diff --git "a/1443.jsonl" "b/1443.jsonl" new file mode 100644--- /dev/null +++ "b/1443.jsonl" @@ -0,0 +1,1159 @@ +{"seq_id": "26116285457", "text": "# 2축 그래프 그리기 \r\n# 기존 축 : 막대그래프 / 보조 축 : 선그래프 그리기\r\n# 보조축 ax2 = ax1.twinx() : 쌍둥이 객체 생성후 ax2에 선그래프 \r\n\r\nimport pandas as pd\r\nimport matplotlib.pyplot as plt\r\n\r\n# matplotlib 한글 폰트 오류 문제 해결\r\nfrom matplotlib import font_manager,rc\r\nfont_path = \"예제/part4/malgun.ttf\" # 폰트 파일 위치 \r\nfont_name = font_manager.FontProperties(fname=font_path).get_name()\r\nrc('font', family = font_name)\r\n\r\nplt.style.use('ggplot')\r\nplt.rcParams['axes.unicode_minus'] = False # 마이너스 부호 출력 설정 \r\n\r\n# 북한 발전량 데이터 \r\ndf = pd.read_excel('예제/part4/남북한발전전력량.xlsx', convert_float= True)\r\ndf = df.loc[5:9]\r\ndf.drop('전력량 (억㎾h)', axis='columns', inplace=True)\r\ndf.set_index('발전 전력별', inplace=True)\r\ndf = df.T \r\n# 증감률 계산\r\ndf = df.rename(columns= {'합계' : '총발전량'})\r\ndf['총발전량 - 1년'] = df['총발전량'].shift(1) # '총발전량' 열 데이터를 1행씩 뒤로 이동시킴\r\nprint(df.head())\r\ndf['증감률'] = ((df['총발전량'] / df['총발전량 - 1년'])- 1) * 100\r\n\r\n# 2축 그래프 그리기 \r\n# ax 1 : 수력, 화력 열 값을 쌓은 세로형 막대 그래프 \r\nax1 = df[['수력','화력']].plot(kind='bar', figsize =(20,10), width = 0.7, stacked = True)\r\nax2= ax1.twinx()\r\n# ax2 : df.index x축 / df.증감률을 y축으로 하는 점 선그래프 \r\nax2.plot(df.index, df.증감률 , ls ='--',\r\n marker ='o', markersize = 20, \r\n color ='red', label ='전년대비 증감률(%)')\r\n\r\nax1.set_ylim(0,500)\r\nax2.set_ylim(-50,50)\r\n\r\nax1.set_xlabel('연도', size=20)\r\nax1.set_ylabel('발전량(억 KWh)')\r\nax2.set_ylabel('전년 대비 증감율(%)')\r\n\r\nplt.title('북한 전력 발전량 (1990 ~ 2016)', size=30)\r\nax1.legend(loc='upper left')\r\n\r\nplt.show()", "repo_name": "jaewon-huh/DA_DS_dreamtree", "sub_path": "실습/4-13.py", "file_name": "4-13.py", "file_ext": "py", "file_size_in_byte": 1860, "program_lang": "python", "lang": "ko", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "matplotlib.font_manager.FontProperties", "line_number": 11, "usage_type": "call"}, {"api_name": "matplotlib.font_manager", "line_number": 11, "usage_type": "name"}, {"api_name": "matplotlib.rc", "line_number": 12, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.style.use", "line_number": 14, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.style", "line_number": 14, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 14, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.rcParams", "line_number": 15, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 15, "usage_type": "name"}, {"api_name": "pandas.read_excel", "line_number": 18, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.title", "line_number": 45, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 45, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 48, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 48, "usage_type": "name"}]} +{"seq_id": "35082440191", "text": "import os\nimport time\nimport tweepy\n\nauth = tweepy.OAuthHandler(os.environ['CONSUMER_KEY'], os.environ['CONSUMER_SECRET'])\nauth.set_access_token(os.environ['ACCESS_TOKEN'], os.environ['ACCESS_TOKEN_SECRET'])\napi = tweepy.API(auth)\n\nimageList = ['test.gif', 'test2.jpg', 'test3.jpg']\n\nfor image in imageList:\n api.update_with_media(image, '#MakingPeaceWithHumans')\n time.sleep(15)\n", "repo_name": "AravindVasudev/twitter-bot", "sub_path": "test_scripts/tweet_images.py", "file_name": "tweet_images.py", "file_ext": "py", "file_size_in_byte": 386, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "tweepy.OAuthHandler", "line_number": 5, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 5, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 6, "usage_type": "attribute"}, {"api_name": "tweepy.API", "line_number": 7, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 13, "usage_type": "call"}]} +{"seq_id": "38726696171", "text": "'''\n###### * User Profile : Keval_78 \nLinkedIn: https://www.linkedin.com/in/kevalpadsala78/\nGithub: https://github.com/Keval78\nLeetcode: https://leetcode.com/Keval_78/\n'''\n\n# Definition for a binary tree node.\n\nfrom typing import List, Optional\n\n\nclass TreeNode:\n def __init__(self, val=0, left=None, right=None):\n self.val = val\n self.left = left\n self.right = right\n\n\nclass Solution:\n def buildTree(self, inorder: List[int], postorder: List[int]) -> Optional[TreeNode]:\n def build(left, right):\n if left > right:\n return None\n root = TreeNode(postorder.pop())\n part_ind = idx_map[root.val]\n root.right = build(part_ind+1, right)\n root.left = build(left, part_ind-1)\n return root\n idx_map = {val: idx for idx, val in enumerate(inorder)}\n n = len(inorder)\n return build(0, n-1)\n", "repo_name": "Keval78/Programming_Solutions", "sub_path": "LeetCode/Daily/106 Construct Binary Tree from Inorder and Postorder Traversal.py", "file_name": "106 Construct Binary Tree from Inorder and Postorder Traversal.py", "file_ext": "py", "file_size_in_byte": 914, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "typing.List", "line_number": 21, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 21, "usage_type": "name"}]} +{"seq_id": "16597311577", "text": "import cv2\nimport numpy as np\n\nimg = np.zeros(shape=(512,512,3), dtype=np.uint8)\ncy = img.shape[0] // 2 #몫 연산자 -> 512/2 = 256\ncx = img.shape[1] // 2\ncv2.circle(img, (cx, cy), radius=50, color=(0,0,255), thickness=-1)\ncv2.circle(img, (cx//2, cy), radius=60, color=(0,0,255), thickness=-1)\ncv2.circle(img, (cx, cy//2), radius=60, color=(0,0,255), thickness=-1)\ncv2.circle(img, (cx//2, cy//2), radius=80, color=(0,0,255), thickness=-1)\ngray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n\nret, bImage = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)\ndist = cv2.distanceTransform(bImage, cv2.DIST_L1, 3) #색깔이 없는 곳과 있는 곳까지의 거리 계산\ndist8 = cv2.normalize(dist, None, 0, 255, cv2.NORM_MINMAX, dtype=cv2.CV_8U) #정규화\ncv2.imshow('bImage', bImage)\ncv2.imshow('dist8', dist8)\n\nminVal, maxVal, minLoc, maxLoc = cv2.minMaxLoc(dist)\nprint('dist :', minVal, maxVal, minLoc, maxLoc)\nmask = (dist > maxVal * 0.5).astype(np.uint8) * 255 #최대값의 절반보다 크면 마스크\ncv2.imshow('mask', mask)\n\nmode = cv2.RETR_LIST #바깥쪽의 경계선만 검출\nmethod = cv2.CHAIN_APPROX_SIMPLE #다각형의 꼭짓점들 반환\ncontours, hierarchy = cv2.findContours(mask, mode, method)\nprint('len(contours) =', len(contours))\n\nmarkers = np.zeros(shape=img.shape[:2], dtype=np.int32)\nfor i, cnt in enumerate(contours):\n cv2.drawContours(markers, [cnt], 0, i+1, -1)\n\n# markers = cv2.normalize(markers, None, 0, 255, cv2.NORM_MINMAX) #contour를 잘그리긴 함\n# cv2.imshow('markers', markers)\ndst = img.copy()\ncv2.watershed(img, markers) #뭔가 여기서 안됨\n\ndst[markers == -1] = [0,0,255] #경계선 빨간색 지정\nfor i in range(len(contours)): #랜덤 색깔 지정\n r = np.random.randint(256)\n g = np.random.randint(256)\n b = np.random.randint(256)\n dst[markers == i+1] = [b,g,r]\n\ncv2.imshow('water', dst)\ndst = cv2.addWeighted(img, 0.4, dst, 0.6, 0) #원본영상과 가중치둬서 출력\n\ncv2.imshow('img', img)\ncv2.imshow('dst', dst)\ncv2.waitKey()\ncv2.destroyAllWindows()", "repo_name": "junho2000/opencv_python", "sub_path": "chap7/7.11.py", "file_name": "7.11.py", "file_ext": "py", "file_size_in_byte": 2041, "program_lang": "python", "lang": "ko", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "numpy.zeros", "line_number": 4, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 4, "usage_type": "attribute"}, {"api_name": "cv2.circle", "line_number": 7, "usage_type": "call"}, {"api_name": "cv2.circle", "line_number": 8, "usage_type": "call"}, {"api_name": "cv2.circle", "line_number": 9, "usage_type": "call"}, {"api_name": "cv2.circle", "line_number": 10, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 11, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 11, "usage_type": "attribute"}, {"api_name": "cv2.threshold", "line_number": 13, "usage_type": "call"}, {"api_name": "cv2.THRESH_BINARY", "line_number": 13, "usage_type": "attribute"}, {"api_name": "cv2.THRESH_OTSU", "line_number": 13, "usage_type": "attribute"}, {"api_name": "cv2.distanceTransform", "line_number": 14, "usage_type": "call"}, {"api_name": "cv2.DIST_L1", "line_number": 14, "usage_type": "attribute"}, {"api_name": "cv2.normalize", "line_number": 15, "usage_type": "call"}, {"api_name": "cv2.NORM_MINMAX", "line_number": 15, "usage_type": "attribute"}, {"api_name": "cv2.CV_8U", "line_number": 15, "usage_type": "attribute"}, {"api_name": "cv2.imshow", "line_number": 16, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 17, "usage_type": "call"}, {"api_name": "cv2.minMaxLoc", "line_number": 19, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 21, "usage_type": "attribute"}, {"api_name": "cv2.imshow", "line_number": 22, "usage_type": "call"}, {"api_name": "cv2.RETR_LIST", "line_number": 24, "usage_type": "attribute"}, {"api_name": "cv2.CHAIN_APPROX_SIMPLE", "line_number": 25, "usage_type": "attribute"}, {"api_name": "cv2.findContours", "line_number": 26, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 29, "usage_type": "call"}, {"api_name": "numpy.int32", "line_number": 29, "usage_type": "attribute"}, {"api_name": "cv2.drawContours", "line_number": 31, "usage_type": "call"}, {"api_name": "cv2.watershed", "line_number": 36, "usage_type": "call"}, {"api_name": "numpy.random.randint", "line_number": 40, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 40, "usage_type": "attribute"}, {"api_name": "numpy.random.randint", "line_number": 41, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 41, "usage_type": "attribute"}, {"api_name": "numpy.random.randint", "line_number": 42, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 42, "usage_type": "attribute"}, {"api_name": "cv2.imshow", "line_number": 45, "usage_type": "call"}, {"api_name": "cv2.addWeighted", "line_number": 46, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 48, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 49, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 50, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 51, "usage_type": "call"}]} +{"seq_id": "8760580375", "text": "import json\nfrom typing import Optional\n\nfrom pydantic import BaseModel\n\n\nclass _BasicModel(BaseModel):\n\n id: Optional[str]\n version: int = 1\n\n def __init__(self, *args, **kargs):\n super().__init__(*args, **kargs)\n self._parse_json_fields()\n\n def to_orm_dict(self, flat=True):\n \"\"\"用于获取向数据库写入的 dict ，将嵌套类转化为 json \"\"\"\n obj = self.dict()\n if flat: # 扁平化，递归项转json字符串\n for k, v in obj.items():\n if isinstance(v, (list, set, dict, tuple, BaseModel)):\n obj[k] = json.dumps(v, ensure_ascii=False)\n return obj\n\n def _parse_json_fields(self):\n for field in self.Config.orm_json_fields:\n value = getattr(self, field)\n if isinstance(value, str):\n value = json.loads(value)\n setattr(self, field, value)\n\n class Config:\n # 数据库加载时需要json解析的字段\n orm_json_fields = []\n\n\nBasicModel = _BasicModel\nBasicEditModel = _BasicModel\n", "repo_name": "Philogag/The-Project-Demo", "sub_path": "backend/model/basic_model.py", "file_name": "basic_model.py", "file_ext": "py", "file_size_in_byte": 1070, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "37", "api": [{"api_name": "pydantic.BaseModel", "line_number": 7, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 9, "usage_type": "name"}, {"api_name": "pydantic.BaseModel", "line_number": 21, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 22, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 29, "usage_type": "call"}]} +{"seq_id": "16893244015", "text": "import pytorch_lightning as pl\nfrom transformers import AutoModelForMaskedLM\nfrom .tokenizer import get_tokenizer\nfrom .argparser import get_args\nimport torch\nimport re\nfrom utils import ModelEvalMixin\nfrom utils.decoder import BeamSearchForMaskedLM\nfrom utils.server import ServerMixin \nargs = get_args()\n\nclass Model(pl.LightningModule,ModelEvalMixin,ServerMixin):\n def __init__(self,args = args):\n super().__init__()\n self.save_hyperparameters(args)\n #\n args = get_args()\n self.tokenizer = get_tokenizer(args.base_model)\n self.model = AutoModelForMaskedLM.from_pretrained(args.base_model)\n self.model.resize_token_embeddings(len(self.tokenizer))\n\n self._type = 'masked_lm'\n\n def forward(self, input_ids,labels=None):\n return self.model(input_ids=input_ids,labels=labels,return_dict=True)\n \n def training_step(self, batch, batch_idx):\n outputs = self(batch[0],batch[1])\n loss = outputs['loss']\n self.log('train_loss',loss)\n return loss\n \n def validation_step(self, batch, batch_idx):\n outputs = self(batch[0],batch[1])\n loss = outputs['loss']\n self.log('dev_loss',loss)\n \n def test_step(self, batch, batch_idx):\n input_ids = batch[0]\n ref_question = batch[1][0]\n # input_ids_len = input_ids.shape[-1]\n batch_size = input_ids.shape[0]\n assert batch_size == 1\n\n decoder = BeamSearchForMaskedLM(self.model,self.tokenizer,beam_size=3,max_token_length=450,device='cuda')\n decode_question = decoder(input_ids)\n print(decode_question)\n self.write_predict(decode_question,ref_question)\n\n def test_epoch_end(self,outputs):\n self.evaluate_predict(dataset=args.dataset)\n self.save_huggingface_model()\n\n def configure_optimizers(self):\n opt = torch.optim.AdamW(self.parameters(), lr=args.lr)\n opt.zero_grad()\n return opt", "repo_name": "p208p2002/Transformer-QG-on-SQuAD", "sub_path": "models/masked_lm/model.py", "file_name": "model.py", "file_ext": "py", "file_size_in_byte": 1958, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 42, "dataset": "github-code", "pt": "37", "api": [{"api_name": "argparser.get_args", "line_number": 10, "usage_type": "call"}, {"api_name": "pytorch_lightning.LightningModule", "line_number": 12, "usage_type": "attribute"}, {"api_name": "utils.ModelEvalMixin", "line_number": 12, "usage_type": "name"}, {"api_name": "utils.server.ServerMixin", "line_number": 12, "usage_type": "name"}, {"api_name": "argparser.get_args", "line_number": 17, "usage_type": "call"}, {"api_name": "tokenizer.get_tokenizer", "line_number": 18, "usage_type": "call"}, {"api_name": "transformers.AutoModelForMaskedLM.from_pretrained", "line_number": 19, "usage_type": "call"}, {"api_name": "transformers.AutoModelForMaskedLM", "line_number": 19, "usage_type": "name"}, {"api_name": "utils.decoder.BeamSearchForMaskedLM", "line_number": 45, "usage_type": "call"}, {"api_name": "torch.optim.AdamW", "line_number": 55, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 55, "usage_type": "attribute"}]} +{"seq_id": "32605165331", "text": "import os\nfrom setuptools import setup\n\n\ndef filter_requirements(fn):\n with open(fn) as fh:\n filtered_requirements = []\n for line in fh.readlines():\n if line[0] in ['#', ' ', '-']:\n continue\n filtered_requirements.append(line)\n return filtered_requirements\n\n\ndef load_version():\n with open('VERSION') as fh:\n res = fh.read()\n return res\n\n\nversion = load_version()\n\nrequired = filter_requirements('requirements.txt')\n\nrequired_test = filter_requirements('requirements-test.txt')\n\nlong_description = 'Script to lay out a large print job on multiple pages with alignment marks'\n\nif os.path.exists('README.rst'):\n with open('README.rst') as fh:\n long_description = fh.read()\n\nsetup(\n name='lps',\n description='Lay out large print jobs as PDFs with alignment marks.',\n long_description=long_description,\n version=version,\n author='Chris Speck',\n author_email='cgspeck@gmail.com',\n url='https://github.com/cgspeck/largeprintsplitter',\n packages=['lps'],\n install_requires=required,\n extras_require={\n 'tests': required_test\n },\n entry_points='''\n [console_scripts]\n lps=lps.cli:run_cli\n ''',\n keywords=['print', 'printing', 'layout', 'pdf', 'plan', 'engineering', 'fabrication'],\n download_url=f'https://github.com/cgspeck/largeprintsplitter/archive/{version}.tar.gz'\n)\n", "repo_name": "cgspeck/largeprintsplitter", "sub_path": "setup.py", "file_name": "setup.py", "file_ext": "py", "file_size_in_byte": 1410, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "os.path.exists", "line_number": 29, "usage_type": "call"}, {"api_name": "os.path", "line_number": 29, "usage_type": "attribute"}, {"api_name": "setuptools.setup", "line_number": 33, "usage_type": "call"}]} +{"seq_id": "16509870435", "text": "import regex as re\nfrom typing import Dict, List, Optional, Generator, Tuple\n\nfrom pie_extended.pipeline.postprocessor.proto import ChainedProcessor, ProcessorPrototype\nfrom pie_extended.pipeline.postprocessor.glue import GlueProcessor\nfrom pie_extended.pipeline.postprocessor.rulebased import RuleBasedProcessor\nfrom pie_extended.utils import roman_number\n\n\nclass LatinRulesProcessor(RuleBasedProcessor):\n \"\"\" Lasla data has no punctuation, we tag it automatically.\n\n \"ne\" token can be two different lemma, but I don't remember why I wrote this part. (ne/nec ?)\n\n >>> p = LatinRulesProcessor()\n >>> p.set_tasks([\"lemma\", \"pos\", \"morph\", \"treated\"])\n ['lemma', 'pos', 'morph', 'treated']\n >>> p.get_dict(\"uinipollens\", ['界pollens', '', '', 'uinipollens']) == [\n ... {'lemma': 'pollens', 'treated': 'uinipollens', 'morph': 'MORPH=empty', 'pos': 'ADJqua',\n ... 'form': '{uinipollens}', 'Dis': '_'}\n ... ]\n True\n >>> p.get_dict(\"similist\", ['界sum', '', '', 'similist']) == [{'lemma': 'sum1', 'treated': 'similist',\n ... 'morph': 'Numb=Sing|Mood=Ind|Tense=Pres|Voice=Act|Person=3',\n ... 'pos': 'VER', 'form': '{similist}', 'Dis': '_'}]\n True\n\n \"\"\"\n PONCTU = re.compile(r\"^\\W+$\")\n GREEK = re.compile(r\"^\\p{Greek}+$\")\n CLITICS_POS = {\n 'audeo': 'VER',\n 'consultum': 'NOM',\n 'cum': 'CON',\n 'ientaculum': 'NOM',\n 'ille': 'PROdem',\n 'ipse': 'PROdem',\n 'is': 'PROdem',\n 'iste': 'PROdem',\n 'ne': 'ADV',\n 'pollens': 'ADJqua',\n 'que': 'CON',\n 'scribo': 'VER',\n 'sum': 'VER',\n 'ue': 'CON',\n 'unus': '',\n 'uolo': ''\n }\n\n CLITICS_MORPH = {\n 'sum': 'Numb=Sing|Mood=Ind|Tense=Pres|Voice=Act|Person=3'\n }\n CLITICS_DIS = {\n 'cum': '3',\n 'ne': '2',\n 'sum': '1'\n }\n\n def rules(self, annotation: Dict[str, str]) -> Dict[str, str]:\n # If Else condition\n token = annotation[\"form\"]\n\n if annotation[\"lemma\"].startswith(\"界\"):\n lem = annotation[\"lemma\"][1:]\n return {\n \"lemma\": lem + self.CLITICS_DIS.get(lem, \"\"),\n \"treated\": annotation[\"treated\"],\n \"morph\": self.CLITICS_MORPH.get(lem, \"MORPH=empty\"),\n \"pos\": self.CLITICS_POS.get(lem, \"UNK\"),\n \"form\": \"{\"+annotation[\"form\"]+\"}\",\n \"Dis\": \"_\"\n }\n\n if self.PONCTU.match(token):\n return {\"form\": token, \"lemma\": token, \"pos\": \"PUNC\", \"morph\": \"MORPH=empty\",\n \"treated\": annotation['treated'], \"Dis\": \"_\"}\n elif self.GREEK.match(token):\n return {\"form\": token, \"lemma\": \"[Greek]\", \"pos\": \"FOR\", \"morph\": \"MORPH=empty\",\n \"treated\": annotation['treated'], \"Dis\": \"_\"}\n elif token.startswith(\"[IGN:\"):\n return {\"form\": token, \"lemma\": \"[IGNORED]\", \"pos\": \"OUT\", \"morph\": \"MORPH=empty\",\n \"treated\": annotation['treated'], \"Dis\": \"_\"}\n elif token.startswith(\"[REF:\"):\n return {\"form\": token, \"lemma\": \"[METADATA]\", \"pos\": \"OUT\", \"morph\": \"MORPH=empty\",\n \"treated\": annotation['treated'], \"Dis\": \"_\"}\n elif annotation[\"lemma\"].isdigit() and annotation[\"treated\"].isdigit() and not token.isnumeric():\n try:\n annotation[\"lemma\"] = str(roman_number(token))\n except KeyError:\n annotation[\"lemma\"] = \"\"\n print(\"Weird behavior on this token\", annotation)\n return annotation\n\n def __init__(self, *args, **kwargs):\n super(LatinRulesProcessor, self).__init__(*args, **kwargs)\n\n\nclass LatinGlueProcessor(GlueProcessor):\n OUTPUT_KEYS = [\"form\", \"lemma\", \"pos\", \"morph\", \"Dis\"]\n GLUE = {\"morph\": [\"Case\", \"Numb\", \"Gend\", \"Deg\", \"Mood\", \"Tense\", \"Voice\", \"Person\"]}\n WHEN_EMPTY = {\"morph\": \"MORPH=empty\"}\n EMPTY_TAG: Dict[str, str] = {\"Case\": \"_\", \"Numb\": \"_\", \"Deg\": \"_\", \"Mood\": \"_\", \"Tense\": \"_\", \"Voice\": \"_\",\n \"Person\": \"_\", \"Gend\": \"_\"}\n\n def __init__(self, *args, **kwargs):\n super(LatinGlueProcessor, self).__init__(*args, **kwargs)\n\n\nclass MoodTenseVoice(ChainedProcessor):\n def __init__(self, head_processor: Optional[ProcessorPrototype],\n empty_value: str = \"_\", **kwargs):\n super(MoodTenseVoice, self).__init__(head_processor=head_processor)\n\n self._out_tasks = []\n self.empty_value = empty_value\n\n def set_tasks(self, tasks):\n self._tasks = self.head_processor.set_tasks(tasks)\n self._out_tasks = [\n subtask\n for task in self._tasks\n for subtask in task.split(\"_\")\n ]\n return self.tasks\n\n def reinsert(self, form: str) -> Dict[str, str]:\n return dict(form=form, **{key: self.empty_value for key in self._out_tasks if key != \"form\"})\n\n def _yield_key(self, dic: Dict[str, str]) -> Generator[Tuple[str, str], None, None]:\n for key, value in dic.items():\n if \"_\" in key:\n keys, values = key.split(\"_\"), value.split(\"|\")\n for k, v in zip(keys, values+[self.empty_value]*(len(keys)-len(values))):\n yield k, v\n else:\n yield key, value\n\n def get_dict(self, token: str, tags: List[str]) -> List[Dict[str, str]]:\n return [\n dict(self._yield_key(dic))\n for dic in self.head_processor.get_dict(token, tags)\n ]\n\n def reset(self):\n self.head_processor.reset()\n\n", "repo_name": "hipster-philology/nlp-pie-taggers", "sub_path": "pie_extended/models/lasla/processor.py", "file_name": "processor.py", "file_ext": "py", "file_size_in_byte": 5596, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 9, "dataset": "github-code", "pt": "37", "api": [{"api_name": "pie_extended.pipeline.postprocessor.rulebased.RuleBasedProcessor", "line_number": 10, "usage_type": "name"}, {"api_name": "regex.compile", "line_number": 29, "usage_type": "call"}, {"api_name": "regex.compile", "line_number": 30, "usage_type": "call"}, {"api_name": "typing.Dict", "line_number": 59, "usage_type": "name"}, {"api_name": "pie_extended.utils.roman_number", "line_number": 88, "usage_type": "call"}, {"api_name": "pie_extended.pipeline.postprocessor.glue.GlueProcessor", "line_number": 98, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 102, "usage_type": "name"}, {"api_name": "pie_extended.pipeline.postprocessor.proto.ChainedProcessor", "line_number": 109, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 110, "usage_type": "name"}, {"api_name": "pie_extended.pipeline.postprocessor.proto.ProcessorPrototype", "line_number": 110, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 126, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 129, "usage_type": "name"}, {"api_name": "typing.Generator", "line_number": 129, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 129, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 138, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 138, "usage_type": "name"}]} +{"seq_id": "556822819", "text": "#!/usr/bin/env python3\n# -*- coding: utf8 -*-\n\n\"\"\"Remove columns from a table (TSV)\n\nUsage:\n remove_columns.py INPUT [--columns=...]\n remove_columns.py (-h | --help)\n remove_columns.py --version\n\nOptions:\n -h --help Show this screen.\n --version Show version.\n INPUT input file\n --counts= Number of methods to consider a sample outlier\n\"\"\"\n\n\n__author__ = \"Matheus Carvalho Bürger\"\n__email__ = \"matheus.cburger@gmail.com\"\n__license__ = \"GPL\"\n\n\nfrom docopt import docopt\n\n\ndef remove_idx(vals, idx):\n return([v for i, v in enumerate(vals) if i not in idx])\n\nif __name__ == \"__main__\":\n args = docopt(__doc__, version='Get outliers from JSON')\n columns = args[\"--columns\"]\n filename = args[\"INPUT\"]\n with open(filename) as fh:\n header_line = fh.readline().strip(\"\\n\")\n header = header_line.split(\"\\t\")\n idx = [i for i, v in enumerate(header) if v in columns]\n print(\"\\t\".join(remove_idx(header, idx)))\n for line in fh:\n line = line.strip(\"\\n\")\n values = line.split(\"\\t\")\n print(\"\\t\".join(remove_idx(values, idx)))\n", "repo_name": "matheuscburger/DengueAnalysis", "sub_path": "src/microarrayAnalysis/remove_columns.py", "file_name": "remove_columns.py", "file_ext": "py", "file_size_in_byte": 1176, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "docopt.docopt", "line_number": 31, "usage_type": "call"}]} +{"seq_id": "31859303716", "text": "#!/usr/bin/env python3\nimport os, shutil, configparser, socket\n# Post install configutation. Can be used to refresh configs on errors.\n\nAPP_NAME = 'pids'\nCONFIG_INI = 'config.ini'\nENC_NAME = 'private.pfx'\n\nif os.name == \"posix\":\n \n # This code will be executed on Linux\n if os.path.exists('/usr/local/etc') and os.access('/usr/local/etc', os.W_OK):\n if not os.path.exists('/usr/local/etc/' + APP_NAME) and not os.path.isdir('/usr/local/etc/' + APP_NAME):\n print(\"Creating directory: /usr/local/etc/\" + APP_NAME)\n os.makedirs(f'/usr/local/etc/{APP_NAME}', exist_ok=True)\n path = f'/usr/local/etc/{APP_NAME}/'\n \n elif os.path.exists('/etc') and os.access('/etc', os.W_OK):\n if os.path.isdir('/etc/' + APP_NAME):\n print(\"Creating directory: /etc/\" + APP_NAME)\n os.makedirs(f'/etc/{APP_NAME}', exist_ok=True)\n path = f'/etc/{APP_NAME}/'\n \n elif os.path.exists(f'{os.environ[\"HOME\"]}/.local') and os.access(f'{os.environ[\"HOME\"]}/.local', os.W_OK):\n path = f'{os.environ[\"HOME\"]}/.local/share/{APP_NAME}/' # fallback to /home/.local/share if neither directory exists\n print(\"Warning: Unable to join user path, attempting default path: \" + path)\n if not os.path.isdir(f'{os.environ[\"HOME\"]}/.local/share/{APP_NAME}/'):\n os.makedirs(f'{os.environ[\"HOME\"]}/.local/share/{APP_NAME}/', exist_ok=True)\n print(f'Creating directory: {os.environ[\"HOME\"]}/.local/share/' + APP_NAME)\n \n else:\n path = f'/.{APP_NAME}/' # fallback to the old ways of /home/. if none of the directories are available\n print(\"Warning: Unable to join path, attempting fallback path: \" + path)\n os.makedirs(f'/.{APP_NAME}/', exist_ok=True)\n print(\"Creating directory: /.\" + APP_NAME)\n \n\n USR_ETC_DIR = path\n SERVICE_FILE = '/etc/systemd/system/pids.service'\n\nelif os.name == \"nt\":\n # This code will be executed on Windows\n if not os.path.exists(os.path.join(os.environ['APPDATA'], APP_NAME, CONFIG_INI)):\n #os.mkdir(os.path.join(os.environ['APPDATA'], APP_NAME ))\n print(\"Creating directory: \" + os.path.join(os.environ['APPDATA'], APP_NAME))\n os.makedirs(os.path.join(os.environ['APPDATA'], APP_NAME), exist_ok=True)\n \n path = os.path.join(os.environ['APPDATA'], APP_NAME)\n \n INSTALL_DIR = 'C:\\Program Files\\pids'\n USR_ETC_DIR = path\n SERVICE_FILE = '%PROGRAMDATA%\\pids'\n \n\ndef prompt(help, **kwargs):\n options = ' '.join(f'[{key}] {val}' for key, val in kwargs.items())\n prompt_args = f'{help} ({\"/\".join(kwargs)}): '\n prompt_opts = f'Try {options} or [x] to force quit: '\n kwargs['x'] = \"Force quit\"\n \n # Prompt the user and get the response\n response = input(prompt_args).lower()\n while response not in kwargs:\n response = input(prompt_opts).lower()\n\n if response.lower() == 'x':\n print(\"Aborted by user.\")\n exit()\n else:\n return response\n \n \ndef find_mqtt_ip():\n # Attempt to locate the ip of the mosquitto broker, otherwise use default ip and customize later\n ip = ''\n default = 'localhost'\n \n try:\n sock = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\n sock.settimeout(0.1)\n sock.connect((\"8.8.8.8\", 1883))\n ip = sock.getsockname()[0]\n sock.close()\n \n except socket.gaierror:\n print(f\"Error: Could not resolve mosquitto ip, using default: {default}\")\n return default\n return ip\n\n\ndef create_config():\n # Create the config.ini file\n config = configparser.ConfigParser()\n config['mqtt-broker'] = {'ip': find_mqtt_ip(), 'port': '1883'}\n config['sqlite'] = {'repository': 'database.db'}\n config['display'] = {\"fullscreen\" : 1}\n config['validation'] = {'token': ''}\n \n try:\n with open(USR_ETC_DIR + CONFIG_INI, 'w') as f:\n config.write(f)\n print(\"config created in \" + USR_ETC_DIR + \"\")\n except IOError as ex:\n print('Failed to create config', ex)\n\n \n \n \ndef install():\n if os.path.isfile(USR_ETC_DIR + CONFIG_INI):\n print(\"Warning: config already exists in \" + USR_ETC_DIR + \".\")\n response = prompt(\"Overwrite config?\", y=\"Overwrite the config file\", n=\"Keep the existing config file\")\n if response == \"y\":\n create_config()\n else: \n print(\"Keeping existing config\")\n else:\n create_config()\n \n if not os.path.isfile(USR_ETC_DIR + ENC_NAME):\n import pis.utils.conf as conf\n \n conf.CONFIG_PATH = os.path.join(path, CONFIG_INI)\n conf.ENC_PATH = os.path.join(path, ENC_NAME)\n \n from pis.install.wizard import prompt_confirmpassword, generate_validation_keys\n import pis.utils.integrity as integrity\n \n print(\"No validation token found. Generating new one...\")\n print(\"Validation uses encrypted storage. Please enter a password to encrypt the token.\")\n pfk = prompt_confirmpassword()\n generate_validation_keys(pfk, None, integrity)\n \n\nif __name__ == \"__main__\":\n install()", "repo_name": "InnovationProject4/platform-info-system", "sub_path": "src/pis/install/postinstall.py", "file_name": "postinstall.py", "file_ext": "py", "file_size_in_byte": 5194, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "os.name", "line_number": 9, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 12, "usage_type": "call"}, {"api_name": "os.path", "line_number": 12, "usage_type": "attribute"}, {"api_name": "os.access", "line_number": 12, "usage_type": "call"}, {"api_name": "os.W_OK", "line_number": 12, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 13, "usage_type": "call"}, {"api_name": "os.path", "line_number": 13, "usage_type": "attribute"}, {"api_name": "os.path.isdir", "line_number": 13, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 15, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 18, "usage_type": "call"}, {"api_name": "os.path", "line_number": 18, "usage_type": "attribute"}, {"api_name": "os.access", "line_number": 18, "usage_type": "call"}, {"api_name": "os.W_OK", "line_number": 18, "usage_type": "attribute"}, {"api_name": "os.path.isdir", "line_number": 19, "usage_type": "call"}, {"api_name": "os.path", "line_number": 19, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 21, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 24, "usage_type": "call"}, {"api_name": "os.path", "line_number": 24, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 24, "usage_type": "attribute"}, {"api_name": "os.access", "line_number": 24, "usage_type": "call"}, {"api_name": "os.W_OK", "line_number": 24, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 25, "usage_type": "attribute"}, {"api_name": "os.path.isdir", "line_number": 27, "usage_type": "call"}, {"api_name": "os.path", "line_number": 27, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 27, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 28, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 28, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 29, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 34, "usage_type": "call"}, {"api_name": "os.name", "line_number": 41, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 43, "usage_type": "call"}, {"api_name": "os.path", "line_number": 43, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 43, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 43, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 45, "usage_type": "call"}, {"api_name": "os.path", "line_number": 45, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 45, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 46, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 46, "usage_type": "call"}, {"api_name": "os.path", "line_number": 46, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 46, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 48, "usage_type": "call"}, {"api_name": "os.path", "line_number": 48, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 48, "usage_type": "attribute"}, {"api_name": "socket.socket", "line_number": 79, "usage_type": "call"}, {"api_name": "socket.AF_INET", "line_number": 79, "usage_type": "attribute"}, {"api_name": "socket.SOCK_DGRAM", "line_number": 79, "usage_type": "attribute"}, {"api_name": "socket.gaierror", "line_number": 85, "usage_type": "attribute"}, {"api_name": "configparser.ConfigParser", "line_number": 93, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 110, "usage_type": "call"}, {"api_name": "os.path", "line_number": 110, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 120, "usage_type": "call"}, {"api_name": "os.path", "line_number": 120, "usage_type": "attribute"}, {"api_name": "pis.utils.conf.CONFIG_PATH", "line_number": 123, "usage_type": "attribute"}, {"api_name": "pis.utils.conf", "line_number": 123, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 123, "usage_type": "call"}, {"api_name": "os.path", "line_number": 123, "usage_type": "attribute"}, {"api_name": "pis.utils.conf.ENC_PATH", "line_number": 124, "usage_type": "attribute"}, {"api_name": "pis.utils.conf", "line_number": 124, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 124, "usage_type": "call"}, {"api_name": "os.path", "line_number": 124, "usage_type": "attribute"}, {"api_name": "pis.install.wizard.prompt_confirmpassword", "line_number": 131, "usage_type": "call"}, {"api_name": "pis.install.wizard.generate_validation_keys", "line_number": 132, "usage_type": "call"}, {"api_name": "pis.utils.integrity", "line_number": 132, "usage_type": "argument"}]} +{"seq_id": "11087215105", "text": "from typing import Optional, Dict\n\nfrom core.services.devices import DevicesService\nfrom modules.apihelper.utility.helpers import get_device_id, hex_digest\n\n\nclass DevicesMethods:\n def __init__(self):\n self.service: Optional[DevicesService] = None\n\n @staticmethod\n def get_default_device_header(account_id: int, headers: Dict = None) -> Dict[str, str]:\n headers = headers or {}\n headers[\"x-rpc-device_id\"] = get_device_id(str(account_id))\n headers[\"x-rpc-device_fp\"] = hex_digest(headers[\"x-rpc-device_id\"])[:13]\n headers[\"x-rpc-device_name\"] = \"Xiaomi\"\n return headers\n\n async def update_device_headers(self, account_id: int, headers: Dict = None) -> Dict[str, str]:\n account_id = account_id or 0\n if not self.service:\n return self.get_default_device_header(account_id, headers)\n device = await self.service.get(account_id)\n if not device:\n return self.get_default_device_header(account_id, headers)\n headers = headers or {}\n headers[\"x-rpc-device_id\"] = device.device_id\n headers[\"x-rpc-device_fp\"] = device.device_fp\n headers[\"x-rpc-device_name\"] = device.device_name or \"Xiaomi\"\n return headers\n\n\ndevices_methods = DevicesMethods()\n", "repo_name": "PaiGramTeam/PaiGram", "sub_path": "modules/apihelper/utility/devices.py", "file_name": "devices.py", "file_ext": "py", "file_size_in_byte": 1275, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 75, "dataset": "github-code", "pt": "37", "api": [{"api_name": "typing.Optional", "line_number": 9, "usage_type": "name"}, {"api_name": "core.services.devices.DevicesService", "line_number": 9, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 12, "usage_type": "name"}, {"api_name": "modules.apihelper.utility.helpers.get_device_id", "line_number": 14, "usage_type": "call"}, {"api_name": "modules.apihelper.utility.helpers.hex_digest", "line_number": 15, "usage_type": "call"}, {"api_name": "typing.Dict", "line_number": 19, "usage_type": "name"}]} +{"seq_id": "34952050450", "text": "from app import db\nfrom app.character import bp\nfrom app.character.forms import CreateCharacterForm, EditCharacterForm, JournalForm\nfrom app.character.helpers import gen_session_choices\nfrom app.character.models import Character, Journal\nfrom app.helpers import page_title, deny_access, upload_profile_picture, delete_profile_picture\nfrom app.party.models import Party\nfrom datetime import datetime\nfrom flask import render_template, flash, redirect, jsonify, request\nfrom flask_login import current_user, login_required\n\nno_perm_url = \"main.index\"\n\n\n@bp.route(\"/create\", methods=[\"GET\", \"POST\"])\n@login_required\ndef create():\n form = CreateCharacterForm()\n\n if form.validate_on_submit():\n char = Character(name=form.name.data,\n race=form.race.data,\n class_=form.class_.data,\n description=form.description.data,\n private_notes=form.private_notes.data,\n user_id=current_user.id,\n is_visible=form.is_visible.data)\n\n success = True\n if form.profile_picture.data:\n success, filename = upload_profile_picture(form.profile_picture.data)\n char.profile_picture = filename\n\n if success is False:\n flash(\"Error while creating character.\", \"error\")\n else:\n db.session.add(char)\n db.session.commit()\n flash(\"Character was created.\", \"success\")\n return redirect(char.view_url())\n\n return render_template(\"character/create.html\", form=form, title=page_title(\"Add Character\"))\n\n\n@bp.route(\"/view//\", methods=[\"GET\"])\n@bp.route(\"/view/\", methods=[\"GET\"])\n@login_required\ndef view(id, name=None):\n char = Character.query.filter_by(id=id).first_or_404()\n\n if not char.is_viewable_by_user():\n return deny_access(no_perm_url)\n\n if char.is_visible is False:\n flash(\"This Character is only visible to you.\", \"warning\")\n\n return render_template(\"character/view.html\", char=char, title=page_title(f\"View Character '{char.name}'\"))\n\n\n# TODO Fix C901\n@bp.route(\"/edit//\", methods=[\"GET\", \"POST\"])\n@login_required\ndef edit(id, name=None): # noqa: C901\n char = Character.query.filter_by(id=id).first_or_404()\n\n if not char.is_editable_by_user():\n return deny_access(no_perm_url)\n\n form = EditCharacterForm()\n\n if not char.is_owned_by_user():\n del form.private_notes\n\n if not char.is_hideable_by_user():\n del form.is_visible\n\n if form.validate_on_submit():\n char.name = form.name.data\n char.race = form.race.data\n char.class_ = form.class_.data\n char.description = form.description.data\n char.edited = datetime.utcnow()\n\n if char.is_owned_by_user():\n char.private_notes = form.private_notes.data\n\n if char.is_hideable_by_user():\n char.is_visible = form.is_visible.data\n\n success = True\n if form.profile_picture.data:\n success, filename = upload_profile_picture(form.profile_picture.data)\n\n if success and char.profile_picture:\n delete_profile_picture(char.profile_picture)\n\n char.profile_picture = filename\n\n if success is False:\n flash(\"Error while editing character.\", \"error\")\n else:\n flash(\"Character was edited.\", \"success\")\n db.session.commit()\n\n return redirect(char.view_url())\n else:\n form.name.data = char.name\n form.race.data = char.race\n form.class_.data = char.class_\n form.description.data = char.description\n\n if char.is_owned_by_user():\n form.private_notes.data = char.private_notes\n\n if char.is_hideable_by_user():\n form.is_visible.data = char.is_visible\n\n return render_template(\"character/edit.html\", form=form, char=char,\n title=page_title(f\"Edit character '{char.name}'\"))\n\n\n@bp.route(\"/list\", methods=[\"GET\"])\n@login_required\ndef list():\n chars = Character.get_visible_items(include_hidden_for_user=True)\n parties = Party.query.all()\n\n return render_template(\"character/list.html\", chars=chars, parties=parties,\n title=page_title(\"Characters and Parties\"))\n\n\n@bp.route(\"/delete//\")\n@login_required\ndef delete(id, name=None):\n char = Character.query.filter_by(id=id).first_or_404()\n\n if not char.is_deletable_by_user():\n return deny_access(no_perm_url)\n\n player = char.player\n\n db.session.delete(char)\n db.session.commit()\n\n flash(\"Character was deleted.\", \"success\")\n return redirect(player.view_url())\n\n\n@bp.route(\"/sidebar\", methods=[\"GET\"])\n@login_required\ndef sidebar():\n chars = Character.get_query_for_visible_items(include_hidden_for_user=True) \\\n .with_entities(Character.id, Character.name).order_by(Character.name.asc()).all()\n\n return jsonify([tuple(c) for c in chars])\n\n\n@bp.route(\"//journal/\", methods=[\"GET\"])\n@login_required\ndef journal_list(c_id, c_name=None):\n char = Character.query.filter_by(id=c_id).first_or_404()\n\n journals = Journal.get_query_for_visible_items(include_hidden_for_user=True).filter_by(character_id=c_id).all()\n\n return render_template(\"character/journal_list.html\", char=char, journals=journals,\n title=page_title(f\"Journals for '{char.name}'\"))\n\n\n@bp.route(\"//journal/create\", methods=[\"GET\", \"POST\"])\n@login_required\ndef journal_create(c_id, c_name=None):\n char = Character.query.filter_by(id=c_id).first_or_404()\n\n if not char.journal_is_creatable_by_user():\n return deny_access(no_perm_url)\n\n heading = f\"Create Journal Entry for {char.name}\"\n\n form = JournalForm()\n form.session.choices = gen_session_choices(char)\n form.submit.label.text = \"Create Journal Entry\"\n\n if form.validate_on_submit():\n journal_entry = Journal(title=form.title.data,\n content=form.content.data,\n is_visible=form.is_visible.data,\n character_id=c_id)\n\n if (form.session.data != 0):\n journal_entry.session_id = form.session.data\n\n db.session.add(journal_entry)\n db.session.commit()\n flash(\"Journal entry was created.\", \"success\")\n\n return redirect(journal_entry.view_url())\n else:\n # set default for visibility\n if request.method == \"GET\":\n form.is_visible.data = True\n\n # pre-select session if get-param was passed\n session_id = request.args.get(\"session\")\n\n # will do nothing if session_id not an int or not in choices\n if session_id:\n try:\n form.session.data = int(session_id)\n except ValueError:\n pass\n\n return render_template(\"character/journal_form.html\", heading=heading, form=form,\n title=page_title(f\"Add Journal Entry for '{char.name}'\"))\n\n\n@bp.route(\"//journal/edit//\", methods=[\"GET\", \"POST\"])\n@login_required\ndef journal_edit(c_id, j_id, c_name=None, j_name=None):\n char = Character.query.filter_by(id=c_id).first_or_404()\n journal = Journal.query.filter_by(id=j_id).first_or_404()\n\n if not journal.is_editable_by_user():\n return deny_access(no_perm_url)\n\n # journal belongs to character\n if journal not in char.journals:\n return deny_access(no_perm_url, \"Journal does not belong to this character.\")\n\n heading = f\"Edit Journal Entry for {char.name}\"\n\n form = JournalForm()\n form.session.choices = gen_session_choices(char)\n form.submit.label.text = \"Save Journal Entry\"\n\n if form.validate_on_submit():\n journal.title = form.title.data\n journal.is_visible = form.is_visible.data\n journal.content = form.content.data\n\n if form.session.data == 0:\n journal.session_id = None\n else:\n journal.session_id = form.session.data\n\n db.session.commit()\n flash(\"Journal entry was changed.\", \"success\")\n return redirect(journal.view_url())\n else:\n form.title.data = journal.title\n form.is_visible.data = journal.is_visible\n form.content.data = journal.content\n form.session.data = journal.session_id\n\n return render_template(\"character/journal_form.html\", heading=heading, form=form,\n title=page_title(f\"Edit Journal Entry '{journal.title}'\"))\n\n\n@bp.route(\"//journal/view//\", methods=[\"GET\"])\n@login_required\ndef journal_view(c_id, j_id, c_name=None, j_name=None):\n char = Character.query.filter_by(id=c_id).first_or_404()\n journal = Journal.query.filter_by(id=j_id).first_or_404()\n\n if not journal.is_viewable_by_user():\n return deny_access(no_perm_url)\n\n # journal belongs to character\n if journal not in char.journals:\n return deny_access(no_perm_url, \"Journal does not belong to this character.\")\n\n if journal.is_visible is False:\n flash(\"This Journal is only visible to you.\", \"warning\")\n\n return render_template(\"character/journal_view.html\", char=char, journal=journal,\n title=page_title(f\"View Journal Entry '{journal.title}'\"))\n\n\n@bp.route(\"//journal/delete//\", methods=[\"GET\"])\n@login_required\ndef journal_delete(c_id, j_id, c_name=None, j_name=None):\n char = Character.query.filter_by(id=c_id).first_or_404()\n journal = Journal.query.filter_by(id=j_id).first_or_404()\n\n if not journal.is_deletable_by_user():\n return deny_access(no_perm_url)\n\n # journal belongs to character\n if journal not in char.journals:\n return deny_access(no_perm_url, \"Journal does not belong to this character.\")\n\n db.session.delete(journal)\n db.session.commit()\n\n flash(\"Journal entry was deleted.\", \"success\")\n return redirect(char.view_url())\n", "repo_name": "TarEnethil/archivar", "sub_path": "app/character/routes.py", "file_name": "routes.py", "file_ext": "py", "file_size_in_byte": 10168, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "37", "api": [{"api_name": "app.character.forms.CreateCharacterForm", "line_number": 18, "usage_type": "call"}, {"api_name": "app.character.models.Character", "line_number": 21, "usage_type": "call"}, {"api_name": "flask_login.current_user.id", "line_number": 26, "usage_type": "attribute"}, {"api_name": "flask_login.current_user", "line_number": 26, "usage_type": "name"}, {"api_name": "app.helpers.upload_profile_picture", "line_number": 31, "usage_type": "call"}, {"api_name": "flask.flash", "line_number": 35, "usage_type": "call"}, {"api_name": "app.db.session.add", "line_number": 37, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 37, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 37, "usage_type": "name"}, {"api_name": "app.db.session.commit", "line_number": 38, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 38, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 38, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 39, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 40, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 42, "usage_type": "call"}, {"api_name": "app.helpers.page_title", "line_number": 42, "usage_type": "call"}, {"api_name": "app.character.bp.route", "line_number": 15, "usage_type": "call"}, {"api_name": "app.character.bp", "line_number": 15, "usage_type": "name"}, {"api_name": "flask_login.login_required", "line_number": 16, "usage_type": "name"}, {"api_name": "app.character.models.Character.query.filter_by", "line_number": 49, "usage_type": "call"}, {"api_name": "app.character.models.Character.query", "line_number": 49, "usage_type": "attribute"}, {"api_name": "app.character.models.Character", "line_number": 49, "usage_type": "name"}, {"api_name": "app.helpers.deny_access", "line_number": 52, "usage_type": "call"}, {"api_name": "flask.flash", "line_number": 55, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 57, "usage_type": "call"}, {"api_name": "app.helpers.page_title", "line_number": 57, "usage_type": "call"}, {"api_name": "app.character.bp.route", "line_number": 45, "usage_type": "call"}, {"api_name": "app.character.bp", "line_number": 45, "usage_type": "name"}, {"api_name": "app.character.bp.route", "line_number": 46, "usage_type": "call"}, {"api_name": "app.character.bp", "line_number": 46, "usage_type": "name"}, {"api_name": "flask_login.login_required", "line_number": 47, "usage_type": "name"}, {"api_name": "app.character.models.Character.query.filter_by", "line_number": 64, "usage_type": "call"}, {"api_name": "app.character.models.Character.query", "line_number": 64, "usage_type": "attribute"}, {"api_name": "app.character.models.Character", "line_number": 64, "usage_type": "name"}, {"api_name": "app.helpers.deny_access", "line_number": 67, "usage_type": "call"}, {"api_name": "app.character.forms.EditCharacterForm", "line_number": 69, "usage_type": "call"}, {"api_name": "datetime.datetime.utcnow", "line_number": 82, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 82, "usage_type": "name"}, {"api_name": "app.helpers.upload_profile_picture", "line_number": 92, "usage_type": "call"}, {"api_name": "app.helpers.delete_profile_picture", "line_number": 95, "usage_type": "call"}, {"api_name": "flask.flash", "line_number": 100, "usage_type": "call"}, {"api_name": "flask.flash", "line_number": 102, "usage_type": "call"}, {"api_name": "app.db.session.commit", "line_number": 103, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 103, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 103, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 105, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 118, "usage_type": "call"}, {"api_name": "app.helpers.page_title", "line_number": 119, "usage_type": "call"}, {"api_name": "app.character.bp.route", "line_number": 61, "usage_type": "call"}, {"api_name": "app.character.bp", "line_number": 61, "usage_type": "name"}, {"api_name": "flask_login.login_required", "line_number": 62, "usage_type": "name"}, {"api_name": "app.character.models.Character.get_visible_items", "line_number": 125, "usage_type": "call"}, {"api_name": "app.character.models.Character", "line_number": 125, "usage_type": "name"}, {"api_name": "app.party.models.Party.query.all", "line_number": 126, "usage_type": "call"}, {"api_name": "app.party.models.Party.query", "line_number": 126, "usage_type": "attribute"}, {"api_name": "app.party.models.Party", "line_number": 126, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 128, "usage_type": "call"}, {"api_name": "app.helpers.page_title", "line_number": 129, "usage_type": "call"}, {"api_name": "app.character.bp.route", "line_number": 122, "usage_type": "call"}, {"api_name": "app.character.bp", "line_number": 122, "usage_type": "name"}, {"api_name": "flask_login.login_required", "line_number": 123, "usage_type": "name"}, {"api_name": "app.character.models.Character.query.filter_by", "line_number": 135, "usage_type": "call"}, {"api_name": "app.character.models.Character.query", "line_number": 135, "usage_type": "attribute"}, {"api_name": "app.character.models.Character", "line_number": 135, "usage_type": "name"}, {"api_name": "app.helpers.deny_access", "line_number": 138, "usage_type": "call"}, {"api_name": "app.db.session.delete", "line_number": 142, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 142, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 142, "usage_type": "name"}, {"api_name": "app.db.session.commit", "line_number": 143, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 143, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 143, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 145, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 146, "usage_type": "call"}, {"api_name": "app.character.bp.route", "line_number": 132, "usage_type": "call"}, {"api_name": "app.character.bp", "line_number": 132, "usage_type": "name"}, {"api_name": "flask_login.login_required", "line_number": 133, "usage_type": "name"}, {"api_name": "app.character.models.Character.get_query_for_visible_items", "line_number": 152, "usage_type": "call"}, {"api_name": "app.character.models.Character", "line_number": 152, "usage_type": "name"}, {"api_name": "app.character.models.Character.id", "line_number": 153, "usage_type": "attribute"}, {"api_name": "app.character.models.Character", "line_number": 153, "usage_type": "name"}, {"api_name": "app.character.models.Character.name", "line_number": 153, "usage_type": "attribute"}, {"api_name": "app.character.models.Character.name.asc", "line_number": 153, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 155, "usage_type": "call"}, {"api_name": "app.character.bp.route", "line_number": 149, "usage_type": "call"}, {"api_name": "app.character.bp", "line_number": 149, "usage_type": "name"}, {"api_name": "flask_login.login_required", "line_number": 150, "usage_type": "name"}, {"api_name": "app.character.models.Character.query.filter_by", "line_number": 161, "usage_type": "call"}, {"api_name": "app.character.models.Character.query", "line_number": 161, "usage_type": "attribute"}, {"api_name": "app.character.models.Character", "line_number": 161, "usage_type": "name"}, {"api_name": "app.character.models.Journal.get_query_for_visible_items", "line_number": 163, "usage_type": "call"}, {"api_name": "app.character.models.Journal", "line_number": 163, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 165, "usage_type": "call"}, {"api_name": "app.helpers.page_title", "line_number": 166, "usage_type": "call"}, {"api_name": "app.character.bp.route", "line_number": 158, "usage_type": "call"}, {"api_name": "app.character.bp", "line_number": 158, "usage_type": "name"}, {"api_name": "flask_login.login_required", "line_number": 159, "usage_type": "name"}, {"api_name": "app.character.models.Character.query.filter_by", "line_number": 172, "usage_type": "call"}, {"api_name": "app.character.models.Character.query", "line_number": 172, "usage_type": "attribute"}, {"api_name": "app.character.models.Character", "line_number": 172, "usage_type": "name"}, {"api_name": "app.helpers.deny_access", "line_number": 175, "usage_type": "call"}, {"api_name": "app.character.forms.JournalForm", "line_number": 179, "usage_type": "call"}, {"api_name": "app.character.helpers.gen_session_choices", "line_number": 180, "usage_type": "call"}, {"api_name": "app.character.models.Journal", "line_number": 184, "usage_type": "call"}, {"api_name": "app.db.session.add", "line_number": 192, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 192, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 192, "usage_type": "name"}, {"api_name": "app.db.session.commit", "line_number": 193, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 193, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 193, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 194, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 196, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 199, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 199, "usage_type": "name"}, {"api_name": "flask.request.args.get", "line_number": 203, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 203, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 203, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 212, "usage_type": "call"}, {"api_name": "app.helpers.page_title", "line_number": 213, "usage_type": "call"}, {"api_name": "app.character.bp.route", "line_number": 169, "usage_type": "call"}, {"api_name": "app.character.bp", "line_number": 169, "usage_type": "name"}, {"api_name": "flask_login.login_required", "line_number": 170, "usage_type": "name"}, {"api_name": "app.character.models.Character.query.filter_by", "line_number": 219, "usage_type": "call"}, {"api_name": "app.character.models.Character.query", "line_number": 219, "usage_type": "attribute"}, {"api_name": "app.character.models.Character", "line_number": 219, "usage_type": "name"}, {"api_name": "app.character.models.Journal.query.filter_by", "line_number": 220, "usage_type": "call"}, {"api_name": "app.character.models.Journal.query", "line_number": 220, "usage_type": "attribute"}, {"api_name": "app.character.models.Journal", "line_number": 220, "usage_type": "name"}, {"api_name": "app.helpers.deny_access", "line_number": 223, "usage_type": "call"}, {"api_name": "app.helpers.deny_access", "line_number": 227, "usage_type": "call"}, {"api_name": "app.character.forms.JournalForm", "line_number": 231, "usage_type": "call"}, {"api_name": "app.character.helpers.gen_session_choices", "line_number": 232, "usage_type": "call"}, {"api_name": "app.db.session.commit", "line_number": 245, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 245, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 245, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 246, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 247, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 254, "usage_type": "call"}, {"api_name": "app.helpers.page_title", "line_number": 255, "usage_type": "call"}, {"api_name": "app.character.bp.route", "line_number": 216, "usage_type": "call"}, {"api_name": "app.character.bp", "line_number": 216, "usage_type": "name"}, {"api_name": "flask_login.login_required", "line_number": 217, "usage_type": "name"}, {"api_name": "app.character.models.Character.query.filter_by", "line_number": 261, "usage_type": "call"}, {"api_name": "app.character.models.Character.query", "line_number": 261, "usage_type": "attribute"}, {"api_name": "app.character.models.Character", "line_number": 261, "usage_type": "name"}, {"api_name": "app.character.models.Journal.query.filter_by", "line_number": 262, "usage_type": "call"}, {"api_name": "app.character.models.Journal.query", "line_number": 262, "usage_type": "attribute"}, {"api_name": "app.character.models.Journal", "line_number": 262, "usage_type": "name"}, {"api_name": "app.helpers.deny_access", "line_number": 265, "usage_type": "call"}, {"api_name": "app.helpers.deny_access", "line_number": 269, "usage_type": "call"}, {"api_name": "flask.flash", "line_number": 272, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 274, "usage_type": "call"}, {"api_name": "app.helpers.page_title", "line_number": 275, "usage_type": "call"}, {"api_name": "app.character.bp.route", "line_number": 258, "usage_type": "call"}, {"api_name": "app.character.bp", "line_number": 258, "usage_type": "name"}, {"api_name": "flask_login.login_required", "line_number": 259, "usage_type": "name"}, {"api_name": "app.character.models.Character.query.filter_by", "line_number": 281, "usage_type": "call"}, {"api_name": "app.character.models.Character.query", "line_number": 281, "usage_type": "attribute"}, {"api_name": "app.character.models.Character", "line_number": 281, "usage_type": "name"}, {"api_name": "app.character.models.Journal.query.filter_by", "line_number": 282, "usage_type": "call"}, {"api_name": "app.character.models.Journal.query", "line_number": 282, "usage_type": "attribute"}, {"api_name": "app.character.models.Journal", "line_number": 282, "usage_type": "name"}, {"api_name": "app.helpers.deny_access", "line_number": 285, "usage_type": "call"}, {"api_name": "app.helpers.deny_access", "line_number": 289, "usage_type": "call"}, {"api_name": "app.db.session.delete", "line_number": 291, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 291, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 291, "usage_type": "name"}, {"api_name": "app.db.session.commit", "line_number": 292, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 292, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 292, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 294, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 295, "usage_type": "call"}, {"api_name": "app.character.bp.route", "line_number": 278, "usage_type": "call"}, {"api_name": "app.character.bp", "line_number": 278, "usage_type": "name"}, {"api_name": "flask_login.login_required", "line_number": 279, "usage_type": "name"}]} +{"seq_id": "12509896994", "text": "import time\nfrom inputs import get_gamepad\nmaxUp = -32768\nmiddle = 0\nminDown = 32767\n\nminESC = 2600.0\nmaxESC = 3000.0\n\n\n\ndef gamepad():\n\twhile 1:\n\t\tevents = get_gamepad()\n\t\tfor event in events:\n\t\t\tif(event.code == \"ABS_Y\"):\n\t\t\t\tprint (\"Left: \"+str(event.state))\n\t\t\tif(event.code == \"ABS_RY\"):\n\t\t\t\tprint (\"Right: \"+str(event.state))\n\ngamepad()\n", "repo_name": "killkelleyr/Jetson", "sub_path": "cont.py", "file_name": "cont.py", "file_ext": "py", "file_size_in_byte": 343, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "inputs.get_gamepad", "line_number": 14, "usage_type": "call"}]} +{"seq_id": "25242664988", "text": "\"\"\"\n\n\"\"\"\n\nimport requests\n\nproxies = {\n 'http': 'socks5://127.0.0.1:9050',\n 'https': 'socks5://127.0.0.1:9050'\n}\n\n\ndef main():\n url = 'https://api.ipify.org'\n\n # Запрос без проксирования\n response = requests.get(url)\n print(f'ip: {response.text.strip()}')\n\n # Запрос с TOR проксированием\n response = requests.get(url, proxies=proxies)\n print(f'tor ip: {response.text.strip()}')\n\n\nif __name__ == '__main__':\n main()\n", "repo_name": "vakhov/local-tor-example", "sub_path": "tor.py", "file_name": "tor.py", "file_ext": "py", "file_size_in_byte": 488, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "requests.get", "line_number": 17, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 21, "usage_type": "call"}]} +{"seq_id": "27507271167", "text": "import pygame\nimport time\nimport os\nimport neat\nimport numpy as np\n\nfrom src.player import Player\nfrom src.ball import Ball\nfrom src.game import Game\nfrom src.constants import *\nfrom pygame.locals import *\n\n\ndef main(genomes, config):\n nets = []\n ge = []\n games = []\n i = 0\n for _, g in genomes:\n net = neat.nn.FeedForwardNetwork.create(g, config)\n nets.append(net)\n games.append(Game())\n g.fitness = 0\n ge.append(g)\n i += 1\n\n global FPSCLOCK, DISPLAYSURF\n pygame.init()\n FPSCLOCK = pygame.time.Clock()\n DISPLAYSURF = pygame.display.set_mode((WINDOWWIDTH, WINDOWHEIGHT))\n\n pygame.display.set_caption('Breakout')\n start_time = pygame.time.get_ticks()\n\n while games:\n new_time = (pygame.time.get_ticks() - start_time) / 1000\n\n draw_blank()\n border_rects = draw_borders()\n\n games[0].draw_scoreboard(new_time, DISPLAYSURF)\n brick_rects = games[0].draw_bricks(DISPLAYSURF)\n for game in games:\n game.draw_player(DISPLAYSURF)\n game.draw_ball(DISPLAYSURF)\n\n # Collision detection\n for x, game in enumerate(games):\n if new_time > 100:\n games.pop(x)\n nets.pop(x)\n ge.pop(x)\n continue\n game.check_border_collisions(border_rects)\n old_brick = game.check_brick_collisions(brick_rects)\n if old_brick:\n for row in range(len(brick_rects)):\n for col in range(len(brick_rects[0])):\n if brick_rects[row][col] == old_brick:\n game.score += 1\n ge[x].fitness += 1\n game.is_brick[row][col] = False\n continue\n game.ball.update_ball_pos(game.ball.x_velocity,\n game.ball.y_velocity)\n\n # Lost a life\n if game.ball.y > WINDOWHEIGHT + game.ball.width:\n if game.lives == 0:\n ge[x].fitness -= 1\n games.pop(x)\n nets.pop(x)\n ge.pop(x)\n continue\n\n time.sleep(1)\n game.ball = Ball(WINDOWWIDTH / 2, BRICKCEILING + 200, 1.0)\n game.lives -= 1\n\n output = nets[x].activate((game.player.x, game.ball.y, game.ball.x,\n bricks_product(game.is_brick)))\n if output[0] > 0.5:\n game.player.move_right(BARWIDTH, WINDOWWIDTH)\n if output[1] > 0.5:\n game.player.move_left(BARWIDTH)\n\n if game.is_win():\n if game.level == 1:\n ge[x].fitness += 100 - new_time\n games.pop(x)\n nets.pop(x)\n ge.pop(x)\n continue\n # reset whole game\n time.sleep(1)\n game.ball = Ball(WINDOWWIDTH / 2, BRICKCEILING + 200, 1.0)\n game.player = Player(WINDOWWIDTH / 2, WINDOWHEIGHT - 10)\n game.is_brick = [[True for _ in range(ROWSIZE)]\n for _ in BRICKCOLORS]\n game.level += 1\n\n pygame.display.update()\n FPSCLOCK.tick(FPS)\n pygame.quit()\n\n\ndef bricks_product(is_brick):\n flat = [b for brick in is_brick for b in brick]\n temp = [brick * prime for brick, prime in zip(flat, PRIMES)]\n temp = [a for a in temp if a != 0]\n return np.prod(np.array(temp))\n\n\n# erases previous drawing of game\ndef draw_blank():\n pygame.draw.rect(DISPLAYSURF, BLACK,\n (BARWIDTH, HEADERHEIGHT + BARWIDTH,\n WINDOWWIDTH - BARWIDTH * 2,\n WINDOWHEIGHT - HEADERHEIGHT - BARWIDTH))\n\n\ndef draw_borders():\n top_bar = pygame.Rect(0, HEADERHEIGHT, WINDOWWIDTH, BARWIDTH)\n left_bar = pygame.Rect(0, HEADERHEIGHT, BARWIDTH, BARHEIGHT)\n right_bar = pygame.Rect(WINDOWWIDTH - BARWIDTH, HEADERHEIGHT, BARWIDTH,\n BARHEIGHT)\n cyan_bar = pygame.Rect(0, BARHEIGHT + HEADERHEIGHT, BARWIDTH, 20)\n red_bar = pygame.Rect(WINDOWWIDTH - BARWIDTH, BARHEIGHT + HEADERHEIGHT,\n BARWIDTH, 20)\n\n pygame.draw.rect(DISPLAYSURF, GRAY, top_bar)\n pygame.draw.rect(DISPLAYSURF, GRAY, left_bar)\n pygame.draw.rect(DISPLAYSURF, GRAY, right_bar)\n pygame.draw.rect(DISPLAYSURF, CYAN, cyan_bar)\n pygame.draw.rect(DISPLAYSURF, RED, red_bar)\n\n return [top_bar, left_bar, right_bar, cyan_bar, red_bar]\n\n\ndef run(config_path):\n config = neat.config.Config(neat.DefaultGenome, neat.DefaultReproduction,\n neat.DefaultSpeciesSet, neat.DefaultStagnation,\n config_path)\n p = neat.Population(config)\n p.add_reporter(neat.StdOutReporter(True))\n stats = neat.StatisticsReporter()\n p.add_reporter(stats)\n\n winner = p.run(main, 5000)\n\n\nif __name__ == '__main__':\n local_dir = os.path.dirname(__file__)\n config_path = os.path.join(local_dir, \"config-feedforward.txt\")\n run(config_path)\n", "repo_name": "bmwade8/DM-Breakout", "sub_path": "src/breakout.py", "file_name": "breakout.py", "file_ext": "py", "file_size_in_byte": 5179, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "neat.nn.FeedForwardNetwork.create", "line_number": 20, "usage_type": "call"}, {"api_name": "neat.nn", "line_number": 20, "usage_type": "attribute"}, {"api_name": "src.game.Game", "line_number": 22, "usage_type": "call"}, {"api_name": "pygame.init", "line_number": 28, "usage_type": "call"}, {"api_name": "pygame.time.Clock", "line_number": 29, "usage_type": "call"}, {"api_name": "pygame.time", "line_number": 29, "usage_type": "attribute"}, {"api_name": "pygame.display.set_mode", "line_number": 30, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 30, "usage_type": "attribute"}, {"api_name": "pygame.display.set_caption", "line_number": 32, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 32, "usage_type": "attribute"}, {"api_name": "pygame.time.get_ticks", "line_number": 33, "usage_type": "call"}, {"api_name": "pygame.time", "line_number": 33, "usage_type": "attribute"}, {"api_name": "pygame.time.get_ticks", "line_number": 36, "usage_type": "call"}, {"api_name": "pygame.time", "line_number": 36, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 76, "usage_type": "call"}, {"api_name": "src.ball.Ball", "line_number": 77, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 95, "usage_type": "call"}, {"api_name": "src.ball.Ball", "line_number": 96, "usage_type": "call"}, {"api_name": "src.player.Player", "line_number": 97, "usage_type": "call"}, {"api_name": "pygame.display.update", "line_number": 102, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 102, "usage_type": "attribute"}, {"api_name": "pygame.quit", "line_number": 104, "usage_type": "call"}, {"api_name": "numpy.prod", "line_number": 111, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 111, "usage_type": "call"}, {"api_name": "pygame.draw.rect", "line_number": 116, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 116, "usage_type": "attribute"}, {"api_name": "pygame.Rect", "line_number": 123, "usage_type": "call"}, {"api_name": "pygame.Rect", "line_number": 124, "usage_type": "call"}, {"api_name": "pygame.Rect", "line_number": 125, "usage_type": "call"}, {"api_name": "pygame.Rect", "line_number": 127, "usage_type": "call"}, {"api_name": "pygame.Rect", "line_number": 128, "usage_type": "call"}, {"api_name": "pygame.draw.rect", "line_number": 131, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 131, "usage_type": "attribute"}, {"api_name": "pygame.draw.rect", "line_number": 132, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 132, "usage_type": "attribute"}, {"api_name": "pygame.draw.rect", "line_number": 133, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 133, "usage_type": "attribute"}, {"api_name": "pygame.draw.rect", "line_number": 134, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 134, "usage_type": "attribute"}, {"api_name": "pygame.draw.rect", "line_number": 135, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 135, "usage_type": "attribute"}, {"api_name": "neat.config.Config", "line_number": 141, "usage_type": "call"}, {"api_name": "neat.config", "line_number": 141, "usage_type": "attribute"}, {"api_name": "neat.DefaultGenome", "line_number": 141, "usage_type": "attribute"}, {"api_name": "neat.DefaultReproduction", "line_number": 141, "usage_type": "attribute"}, {"api_name": "neat.DefaultSpeciesSet", "line_number": 142, "usage_type": "attribute"}, {"api_name": "neat.DefaultStagnation", "line_number": 142, "usage_type": "attribute"}, {"api_name": "neat.Population", "line_number": 144, "usage_type": "call"}, {"api_name": "neat.StdOutReporter", "line_number": 145, "usage_type": "call"}, {"api_name": "neat.StatisticsReporter", "line_number": 146, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 153, "usage_type": "call"}, {"api_name": "os.path", "line_number": 153, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 154, "usage_type": "call"}, {"api_name": "os.path", "line_number": 154, "usage_type": "attribute"}]} +{"seq_id": "35890298398", "text": "import os\nimport sys\nimport cocotb\nimport logging\nfrom cocotb.result import TestFailure\nfrom cocotb.result import TestSuccess\nfrom cocotb.clock import Clock\nfrom cocotb.triggers import Timer , RisingEdge\nfrom AXI4_MASTER_Driver import AXI4_master\nimport b_ram_predefined_signals as up\n\nCLK_PERIOD = 10\ndef setup_dut(dut):\n\tcocotb.fork(Clock(dut.CLK, CLK_PERIOD).start())\n\"\"\"\naddress = address of the first transfer in a write burst transaction in uart case\n\nno_of_beats_in_burst = the exact number of transfers or beats in a burst . \n\nsize_of_beat_in_bytes = the size(in bytes) of each transfer or beats in the burst.\nin case uart\n\ndata = the data to be written on the memory\n\nlast_beat_of_burst= True (indicates the last transfer in a write burst)\n\t\t\t\t = False (indicates the not last transfer in a write burst)\n\n\"\"\"\n\n@cocotb.test(skip = False)\ndef write_read(dut):\n\n\tsetup_dut(dut)\n\tdut.RST_N <= 0\n\tyield Timer(CLK_PERIOD * 10)\n\taxim = AXI4_master(dut, \"axi_slave_slave\", dut.CLK)\n\t\n\tdut.RST_N <= 1\n\tyield Timer(CLK_PERIOD)\n\t\n\t\"\"\"\n\tsetting the mem_address (0) = 5\n\t\"\"\"\n\taddress = 0\n\tno_of_beats_in_burst = 1 \n\tsize_of_beat_in_bytes = 1\n\t\n\n\tyield axim._send_write_address(address,up.AxID,up.AxPROT,no_of_beats_in_burst,size_of_beat_in_bytes,up.burst_type)\n\tdut.log.info(\" address(0) was read by slave\")\n\n\t\n\tdata = 0x0005\n\tlast_beat_of_burst = True\n\t\n\tyield axim._send_write_data(data,up.WID,up.Baud_WSTRB,last_beat_of_burst)\n\tdut.log.info(\" data was read by slave\")\n\n\n\t_BRESP = yield axim._get_write_response()\n\tdut.log.info(\"the value of BRESP = %s\" %_BRESP)\n\tyield Timer(CLK_PERIOD * 10)\n\t\"\"\"\n\treading back the data from the Baudreg\n\t\"\"\"\n\tno_of_beats_in_burst = 1 \n\tsize_of_beat_in_bytes = 1\n\n\n\tyield axim._send_Read_address(address,up.AxID,up.AxPROT,no_of_beats_in_burst,size_of_beat_in_bytes,up.burst_type)\n\tdut.log.info(\" address(0) was read by slave\")\n\n\t_RDATA=yield axim._get_Read_data()\n\tif int(str(_RDATA)[0:16] , 2) == int(data) :\n\t\tdut.log.info(\"the value = %d\" %int(str(_RDATA)[0:8] , 2))\t\n\telse:\n\t\traise TestFailure(\"Data read is incorrect %d \"%int(str(_RDATA)[0:8] , 2))\n\n\n\n\n", "repo_name": "cbkathir/shakti_intern", "sub_path": "py-uvm/b_ram/tests/b_ram_test_1.py", "file_name": "b_ram_test_1.py", "file_ext": "py", "file_size_in_byte": 2094, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "cocotb.fork", "line_number": 14, "usage_type": "call"}, {"api_name": "cocotb.clock.Clock", "line_number": 14, "usage_type": "call"}, {"api_name": "cocotb.triggers.Timer", "line_number": 35, "usage_type": "call"}, {"api_name": "AXI4_MASTER_Driver.AXI4_master", "line_number": 36, "usage_type": "call"}, {"api_name": "cocotb.triggers.Timer", "line_number": 39, "usage_type": "call"}, {"api_name": "b_ram_predefined_signals.AxID", "line_number": 49, "usage_type": "attribute"}, {"api_name": "b_ram_predefined_signals.AxPROT", "line_number": 49, "usage_type": "attribute"}, {"api_name": "b_ram_predefined_signals.burst_type", "line_number": 49, "usage_type": "attribute"}, {"api_name": "b_ram_predefined_signals.WID", "line_number": 56, "usage_type": "attribute"}, {"api_name": "b_ram_predefined_signals.Baud_WSTRB", "line_number": 56, "usage_type": "attribute"}, {"api_name": "cocotb.triggers.Timer", "line_number": 62, "usage_type": "call"}, {"api_name": "b_ram_predefined_signals.AxID", "line_number": 70, "usage_type": "attribute"}, {"api_name": "b_ram_predefined_signals.AxPROT", "line_number": 70, "usage_type": "attribute"}, {"api_name": "b_ram_predefined_signals.burst_type", "line_number": 70, "usage_type": "attribute"}, {"api_name": "cocotb.result.TestFailure", "line_number": 77, "usage_type": "call"}, {"api_name": "cocotb.test", "line_number": 30, "usage_type": "call"}]} +{"seq_id": "11679596309", "text": "from setuptools import setup, Command\n\nversion = '0.3.4'\n\n\nclass TestCommand(Command):\n user_options = []\n\n def initialize_options(self):\n pass\n\n def finalize_options(self):\n pass\n\n def run(self):\n from django.conf import settings\n settings.configure(\n DATABASES={\n 'default': {\n 'NAME': ':memory:',\n 'ENGINE': 'django.db.backends.sqlite3',\n },\n 'other': {\n 'NAME': ':memory:',\n 'ENGINE': 'django.db.backends.sqlite3',\n },\n },\n INSTALLED_APPS=('django_uidfield', 'django.contrib.contenttypes'),\n DEFAULT_AUTO_FIELD='django.db.models.AutoField',\n )\n from django.core.management import call_command\n import django\n\n django.setup()\n\n call_command('test', 'django_uidfield')\n\n\nsetup(\n name='django-uidfield',\n version=version,\n description='django-uidfield is a library which includes class '\n 'UIDField for models.',\n long_description=open('README.rst').read(),\n keywords='django model field',\n license='MIT',\n author='ivelum',\n author_email='info@ivelum.com',\n url='https://github.com/ivelum/django-uidfield/',\n install_requires=[\n 'django',\n ],\n classifiers=[\n 'Development Status :: 4 - Beta',\n 'Environment :: Plugins',\n 'Framework :: Django',\n 'Intended Audience :: Developers',\n 'License :: OSI Approved :: MIT License',\n 'Programming Language :: Python',\n 'Topic :: Software Development :: Libraries :: Python Modules',\n ],\n packages=['django_uidfield'],\n include_package_data=True,\n cmdclass={'test': TestCommand},\n)\n", "repo_name": "ivelum/django-uidfield", "sub_path": "setup.py", "file_name": "setup.py", "file_ext": "py", "file_size_in_byte": 1796, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 21, "dataset": "github-code", "pt": "37", "api": [{"api_name": "setuptools.Command", "line_number": 6, "usage_type": "name"}, {"api_name": "django.conf.settings.configure", "line_number": 17, "usage_type": "call"}, {"api_name": "django.conf.settings", "line_number": 17, "usage_type": "name"}, {"api_name": "django.setup", "line_number": 34, "usage_type": "call"}, {"api_name": "django.core.management.call_command", "line_number": 36, "usage_type": "call"}, {"api_name": "setuptools.setup", "line_number": 39, "usage_type": "call"}]} +{"seq_id": "70604352427", "text": "# Importing Libraries\r\n\r\nimport warnings\r\nwarnings.filterwarnings('ignore')\r\n\r\nimport numpy as np\r\nimport pandas as pd\r\nimport matplotlib.pyplot as plt\r\nimport seaborn as sns\r\nfrom itertools import permutations #Permutation and Combination in Python\r\n\r\nfrom sklearn.ensemble import RandomForestClassifier\r\nfrom sklearn.preprocessing import StandardScaler\r\nfrom sklearn.ensemble import VotingClassifier\r\nfrom sklearn.svm import SVC\r\nfrom sklearn.model_selection import RandomizedSearchCV, train_test_split, GridSearchCV\r\nfrom sklearn.linear_model import LogisticRegression\r\nfrom sklearn.metrics import confusion_matrix, classification_report, accuracy_score, roc_curve, roc_auc_score\r\nfrom sklearn.model_selection import cross_val_score\r\n\r\n# Getting our data\r\n\r\ndata = pd.read_csv('heart.csv')\r\n\r\n# Renaming the columns for better understanding of features\r\n\r\ndata.columns = ['age', 'sex', 'chest_pain_type', 'resting_blood_pressure', 'serum_cholesterol', 'fasting_blood_sugar', 'rest_ecg', 'max_heart_rate',\r\n 'exercise_angina', 'st_depression', 'st_slope', 'num_major_vessels', 'thalassemia', 'target']\r\n\r\n# Lets see how our features correlate with the target variable\r\n#corr() function to find the correlation among the columns in the dataframe\r\nx = data.corr()\r\npd.DataFrame(x['target']).sort_values(by='target',ascending = False).style.background_gradient(cmap = 'copper')\r\n\r\ndata.chest_pain_type = data.chest_pain_type.map({1:'angina pectoris', 2:'atypical angina', 3:'non-anginal pain', 4:'SMI', 0:'absent'})\r\n\r\ndata.st_slope = data.st_slope.map({1:'upsloping', 2:'horizontal', 3:'downsloping', 0:'absent'})\r\n\r\ndata.thalassemia = data.thalassemia.map({1:'normal', 2:'fixed defect', 3:'reversable defect', 0:'absent'})\r\n\r\n# Seperating out predictors(X) and target(Y)\r\n\r\nX = data.iloc[:, 0:13] #all rows and 0 to 12 columns of data frame\r\n\r\nY = data.iloc[:, -1] #last column of data frame\r\n\r\n# Encoding the categorical variables using get_dummies()\r\ncategorical_columns = ['chest_pain_type', 'thalassemia', 'st_slope']\r\n\r\nfor column in categorical_columns:\r\n dummies = pd.get_dummies(X[column], drop_first = True)\r\n X[dummies.columns] = dummies\r\n X.drop(column, axis =1, inplace = True)\r\n\r\n# Let us again look at the correlation against our target variable\r\n\r\ntemp = X.copy()\r\ntemp['target'] = Y\r\n\r\nd = temp.corr()\r\npd.DataFrame(d['target']).sort_values(by='target',ascending = False).style.background_gradient(cmap = 'copper')\r\n\r\n# Splitting the data into test and train\r\n\r\nX_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.2, random_state=42)\r\n\r\n# Scaling the continous data\r\n\r\nnum_columns = ['resting_blood_pressure','serum_cholesterol', 'age', 'max_heart_rate', 'st_depression']\r\n\r\nscaler = StandardScaler()\r\n\r\nscaler.fit(X_train[num_columns])\r\n\r\nX_train[num_columns] = scaler.transform(X_train[num_columns])\r\n\r\nX_test[num_columns] = scaler.transform(X_test[num_columns])\r\n\r\n#\r\n#Modeling\r\n#\r\n# Creating a function to plot correlation matrix and roc_auc_curve\r\n\r\ndef show_metrics(model):\r\n fig = plt.figure(figsize=(25, 10))\r\n\r\n # Confusion matrix\r\n fig.add_subplot(121)\r\n sns.heatmap(confusion_matrix(y_test, y_pred), annot=True, annot_kws={\"size\": 16}, fmt='g')\r\n\r\n # ROC Curve\r\n fig.add_subplot(122)\r\n\r\n auc_roc = roc_auc_score(y_test, model.predict(X_test))\r\n fpr, tpr, thresholds = roc_curve(y_test, model.predict_proba(X_test)[:, 1])\r\n\r\n plt.plot(fpr, tpr, color='darkorange', lw=2, marker='o', label='Trained Model (area = {0:0.3f})'.format(auc_roc))\r\n plt.plot([0, 1], [0, 1], color='blue', lw=2, linestyle='--', label='No Skill (area = 0.500)')\r\n plt.xlim([0.0, 1.0])\r\n plt.ylim([0.0, 1.05])\r\n plt.xlabel('False Positive Rate')\r\n plt.ylabel('True Positive Rate')\r\n plt.title('Receiver operating characteristic')\r\n plt.legend(loc=\"lower right\")\r\n plt.show()\r\n\r\n# creating our model instance\r\nlog_reg = LogisticRegression()\r\n\r\n# fitting the model\r\nlog_reg.fit(X_train, y_train)\r\n\r\n# predicting the target vectors\r\ny_pred=log_reg.predict(X_test)\r\n\r\n# let's look at our accuracy of Logistic regression\r\naccuracy = accuracy_score(y_pred, y_test)\r\n#print(f\"The accuracy on test set using Logistic Regression is: {np.round(accuracy, 3)*100.0}%\")\r\n\r\n#\r\n# KNN\r\n#\r\n\r\nfrom sklearn.neighbors import KNeighborsClassifier\r\n\r\n# creating a list of K's for performing KNN\r\nmy_list = list(range(0,30))\r\n\r\n# filtering out only the odd K values\r\nneighbors = list(filter(lambda x: x % 2 != 0, my_list))\r\n\r\n# list to hold the cv scores\r\ncv_scores = []\r\n\r\n# perform 10-fold cross validation with default weights\r\nfor k in neighbors:\r\n Knn = KNeighborsClassifier(n_neighbors = k, algorithm = 'brute')\r\n scores = cross_val_score(Knn, X_train, y_train, cv=10, scoring='accuracy', n_jobs = -1)\r\n cv_scores.append(scores.mean())\r\n\r\n# finding the optimal k\r\noptimal_k = neighbors[cv_scores.index(max(cv_scores))]\r\n\r\n# Finding the accuracy of KNN with optimal K\r\n\r\nfrom sklearn.metrics import accuracy_score\r\n\r\n# create instance of classifier\r\nknn_optimal = KNeighborsClassifier(n_neighbors = optimal_k, algorithm = 'kd_tree',\r\n n_jobs = -1)\r\n\r\n# fit the model\r\nknn_optimal.fit(X_train, y_train)\r\n\r\n# predict on test vector\r\ny_pred = knn_optimal.predict(X_test)\r\n\r\n# evaluate accuracy score\r\naccuracy = accuracy_score(y_test, y_pred)*100\r\n\r\n#\r\n# SVM\r\n#\r\n\r\n# Creating an instance of the classifier\r\nsvm = SVC()\r\n\r\n# training on train data\r\nsvm.fit(X_train, y_train)\r\n\r\n# predicting on test data\r\ny_pred = svm.predict(X_test)\r\n\r\n# let's look at our accuracy\r\naccuracy = accuracy_score(y_pred, y_test)\r\n\r\n#print(f\"The accuracy on test set using SVC is: {np.round(accuracy, 3)*100.0}%\")\r\n\r\n# Number of trees in random forest\r\nn_estimators = [int(x) for x in np.linspace(start = 200, stop = 2000, num = 10)]\r\n\r\n# Number of features to consider at every split\r\nmax_features = ['auto', 'sqrt']\r\n\r\n# Maximum number of levels in tree\r\nmax_depth = [int(x) for x in np.linspace(10, 110, num = 11)]\r\nmax_depth.append(None)\r\n\r\n# Minimum number of samples required to split a node\r\nmin_samples_split = [2, 5, 10]\r\n\r\n# Minimum number of samples required at each leaf node\r\nmin_samples_leaf = [1, 2, 4]\r\n\r\n# Method of selecting samples for training each tree\r\nbootstrap = [True, False]\r\n\r\n# Create the random grid\r\nrandom_grid = {'n_estimators': n_estimators,\r\n 'max_features': max_features,\r\n 'max_depth': max_depth,\r\n 'min_samples_split': min_samples_split,\r\n 'min_samples_leaf': min_samples_leaf,\r\n 'bootstrap': bootstrap}\r\n\r\n# Use the random grid to search for best hyperparameters\r\n\r\n# First create the base model to tune\r\nrf = RandomForestClassifier()\r\n\r\n# Random search of parameters, using 3 fold cross validation,\r\n# search across 100 different combinations, and use all available cores\r\nrf_random = RandomizedSearchCV(estimator = rf, param_distributions = random_grid, n_iter = 100, cv = 3, verbose=2, random_state=42, n_jobs = -1)\r\n\r\n# Fit the random search model\r\nrf_random.fit(X_train, y_train)\r\n\r\n# Creating an instance for the classifier\r\nrf_best = RandomForestClassifier(**rf_random.best_params_)\r\n\r\n# fitting the model\r\nrf_best.fit(X_train, y_train)\r\n\r\n# predict the labels\r\ny_pred = rf_best.predict(X_test)\r\n\r\naccuracy = accuracy_score(y_pred, y_test)\r\n\r\n#\r\n# Ensemble Voting classifier\r\n#\r\n# creating a list of our models\r\nensembles = [log_reg, knn_optimal, rf_best, svm]\r\n\r\n# Train each of the model\r\nfor estimator in ensembles:\r\n print(\"Training the\", estimator)\r\n estimator.fit(X_train,y_train)\r\n\r\n# Find the scores of each estimator\r\nscores = [estimator.score(X_test, y_test) for estimator in ensembles]\r\n\r\n# Lets define our estimators in a list\r\n\r\nnamed_estimators = [\r\n (\"log_reg\",log_reg),\r\n ('random_forest', rf_best),\r\n ('svm',svm),\r\n ('knn', knn_optimal),\r\n]\r\n\r\n# Creating an instance for our Voting classifier\r\n\r\nvoting_clf = VotingClassifier(named_estimators)\r\n\r\n# Fit the classifier\r\n\r\nvoting_clf.fit(X_train,y_train)\r\n\r\nvote_model = voting_clf.fit(X_train,y_train)\r\n\r\n# Let's look at our accuracy\r\nacc = voting_clf.score(X_test,y_test)\r\n\r\n#print(f\"The accuracy on test set using voting classifier is {np.round(acc, 3)*100}%\")\r\n\r\n\r\nimport pickle\r\n# open a file, where you ant to store the data\r\nfile = open('Heart_model_new.pkl', 'wb')\r\n\r\n# dump information to that file\r\npickle.dump(vote_model, file)", "repo_name": "frason88/Heart-Pred-Project", "sub_path": "Heart_model_new.py", "file_name": "Heart_model_new.py", "file_ext": "py", "file_size_in_byte": 8467, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "warnings.filterwarnings", "line_number": 4, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 23, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 33, "usage_type": "call"}, {"api_name": "pandas.get_dummies", "line_number": 51, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 61, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 65, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.StandardScaler", "line_number": 71, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 85, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 85, "usage_type": "name"}, {"api_name": "seaborn.heatmap", "line_number": 89, "usage_type": "call"}, {"api_name": "sklearn.metrics.confusion_matrix", "line_number": 89, "usage_type": "call"}, {"api_name": "sklearn.metrics.roc_auc_score", "line_number": 94, "usage_type": "call"}, {"api_name": "sklearn.metrics.roc_curve", "line_number": 95, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 97, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 97, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 98, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 98, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 99, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 99, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 100, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 100, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 101, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 101, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 102, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 102, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 103, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 103, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 104, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 104, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 105, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 105, "usage_type": "name"}, {"api_name": "sklearn.linear_model.LogisticRegression", "line_number": 108, "usage_type": "call"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 117, "usage_type": "call"}, {"api_name": "sklearn.neighbors.KNeighborsClassifier", "line_number": 137, "usage_type": "call"}, {"api_name": "sklearn.model_selection.cross_val_score", "line_number": 138, "usage_type": "call"}, {"api_name": "sklearn.neighbors.KNeighborsClassifier", "line_number": 149, "usage_type": "call"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 159, "usage_type": "call"}, {"api_name": "sklearn.svm.SVC", "line_number": 166, "usage_type": "call"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 175, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 180, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 186, "usage_type": "call"}, {"api_name": "sklearn.ensemble.RandomForestClassifier", "line_number": 209, "usage_type": "call"}, {"api_name": "sklearn.model_selection.RandomizedSearchCV", "line_number": 213, "usage_type": "call"}, {"api_name": "sklearn.ensemble.RandomForestClassifier", "line_number": 219, "usage_type": "call"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 227, "usage_type": "call"}, {"api_name": "sklearn.ensemble.VotingClassifier", "line_number": 254, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 273, "usage_type": "call"}]} +{"seq_id": "18662534931", "text": "from multiprocessing import Process,Pipe\nimport os,time\n#创建一个双向管道\nfd1,fd2 = Pipe()\n\ndef fun(name):\n\ttime.sleep(1)\n\t#发送字符串到管道\n\tfd1.send('hello'+str(name))\n\tprint(os.getppid(),'------',os.getpid())\n\njobs = []\n\nif __name__ == '__main__':\n\tfor i in range(5):\n\t\tp = Process(target = fun,args = (i,))\n\t\tjobs.append(p)\n\t\tp.start()\n\tprint(os.getpid())\n\n#接受子进程发送的消息\n\tfor i in range(5):\n\t\tdata = fd2.recv()\n\t\tprint(data)\n\n\tfor i in jobs:\n\t\ti.join()", "repo_name": "jiyabing/learning", "sub_path": "开班笔记/python网络编程及MySQL部分/day32/code/pipe.py", "file_name": "pipe.py", "file_ext": "py", "file_size_in_byte": 490, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "multiprocessing.Pipe", "line_number": 4, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 7, "usage_type": "call"}, {"api_name": "os.getppid", "line_number": 10, "usage_type": "call"}, {"api_name": "os.getpid", "line_number": 10, "usage_type": "call"}, {"api_name": "multiprocessing.Process", "line_number": 16, "usage_type": "call"}, {"api_name": "os.getpid", "line_number": 19, "usage_type": "call"}]} +{"seq_id": "15963421216", "text": "# import relevant libraries\n# you may have to install pygame with: python3 -m pip install -U pygame --user\nimport pygame\nfrom components import board\n\n# initialize pygame\npygame.init()\n\n# initialize the board\nboard = board(25, 24, 24, 50)\n\n\n# set window dimensions\nwin = pygame.display.set_mode(board.dimensions)\n\n# set the title of the window\npygame.display.set_caption(\"Snekbot\")\n\n# set the clock used for managing fps in the main loop\nclock = pygame.time.Clock()\n\n\n\n# main loop\nrun = True\nwhile run:\n # set the fps\n clock.tick(30)\n\n # end the loop when the user closes the window\n for event in pygame.event.get():\n if event.type == pygame.QUIT:\n run = False\n\n keys = pygame.key.get_pressed()\n\n if keys[pygame.K_LEFT]:\n board.snek.set_direction(\"left\")\n elif keys[pygame.K_RIGHT]:\n board.snek.set_direction(\"right\")\n elif keys[pygame.K_UP]:\n board.snek.set_direction(\"up\")\n elif keys[pygame.K_DOWN]:\n board.snek.set_direction(\"down\")\n\n\n # draw the board\n board.draw(win)\n\n # refresh the window\n pygame.display.update()\n\npygame.quit()\n", "repo_name": "zhandavidz/snekbot", "sub_path": "snek.py", "file_name": "snek.py", "file_ext": "py", "file_size_in_byte": 1121, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "37", "api": [{"api_name": "pygame.init", "line_number": 7, "usage_type": "call"}, {"api_name": "components.board", "line_number": 10, "usage_type": "name"}, {"api_name": "pygame.display.set_mode", "line_number": 14, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 14, "usage_type": "attribute"}, {"api_name": "components.board.dimensions", "line_number": 14, "usage_type": "attribute"}, {"api_name": "components.board", "line_number": 14, "usage_type": "name"}, {"api_name": "pygame.display.set_caption", "line_number": 17, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 17, "usage_type": "attribute"}, {"api_name": "pygame.time.Clock", "line_number": 20, "usage_type": "call"}, {"api_name": "pygame.time", "line_number": 20, "usage_type": "attribute"}, {"api_name": "pygame.event.get", "line_number": 31, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 31, "usage_type": "attribute"}, {"api_name": "pygame.QUIT", "line_number": 32, "usage_type": "attribute"}, {"api_name": "pygame.key.get_pressed", "line_number": 35, "usage_type": "call"}, {"api_name": "pygame.key", "line_number": 35, "usage_type": "attribute"}, {"api_name": "pygame.K_LEFT", "line_number": 37, "usage_type": "attribute"}, {"api_name": "components.board.snek.set_direction", "line_number": 38, "usage_type": "call"}, {"api_name": "components.board.snek", "line_number": 38, "usage_type": "attribute"}, {"api_name": "components.board", "line_number": 38, "usage_type": "name"}, {"api_name": "pygame.K_RIGHT", "line_number": 39, "usage_type": "attribute"}, {"api_name": "components.board.snek.set_direction", "line_number": 40, "usage_type": "call"}, {"api_name": "components.board.snek", "line_number": 40, "usage_type": "attribute"}, {"api_name": "components.board", "line_number": 40, "usage_type": "name"}, {"api_name": "pygame.K_UP", "line_number": 41, "usage_type": "attribute"}, {"api_name": "components.board.snek.set_direction", "line_number": 42, "usage_type": "call"}, {"api_name": "components.board.snek", "line_number": 42, "usage_type": "attribute"}, {"api_name": "components.board", "line_number": 42, "usage_type": "name"}, {"api_name": "pygame.K_DOWN", "line_number": 43, "usage_type": "attribute"}, {"api_name": "components.board.snek.set_direction", "line_number": 44, "usage_type": "call"}, {"api_name": "components.board.snek", "line_number": 44, "usage_type": "attribute"}, {"api_name": "components.board", "line_number": 44, "usage_type": "name"}, {"api_name": "components.board.draw", "line_number": 48, "usage_type": "call"}, {"api_name": "components.board", "line_number": 48, "usage_type": "name"}, {"api_name": "pygame.display.update", "line_number": 51, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 51, "usage_type": "attribute"}, {"api_name": "pygame.quit", "line_number": 53, "usage_type": "call"}]} +{"seq_id": "31810985932", "text": "import os\nimport argparse\nfrom docx import Document\nfrom modules.tools import convert_docx\n\nif __name__=='__main__':\n parser = argparse.ArgumentParser()\n parser.add_argument('-f', '--file', type = str, required = True)\n args = parser.parse_args( )\n if not os.path.isfile(args.file):\n raise ValueError(\"The file does not exist.\")\n\n folder = os.path.dirname(args.file) + \"/\"\n filename = os.path.basename(args.file)\n output_filename = filename.split('.')[0] + \"-converted.docx\"\n diff_filename = filename.split('.')[0] + \"-diff.csv\"\n\n document = Document(folder + filename)\n document, df = convert_docx(document)\n\n document.save(folder + output_filename)\n df.to_csv(folder + diff_filename, index = False)\n\n\n", "repo_name": "Daisuke0209/word-conversion", "sub_path": "convert.py", "file_name": "convert.py", "file_ext": "py", "file_size_in_byte": 718, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 7, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 10, "usage_type": "call"}, {"api_name": "os.path", "line_number": 10, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 13, "usage_type": "call"}, {"api_name": "os.path", "line_number": 13, "usage_type": "attribute"}, {"api_name": "os.path.basename", "line_number": 14, "usage_type": "call"}, {"api_name": "os.path", "line_number": 14, "usage_type": "attribute"}, {"api_name": "docx.Document", "line_number": 18, "usage_type": "call"}, {"api_name": "modules.tools.convert_docx", "line_number": 19, "usage_type": "call"}]} +{"seq_id": "29984274274", "text": "from flask import Flask, request\nfrom flask.helpers import make_response\nfrom werkzeug.utils import redirect\n\napp = Flask(__name__)\n\n@app.route('/')\ndef index():\n user_ip = request.remote_addr\n response = make_response(redirect('/hello'))\n response.set_cookie('user_ip', user_ip)\n return response\n \n@app.route('/hello')\ndef hello():\n user_ip = request.cookies.get('user_ip')\n return \"Estado anterior, su ip es {}\".format(user_ip)\n\nif __name__ == '__main__': \n app.run()\n ", "repo_name": "JazzzFM/JazzzDevelopmentWithPython", "sub_path": "Flask/1-hello-word.py/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 498, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "37", "api": [{"api_name": "flask.Flask", "line_number": 5, "usage_type": "call"}, {"api_name": "flask.request.remote_addr", "line_number": 9, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 9, "usage_type": "name"}, {"api_name": "flask.helpers.make_response", "line_number": 10, "usage_type": "call"}, {"api_name": "werkzeug.utils.redirect", "line_number": 10, "usage_type": "call"}, {"api_name": "flask.request.cookies.get", "line_number": 16, "usage_type": "call"}, {"api_name": "flask.request.cookies", "line_number": 16, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 16, "usage_type": "name"}]} +{"seq_id": "8565192158", "text": "from django.contrib.auth.models import AbstractUser\nfrom django.db import models\n\n\nclass User(AbstractUser):\n pass\n \n\nclass Listing(models.Model):\n bid_owner = models.ForeignKey(User, on_delete=models.CASCADE, related_name=\"owners\")\n bid_title= models.CharField(max_length=64)\n bid_description= models.CharField(max_length=200)\n bid_image=models.CharField(max_length=200, blank=True)\n bid_starting_price = models.IntegerField()\n bid_time= models.DateTimeField(auto_now_add=True, blank=True)\n bid_status = models.BooleanField(default=1)\n bid_price = models.IntegerField(blank=True)\n def __str__(self):\n return f\"{self.bid_title}: is being sold by {self.bid_owner}\"\n\nclass Bid(models.Model):\n bid_id=models.ForeignKey(Listing, on_delete=models.CASCADE, related_name=\"current_bidder_id\")\n time = models.DateTimeField(auto_now_add=True, blank=True)\n user = models.ForeignKey(User, on_delete=models.CASCADE, related_name=\"user_bids\")\n price = models.IntegerField()\n\n def __str__(self):\n return f\"{self.user} put a bid in for {self.price}\"\n\n\nclass Category(models.Model):\n bid=models.ManyToManyField(Listing, related_name=\"tags\", blank=True)\n category=models.CharField(max_length=20, blank=True)\n\nclass Comment(models.Model):\n bid=models.ForeignKey(Listing,related_name=\"user_coms\", on_delete=models.CASCADE)\n user = models.ForeignKey(User, on_delete=models.CASCADE, related_name=\"coms_user\",blank=True)\n title = models.CharField(max_length=25, default=\"\",blank=True)\n comment = models.CharField(max_length=255, blank=True)\n time = models.DateTimeField(auto_now_add=True)\n\n def __str__(self):\n return f\"{self.title}: {self.comment}\"\n\n\nclass Watchlist(models.Model):\n user = models.OneToOneField(User, on_delete=models.CASCADE,blank=True)\n listing = models.ForeignKey(Listing, on_delete=models.CASCADE, blank=True)\n\nclass Winners(models.Model):\n user= models.OneToOneField(User, on_delete=models.CASCADE)\n win_price= models.IntegerField()\n listing = models.OneToOneField(Listing,on_delete=models.CASCADE)\n\n\n\n\n", "repo_name": "Waseem0912-coder/Cs50-Web", "sub_path": "v2/2.2/commerce/auctions/models.py", "file_name": "models.py", "file_ext": "py", "file_size_in_byte": 2107, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "django.contrib.auth.models.AbstractUser", "line_number": 5, "usage_type": "name"}, {"api_name": "django.db.models.Model", "line_number": 9, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 9, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 10, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 10, "usage_type": "name"}, {"api_name": "django.db.models.CASCADE", "line_number": 10, "usage_type": "attribute"}, {"api_name": "django.db.models.CharField", "line_number": 11, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 11, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 12, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 12, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 13, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 13, "usage_type": "name"}, {"api_name": "django.db.models.IntegerField", "line_number": 14, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 14, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 15, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 15, "usage_type": "name"}, {"api_name": "django.db.models.BooleanField", "line_number": 16, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 16, "usage_type": "name"}, {"api_name": "django.db.models.IntegerField", "line_number": 17, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 17, "usage_type": "name"}, {"api_name": "django.db.models.Model", "line_number": 21, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 21, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 22, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 22, "usage_type": "name"}, {"api_name": "django.db.models.CASCADE", "line_number": 22, "usage_type": "attribute"}, {"api_name": "django.db.models.DateTimeField", "line_number": 23, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 23, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 24, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 24, "usage_type": "name"}, {"api_name": "django.db.models.CASCADE", "line_number": 24, "usage_type": "attribute"}, {"api_name": "django.db.models.IntegerField", "line_number": 25, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 25, "usage_type": "name"}, {"api_name": "django.db.models.Model", "line_number": 31, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 31, "usage_type": "name"}, {"api_name": "django.db.models.ManyToManyField", "line_number": 32, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 32, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 33, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 33, "usage_type": "name"}, {"api_name": "django.db.models.Model", "line_number": 35, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 35, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 36, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 36, "usage_type": "name"}, {"api_name": "django.db.models.CASCADE", "line_number": 36, "usage_type": "attribute"}, {"api_name": "django.db.models.ForeignKey", "line_number": 37, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 37, "usage_type": "name"}, {"api_name": "django.db.models.CASCADE", "line_number": 37, "usage_type": "attribute"}, {"api_name": "django.db.models.CharField", "line_number": 38, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 38, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 39, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 39, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 40, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 40, "usage_type": "name"}, {"api_name": "django.db.models.Model", "line_number": 46, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 46, "usage_type": "name"}, {"api_name": "django.db.models.OneToOneField", "line_number": 47, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 47, "usage_type": "name"}, {"api_name": "django.db.models.CASCADE", "line_number": 47, "usage_type": "attribute"}, {"api_name": "django.db.models.ForeignKey", "line_number": 48, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 48, "usage_type": "name"}, {"api_name": "django.db.models.CASCADE", "line_number": 48, "usage_type": "attribute"}, {"api_name": "django.db.models.Model", "line_number": 50, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 50, "usage_type": "name"}, {"api_name": "django.db.models.OneToOneField", "line_number": 51, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 51, "usage_type": "name"}, {"api_name": "django.db.models.CASCADE", "line_number": 51, "usage_type": "attribute"}, {"api_name": "django.db.models.IntegerField", "line_number": 52, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 52, "usage_type": "name"}, {"api_name": "django.db.models.OneToOneField", "line_number": 53, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 53, "usage_type": "name"}, {"api_name": "django.db.models.CASCADE", "line_number": 53, "usage_type": "attribute"}]} +{"seq_id": "73511467948", "text": "import tensorflow as tf\nfrom pathlib import Path\nimport os\nimport numpy as np\nimport mlflow\nimport mlflow.keras\nimport pandas as pd\nfrom urllib.parse import urlparse\nfrom sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, classification_report\nfrom sklearn.preprocessing import MinMaxScaler\nfrom IOT_NIDS.entity.config_entity import EvaluationConfig\nfrom IOT_NIDS.utils.common import save_json\n\n\nclass Evaluation:\n def __init__(self, config: EvaluationConfig):\n self.config=config\n\n def eval_metrics(self, actual, pred): \n accuracy = accuracy_score(actual, pred)\n precision_per_class = precision_score(actual, pred, average=None)\n recall_per_class = recall_score(actual, pred, average=None)\n f1_score_per_class = f1_score(actual, pred, average=None)\n return accuracy, precision_per_class, recall_per_class, f1_score_per_class\n \n\n def log_into_mlflow(self):\n min_max_scaler = MinMaxScaler()\n x_test = pd.read_csv(self.config.test_data_path)\n y_test= pd.read_csv(self.config.lables_data_path)\n model = tf.keras.models.load_model(self.config.model_path)\n\n \n x_test = min_max_scaler.fit_transform(x_test)\n\n mlflow.set_registry_uri(self.config.mlflow_uri)\n tracking_url_type_store = urlparse(mlflow.get_tracking_uri()).scheme\n\n\n with mlflow.start_run():\n\n predicted_lable = model.predict(x_test)\n predicted_ids=np.argmax(predicted_lable, axis=1)\n\n (acc, pre, recal,f1sc) = self.eval_metrics(y_test, predicted_ids)\n \n \n # Saving metrics as local\n scores = {\"acc\": acc, \"pre\": pre.tolist(), \"recl\": recal.tolist(), \"f1s\": f1sc.tolist()}\n \n # for key, value in scores.items():\n # if isinstance(value, np.ndarray):\n # scores[key] = value.tolist()\n\n save_json(path=Path(self.config.metric_file_name), data=scores)\n \n mlflow.log_metric(\"accuracy\", np.mean(acc))\n mlflow.log_metric(\"precision\", np.mean(pre))\n mlflow.log_metric(\"recall\", np.mean(recal))\n mlflow.log_metric(\"f1_score\", np.mean(f1sc))\n # Model registry does not work with file store\n if tracking_url_type_store != \"file\":\n\n # Register the model\n # There are other ways to use the Model Registry, which depends on the use case,\n # please refer to the doc for more information:\n # https://mlflow.org/docs/latest/model-registry.html#api-workflow\n mlflow.sklearn.log_model(model, \"model\", registered_model_name=\"GRUModel\")\n else:\n mlflow.sklearn.log_model(model, \"model\")\n\n \n ", "repo_name": "RCgit123/newsite", "sub_path": "src/IOT_NIDS/components/model_evaluation.py", "file_name": "model_evaluation.py", "file_ext": "py", "file_size_in_byte": 2783, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "IOT_NIDS.entity.config_entity.EvaluationConfig", "line_number": 16, "usage_type": "name"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 20, "usage_type": "call"}, {"api_name": "sklearn.metrics.precision_score", "line_number": 21, "usage_type": "call"}, {"api_name": "sklearn.metrics.recall_score", "line_number": 22, "usage_type": "call"}, {"api_name": "sklearn.metrics.f1_score", "line_number": 23, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.MinMaxScaler", "line_number": 28, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 29, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 30, "usage_type": "call"}, {"api_name": "tensorflow.keras.models.load_model", "line_number": 31, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 31, "usage_type": "attribute"}, {"api_name": "mlflow.set_registry_uri", "line_number": 36, "usage_type": "call"}, {"api_name": "urllib.parse.urlparse", "line_number": 37, "usage_type": "call"}, {"api_name": "mlflow.get_tracking_uri", "line_number": 37, "usage_type": "call"}, {"api_name": "mlflow.start_run", "line_number": 40, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 43, "usage_type": "call"}, {"api_name": "IOT_NIDS.utils.common.save_json", "line_number": 55, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 55, "usage_type": "call"}, {"api_name": "mlflow.log_metric", "line_number": 57, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 57, "usage_type": "call"}, {"api_name": "mlflow.log_metric", "line_number": 58, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 58, "usage_type": "call"}, {"api_name": "mlflow.log_metric", "line_number": 59, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 59, "usage_type": "call"}, {"api_name": "mlflow.log_metric", "line_number": 60, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 60, "usage_type": "call"}, {"api_name": "mlflow.sklearn.log_model", "line_number": 68, "usage_type": "call"}, {"api_name": "mlflow.sklearn", "line_number": 68, "usage_type": "attribute"}, {"api_name": "mlflow.sklearn.log_model", "line_number": 70, "usage_type": "call"}, {"api_name": "mlflow.sklearn", "line_number": 70, "usage_type": "attribute"}]} +{"seq_id": "18741284780", "text": "import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom constant import PAD_INDEX, RPAD_INDEX\nfrom config import ARGS\nfrom network.util_network import get_pad_mask, get_subsequent_mask, clones\nfrom network.SAKT import *\n\n\nclass TransformerBlock(nn.Module):\n \"\"\"\n Single Transformer block of SAINT\n \"\"\"\n def __init__(self, hidden_dim, num_head, dropout):\n super().__init__()\n self._self_attn = MultiHeadedAttention(num_head, hidden_dim, dropout)\n self._ffn = PositionwiseFeedForward(hidden_dim, hidden_dim, dropout)\n self._layernorms = clones(nn.LayerNorm(hidden_dim, eps=1e-8), 2)\n\n def forward(self, query, key, value, mask):\n output = self._self_attn(query=query, key=key, value=value, mask=mask)\n output = self._layernorms[0](key + output)\n output = self._layernorms[1](output + self._ffn(output))\n return output\n\n\nclass SAINT(nn.Module):\n def __init__(self, hidden_dim, question_num,\n num_enc_layers, num_dec_layers, num_head, dropout):\n super().__init__()\n self._hidden_dim = hidden_dim\n self._question_num = question_num\n\n # Encoder blocks\n self._encoder = clones(TransformerBlock(hidden_dim, num_head, dropout),\n num_enc_layers)\n # Decoder blocks\n self._decoder = clones(TransformerBlock(hidden_dim, num_head, dropout),\n num_dec_layers)\n\n # Embedding layers\n self._positional_embedding = nn.Embedding(\n ARGS.seq_size+2, hidden_dim, padding_idx=PAD_INDEX)\n self._question_embedding = nn.Embedding(\n question_num+1, hidden_dim, padding_idx=PAD_INDEX)\n self._response_embedding = nn.Embedding(\n 2+2, hidden_dim, padding_idx=RPAD_INDEX)\n self._prediction = nn.Linear(hidden_dim, 1)\n\n def _transform_interaction_to_question_id(self, interaction):\n \"\"\"\n get question_id from interaction index\n if interaction index is a number in [0, question_num], then leave it as-is\n if interaction index is bigger than question_num (in [question_num + 1, 2 * question_num]\n then subtract question_num\n interaction: integer tensor of shape (batch_size, sequence_size)\n \"\"\"\n return interaction - self._question_num *\\\n (interaction > self._question_num).long()\n\n def _transform_interaction_to_response_id(self, interaction):\n question_id = interaction - self._question_num *\\\n (interaction > self._question_num).long()\n pads = interaction == 0\n response_id = (interaction > self._question_num).long()\n response_id = response_id.masked_fill(pads, RPAD_INDEX)\n return response_id.long()\n\n def _get_position_index(self, input, for_decoder=False):\n batch_size = input.shape[0]\n seq_len = ARGS.seq_size\n position_indices = []\n if not for_decoder:\n for i in range(batch_size):\n question_id = input\n non_padding_num = (question_id[i] != PAD_INDEX).sum(-1).item()\n position_index = [0] * (seq_len - non_padding_num) +\\\n list(range(1, non_padding_num+1))\n position_indices.append(position_index)\n else:\n for i in range(batch_size):\n response_id = input\n non_padding_num = (response_id[i] != RPAD_INDEX).sum(-1).item()\n position_index = [0] * min(seq_len - non_padding_num, seq_len-1) +\\\n [seq_len+1] + list(range(1, non_padding_num))\n position_indices.append(position_index)\n return torch.tensor(position_indices, dtype=int).to(ARGS.device)\n\n def forward(self, interaction_id, target_id):\n question_id =\\\n self._transform_interaction_to_question_id(interaction_id)\n question_id = torch.cat([question_id[:, 1:], target_id], dim=-1)\n question_vector = self._question_embedding(question_id)\n\n position_index = self._get_position_index(question_id)\n position_vector = self._positional_embedding(position_index)\n\n response_id =\\\n self._transform_interaction_to_response_id(interaction_id)\n response_vector = self._response_embedding(response_id)\n\n response_position_index =\\\n self._get_position_index(response_id, for_decoder=True)\n response_position_vector =\\\n self._positional_embedding(response_position_index)\n\n x = question_vector + position_vector\n decoder_input = response_vector + response_position_vector\n\n mask = get_pad_mask(question_id, PAD_INDEX) &\\\n get_subsequent_mask(question_id)\n\n # encoder forward pass\n for layer in self._encoder:\n x = layer(query=x, key=x, value=x, mask=mask)\n encoder_out = x\n # decoder forward pass\n for n, layer in enumerate(self._decoder):\n if n == 0:\n decoder_out = layer(query=decoder_input, key=decoder_input,\n value=decoder_input, mask=mask)\n else:\n decoder_out = layer(query=decoder_out, key=encoder_out,\n value=encoder_out, mask=mask)\n\n output = self._prediction(decoder_out)[:, -1, :]\n return output\n", "repo_name": "14heeseok/DiKT_ITS21", "sub_path": "network/SAINT.py", "file_name": "SAINT.py", "file_ext": "py", "file_size_in_byte": 5374, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "37", "api": [{"api_name": "torch.nn.Module", "line_number": 11, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 11, "usage_type": "name"}, {"api_name": "network.util_network.clones", "line_number": 19, "usage_type": "call"}, {"api_name": "torch.nn.LayerNorm", "line_number": 19, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 19, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 28, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 28, "usage_type": "name"}, {"api_name": "network.util_network.clones", "line_number": 36, "usage_type": "call"}, {"api_name": "network.util_network.clones", "line_number": 39, "usage_type": "call"}, {"api_name": "torch.nn.Embedding", "line_number": 43, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 43, "usage_type": "name"}, {"api_name": "config.ARGS.seq_size", "line_number": 44, "usage_type": "attribute"}, {"api_name": "config.ARGS", "line_number": 44, "usage_type": "name"}, {"api_name": "constant.PAD_INDEX", "line_number": 44, "usage_type": "name"}, {"api_name": "torch.nn.Embedding", "line_number": 45, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 45, "usage_type": "name"}, {"api_name": "constant.PAD_INDEX", "line_number": 46, "usage_type": "name"}, {"api_name": "torch.nn.Embedding", "line_number": 47, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 47, "usage_type": "name"}, {"api_name": "constant.RPAD_INDEX", "line_number": 48, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 49, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 49, "usage_type": "name"}, {"api_name": "constant.RPAD_INDEX", "line_number": 67, "usage_type": "argument"}, {"api_name": "config.ARGS.seq_size", "line_number": 72, "usage_type": "attribute"}, {"api_name": "config.ARGS", "line_number": 72, "usage_type": "name"}, {"api_name": "constant.PAD_INDEX", "line_number": 77, "usage_type": "name"}, {"api_name": "constant.RPAD_INDEX", "line_number": 84, "usage_type": "name"}, {"api_name": "torch.tensor", "line_number": 88, "usage_type": "call"}, {"api_name": "config.ARGS.device", "line_number": 88, "usage_type": "attribute"}, {"api_name": "config.ARGS", "line_number": 88, "usage_type": "name"}, {"api_name": "torch.cat", "line_number": 93, "usage_type": "call"}, {"api_name": "network.util_network.get_pad_mask", "line_number": 111, "usage_type": "call"}, {"api_name": "constant.PAD_INDEX", "line_number": 111, "usage_type": "argument"}, {"api_name": "network.util_network.get_subsequent_mask", "line_number": 112, "usage_type": "call"}]} +{"seq_id": "21243848104", "text": "import collections\nimport functools\n\nfrom test_framework import generic_test\nfrom test_framework.test_utils import enable_executor_hook\n\n\"\"\"13.6 Render a calendar\nConsider the problem of designing an online calendaring application. One\ncomponent of the design is to render the calendar, i.e. display it visually\n\nSuppose each day consist of a number of events, where an event is specified as a\nstart time and finish time. Individual events for a day are to be rendered as\nnonoverlapping rectangular regions whose sides are parallel to the X- and Y-axes\n. Let the X-axis correspond to time. If an event starts at time b and ends at\ntime e, the upper and lower sides of its corresponding rectangle must be at b and\ne, respectively.\n\nSuppose the y-coordinates for each day's events must lie between 0 and L ( a\npre-specified constant), and each event's rectangle must have the same \"height\"\n(distance between the sides parallel to the X-axis). Your task is to compute the\nmaximum height an event rectangle can have. In essence, this is equivalent to\nthe following problem.\n\nWrite a program that takes a set of events, and determines the maximum number of\nevents that take place concurrently.\n\nBasic Algorithm: Brute force algorithm, for each endpoint, compute the number of\nevents that contain it. The maximum of this quantity over all endpoints. If there\nare n endpoints, then we'll have to scan over the array n times, which would make this\nalgorithm O(n ^ 2)\n\nThe problem with this solution, is it does not take into account of locality.\nWhen we move from 1 point to the next, we know 2 things, if it's a start and its\nvalue. If we sort all endpoints, and we have 3 events that happen in a row,\nwithout their endtimes being seen, then that means there are 3 events in a row\n\nexample: [(0,3), (1, 5), (2, 3)]\nIf we sort this into something into a tuple containing, (value, is_start),\nWe check each event and keep a count of how many events are going on at the same time.\nYou break ties between event times by putting start times first depending on\nwhether the interviewer will have start_times and end times being equal if\nthat is considered \"overlapping\"\n\nsorted_array = [\n (0, True), (1, True), (2, True), (3, False), (3, False), (5, False)\n]\nnum_events 1 2 3 2 1 0\nFrom this we can see that there are at max 3 events going on at the same time\n\n[ ATTEMPTED ] - 6/3\n\"\"\"\n# Event is a tuple (start_time, end_time)\nEvent = collections.namedtuple('Event', ('start', 'finish'))\n\n# Endpoint is a tuple (start_time, 0) or (end_time, 1) so that if times\n# are equal, start_time comes first\nEndpoint = collections.namedtuple('Endpoint', ('time', 'is_start'))\n\n# O(N) time and O(N) space\ndef find_max_simultaneous_events(A):\n starts = sorted([e.start for e in A])\n ends = sorted([e.finish for e in A])\n num_sim_events = 0\n i, j = 0, 0\n while i < len(starts):\n if starts[i] > ends[j]:\n num_sim_events -= 1\n j += 1\n num_sim_events += 1\n i += 1\n return num_sim_events\n\n# O(N) time and O(N) space\ndef find_max_simultaneous_events(A):\n\n # Builds an array of all endpoints.\n E = [\n p for event in A for p in (Endpoint(event.start, True),\n Endpoint(event.finish, False))\n ]\n # Sorts the endpoint array according to the time, breaking ties by putting\n # start times before end times.\n E.sort(key=lambda e: (e.time, not e.is_start))\n\n # Track the number of simultaneous events, record the maximum number of\n # simultaneous events.\n max_num_simultaneous_events, num_simultaneous_events = 0, 0\n for e in E:\n if e.is_start:\n num_simultaneous_events += 1\n max_num_simultaneous_events = max(num_simultaneous_events,\n max_num_simultaneous_events)\n else:\n num_simultaneous_events -= 1\n return max_num_simultaneous_events\n\n# Heap Based Solution\n# Think of each interval, as is it's own bucket. Whether or not you can\n# put a new interval in a new \"bucket\", depends on the end time of the latest\n# interval. (new_interval.start >= some_interval.finish). If there are\n# multiple rooms that could fit, choose the room with the earliest finish time\n# Based off leetcode https://bit.ly/2ZpWCSi\ndef find_max_simultaneous_events(A):\n A.sort(key=lambda event: event.start)\n res = 1\n heap = [A[0].finish]\n for i in range(1, len(A)):\n if A[i].start > heap[0]:\n heapq.heappushpop(heap, A[i].finish)\n else:\n heapq.heappush(heap, A[i].finish)\n res = max(len(heap), res)\n return res\n\n@enable_executor_hook\ndef find_max_simultaneous_events_wrapper(executor, events):\n events = [Event(*x) for x in events]\n return executor.run(\n functools.partial(find_max_simultaneous_events, events))\n\n\nif __name__ == '__main__':\n exit(\n generic_test.generic_test_main(\"calendar_rendering.py\",\n 'calendar_rendering.tsv',\n find_max_simultaneous_events_wrapper))\n", "repo_name": "aarboleda1/Elements-Of-Programming-Interviews", "sub_path": "epi_judge_python_solutions/calendar_rendering.py", "file_name": "calendar_rendering.py", "file_ext": "py", "file_size_in_byte": 5138, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "37", "api": [{"api_name": "collections.namedtuple", "line_number": 53, "usage_type": "call"}, {"api_name": "collections.namedtuple", "line_number": 57, "usage_type": "call"}, {"api_name": "functools.partial", "line_number": 119, "usage_type": "call"}, {"api_name": "test_framework.test_utils.enable_executor_hook", "line_number": 115, "usage_type": "name"}, {"api_name": "test_framework.generic_test.generic_test_main", "line_number": 124, "usage_type": "call"}, {"api_name": "test_framework.generic_test", "line_number": 124, "usage_type": "name"}]} +{"seq_id": "19471340019", "text": "import torch\r\nfrom torch import nn, optim\r\nfrom torch.utils.data import DataLoader\r\nfrom dataset_sampling import MyDataset\r\nfrom Model import Net\r\nfrom ResNet18 import ResNet18, BasicBlock\r\nimport os\r\nimport numpy as np\r\nimport random\r\n\r\ndef setup_seed(seed):\r\n torch.manual_seed(seed)\r\n torch.cuda.manual_seed_all(seed)\r\n np.random.seed(seed)\r\n random.seed(seed)\r\n torch.backends.cudnn.deterministic = True\r\n\r\n\r\nif __name__ == '__main__':\r\n data_path = r\"E:\\cat_dog_classify\"\r\n setup_seed(20)\r\n sample_test = MyDataset(data_path, 'test')\r\n net = ResNet18(BasicBlock)\r\n net.load_state_dict(torch.load(\"./params.pth\"))\r\n\r\n batch_size = 100\r\n test_loader = DataLoader(sample_test, batch_size=batch_size, shuffle=True)\r\n\r\n device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\r\n net = net.to(device)\r\n test_correct=0\r\n for batch in test_loader:\r\n img, label = batch\r\n img = img.to(device)\r\n label = label.to(device)\r\n with torch.no_grad():\r\n output = net(img)\r\n argmax = torch.argmax(output, 1)\r\n test_correct += (argmax == label).sum().item()\r\n del img, label\r\n torch.cuda.empty_cache()\r\n test_acc = test_correct / len(sample_test)\r\n print('Final Test Acc: {:.3f}'.format(test_acc))\r\n\r\n", "repo_name": "akEliza/cat_dog_classify", "sub_path": "Pred.py", "file_name": "Pred.py", "file_ext": "py", "file_size_in_byte": 1340, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "torch.manual_seed", "line_number": 12, "usage_type": "call"}, {"api_name": "torch.cuda.manual_seed_all", "line_number": 13, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 13, "usage_type": "attribute"}, {"api_name": "numpy.random.seed", "line_number": 14, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 14, "usage_type": "attribute"}, {"api_name": "random.seed", "line_number": 15, "usage_type": "call"}, {"api_name": "torch.backends", "line_number": 16, "usage_type": "attribute"}, {"api_name": "dataset_sampling.MyDataset", "line_number": 22, "usage_type": "call"}, {"api_name": "ResNet18.ResNet18", "line_number": 23, "usage_type": "call"}, {"api_name": "ResNet18.BasicBlock", "line_number": 23, "usage_type": "argument"}, {"api_name": "torch.load", "line_number": 24, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 27, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 29, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 29, "usage_type": "attribute"}, {"api_name": "torch.device", "line_number": 29, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 36, "usage_type": "call"}, {"api_name": "torch.argmax", "line_number": 38, "usage_type": "call"}, {"api_name": "torch.cuda.empty_cache", "line_number": 41, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 41, "usage_type": "attribute"}]} +{"seq_id": "16639426219", "text": "import torch as T\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch.optim as optim\nimport numpy as np\nimport src.setup as setup\nimport json\nfrom enum import Enum\nfrom os.path import exists\n\nclass DeepQNetwork(nn.Module):\n\tdef __init__(self, lr, input_dims, fc1_dims, fc2_dims, n_outputs):\n\t\tsuper(DeepQNetwork, self).__init__()\n\t\tself.input_dims = input_dims\n\t\tself.fc1_dims = fc1_dims\n\t\tself.fc2_dims = fc2_dims\n\t\tself.n_outputs = n_outputs\n\t\tself.fc1 = nn.Linear(*self.input_dims, self.fc1_dims)\n\t\tself.fc2 = nn.Linear(self.fc1_dims, self.fc2_dims)\n\t\tself.fc3 = nn.Linear(self.fc2_dims, self.n_outputs) \n\t\tself.optimizer = optim.Adam(self.parameters(), lr=lr)\n\t\tself.loss = nn.MSELoss()\n\t\tself.device = T.device(\"cuda:0\" if T.cuda.is_available() else \"cpu\")\n\t\tself.to(self.device)\n\n\tdef forward(self, state):\n\t\tx = F.relu(self.fc1(state))\n\t\tx = F.relu(self.fc2(x))\n\t\tactions = self.fc3(x)\n\n\t\treturn actions\n\nclass Agent():\n\tdef __init__(self, gamma, epsilon, lr, input_dims, batch_size, n_outputs, max_mem_size=100000, eps_end=0.01, eps_dec=5e-4):\n\t\tself.gamma = gamma\n\t\tself.epsilon = epsilon\n\t\tself.eps_min = eps_end\n\t\tself.eps_dec = eps_dec\n\t\tself.lr = lr\n\t\tself.action_space = [i for i in range(n_outputs)]\n\t\tself.mem_size = max_mem_size\n\t\tself.batch_size = batch_size\n\t\tself.mem_counter = 0\n\n\t\tself.Q_eval = DeepQNetwork(lr, n_outputs=n_outputs, input_dims=input_dims, fc1_dims=256, fc2_dims=256)\n\n\t\tself.state_memory = np.zeros((self.mem_size, *input_dims), dtype=np.float32)\n\t\tself.reward_memory = np.zeros(self.mem_size, dtype=np.float32)\n\t\tself.action_memory = np.zeros(self.mem_size, dtype=np.int32)\n\n\tdef store_transition(self, state, action, reward):\n\t\tindex = self.mem_counter % self.mem_size\n\t\tself.state_memory[index] = state\n\t\tself.action_memory[index] = action\n\t\tself.reward_memory[index] = reward\n\n\t\tself.mem_counter += 1\n\n\n\tdef calculate_actions(self, steer_profile):\n\t\tresults = {}\n\t\tfor candidate_action in setup.Actions:\n\t\t\tstate = T.tensor(np.array(steer_profile, dtype=np.float32)).to(self.Q_eval.device)\n\t\t\taction_eval = self.Q_eval.forward(state)\n\t\t\tresults[candidate_action] = action_eval[candidate_action].item()\n\n\t\treturn results\n\n\tdef learn(self):\n\t\tif self.mem_counter < self.batch_size:\n\t\t\treturn\n\n\t\tprint(\"learning\")\n\t\tself.Q_eval.optimizer.zero_grad()\n\n\t\tmax_mem = min(self.mem_counter, self.mem_size)\n\t\tbatch = np.random.choice(max_mem, self.batch_size, replace=False)\n\t\tbatch_index = np.arange(self.batch_size, dtype=np.int32)\n\n\t\tstate_batch = T.tensor(self.state_memory[batch]).to(self.Q_eval.device)\n\t\taction_batch = self.action_memory[batch]\n\n\t\tq_eval = self.Q_eval.forward(state_batch)[batch_index, action_batch]\n\t\tq_target = T.tensor(self.reward_memory[batch]).to(self.Q_eval.device)\n\n\t\tloss = self.Q_eval.loss(q_target, q_eval).to(self.Q_eval.device)\n\t\tloss.backward()\n\t\tself.Q_eval.optimizer.step()\n\n\t\tself.epsilon = self.epsilon - self.eps_dec if self.epsilon > self.eps_min else self.eps_min\n\n\tdef write_training_input(self, steer_profile, time, action, file_name=\"./training_input.json\"):\n\t\toutput = {\n\t\t\t\"steer_profile\" : steer_profile,\n\t\t\t\"time\" : time,\n\t\t\t\"action\": action\n\t\t}\n\t\twith open(file_name, 'w') as f:\n\t\t\tjson.dump(output, f, indent=4, separators=(',', ': '))\n\n\tdef read_training_input(self, file_name=\"./training_input.json\"):\n\t\twith open(file_name) as f:\n\t\t\tself.input = json.load(f)\n\t\t\n\t\treturn self.input\n\n\n\tdef mainloop():\n\t\tagent = Agent(gamma=0.99, epsilon = 1.0, batch_size = 64, n_outputs = 1, eps_end = 0.01,\n\t\t\tinput_dims=[10], lr=0.03)\n\t\twhile True:\n\t\t\taction = agent.choose_action(observation)\n\t\t\tobservation_, reward = getResult()\n\t\t\tagent.store_transition(observation, action, reward)\n\t\t\tagent.learn()\n\n\tdef calc_reward(self, last_profile, last_time, current_profile, current_time, target_time):\n\t\told_square_profile_sum = np.sum(last_profile)\n\t\tnew_square_profile_sum = np.sum(current_profile)\n\t\tprofile_diff = old_square_profile_sum - new_square_profile_sum\n\n\t\ttime_diff = (last_time - current_time) * np.exp(target_time-current_time)\n\n\t\treturn profile_diff + time_diff\n\n\tdef save(self, path=\"./data/nn_data\"):\n\t\tT.save(self.Q_eval.state_dict(), path+\".pt\")\n\t\tnp.save(path+\"_mem_counter.npy\", [self.mem_counter])\n\t\tnp.save(path+\"_state_memory.npy\", self.state_memory)\n\t\tnp.save(path+\"_action_memory.npy\", self.action_memory)\n\t\tnp.save(path+\"_reward_memory.npy\", self.reward_memory)\n\n \n\tdef load(self, path=\"./data/nn_data\"):\n\t\tif exists(path+\".pt\"):\n\t\t\tself.Q_eval.load_state_dict(T.load(path+\".pt\"))\n\t\t\tself.Q_eval.eval()\n\n\t\t\tself.mem_counter = np.load(path+\"_mem_counter.npy\", allow_pickle=True)[0]\n\t\t\tself.state_memory = np.load(path+\"_state_memory.npy\", allow_pickle=True)\n\t\t\tself.action_memory = np.load(path+\"_action_memory.npy\", allow_pickle=True)\n\t\t\tself.reward_memory = np.load(path+\"_reward_memory.npy\", allow_pickle=True)\n\n\n\n", "repo_name": "nPeMu9I-DapBuHa/acc-setup-engineer", "sub_path": "src/ldparser/q_learning.py", "file_name": "q_learning.py", "file_ext": "py", "file_size_in_byte": 4837, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "torch.nn.Module", "line_number": 11, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 11, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 18, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 18, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 19, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 19, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 20, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 20, "usage_type": "name"}, {"api_name": "torch.optim.Adam", "line_number": 21, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 21, "usage_type": "name"}, {"api_name": "torch.nn.MSELoss", "line_number": 22, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 22, "usage_type": "name"}, {"api_name": "torch.device", "line_number": 23, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 23, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 23, "usage_type": "attribute"}, {"api_name": "torch.nn.functional.relu", "line_number": 27, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 27, "usage_type": "name"}, {"api_name": "torch.nn.functional.relu", "line_number": 28, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 28, "usage_type": "name"}, {"api_name": "numpy.zeros", "line_number": 47, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 47, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 48, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 48, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 49, "usage_type": "call"}, {"api_name": "numpy.int32", "line_number": 49, "usage_type": "attribute"}, {"api_name": "src.setup.Actions", "line_number": 62, "usage_type": "attribute"}, {"api_name": "src.setup", "line_number": 62, "usage_type": "name"}, {"api_name": "torch.tensor", "line_number": 63, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 63, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 63, "usage_type": "attribute"}, {"api_name": "numpy.random.choice", "line_number": 77, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 77, "usage_type": "attribute"}, {"api_name": "numpy.arange", "line_number": 78, "usage_type": "call"}, {"api_name": "numpy.int32", "line_number": 78, "usage_type": "attribute"}, {"api_name": "torch.tensor", "line_number": 80, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 84, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 99, "usage_type": "call"}, {"api_name": "json.load", "line_number": 103, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 118, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 119, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 122, "usage_type": "call"}, {"api_name": "torch.save", "line_number": 127, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 128, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 129, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 130, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 131, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 135, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 136, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 139, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 140, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 141, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 142, "usage_type": "call"}]} +{"seq_id": "11971996457", "text": "import numpy as np\nimport yiq\nfrom PIL import Image\nimport sys\n\n\ndef negative(image):\n # cria um array multidimensional(1x1x3) preenchido por zeros usando as\n # dimensões da\n neg = np.zeros((image.height, image.width, 3), 'uint8')\n # imagem e a quantidade de componentes que existe por cada pixel(RGB)\n for i in range(image.width):\n for j in range(image.height): # percorre todos os pixels da imagem\n r, g, b = image.getpixel((i, j)) # retorna os valores de r ,g e b\n # do pixel(i,j)\n\n # subtrai de 255 o valor capturado em r, g e b do pixel\n neg[j, i, 0] = 255 - r\n neg[j, i, 1] = 255 - g\n neg[j, i, 2] = 255 - b\n\n return neg\n\n\ndef negativeR(image):\n neg = np.zeros((image.height, image.width, 3), 'uint8')\n for i in range(image.width):\n for j in range(image.height):\n r, g, b = image.getpixel((i, j))\n\n # subtrai de 255 o valor capturado em r do pixel\n neg[j, i, 0] = 255 - r\n neg[j, i, 1] = g\n neg[j, i, 2] = b\n\n return neg\n\n\ndef negativeG(image):\n neg = np.zeros((image.height, image.width, 3), 'uint8')\n for i in range(image.width):\n for j in range(image.height):\n r, g, b = image.getpixel((i, j))\n\n neg[j, i, 0] = r\n # subtrai de 255 o valor capturado em g do pixel\n neg[j, i, 1] = 255 - g\n neg[j, i, 2] = b\n\n return neg\n\n\ndef negativeB(image):\n neg = np.zeros((image.height, image.width, 3), 'uint8')\n for i in range(image.width):\n for j in range(image.height):\n r, g, b = image.getpixel((i, j))\n\n neg[j, i, 0] = r\n neg[j, i, 1] = g\n # subtrai de 255 o valor capturado em b do pixel\n neg[j, i, 2] = 255 - b\n\n return neg\n\n\ndef negative_y(image, width, height):\n neg = np.zeros((height, width, 3), 'float32')\n # percorre todos os pixels da imagem\n for k in range(width):\n for j in range(height):\n y = image[j, k, 0]\n i = image[j, k, 1]\n q = image[j, k, 2]\n\n # realiza a subtração do valor máximo no\n # sistema YIQ e o próprio y capturado no pixel\n neg[j, k, 0] = 1-y\n neg[j, k, 1] = i\n neg[j, k, 2] = q\n\n return neg\n\n\ndef main(path):\n\n img = Image.open(path) # abre a imagem passada como argumento\n\n while 1:\n print(\"\\n\\tMenu Negativo:\\n\")\n print(\"\\t1 - Aplicar negativo em RGB\")\n print(\"\\t2 - Aplicar negativo em R\")\n print(\"\\t3 - Aplicar negativo em G\")\n print(\"\\t4 - Aplicar negativo em B\")\n print(\"\\t5 - Aplicar negativo em Y\")\n print(\"\\t0 - Voltar ao Menu Principal\")\n\n option = int(input(\"Selecione uma opção: \"))\n\n if option == 1:\n # retorna um array após deixar as cores negativas em RGB\n im = negative(img)\n im = Image.fromarray(im) # constrói a imagem\n im.show() # mostra a imagem construída\n\n elif option == 2:\n # retorna um array após deixar as cores negativas em R\n im = negativeR(img)\n im = Image.fromarray(im)\n im.show()\n\n elif option == 3:\n # retorna um array após deixar as cores negativas em G\n im = negativeG(img)\n im = Image.fromarray(im)\n im.show()\n\n elif option == 4:\n # retorna um array após deixar as cores negativas em B\n im = negativeB(img)\n im = Image.fromarray(im)\n im.show()\n\n elif option == 5:\n # converte a imagem de rgb para yiq\n im = yiq.rgb2yiq(img)\n # utiliza a nossa função para deixar deixar a imagem negativa\n im = negative_y(im, img.width, img.height)\n # converte de yiq para rgb\n im = yiq.yiq2rgb(im, img.width, img.height)\n im = Image.fromarray(im)\n im.show()\n\n else:\n return\n", "repo_name": "mejnour/pdi-trab1", "sub_path": "negativo.py", "file_name": "negativo.py", "file_ext": "py", "file_size_in_byte": 4032, "program_lang": "python", "lang": "pt", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "numpy.zeros", "line_number": 10, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 26, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 40, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 54, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 68, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 87, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 87, "usage_type": "name"}, {"api_name": "PIL.Image.fromarray", "line_number": 103, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 103, "usage_type": "name"}, {"api_name": "PIL.Image.fromarray", "line_number": 109, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 109, "usage_type": "name"}, {"api_name": "PIL.Image.fromarray", "line_number": 115, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 115, "usage_type": "name"}, {"api_name": "PIL.Image.fromarray", "line_number": 121, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 121, "usage_type": "name"}, {"api_name": "yiq.rgb2yiq", "line_number": 126, "usage_type": "call"}, {"api_name": "yiq.yiq2rgb", "line_number": 130, "usage_type": "call"}, {"api_name": "PIL.Image.fromarray", "line_number": 131, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 131, "usage_type": "name"}]} +{"seq_id": "5180895156", "text": "import random\n\nimport numpy as np\nfrom matplotlib import pyplot as plt\n\nfrom nearest_neighbour import learnknn, predictknn\nfrom utils import gensmallm\n\nLABELS = [2, 3, 5, 6]\nCORRUPTED_FACTOR = 0.15\n\n\ndef run_knn_over_sample_size(k: int, sample_size_max: int, sample_sizes_steps: int, repeats: int, title: str):\n print(f\"Running knn with k={k}, sample_size_max={sample_size_max} \"\n f\"sample_sizes_steps={sample_sizes_steps}, repeats={repeats}\"\n f\"...\")\n train_sample_sizes = np.linspace(1, sample_size_max, num=sample_sizes_steps, dtype=int)\n\n avg_errors, min_errors, max_errors = [], [], []\n for sample_size in train_sample_sizes:\n print(f\"sample_size: {sample_size}\")\n avg_error, min_error, max_error = calculate_errors_with_repeats(sample_size, k, repeats, is_corrupted=False)\n avg_errors.append(avg_error)\n min_errors.append(min_error)\n max_errors.append(max_error)\n\n error_bar = calculate_error_bar(avg_errors, min_errors, max_errors)\n show_results(x_axis=train_sample_sizes, y_axis=np.array(avg_errors), repeats=repeats, error_bar=error_bar,\n title=title, x_label='sample size')\n print(\"done!\")\n\n\ndef run_knn_over_k(max_k: int, sample_size, repeats: int, title: str, is_corrupted):\n print(f\"Running knn with max_k={max_k}, \"\n f\"sample_size={sample_size}, repeats={repeats}\"\n f\"...\")\n\n k_values = np.linspace(1, max_k, num=max_k, dtype=int)\n avg_errors, min_errors, max_errors = [], [], []\n for k in k_values:\n print(f\"k: {k}\")\n avg_error, min_error, max_error = calculate_errors_with_repeats(sample_size, k, repeats, is_corrupted)\n avg_errors.append(avg_error)\n min_errors.append(min_error)\n max_errors.append(max_error)\n\n error_bar = calculate_error_bar(avg_errors, min_errors, max_errors)\n show_results(x_axis=k_values, y_axis=np.array(avg_errors), repeats=repeats, error_bar=error_bar,\n title=title, x_label='k')\n print(\"done!\")\n\n\ndef calculate_error_bar(avg_errors, min_errors, max_errors):\n avg_errors, min_errors, max_errors = np.array(avg_errors), np.array(min_errors), np.array(max_errors)\n avg_distance_min = avg_errors - min_errors\n avg_distance_max = max_errors - avg_errors\n return np.vstack((avg_distance_min, avg_distance_max))\n\n\ndef calculate_errors_with_repeats(train_sample_size: int, k: int, repeats: int, is_corrupted):\n repeats_errors = [calculate_error(k, train_sample_size, is_corrupted) for _ in range(repeats)]\n return np.mean(repeats_errors), min(repeats_errors), max(repeats_errors)\n\n\ndef calculate_error(k: int, train_sample_size: int, is_corrupted) -> float:\n train_data = [data[f'train{label}'] for label in LABELS]\n test_data = [data[f'test{label}'] for label in LABELS]\n test_size = sum(map(lambda test: test.shape[0], test_data))\n\n x_train, y_train = gensmallm(train_data, LABELS, train_sample_size)\n x_test, y_test = gensmallm(test_data, LABELS, test_size)\n\n if is_corrupted:\n corrupt(y_train)\n corrupt(y_test)\n\n classifier = learnknn(k, x_train, y_train)\n preds = predictknn(classifier, x_test)\n # fix shape from (n,) -> (n,1)\n y_test = np.expand_dims(y_test, axis=1)\n return float(np.mean(y_test != preds))\n\n\ndef corrupt(y):\n train_corrupted_indices = random.sample(range(len(y)), int(CORRUPTED_FACTOR * len(y)))\n for train_corrupted_index in train_corrupted_indices:\n label_to_change = y[train_corrupted_index]\n y[train_corrupted_index] = random.choice([label for label in LABELS if label != label_to_change])\n\n\ndef show_results(x_axis, y_axis, repeats: int, error_bar, title: str, x_label: str):\n fig, ax = plt.subplots()\n ax.set_xlabel(x_label)\n ax.set_ylabel(f'mean error {repeats} repeats')\n ax.set_title(f\"{title}\")\n plt.errorbar(x=x_axis, y=y_axis, yerr=error_bar, marker='o', ecolor='red', capsize=3)\n plt.show()\n\n\nif __name__ == '__main__':\n # before submitting, make sure that the function simple_test runs without errors\n data = np.load('mnist_all.npz')\n # question 2a\n run_knn_over_sample_size(title=\"Question 2A (k=1)\",\n k=1,\n sample_size_max=100,\n sample_sizes_steps=10,\n repeats=10)\n # question 2e\n run_knn_over_k(title=\"Question 2e (sample=200)\",\n max_k=11,\n sample_size=200,\n repeats=10,\n is_corrupted=False)\n\n # question 2f\n run_knn_over_k(title=\"Question 2f (sample=200)\",\n max_k=11,\n sample_size=200,\n repeats=10,\n is_corrupted=True)\n", "repo_name": "OmriGalShen/intro_ml_1", "sub_path": "knn_runner.py", "file_name": "knn_runner.py", "file_ext": "py", "file_size_in_byte": 4757, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "numpy.linspace", "line_number": 17, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 28, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 38, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 48, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 54, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 57, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 62, "usage_type": "call"}, {"api_name": "utils.gensmallm", "line_number": 70, "usage_type": "call"}, {"api_name": "utils.gensmallm", "line_number": 71, "usage_type": "call"}, {"api_name": "nearest_neighbour.learnknn", "line_number": 77, "usage_type": "call"}, {"api_name": "nearest_neighbour.predictknn", "line_number": 78, "usage_type": "call"}, {"api_name": "numpy.expand_dims", "line_number": 80, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 81, "usage_type": "call"}, {"api_name": "random.sample", "line_number": 85, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 88, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 92, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 92, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.errorbar", "line_number": 96, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 96, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 97, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 97, "usage_type": "name"}, {"api_name": "numpy.load", "line_number": 102, "usage_type": "call"}]} +{"seq_id": "18126918367", "text": "import pygame\r\nimport time\r\nimport runpy\r\n\r\npygame.init()\r\nx = 800\r\ny = 600\r\ndisplay = pygame.display.set_mode((x, y))\r\nback = pygame.image.load('background.png')\r\n\r\nblack = (0, 0, 0)\r\nblue = (0, 0, 255)\r\nred = (255, 0, 0)\r\nlight_red = (155, 0, 0)\r\nwhite = (255, 255, 255)\r\ngrey = (128,128,128)\r\nboardemBlaster_memebers = [\"Gabriel Menezes, \", \"Kat Swannell, \", \"Joe Muana, \", \"Matthew Nicholas, \", \"Camaron Turner\"]\r\npygame.display.set_caption(\"Boredom Blasters Game Library\")\r\n\r\nGames = [\"Connect 4\", \"Pong\", \"Snake\", \"Tron\", \"OXO\", \"Blackholes\", \"Mancala\", \"Adventure\"]\r\n\r\n\r\ndef footer():\r\n adp_footer = \"Agile Development Project, Boredom Blasters, Games by: \"\r\n\r\n for name in boardemBlaster_memebers:\r\n adp_footer += name\r\n font = pygame.font.SysFont(None, 15)\r\n\r\n button_msg = font.render(adp_footer, True, white)\r\n display.blit(button_msg, [20, 580])\r\n\r\n\r\ndef close():\r\n menu_is_on = True\r\n for event in pygame.event.get():\r\n if event.type == pygame.QUIT:\r\n menu_is_on = False\r\n\r\n return menu_is_on\r\n\r\n\r\ndef text(i, x, y):\r\n font = pygame.font.SysFont(None, 30)\r\n\r\n button_msg = font.render(Games[i], True, black)\r\n display.blit(button_msg, [x+20, y+15])\r\n\r\n\r\ndef button(X, Y):\r\n global x\r\n global y\r\n display.blit(back, (0, 0))\r\n mouse = pygame.mouse.get_pos()\r\n click = pygame.mouse.get_pressed()\r\n for buttons in range(0, 8):\r\n\r\n if X + 150 > mouse[0] > X and Y + 50 > mouse[1] > Y:\r\n pygame.draw.rect(display, grey, [X, Y, 150, 50])\r\n text(buttons, X, Y)\r\n if click[0] == 1 and buttons == 0:\r\n runpy.run_path(\"Connect4.py\")\r\n if click[0] == 1 and buttons == 1:\r\n runpy.run_path(\"Pong.py\")\r\n if click[0] == 1 and buttons == 2:\r\n runpy.run_path(\"Snake.py\")\r\n if click[0] == 1 and buttons == 3:\r\n runpy.run_path(\"Tron.py\")\r\n if click[0] == 1 and buttons == 4:\r\n runpy.run_path(\"OXO.py\")\r\n if click[0] == 1 and buttons == 5:\r\n runpy.run_path(\"Blackholes.py\")\r\n if click[0] == 1 and buttons == 6:\r\n runpy.run_path(\"Mancala.py\")\r\n if click[0] == 1 and buttons == 7:\r\n runpy.run_path(\"Adventure_game1.py\")\r\n\r\n else:\r\n pygame.draw.rect(display, white, [X, Y, 150, 50])\r\n text(buttons, X, Y)\r\n\r\n Y += 70\r\n\r\n if Y >= 500:\r\n X += 170\r\n Y = 250\r\n\r\n footer()\r\n pygame.display.update()\r\n\r\n\r\ndef main():\r\n menu_on = True\r\n\r\n pygame.display.update()\r\n while menu_on:\r\n\r\n button(50, 250)\r\n menu_on = close()\r\n\r\n pygame.quit()\r\n quit()\r\n\r\n\r\nmain()", "repo_name": "GabrielM97/LibraryOfGames", "sub_path": "ADP-Group Project/Menu.py", "file_name": "Menu.py", "file_ext": "py", "file_size_in_byte": 2753, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "pygame.init", "line_number": 5, "usage_type": "call"}, {"api_name": "pygame.display.set_mode", "line_number": 8, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 8, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 9, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 9, "usage_type": "attribute"}, {"api_name": "pygame.display.set_caption", "line_number": 18, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 18, "usage_type": "attribute"}, {"api_name": "pygame.font.SysFont", "line_number": 28, "usage_type": "call"}, {"api_name": "pygame.font", "line_number": 28, "usage_type": "attribute"}, {"api_name": "pygame.event.get", "line_number": 36, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 36, "usage_type": "attribute"}, {"api_name": "pygame.QUIT", "line_number": 37, "usage_type": "attribute"}, {"api_name": "pygame.font.SysFont", "line_number": 44, "usage_type": "call"}, {"api_name": "pygame.font", "line_number": 44, "usage_type": "attribute"}, {"api_name": "pygame.mouse.get_pos", "line_number": 54, "usage_type": "call"}, {"api_name": "pygame.mouse", "line_number": 54, "usage_type": "attribute"}, {"api_name": "pygame.mouse.get_pressed", "line_number": 55, "usage_type": "call"}, {"api_name": "pygame.mouse", "line_number": 55, "usage_type": "attribute"}, {"api_name": "pygame.draw.rect", "line_number": 59, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 59, "usage_type": "attribute"}, {"api_name": "runpy.run_path", "line_number": 62, "usage_type": "call"}, {"api_name": "runpy.run_path", "line_number": 64, "usage_type": "call"}, {"api_name": "runpy.run_path", "line_number": 66, "usage_type": "call"}, {"api_name": "runpy.run_path", "line_number": 68, "usage_type": "call"}, {"api_name": "runpy.run_path", "line_number": 70, "usage_type": "call"}, {"api_name": "runpy.run_path", "line_number": 72, "usage_type": "call"}, {"api_name": "runpy.run_path", "line_number": 74, "usage_type": "call"}, {"api_name": "runpy.run_path", "line_number": 76, "usage_type": "call"}, {"api_name": "pygame.draw.rect", "line_number": 79, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 79, "usage_type": "attribute"}, {"api_name": "pygame.display.update", "line_number": 89, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 89, "usage_type": "attribute"}, {"api_name": "pygame.display.update", "line_number": 95, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 95, "usage_type": "attribute"}, {"api_name": "pygame.quit", "line_number": 101, "usage_type": "call"}]} +{"seq_id": "74204329387", "text": "from .tile import Tile\nfrom .player import PlayerType\nfrom enum import Enum\nfrom typing import Callable\n\n\nclass PathPair:\n \"\"\"\n Represents a pair of paths for the light and dark players to\n move their pieces along in a game of the Royal Game of Ur.\n \"\"\"\n __slots__ = (\n \"_name\", \"_light_with_ends\", \"_dark_with_ends\",\n \"_light\", \"_dark\",\n )\n\n _name: str\n _light_with_ends: list[Tile]\n _dark_with_ends: list[Tile]\n _light: list[Tile]\n _dark: list[Tile]\n\n def __init__(\n self,\n name: str,\n lightWithStartEnd: list[Tile],\n darkWithStartEnd: list[Tile],\n ):\n self._name = name\n self._light_with_ends = [*lightWithStartEnd]\n self._dark_with_ends = [*darkWithStartEnd]\n self._light = self._light_with_ends[1:-1]\n self._dark = self._dark_with_ends[1:-1]\n\n @property\n def name(self) -> str:\n \"\"\"\n The name of this path pair.\n \"\"\"\n return self.name\n\n @property\n def light_with_ends(self) -> list[Tile]:\n \"\"\"\n The path that light players take around the board, including\n the start and end tiles that exist off the board.\n \"\"\"\n return self._light_with_ends\n\n @property\n def dark_with_ends(self) -> list[Tile]:\n \"\"\"\n The path that dark players take around the board, including\n the start and end tiles that exist off the board.\n \"\"\"\n return self._dark_with_ends\n\n @property\n def light(self) -> list[Tile]:\n \"\"\"\n The path that light players take around the board, excluding\n the start and end tiles that exist off the board.\n \"\"\"\n return self._light\n\n @property\n def dark(self) -> list[Tile]:\n \"\"\"\n The path that dark players take around the board, excluding\n the start and end tiles that exist off the board.\n \"\"\"\n return self._dark\n\n @property\n def light_start(self) -> Tile:\n \"\"\"\n The start tile of the light player that exists off the board.\n \"\"\"\n return self._light_with_ends[0]\n\n @property\n def light_end(self) -> Tile:\n \"\"\"\n The end tile of the light player that exists off the board.\n \"\"\"\n return self._light_with_ends[-1]\n\n @property\n def dark_start(self) -> Tile:\n \"\"\"\n The start tile of the dark player that exists off the board.\n \"\"\"\n return self._dark_with_ends[0]\n\n @property\n def dark_end(self) -> Tile:\n \"\"\"\n The end tile of the dark player that exists off the board.\n \"\"\"\n return self._dark_with_ends[-1]\n\n def get(self, player: PlayerType) -> list[Tile]:\n \"\"\"\n Gets the path of the given player, excluding the start and\n end tiles that exist off the board.\n \"\"\"\n if player == PlayerType.LIGHT:\n return self._light\n elif player == PlayerType.DARK:\n return self._dark\n else:\n raise ValueError(f\"Unknown PlayerType {player}\")\n\n def get_with_ends(self, player: PlayerType) -> list[Tile]:\n \"\"\"\n Gets the path of the given player, including the start and\n end tiles that exist off the board.\n \"\"\"\n if player == PlayerType.LIGHT:\n return self._light_with_ends\n elif player == PlayerType.DARK:\n return self._dark_with_ends\n else:\n raise ValueError(f\"Unknown PlayerType {player}\")\n\n def get_start(self, player: PlayerType) -> list[Tile]:\n \"\"\"\n Gets the start tile of the given player, which exists off the board.\n \"\"\"\n return self.get_with_ends(player)[0]\n\n def get_end(self, player: PlayerType) -> list[Tile]:\n \"\"\"\n Gets the end tile of the given player, which exists off the board.\n \"\"\"\n return self.get_with_ends(player)[-1]\n\n def is_equivalent(self, other: 'PathPair') -> bool:\n \"\"\"\n Determines whether this set of paths and other cover the\n same tiles, in the same order. This does not check the\n start and end tiles that exist off the board, or the name\n of the paths.\n \"\"\"\n return self._light == other._light\n\n def __eq__(self, other: object) -> bool:\n if type(other) is not type(self):\n return False\n\n return self._name == other._name \\\n and self._light_with_ends == other._light_with_ends \\\n and self._dark_with_ends == other._dark_with_ends\n\n\nclass AsebPathPair(PathPair):\n \"\"\"\n The standard paths that are used for Aseb.\n\n Citation: W. Crist, A.E. Dunn-Vaturi, and A. de Voogt,\n Ancient Egyptians at Play: Board Games Across Borders,\n Bloomsbury Egyptology, Bloomsbury Academic, London, 2016.\n \"\"\"\n __slots__ = ()\n\n NAME: str = \"Aseb\"\n \"\"\"\n The name of this type of path pair.\n \"\"\"\n\n LIGHT_PATH: list[Tile] = Tile.create_path(\n (1, 5),\n (1, 1),\n (2, 1),\n (2, 12),\n (1, 12),\n )\n \"\"\"\n The path of the light player's pieces.\n \"\"\"\n\n DARK_PATH: list[Tile] = Tile.create_path(\n (3, 5),\n (3, 1),\n (2, 1),\n (2, 12),\n (3, 12),\n )\n \"\"\"\n The path of the dark player's pieces.\n \"\"\"\n\n def __init__(self):\n super().__init__(\n AsebPathPair.NAME,\n AsebPathPair.LIGHT_PATH,\n AsebPathPair.DARK_PATH\n )\n\n\nclass BellPathPair(PathPair):\n \"\"\"\n The paths proposed by Bell for the Royal Game of Ur.\n\n Citation: R.C. Bell, Board and Table Games From Many\n Civilizations, revised ed., Vol. 1 and 2, Dover\n Publications, Inc., New York, 1979.\n \"\"\"\n __slots__ = ()\n\n NAME: str = \"Bell\"\n \"\"\"\n The name of this type of path pair.\n \"\"\"\n\n LIGHT_PATH: list[Tile] = Tile.create_path(\n (1, 5),\n (1, 1),\n (2, 1),\n (2, 8),\n (1, 8),\n (1, 6),\n )\n \"\"\"\n The path of the light player's pieces.\n \"\"\"\n\n DARK_PATH: list[Tile] = Tile.create_path(\n (3, 5),\n (3, 1),\n (2, 1),\n (2, 8),\n (3, 8),\n (3, 6),\n )\n \"\"\"\n The path of the dark player's pieces.\n \"\"\"\n\n def __init__(self):\n super().__init__(\n BellPathPair.NAME,\n BellPathPair.LIGHT_PATH,\n BellPathPair.DARK_PATH\n )\n\n\nclass MastersPathPair(PathPair):\n \"\"\"\n The paths proposed by Masters for the Royal Game of Ur.\n\n Citation: J. Masters, The Royal Game of Ur & The\n Game of 20 Squares (2021). Available at\n https://www.tradgames.org.uk/games/Royal-Game-Ur.htm.\n \"\"\"\n __slots__ = ()\n\n NAME: str = \"Masters\"\n \"\"\"\n The name of this type of path pair.\n \"\"\"\n\n LIGHT_PATH: list[Tile] = Tile.create_path(\n (1, 5),\n (1, 1),\n (2, 1),\n (2, 7),\n (3, 7),\n (3, 8),\n (1, 8),\n (1, 6),\n )\n \"\"\"\n The path of the light player's pieces.\n \"\"\"\n\n DARK_PATH: list[Tile] = Tile.create_path(\n (3, 5),\n (3, 1),\n (2, 1),\n (2, 7),\n (1, 7),\n (1, 8),\n (3, 8),\n (3, 6),\n )\n \"\"\"\n The path of the dark player's pieces.\n \"\"\"\n\n def __init__(self):\n super().__init__(\n MastersPathPair.NAME,\n MastersPathPair.LIGHT_PATH,\n MastersPathPair.DARK_PATH\n )\n\n\nclass MurrayPathPair(PathPair):\n \"\"\"\n The paths proposed by Murray for the Royal Game of Ur.\n\n Citation: H.J.R. Murray, A History of Board-games\n Other Than Chess, Oxford University Press, Oxford, 1952.\n \"\"\"\n __slots__ = ()\n\n NAME: str = \"Murray\"\n \"\"\"\n The name of this type of path pair.\n \"\"\"\n\n LIGHT_PATH: list[Tile] = Tile.create_path(\n (1, 5),\n (1, 1),\n (2, 1),\n (2, 7),\n (3, 7),\n (3, 8),\n (1, 8),\n (1, 7),\n (2, 7),\n (2, 1),\n (3, 1),\n (3, 5),\n )\n \"\"\"\n The path of the light player's pieces.\n \"\"\"\n\n DARK_PATH: list[Tile] = Tile.create_path(\n (3, 5),\n (3, 1),\n (2, 1),\n (2, 7),\n (1, 7),\n (1, 8),\n (3, 8),\n (3, 7),\n (2, 7),\n (2, 1),\n (1, 1),\n (1, 5),\n )\n \"\"\"\n The path of the dark player's pieces.\n \"\"\"\n\n def __init__(self):\n super().__init__(\n MurrayPathPair.NAME,\n MurrayPathPair.LIGHT_PATH,\n MurrayPathPair.DARK_PATH\n )\n\n\nclass SkiriukPathPair(PathPair):\n \"\"\"\n The paths proposed by Skiriuk for the Royal Game of Ur.\n\n Citation: D. Skiriuk, The rules of royal game of ur (2021).\n Available at https://skyruk.livejournal.com/231444.html.\n \"\"\"\n __slots__ = ()\n\n NAME: str = \"Skiriuk\"\n \"\"\"\n The name of this type of path pair.\n \"\"\"\n\n LIGHT_PATH: list[Tile] = Tile.create_path(\n (1, 5),\n (1, 1),\n (2, 1),\n (2, 7),\n (3, 7),\n (3, 8),\n (1, 8),\n (1, 7),\n (2, 7),\n (2, 0),\n )\n \"\"\"\n The path of the light player's pieces.\n \"\"\"\n\n DARK_PATH: list[Tile] = Tile.create_path(\n (3, 5),\n (3, 1),\n (2, 1),\n (2, 7),\n (1, 7),\n (1, 8),\n (3, 8),\n (3, 7),\n (2, 7),\n (2, 0),\n )\n \"\"\"\n The path of the dark player's pieces.\n \"\"\"\n\n def __init__(self):\n super().__init__(\n SkiriukPathPair.NAME,\n SkiriukPathPair.LIGHT_PATH,\n SkiriukPathPair.DARK_PATH\n )\n\n\nclass PathType(Enum):\n \"\"\"\n The type of path to use in a game.\n \"\"\"\n\n BELL = (1, BellPathPair.NAME, lambda: BellPathPair())\n \"\"\"\n The path proposed by Bell for the Royal Game of Ur.\n \"\"\"\n\n ASEB = (2, AsebPathPair.NAME, lambda: AsebPathPair())\n \"\"\"\n The standard path used for Aseb.\n \"\"\"\n\n MASTERS = (3, MastersPathPair.NAME, lambda: MastersPathPair())\n \"\"\"\n The path proposed by Masters for the Royal Game of Ur.\n \"\"\"\n\n MURRAY = (4, MurrayPathPair.NAME, lambda: MurrayPathPair())\n \"\"\"\n The path proposed by Murray for the Royal Game of Ur.\n \"\"\"\n\n SKIRIUK = (5, SkiriukPathPair.NAME, lambda: SkiriukPathPair())\n \"\"\"\n The path proposed by Skiriuk for the Royal Game of Ur.\n \"\"\"\n\n def __init__(\n self,\n value: int,\n text_name: str,\n create_path_pair: Callable[[], PathPair]\n ):\n self._value_ = value\n self._text_name = text_name\n self._create_path_pair = create_path_pair\n\n @property\n def text_name(self) -> str:\n \"\"\"\n The name of these paths.\n \"\"\"\n return self._text_name\n\n def create_path_pair(self) -> PathPair:\n \"\"\"\n Create an instance of the paths.\n \"\"\"\n return self._create_path_pair()\n", "repo_name": "RoyalUr/RoyalUr-Python", "sub_path": "royalur/model/path.py", "file_name": "path.py", "file_ext": "py", "file_size_in_byte": 10891, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "37", "api": [{"api_name": "tile.Tile", "line_number": 18, "usage_type": "name"}, {"api_name": "tile.Tile", "line_number": 19, "usage_type": "name"}, {"api_name": "tile.Tile", "line_number": 20, "usage_type": "name"}, {"api_name": "tile.Tile", "line_number": 21, "usage_type": "name"}, {"api_name": "tile.Tile", "line_number": 26, "usage_type": "name"}, {"api_name": "tile.Tile", "line_number": 27, "usage_type": "name"}, {"api_name": "tile.Tile", "line_number": 43, "usage_type": "name"}, {"api_name": "tile.Tile", "line_number": 51, "usage_type": "name"}, {"api_name": "tile.Tile", "line_number": 59, "usage_type": "name"}, {"api_name": "tile.Tile", "line_number": 67, "usage_type": "name"}, {"api_name": "tile.Tile", "line_number": 75, "usage_type": "name"}, {"api_name": "tile.Tile", "line_number": 82, "usage_type": "name"}, {"api_name": "tile.Tile", "line_number": 89, "usage_type": "name"}, {"api_name": "tile.Tile", "line_number": 96, "usage_type": "name"}, {"api_name": "player.PlayerType", "line_number": 102, "usage_type": "name"}, {"api_name": "player.PlayerType.LIGHT", "line_number": 107, "usage_type": "attribute"}, {"api_name": "player.PlayerType", "line_number": 107, "usage_type": "name"}, {"api_name": "player.PlayerType.DARK", "line_number": 109, "usage_type": "attribute"}, {"api_name": "player.PlayerType", "line_number": 109, "usage_type": "name"}, {"api_name": "tile.Tile", "line_number": 102, "usage_type": "name"}, {"api_name": "player.PlayerType", "line_number": 114, "usage_type": "name"}, {"api_name": "player.PlayerType.LIGHT", "line_number": 119, "usage_type": "attribute"}, {"api_name": "player.PlayerType", "line_number": 119, "usage_type": "name"}, {"api_name": "player.PlayerType.DARK", "line_number": 121, "usage_type": "attribute"}, {"api_name": "player.PlayerType", "line_number": 121, "usage_type": "name"}, {"api_name": "tile.Tile", "line_number": 114, "usage_type": "name"}, {"api_name": "player.PlayerType", "line_number": 126, "usage_type": "name"}, {"api_name": "tile.Tile", "line_number": 126, "usage_type": "name"}, {"api_name": "player.PlayerType", "line_number": 132, "usage_type": "name"}, {"api_name": "tile.Tile", "line_number": 132, "usage_type": "name"}, {"api_name": "tile.Tile", "line_number": 171, "usage_type": "name"}, {"api_name": "tile.Tile.create_path", "line_number": 171, "usage_type": "call"}, {"api_name": "tile.Tile", "line_number": 182, "usage_type": "name"}, {"api_name": "tile.Tile.create_path", "line_number": 182, "usage_type": "call"}, {"api_name": "tile.Tile", "line_number": 216, "usage_type": "name"}, {"api_name": "tile.Tile.create_path", "line_number": 216, "usage_type": "call"}, {"api_name": "tile.Tile", "line_number": 228, "usage_type": "name"}, {"api_name": "tile.Tile.create_path", "line_number": 228, "usage_type": "call"}, {"api_name": "tile.Tile", "line_number": 263, "usage_type": "name"}, {"api_name": "tile.Tile.create_path", "line_number": 263, "usage_type": "call"}, {"api_name": "tile.Tile", "line_number": 277, "usage_type": "name"}, {"api_name": "tile.Tile.create_path", "line_number": 277, "usage_type": "call"}, {"api_name": "tile.Tile", "line_number": 313, "usage_type": "name"}, {"api_name": "tile.Tile.create_path", "line_number": 313, "usage_type": "call"}, {"api_name": "tile.Tile", "line_number": 331, "usage_type": "name"}, {"api_name": "tile.Tile.create_path", "line_number": 331, "usage_type": "call"}, {"api_name": "tile.Tile", "line_number": 371, "usage_type": "name"}, {"api_name": "tile.Tile.create_path", "line_number": 371, "usage_type": "call"}, {"api_name": "tile.Tile", "line_number": 387, "usage_type": "name"}, {"api_name": "tile.Tile.create_path", "line_number": 387, "usage_type": "call"}, {"api_name": "enum.Enum", "line_number": 411, "usage_type": "name"}, {"api_name": "typing.Callable", "line_number": 445, "usage_type": "name"}]} +{"seq_id": "24698101476", "text": "import torch\nfrom torch.utils.data import Dataset\nimport numpy as np\n\nclass Salicon_loader(Dataset):\n def __init__(self,data,mask):\n \n self.data = data\n\n #reshape to N*C*H*W\n # self.data = self.data.reshape(self.data.shape[0],3,48,48) \n self.data = self.data.transpose([0,3,1,2]) \n self.mask = mask \n\n \n def __getitem__(self, index, bgr_mean=np.array([103.939, 116.779, 123.68])):\n #every time the data loader is called, it will input a index, \n #the getitem function will return the image based on the index\n #the maximum index number is defined in __len__ method below\n #for each calling, you could do the image preprocessing, flipping or cropping\n \n img = self.data[index,:,:,:]\n img = img[::-1,:,:]\n \n self.mean_val = bgr_mean\n # self.std = np.std(img,axis=0)\n\n # use broadcasting to vectorizely normalize image\n img = (img - self.mean_val.reshape(1,3,1,1))#/(self.std.reshape(1,3,1,1))\n mask = self.mask[index,:,:]\n\n # convert numpy array to torch tensor variable\n img = torch.from_numpy(img.astype(np.float32))\n mask = torch.from_numpy(mask.astype(np.float32)) \n \n return img,mask\n\n def __len__(self):\n #this function define the upper bound of input index\n #it's usually set to the data image number\n\n #return self.data.shape[0]\n return 2000\n", "repo_name": "varungupta3/saliency-guided-activity-recognition", "sub_path": "scripts/Salicon_loader.py", "file_name": "Salicon_loader.py", "file_ext": "py", "file_size_in_byte": 1475, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "37", "api": [{"api_name": "torch.utils.data.Dataset", "line_number": 5, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 16, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 33, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 33, "usage_type": "attribute"}, {"api_name": "torch.from_numpy", "line_number": 34, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 34, "usage_type": "attribute"}]} +{"seq_id": "7903489821", "text": "import csv\nimport sys\nimport traceback\n\nfrom PyQt5.QtWidgets import *\nfrom PyQt5.QtGui import QIcon, QFont, QCursor\nfrom PyQt5.QtCore import Qt, QSize, QTimer\n\nfrom os import startfile, getcwd\nfrom webbrowser import open as openweb\nfrom keyboard import add_hotkey\n\ncwd = getcwd() # Place of this program\nwith open('Description.txt', mode='r', encoding='utf-8') as man:\n DESCRIPTION = [i.replace('\\n', '') for i in man.readlines()] # Import description of this program\n\n\ndef restart(): # Restart this program\n try:\n startfile(sys.argv[0])\n sys.exit(-23)\n except Exception:\n traceback.print_exc()\n erw = QErrorMessage()\n erw.showMessage(str(error))\n erw.exec_()\n\n\nclass MainWindow(QWidget):\n \"\"\"This is a main program window\"\"\"\n\n def __init__(self):\n super().__init__(flags=Qt.WindowStaysOnTopHint)\n self.settings_window = SettingsWindow() # add a settings window\n self.descr_window = DescrWindow() # add a description window\n self.add_window = AddWindow() # add an add window\n self.initUI()\n\n def initUI(self):\n try:\n self.setMaximumSize(700, 700)\n self.setMinimumSize(700, 700)\n self.setWindowTitle('Filer Manager')\n self.setWindowIcon(QIcon('icon.png'))\n self.setStyleSheet('''QWidget {\n background-color: rgb(10, 10, 20); \n }''')\n\n self.menuf = QGroupBox('Tools', self) # Group of tools buttons\n self.menuf.setStyleSheet('''QGroupBox {\n margin-top: 2ex;\n }\n QGroupBox:enabled {\n border: 1px solid rgb(180, 1, 1);\n border-radius: 8px;\n }\n QGroupBox:title {\n subcontrol-origin: margin;\n left: 3ex;\n color: rgb(180, 1, 1);\n }''')\n\n self.menufg = QVBoxLayout() # Box of tools buttons\n\n self.settings = QPushButton('Settings', self) # Opens a settings window\n self.settings.setStyleSheet('''QPushButton {\n background-color: rgb(60, 60, 120); \n border-style: outset; \n border-width: 2px; \n border-radius: 10px; \n border-color: black; \n color: white; \n font: bold 24px; \n padding: 4px;\n width: 6em;\n }\n QPushButton:hover {\n background-color: rgb(50, 50, 100); \n border-color: rgb(80, 80, 100); \n border-style: inset;\n }''')\n self.settings.clicked.connect(lambda: self.settings_window.shower())\n self.settings.setCursor(QCursor(Qt.PointingHandCursor))\n self.menufg.addWidget(self.settings, alignment=Qt.Alignment())\n\n self.add = QPushButton('Addfile', self) # Opens an add window\n self.add.setStyleSheet('''QPushButton {\n background-color: rgb(60, 60, 120); \n border-style: outset; \n border-width: 2px; \n border-radius: 10px; \n border-color: black; \n color: white; \n font: bold 24px; \n padding: 4px;\n width: 6em;\n }\n QPushButton:hover {\n background-color: rgb(50, 50, 100); \n border-color: rgb(80, 80, 100); \n border-style: inset;\n }''')\n self.add.clicked.connect(self.add_window.shower)\n self.add.setCursor(QCursor(Qt.PointingHandCursor))\n self.menufg.addWidget(self.add, alignment=Qt.Alignment())\n\n self.descr = QPushButton('Program', self) # Opens a description window\n self.descr.setStyleSheet('''QPushButton {\n background-color: rgb(60, 60, 120); \n border-style: outset; \n border-width: 2px; \n border-radius: 10px; \n border-color: black; \n color: white; \n font: bold 24px; \n padding: 4px;\n width: 6em;\n }\n QPushButton:hover {\n background-color: rgb(50, 50, 100); \n border-color: rgb(80, 80, 100); \n border-style: inset;\n }''')\n self.descr.clicked.connect(lambda: self.descr_window.shower())\n self.descr.setCursor(QCursor(Qt.PointingHandCursor))\n self.menufg.addWidget(self.descr, alignment=Qt.Alignment())\n\n self.exitting = QPushButton('Exit', self) # Closes a program\n self.exitting.setStyleSheet('''QPushButton {\n background-color: rgb(60, 60, 120); \n border-style: outset; \n border-width: 2px; \n border-radius: 10px; \n border-color: black; \n color: white; \n font: bold 24px; \n padding: 4px;\n width: 6em;\n }\n QPushButton:hover {\n background-color: rgb(160, 40, 40); \n border-color: rgb(240, 60, 60); \n border-style: inset;\n }''')\n self.exitting.clicked.connect(lambda: sys.exit())\n self.exitting.setCursor(QCursor(Qt.PointingHandCursor))\n self.menufg.addWidget(self.exitting, alignment=Qt.Alignment())\n\n self.menuf.setLayout(self.menufg)\n self.menuf.move(20, 20)\n\n self.packsf = QGroupBox('Packs', self) # Group of pack buttons\n self.packsf.setStyleSheet('''QGroupBox {\n margin-top: 2ex;\n }\n QGroupBox:enabled {\n border: 1px solid rgb(180, 1, 1);\n border-radius: 8px;\n }\n QGroupBox:title {\n subcontrol-origin: margin;\n left: 3ex;\n color: rgb(180, 1, 1);\n }''')\n\n self.pack1 = QPushButton('Pack 1', self) # Button opens all programs in \"Pack 1\" directory\n self.pack1.setStyleSheet('''QPushButton {\n background-color: rgb(70, 70, 120); \n border-style: outset; \n border-width: 2px; \n border-radius: 8px; \n border-color: black; \n color: white; \n font: bold 24px; \n padding: 4px;\n width: 6em;\n }\n QPushButton:hover {\n background-color: rgb(50, 50, 100); \n border-color: rgb(100, 100, 100); \n border-style: inset;\n }''')\n self.pack1.clicked.connect(self.open1)\n self.pack1.setCursor(QCursor(Qt.PointingHandCursor))\n\n self.pack2 = QPushButton('Pack 2', self) # Button opens all programs in \"Pack 2\" directory\n self.pack2.setStyleSheet('''QPushButton {\n background-color: rgb(70, 70, 120); \n border-style: outset; \n border-width: 2px; \n border-radius: 8px; \n border-color: black; \n color: white; \n font: bold 24px; \n padding: 4px;\n width: 6em;\n }\n QPushButton:hover {\n background-color: rgb(50, 50, 100); \n border-color: rgb(100, 100, 100); \n border-style: inset;\n }''')\n self.pack2.clicked.connect(self.open2)\n self.pack2.setCursor(QCursor(Qt.PointingHandCursor))\n\n self.pack3 = QPushButton('Pack 3', self) # Button opens all programs in \"Pack 3\" directory\n self.pack3.setStyleSheet('''QPushButton {\n background-color: rgb(70, 70, 120); \n border-style: outset; \n border-width: 2px; \n border-radius: 8px; \n border-color: black; \n color: white; \n font: bold 24px; \n padding: 4px;\n width: 6em;\n }\n QPushButton:hover {\n background-color: rgb(50, 50, 100); \n border-color: rgb(100, 100, 100); \n border-style: inset;\n }''')\n self.pack3.clicked.connect(self.open3)\n self.pack3.setCursor(QCursor(Qt.PointingHandCursor))\n\n self.packsfg = QVBoxLayout() # Box of pack buttons\n self.packsfg.addWidget(self.pack1, alignment=Qt.Alignment())\n self.packsfg.addWidget(self.pack2, alignment=Qt.Alignment())\n self.packsfg.addWidget(self.pack3, alignment=Qt.Alignment())\n\n self.packsf.setLayout(self.packsfg)\n self.packsf.move(20, 250)\n\n self.fr = QScrollArea(self) # Group of programs in \"Main\" directory\n self.fr.setStyleSheet('''QScrollArea {\n border: 2px solid rgb(180, 1, 1);\n border-radius: 4px;\n }''')\n self.fr.verticalScrollBar().setStyleSheet('''QScrollBar {\n background-color: rgb(60, 60, 60); \n }''')\n self.fr.setGeometry(0, 0, 430, 660)\n self.fr.setVerticalScrollBarPolicy(Qt.ScrollBarAlwaysOn)\n self.fr.setHorizontalScrollBarPolicy(Qt.ScrollBarAlwaysOff)\n\n self.frw = QWidget(flags=Qt.WindowFlags()) # Box of buttons\n self.frg = QVBoxLayout()\n self.data = {}\n with open('Main.csv', mode='r', encoding=\"utf8\") as csvfile:\n readerpath = csv.DictReader(csvfile, delimiter='|', quotechar='\"')\n for pathname in sorted(readerpath, key=lambda p: str(p['Name']).lower()): # List of programs\n if pathname['Type'] == 'Main':\n btn = QPushButton(pathname['Name'], self)\n btn.setStyleSheet('''QPushButton {\n background-color: rgb(90, 90, 150); \n border-style: outset; \n border-width: 1px; \n border-radius: 5px; \n border-color: rgb(80, 80, 100); \n color: white; \n font: bold 20px; \n padding: 5px;\n width: 16em;\n }\n QPushButton:hover {\n background-color: rgb(80, 80, 140); \n border-style: inset;\n }''')\n btn.setCursor(QCursor(Qt.PointingHandCursor))\n btn.clicked.connect(self.opener)\n self.data[pathname['Name']] = pathname['Path']\n self.frg.addWidget(btn, alignment=Qt.Alignment())\n\n if len(self.data) == 0: # If \"Main\" is empty\n none_label = QLabel('EMPTY')\n none_label.setStyleSheet('''QLabel {\n color: white;\n font: bold 32px;\n }''')\n self.frg.addWidget(none_label, alignment=Qt.AlignCenter)\n self.add.setStyleSheet('''QPushButton {\n background-color: rgb(80, 60, 60); \n border-style: outset; \n border-width: 2px; \n border-radius: 10px; \n border-color: red; \n color: red; \n font: bold 24px; \n padding: 4px;\n width: 6em;\n }\n QPushButton:hover {\n border-color: rgb(200, 20, 20); \n color: rgb(200, 20, 20); \n border-style: inset;\n }''')\n self.frw.setLayout(self.frg)\n self.fr.setWidget(self.frw)\n self.fr.move(250, 20)\n\n self.adf = QGroupBox('Advertising', self) # Group of advertising\n self.adf.setCursor(QCursor(Qt.OpenHandCursor))\n self.adf.setStyleSheet('''QGroupBox {\n background-color: rgb(30, 30, 45); \n margin-top: 2ex;\n }\n QGroupBox:enabled {\n border: 1px solid rgb(180, 1, 1);\n border-radius: 8px;\n }\n QGroupBox:title {\n subcontrol-origin: margin;\n left: 3ex;\n color: rgb(180, 1, 1);\n }''')\n self.adfg = QVBoxLayout()\n self.adyp = QIcon('Yandex_logo_2021_Russian.png')\n self.ady = QPushButton(self)\n self.ady.setIcon(self.adyp)\n self.ady.setIconSize(QSize(175, 60))\n self.ady.setStyleSheet('''QPushButton,\n QPushButton:default,\n QPushButton:hover,\n QPushButton:selected,\n QPushButton:disabled,\n QPushButton:pressed {\n background-color: transparent;\n border-color: transparent;\n color: transparent;\n }''')\n self.ady.clicked.connect(self.adshow)\n self.adfg.addWidget(self.ady, alignment=Qt.Alignment())\n self.adf.setLayout(self.adfg)\n self.adf.move(20, 430)\n\n self.logo = QPushButton(f'{chr(169)} Varenik Vladimir', self) # Copyright\n self.logo.move(20, 620)\n self.logo.resize(200, 60)\n self.logo.setFont(QFont('Arial', 15))\n self.logo.setStyleSheet('''QPushButton,\n QPushButton:default,\n QPushButton:hover,\n QPushButton:selected,\n QPushButton:disabled,\n QPushButton:pressed {\n background-color: transparent;\n border-color: transparent;\n color: rgb(30, 30, 30);\n }''')\n self.logo.clicked.connect(self.authorwindow)\n\n except Exception as error: # Print a error\n traceback.print_exc()\n erw = QErrorMessage()\n erw.showMessage(str(error))\n erw.exec_()\n\n def closeEvent(self, event):\n event.ignore()\n self.hide()\n\n def opener(self): # Opens a link\n openweb(self.data[self.sender().text()])\n\n def open1(self): # for \"Pack 1\" button\n with open('Main.csv', mode='r', encoding=\"utf8\") as csvfile:\n readerp = csv.DictReader(csvfile, delimiter='|', quotechar='\"')\n for path in readerp:\n if path['Type'] == 'Pack 1':\n openweb(path['Path'])\n\n def open2(self): # for \"Pack 2\" button\n with open('Main.csv', mode='r', encoding=\"utf8\") as csvfile:\n readerp = csv.DictReader(csvfile, delimiter='|', quotechar='\"')\n for path in readerp:\n if path['Type'] == 'Pack 2':\n openweb(path['Path'])\n\n def open3(self): # for \"Pack 3\" button\n with open('Main.csv', mode='r', encoding=\"utf8\") as csvfile:\n readerp = csv.DictReader(csvfile, delimiter='|', quotechar='\"')\n for path in readerp:\n if path['Type'] == 'Pack 3':\n openweb(path['Path'])\n\n def adshow(self): # Go to Yandex site (not always)\n openweb('https://rroll.to/chyj2Q')\n self.hide()\n self.settings_window.hide()\n self.add_window.hide()\n self.descr_window.hide()\n QTimer().singleShot(30000, lambda: self.show())\n\n def authorwindow(self):\n self.aw = QDialog(flags=Qt.WindowStaysOnTopHint)\n self.aw.setWindowTitle('Contacts')\n self.secr = QLabel('Contacts\\nTelegram/WhatsApp:\\n+79047613727', self.aw)\n self.secr.setAlignment(Qt.AlignCenter)\n self.secr.resize(120, 60)\n self.aw.show()\n\n\nclass SettingsWindow(QWidget):\n \"\"\"This is a settings window\"\"\"\n\n def __init__(self):\n super().__init__(flags=Qt.WindowStaysOnTopHint)\n self.initUI()\n\n def initUI(self):\n self.move(200, 200)\n self.setMaximumSize(200, 130)\n self.setMinimumSize(200, 130)\n self.setWindowTitle('Settings')\n self.setWindowIcon(QIcon('icon.png'))\n\n self.clear = QPushButton('Clear data', self)\n self.clear.move(10, 10)\n self.clear.resize(180, 50)\n self.clear.setStyleSheet('''QPushButton {\n background-color: rgb(65, 65, 65); \n border-style: outset; \n border-width: 2px; \n border-radius: 10px; \n border-color: black; \n color: rgb(215, 215, 215); \n font: 24px; \n padding: 4px;\n width: 6em;\n }\n QPushButton:hover {\n border-color: rgb(200, 20, 20); \n color: rgb(200, 20, 20); \n border-style: inset;\n }''')\n self.clear.clicked.connect(cleardata)\n\n self.over = QPushButton('Files overview', self)\n self.over.move(10, 70)\n self.over.resize(180, 50)\n self.over.setStyleSheet('''QPushButton {\n background-color: rgb(65, 65, 65); \n border-style: outset; \n border-width: 2px; \n border-radius: 10px; \n border-color: black; \n color: rgb(215, 215, 215); \n font: 24px; \n padding: 4px;\n width: 6em;\n }\n QPushButton:hover {\n border-color: rgb(40, 40, 40); \n color: rgb(165, 165, 165); \n border-style: inset;\n }''')\n self.over.clicked.connect(lambda: startfile(cwd))\n\n def shower(self):\n if self.isHidden():\n self.show()\n else:\n self.hide()\n\n\nclass AddWindow(QWidget):\n \"\"\"This window add a new links or programs\"\"\"\n\n def __init__(self):\n super().__init__(flags=Qt.WindowStaysOnTopHint)\n self.initUI()\n\n def initUI(self):\n self.setWindowTitle('Add...')\n self.setWindowIcon(QIcon('icon.png'))\n self.setGeometry(200, 400, 300, 240)\n self.setStyleSheet('''QWidget {\n background-color: rgb(80, 80, 80); \n }''')\n\n self.getpath = QLineEdit('path', self) # Get a full path\n self.getpath.move(10, 10)\n self.getpath.resize(280, 35)\n self.getpath.setStyleSheet('''QLineEdit {\n background-color: rgb(65, 65, 65); \n border-style: outset; \n border-width: 2px; \n border-radius: 8px; \n border-color: black; \n color: rgb(240, 240, 240); \n font: 18px; \n padding: 4px;\n width: 6em;\n }\n QLineEdit:hover {\n color: rgb(215, 215, 215); \n border-style: inset;\n }''')\n\n self.getname = QLineEdit('name', self) # Get a name\n self.getname.move(10, 70)\n self.getname.resize(280, 35)\n self.getname.setStyleSheet('''QLineEdit {\n background-color: rgb(65, 65, 65); \n border-style: outset; \n border-width: 2px; \n border-radius: 8px; \n border-color: black; \n color: rgb(240, 240, 240); \n font: 18px; \n padding: 4px;\n width: 6em;\n }\n QLineEdit:hover {\n color: rgb(215, 215, 215); \n border-style: inset;\n }''')\n\n self.getpl = QComboBox(self) # Get a pack\n self.getpl.addItems(['Main', 'Pack 1', 'Pack 2', 'Pack 3'])\n self.getpl.move(10, 130)\n self.getpl.resize(280, 35)\n self.getpl.setStyleSheet('''QComboBox {\n background-color: rgb(65, 65, 65); \n border-style: outset; \n border-width: 2px; \n border-radius: 8px; \n border-color: black; \n color: rgb(215, 215, 215); \n font: 18px; \n padding: 4px;\n width: 6em;\n }\n QComboBox:hover {\n border-color: rgb(40, 40, 40); \n color: rgb(165, 165, 165); \n border-style: inset;\n }\n QComboBox::drop-down {\n background-color: rgb(80, 80, 80); \n border-radius: 8px; \n }''')\n\n self.go = QPushButton('Save', self) # Save\n self.go.move(10, 190)\n self.go.resize(135, 40)\n self.go.clicked.connect(self.enter)\n self.go.setStyleSheet('''QPushButton {\n background-color: rgb(65, 65, 65); \n border-style: outset; \n border-width: 2px; \n border-radius: 10px; \n border-color: black; \n color: rgb(215, 215, 215); \n font: 24px; \n padding: 4px;\n width: 6em;\n }\n QPushButton:hover {\n border-color: rgb(40, 40, 40); \n color: rgb(165, 165, 165); \n border-style: inset;\n }''')\n\n self.overb = QPushButton('Overview', self) # Choose in explorer\n self.overb.move(155, 190)\n self.overb.resize(135, 40)\n self.overb.clicked.connect(self.filedial)\n self.overb.setStyleSheet('''QPushButton {\n background-color: rgb(65, 65, 65); \n border-style: outset; \n border-width: 2px; \n border-radius: 10px; \n border-color: black; \n color: rgb(215, 215, 215); \n font: 24px; \n padding: 4px;\n width: 6em;\n }\n QPushButton:hover {\n border-color: rgb(40, 40, 40); \n color: rgb(165, 165, 165); \n border-style: inset;\n }''')\n\n def enter(self): # Save\n with open('Main.csv', mode='a', newline='', encoding=\"utf8\") as csvfile:\n writer = csv.DictWriter(csvfile, fieldnames=['Type', 'Path', 'Name'], delimiter='|',\n quotechar='\"', quoting=csv.QUOTE_MINIMAL)\n new = {'Type': self.getpl.currentText(),\n 'Path': self.getpath.text(),\n 'Name': self.getname.text()}\n writer.writerow(new)\n restart()\n\n def filedial(self): # Get path in explorer\n path = QFileDialog().getOpenFileUrl()[0].path()[1:]\n if path:\n self.getpath.setText(path)\n\n def shower(self):\n if self.isHidden():\n self.show()\n else:\n self.hide()\n\n\nclass DescrWindow(QWidget):\n \"\"\"This window is a description\"\"\"\n\n def __init__(self):\n super().__init__(flags=Qt.WindowStaysOnTopHint)\n self.initUI()\n\n def initUI(self):\n self.setGeometry(1350, 200, 400, 400)\n self.setWindowTitle('Description')\n self.setWindowIcon(QIcon('icon.png'))\n self.setStyleSheet('''QWidget {\n background-color: rgb(80, 80, 80); \n }''')\n\n self.lay = QVBoxLayout()\n self.descr = QPlainTextEdit(self) # Text\n self.descr.appendPlainText('\\n'.join(DESCRIPTION))\n self.descr.setReadOnly(True)\n self.descr.setStyleSheet('''QPlainTextEdit {\n background-color: rgb(25, 25, 25); \n border-style: outset; \n border-width: 2px; \n border-radius: 6px; \n border-color: black; \n color: white; \n font: 12px; \n padding: 4px;\n width: 6em;\n }''')\n self.lay.addWidget(self.descr, alignment=Qt.Alignment())\n self.setLayout(self.lay)\n\n def shower(self):\n if self.isHidden():\n self.show()\n else:\n self.hide()\n\n\ndef cleardata():\n \"\"\"Clear a data\"\"\"\n with open('Main.csv', mode='w', newline='', encoding=\"utf8\") as csvfile:\n writer = csv.DictWriter(csvfile, fieldnames=['Type', 'Path', 'Name'], delimiter='|',\n quotechar='\"', quoting=csv.QUOTE_MINIMAL)\n writer.writeheader()\n restart()\n\n\ndef hotkey():\n \"\"\"This is a hotkey work (secret function of this program)\"\"\"\n if form.isHidden():\n form.show()\n else:\n form.hide()\n\n\nif __name__ == '__main__': # Launch of program\n try:\n add_hotkey('ctrl + shift + f', hotkey) # Secret hotkey\n QApplication.setAttribute(Qt.AA_EnableHighDpiScaling, True) # For big displays\n QApplication.setAttribute(Qt.AA_UseHighDpiPixmaps, True)\n app = QApplication(sys.argv) # Application\n app.setWindowIcon(QIcon('icon.png'))\n form = MainWindow() # Main window\n form.show()\n sys.exit(app.exec()) # Exit\n except Exception as error: # Send a error\n traceback.print_exc()\n erw = QErrorMessage()\n erw.showMessage(str(error))\n erw.exec_()\n", "repo_name": "VVV33301/FilerManager", "sub_path": "FilerManager.py", "file_name": "FilerManager.py", "file_ext": "py", "file_size_in_byte": 24587, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "os.getcwd", "line_number": 13, "usage_type": "call"}, {"api_name": "os.startfile", "line_number": 20, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 20, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 21, "usage_type": "call"}, {"api_name": "traceback.print_exc", "line_number": 23, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.Qt.WindowStaysOnTopHint", "line_number": 33, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 33, "usage_type": "name"}, {"api_name": "PyQt5.QtGui.QIcon", "line_number": 44, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QCursor", "line_number": 83, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.Qt.PointingHandCursor", "line_number": 83, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 83, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt.Alignment", "line_number": 84, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 84, "usage_type": "name"}, {"api_name": "PyQt5.QtGui.QCursor", "line_number": 104, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.Qt.PointingHandCursor", "line_number": 104, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 104, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt.Alignment", "line_number": 105, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 105, "usage_type": "name"}, {"api_name": "PyQt5.QtGui.QCursor", "line_number": 125, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.Qt.PointingHandCursor", "line_number": 125, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 125, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt.Alignment", "line_number": 126, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 126, "usage_type": "name"}, {"api_name": "sys.exit", "line_number": 145, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QCursor", "line_number": 146, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.Qt.PointingHandCursor", "line_number": 146, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 146, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt.Alignment", "line_number": 147, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 147, "usage_type": "name"}, {"api_name": "PyQt5.QtGui.QCursor", "line_number": 184, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.Qt.PointingHandCursor", "line_number": 184, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 184, "usage_type": "name"}, {"api_name": "PyQt5.QtGui.QCursor", "line_number": 204, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.Qt.PointingHandCursor", "line_number": 204, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 204, "usage_type": "name"}, {"api_name": "PyQt5.QtGui.QCursor", "line_number": 224, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.Qt.PointingHandCursor", "line_number": 224, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 224, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt.Alignment", "line_number": 227, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 227, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt.Alignment", "line_number": 228, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 228, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt.Alignment", "line_number": 229, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 229, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt.ScrollBarAlwaysOn", "line_number": 243, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 243, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt.ScrollBarAlwaysOff", "line_number": 244, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 244, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt.WindowFlags", "line_number": 246, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 246, "usage_type": "name"}, {"api_name": "csv.DictReader", "line_number": 250, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QCursor", "line_number": 269, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.Qt.PointingHandCursor", "line_number": 269, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 269, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt.Alignment", "line_number": 272, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 272, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt.AlignCenter", "line_number": 280, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 280, "usage_type": "name"}, {"api_name": "PyQt5.QtGui.QCursor", "line_number": 302, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.Qt.OpenHandCursor", "line_number": 302, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 302, "usage_type": "name"}, {"api_name": "PyQt5.QtGui.QIcon", "line_number": 317, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.QSize", "line_number": 320, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.Qt.Alignment", "line_number": 332, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 332, "usage_type": "name"}, {"api_name": "PyQt5.QtGui.QFont", "line_number": 339, "usage_type": "call"}, {"api_name": "traceback.print_exc", "line_number": 353, "usage_type": "call"}, {"api_name": "webbrowser.open", "line_number": 363, "usage_type": "call"}, {"api_name": "csv.DictReader", "line_number": 367, "usage_type": "call"}, {"api_name": "webbrowser.open", "line_number": 370, "usage_type": "call"}, {"api_name": "csv.DictReader", "line_number": 374, "usage_type": "call"}, {"api_name": "webbrowser.open", "line_number": 377, "usage_type": "call"}, {"api_name": "csv.DictReader", "line_number": 381, "usage_type": "call"}, {"api_name": "webbrowser.open", "line_number": 384, "usage_type": "call"}, {"api_name": "webbrowser.open", "line_number": 387, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.QTimer", "line_number": 392, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.Qt.WindowStaysOnTopHint", "line_number": 395, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 395, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt.AlignCenter", "line_number": 398, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 398, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt.WindowStaysOnTopHint", "line_number": 407, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 407, "usage_type": "name"}, {"api_name": "PyQt5.QtGui.QIcon", "line_number": 415, "usage_type": "call"}, {"api_name": "os.startfile", "line_number": 457, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.Qt.WindowStaysOnTopHint", "line_number": 470, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 470, "usage_type": "name"}, {"api_name": "PyQt5.QtGui.QIcon", "line_number": 475, "usage_type": "call"}, {"api_name": "csv.DictWriter", "line_number": 588, "usage_type": "call"}, {"api_name": "csv.QUOTE_MINIMAL", "line_number": 589, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt.WindowStaysOnTopHint", "line_number": 612, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 612, "usage_type": "name"}, {"api_name": "PyQt5.QtGui.QIcon", "line_number": 618, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.Qt.Alignment", "line_number": 638, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 638, "usage_type": "name"}, {"api_name": "csv.DictWriter", "line_number": 651, "usage_type": "call"}, {"api_name": "csv.QUOTE_MINIMAL", "line_number": 652, "usage_type": "attribute"}, {"api_name": "keyboard.add_hotkey", "line_number": 667, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.Qt.AA_EnableHighDpiScaling", "line_number": 668, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 668, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt.AA_UseHighDpiPixmaps", "line_number": 669, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 669, "usage_type": "name"}, {"api_name": "sys.argv", "line_number": 670, "usage_type": "attribute"}, {"api_name": "PyQt5.QtGui.QIcon", "line_number": 671, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 674, "usage_type": "call"}, {"api_name": "traceback.print_exc", "line_number": 676, "usage_type": "call"}]} +{"seq_id": "14273735466", "text": "import torch\nfrom torch import nn\nfrom torch.nn import functional as F\n\nclass Lenet5(nn.Module):\n \"\"\"\n for cifar10 dataset.\n \"\"\"\n def __init__(self):\n super(Lenet5, self).__init__()\n\n self.conv1 = nn.Conv2d(1, 6, kernel_size=5, stride=1, padding=0)\n self.pool1 = nn.AvgPool2d(kernel_size=2, stride=2, padding=0)\n self.conv2 = nn.Conv2d(6, 16, kernel_size=5, stride=1, padding=0)\n self.fc1 = nn.Linear(16*5*5, 120)\n self.fc2 = nn.Linear(120, 84)\n self.fc3 = nn.Linear(84, 10)\n\n def forward(self, x):\n print('input: ', x.shape)\n x = F.relu(self.conv1(x))\n print('conv1',x.shape)\n x = self.pool1(x)\n print('pool1: ', x.shape)\n x = F.relu(self.conv2(x))\n print('conv2',x.shape)\n x = self.pool1(x)\n print('pool2',x.shape)\n x = x.view(x.size(0), -1)\n print('view: ', x.shape)\n x = F.relu(self.fc1(x))\n print('fc1: ', x.shape)\n x = F.relu(self.fc2(x))\n x = F.softmax(self.fc3(x), dim=1)\n return x\n\ndef main():\n print('cuda device count: ', torch.cuda.device_count())\n torch.manual_seed(1234)\n net = Lenet5()\n net = net.to('cuda:0')\n net.eval()\n tmp = torch.ones(1, 1, 32, 32).to('cuda:0')\n out = net(tmp)\n print('lenet out shape:', out.shape)\n print('lenet out:', out)\n torch.save(net, \"lenet5.pth\")\n\nif __name__ == '__main__':\n main()\n\n", "repo_name": "wang-xinyu/pytorchx", "sub_path": "lenet/lenet5.py", "file_name": "lenet5.py", "file_ext": "py", "file_size_in_byte": 1439, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 170, "dataset": "github-code", "pt": "37", "api": [{"api_name": "torch.nn.Module", "line_number": 5, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 5, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 12, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 12, "usage_type": "name"}, {"api_name": "torch.nn.AvgPool2d", "line_number": 13, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 13, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 14, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 14, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 15, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 15, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 16, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 16, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 17, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 17, "usage_type": "name"}, {"api_name": "torch.nn.functional.relu", "line_number": 21, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 21, "usage_type": "name"}, {"api_name": "torch.nn.functional.relu", "line_number": 25, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 25, "usage_type": "name"}, {"api_name": "torch.nn.functional.relu", "line_number": 31, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 31, "usage_type": "name"}, {"api_name": "torch.nn.functional.relu", "line_number": 33, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 33, "usage_type": "name"}, {"api_name": "torch.nn.functional.softmax", "line_number": 34, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 34, "usage_type": "name"}, {"api_name": "torch.cuda.device_count", "line_number": 38, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 38, "usage_type": "attribute"}, {"api_name": "torch.manual_seed", "line_number": 39, "usage_type": "call"}, {"api_name": "torch.ones", "line_number": 43, "usage_type": "call"}, {"api_name": "torch.save", "line_number": 47, "usage_type": "call"}]} +{"seq_id": "19069061184", "text": "from keras.layers import Input, Dense, BatchNormalization, Dropout, GaussianNoise, GaussianDropout\nfrom keras.models import Model\nimport keras.backend as backend\nimport numpy as np\nfrom sklearn.preprocessing import LabelEncoder, normalize\n\nfrom keras.utils import np_utils\nfrom keras.callbacks import CSVLogger, History\nimport pandas as pd\n\nlocal_dataset_folder = './Data/'\nlocal_results_folder = './Data/Result/'\ndropout_model_name = \"-dropout.csv\"\ngaussian_model_name = \"-gaussian.csv\"\nmodel_spec = \"optimal_\" # or hyperopt_\n\n\ndef contractive_dropout_autoencoder(local_data_folder, local_result_folder, model_specific, seed=4018):\n seed = seed\n np.random.seed(seed=seed)\n dataset_folder = local_data_folder\n df_m_rna_address = dataset_folder + \"fpkm.csv\"\n df_mi_rna_address = dataset_folder + \"miRNA.csv\"\n df_tissue_address = dataset_folder + \"tissue.csv\"\n df_disease_address = dataset_folder + \"disease.csv\"\n\n df_m_rna = np.loadtxt(df_m_rna_address, delimiter=\",\")\n # df_singlecell = np.loadtxt(df_singlecell_address, delimiter=\",\")\n df_mi_rna = np.loadtxt(df_mi_rna_address, delimiter=\",\")\n df_tissue = np.ravel(pd.DataFrame.as_matrix(pd.read_csv(df_tissue_address, delimiter=\",\", header=None)))\n df_disease = np.ravel(pd.DataFrame.as_matrix(pd.read_csv(df_disease_address, delimiter=\",\", header=None)))\n\n df_m_rna = normalize(X=df_m_rna, axis=0, norm=\"max\")\n # df_singlecell = normalize(X=df_singlecell, axis=0, norm=\"max\")\n df_mi_rna = normalize(X=df_mi_rna, axis=0, norm=\"max\")\n\n label_encoder_tissue = LabelEncoder()\n label_encoder_tissue.fit(df_tissue)\n encoded_tissue = label_encoder_tissue.transform(df_tissue)\n\n label_encoder_disease = LabelEncoder()\n label_encoder_disease.fit(df_disease)\n encoded_disease = label_encoder_disease.transform(df_disease)\n\n categorical_tissue = np_utils.to_categorical(encoded_tissue)\n categorical_disease = np_utils.to_categorical(encoded_disease)\n m_rna = df_m_rna\n mi_rna = df_mi_rna\n\n print(\"data loading has just been finished\")\n print(m_rna.shape, mi_rna.shape, categorical_tissue.shape, categorical_disease.shape)\n\n batch_size = 64\n nb_epochs = 200\n\n def create_model():\n inputs = Input(shape=(m_rna.shape[1],), name=\"inputs\")\n inputs_noise = GaussianNoise(stddev=0.025)(inputs)\n inputs_noise = GaussianDropout(rate=0.025 ** 2 / (1 + 0.025 ** 2))(inputs_noise)\n inputs_0 = BatchNormalization(name=\"inputs_0\")(inputs_noise)\n inputs_0 = Dropout(rate=0.0, name='dropout_1')(inputs_0)\n inputs_1 = Dense(1024, activation=\"softplus\", name=\"inputs_1\")(inputs_0)\n inputs_2 = BatchNormalization(name=\"inputs_2\")(inputs_1)\n inputs_2 = Dropout(rate=0.0, name='dropout_2')(inputs_2)\n inputs_3 = Dense(256, activation=\"softplus\", name=\"inputs_3\")(inputs_2)\n inputs_4 = BatchNormalization(name=\"inputs_4\")(inputs_3)\n inputs_4 = Dropout(rate=0.25, name='dropout_3')(inputs_4)\n\n encoded = Dense(units=12, activation='relu', name='encoded')(inputs_4)\n\n inputs_5 = Dense(512, activation=\"linear\", name=\"inputs_5\")(encoded)\n inputs_5 = Dropout(rate=0.25, name='dropout_4')(inputs_5)\n\n decoded_tcga = Dense(units=m_rna.shape[1], activation='relu', name=\"m_rna\")(inputs_5)\n decoded_micro_rna = Dense(units=mi_rna.shape[1], activation='relu', name=\"mi_rna\")(inputs_5)\n cl_0 = Dense(units=categorical_tissue.shape[1], activation=\"softmax\", name=\"cl_tissue\")(encoded)\n cl_2 = Dense(units=categorical_disease.shape[1], activation=\"softmax\", name=\"cl_disease\")(encoded)\n\n scae = Model(inputs=inputs, outputs=[decoded_tcga, decoded_micro_rna, cl_0, cl_2])\n\n lambda_value = 9.5581e-3\n\n def contractive_loss(y_pred, y_true):\n mse = backend.mean(backend.square(y_true - y_pred), axis=1)\n\n w = backend.variable(value=scae.get_layer('encoded').get_weights()[0]) # N inputs N_hidden\n w = backend.transpose(w) # N_hidden inputs N\n h = scae.get_layer('encoded').output\n dh = h * (1 - h) # N_batch inputs N_hidden\n\n # N_batch inputs N_hidden * N_hidden inputs 1 = N_batch inputs 1\n contractive = lambda_value * backend.sum(dh ** 2 * backend.sum(w ** 2, axis=1), axis=1)\n\n return mse + contractive\n\n scae.compile(optimizer='nadam',\n loss=[contractive_loss, \"mse\", \"cosine_proximity\", \"cosine_proximity\"],\n loss_weights=[0.001, 0.001, 0.5, 0.5],\n metrics={\"m_rna\": [\"mae\", \"mse\"], \"mi_rna\": [\"mae\", \"mse\"], \"cl_tissue\": \"acc\", \"cl_disease\": \"acc\"})\n\n return scae\n\n def k_fold(num_fold):\n for k in range(num_fold):\n model = create_model()\n m_rna_train = np.array([x for i, x in enumerate(m_rna) if i % num_fold != k])\n m_rna_test = np.array([x for i, x in enumerate(m_rna) if i % num_fold == k])\n mi_rna_train = np.array([x for i, x in enumerate(mi_rna) if i % num_fold != k])\n mi_rna_test = np.array([x for i, x in enumerate(mi_rna) if i % num_fold == k])\n categorical_tissue_train = np.array([x for i, x in enumerate(categorical_tissue) if i % num_fold != k])\n categorical_tissue_test = np.array([x for i, x in enumerate(categorical_tissue) if i % num_fold == k])\n categorical_disease_train = np.array([x for i, x in enumerate(categorical_disease) if i % num_fold != k])\n categorical_disease_test = np.array([x for i, x in enumerate(categorical_disease) if i % num_fold == k])\n\n # result_folder = \"/home/ermia/Desktop/Deep Learning-Bioinfo/Results/\"\n result_folder = '/s/' + machine_name + local_result_folder + model_specific + str(k) + \"_th_fold_\"\n\n file_name = \"best-scae-dropout.log\"\n\n csv_logger = CSVLogger(result_folder + file_name)\n history = History()\n model.fit(m_rna_train, [m_rna_train, mi_rna_train, categorical_tissue_train, categorical_disease_train], batch_size=batch_size, epochs=nb_epochs,\n callbacks=[csv_logger, history],\n validation_data=(m_rna_test, [m_rna_test, mi_rna_test, categorical_tissue_test, categorical_disease_test]), verbose=2)\n\n print(history.history.keys())\n print(\"fitting has just been finished\")\n # save the model and encoded-layer output\n model.save(filepath=result_folder + \"scae-dropout.h5\")\n\n layer_name = \"encoded\"\n encoded_layer_model = Model(inputs=model.input, outputs=model.get_layer(layer_name).output)\n encoded_output = encoded_layer_model.predict(df_m_rna)\n np.savetxt(X=encoded_output, fname=result_folder + \"encoded_scae_dropout.csv\", delimiter=\",\")\n\n # save the result and prediction value\n\n data_pred = model.predict(m_rna_test, batch_size=batch_size, verbose=2)\n np.savetxt(X=m_rna_test, fname=result_folder + \"tcga_genes_scae_dropout.csv\", delimiter=\",\", fmt='%1.3f')\n np.savetxt(X=mi_rna_test, fname=result_folder + \"micro_rna_scae_dropout.csv\", delimiter=\",\", fmt='%1.3f')\n np.savetxt(X=categorical_tissue_test, fname=result_folder + \"categorical_tissue_scae_dropout.csv\", delimiter=\",\", fmt='%1.3f')\n np.savetxt(X=categorical_disease_test, fname=result_folder + \"categorical_disease_scae_dropout.csv\", delimiter=\",\", fmt='%1.3f')\n\n np.savetxt(X=data_pred[0], fname=result_folder + \"tcga_genes_scae_dropout_pred.csv\", delimiter=\",\", fmt='%1.3f')\n np.savetxt(X=data_pred[1], fname=result_folder + \"micro_rna_scae_dropout_pred.csv\", delimiter=\",\", fmt='%1.3f')\n np.savetxt(X=data_pred[2], fname=result_folder + \"categorical_tissue_scae_dropout_pred.csv\", delimiter=\",\", fmt='%1.3f')\n np.savetxt(X=data_pred[3], fname=result_folder + \"categorical_disease_scae_dropout_pred.csv\", delimiter=\",\", fmt='%1.3f')\n\n print(\"prediction process has just been finished\")\n\n k_fold(num_fold=10)\n\n\ncontractive_dropout_autoencoder(machine_name=machine, local_data_folder=local_dataset_folder, local_result_folder=local_results_folder, model_specific=model_spec)\n\nprint(\"run has just been finished\")\n", "repo_name": "SharifBioinf/DeePathology", "sub_path": "DeepLearning/Cross-Val-Contractive-Dropout-CAE.py", "file_name": "Cross-Val-Contractive-Dropout-CAE.py", "file_ext": "py", "file_size_in_byte": 8284, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 12, "dataset": "github-code", "pt": "37", "api": [{"api_name": "numpy.random.seed", "line_number": 20, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 20, "usage_type": "attribute"}, {"api_name": "numpy.loadtxt", "line_number": 27, "usage_type": "call"}, {"api_name": "numpy.loadtxt", "line_number": 29, "usage_type": "call"}, {"api_name": "numpy.ravel", "line_number": 30, "usage_type": "call"}, {"api_name": "pandas.DataFrame.as_matrix", "line_number": 30, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 30, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 30, "usage_type": "call"}, {"api_name": "numpy.ravel", "line_number": 31, "usage_type": "call"}, {"api_name": "pandas.DataFrame.as_matrix", "line_number": 31, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 31, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 31, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.normalize", "line_number": 33, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.normalize", "line_number": 35, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.LabelEncoder", "line_number": 37, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.LabelEncoder", "line_number": 41, "usage_type": "call"}, {"api_name": "keras.utils.np_utils.to_categorical", "line_number": 45, "usage_type": "call"}, {"api_name": "keras.utils.np_utils", "line_number": 45, "usage_type": "name"}, {"api_name": "keras.utils.np_utils.to_categorical", "line_number": 46, "usage_type": "call"}, {"api_name": "keras.utils.np_utils", "line_number": 46, "usage_type": "name"}, {"api_name": "keras.layers.Input", "line_number": 57, "usage_type": "call"}, {"api_name": "keras.layers.GaussianNoise", "line_number": 58, "usage_type": "call"}, {"api_name": "keras.layers.GaussianDropout", "line_number": 59, "usage_type": "call"}, {"api_name": "keras.layers.BatchNormalization", "line_number": 60, "usage_type": "call"}, {"api_name": "keras.layers.Dropout", "line_number": 61, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 62, "usage_type": "call"}, {"api_name": "keras.layers.BatchNormalization", "line_number": 63, "usage_type": "call"}, {"api_name": "keras.layers.Dropout", "line_number": 64, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 65, "usage_type": "call"}, {"api_name": "keras.layers.BatchNormalization", "line_number": 66, "usage_type": "call"}, {"api_name": "keras.layers.Dropout", "line_number": 67, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 69, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 71, "usage_type": "call"}, {"api_name": "keras.layers.Dropout", "line_number": 72, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 74, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 75, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 76, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 77, "usage_type": "call"}, {"api_name": "keras.models.Model", "line_number": 79, "usage_type": "call"}, {"api_name": "keras.backend.mean", "line_number": 84, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 84, "usage_type": "name"}, {"api_name": "keras.backend.square", "line_number": 84, "usage_type": "call"}, {"api_name": "keras.backend.variable", "line_number": 86, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 86, "usage_type": "name"}, {"api_name": "keras.backend.transpose", "line_number": 87, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 87, "usage_type": "name"}, {"api_name": "keras.backend.sum", "line_number": 92, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 92, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 106, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 107, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 108, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 109, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 110, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 111, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 112, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 113, "usage_type": "call"}, {"api_name": "keras.callbacks.CSVLogger", "line_number": 120, "usage_type": "call"}, {"api_name": "keras.callbacks.History", "line_number": 121, "usage_type": "call"}, {"api_name": "keras.models.Model", "line_number": 132, "usage_type": "call"}, {"api_name": "numpy.savetxt", "line_number": 134, "usage_type": "call"}, {"api_name": "numpy.savetxt", "line_number": 139, "usage_type": "call"}, {"api_name": "numpy.savetxt", "line_number": 140, "usage_type": "call"}, {"api_name": "numpy.savetxt", "line_number": 141, "usage_type": "call"}, {"api_name": "numpy.savetxt", "line_number": 142, "usage_type": "call"}, {"api_name": "numpy.savetxt", "line_number": 144, "usage_type": "call"}, {"api_name": "numpy.savetxt", "line_number": 145, "usage_type": "call"}, {"api_name": "numpy.savetxt", "line_number": 146, "usage_type": "call"}, {"api_name": "numpy.savetxt", "line_number": 147, "usage_type": "call"}]} +{"seq_id": "28792305691", "text": "from os import listdir, getcwd, system, walk\nfrom subprocess import call\nfrom utils import checkIfScss, getNameWithoutScssExtension\n\n\nfor root, dirs, files in walk (getcwd ()):\n for fileName in files: \n # print (f\"fileName: {fileName}\")\n if checkIfScss (fileName):\n print (f\"Compiling SCSS file with name: {fileName}\")\n nameWithoutScssExtension = getNameWithoutScssExtension (fileName)\n outputName = fileName.replace (\".scss\", \".css\")\n\n system (f\"sass {root}/{fileName} {root}/{nameWithoutScssExtension + '.css'}\") \n", "repo_name": "reoyamanaka/compileAllScss", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 577, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "os.walk", "line_number": 6, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 6, "usage_type": "call"}, {"api_name": "utils.checkIfScss", "line_number": 9, "usage_type": "call"}, {"api_name": "utils.getNameWithoutScssExtension", "line_number": 11, "usage_type": "call"}, {"api_name": "os.system", "line_number": 14, "usage_type": "call"}]} +{"seq_id": "71172993386", "text": "import networkx as nx\nimport numpy as np\nfrom matplotlib import pyplot as plt\n\n\nclass BundledGraph:\n def __init__(self, graph: nx.Graph, curves: np.ndarray):\n self.curves = curves\n self.graph = graph\n\n def plot(self, fig=None, ax=None, show=True, edges=True):\n if fig is None and ax is None:\n fig = plt.figure()\n ax = fig.add_subplot(1, 1, 1)\n elif fig is None:\n ax.cla()\n elif ax is None:\n raise Exception(\"Cannot pass figure without axes\")\n\n ax.set_aspect('equal')\n\n pos = {\n n: (d['x'], d['y']) for n, d in self.graph.nodes(data=True)\n }\n\n nx.draw_networkx_nodes(self.graph, pos,\n ax=ax,\n node_size=100)\n\n if edges:\n for curve in self.curves:\n ax.plot(curve[0], curve[1], 'k-', linewidth=1)\n\n if show:\n plt.show()\n", "repo_name": "dpvdberg/pyedgebundle", "sub_path": "data/BundledGraph.py", "file_name": "BundledGraph.py", "file_ext": "py", "file_size_in_byte": 952, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "37", "api": [{"api_name": "networkx.Graph", "line_number": 7, "usage_type": "attribute"}, {"api_name": "numpy.ndarray", "line_number": 7, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 13, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 13, "usage_type": "name"}, {"api_name": "networkx.draw_networkx_nodes", "line_number": 26, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 35, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 35, "usage_type": "name"}]} +{"seq_id": "72868892268", "text": "# -*- coding: utf-8 -*-\nimport scrapy\nimport re\nimport pprint\nfrom scrapy.spiders import Spider\nfrom scrapy.selector import Selector\nfrom scrapy.http import Request\nfrom scrapy.crawler import Crawler,CrawlerRunner\nfrom datetime import datetime\nfrom twisted.internet import reactor\nfrom scrapy.utils.project import get_project_settings\n#from scrapy import log\n#from tutorial.my_settings import keyword,name_file\n\n\n#custom setting\nkeyword = [\"feature film\", \"indie film\", \"film production\", \"independent film\", \"film casting\", \"movie casting\",\n \"extras casting\", \"film editor\", \"movie editor\", \"post production\", \"movie production\", \"line producer\",\n \"production manager\", \"editor\", \"colorist\", \"visual effects\", \"sound design\", \"VFX\", \"motion picture\",\n \"film sales\", \"film distribution\", \"film budget\"]\n\n\n# options = {\n# 'CONCURRENT_ITEMS': 150,\n# 'USER_AGENT': \"Mozilla/5.0 (Windows NT 6.2; WOW64) AppleWebKit/535.24 (KHTML, like Gecko) \"\n# \"Chrome/19.0.1055.1 Safari/535.24\",\n# 'CONCURRENT_REQUESTS': 5,\n# 'SW_SAVE_BUFFER': 30,\n# 'DOWNLOAD_DELAY': 1.5,\n# 'COOKIES_ENABLED': False,\n# }\nname_file = '../outfile.txt'\n\n#processing\nkeyword = list(map(lambda x :re.sub(' ','+',x),keyword))\nprint(\"keyword:\"+str(keyword))\n\nfile =open (name_file,'a')\n\nemail_in_file = open(name_file,'r').readlines()\n\n\nclass EntertainmentcareersSpider(Spider):\n name = 'entertainmentcareers'\n allowed_domains = ['entertainmentcareers.net']\n \n start_urls = ['http://www.entertainmentcareers.net/psearch/?zoom_query={}'.format(key)\n for key in keyword]\n\n def parse(self, response):\n sel = Selector(response)\n\n links=sel.xpath('//*[@class = \"result_title\"]/a/@href').extract()\n pprint.pprint(links)\n\n for link in links:\n yield Request(url=link,callback=self.parse_detail_page)\n\n\n def parse_detail_page(self,response):\n print(response.url)\n sel = Selector(response)\n \n \n pass\n\ndef run():\n options = {\n 'CONCURRENT_ITEMS': 250,\n #'USER_AGENT': 'Googlebot/2.1 (+http://www.google.com/bot.html)',\n 'CONCURRENT_REQUESTS': 30,\n 'DOWNLOAD_DELAY': 0.5,\n 'COOKIES_ENABLED': False,\n }\n\n spider = EntertainmentcareersSpider()\n\n settings = get_project_settings()\n settings.update(options)\n\n runner= CrawlerRunner(settings)\n runner.crawl(spider)\n\n d= runner.join()\n d.addBoth(lambda _:reactor.stop())\n #crawler = Crawler(settings)\n #crawler.signals.connect(reactor.stop, signal=signals.spider_closed)\n #crawler.install()\n #crawler.configure()\n #crawler.crawl(spider)\n #crawler.start()\n #log.start(logfile=\"results.log\", loglevel=log.DEBUG, crawler=crawler, logstdout=False)\n reactor.run()\n\n\n\nif __name__ == '__main__':\n run()\n \n \n \n", "repo_name": "Andy-wangke/Scrapy_Samples", "sub_path": "tutorial/tutorial/spiders/entertainmentcareers_basic.py", "file_name": "entertainmentcareers_basic.py", "file_ext": "py", "file_size_in_byte": 2932, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "re.sub", "line_number": 35, "usage_type": "call"}, {"api_name": "scrapy.spiders.Spider", "line_number": 43, "usage_type": "name"}, {"api_name": "scrapy.selector.Selector", "line_number": 51, "usage_type": "call"}, {"api_name": "pprint.pprint", "line_number": 54, "usage_type": "call"}, {"api_name": "scrapy.http.Request", "line_number": 57, "usage_type": "call"}, {"api_name": "scrapy.selector.Selector", "line_number": 62, "usage_type": "call"}, {"api_name": "scrapy.utils.project.get_project_settings", "line_number": 78, "usage_type": "call"}, {"api_name": "scrapy.crawler.CrawlerRunner", "line_number": 81, "usage_type": "call"}, {"api_name": "twisted.internet.reactor.stop", "line_number": 85, "usage_type": "call"}, {"api_name": "twisted.internet.reactor", "line_number": 85, "usage_type": "name"}, {"api_name": "twisted.internet.reactor.run", "line_number": 93, "usage_type": "call"}, {"api_name": "twisted.internet.reactor", "line_number": 93, "usage_type": "name"}]} +{"seq_id": "70559163629", "text": "import json\nimport datetime\nimport requests\nimport pandas as pd\nfrom load_articles import read_articles, write_articles\n\n\ndef write_hourlyStock(hourlyStock):\n with open('hourlyStock.json', 'w') as fp:\n json.dump(hourlyStock, fp)\n\n\ndef read_hourlyStock():\n with open('hourlyStock.json') as f:\n return json.load(f)\n\n\ndef updateJSON_prices(sym):\n APIKey = \"442ONKXSVHA79170\" # redact in submissions\n APIbase = \"https://www.alphavantage.co/query\"\n\n def rqstStockTSIntraDay(sym):\n r = None\n tries = 0\n maxTries = 10\n while not r and tries < maxTries:\n r = requests.get(APIbase, params={\n \"function\": \"TIME_SERIES_INTRADAY\", \"symbol\": sym, \"interval\": \"60min\", \"outputsize\": \"full\", \"apikey\": APIKey})\n tries += 1\n\n if not r:\n raise ValueError(\"Something unexpected happened.\")\n\n return r.json()[\"Time Series (IntraDay)\"]\n\n def getTSDataIntraDay(stockDat, t):\n try:\n e = stockDat[t.strftime(\"%Y-%m-%d %H:%M:%S\")]\n return {\"open\": round(float(e[\"1. open\"]), 2),\n \"close\": round(float(e[\"4. close\"]), 2),\n \"high\": round(float(e[\"2. high\"]), 2),\n \"low\": round(float(e[\"3. low\"]), 2),\n \"volume\": int(e[\"5. volume\"])}\n except KeyError:\n return False\n\n def getTSDataDailyForceSuccessFuture(stockDat, t, maxFail):\n y = getTSDataIntraDay(stockDat, t)\n i = 0\n while not y:\n t += datetime.timedelta(days=1)\n y = getTSDataIntraDay(stockDat, t)\n i += 1\n if i == maxFail:\n return False\n\n return (t, y)\n\n def get_datetime(data, i):\n return datetime.datetime.strptime(data['datetime'][i], \"%Y-%m-%d %H:%M:%S\")\n\n def get_timeframe(data):\n dt_start = get_datetime(data, 0)\n dt_end = get_datetime(data, len(data)-1)\n diff = dt_end - dt_start\n tot_hours = diff.days * 24 + diff.seconds / 3600 + 1\n timeframe = pd.date_range(start=str(dt_start), end=str(dt_end), periods=tot_hours)\n return timeframe\n\n # Updates hourlyStock\n intraDayDat = rqstStockTSIntraDay(sym)\n dat = pd.read_csv('X.csv')\n timeframe = get_timeframe(dat)\n hourlyStock = dict.fromkeys(timeframe)\n for t in timeframe:\n result = getTSDataDailyForceSuccessFuture(intraDayDat, t, 1000)\n hourlyStock[t] = result[1][\"close\"] - result[1][\"open\"]\n hourlyStock = {str(k): v for k, v in hourlyStock.items()}\n write_hourlyStock(hourlyStock)", "repo_name": "ericygu/StocksAndStringsDuo", "sub_path": "experimental/hourlystock_parse.py", "file_name": "hourlystock_parse.py", "file_ext": "py", "file_size_in_byte": 2600, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "json.dump", "line_number": 10, "usage_type": "call"}, {"api_name": "json.load", "line_number": 15, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 27, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 51, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 60, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 60, "usage_type": "attribute"}, {"api_name": "pandas.date_range", "line_number": 67, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 72, "usage_type": "call"}]} +{"seq_id": "28129212456", "text": "import json\nfrom flask import Flask, Response\nfrom balance import Balance\n\nbalance = Balance()\napp = Flask(__name__, static_url_path=\"\", static_folder=\"public\")\n\n@app.route('/balances')\ndef balances_all():\n balances = [{\n 'id': x[0],\n 'timestamp': x[1],\n 'weight': x[2],\n 'user_id': x[3]\n } for x in balance.all()]\n response = Response(json.dumps(balances), mimetype='application/json')\n return response\n\nif __name__ == \"__main__\":\n app.run(host='0.0.0.0', debug=True)\n", "repo_name": "rephus/smartscale", "sub_path": "smartscale-server.py", "file_name": "smartscale-server.py", "file_ext": "py", "file_size_in_byte": 506, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "balance.Balance", "line_number": 5, "usage_type": "call"}, {"api_name": "flask.Flask", "line_number": 6, "usage_type": "call"}, {"api_name": "balance.all", "line_number": 15, "usage_type": "call"}, {"api_name": "flask.Response", "line_number": 16, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 16, "usage_type": "call"}]} +{"seq_id": "21539691617", "text": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nfrom __future__ import print_function\nimport time\nfrom flask import Flask, render_template, request, redirect, url_for\nimport csv\nimport os\nimport numpy as np\nfrom matplotlib import pyplot as plt\nfrom scipy import stats\nfrom figure import Figure\n\napp = Flask(__name__)\n\n@app.route(\"/\")\ndef index():\n return render_template('index.html')\n\n@app.route('/analysis', methods=['GET', 'POST'])\ndef analysis():\n file = request.files['file'] \n csv_lines = []\n\n for line in file.stream.readlines():\n csv_lines.append(line.decode('utf-8'))\n\n # if there is no directory, then create\n if not os.path.isdir(\"csv\"):\n os.makedirs(\"csv\")\n\n # save csv in local directory\n with open('csv/upload.csv', 'w') as f:\n writer = csv.writer(f, lineterminator='\\n')\n writer.writerows(csv.reader(csv_lines, delimiter=\",\", quotechar='\"'))\n\n # analysis\n figure = Figure('csv/upload.csv')\n figure.make()\n\n return render_template('index.html')\n\nif __name__ == \"__main__\":\n app.debug = True # デバッグモード有効化\n app.run(host=\"127.0.0.1\", port=5000)\n", "repo_name": "egusahiroaki/data-analysis-web-app", "sub_path": "index.py", "file_name": "index.py", "file_ext": "py", "file_size_in_byte": 1139, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "flask.Flask", "line_number": 14, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 18, "usage_type": "call"}, {"api_name": "flask.request.files", "line_number": 22, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 22, "usage_type": "name"}, {"api_name": "os.path.isdir", "line_number": 29, "usage_type": "call"}, {"api_name": "os.path", "line_number": 29, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 30, "usage_type": "call"}, {"api_name": "csv.writer", "line_number": 34, "usage_type": "call"}, {"api_name": "csv.reader", "line_number": 35, "usage_type": "call"}, {"api_name": "figure.Figure", "line_number": 38, "usage_type": "call"}, {"api_name": "figure.make", "line_number": 39, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 41, "usage_type": "call"}]} +{"seq_id": "14735672934", "text": "\"\"\"\nA websocket server that allows clients to monitor a URL (or channel) for notifications.\n\nVery simple, very fast.\n\n\"\"\"\n\nfrom __future__ import print_function\nfrom __future__ import unicode_literals\n\nimport json\nimport logging\nfrom collections import defaultdict\nfrom weakref import WeakSet\n\nfrom tornado import websocket, web, gen\n\nlog = logging.getLogger('notifier')\n\n\nclass WatchHandler(websocket.WebSocketHandler):\n \"\"\"Watch a URL (resource).\"\"\"\n\n watching = defaultdict(WeakSet)\n\n def initialize(self):\n self.ip = self.request.remote_ip\n self.path = None\n\n def __repr__(self):\n \"\"\"Show the IP of present.\"\"\"\n if self.ip is not None:\n return \"\".format(self.ip)\n else:\n return \"\"\n\n def check_origin(self, origin):\n return True\n\n def open(self, path):\n self.set_nodelay(True)\n log.debug('%s connected', self.ip)\n self.path = path = '/' + path.lstrip('/')\n self.watching[path].add(self)\n log.info('%s client(s) watching %s', len(self.watching[path]), path)\n\n def on_close(self):\n path = self.path\n log.debug('%s client left', self.ip)\n self.watching[path].discard(self)\n log.info('%s client(s) watching %s', len(self.watching[path]), path)\n\n\nclass NotifyHandler(websocket.WebSocketHandler):\n \"\"\"Broadcast notification for resource.\"\"\"\n\n def initialize(self, secret=None):\n self.secret = secret\n\n def check_origin(self, origin):\n return True\n\n def open(self):\n try:\n secret = self.request.query_arguments.get('secret', [])[0]\n except IndexError:\n log.debug('secret key not supplied')\n self.close()\n else:\n if self.secret == secret:\n log.debug('%s connected', self.request.remote_ip)\n else:\n log.debug('secret key invalid')\n self.close()\n\n @gen.coroutine\n def on_message(self, message_json):\n try:\n notify_list = json.loads(message_json)\n except:\n log.exception('failed to decode message')\n self.close()\n return\n\n try:\n for path, instruction in notify_list:\n if path in WatchHandler.watching:\n yield self.notify(path, instruction)\n finally:\n self.close()\n\n @gen.coroutine\n def notify(self, path, instruction):\n watching = WatchHandler.watching[path]\n log.info('notify %s client(s) watching %s', len(watching), path)\n for handler in watching:\n try:\n yield handler.write_message(instruction)\n except:\n pass\n else:\n log.debug(' %r', handler)\n\n def on_close(self):\n log.debug('%s left', self.request.remote_ip)\n\n\ndef make_app(secret):\n \"\"\"Create the Tornado web application object.\"\"\"\n app = web.Application([\n (r'^/watch/(?P.*?)$', WatchHandler),\n (r'^/notify/$', NotifyHandler, {'secret': secret}),\n ])\n return app\n", "repo_name": "willmcgugan/timeline", "sub_path": "notifier/notifier/server.py", "file_name": "server.py", "file_ext": "py", "file_size_in_byte": 3124, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "37", "api": [{"api_name": "logging.getLogger", "line_number": 18, "usage_type": "call"}, {"api_name": "tornado.websocket.WebSocketHandler", "line_number": 21, "usage_type": "attribute"}, {"api_name": "tornado.websocket", "line_number": 21, "usage_type": "name"}, {"api_name": "collections.defaultdict", "line_number": 24, "usage_type": "call"}, {"api_name": "weakref.WeakSet", "line_number": 24, "usage_type": "argument"}, {"api_name": "tornado.websocket.WebSocketHandler", "line_number": 54, "usage_type": "attribute"}, {"api_name": "tornado.websocket", "line_number": 54, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 79, "usage_type": "call"}, {"api_name": "tornado.gen.coroutine", "line_number": 76, "usage_type": "attribute"}, {"api_name": "tornado.gen", "line_number": 76, "usage_type": "name"}, {"api_name": "tornado.gen.coroutine", "line_number": 92, "usage_type": "attribute"}, {"api_name": "tornado.gen", "line_number": 92, "usage_type": "name"}, {"api_name": "tornado.web.Application", "line_number": 110, "usage_type": "call"}, {"api_name": "tornado.web", "line_number": 110, "usage_type": "name"}]} +{"seq_id": "24200198621", "text": "import pandas as pd\r\nimport numpy as np\r\nfrom sklearn.preprocessing import StandardScaler\r\nimport statsmodels.api as sm\r\n\r\n\r\n# Load the World Indicators data from \"World Indicators.csv\"\r\n\r\nWord_indicators = read_csv(\"World Indicators.csv\")\r\n\r\n'''\r\n\r\nPreferably run the below programs in google colab\r\n\r\nMake sure to upload the csv file in the directory\r\n\r\nChange read csv directory to suitable directory\r\n\r\n\r\n'''\r\n\r\n# Your code starts after this line\r\n\r\nimport numpy as np\r\nimport sklearn\r\ndf = pd.read_csv(\"World Indicators.csv\")\r\n\r\n\r\n# Your code ends before this line\r\n\r\n# Create a linear model between year and population in the US\r\n\r\n# Your code starts after this line\r\n\r\ndataset = pd.DataFrame(df,columns=[\"Country/Region\",\"Year\",\"Population Total\"])\r\nc = dataset.iloc[:,0].values\r\ny = dataset.iloc[:,1].values\r\np = dataset.iloc[:,2].values\r\nyearl = []\r\npopu = []\r\nfor i,j,k in zip(c,y,p):\r\n if i == \"United States\":\r\n yearl.append(j)\r\n popu.append(k)\r\ndata = {\"year\":yearl,\"population\":popu}\r\nunited = pd.DataFrame(data)\r\nx = united.iloc[:,0:1]\r\ny = united.iloc[:,-1]\r\nfrom sklearn.model_selection import train_test_split\r\n\r\nX_train,X_test,y_train,y_test = train_test_split(\r\n x,y, test_size=0.30, random_state=42)\r\nfrom sklearn.linear_model import LinearRegression\r\n#cross validation\r\nfrom sklearn.model_selection import cross_val_score\r\nregression = LinearRegression()\r\nregression.fit(X_train,y_train)\r\nreg_pred= regression.predict(X_test)\r\nprint(regression.score(X_test,y_test)*100)\r\n\r\n \r\n# Your code ends before this line\r\n\r\n# Predict the expected population in the US in 2015\r\n\r\n# Your code starts after this line\r\n\r\nxpred = [[2015.0]]\r\nprint(regression.predict(xpred))\r\n\r\n# Your code ends before this line\r\n\r\n# For the data from Europe\r\n# Create a linear model between Life Expectancy Female and the significant predictors among\r\n# Birth Rate\r\n# GDP\r\n# Health Exp % GDP\r\n# Infant Mortality Rate\r\n# Life Expectancy Male\r\n\r\n# Summarize your model (only the final one)\r\n\r\n# Hint: if you hit an issue with NaNs in the values consider using this: missing='drop'\r\n\r\n# Your code starts after this line\r\n\r\ndataset = pd.DataFrame(df,columns=[\"Region\",\"Birth Rate\",\"GDP\",\"Health Exp % GDP\",\"Infant Mortality Rate\",\"Life Expectancy Male\",\"Life Expectancy Female\"])\r\nr = dataset.iloc[:,0].values\r\nb = dataset.iloc[:,1].values\r\ng = dataset.iloc[:,2].values\r\nh = dataset.iloc[:,3].values\r\ni = dataset.iloc[:,4].values\r\nm = dataset.iloc[:,5].values\r\nf = dataset.iloc[:,6].values\r\nbirth = []\r\ngdp = []\r\nhealth = []\r\ninfant = []\r\nmale = []\r\nfemale = []\r\nfor A,B,C,D,E,F,G in zip(r,b,g,h,i,m,f):\r\n if A == \"Europe\":\r\n birth.append(B)\r\n gdp.append(C)\r\n health.append(D)\r\n infant.append(E)\r\n male.append(F)\r\n female.append(G)\r\ndata = {\"Birth Rate\":birth,\"GDP\":gdp,\"Health\":health,\"Infant\":infant,\"Male\":male,\"Female\":female}\r\neurope = pd.DataFrame(data)\r\neurope = europe.dropna()\r\nx = europe.iloc[:,0:5]\r\ny = europe.iloc[:,-1]\r\nX_train,X_test,y_train,y_test = train_test_split(\r\n x,y, test_size=0.30, random_state=42)\r\nregression.fit(X_train,y_train)\r\nprint(regression.score(X_test,y_test)*100)\r\nreg_pred= regression.predict(X_test)\r\n\r\n# Your code ends before this line\r\n\r\n# Predict the Expected Life Expectancy Female of a country with this characteristics\r\n# Birth Rate = 3%\r\n# GDP = 1 billion\r\n# Health Exp % GDP = 4%\r\n# Infant Mortality Rate = 5%\r\n# Life Expectancy Male = 80\r\n# Round the prediction to two decimal points\r\n\r\n# Your code starts after this line\r\n\r\nprint(regression.predict([[0.003,1e+09,0.004,0.005,80.0]]))\r\n\r\n# Your code ends before this line\r\n\r\n", "repo_name": "mrRio8936/Hackathon-Questions", "sub_path": "hq/Problem.py", "file_name": "Problem.py", "file_ext": "py", "file_size_in_byte": 3605, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "pandas.read_csv", "line_number": 26, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 35, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 46, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 51, "usage_type": "call"}, {"api_name": "sklearn.linear_model.LinearRegression", "line_number": 56, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 87, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 110, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 114, "usage_type": "call"}]} +{"seq_id": "86568326317", "text": " \nimport cv2\nimport numpy as np\nimport math\nimport time\nimport testAAE\nimport testAAEWithClassifier\nimport region\n\ndef quantizeAngle(angle):\n if angle >= 0:\n if angle >= 90:\n if angle >= 45:\n quantized = 2\n else:\n quantized = 1\n elif angle >= 135:\n quantized = 4\n else:\n quantized = 8\n elif angle <= -90:\n if angle <= -135:\n quantized = 16\n else:\n quantized = 32\n elif angle <= -45:\n quantized = 64\n else:\n quantized = 128\n return int(quantized)\n\ndef angleFilter(mask, quantized, quantized_flag = False):\n temp_angle = mask*quantized\n kernal = 9\n m,n = mask.shape\n hist = {}\n hist_sorted = []\n strong_angle = np.zeros(mask.shape, np.uint8)\n contour = np.zeros(mask.shape, np.uint8)\n score_map = np.array([[5,3,1,0,0,0,1,3],\n [3,5,3,1,0,0,0,1],\n [1,3,5,3,1,0,0,0],\n [0,1,3,5,3,1,0,0],\n [0,0,1,3,5,3,1,0],\n [0,0,0,1,3,5,3,1],\n [1,0,0,0,1,3,5,3],\n [3,1,0,0,0,1,3,5]])\n \n bias = math.floor(kernal /2)\n qt_angle = np.array([1,2,4,8,16,32,64,128])\n for i in range(m):\n for j in range(n):\n if mask[i,j] > 0:\n if i-bias < 0: \n h_t=0\n else: \n h_t = i-bias\n if i+bias > m-1: \n h_b=m-1\n else: \n h_b = i+bias\n if j-bias < 0: \n w_l=0\n else: \n w_l=j-bias\n if j+bias > m-1:\n w_r=m-1\n else: \n w_r=j+bias\n temp = temp_angle[h_t:h_b+1,w_l:w_r+1]\n a,b = temp.shape\n temp = temp.flat[:]\n if not quantized_flag:\n for k in range(a*b):\n if temp[k] > 0:\n temp[k] = quantizeAngle(temp[k])\n temp = temp.astype(np.uint8)\n\n temp_ = temp[temp.nonzero()]\n bcounts = np.bincount(temp_)\n strong_temp = np.zeros(a*b)\n score_temp = np.zeros(a*b)\n hist.clear\n hist_sorted.clear\n hist = dict(zip(np.unique(temp_),bcounts[bcounts.nonzero()]))\n hist_sorted = sorted(hist.items(), key=lambda x: x[1], reverse=True) \n max_count = hist_sorted[0][1]\n strong_angle[i,j] = hist_sorted[0][0]\n count = 0\n for c in range(a*b):\n if temp[c] > 0:\n score_temp[c] = score_map[int(math.log2(quantizeAngle(temp_angle[i,j]))),int(math.log2(temp[c]))]\n strong_temp[c] = score_map[int(math.log2(strong_angle[i,j])),int(math.log2(temp[c]))]\n count+=1\n pix_score = np.sum(score_temp)/count\n strong_score = np.sum(strong_temp)/count\n if max_count > 5 and (pix_score > 2 or strong_score > 2):\n contour[i,j] = 1\n return contour\n\ndef preprocessing():\n total_num = 28\n sample_id = 0 \n threshold = 160\n exposure = 6\n write_flag = False\n\n sobel_mask_vect = []\n src_vect = []\n sobel_x =np.array([[-1, 0, 1],[-1, 0, 1],[-1, 0, 1]], dtype=np.float32)\n sobel_y =np.array([[1, 1, 1],[0, 0, 0],[-1, -1, -1]], dtype=np.float32)\n new_img = np.zeros((256,256), np.uint8)\n for pic_num in range(1, total_num):\n if write_flag:\n src_file = '../data/sample_' + str(sample_id) + '/{:03d}'.format(exposure) + '/' + str(pic_num) + '.jpg'\n output_file = '../data/sample_' + str(sample_id) + '/{:03d}'.format(exposure) + '/' + str(pic_num) + '.png'\n\n IN_src_file = '../data/sample_' + str(sample_id) + '/{:03d}'.format(exposure) + '_IN/' + 'SQI' + '/' + '{:02d}'.format(pic_num) + '.png'\n # output_file = '../data/sample_' + str(sample_id) + '/{:03d}'.format(exposure) + '_IN/' + 'TT' + '/' + '{:02d}'.format(pic_num) + '.png'\n # region_file = './roi/region_' + str(pic_num) + '.png'\n\n print(src_file)\n img = cv2.imread(src_file)\n img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n m,n = img.shape\n img = img[0:n]\n new_img[3:253,3:253] = img\n cv2.imwrite(output_file, new_img)\n new_img_copy = new_img.copy()\n\n IN_img = cv2.imread(IN_src_file)\n IN_img = cv2.cvtColor(IN_img, cv2.COLOR_BGR2GRAY)\n src_vect.append(IN_img)\n else:\n src_file = '../data/sample_' + str(sample_id) + '/{:03d}'.format(exposure) + '/' + str(pic_num) + '.png'\n IN_src_file = '../data/sample_' + str(sample_id) + '/{:03d}'.format(exposure) + '_IN/' + 'SQI' + '/' + '{:02d}'.format(pic_num) + '.png'\n new_img = cv2.imread(src_file)\n new_img = cv2.cvtColor(new_img,cv2.COLOR_BGR2GRAY)\n IN_img = cv2.imread(IN_src_file)\n IN_img = cv2.cvtColor(IN_img, cv2.COLOR_BGR2GRAY)\n src_vect.append(IN_img)\n\n\n sobel_mag = np.zeros(new_img.shape, np.float)\n sobel_angle = np.zeros(new_img.shape, np.float)\n quantized_angle = np.zeros(new_img.shape, np.uint8)\n sobel_mask = np.zeros(new_img.shape, np.uint8)\n\n # img_Guassian = cv2.GaussianBlur(new_img,(5,5),0)\n # img_Guassian.astype(np.uint8)\n # m,n = img_Guassian.shape\n\n # m,n = new_img.shape\n # for i in range(2,m-1):\n # for j in range(2,n-1):\n # Gx = np.sum(new_img[i-1:i+2, j-1:j+2] * sobel_x)\n # Gy = np.sum(new_img[i-1:i+2, j-1:j+2] * sobel_y) \n # sobel_mag[i,j] = math.sqrt(math.pow(Gx,2) + math.pow(Gy,2))\n # sobel_angle[i,j] = math.atan2(Gy, Gx) * 180 / math.pi\n # # quantized_angle[i,j] = quantizeAngle(sobel_angle[i,j])\n # if sobel_mag[i,j] >= threshold:\n # sobel_mask[i,j] = 1\n # contour = angleFilter(sobel_mask, quantized_angle)\n # contour = cv2.blur(contour, (3,3))\n # sobelx = cv2.Sobel(new_img,cv2.CV_32F,1,0) #默认ksize=3\n # sobely = cv2.Sobel(new_img,cv2.CV_32F,0,1)\n\n sobelx = cv2.filter2D(new_img, cv2.CV_32F, sobel_x)\n sobely = cv2.filter2D(new_img, cv2.CV_32F, sobel_y)\n sobel_mag = np.sqrt(pow(sobelx,2) + pow(sobely,2))\n sobel_angle = np.arctan2(sobely,sobelx) * 180 /math.pi\n sobel_mag = cv2.convertScaleAbs(sobel_mag)\n _, sobel_mask = cv2.threshold(sobel_mag, threshold, 255, 0)\n\n # contour = angleFilter(sobel_mask, sobel_angle)\n # contour = cv2.blur(contour, (3,3))\n # sobel_mask = cv2.blur(sobel_mask, (3,3))\n # contour_vect.append(contour)\n\n # cv2.imshow('sobel', sobel_mask)\n # cv2.waitKey(0)\n sobel_mask_vect.append(sobel_mask)\n\n return sobel_mask_vect, src_vect\n\nif __name__ == \"__main__\":\n\n time_start = time.time()\n sobel_mask_vect, src_vect = preprocessing()\n time_end = time.time()\n print('Proprecessing time cost:{:.3f}'.format(time_end - time_start))\n # for sobel_mask in sobel_mask_vect:\n # # cv2.imshow(\"sobel\",255*sobel_mask.astype(np.uint8))\n # cv2.imshow(\"sobel\",sobel_mask)\n # # cv2.imshow(\"extend\", 255*contour.astype(np.uint8))\n # # cv2.imshow(\"sub\",255*(sobel_mask - contour).astype(np.uint8))\n # cv2.waitKey(0)\n \n\n\n output_img_vect = testAAE.AEprocessing(sobel_mask_vect)\n # output_img_vect = testAAEWithClassifier.AEprocessing(sobel_mask_vect)\n \n print('AAE time cost:{:.3f}'.format(time.time() - time_end))\n\n for i, singleimg in enumerate(output_img_vect):\n # singleimg = np.squeeze(singleimg, axis=(2,))\n singleimg = singleimg.astype(np.uint8)\n src = src_vect[i]\n # cv2.imshow('src',src)\n # cv2.waitKey(0)\n region_file = '../roi/region_{:02d}'.format(i) + '.png'\n mask_file = '../Template/bin_mask/region_{:02d}'.format(i) + '.png'\n mask = region.regionGenerate(singleimg)\n \n kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(3, 3))\n eroded = cv2.erode(mask,kernel)\n eroded_2 = cv2.erode(eroded,kernel)\n eroded_3 = cv2.erode(eroded_2,kernel)\n roi = cv2.bitwise_and(src, src, mask=eroded)\n sub = eroded - eroded_3\n m,n = sub.shape\n for row in range(m):\n for col in range(n):\n if sub[row, col] and roi[row, col] < 80:\n roi[row,col] = 0\n eroded[row, col] = 0\n\n\n background = cv2.bitwise_not(eroded) \n # cv2.imwrite(region_file, roi)\n # cv2.imwrite(mask_file, eroded)\n # cv2.imshow('region', roi+background)\n # cv2.waitKey(0)\n\n print('Totally time cost:{:.3f}'.format(time.time() - time_start)) \n\n \n \n ", "repo_name": "SeanYan604/ImgPreprocessing", "sub_path": "src/Imgprocessing.py", "file_name": "Imgprocessing.py", "file_ext": "py", "file_size_in_byte": 9082, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "37", "api": [{"api_name": "numpy.zeros", "line_number": 38, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 38, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 39, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 39, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 40, "usage_type": "call"}, {"api_name": "math.floor", "line_number": 49, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 50, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 77, "usage_type": "attribute"}, {"api_name": "numpy.bincount", "line_number": 80, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 81, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 82, "usage_type": "call"}, {"api_name": "numpy.unique", "line_number": 85, "usage_type": "call"}, {"api_name": "math.log2", "line_number": 92, "usage_type": "call"}, {"api_name": "math.log2", "line_number": 93, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 95, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 96, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 110, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 110, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 111, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 111, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 112, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 112, "usage_type": "attribute"}, {"api_name": "cv2.imread", "line_number": 123, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 124, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 124, "usage_type": "attribute"}, {"api_name": "cv2.imwrite", "line_number": 128, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 131, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 132, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 132, "usage_type": "attribute"}, {"api_name": "cv2.imread", "line_number": 137, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 138, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 138, "usage_type": "attribute"}, {"api_name": "cv2.imread", "line_number": 139, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 140, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 140, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 144, "usage_type": "call"}, {"api_name": "numpy.float", "line_number": 144, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 145, "usage_type": "call"}, {"api_name": "numpy.float", "line_number": 145, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 146, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 146, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 147, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 147, "usage_type": "attribute"}, {"api_name": "cv2.filter2D", "line_number": 168, "usage_type": "call"}, {"api_name": "cv2.CV_32F", "line_number": 168, "usage_type": "attribute"}, {"api_name": "cv2.filter2D", "line_number": 169, "usage_type": "call"}, {"api_name": "cv2.CV_32F", "line_number": 169, "usage_type": "attribute"}, {"api_name": "numpy.sqrt", "line_number": 170, "usage_type": "call"}, {"api_name": "numpy.arctan2", "line_number": 171, "usage_type": "call"}, {"api_name": "math.pi", "line_number": 171, "usage_type": "attribute"}, {"api_name": "cv2.convertScaleAbs", "line_number": 172, "usage_type": "call"}, {"api_name": "cv2.threshold", "line_number": 173, "usage_type": "call"}, {"api_name": "time.time", "line_number": 188, "usage_type": "call"}, {"api_name": "time.time", "line_number": 190, "usage_type": "call"}, {"api_name": "testAAE.AEprocessing", "line_number": 201, "usage_type": "call"}, {"api_name": "time.time", "line_number": 204, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 208, "usage_type": "attribute"}, {"api_name": "region.regionGenerate", "line_number": 214, "usage_type": "call"}, {"api_name": "cv2.getStructuringElement", "line_number": 216, "usage_type": "call"}, {"api_name": "cv2.MORPH_ELLIPSE", "line_number": 216, "usage_type": "attribute"}, {"api_name": "cv2.erode", "line_number": 217, "usage_type": "call"}, {"api_name": "cv2.erode", "line_number": 218, "usage_type": "call"}, {"api_name": "cv2.erode", "line_number": 219, "usage_type": "call"}, {"api_name": "cv2.bitwise_and", "line_number": 220, "usage_type": "call"}, {"api_name": "cv2.bitwise_not", "line_number": 230, "usage_type": "call"}, {"api_name": "time.time", "line_number": 236, "usage_type": "call"}]} +{"seq_id": "37328426197", "text": "from ..abstract_model import AbstractModel\nfrom ..datautils import *\nimport pandas as pd\nimport numpy as np\n\nfrom typing import Optional, List\nfrom tqdm import tqdm\n\nfrom tensorflow.keras.models import Model\nfrom tensorflow.keras.layers import Dense, LSTM, RepeatVector, TimeDistributed, Input\nfrom tensorflow.keras.callbacks import EarlyStopping\n\nimport pickle\n\nclass LSTMRegressor(AbstractModel):\n \"\"\"\n\n \"\"\"\n\n def __init__(\n self,\n n_timesteps: int = 80,\n n_features: int = 5,\n n_LSTM_layers: int = 2,\n LSTM_size: int = 64,\n random_seed: Optional[int] = None\n ):\n \"\"\"\n Initialize the model, with model structure and hyperparameters\n\n Input:\n n_timesteps:\n The number of timesteps to process at once when reading the data. In the Google Data, it is 80 per breath.\n n_features:\n The number of input features to build the model for.\n n_LSTM_layers:\n The number of LSTM layers to have in the model.\n LSTM_size:\n The size of each LSTM layer.\n random_seed:\n The integer seed to use for reproducibility\n \"\"\"\n\n self.n_timesteps = n_timesteps\n self.n_features = n_features\n self.random_seed = random_seed\n\n self.model = self._define_model(n_LSTM_layers, LSTM_size)\n\n def _define_model(\n self,\n n_LSTM_layers,\n LSTM_size\n ) -> Model:\n \"\"\"\n\n \"\"\"\n #Create model input layer\n inputs = Input(shape=(self.n_timesteps, self.n_features))\n\n #Iterate, adding LSTM layers as needed\n current = inputs\n for i in range(n_LSTM_layers):\n if (i == n_LSTM_layers - 1):\n current = LSTM(LSTM_size, return_sequences=True)(current)\n else:\n current = LSTM(LSTM_size)(current)\n current = RepeatVector(self.n_timesteps)(current)\n\n #Add the final TimeDistributed Dense layer for output\n current = TimeDistributed(Dense(1))(current)\n\n #Combine all layers into one model object\n model = Model(inputs, current, name=\"lstm_regressor\")\n\n #Compile the model for use\n model.compile(optimizer=\"adam\", loss=\"mae\")\n\n return model\n\n\n def fit(\n self,\n X: np.ndarray,\n Y: np.ndarray,\n val_X: Optional[np.ndarray] = None,\n val_Y: Optional[np.ndarray] = None,\n epochs: int = 300,\n patience: int = 25,\n verbose: int = 1\n ):\n \"\"\"\n A method to train the model, using some data\n \"\"\"\n\n #Data Checking\n assert X.shape[2] == self.n_features\n assert X.shape[0] == Y.shape[0]\n assert X.shape[1] == Y.shape[1]\n assert X.shape[1] == 80\n\n\n #Train model\n if val_X is None or val_Y is None:\n es = EarlyStopping(\n monitor=\"loss\",\n patience=patience,\n verbose=verbose,\n restore_best_weights=True\n )\n\n self.model.fit(\n X,\n Y,\n epochs=epochs,\n verbose=verbose,\n callbacks=[es]\n )\n else:\n assert val_X.shape[2] == self.n_features\n assert val_X.shape[0] == val_Y.shape[0]\n assert val_X.shape[1] == val_Y.shape[1]\n assert val_X.shape[1] == 80\n\n es = EarlyStopping(\n monitor=\"val_loss\",\n patience=patience,\n verbose=verbose,\n restore_best_weights=True\n )\n\n self.model.fit(\n X,\n Y,\n epochs=epochs,\n verbose=verbose,\n validation_data=(val_X, val_Y),\n callbacks=[es]\n )\n\n\n def predict(self, X: np.ndarray) -> np.ndarray:\n \"\"\"\n A method to use the model, predicting on some data\n \"\"\"\n\n assert X.shape[2] == self.n_features\n assert X.shape[1] == 80\n\n Y = self.model.predict(X)\n\n return Y\n\n def save_model(self, model_path: str):\n \"\"\"\n Save the current model to a file\n \"\"\"\n\n def load_model(self, model_path: str):\n \"\"\"\n Load the model from a file\n \"\"\"\n", "repo_name": "trevorWieland/kaggle-ventilator-model", "sub_path": "ventilatormodels/lstm_regressor/lstm_regressor.py", "file_name": "lstm_regressor.py", "file_ext": "py", "file_size_in_byte": 4365, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "abstract_model.AbstractModel", "line_number": 15, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 26, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.Input", "line_number": 59, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.LSTM", "line_number": 65, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.LSTM", "line_number": 67, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.RepeatVector", "line_number": 68, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.TimeDistributed", "line_number": 71, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Dense", "line_number": 71, "usage_type": "call"}, {"api_name": "tensorflow.keras.models.Model", "line_number": 74, "usage_type": "call"}, {"api_name": "tensorflow.keras.models.Model", "line_number": 54, "usage_type": "name"}, {"api_name": "numpy.ndarray", "line_number": 84, "usage_type": "attribute"}, {"api_name": "numpy.ndarray", "line_number": 85, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 86, "usage_type": "name"}, {"api_name": "numpy.ndarray", "line_number": 86, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 87, "usage_type": "name"}, {"api_name": "numpy.ndarray", "line_number": 87, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.callbacks.EarlyStopping", "line_number": 105, "usage_type": "call"}, {"api_name": "tensorflow.keras.callbacks.EarlyStopping", "line_number": 125, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 142, "usage_type": "attribute"}]} +{"seq_id": "5933436108", "text": "import torch\nimport torch.nn as nn\nfrom torch.nn.parameter import Parameter\nfrom torch.nn import functional as F\nfrom torch.nn.modules.batchnorm import _BatchNorm\nimport re\n\n__all__ = [\n 'WeightNorm',\n 'InstanceNorm1d', 'InstanceNorm2d', 'InstanceNorm3d',\n 'LayerNorm',\n 'BatchInstanceNorm1d', 'BatchInstanceNorm2d', 'BatchInstanceNorm3d',\n 'SpectralNorm',\n 'GroupNorm',\n 'IterNorm_Single', 'IterNorm',\n 'SwitchNorm1d', 'SwitchNorm2d', 'SwitchNorm3d',\n 'SPADE'\n\n]\n\n# TODO: Kalman Norm, https://github.com/wanggrun/Kalman-Normalization/blob/master/KalmanNorm/kalman_norm.py, NIPS 2018\n# Dynamic Normalization, ICML 2019.\n# Gradient Norm `https://arxiv.org/pdf/1711.02257.pdf`, ICML 2018\n# Decorrelated BN `https://github.com/princeton-vl/DecorrelatedBN` CVPR 2018\n\n# ICLR 2018, Spectral Normalization for Generative Adversarial Networks\nSpectralNorm = torch.nn.utils.spectral_norm\n'''\n(module, name='weight', n_power_iteration=1, eps=1e-12, dim=None)\n'''\nWeightNorm = torch.nn.utils.weight_norm\n'''\n(module, name='weight', dim=0)\n'''\nInstanceNorm1d = torch.nn.InstanceNorm1d\nInstanceNorm2d = torch.nn.InstanceNorm2d\nInstanceNorm3d = torch.nn.InstanceNorm3d\n'''\n(num_features, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n'''\nLayerNorm = torch.nn.LayerNorm\n'''\n(normalized_shape, eps=1e-05, elementwise_affine=True)\n'''\n\n# NIPS 2018, Batch-Instance Normalization for Adaptively Style-Invariant Neural Networks\nclass _BatchInstanceNorm(_BatchNorm):\n def __init__(self, num_features, eps=1e-5, momentum=0.1, affine=True):\n ''' refer to `https://github.com/hyeonseobnam/Batch-Instance-Normalization`\n '''\n super(_BatchInstanceNorm, self).__init__(num_features, eps, momentum, affine)\n self.gate = Parameter(torch.Tensor(num_features))\n self.gate.data.fill_(1)\n setattr(self.gate, 'bin_gate', True)\n\n def forward(self, input):\n self._check_input_dim(input)\n\n # Batch norm\n if self.affine:\n bn_w = self.weight * self.gate\n else:\n bn_w = self.gate\n out_bn = F.batch_norm(\n input, self.running_mean, self.running_var, bn_w, self.bias,\n self.training, self.momentum, self.eps)\n\n # Instance norm\n b, c = input.size(0), input.size(1)\n if self.affine:\n in_w = self.weight * (1 - self.gate)\n else:\n in_w = 1 - self.gate\n input = input.view(1, b * c, *input.size()[2:])\n out_in = F.batch_norm(\n input, None, None, None, None,\n True, self.momentum, self.eps)\n out_in = out_in.view(b, c, *input.size()[2:])\n out_in.mul_(in_w[None, :, None, None])\n\n return out_bn + out_in\n\n\nclass BatchInstanceNorm1d(_BatchInstanceNorm):\n def _check_input_dim(self, input):\n if input.dim() != 2 and input.dim() != 3:\n raise ValueError('expected 2D or 3D input (got {}D input)'.format(input.dim()))\n\n\nclass BatchInstanceNorm2d(_BatchInstanceNorm):\n def _check_input_dim(self, input):\n if input.dim() != 4:\n raise ValueError('expected 4D input (got {}D input)'.format(input.dim()))\n\n\nclass BatchInstanceNorm3d(_BatchInstanceNorm):\n def _check_input_dim(self, input):\n if input.dim() != 5:\n raise ValueError('expected 5D input (got {}D input)'.format(input.dim()))\n\n# ECCV 2018, Group Normalization\nGroupNorm = torch.nn.GroupNorm\n'''\n(num_groups, num_channels, eps=1e-05, affine=True)\nnum_groups (python:int) number of groups to separate the channels into\nnum_channels (python:int) – number of channels expected in input\n'''\n\n# refer to `https://github.com/huangleiBuaa/IterNorm`\nclass iterative_normalization_py(torch.autograd.Function):\n @staticmethod\n def forward(ctx, *args, **kwargs):\n X, running_mean, running_wmat, nc, ctx.T, eps, momentum, training = args\n # change NxCxHxW to Dx(NxHxW), i.e., d*m\n ctx.g = X.size(1) // nc\n x = X.transpose(0, 1).contiguous().view(nc, -1)\n d, m = x.size()\n saved = []\n if training:\n # calculate centered activation by subtracted mini-batch mean\n mean = x.mean(-1, keepdim=True)\n xc = x - mean\n saved.append(xc)\n # calculate covariance matrix\n P = [None] * (ctx.T + 1)\n P[0] = torch.eye(d).to(X)\n Sigma = torch.addmm(eps, P[0], 1. / m, xc, xc.transpose(0, 1))\n # reciprocal of trace of Sigma: shape [g, 1, 1]\n rTr = (Sigma * P[0]).sum((0, 1), keepdim=True).reciprocal_()\n saved.append(rTr)\n Sigma_N = Sigma * rTr\n saved.append(Sigma_N)\n for k in range(ctx.T):\n P[k + 1] = torch.addmm(1.5, P[k], -0.5, torch.matrix_power(P[k], 3), Sigma_N)\n saved.extend(P)\n wm = P[ctx.T].mul_(rTr.sqrt()) # whiten matrix: the matrix inverse of Sigma, i.e., Sigma^{-1/2}\n running_mean.copy_(momentum * mean + (1. - momentum) * running_mean)\n running_wmat.copy_(momentum * wm + (1. - momentum) * running_wmat)\n else:\n xc = x - running_mean\n wm = running_wmat\n xn = wm.mm(xc)\n Xn = xn.view(X.size(1), X.size(0), *X.size()[2:]).transpose(0, 1).contiguous()\n ctx.save_for_backward(*saved)\n return Xn\n\n @staticmethod\n def backward(ctx, *grad_outputs):\n grad, = grad_outputs\n saved = ctx.saved_variables\n xc = saved[0] # centered input\n rTr = saved[1] # trace of Sigma\n sn = saved[2].transpose(-2, -1) # normalized Sigma\n P = saved[3:] # middle result matrix,\n d, m = xc.size()\n\n g_ = grad.transpose(0, 1).contiguous().view_as(xc)\n g_wm = g_.mm(xc.transpose(-2, -1))\n g_P = g_wm * rTr.sqrt()\n wm = P[ctx.T]\n g_sn = 0\n for k in range(ctx.T, 1, -1):\n P[k - 1].transpose_(-2, -1)\n P2 = P[k - 1].mm(P[k - 1])\n g_sn += P2.mm(P[k - 1]).mm(g_P)\n g_tmp = g_P.mm(sn)\n g_P.addmm_(1.5, -0.5, g_tmp, P2)\n g_P.addmm_(1, -0.5, P2, g_tmp)\n g_P.addmm_(1, -0.5, P[k - 1].mm(g_tmp), P[k - 1])\n g_sn += g_P\n # g_sn = g_sn * rTr.sqrt()\n g_tr = ((-sn.mm(g_sn) + g_wm.transpose(-2, -1).mm(wm)) * P[0]).sum((0, 1), keepdim=True) * P[0]\n g_sigma = (g_sn + g_sn.transpose(-2, -1) + 2. * g_tr) * (-0.5 / m * rTr)\n # g_sigma = g_sigma + g_sigma.transpose(-2, -1)\n g_x = torch.addmm(wm.mm(g_ - g_.mean(-1, keepdim=True)), g_sigma, xc)\n grad_input = g_x.view(grad.size(1), grad.size(0), *grad.size()[2:]).transpose(0, 1).contiguous()\n return grad_input, None, None, None, None, None, None, None\n\n\nclass IterNorm_Single(torch.nn.Module):\n def __init__(self, num_features, num_groups=1, num_channels=None, T=5, dim=4, eps=1e-5, momentum=0.1, affine=True,\n *args, **kwargs):\n super(IterNorm_Single, self).__init__()\n # assert dim == 4, 'IterNorm is not support 2D'\n self.T = T\n self.eps = eps\n self.momentum = momentum\n self.num_features = num_features\n self.affine = affine\n self.dim = dim\n shape = [1] * dim\n shape[1] = self.num_features\n\n self.register_buffer('running_mean', torch.zeros(num_features, 1))\n # running whiten matrix\n self.register_buffer('running_wm', torch.eye(num_features))\n\n def forward(self, X: torch.Tensor):\n X_hat = iterative_normalization_py.apply(X, self.running_mean, self.running_wm, self.num_features, self.T,\n self.eps, self.momentum, self.training)\n return X_hat\n\n\nclass IterNorm(torch.nn.Module):\n def __init__(self, num_features, num_groups=1, num_channels=None, T=5, dim=4, eps=1e-5, momentum=0.1, affine=True,\n *args, **kwargs):\n super(IterNorm, self).__init__()\n # assert dim == 4, 'IterNorm is not support 2D'\n self.T = T\n self.eps = eps\n self.momentum = momentum\n self.num_features = num_features\n self.num_channels = num_channels\n num_groups = (self.num_features - 1) // self.num_channels + 1\n self.num_groups = num_groups\n self.iterNorm_Groups = torch.nn.ModuleList(\n [IterNorm_Single(num_features=self.num_channels, eps=eps, momentum=momentum, T=T) for _ in\n range(self.num_groups - 1)]\n )\n num_channels_last = self.num_features - self.num_channels * (self.num_groups - 1)\n self.iterNorm_Groups.append(IterNorm_Single(num_features=num_channels_last, eps=eps, momentum=momentum, T=T))\n\n self.affine = affine\n self.dim = dim\n shape = [1] * dim\n shape[1] = self.num_features\n if self.affine:\n self.weight = Parameter(torch.Tensor(*shape))\n self.bias = Parameter(torch.Tensor(*shape))\n else:\n self.register_parameter('weight', None)\n self.register_parameter('bias', None)\n self.reset_parameters()\n\n def reset_parameters(self):\n # self.reset_running_stats()\n if self.affine:\n torch.nn.init.ones_(self.weight)\n torch.nn.init.zeros_(self.bias)\n\n def forward(self, X: torch.Tensor):\n X_splits = torch.split(X, self.num_channels, dim=1)\n X_hat_splits = []\n for i in range(self.num_groups):\n X_hat_tmp = self.iterNorm_Groups[i](X_splits[i])\n X_hat_splits.append(X_hat_tmp)\n X_hat = torch.cat(X_hat_splits, dim=1)\n # affine\n if self.affine:\n return X_hat * self.weight + self.bias\n else:\n return X_hat\n\n def extra_repr(self):\n return '{num_features}, num_channels={num_channels}, T={T}, eps={eps}, ' \\\n 'momentum={momentum}, affine={affine}'.format(**self.__dict__)\n\n\n# `https://github.com/switchablenorms/Switchable-Normalization/blob/master/devkit/ops/switchable_norm.py`\nclass SwitchNorm1d(nn.Module):\n def __init__(self, num_features, eps=1e-5, momentum=0.997, using_moving_average=True):\n super(SwitchNorm1d, self).__init__()\n self.eps = eps\n self.momentum = momentum\n self.using_moving_average = using_moving_average\n self.weight = nn.Parameter(torch.ones(1, num_features))\n self.bias = nn.Parameter(torch.zeros(1, num_features))\n self.mean_weight = nn.Parameter(torch.ones(2))\n self.var_weight = nn.Parameter(torch.ones(2))\n self.register_buffer('running_mean', torch.zeros(1, num_features))\n self.register_buffer('running_var', torch.zeros(1, num_features))\n self.reset_parameters()\n\n def reset_parameters(self):\n self.running_mean.zero_()\n self.running_var.zero_()\n self.weight.data.fill_(1)\n self.bias.data.zero_()\n\n def _check_input_dim(self, input):\n if input.dim() != 2:\n raise ValueError('expected 2D input (got {}D input)'\n .format(input.dim()))\n\n def forward(self, x):\n self._check_input_dim(x)\n mean_ln = x.mean(1, keepdim=True)\n var_ln = x.var(1, keepdim=True)\n\n if self.training:\n mean_bn = x.mean(0, keepdim=True)\n var_bn = x.var(0, keepdim=True)\n if self.using_moving_average:\n self.running_mean.mul_(self.momentum)\n self.running_mean.add_((1 - self.momentum) * mean_bn.data)\n self.running_var.mul_(self.momentum)\n self.running_var.add_((1 - self.momentum) * var_bn.data)\n else:\n self.running_mean.add_(mean_bn.data)\n self.running_var.add_(mean_bn.data ** 2 + var_bn.data)\n else:\n mean_bn = torch.autograd.Variable(self.running_mean)\n var_bn = torch.autograd.Variable(self.running_var)\n\n softmax = nn.Softmax(0)\n mean_weight = softmax(self.mean_weight)\n var_weight = softmax(self.var_weight)\n\n mean = mean_weight[0] * mean_ln + mean_weight[1] * mean_bn\n var = var_weight[0] * var_ln + var_weight[1] * var_bn\n\n x = (x - mean) / (var + self.eps).sqrt()\n return x * self.weight + self.bias\n\n\nclass SwitchNorm2d(nn.Module):\n def __init__(self, num_features, eps=1e-5, momentum=0.9, using_moving_average=True, using_bn=True,\n last_gamma=False):\n super(SwitchNorm2d, self).__init__()\n self.eps = eps\n self.momentum = momentum\n self.using_moving_average = using_moving_average\n self.using_bn = using_bn\n self.last_gamma = last_gamma\n self.weight = nn.Parameter(torch.ones(1, num_features, 1, 1))\n self.bias = nn.Parameter(torch.zeros(1, num_features, 1, 1))\n if self.using_bn:\n self.mean_weight = nn.Parameter(torch.ones(3))\n self.var_weight = nn.Parameter(torch.ones(3))\n else:\n self.mean_weight = nn.Parameter(torch.ones(2))\n self.var_weight = nn.Parameter(torch.ones(2))\n if self.using_bn:\n self.register_buffer('running_mean', torch.zeros(1, num_features, 1))\n self.register_buffer('running_var', torch.zeros(1, num_features, 1))\n\n self.reset_parameters()\n\n def reset_parameters(self):\n if self.using_bn:\n self.running_mean.zero_()\n self.running_var.zero_()\n if self.last_gamma:\n self.weight.data.fill_(0)\n else:\n self.weight.data.fill_(1)\n self.bias.data.zero_()\n\n def _check_input_dim(self, input):\n if input.dim() != 4:\n raise ValueError('expected 4D input (got {}D input)'\n .format(input.dim()))\n\n def forward(self, x):\n self._check_input_dim(x)\n N, C, H, W = x.size()\n x = x.view(N, C, -1)\n mean_in = x.mean(-1, keepdim=True)\n var_in = x.var(-1, keepdim=True)\n\n mean_ln = mean_in.mean(1, keepdim=True)\n temp = var_in + mean_in ** 2\n var_ln = temp.mean(1, keepdim=True) - mean_ln ** 2\n\n if self.using_bn:\n if self.training:\n mean_bn = mean_in.mean(0, keepdim=True)\n var_bn = temp.mean(0, keepdim=True) - mean_bn ** 2\n if self.using_moving_average:\n self.running_mean.mul_(self.momentum)\n self.running_mean.add_((1 - self.momentum) * mean_bn.data)\n self.running_var.mul_(self.momentum)\n self.running_var.add_((1 - self.momentum) * var_bn.data)\n else:\n self.running_mean.add_(mean_bn.data)\n self.running_var.add_(mean_bn.data ** 2 + var_bn.data)\n else:\n mean_bn = torch.autograd.Variable(self.running_mean)\n var_bn = torch.autograd.Variable(self.running_var)\n\n softmax = nn.Softmax(0)\n mean_weight = softmax(self.mean_weight)\n var_weight = softmax(self.var_weight)\n\n if self.using_bn:\n mean = mean_weight[0] * mean_in + mean_weight[1] * mean_ln + mean_weight[2] * mean_bn\n var = var_weight[0] * var_in + var_weight[1] * var_ln + var_weight[2] * var_bn\n else:\n mean = mean_weight[0] * mean_in + mean_weight[1] * mean_ln\n var = var_weight[0] * var_in + var_weight[1] * var_ln\n\n x = (x-mean) / (var+self.eps).sqrt()\n x = x.view(N, C, H, W)\n return x * self.weight + self.bias\n\n\nclass SwitchNorm3d(nn.Module):\n def __init__(self, num_features, eps=1e-5, momentum=0.997, using_moving_average=True, using_bn=True,\n last_gamma=False):\n super(SwitchNorm3d, self).__init__()\n self.eps = eps\n self.momentum = momentum\n self.using_moving_average = using_moving_average\n self.using_bn = using_bn\n self.last_gamma = last_gamma\n self.weight = nn.Parameter(torch.ones(1, num_features, 1, 1, 1))\n self.bias = nn.Parameter(torch.zeros(1, num_features, 1, 1, 1))\n if self.using_bn:\n self.mean_weight = nn.Parameter(torch.ones(3))\n self.var_weight = nn.Parameter(torch.ones(3))\n else:\n self.mean_weight = nn.Parameter(torch.ones(2))\n self.var_weight = nn.Parameter(torch.ones(2))\n if self.using_bn:\n self.register_buffer('running_mean', torch.zeros(1, num_features, 1))\n self.register_buffer('running_var', torch.zeros(1, num_features, 1))\n\n self.reset_parameters()\n\n def reset_parameters(self):\n if self.using_bn:\n self.running_mean.zero_()\n self.running_var.zero_()\n if self.last_gamma:\n self.weight.data.fill_(0)\n else:\n self.weight.data.fill_(1)\n self.bias.data.zero_()\n\n def _check_input_dim(self, input):\n if input.dim() != 5:\n raise ValueError('expected 5D input (got {}D input)'\n .format(input.dim()))\n\n def forward(self, x):\n self._check_input_dim(x)\n N, C, D, H, W = x.size()\n x = x.view(N, C, -1)\n mean_in = x.mean(-1, keepdim=True)\n var_in = x.var(-1, keepdim=True)\n\n mean_ln = mean_in.mean(1, keepdim=True)\n temp = var_in + mean_in ** 2\n var_ln = temp.mean(1, keepdim=True) - mean_ln ** 2\n\n if self.using_bn:\n if self.training:\n mean_bn = mean_in.mean(0, keepdim=True)\n var_bn = temp.mean(0, keepdim=True) - mean_bn ** 2\n if self.using_moving_average:\n self.running_mean.mul_(self.momentum)\n self.running_mean.add_((1 - self.momentum) * mean_bn.data)\n self.running_var.mul_(self.momentum)\n self.running_var.add_((1 - self.momentum) * var_bn.data)\n else:\n self.running_mean.add_(mean_bn.data)\n self.running_var.add_(mean_bn.data ** 2 + var_bn.data)\n else:\n mean_bn = torch.autograd.Variable(self.running_mean)\n var_bn = torch.autograd.Variable(self.running_var)\n\n softmax = nn.Softmax(0)\n mean_weight = softmax(self.mean_weight)\n var_weight = softmax(self.var_weight)\n\n if self.using_bn:\n mean = mean_weight[0] * mean_in + mean_weight[1] * mean_ln + mean_weight[2] * mean_bn\n var = var_weight[0] * var_in + var_weight[1] * var_ln + var_weight[2] * var_bn\n else:\n mean = mean_weight[0] * mean_in + mean_weight[1] * mean_ln\n var = var_weight[0] * var_in + var_weight[1] * var_ln\n\n x = (x - mean) / (var + self.eps).sqrt()\n x = x.view(N, C, D, H, W)\n return x * self.weight + self.bias\n\n# refer to `https://github.com/NVlabs/SPADE/blob/master/models/networks/normalization.py`\ndef get_nonspade_norm_layer(opt, norm_type='instance'):\n # helper function to get # output channels of the previous layer\n def get_out_channel(layer):\n if hasattr(layer, 'out_channels'):\n return getattr(layer, 'out_channels')\n return layer.weight.size(0)\n\n # this function will be returned\n def add_norm_layer(layer):\n nonlocal norm_type\n if norm_type.startswith('spectral'):\n layer = SpectralNorm(layer)\n subnorm_type = norm_type[len('spectral'):]\n\n if subnorm_type == 'none' or len(subnorm_type) == 0:\n return layer\n\n # remove bias in the previous layer, which is meaningless\n # since it has no effect after normalization\n if getattr(layer, 'bias', None) is not None:\n delattr(layer, 'bias')\n layer.register_parameter('bias', None)\n\n if subnorm_type == 'batch':\n norm_layer = nn.BatchNorm2d(get_out_channel(layer), affine=True)\n # elif subnorm_type == 'sync_batch':\n # norm_layer = SynchronizedBatchNorm2d(get_out_channel(layer), affine=True)\n elif subnorm_type == 'instance':\n norm_layer = nn.InstanceNorm2d(get_out_channel(layer), affine=False)\n else:\n raise ValueError('normalization layer %s is not recognized' % subnorm_type)\n\n return nn.Sequential(layer, norm_layer)\n\n return add_norm_layer\n\n\n# Creates SPADE normalization layer based on the given configuration\n# SPADE consists of two steps. First, it normalizes the activations using\n# your favorite normalization method, such as Batch Norm or Instance Norm.\n# Second, it applies scale and bias to the normalized output, conditioned on\n# the segmentation map.\n# The format of |config_text| is spade(norm)(ks), where\n# (norm) specifies the type of parameter-free normalization.\n# (e.g. syncbatch, batch, instance)\n# (ks) specifies the size of kernel in the SPADE module (e.g. 3x3)\n# Example |config_text| will be spadesyncbatch3x3, or spadeinstance5x5.\n# Also, the other arguments are\n# |norm_nc|: the #channels of the normalized activations, hence the output dim of SPADE\n# |label_nc|: the #channels of the input semantic map, hence the input dim of SPADE\nclass SPADE(nn.Module):\n def __init__(self, config_text, norm_nc, label_nc):\n super().__init__()\n\n assert config_text.startswith('spade')\n parsed = re.search('spade(\\D+)(\\d)x\\d', config_text)\n param_free_norm_type = str(parsed.group(1))\n ks = int(parsed.group(2))\n\n if param_free_norm_type == 'instance':\n self.param_free_norm = nn.InstanceNorm2d(norm_nc, affine=False)\n # elif param_free_norm_type == 'syncbatch':\n # self.param_free_norm = SynchronizedBatchNorm2d(norm_nc, affine=False)\n elif param_free_norm_type == 'batch':\n self.param_free_norm = nn.BatchNorm2d(norm_nc, affine=False)\n else:\n raise ValueError('%s is not a recognized param-free norm type in SPADE'\n % param_free_norm_type)\n\n # The dimension of the intermediate embedding space. Yes, hardcoded.\n nhidden = 128\n\n pw = ks // 2\n self.mlp_shared = nn.Sequential(\n nn.Conv2d(label_nc, nhidden, kernel_size=ks, padding=pw),\n nn.ReLU()\n )\n self.mlp_gamma = nn.Conv2d(nhidden, norm_nc, kernel_size=ks, padding=pw)\n self.mlp_beta = nn.Conv2d(nhidden, norm_nc, kernel_size=ks, padding=pw)\n\n def forward(self, x, segmap):\n\n # Part 1. generate parameter-free normalized activations\n normalized = self.param_free_norm(x)\n\n # Part 2. produce scaling and bias conditioned on semantic map\n segmap = F.interpolate(segmap, size=x.size()[2:], mode='nearest')\n actv = self.mlp_shared(segmap)\n gamma = self.mlp_gamma(actv)\n beta = self.mlp_beta(actv)\n\n # apply scale and bias\n out = normalized * (1 + gamma) + beta\n\n return out", "repo_name": "yantijin/DynaSysML", "sub_path": "DynaSysML/Layers/norm.py", "file_name": "norm.py", "file_ext": "py", "file_size_in_byte": 22905, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "37", "api": [{"api_name": "torch.nn", "line_number": 27, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 31, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 35, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 36, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 37, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 41, "usage_type": "attribute"}, {"api_name": "torch.nn.modules.batchnorm._BatchNorm", "line_number": 47, "usage_type": "name"}, {"api_name": "torch.nn.parameter.Parameter", "line_number": 52, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 52, "usage_type": "call"}, {"api_name": "torch.nn.functional.batch_norm", "line_number": 64, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 64, "usage_type": "name"}, {"api_name": "torch.nn.functional.batch_norm", "line_number": 75, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 75, "usage_type": "name"}, {"api_name": "torch.nn", "line_number": 102, "usage_type": "attribute"}, {"api_name": "torch.autograd", "line_number": 110, "usage_type": "attribute"}, {"api_name": "torch.eye", "line_number": 126, "usage_type": "call"}, {"api_name": "torch.addmm", "line_number": 127, "usage_type": "call"}, {"api_name": "torch.addmm", "line_number": 134, "usage_type": "call"}, {"api_name": "torch.matrix_power", "line_number": 134, "usage_type": "call"}, {"api_name": "torch.addmm", "line_number": 175, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 180, "usage_type": "attribute"}, {"api_name": "torch.zeros", "line_number": 194, "usage_type": "call"}, {"api_name": "torch.eye", "line_number": 196, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 198, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 204, "usage_type": "attribute"}, {"api_name": "torch.nn.ModuleList", "line_number": 216, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 216, "usage_type": "attribute"}, {"api_name": "torch.nn.parameter.Parameter", "line_number": 228, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 228, "usage_type": "call"}, {"api_name": "torch.nn.parameter.Parameter", "line_number": 229, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 229, "usage_type": "call"}, {"api_name": "torch.nn.init.ones_", "line_number": 238, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 238, "usage_type": "attribute"}, {"api_name": "torch.nn.init.zeros_", "line_number": 239, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 239, "usage_type": "attribute"}, {"api_name": "torch.Tensor", "line_number": 241, "usage_type": "attribute"}, {"api_name": "torch.split", "line_number": 242, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 247, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 260, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 260, "usage_type": "name"}, {"api_name": "torch.nn.Parameter", "line_number": 266, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 266, "usage_type": "name"}, {"api_name": "torch.ones", "line_number": 266, "usage_type": "call"}, {"api_name": "torch.nn.Parameter", "line_number": 267, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 267, "usage_type": "name"}, {"api_name": "torch.zeros", "line_number": 267, "usage_type": "call"}, {"api_name": "torch.nn.Parameter", "line_number": 268, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 268, "usage_type": "name"}, {"api_name": "torch.ones", "line_number": 268, "usage_type": "call"}, {"api_name": "torch.nn.Parameter", "line_number": 269, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 269, "usage_type": "name"}, {"api_name": "torch.ones", "line_number": 269, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 270, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 271, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 302, "usage_type": "call"}, {"api_name": "torch.autograd", "line_number": 302, "usage_type": "attribute"}, {"api_name": "torch.autograd.Variable", "line_number": 303, "usage_type": "call"}, {"api_name": "torch.autograd", "line_number": 303, "usage_type": "attribute"}, {"api_name": "torch.nn.Softmax", "line_number": 305, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 305, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 316, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 316, "usage_type": "name"}, {"api_name": "torch.nn.Parameter", "line_number": 325, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 325, "usage_type": "name"}, {"api_name": "torch.ones", "line_number": 325, "usage_type": "call"}, {"api_name": "torch.nn.Parameter", "line_number": 326, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 326, "usage_type": "name"}, {"api_name": "torch.zeros", "line_number": 326, "usage_type": "call"}, {"api_name": "torch.nn.Parameter", "line_number": 328, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 328, "usage_type": "name"}, {"api_name": "torch.ones", "line_number": 328, "usage_type": "call"}, {"api_name": "torch.nn.Parameter", "line_number": 329, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 329, "usage_type": "name"}, {"api_name": "torch.ones", "line_number": 329, "usage_type": "call"}, {"api_name": "torch.nn.Parameter", "line_number": 331, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 331, "usage_type": "name"}, {"api_name": "torch.ones", "line_number": 331, "usage_type": "call"}, {"api_name": "torch.nn.Parameter", "line_number": 332, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 332, "usage_type": "name"}, {"api_name": "torch.ones", "line_number": 332, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 334, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 335, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 378, "usage_type": "call"}, {"api_name": "torch.autograd", "line_number": 378, "usage_type": "attribute"}, {"api_name": "torch.autograd.Variable", "line_number": 379, "usage_type": "call"}, {"api_name": "torch.autograd", "line_number": 379, "usage_type": "attribute"}, {"api_name": "torch.nn.Softmax", "line_number": 381, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 381, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 397, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 397, "usage_type": "name"}, {"api_name": "torch.nn.Parameter", "line_number": 406, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 406, "usage_type": "name"}, {"api_name": "torch.ones", "line_number": 406, "usage_type": "call"}, {"api_name": "torch.nn.Parameter", "line_number": 407, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 407, "usage_type": "name"}, {"api_name": "torch.zeros", "line_number": 407, "usage_type": "call"}, {"api_name": "torch.nn.Parameter", "line_number": 409, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 409, "usage_type": "name"}, {"api_name": "torch.ones", "line_number": 409, "usage_type": "call"}, {"api_name": "torch.nn.Parameter", "line_number": 410, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 410, "usage_type": "name"}, {"api_name": "torch.ones", "line_number": 410, "usage_type": "call"}, {"api_name": "torch.nn.Parameter", "line_number": 412, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 412, "usage_type": "name"}, {"api_name": "torch.ones", "line_number": 412, "usage_type": "call"}, {"api_name": "torch.nn.Parameter", "line_number": 413, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 413, "usage_type": "name"}, {"api_name": "torch.ones", "line_number": 413, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 415, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 416, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 459, "usage_type": "call"}, {"api_name": "torch.autograd", "line_number": 459, "usage_type": "attribute"}, {"api_name": "torch.autograd.Variable", "line_number": 460, "usage_type": "call"}, {"api_name": "torch.autograd", "line_number": 460, "usage_type": "attribute"}, {"api_name": "torch.nn.Softmax", "line_number": 462, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 462, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 502, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 502, "usage_type": "name"}, {"api_name": "torch.nn.InstanceNorm2d", "line_number": 506, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 506, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 510, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 510, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 528, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 528, "usage_type": "name"}, {"api_name": "re.search", "line_number": 533, "usage_type": "call"}, {"api_name": "torch.nn.InstanceNorm2d", "line_number": 538, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 538, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 542, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 542, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 551, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 551, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 552, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 552, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 553, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 553, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 555, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 555, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 556, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 556, "usage_type": "name"}, {"api_name": "torch.nn.functional.interpolate", "line_number": 564, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 564, "usage_type": "name"}]} +{"seq_id": "24207531398", "text": "\n# http://m801.music.126.net/20190611134453/57c407e75f3995a39bdfa700e87672c5/jdyyaac/5153/0e5d/565f/f9b40b31509708491f05a5a230727ec8.m4a\n# https://music.163.com/weapi/song/enhance/player/url/v1?csrf_token=\n# /song?id=34923289\n#http://musci.163.com/song/media/outer/url?id=502238497.m4a\n\nfrom tkinter import *\nfrom tkinter.filedialog import askdirectory\n\n\nfrom PIL import ImageTk\nimport os\nimport requests\nfrom lxml import etree\nclass Spider(object):\n\n def __init__(self):\n self.headers = {\n\n \"Accept\": \"text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8\",\n \"Accept-Encoding\": \"gzip, deflate\",\n \"Accept-Language\": \"en-US,en;q=0.5\",\n \"Connection\": \"keep-alive\",\n \"User-Agent\": \"Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:39.0) Gecko/20100101 Firefox/39.0\"\n }\n\n def Main(self,info,listbox):\n global net\n count = 0\n dicts = {}\n def CallOn(event):\n global net2\n if net2==False:\n key=listbox.get(listbox.curselection())\n if key in dicts:\n spider.Main2(dicts[key])\n url = \"https://www.ximalaya.com/search/\"+info\n html = requests.get(url, headers=self.headers).text\n datas =etree.HTML(html)\n titls=datas.xpath(\"//a[@class='xm-album-title ellipsis-2']/div/text()\")\n hrefs = datas.xpath(\"//a[@class='xm-album-title ellipsis-2']/@href\")\n for tit,href in zip(titls,hrefs):\n dicts[tit] =href\n count+=1\n for name in dicts:\n listbox.insert(count,(name))\n\n listbox.bind('',CallOn)\n def Main2(self,values):\n import re\n values=values.split('/')[-2]\n url =\"https://www.ximalaya.com/revision/play/album?albumId={}&pageNum=1&sort=1&pageSize=30\".format(values)\n json = requests.get(url,headers=self.headers).text\n trackName = re.findall(r'\"trackName\":(.*?),', json)\n src = re.findall(r'\"src\":(.*?),', json)\n tkinters.Windows(trackName,src)\n\n\n\n\nclass Tkinter(object):\n\n def __init__(self):\n self.root = Tk()\n self.root.title('專屬音樂盒')\n #窗口大小\n self.root.geometry('900x700')\n #窗口位置\n self.root.geometry('+1000+280')\n def Set(self):\n #滾定條\n scrollbar =Scrollbar(self.root,orient='horizontal')\n scrollbar.grid(row=2, column=0,sticky='ew')\n #font大小,字體\n lable = Label(self.root,text=\"查找歌手,或歌名:\",font=('標楷體',20))\n #布局默認行列(0,0)\n lable.grid(row=0,sticky=E)\n #內容被獲取用\n # lastring = tk.StringVar()\n #輸入框 width=框的寬度\n entry = Entry(self.root,font=(13),width=40)\n entry.grid(row=0,column=1,sticky=E)\n # 列表框\n listbox = Listbox(self.root, font=('標楷體', 15), width=60, height=25)\n listbox.grid(row=1, columnspan=2)\n listbox.configure(xscrollcommand=scrollbar.set)\n # 發送按鈕\n outside = Button(text=\"宏哥快搜\",font=('標楷體',13),width=10,command= lambda:GetInfo(entry,listbox))\n outside.grid(row=0,column=2)\n scrollbar.configure(command=listbox.xview)\n self.root.mainloop()\n def Windows(self,trackName,src):\n\n def CallOn(event):\n global net2\n global off\n global off2\n print(off)\n if net2 == False:\n name = listbox.get(listbox.curselection())\n if '\"'+name+'\"' in trackName:\n index=trackName.index('\"'+name+'\"')\n if src[index]=='null':\n listbox2.insert(END,'這連結爆了換別的吧')\n elif off==True and off2==True:\n listbox2.insert(END, name + '下載中,請稍後....')\n listbox2.see(END)\n listbox2.update()\n href =src[index]\n Downlod(name,href,getpath(),listbox2)\n else:\n root = Tk()\n root.geometry('+1250+480')\n Label(root, text=\"尚未設置路徑,請先設置!!\").grid(row=0, column=0)\n\n def selectPath():\n global off2\n path_ = askdirectory()\n path.set(path_)\n print(path)\n off2 = True\n def getpath():\n global off\n paths = path.get()\n Label(root2, text=paths).grid(row=1, column=3)\n off =True\n return paths\n global off,off2\n off = False\n off2 =False\n count =0\n root2 =Tk()\n root2.title('下載視窗')\n # 窗口大小\n root2.geometry('1200x700')\n # 窗口位置\n root2.geometry('+950+280')\n lable = Label(root2, text=\"點擊音樂下載:\", font=('標楷體', 20))\n # 布局默認行列(0,0)\n lable.grid(row=0, sticky=E)\n listbox = Listbox(root2, font=('標楷體', 15), width=55, height=25)\n listbox.grid(row=1, column=0)\n listbox2 = Listbox(root2, font=('標楷體', 15), width=40, height=25)\n listbox2.grid(row=1, column=1)\n path = StringVar()\n Label(root2, text=\"目標路徑:\").grid(row=1, column=2)\n print(path)\n Button(root2, text=\"下載路徑設置\", command=selectPath).grid(row=0, column=2)\n Button(root2, text=\"確定\", command=getpath).grid(row=0, column=3)\n for name in trackName:\n count+=1\n listbox.insert(count,name.strip('\"'))\n listbox.bind('', CallOn)\n\n\ndef GetInfo(entry,listbox):\n # 判斷結束爬蟲後才被執行\n if net == False:\n info = entry.get()\n spider.Main(info,listbox)\ndef Downlod(name,href,path,listbox2):\n print(href,path)\n\n music = requests.get(href.strip('\"'), headers=spider.headers)\n with open(path + \"\\\\\" + name.strip() + '.m4a', 'wb')as f:\n f.write(music.content)\n listbox2.insert(END, '下載完畢...')\n\nif __name__ ==\"__main__\":\n net =False\n net2=False\n spider=Spider()\n tkinters=Tkinter()\n tkinters.Set()\n", "repo_name": "BigChoCho/untitled1", "sub_path": "py-re-62/spider_re_100.py", "file_name": "spider_re_100.py", "file_ext": "py", "file_size_in_byte": 6240, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "requests.get", "line_number": 38, "usage_type": "call"}, {"api_name": "lxml.etree.HTML", "line_number": 39, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 39, "usage_type": "name"}, {"api_name": "requests.get", "line_number": 53, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 54, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 55, "usage_type": "call"}, {"api_name": "tkinter.filedialog.askdirectory", "line_number": 118, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 164, "usage_type": "call"}]} +{"seq_id": "5355021642", "text": "import numpy as np\nimport matplotlib.pyplot as plt\nimport itertools\nfrom scipy import signal\n\nN_TEMPERATURE = 41\n\ndef calculate_energy(spin_config, Nx, Ny):\n ret = 0.0\n for nx in range(Nx):\n for ny in range(Ny):\n ret += -0.5 * spin_config[nx * Ny + ny] * (spin_config[((nx - 1) % Nx) * Ny + ny]\\\n + spin_config[((nx + 1) % Nx) * Ny + ny] + spin_config[nx * Ny + (ny - 1) % Ny]\\\n + spin_config[nx * Ny + (ny + 1) % Ny])\n return ret\n\ndef calculate_magnetization(spin_config):\n return np.sum(spin_config)\n\ndef ising_mean(Nx, Ny, T):\n Z = 0.0\n mean_energy = 0.0\n mean_magnetization = 0.0\n iterator = itertools.product([-1, +1], repeat = Nx*Ny)\n for i in iterator:\n m = calculate_magnetization(list(i))\n E = calculate_energy(list(i), Nx, Ny)\n boltzmann = np.exp( -E / T)\n Z += boltzmann\n mean_energy += E * boltzmann\n mean_magnetization += np.abs(calculate_magnetization(list(i))) * boltzmann\n mean_energy /= (Z * Nx * Ny)\n mean_magnetization /= (Z * Nx * Ny)\n return mean_energy, mean_magnetization\n\ndef ising_monte_carlo(N_MC_STEPS, Nx, Ny, T):\n N_SPINS = Nx * Ny\n spin_config = [(-1) ** np.random.randint(0, high=2) for i in range(N_SPINS)]\n energy = calculate_energy(spin_config, Nx, Ny)\n magnetization = calculate_magnetization(spin_config)\n mean_energy = 0.0\n mean_magnetization = 0.0\n \n energy_time_series = []\n magnetization_time_series = []\n \n for i in range(N_MC_STEPS):\n #Choose a random spin to flip\n random_spin = np.random.randint(0, high=N_SPINS)\n \n #Calculate the coordinates of the spin\n ny = random_spin % Ny\n nx = int((random_spin - ny) / Ny)\n \n #Calculate the new energy for this configuration\n energy_trial = energy + 2 * spin_config[nx * Ny + ny] * (spin_config[((nx - 1) % Nx) * Ny + ny]\\\n + spin_config[((nx + 1) % Nx) * Ny + ny] + spin_config[nx * Ny + (ny - 1) % Ny]\\\n + spin_config[nx * Ny + (ny + 1) % Ny])\n \n #Decide if new spin configuration is accepted\n if np.random.random_sample() < min(1, np.exp( (energy - energy_trial)/ T)):\n spin_config[random_spin] *= -1\n energy = energy_trial\n magnetization += 2 * spin_config[random_spin]\n \n energy_time_series.append(energy)\n magnetization_time_series.append(magnetization)\n \n mean_energy += energy\n mean_magnetization += np.abs(magnetization)\n \n mean_energy /= (N_MC_STEPS * N_SPINS)\n mean_magnetization /= (N_MC_STEPS * N_SPINS)\n return mean_energy, mean_magnetization, np.array(energy_time_series), np.array(magnetization_time_series)\n\n#def error_analysis(O):\n# #calculate the mean value of the observables\n# mean_value = np.mean(O)\n# variance = np.mean((O - mean_value) ** 2)\n# \n# N = len(O)\n# \n# ac = signal.correlate((O - mean_value), (O - mean_value), mode='same')\n# \n# plt.plot(ac)\n# plt.show()\n# \n# tau_int = 0.5 * np.sum(ac) / max(ac)\n# \n# #calculate the effective statistics\n# N_eff = N / (2 * tau_int)\n# \n# #calculate the error of the observables mean value\n# error = np.sqrt(variance / N_eff)\n# \n# return mean_value, tau_int, N_eff, error\n\n#def bivariate_gaussian(N, rho):\n# e = np.random.normal(size=N)\n# for i in range(N):\n# e[i] = rho * e[i-1] + np.sqrt(1 - rho ** 2) * e[i]\n# return e\n\n#Test the if error_analysis returns the correct autocorrelation time\n#N_TEST = 1000000\n#rho = 0.99005\n#e = bivariate_gaussian(N_TEST, rho)\n#tau_exact = 0.5 * (1 + rho) / (1 - rho)\n#tau_error_analysis = error_analysis(e)\n#print(tau_exact)\n#print(tau_error_analysis)\n\n\nE_exact = np.zeros(N_TEMPERATURE)\nM_exact = np.zeros(N_TEMPERATURE)\n\nE_MC = np.zeros(N_TEMPERATURE)\nM_MC = np.zeros(N_TEMPERATURE)\n\nenergy_time_series = np.zeros([N_TEMPERATURE, 10000])\nmagnetization_time_series = np.zeros([N_TEMPERATURE, 10000])\n\nfor i in range(N_TEMPERATURE):\n T = 1.0 + i*0.1\n# E_exact[i], M_exact[i] = ising_mean(4, 4, T)\n E_MC[i], M_MC[i], energy_time_series[i,:], magnetization_time_series[i,:] = ising_monte_carlo(10000, 4, 4, T)\n# print(E_exact[i])\n \nprint(ising_mean(4,4,5))\n\n#Analyse the time series from the Monte Carlo simulation\n#for i in range(N_TEMPERATURE):\n# statistics_energy[i] = error_analysis(energy_time_series[i,:])\n# statistics_magnetization[i] = error_analysis(magnetization_time_series[i,:])\n\n\n#Create Plots\nT_range = np.linspace(1.0, 5.0, num = 41, endpoint=True)\n\nwidth = 5.787\nheight = width*0.6\nplt.rc('figure', figsize=(width,height))\nplt.rc('text', usetex=True)\n\n#plt.plot(T_range, E_exact, label=r'exact summation')\nplt.plot(T_range, E_MC, label=r'Monte Carlo')\nplt.xlabel(r'$\\mathrm{dimensionless}$ $\\mathrm{temperature}$ $\\frac{k_\\mathrm{B}T}{J}$')\nplt.ylabel(r'$\\mathrm{mean}$ $\\mathrm{energy}$ $\\frac{E}{J}$')\nplt.tight_layout()\nplt.legend()\nplt.show()\n\n#plt.plot(T_range, M_exact, label=r'exact summation')\nplt.plot(T_range, M_MC, label=r'Monte Carlo')\nplt.xlabel(r'$\\mathrm{dimensionless}$ $\\mathrm{temperature}$ $\\frac{k_\\mathrm{B}T}{J}$')\nplt.ylabel(r'$\\mathrm{mean}$ $\\mathrm{magnetization}$ $\\frac{\\left|\\mu\\right|}{m}$')\nplt.tight_layout()\nplt.legend()\nplt.show()\n", "repo_name": "Sekuraz/SimPhys", "sub_path": "Winter/5/solutions/ex_5_4.py", "file_name": "ex_5_4.py", "file_ext": "py", "file_size_in_byte": 5326, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "numpy.sum", "line_number": 18, "usage_type": "call"}, {"api_name": "itertools.product", "line_number": 24, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 28, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 31, "usage_type": "call"}, {"api_name": "numpy.random.randint", "line_number": 38, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 38, "usage_type": "attribute"}, {"api_name": "numpy.random.randint", "line_number": 49, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 49, "usage_type": "attribute"}, {"api_name": "numpy.random.random_sample", "line_number": 61, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 61, "usage_type": "attribute"}, {"api_name": "numpy.exp", "line_number": 61, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 70, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 74, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 114, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 115, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 117, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 118, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 120, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 121, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 138, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.rc", "line_number": 142, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 142, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.rc", "line_number": 143, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 143, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 146, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 146, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 147, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 147, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 148, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 148, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tight_layout", "line_number": 149, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 149, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 150, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 150, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 151, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 151, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 154, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 154, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 155, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 155, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 156, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 156, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tight_layout", "line_number": 157, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 157, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 158, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 158, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 159, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 159, "usage_type": "name"}]} +{"seq_id": "4630322192", "text": "import os\nfrom os.path import join\nimport tkinter as tk\nfrom PIL import ImageTk\nimport pandas as pd\n\n\n# Create a list of images\nimages = []\n\n\n\ndf_sides = pd.read_csv('sides.csv')\nfor index, row in df_sides.iterrows():\n piece_file_name = join(\"threshold\", row['Piece']+\".png\")\n \n images.append(piece_file_name)\n\n\n# Create the main window\nwindow = tk.Tk()\n\n# Create a label to display the image\nlabel = tk.Label(window)\n\n# Create a function to load the next image\ndef next_image():\n global current_image\n current_image = (current_image + 1) % len(images)\n image = ImageTk.PhotoImage(Image.open(images[current_image]))\n label.configure(image=image)\n\n# Create a function to load the previous image\ndef previous_image():\n global current_image\n current_image = (current_image - 1) % len(images)\n image = ImageTk.PhotoImage(Image.open(images[current_image]))\n label.configure(image=image)\n\n# Create a left arrow button\nleft_arrow = tk.Button(window, text=\"Left\", command=previous_image)\n\n# Create a right arrow button\nright_arrow = tk.Button(window, text=\"Right\", command=next_image)\n\n# Pack the widgets\nlabel.pack()\nleft_arrow.pack()\nright_arrow.pack()\n\n# Set the current image\ncurrent_image = 0\n\n# Start the main loop\nwindow.mainloop()", "repo_name": "ehsanamid/jigsaw-puzzle", "sub_path": "graphic.py", "file_name": "graphic.py", "file_ext": "py", "file_size_in_byte": 1263, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "pandas.read_csv", "line_number": 13, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 15, "usage_type": "call"}, {"api_name": "tkinter.Tk", "line_number": 21, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 24, "usage_type": "call"}, {"api_name": "PIL.ImageTk.PhotoImage", "line_number": 30, "usage_type": "call"}, {"api_name": "PIL.ImageTk", "line_number": 30, "usage_type": "name"}, {"api_name": "PIL.ImageTk.PhotoImage", "line_number": 37, "usage_type": "call"}, {"api_name": "PIL.ImageTk", "line_number": 37, "usage_type": "name"}, {"api_name": "tkinter.Button", "line_number": 41, "usage_type": "call"}, {"api_name": "tkinter.Button", "line_number": 44, "usage_type": "call"}]} +{"seq_id": "42708931820", "text": "import numpy as np\nfrom numpy.random import randint\nfrom copy import deepcopy\nimport argparse\n\nparser = argparse.ArgumentParser(description=\"simulate the tiger problem\")\nparser.add_argument('--outfile',\n\t\t\thelp=\"name of file to write output to; defaults to stdio\",\n\t\t\tdefault=None)\nparser.add_argument('--trials', type=int,\n\t\t\thelp=\"number of trials to simulate; default 10\",\n default=10)\nparser.add_argument('--steps', type=int,\n\t\t\thelp=\"number of decision steps to simulate for each trial; default 10\",\n default=10)\n\nargs = parser.parse_args()\n\nfrom functools import reduce\nout_array = [[\"Idnum\"]+reduce(lambda x,y: x+y, [[\"action\"+str(i),\"observation\"+str(i),\"reward\"+str(i)] for i in range(1,args.steps+1)])]\n\n\nfor i in range(1, args.trials+1):\n tiger_side = randint(1,3) # 1 is left, 2 is right.\n if args.outfile:\n log = [i]\n else:\n print(\"\\ngame: \"+str(i))\n print(\"[t,action,observation,utility]:\")\n observation = 0\n for t in range(args.steps):\n # 0 is listen, 1 is open left, 2 is open right\n # 2/3 of the time listens, 1/3 of the time chooses random door\n action = 0 if randint(5)<4 else randint(1,3)\n utility = -1\n if action != 0:\n utility = -100 if tiger_side==action else 10\n tiger_side = randint(1,3) # on opening door, reset tiger location\n if args.outfile:\n log += [observation,action,utility]\n else:\n print([t,observation,action,utility])\n if action == 0:\n if randint(1,101) <= 85: # 85% accuracy of observation\n observation = tiger_side\n else:\n observation = 2 if tiger_side==1 else 1\n else:\n observation = 0\n if args.outfile:\n out_array.append(log)\n\n\nif args.outfile:\n import csv\n with open(args.outfile, 'w') as myfile:\n \twr = csv.writer(myfile,delimiter='\\t')\n \tfor log in out_array:\n \t\t\twr.writerow(log)\n", "repo_name": "minimum-LaytonC/SRSPMN_dataset_generators", "sub_path": "tiger_problem.py", "file_name": "tiger_problem.py", "file_ext": "py", "file_size_in_byte": 1977, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 6, "usage_type": "call"}, {"api_name": "functools.reduce", "line_number": 20, "usage_type": "call"}, {"api_name": "numpy.random.randint", "line_number": 24, "usage_type": "call"}, {"api_name": "numpy.random.randint", "line_number": 34, "usage_type": "call"}, {"api_name": "numpy.random.randint", "line_number": 38, "usage_type": "call"}, {"api_name": "numpy.random.randint", "line_number": 44, "usage_type": "call"}, {"api_name": "csv.writer", "line_number": 57, "usage_type": "call"}]} +{"seq_id": "38157664430", "text": "from SudokuSolver import SudokuSolver\r\nfrom NumberRecognizer import NumberRecognizer\r\nfrom SudokuAlgorithm import SudokuAlgorithm\r\nimport cv2\r\n\r\nrecognizer = NumberRecognizer('./deeplearning/models/cnn/best_model')\r\nsolver = SudokuSolver(recognizer)\r\n\r\n\r\npaths = [\r\n # \"./inputs/c.jpg\",\r\n # \"./inputs/d.jpg\",\r\n # \"./inputs/e.jpg\",\r\n # \"./inputs/g.jpg\",\r\n # \"./inputs/n.png\",\r\n # \"./inputs/o.png\",\r\n # \"./inputs/p.png\",\r\n # \"./inputs/q.png\",\r\n # \"./inputs/r.png\",\r\n # \"./inputs/s.png\",\r\n # \"./inputs/u.png\",\r\n # \"./inputs/v.png\",\r\n # \"./inputs/x.png\",\r\n \"./inputs/fail/y.jpeg\",\r\n]\r\n\r\nfor path in paths:\r\n try:\r\n img, grid = solver.imageToGrid(cv2.imread(path))\r\n img = cv2.resize(img, (600, 600))\r\n h, w, ch = img.shape\r\n\r\n if grid != None:\r\n algorithm = SudokuAlgorithm(grid)\r\n solution = algorithm.solve()\r\n if solution != None:\r\n solver.showNumbersOnImage(img, solution, h // 9)\r\n\r\n except:\r\n print('error in ' + path)\r\n solver.showNumbersOnImage(img, grid, h // 9)\r\n\r\n cv2.waitKey(0)\r\n cv2.destroyAllWindows()\r\n", "repo_name": "Ibrahim9595/SudokuSolverCV", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 1158, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "NumberRecognizer.NumberRecognizer", "line_number": 6, "usage_type": "call"}, {"api_name": "SudokuSolver.SudokuSolver", "line_number": 7, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 29, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 30, "usage_type": "call"}, {"api_name": "SudokuAlgorithm.SudokuAlgorithm", "line_number": 34, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 43, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 44, "usage_type": "call"}]} +{"seq_id": "43699903617", "text": "import os\nimport cv2\nimport numpy as np\nfrom PIL import Image\n\nclass Scan():\n def scan_from_directory(self, source_path, output_path):\n if not os.path.exists(output_path):\n os.makedirs(output_path)\n\n files = os.listdir(source_path)\n for file in files:\n img_path = os.path.join(source_path, file)\n scanned = self.scan_journal(img_path)\n scanned = Image.fromarray(scanned)\n scanned.save(os.path.join(output_path, file), dpi=(300, 300))\n # cv2.imwrite(os.path.join(output_path, file), scanned)\n\n\n def scan_journal(self, img_path):\n image = cv2.imread(img_path)\n origin_img = image.copy()\n\n # 左下->右下->左上->右上\n source_left_page = np.array([[190., 971.], [900., 959.], [240., 65.], [900., 95.]], dtype = \"float32\")\n source_right_page = np.array([[990., 958.], [1650., 959.], [990., 100.], [1660., 77.]], dtype = \"float32\")\n\n # map\n target_points = np.array([[0, 800], [680, 800], [0, 0], [680, 0]], dtype = \"float32\")\n\n left = self.transform(origin_img, source_left_page, target_points)\n right = self.transform(origin_img, source_right_page, target_points)\n\n # concatenate\n concate = np.concatenate((left, right), axis=1)\n\n return self.remove_shadow(concate)\n\n def transform(self, img, source, target, targer_size=(680, 800)):\n transform_img = cv2.getPerspectiveTransform(source, target)\n target = cv2.warpPerspective(img, transform_img, targer_size)\n return target\n\n def remove_shadow(self, img):\n # from https://stackoverflow.com/questions/44752240/how-to-remove-shadow-from-scanned-images-using-opencv\n rgb_planes = cv2.split(img)\n\n result_planes = []\n result_norm_planes = []\n for plane in rgb_planes:\n dilated_img = cv2.dilate(plane, np.ones((9, 9), np.uint8))\n bg_img = cv2.medianBlur(dilated_img, 21)\n diff_img = 255 - cv2.absdiff(plane, bg_img)\n norm_img = cv2.normalize(diff_img, None, alpha=0, beta=270, norm_type=cv2.NORM_MINMAX, dtype=cv2.CV_8UC1)\n result_planes.append(diff_img)\n result_norm_planes.append(norm_img)\n\n result = cv2.merge(result_planes)\n result = cv2.cvtColor(result, cv2.COLOR_BGR2GRAY)\n result_norm = cv2.merge(result_norm_planes)\n result_norm = cv2.cvtColor(result_norm, cv2.COLOR_BGR2GRAY)\n\n return result_norm\n\n\nif __name__=='__main__':\n scanner = Scan()\n # scanner.scan_journal('20191213162042_1.jpg')\n scanner.scan_from_directory('test', 'output/')\n", "repo_name": "charlieeWang/RDR2_Scanner_for_Arthur_Morgan-s_journal", "sub_path": "Scanner.py", "file_name": "Scanner.py", "file_ext": "py", "file_size_in_byte": 2642, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "os.path.exists", "line_number": 8, "usage_type": "call"}, {"api_name": "os.path", "line_number": 8, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 9, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 11, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 13, "usage_type": "call"}, {"api_name": "os.path", "line_number": 13, "usage_type": "attribute"}, {"api_name": "PIL.Image.fromarray", "line_number": 15, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 15, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 16, "usage_type": "call"}, {"api_name": "os.path", "line_number": 16, "usage_type": "attribute"}, {"api_name": "cv2.imread", "line_number": 21, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 25, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 26, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 29, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 35, "usage_type": "call"}, {"api_name": "cv2.getPerspectiveTransform", "line_number": 40, "usage_type": "call"}, {"api_name": "cv2.warpPerspective", "line_number": 41, "usage_type": "call"}, {"api_name": "cv2.split", "line_number": 46, "usage_type": "call"}, {"api_name": "cv2.dilate", "line_number": 51, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 51, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 51, "usage_type": "attribute"}, {"api_name": "cv2.medianBlur", "line_number": 52, "usage_type": "call"}, {"api_name": "cv2.absdiff", "line_number": 53, "usage_type": "call"}, {"api_name": "cv2.normalize", "line_number": 54, "usage_type": "call"}, {"api_name": "cv2.NORM_MINMAX", "line_number": 54, "usage_type": "attribute"}, {"api_name": "cv2.CV_8UC1", "line_number": 54, "usage_type": "attribute"}, {"api_name": "cv2.merge", "line_number": 58, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 59, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 59, "usage_type": "attribute"}, {"api_name": "cv2.merge", "line_number": 60, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 61, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 61, "usage_type": "attribute"}]} +{"seq_id": "33531889229", "text": "from airflow.hooks.postgres_hook import PostgresHook\nfrom airflow.models import BaseOperator\nfrom airflow.utils.decorators import apply_defaults\n\nfrom helpers import SqlQueries\n\nclass DataQualityOperator(BaseOperator):\n\n ui_color = '#89DA59'\n\n @apply_defaults\n def __init__(self,\n redshift_table_names,\n *args, **kwargs):\n\n super(DataQualityOperator, self).__init__(*args, **kwargs)\n self.table_names = redshift_table_names\n\n def execute(self, context):\n redshift_hook = PostgresHook(\"redshift\")\n for table_name in self.table_names:\n records = redshift_hook.get_records(SqlQueries.count_rows.format(table_name=table_name))\n\n if len(records) < 1 or len(records[0]) < 1:\n raise ValueError(f\"Data quality check failed. {table_name} returned no results\")\n num_records = records[0][0]\n if num_records < 1:\n raise ValueError(f\"Data quality check failed. {table_name} contained 0 rows\")\n\n self.log.info(f\"Data quality on table {table_name} check passed with {records[0][0]} records\")\n \n \n", "repo_name": "MBtech/data-eng-capstone", "sub_path": "airflow/plugins/operators/data_quality.py", "file_name": "data_quality.py", "file_ext": "py", "file_size_in_byte": 1152, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "airflow.models.BaseOperator", "line_number": 7, "usage_type": "name"}, {"api_name": "airflow.utils.decorators.apply_defaults", "line_number": 11, "usage_type": "name"}, {"api_name": "airflow.hooks.postgres_hook.PostgresHook", "line_number": 20, "usage_type": "call"}, {"api_name": "helpers.SqlQueries.count_rows.format", "line_number": 22, "usage_type": "call"}, {"api_name": "helpers.SqlQueries.count_rows", "line_number": 22, "usage_type": "attribute"}, {"api_name": "helpers.SqlQueries", "line_number": 22, "usage_type": "name"}]} +{"seq_id": "10454629333", "text": "import datetime\nimport io\nimport logging\nimport math\nimport pickle\nimport sys\n\nimport tracktable.domain\nfrom tracktable.core import geomath\nfrom tracktable.feature.interpolated_points import TrajectoryPointSource\nfrom tracktable.domain.terrestrial import Trajectory as TerrestrialTrajectory\nfrom tracktable.domain.terrestrial import TrajectoryPoint as TerrestrialTrajectoryPoint\n\ndef test_compute_bounding_box_after_pickle():\n error_count = 0\n\n albuquerque = TerrestrialTrajectoryPoint(-106.6504, 35.0844)\n albuquerque.timestamp = datetime.datetime(year=2020, month=1, day=1, hour=12)\n san_francisco = TerrestrialTrajectoryPoint( -122.4194, 37.7749)\n san_francisco.timestamp = albuquerque.timestamp + datetime.timedelta(hours=3)\n tokyo = TerrestrialTrajectoryPoint(-221.6917, 35.6895)\n tokyo.timestamp = albuquerque.timestamp + datetime.timedelta(hours=12)\n\n trajectory_generator = TrajectoryPointSource()\n trajectory_generator.start_point = albuquerque\n trajectory_generator.end_point = tokyo\n trajectory_generator.num_points = 20\n\n print(\"DEBUG: TerrestrialTrajectory: {}\".format(TerrestrialTrajectory))\n albuquerque_to_tokyo = TerrestrialTrajectory.from_position_list(list(trajectory_generator.points()))\n\n expected_min_corner = tracktable.domain.domain_class_for_object(albuquerque, 'BasePoint')()\n expected_max_corner = tracktable.domain.domain_class_for_object(albuquerque, 'BasePoint')()\n\n expected_min_corner[0] = min(albuquerque[0], tokyo[0])\n expected_min_corner[1] = min(albuquerque[1], tokyo[1])\n expected_max_corner[0] = max(albuquerque[0], tokyo[0])\n expected_max_corner[1] = max(albuquerque[1], tokyo[1])\n\n bbox_before_pickling = geomath.compute_bounding_box(albuquerque_to_tokyo)\n\n store = io.BytesIO()\n pickle.dump(albuquerque_to_tokyo, store)\n store.seek(0)\n restored_trajectory = pickle.load(store)\n bbox_after_pickling = geomath.compute_bounding_box(restored_trajectory)\n\n print(\"Bounding box before pickling: ({} {}) - ({} {})\".format(\n bbox_before_pickling.min_corner[0],\n bbox_before_pickling.min_corner[1],\n bbox_before_pickling.max_corner[0],\n bbox_before_pickling.max_corner[1]))\n print(\"Bounding box after pickling: ({} {}) - ({} {})\".format(\n bbox_after_pickling.min_corner[0],\n bbox_after_pickling.min_corner[1],\n bbox_after_pickling.max_corner[0],\n bbox_after_pickling.max_corner[1]))\n\n bbox_min_delta = (bbox_after_pickling.min_corner[0] -\n bbox_before_pickling.min_corner[0],\n bbox_after_pickling.min_corner[1] -\n bbox_before_pickling.min_corner[1])\n bbox_max_delta = (bbox_after_pickling.max_corner[0] -\n bbox_before_pickling.max_corner[0],\n bbox_after_pickling.max_corner[1] -\n bbox_before_pickling.max_corner[1])\n\n if (math.fabs(bbox_min_delta[0]) > 0.01 or\n math.fabs(bbox_min_delta[1]) > 0.01 or\n math.fabs(bbox_max_delta[0]) > 0.01 or\n math.fabs(bbox_max_delta[1]) > 0.01):\n print((\"ERROR: Expected delta between bounding box before and after \"\n \"pickling to be zero. Delta for minimum corner is {}. \"\n \"Delta for maximum corner is {}.\").format(\n bbox_min_delta, bbox_max_delta))\n error_count += 1\n\n return error_count\n\n# ----------------------------------------------------------------------\n\n\ndef main():\n return test_compute_bounding_box_after_pickle()\n\n# ----------------------------------------------------------------------\n\n\nif __name__ == '__main__':\n sys.exit(main())\n", "repo_name": "sandialabs/tracktable", "sub_path": "tracktable/Python/tracktable/core/tests/test_compute_bounding_box_after_pickle.py", "file_name": "test_compute_bounding_box_after_pickle.py", "file_ext": "py", "file_size_in_byte": 3687, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 36, "dataset": "github-code", "pt": "37", "api": [{"api_name": "tracktable.domain.terrestrial.TrajectoryPoint", "line_number": 17, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 18, "usage_type": "call"}, {"api_name": "tracktable.domain.terrestrial.TrajectoryPoint", "line_number": 19, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 20, "usage_type": "call"}, {"api_name": "tracktable.domain.terrestrial.TrajectoryPoint", "line_number": 21, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 22, "usage_type": "call"}, {"api_name": "tracktable.feature.interpolated_points.TrajectoryPointSource", "line_number": 24, "usage_type": "call"}, {"api_name": "tracktable.domain.terrestrial.Trajectory", "line_number": 29, "usage_type": "argument"}, {"api_name": "tracktable.domain.terrestrial.Trajectory.from_position_list", "line_number": 30, "usage_type": "call"}, {"api_name": "tracktable.domain.terrestrial.Trajectory", "line_number": 30, "usage_type": "name"}, {"api_name": "tracktable.domain.domain.domain_class_for_object", "line_number": 32, "usage_type": "call"}, {"api_name": "tracktable.domain.domain", "line_number": 32, "usage_type": "attribute"}, {"api_name": "tracktable.domain", "line_number": 32, "usage_type": "name"}, {"api_name": "tracktable.domain.domain.domain_class_for_object", "line_number": 33, "usage_type": "call"}, {"api_name": "tracktable.domain.domain", "line_number": 33, "usage_type": "attribute"}, {"api_name": "tracktable.domain", "line_number": 33, "usage_type": "name"}, {"api_name": "tracktable.core.geomath.compute_bounding_box", "line_number": 40, "usage_type": "call"}, {"api_name": "tracktable.core.geomath", "line_number": 40, "usage_type": "name"}, {"api_name": "io.BytesIO", "line_number": 42, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 43, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 45, "usage_type": "call"}, {"api_name": "tracktable.core.geomath.compute_bounding_box", "line_number": 46, "usage_type": "call"}, {"api_name": "tracktable.core.geomath", "line_number": 46, "usage_type": "name"}, {"api_name": "math.fabs", "line_number": 68, "usage_type": "call"}, {"api_name": "math.fabs", "line_number": 69, "usage_type": "call"}, {"api_name": "math.fabs", "line_number": 70, "usage_type": "call"}, {"api_name": "math.fabs", "line_number": 71, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 90, "usage_type": "call"}]} +{"seq_id": "5893607967", "text": "# -*- coding: utf-8 -*-\nimport contextlib\nimport itertools\nimport os, sys, time, pdb\nimport numpy as np\nimport torch\nimport torch.nn\n\n\nfrom torch.distributions import (OneHotCategorical)\nfrom torch.nn import (Module,Parameter,)\n\n\n\nclass World(Module):\n def parameters(self):\n s = set(self.structural_parameters())\n l = [p for p in super().parameters() if p not in s]\n return iter(l)\n \n def structural_parameters(self):\n return iter([self.gamma])\n\n\n\nclass CategoricalWorld(World):\n def __init__(self, a, *args, **kwargs):\n super().__init__()\n self.a = a\n self.init(*args, **kwargs)\n \n def init(self, *args, **kwargs):\n self.initgraph()\n \n ########################################################################\n # Weights of the ground-truth model\n ########################################################################\n # register_buffer() to set a parameter which is not a weight of the model\n\n # DIMS\n # For each of the M variables, a tensor [Hgt x M x N]\n # Hgt: hidden units of learner\n self.register_buffer (\"W0gt\", torch.FloatTensor(self.M, self.Hgt, self.M, self.N))\n self.register_buffer (\"B0gt\", torch.FloatTensor(self.M, self.Hgt ))\n self.register_buffer (\"W1gt\", torch.FloatTensor(self.M, self.N, self.Hgt ))\n self.register_buffer (\"B1gt\", torch.FloatTensor(self.M, self.N ))\n\n\n # NOTE the ground truth model has also the parameter \"gammagt\" [M x N]\n # initialized in self.initgraph() according to the structure of the causal graph\n # passed with the \" --graph \" option\n\n \n ########################################################################\n # Weights of the learner model\n ########################################################################\n # register_parameter() to set a parameter which is a weight of the model\n # structural params\n self.register_parameter(\"gamma\", Parameter(torch.zeros_like(self.gammagt)))\n \n # Slow params\n # DIMS\n # For each of the M variables, a tensor [Hlr x M x N]\n # Hlr: hidden units of learner\n self.register_parameter(\"W0slow\", Parameter(torch.zeros((self.M, self.Hlr, self.M, self.N), dtype=self.W0gt.dtype)))\n self.register_parameter(\"B0slow\", Parameter(torch.zeros((self.M, self.Hlr ), dtype=self.B0gt.dtype)))\n self.register_parameter(\"W1slow\", Parameter(torch.zeros((self.M, self.N, self.Hlr ), dtype=self.W1gt.dtype)))\n self.register_parameter(\"B1slow\", Parameter(torch.zeros((self.M, self.N ), dtype=self.B1gt.dtype)))\n \n # Fast params\n self.register_parameter(\"W0fast\", Parameter(torch.zeros_like(self.W0slow)))\n self.register_parameter(\"B0fast\", Parameter(torch.zeros_like(self.B0slow)))\n self.register_parameter(\"W1fast\", Parameter(torch.zeros_like(self.W1slow)))\n self.register_parameter(\"B1fast\", Parameter(torch.zeros_like(self.B1slow)))\n \n self.leaky_relu = torch.nn.LeakyReLU(negative_slope=0.1)\n \n for i in range(self.M):\n torch.nn.init.orthogonal_(self.W0slow[i])\n torch.nn.init.orthogonal_(self.W1slow[i])\n torch.nn.init.uniform_(self.B0slow, -.1, +.1)\n torch.nn.init.uniform_(self.B1slow, -.1, +.1)\n torch.nn.init.uniform_(self.gamma, -.1, +.1)\n with torch.no_grad(): self.gamma.diagonal().fill_(float(\"-inf\"))\n \n self.alterdists()\n self.reconstrain_theta(force=self.a.structural_init)\n \n @property\n def Hgt(self):\n if self.a.hidden_truth is None:\n if self.M > self.N: return 4*self.M\n else: return 4*self.N\n else:\n return int(self.a.hidden_truth)\n \n @property\n def Hlr(self):\n if self.a.hidden_learn is None:\n if self.M > self.N: return 4*self.M\n else: return 4*self.N\n else:\n return int(self.a.hidden_learn)\n \n def initgraph(self):\n \"\"\" Initialize the graph:\n - Set M, N, gammagt\n - \n \"\"\"\n if self.a.graph is None:\n self.M = self.a.num_vars\n self.N = self.a.num_cats\n self.initgammagt()\n \n expParents = self.a.num_parents\n idx = np.arange(self.M).astype(np.float32)[:,np.newaxis]\n idx_maxed = np.minimum(idx*0.5, expParents)\n p = np.broadcast_to(idx_maxed/(idx+1), (self.M, self.M))\n B = np.random.binomial(1, p)\n B = np.tril(B, -1)\n self.gammagt.copy_(torch.as_tensor(B))\n else:\n self.M = self.a.num_vars\n self.N = self.a.num_cats\n self.initgammagt()\n \n for g in self.a.graph:\n for e in g.split(\",\"):\n if e == \"\": continue\n nodes = e.split(\"->\")\n if len(nodes) <= 1: continue\n nodes = [int(n) for n in nodes]\n for src, dst in zip(nodes[:-1], nodes[1:]):\n if dst > src:\n self.gammagt[dst,src] = 1\n elif dst == src:\n raise ValueError(\"Edges are not allowed from \" +\n str(src) + \" to oneself!\")\n else:\n raise ValueError(\"Edges are not allowed from \" +\n str(src) + \" to ancestor \" +\n str(dst) + \" !\")\n \n return self\n \n def initgammagt(self):\n if not hasattr(self, \"gammagt\"):\n self.register_buffer(\"gammagt\", torch.empty((self.M, self.M)))\n self.gammagt.zero_()\n \n def vizualize_gamma(self):\n \"\"\"Generate a rendering of a gamma matrix vs its ground truth.\"\"\"\n \n with torch.no_grad():\n RGBPALETTE = torch.as_tensor([\n [213, 62, 79], # Ruby Red\n [244,109, 67],\n [253,174, 97],\n [254,224,139],\n [255,255,191], # Pale Yellow\n [230,245,152],\n [171,221,164],\n [102,194,165],\n [ 50,136,189], # Deep Blue\n ][::-1], dtype=torch.float32)\n \n GAMMAGT = self.gammagt\n GAMMALR = self.gamma.sigmoid()\n INDEXGT = GAMMAGT.float().mul(len(RGBPALETTE)-1)\n INDEXLR = GAMMALR.float().mul(len(RGBPALETTE)-1)\n INDEXGTL = INDEXGT.floor().long()\n INDEXGTF = INDEXGT.float()-INDEXGTL.float()\n INDEXGTU = INDEXGT.ceil ().long()\n INDEXLRL = INDEXLR.floor().long()\n INDEXLRF = INDEXLR.float()-INDEXLRL.float()\n INDEXLRU = INDEXLR.ceil ().long()\n PIXELGTL = torch.index_select(RGBPALETTE, 0, INDEXGTL.view(-1))\n PIXELGTU = torch.index_select(RGBPALETTE, 0, INDEXGTU.view(-1))\n PIXELLRL = torch.index_select(RGBPALETTE, 0, INDEXLRL.view(-1))\n PIXELLRU = torch.index_select(RGBPALETTE, 0, INDEXLRU.view(-1))\n PIXELGTL = PIXELGTL.view(INDEXGTL.shape + RGBPALETTE.shape[1:])\n PIXELGTU = PIXELGTU.view(INDEXGTU.shape + RGBPALETTE.shape[1:])\n PIXELLRL = PIXELLRL.view(INDEXLRL.shape + RGBPALETTE.shape[1:])\n PIXELLRU = PIXELLRU.view(INDEXLRU.shape + RGBPALETTE.shape[1:])\n PIXELGT = PIXELGTU*INDEXGTF.unsqueeze(-1) + PIXELGTL*(1-INDEXGTF).unsqueeze(-1)\n PIXELLR = PIXELLRU*INDEXLRF.unsqueeze(-1) + PIXELLRL*(1-INDEXLRF).unsqueeze(-1)\n PIXELGT = PIXELGT.round().clamp(0., 255.).byte()\n PIXELLR = PIXELLR.round().clamp(0., 255.).byte()\n PIXELLR = PIXELLR.repeat_interleave(20, dim=0) \\\n .repeat_interleave(20, dim=1)\n for x,y in [ ( 7, 8), ( 7, 9), ( 7,10), ( 7,11),\n ( 8, 7), ( 8, 8), ( 8, 9), ( 8,10), ( 8,11), ( 8,12),\n ( 9, 7), ( 9, 8), ( 9, 9), ( 9,10), ( 9,11), ( 9,12),\n (10, 7), (10, 8), (10, 9), (10,10), (10,11), (10,12),\n (11, 7), (11, 8), (11, 9), (11,10), (11,11), (11,12),\n (12, 8), (12, 9), (12,10), (12,11)]:\n PIXELLR[x::20,y::20] = PIXELGT\n \n return PIXELLR\n \n def alterdists(self):\n \"\"\"For randomly-initialized distributions, alter them entirely.\"\"\"\n for i in range(self.M):\n torch.nn.init.orthogonal_(self.W0gt[i], 2.5)\n torch.nn.init.orthogonal_(self.W1gt[i], 2.5)\n torch.nn.init.uniform_(self.B0gt[i], -1.1, +1.1)\n torch.nn.init.uniform_(self.B1gt[i], -1.1, +1.1)\n return self\n \n @contextlib.contextmanager\n def intervene(self, *args, **kwargs):\n \"\"\"Perform an intervention, then undo it.\"\"\"\n \n class CategoricalWorldIntervention:\n def __init__(salf, i=None):\n salf.node = [np.random.randint(0, self.M) if i is None else i]\n \n def __iter__(salf):\n return iter(salf.node)\n \n def do(salf):\n with torch.no_grad():\n salf.clones = [t.clone() for t in self.parameters_gt()]\n for i in salf.node:\n torch.nn.init.orthogonal_(self.W0gt[i], 2.5)\n torch.nn.init.orthogonal_(self.W1gt[i], 2.5)\n torch.nn.init.uniform_(self.B0gt[i], -1.1, +1.1)\n torch.nn.init.uniform_(self.B1gt[i], -1.1, +1.1)\n \n def undo(salf):\n with torch.no_grad():\n for tnew, told in zip(self.parameters_gt(), salf.clones):\n tnew.copy_(told)\n \n intervention = CategoricalWorldIntervention(*args, **kwargs)\n try:\n intervention.do()\n yield intervention\n except: raise\n finally: intervention.undo()\n \n def configpretrainiter(self, full_connect=False):\n \"\"\"\n Sample a configuration for pretraining.\n \"\"\"\n if not full_connect:\n yield from self.configiter()\n else:\n yield from self.configiter_pretrain()\n\n def configiter(self):\n \"\"\"Sample a configuration of the causal graph from the learner\n belief (gamma).\n\n Returns\n An iterator of gamma configurations (binary MxM matrix )\n\n \"\"\"\n while True:\n with torch.no_grad():\n gammaexp = self.gamma.sigmoid()\n\n # computes a vector V [M x M] randomly sampled from uniform distrib\n # Then returns a binary vector gammaexp = V < gammaexp (elementwise)\n gammaexp = torch.empty_like(gammaexp).uniform_().lt_(gammaexp)\n # set the diagonal of gamma to 0, a node cannot be parent of himself\n gammaexp.diagonal().zero_() \n yield gammaexp\n\n def configiter_pretrain(self):\n \"\"\"Sample a configuration from this world.\"\"\"\n while True:\n with torch.no_grad():\n gammaexp = torch.ones_like(self.gamma)\n gammaexp.diagonal().zero_()\n yield gammaexp\n\n def sampleiter_pretrain(self, bs=1):\n return torch.ones([bs, self.M, self.N])\n\n def sampleiter(self, bs=1):\n \"\"\"\n Extract a bs samples from the ground truth model\n \n 1 sample is a tensor (1, M, N).\n A minibatch of samples is a tensor (bs, M, N).\n 1 variable is a tensor (bs, 1, N)\n\n Args:\n bs: (int) batch size\n Returns\n A bach of samples of shape (bs, M, N)\n \"\"\"\n while True:\n with torch.no_grad():\n h = [] # Hard (onehot) samples (bs,1,N)\n for i in range(self.M):\n O = torch.zeros(bs, self.M-i, self.N) # (bs,M-i,N)\n v = torch.cat(h+[O], dim=1) # Concat: (bs,M-i,N) + (bs,1,N)*i\n\n # Apply the gammagt masking to W0gt[i] [Hgt x M x N] so that only the \n # parents of the node i are considered in the computation\n v = torch.einsum(\"hik,i,bik->bh\", self.W0gt[i], self.gammagt[i], v)\n v = v + self.B0gt[i].unsqueeze(0)\n v = self.leaky_relu(v)\n v = torch.einsum(\"oh,bh->bo\", self.W1gt[i], v)\n v = v + self.B1gt[i].unsqueeze(0)\n v = v.softmax(dim=1).unsqueeze(1)\n\n # v (bs, 1, N) is a set of values for the variable i (**deterministic**)\n # with OneHotCategorical(v).sample() v is considered a probability distribution\n # And a one-hot sample is drawn from the N values for each bs sample\n h.append(OneHotCategorical(v).sample())\n s = torch.cat(h, dim=1)\n yield s\n \n def logits(self, sample, config, traingt=False):\n \"\"\"\n Forward propagation through the ground truth or learner network \n\n Args:\n sample = (bs, M, N) the input to the network\n config = (M, M) the configuration taken from the gamma params\n traingt: (bool) whether to use the ground truth network or the learner\n return logits = (bs, M, N)\n \"\"\"\n W0 = self.W0gt if traingt else self.W0fast+self.W0slow\n W1 = self.W1gt if traingt else self.W1fast+self.W1slow\n B0 = self.B0gt if traingt else self.B0fast+self.B0slow\n B1 = self.B1gt if traingt else self.B1fast+self.B1slow\n \n v = torch.einsum(\"ihjk,ij,bjk->bih\", W0, config, sample)\n v = v + B0.unsqueeze(0)\n v = self.leaky_relu(v)\n v = torch.einsum(\"ioh,bih->bio\", W1, v)\n v = v + B1.unsqueeze(0)\n return v\n \n def logprob(self, sample, config, block=(), traingt=False):\n \"\"\"\n\n\n Log-probability of sample variables given sampled configuration.\n input sample = (bs, M, N) # Actual value of the sample\n input config = (M, M) # Configuration\n return logprob = (bs, M)\n \"\"\"\n \n block = [block] if isinstance(block, int) else list(set(iter(block)))\n block = torch.as_tensor(block, dtype=torch.long, device=sample.device)\n # Compute the network output\n block = torch.ones(self.M, device=sample.device).index_fill_(0, block, 0)\n v = self.logits(sample, config, traingt=traingt) / self.a.temperature\n v = v.log_softmax(dim=2)\n # compute the loss\n v = torch.einsum(\"bio,bio->bi\", v, sample)\n vn = torch.einsum(\"bi,i->bi\", v, 0+block)\n vi = torch.einsum(\"bi,i->bi\", v, 1-block)\n return vn, vi\n \n def forward(self, sample, config, block=()):\n \"\"\"Returns the NLL of the samples under the given configuration\"\"\"\n return self.logprob(sample, config, block)\n \n def reconstrain_gamma(self):\n with torch.no_grad():\n self.gamma.clamp_(-5,+5)\n self.gamma.diagonal().fill_(float(\"-inf\"))\n \n def reconstrain_theta(self, force=False):\n if force or self.a.structural_only:\n if self.Hlr == self.Hgt:\n # --structural-* flags are only meaningful if the ground-truth\n # and learner are of the same architecture.\n with torch.no_grad():\n self.W0slow.copy_(self.W0gt)\n self.W1slow.copy_(self.W1gt)\n self.B0slow.copy_(self.B0gt)\n self.B1slow.copy_(self.B1gt)\n \n def parameters_gt(self):\n return iter([self.W0gt, self.B0gt, self.W1gt, self.B1gt])\n def parameters_fastslow(self):\n return zip(iter([self.W0fast, self.B0fast, self.W1fast, self.B1fast]),\n iter([self.W0slow, self.B0slow, self.W1slow, self.B1slow]))\n def parameters_fast(self):\n for f,s in self.parameters_fastslow(): yield f\n def parameters_slow(self):\n for f,s in self.parameters_fastslow(): yield s\n def parameters(self):\n for f,s in self.parameters_fastslow(): yield f+s\n def zero_fastparams(self):\n with torch.no_grad():\n for f in self.parameters_fast(): f.zero_()\n\n\n\nclass AsiaWorld(CategoricalWorld):\n def init(self, *args, **kwargs):\n self.initgraph()\n \n self.register_buffer (\"table_asia_gt\", torch.zeros(2))\n self.register_buffer (\"table_tub_gt\", torch.zeros(2,2))\n self.register_buffer (\"table_smoke_gt\", torch.zeros(2))\n self.register_buffer (\"table_lung_gt\", torch.zeros(2,2))\n self.register_buffer (\"table_bronc_gt\", torch.zeros(2,2))\n self.register_buffer (\"table_either_gt\", torch.zeros(2,2,2))\n self.register_buffer (\"table_xray_gt\", torch.zeros(2,2))\n self.register_buffer (\"table_dysp_gt\", torch.zeros(2,2,2))\n \n self.register_parameter(\"gamma\", Parameter(torch.zeros_like(self.gammagt)))\n \n self.register_parameter(\"W0slow\", Parameter(torch.zeros(self.M, self.Hlr, self.M, self.N)))\n self.register_parameter(\"B0slow\", Parameter(torch.zeros(self.M, self.Hlr )))\n self.register_parameter(\"W1slow\", Parameter(torch.zeros(self.M, self.N, self.Hlr )))\n self.register_parameter(\"B1slow\", Parameter(torch.zeros(self.M, self.N )))\n \n self.register_parameter(\"W0fast\", Parameter(torch.zeros_like(self.W0slow)))\n self.register_parameter(\"B0fast\", Parameter(torch.zeros_like(self.B0slow)))\n self.register_parameter(\"W1fast\", Parameter(torch.zeros_like(self.W1slow)))\n self.register_parameter(\"B1fast\", Parameter(torch.zeros_like(self.B1slow)))\n \n self.leaky_relu = torch.nn.LeakyReLU(negative_slope=0.1)\n \n for i in range(self.M):\n torch.nn.init.orthogonal_(self.W0slow[i])\n torch.nn.init.orthogonal_(self.W1slow[i])\n torch.nn.init.uniform_(self.B0slow, -.1, +.1)\n torch.nn.init.uniform_(self.B1slow, -.1, +.1)\n torch.nn.init.uniform_(self.gamma, -.1, +.1)\n with torch.no_grad(): self.gamma.diagonal().fill_(float(\"-inf\"))\n \n self.alterdists()\n self.reconstrain_theta(force=self.a.structural_init)\n \n def initgraph(self):\n self.M = self.a.num_vars = 8\n self.N = self.a.num_cats = 2\n self.initgammagt()\n self.gammagt.zero_()\n # 0->1->5->6,2->3->5->7,2->4->7\n self.gammagt[1,0] = 1\n self.gammagt[5,1] = 1\n self.gammagt[6,5] = 1\n self.gammagt[3,2] = 1\n self.gammagt[5,3] = 1\n self.gammagt[7,5] = 1\n self.gammagt[4,2] = 1\n self.gammagt[7,4] = 1\n \n def alterdists(self):\n self.table_asia_gt .copy_(torch.as_tensor([ 0.01, 0.99 ]))\n self.table_tub_gt .copy_(torch.as_tensor([ [0.05, 0.95],\n [0.01, 0.99] ]))\n self.table_smoke_gt .copy_(torch.as_tensor([ 0.5, 0.5 ]))\n self.table_lung_gt .copy_(torch.as_tensor([ [0.1, 0.9 ],\n [0.01, 0.99] ]))\n self.table_bronc_gt .copy_(torch.as_tensor([ [0.6, 0.4 ],\n [0.3, 0.7 ] ]))\n self.table_either_gt.copy_(torch.as_tensor([[[1.0, 0.0 ],\n [1.0, 0.0 ]],\n [[1.0, 0.0 ],\n [0.0, 1.0 ]]]))\n self.table_xray_gt .copy_(torch.as_tensor([ [0.98, 0.02],\n [0.05, 0.95] ]))\n self.table_dysp_gt .copy_(torch.as_tensor([[[0.9, 0.1 ],\n [0.7, 0.3 ]],\n [[0.8, 0.2 ],\n [0.1, 0.9 ]]]))\n \n @contextlib.contextmanager\n def intervene(self, *args, **kwargs):\n \"\"\"Perform an intervention, then undo it.\"\"\"\n \n class AsiaWorldIntervention:\n def __init__(salf, i=None):\n salf.node = [np.random.randint(0, self.M) if i is None else i]\n \n def __iter__(salf):\n return iter(salf.node)\n \n def do(salf):\n params = list(self.parameters_gt())\n with torch.no_grad():\n salf.clones = [t.clone() for t in params]\n for i in salf.node:\n with torch.no_grad():\n torch.nn.init.uniform_(params[i], -4, +4)\n params[i].copy_(params[i].softmax(-1))\n \n def undo(salf):\n with torch.no_grad():\n for tnew, told in zip(self.parameters_gt(), salf.clones):\n tnew.copy_(told)\n \n intervention = AsiaWorldIntervention(*args, **kwargs)\n try:\n intervention.do()\n yield intervention\n except: raise\n finally: intervention.undo()\n \n def sampleiter(self, bs=1):\n \"\"\"Ancestral Sampling from Conditional Probability Tables\"\"\"\n while True:\n h = []\n h.append(OneHotCategorical(torch.einsum( \"i->i\", self.table_asia_gt )) .sample((bs,)))\n h.append(OneHotCategorical(torch.einsum( \"ai,za->zi\", self.table_tub_gt, h[0])) .sample())\n h.append(OneHotCategorical(torch.einsum( \"i->i\", self.table_smoke_gt )) .sample((bs,)))\n h.append(OneHotCategorical(torch.einsum( \"ai,za->zi\", self.table_lung_gt, h[2])) .sample())\n h.append(OneHotCategorical(torch.einsum( \"ai,za->zi\", self.table_bronc_gt, h[2])) .sample())\n h.append(OneHotCategorical(torch.einsum(\"bai,za,zb->zi\", self.table_either_gt, h[1], h[3])).sample())\n h.append(OneHotCategorical(torch.einsum( \"ai,za->zi\", self.table_xray_gt, h[5])) .sample())\n h.append(OneHotCategorical(torch.einsum(\"bai,za,zb->zi\", self.table_dysp_gt, h[4], h[5])).sample())\n yield torch.stack(h, dim=1)\n \n def logits(self, sample, config, traingt=False):\n \"\"\"\n Logits of sample variables given sampled configuration.\n input sample = (bs, M, N) # Actual value of the sample\n input config = (M, M) # Configuration\n return logits = (bs, M, N)\n \"\"\"\n assert(not traingt)\n return super().logits(sample, config, traingt)\n \n def parameters_gt(self):\n return iter([self.table_asia_gt,\n self.table_tub_gt,\n self.table_smoke_gt,\n self.table_lung_gt,\n self.table_bronc_gt,\n self.table_either_gt,\n self.table_xray_gt,\n self.table_dysp_gt,\n ])\n\n", "repo_name": "Simosound94/causality_experiments", "sub_path": "causal_learning_unknown_interventions/causal/models.py", "file_name": "models.py", "file_ext": "py", "file_size_in_byte": 23816, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "37", "api": [{"api_name": "torch.nn.Module", "line_number": 15, "usage_type": "name"}, {"api_name": "torch.FloatTensor", "line_number": 43, "usage_type": "call"}, {"api_name": "torch.FloatTensor", "line_number": 44, "usage_type": "call"}, {"api_name": "torch.FloatTensor", "line_number": 45, "usage_type": "call"}, {"api_name": "torch.FloatTensor", "line_number": 46, "usage_type": "call"}, {"api_name": "torch.nn.Parameter", "line_number": 59, "usage_type": "call"}, {"api_name": "torch.zeros_like", "line_number": 59, "usage_type": "call"}, {"api_name": "torch.nn.Parameter", "line_number": 65, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 65, "usage_type": "call"}, {"api_name": "torch.nn.Parameter", "line_number": 66, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 66, "usage_type": "call"}, {"api_name": "torch.nn.Parameter", "line_number": 67, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 67, "usage_type": "call"}, {"api_name": "torch.nn.Parameter", "line_number": 68, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 68, "usage_type": "call"}, {"api_name": "torch.nn.Parameter", "line_number": 71, "usage_type": "call"}, {"api_name": "torch.zeros_like", "line_number": 71, "usage_type": "call"}, {"api_name": "torch.nn.Parameter", "line_number": 72, "usage_type": "call"}, {"api_name": "torch.zeros_like", "line_number": 72, "usage_type": "call"}, {"api_name": "torch.nn.Parameter", "line_number": 73, "usage_type": "call"}, {"api_name": "torch.zeros_like", "line_number": 73, "usage_type": "call"}, {"api_name": "torch.nn.Parameter", "line_number": 74, "usage_type": "call"}, {"api_name": "torch.zeros_like", "line_number": 74, "usage_type": "call"}, {"api_name": "torch.nn.LeakyReLU", "line_number": 76, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 76, "usage_type": "attribute"}, {"api_name": "torch.nn.init.orthogonal_", "line_number": 79, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 79, "usage_type": "attribute"}, {"api_name": "torch.nn.init.orthogonal_", "line_number": 80, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 80, "usage_type": "attribute"}, {"api_name": "torch.nn.init.uniform_", "line_number": 81, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 81, "usage_type": "attribute"}, {"api_name": "torch.nn.init.uniform_", "line_number": 82, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 82, "usage_type": "attribute"}, {"api_name": "torch.nn.init.uniform_", "line_number": 83, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 83, "usage_type": "attribute"}, {"api_name": "torch.no_grad", "line_number": 84, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 116, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 116, "usage_type": "attribute"}, {"api_name": "numpy.newaxis", "line_number": 116, "usage_type": "attribute"}, {"api_name": "numpy.minimum", "line_number": 117, "usage_type": "call"}, {"api_name": "numpy.broadcast_to", "line_number": 118, "usage_type": "call"}, {"api_name": "numpy.random.binomial", "line_number": 119, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 119, "usage_type": "attribute"}, {"api_name": "numpy.tril", "line_number": 120, "usage_type": "call"}, {"api_name": "torch.as_tensor", "line_number": 121, "usage_type": "call"}, {"api_name": "torch.empty", "line_number": 148, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 154, "usage_type": "call"}, {"api_name": "torch.as_tensor", "line_number": 155, "usage_type": "call"}, {"api_name": "torch.float32", "line_number": 165, "usage_type": "attribute"}, {"api_name": "torch.index_select", "line_number": 177, "usage_type": "call"}, {"api_name": "torch.index_select", "line_number": 178, "usage_type": "call"}, {"api_name": "torch.index_select", "line_number": 179, "usage_type": "call"}, {"api_name": "torch.index_select", "line_number": 180, "usage_type": "call"}, {"api_name": "torch.nn.init.orthogonal_", "line_number": 204, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 204, "usage_type": "attribute"}, {"api_name": "torch.nn.init.orthogonal_", "line_number": 205, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 205, "usage_type": "attribute"}, {"api_name": "torch.nn.init.uniform_", "line_number": 206, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 206, "usage_type": "attribute"}, {"api_name": "torch.nn.init.uniform_", "line_number": 207, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 207, "usage_type": "attribute"}, {"api_name": "numpy.random.randint", "line_number": 216, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 216, "usage_type": "attribute"}, {"api_name": "torch.no_grad", "line_number": 222, "usage_type": "call"}, {"api_name": "torch.nn.init.orthogonal_", "line_number": 225, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 225, "usage_type": "attribute"}, {"api_name": "torch.nn.init.orthogonal_", "line_number": 226, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 226, "usage_type": "attribute"}, {"api_name": "torch.nn.init.uniform_", "line_number": 227, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 227, "usage_type": "attribute"}, {"api_name": "torch.nn.init.uniform_", "line_number": 228, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 228, "usage_type": "attribute"}, {"api_name": "torch.no_grad", "line_number": 231, "usage_type": "call"}, {"api_name": "contextlib.contextmanager", "line_number": 210, "usage_type": "attribute"}, {"api_name": "torch.no_grad", "line_number": 260, "usage_type": "call"}, {"api_name": "torch.empty_like", "line_number": 265, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 273, "usage_type": "call"}, {"api_name": "torch.ones_like", "line_number": 274, "usage_type": "call"}, {"api_name": "torch.ones", "line_number": 279, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 295, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 298, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 299, "usage_type": "call"}, {"api_name": "torch.einsum", "line_number": 303, "usage_type": "call"}, {"api_name": "torch.einsum", "line_number": 306, "usage_type": "call"}, {"api_name": "torch.distributions.OneHotCategorical", "line_number": 313, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 314, "usage_type": "call"}, {"api_name": "torch.einsum", "line_number": 332, "usage_type": "call"}, {"api_name": "torch.einsum", "line_number": 335, "usage_type": "call"}, {"api_name": "torch.as_tensor", "line_number": 350, "usage_type": "call"}, {"api_name": "torch.long", "line_number": 350, "usage_type": "attribute"}, {"api_name": "torch.ones", "line_number": 352, "usage_type": "call"}, {"api_name": "torch.einsum", "line_number": 356, "usage_type": "call"}, {"api_name": "torch.einsum", "line_number": 357, "usage_type": "call"}, {"api_name": "torch.einsum", "line_number": 358, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 366, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 375, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 393, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 402, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 403, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 404, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 405, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 406, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 407, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 408, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 409, "usage_type": "call"}, {"api_name": "torch.nn.Parameter", "line_number": 411, "usage_type": "call"}, {"api_name": "torch.zeros_like", "line_number": 411, "usage_type": "call"}, {"api_name": "torch.nn.Parameter", "line_number": 413, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 413, "usage_type": "call"}, {"api_name": "torch.nn.Parameter", "line_number": 414, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 414, "usage_type": "call"}, {"api_name": "torch.nn.Parameter", "line_number": 415, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 415, "usage_type": "call"}, {"api_name": "torch.nn.Parameter", "line_number": 416, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 416, "usage_type": "call"}, {"api_name": "torch.nn.Parameter", "line_number": 418, "usage_type": "call"}, {"api_name": "torch.zeros_like", "line_number": 418, "usage_type": "call"}, {"api_name": "torch.nn.Parameter", "line_number": 419, "usage_type": "call"}, {"api_name": "torch.zeros_like", "line_number": 419, "usage_type": "call"}, {"api_name": "torch.nn.Parameter", "line_number": 420, "usage_type": "call"}, {"api_name": "torch.zeros_like", "line_number": 420, "usage_type": "call"}, {"api_name": "torch.nn.Parameter", "line_number": 421, "usage_type": "call"}, {"api_name": "torch.zeros_like", "line_number": 421, "usage_type": "call"}, {"api_name": "torch.nn.LeakyReLU", "line_number": 423, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 423, "usage_type": "attribute"}, {"api_name": "torch.nn.init.orthogonal_", "line_number": 426, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 426, "usage_type": "attribute"}, {"api_name": "torch.nn.init.orthogonal_", "line_number": 427, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 427, "usage_type": "attribute"}, {"api_name": "torch.nn.init.uniform_", "line_number": 428, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 428, "usage_type": "attribute"}, {"api_name": "torch.nn.init.uniform_", "line_number": 429, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 429, "usage_type": "attribute"}, {"api_name": "torch.nn.init.uniform_", "line_number": 430, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 430, "usage_type": "attribute"}, {"api_name": "torch.no_grad", "line_number": 431, "usage_type": "call"}, {"api_name": "torch.as_tensor", "line_number": 452, "usage_type": "call"}, {"api_name": "torch.as_tensor", "line_number": 453, "usage_type": "call"}, {"api_name": "torch.as_tensor", "line_number": 455, "usage_type": "call"}, {"api_name": "torch.as_tensor", "line_number": 456, "usage_type": "call"}, {"api_name": "torch.as_tensor", "line_number": 458, "usage_type": "call"}, {"api_name": "torch.as_tensor", "line_number": 460, "usage_type": "call"}, {"api_name": "torch.as_tensor", "line_number": 464, "usage_type": "call"}, {"api_name": "torch.as_tensor", "line_number": 466, "usage_type": "call"}, {"api_name": "numpy.random.randint", "line_number": 477, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 477, "usage_type": "attribute"}, {"api_name": "torch.no_grad", "line_number": 484, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 487, "usage_type": "call"}, {"api_name": "torch.nn.init.uniform_", "line_number": 488, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 488, "usage_type": "attribute"}, {"api_name": "torch.no_grad", "line_number": 492, "usage_type": "call"}, {"api_name": "contextlib.contextmanager", "line_number": 471, "usage_type": "attribute"}, {"api_name": "torch.distributions.OneHotCategorical", "line_number": 507, "usage_type": "call"}, {"api_name": "torch.einsum", "line_number": 507, "usage_type": "call"}, {"api_name": "torch.distributions.OneHotCategorical", "line_number": 508, "usage_type": "call"}, {"api_name": "torch.einsum", "line_number": 508, "usage_type": "call"}, {"api_name": "torch.distributions.OneHotCategorical", "line_number": 509, "usage_type": "call"}, {"api_name": "torch.einsum", "line_number": 509, "usage_type": "call"}, {"api_name": "torch.distributions.OneHotCategorical", "line_number": 510, "usage_type": "call"}, {"api_name": "torch.einsum", "line_number": 510, "usage_type": "call"}, {"api_name": "torch.distributions.OneHotCategorical", "line_number": 511, "usage_type": "call"}, {"api_name": "torch.einsum", "line_number": 511, "usage_type": "call"}, {"api_name": "torch.distributions.OneHotCategorical", "line_number": 512, "usage_type": "call"}, {"api_name": "torch.einsum", "line_number": 512, "usage_type": "call"}, {"api_name": "torch.distributions.OneHotCategorical", "line_number": 513, "usage_type": "call"}, {"api_name": "torch.einsum", "line_number": 513, "usage_type": "call"}, {"api_name": "torch.distributions.OneHotCategorical", "line_number": 514, "usage_type": "call"}, {"api_name": "torch.einsum", "line_number": 514, "usage_type": "call"}, {"api_name": "torch.stack", "line_number": 515, "usage_type": "call"}]} +{"seq_id": "32444475489", "text": "#!/usr/bin/env python\n#coding:utf-8\n\"\"\"\n Author: ppc_pipeline --<>\n Purpose: Run simulation, P+T, null model and P+C\n Created: 10/04/17\n\"\"\"\nimport os\nimport argparse\nimport numpy as np\nimport pandas as pd\nfrom ppt import pplust\nfrom ranumo import ranumo\nfrom plinkGWAS import plink_gwas\nfrom prankcster import prankcster\nfrom qtraitsimulation_old import qtraits_simulation\n\ndef execute(args):\n \"\"\"\n Execute pipeline\n \"\"\"\n # TODO: expand the Tar files if relaunched\n rstart, rstop, rstep = args.r2range\n Ps = [float('%.1g' % float(x)) for x in args.pvals.split(',')]\n cwd = os.getcwd()\n ref, tar = args.labels\n if not os.path.isdir(ref):\n os.mkdir(ref)\n os.chdir(ref)\n prs_ref, validsnpfile = qtraits_simulation(ref, args.refb, args.h2, \n args.ncausal, args.plinkexe,\n maxmem=args.maxmem, \n threads=args.threads)\n gwas_ref, sumstats, train_eur, test_eur = plink_gwas(\n args.plinkexe, args.refb, args.prefix, '%s.pheno' % ref, nosex=True, \n threads=args.threads, maxmem=args.maxmem, validate=5,\n validsnpsfile=validsnpfile, plot=True)\n r2range = [x if x <= 0.99 else 0.99 for x in sorted(np.arange(\n rstart, rstop + rstep, rstep), reverse=True)]\n phenoref = os.path.join(os.getcwd(), '%s.pheno' % ref)\n resE, pptrfn = pplust(ref, test_eur, sumstats, r2range, Ps, args.LDwindow, \n phenoref, args.plinkexe, plot=True, \n clean=True)\n causaleff = os.path.join(os.getcwd(), '%s.full' % ref)\n assert os.path.isfile(causaleff)\n os.chdir(cwd)\n if not os.path.isdir(tar):\n os.mkdir(tar)\n os.chdir(tar)\n tarpref = '%s-%s' % (tar, ref)\n prs_tar, validsnpfile = qtraits_simulation(tarpref, args.tarb, \n args.h2, args.ncausal, \n args.plinkexe, \n maxmem=args.maxmem, \n threads=args.threads,\n causaleff=causaleff)\n phenotar = os.path.join(cwd, tar, '%s.pheno' % tarpref)\n resT, pptfn = pplust(tarpref, args.tarb, sumstats, r2range,Ps,args.LDwindow, \n phenotar, args.plinkexe, plot=True, \n clean=True) \n os.chdir(cwd)\n if not os.path.isdir('Null'):\n os.mkdir('Null')\n os.chdir('Null')\n sortresults, qrfn = ranumo(args.prefix, args.tarb, args.refb, sumstats, \n args.cotagfn, args.plinkexe, args.labels, phenotar, \n phenoref, pptR=resE, pptT=resT, hline=args.h2,\n check_freqs=args.freq_threshold, step=args.prune_step,\n quality='pdf')\n os.chdir(cwd)\n prankcster(args.prefix, args.tarb, args.refb, args.cotagfn, pptfn, pptrfn, \n sumstats, phenotar, args.plinkexe, args.alpha_step, args.labels, \n args.prune_step, sortresults, freq_threshold=args.freq_threshold,\n h2=args.h2, qrangefn=qrfn, maxmem=args.maxmem, \n threads=args.threads)\n \n\nif __name__ == '__main__':\n parser = argparse.ArgumentParser()\n parser.add_argument('-p', '--prefix', help='prefix for outputs', \n required=True)\n parser.add_argument('-m', '--ncausal', type=int, default=100)\n parser.add_argument('-b', '--refb', help=('prefix of the bed fileset in '\n 'reference'), \n required=True) \n parser.add_argument('-c', '--tarb', help=('prefix of the bed fileset in '\n 'target'), required=True)\n parser.add_argument('-L', '--labels', help=('Space separated string with '\n 'labels of reference and target '\n 'populations'), nargs=2)\n parser.add_argument('-d', '--cotagfn', help=('Filename tsv with cotag '\n 'results'), required=True) \n parser.add_argument('-S', '--alpha_step', help=('Step for the granularity of'\n ' the grid search. Default: '\n '.1'), default=0.1, \n type=float) \n parser.add_argument('-E', '--prune_step', help=('Percentage of snps to be '\n 'tested at each step is 1'\n ), default=1, type=float) \n parser.add_argument('-v', '--pvals', default='1.0,0.5,0.1,10E-3,10E-7')\n parser.add_argument('-r', '--r2range', help=('Space separated rstart, rstop'\n ', rstep for LD clumping'), \n nargs=3, type=float)\n parser.add_argument('-P', '--plinkexe')\n parser.add_argument('-t', '--threads', default=-1, type=int) \n parser.add_argument('-H', '--h2', default=0.66, type=float, \n help=('Heritability of the simulated phenotype')) \n parser.add_argument('-M', '--maxmem', default=1700, type=int) \n parser.add_argument('-F', '--freq_threshold', default=0.1, type=float) \n parser.add_argument('-Q', '--qrangefn', default=None, help=(\n 'Specific pre-made qrange file')) \n parser.add_argument('-l', '--LDwindow', help='Physical distance threshold '+\n 'for clumping in kb (250kb by default)', type=int, \n default=250) \n args = parser.parse_args()\n args.refb = os.path.abspath(args.refb)\n args.tarb = os.path.abspath(args.tarb)\n args.plinkexe = os.path.abspath(args.plinkexe)\n args.cotagfn = os.path.abspath(args.cotagfn)\n execute(args)", "repo_name": "jshleap/Cotagging_playground", "sub_path": "ppc_pipeline.py", "file_name": "ppc_pipeline.py", "file_ext": "py", "file_size_in_byte": 6010, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "37", "api": [{"api_name": "os.getcwd", "line_number": 25, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 27, "usage_type": "call"}, {"api_name": "os.path", "line_number": 27, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 28, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 29, "usage_type": "call"}, {"api_name": "qtraitsimulation_old.qtraits_simulation", "line_number": 30, "usage_type": "call"}, {"api_name": "plinkGWAS.plink_gwas", "line_number": 34, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 38, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 40, "usage_type": "call"}, {"api_name": "os.path", "line_number": 40, "usage_type": "attribute"}, {"api_name": "os.getcwd", "line_number": 40, "usage_type": "call"}, {"api_name": "ppt.pplust", "line_number": 41, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 44, "usage_type": "call"}, {"api_name": "os.path", "line_number": 44, "usage_type": "attribute"}, {"api_name": "os.getcwd", "line_number": 44, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 45, "usage_type": "call"}, {"api_name": "os.path", "line_number": 45, "usage_type": "attribute"}, {"api_name": "os.chdir", "line_number": 46, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 47, "usage_type": "call"}, {"api_name": "os.path", "line_number": 47, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 48, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 49, "usage_type": "call"}, {"api_name": "qtraitsimulation_old.qtraits_simulation", "line_number": 51, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 57, "usage_type": "call"}, {"api_name": "os.path", "line_number": 57, "usage_type": "attribute"}, {"api_name": "ppt.pplust", "line_number": 58, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 61, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 62, "usage_type": "call"}, {"api_name": "os.path", "line_number": 62, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 63, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 64, "usage_type": "call"}, {"api_name": "ranumo.ranumo", "line_number": 65, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 70, "usage_type": "call"}, {"api_name": "prankcster.prankcster", "line_number": 71, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 79, "usage_type": "call"}, {"api_name": "os.path.abspath", "line_number": 116, "usage_type": "call"}, {"api_name": "os.path", "line_number": 116, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 117, "usage_type": "call"}, {"api_name": "os.path", "line_number": 117, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 118, "usage_type": "call"}, {"api_name": "os.path", "line_number": 118, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 119, "usage_type": "call"}, {"api_name": "os.path", "line_number": 119, "usage_type": "attribute"}]} +{"seq_id": "36289260275", "text": "import numpy as np\nimport tensorflow as tf\n# import tf.keras.layers.Conv2D\nfrom tensorflow import keras\nimport matplotlib.pyplot as plt\n\nprint(tf.__version__)\nprint(keras.__version__)\n\ntf.enable_eager_execution()\n\nimage = tf.constant([[[[1], [2], [3]],\n [[4], [5], [6]],\n [[7], [8], [9]]]], dtype=np.float32) # 4차원 layer의 image\n\nweight = np.array([[[[1.]], [[1.]]],\n [[[1.]], [[1.]]]])\n\n\n# print(image.shape)\n# # batch, height, width, channel\n# plt.imshow(image.numpy().reshape(3,3), cmap='Greys')\n# plt.show()\nprint(weight.shape) # 2,2,2,1\n\nweight_init = tf.constant_initializer(weight)\n\nconv2d = keras.layers.Conv2D(filters=1, kernel_size=2, padding='VALID', kernel_initializer=weight_init)(image)\nprint('conv2d.shape : ',conv2d.shape)\nprint(conv2d.numpy().reshape(2,2))\nplt.imshow(conv2d.numpy().reshape(2,2), cmap='gray')\nplt.show()\n\n# padding을 VALID가 아닌 SAME으로 준다면 출력 또한 3*3으로 나오게 된다.\n# FILTER를 여러개 주고 싶다면 WEIGHT의 모양이 2,2,1,3 형태로 나오도록 하고, Conv2D의 filter도 3으로 지정한다.\nmulti_weight = np.array([[[[1., 10., -1.]], [[1., 10., -1]]],\n [[[1., 10., -1]], [[1., 10., -1.]]]])\n\nmulti_weight_init = tf.constant_initializer(multi_weight)\n\nmulti_conv2d = keras.layers.Conv2D(filters=3, kernel_size=2, padding='SAME', kernel_initializer=multi_weight_init)(image)\n\nfeature_maps = np.swapaxes(multi_conv2d, 0, 3) # feature도 3개\n\nfor i, feature_map in enumerate(feature_maps):\n print(feature_map.reshape(3,3))\n plt.subplot(1, 3, i+1), plt.imshow(feature_map.reshape(3,3), cmap='gray')\nplt.show()", "repo_name": "eprj453/Tensorflow", "sub_path": "CNN/01_layer/01_2D_LAYER.py", "file_name": "01_2D_LAYER.py", "file_ext": "py", "file_size_in_byte": 1675, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "tensorflow.__version__", "line_number": 7, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.__version__", "line_number": 8, "usage_type": "attribute"}, {"api_name": "tensorflow.keras", "line_number": 8, "usage_type": "name"}, {"api_name": "tensorflow.enable_eager_execution", "line_number": 10, "usage_type": "call"}, {"api_name": "tensorflow.constant", "line_number": 12, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 14, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 16, "usage_type": "call"}, {"api_name": "tensorflow.constant_initializer", "line_number": 26, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Conv2D", "line_number": 28, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 28, "usage_type": "attribute"}, {"api_name": "tensorflow.keras", "line_number": 28, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 31, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 31, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 32, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 32, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 36, "usage_type": "call"}, {"api_name": "tensorflow.constant_initializer", "line_number": 39, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Conv2D", "line_number": 41, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 41, "usage_type": "attribute"}, {"api_name": "tensorflow.keras", "line_number": 41, "usage_type": "name"}, {"api_name": "numpy.swapaxes", "line_number": 43, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 47, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 47, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 47, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 48, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 48, "usage_type": "name"}]} +{"seq_id": "29411702396", "text": "import torch\nimport pprint, random\nimport unittest\nfrom visdom import Visdom\nfrom pytorchart import FlexLogger, TraceLogger\nimport numpy as np\n\n\ndes = {'win': None,\n 'opts': {'markersize': 10,\n 'colormap': 'Viridis',\n 'mode': 'lines',\n 'markers': False,\n 'fillarea': False,\n 'markersymbol': 'dot'},\n 'data': [{'type': 'scatter',\n 'y': [4, 5, 6],\n 'mode': 'lines',\n 'marker': {'size': 10,\n 'symbol': 'dot',\n 'line': {'width': 0.5, 'color': '#000000'}},\n 'x': [1, 2, 3], 'name': '1'}],\n 'eid': None,\n 'layout': {'showlegend': False,\n 'margin': {'r': 60, 't': 60, 'l': 60, 'b': 60}}}\n\n\ndes2 = {'opts': {'markersize': 10,\n 'colormap': 'Viridis',\n 'markersymbol': 'dot',\n 'markers': False,\n 'fillarea': False,\n 'mode': 'lines'},\n 'data': [{'name': '1',\n 'type': 'scatter',\n 'marker': {'size': 10,\n 'symbol': 'dot',\n 'color': 'red',\n 'line': {'width': 0.5, 'color': '#000000'}},\n 'mode': 'markers+lines',\n 'line': {'color': 'red',\n 'dash':'dash'},\n },\n {'name': '2',\n 'type': 'scatter',\n 'marker': {'size': 10,\n 'symbol': 'dot',\n 'line': {'width': 0.5, 'color': '#000000'}},\n 'mode': 'lines'}],\n 'layout': {'margin': {'t': 60, 'r': 60, 'b': 60, 'l': 60}, 'showlegend': False},\n 'win': None,\n 'eid': None}\n\nupd = {'layout': {'margin': {'l': 60, 't': 60, 'r': 60, 'b': 60}, 'showlegend': False},\n 'append': True,\n 'name': None,\n 'data': [{'y': [1.5], 'type': 'scatter', 'x': [2.0], 'name': '1',\n 'marker': {'symbol': 'dot', 'line': {'width': 0.5, 'color': '#000000'},\n 'size': 10}, 'mode': 'lines'},\n {'y': [1.6],\n 'type': 'scatter', 'x': [2.0], 'name': '2',\n 'marker': {'symbol': 'dot',\n 'line': {'width': 0.5, 'color': '#000000'},\n 'size': 10},\n 'mode': 'lines'}],\n 'win': 'window_363b9f162e3d1e',\n 'eid': None,\n 'opts': {'fillarea': False,\n 'colormap': 'Viridis',\n 'markersymbol': 'dot',\n 'markers': False,\n 'markersize': 10,\n 'mode': 'lines'}}\n\n\nclass TestSample(unittest.TestCase):\n def test_traces(self):\n titles = ['1', '2']\n data = {o['name']: o for o in des2['data']}\n opts = {}\n opts['data'] = data\n tlg = TraceLogger(win='test', title=titles, opts=opts)\n pprint.pprint(tlg._lines)\n tlg.log([3, 3], [5, 6])\n tlg.log([4, 4], [4, 5])\n\n # print(str(tlg))\n # pprint.pprint(res)\n\n def test_opts(self):\n titles = ['1', '2']\n opts = {o['name']: o for o in des2['data']}\n\n lines = TraceLogger.init_lines(titles, opts)\n pprint.pprint(lines)\n\n\n def test_baseline(self):\n v = Visdom()\n w = v.line(X=np.array([[1, 1]]), Y=np.array([[1, 2]]))\n v.line(X=np.array([[2, 2]]), Y=np.array([[1.5, 1.6]]), win=w, update='append')\n", "repo_name": "psavine42/pytorchart", "sub_path": "test/test_sample.py", "file_name": "test_sample.py", "file_ext": "py", "file_size_in_byte": 3574, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "unittest.TestCase", "line_number": 76, "usage_type": "attribute"}, {"api_name": "pytorchart.TraceLogger", "line_number": 82, "usage_type": "call"}, {"api_name": "pprint.pprint", "line_number": 83, "usage_type": "call"}, {"api_name": "pytorchart.TraceLogger.init_lines", "line_number": 94, "usage_type": "call"}, {"api_name": "pytorchart.TraceLogger", "line_number": 94, "usage_type": "name"}, {"api_name": "pprint.pprint", "line_number": 95, "usage_type": "call"}, {"api_name": "visdom.Visdom", "line_number": 99, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 100, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 101, "usage_type": "call"}]} +{"seq_id": "24380420389", "text": "#!/usr/bin/python\nfrom modem.lib.geventdaemon import GeventDaemon\nimport sys\nimport logging\nimport os\nlogfile = 'smsd.log'\npidfile = 'smsd.pid'\nlogging.basicConfig(filename=logfile,level=logging.INFO)\n\nclass ModemDaemon(GeventDaemon):\n\tdef run(self, **kwargs):\n\t\timport sys\n\t\tfrom modem import Gateway\n\n\t\ttry:\n\t\t\tgate = Gateway(**kwargs)\n\t\t\tgate.run()\n\n\t\t\t\n\t\texcept SystemExit:\n\t\t\tlogging.error(\"%s: Gateway SIGTERM, exiting\" % time.strftime(\"%d%b%Y,%H:%M\"))\n\t\t\tsys.exit(0)\n\n\nif __name__ == \"__main__\":\n\timport time\n\tlogging.info(\"%s: Starting Gateway\" % (time.strftime(\"%d%b%Y,%H:%M\")) )\n\t\n\tmydaemon = ModemDaemon(pidfile)\n\ttry:\n\t\tconnect = os.environ[\"CONNECT\"]\n\t\tmydaemon.start(server=connect)\n\texcept KeyError:\n\t\tconnect = \"184.164.136.144\"\n\t\tmydaemon.start(server=connect)\n\t\n", "repo_name": "wends155/modem", "sub_path": "smsd.py", "file_name": "smsd.py", "file_ext": "py", "file_size_in_byte": 780, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "37", "api": [{"api_name": "logging.basicConfig", "line_number": 8, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 8, "usage_type": "attribute"}, {"api_name": "modem.lib.geventdaemon.GeventDaemon", "line_number": 10, "usage_type": "name"}, {"api_name": "modem.Gateway", "line_number": 16, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 21, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 22, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 27, "usage_type": "call"}, {"api_name": "time.strftime", "line_number": 27, "usage_type": "call"}, {"api_name": "{'sys': 'sys', 'Gateway': 'modem.Gateway'}", "line_number": 29, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 31, "usage_type": "attribute"}]} +{"seq_id": "70617421227", "text": "from flask import Flask\n\nfrom controller.FileSystem import *\nfrom controller.index import *\n\n\nclass Config(object):\n JOBS = []\n\n\napp = Flask(__name__, template_folder='templates', static_url_path='/', static_folder='static')\napp.config.from_object(Config())\n\napp.register_blueprint(index)\n\napp.register_blueprint(file)\n\napp.run(threaded=True, debug=True)\n", "repo_name": "FAWC438/Operating-System-Simulator", "sub_path": "app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 358, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 6, "dataset": "github-code", "pt": "37", "api": [{"api_name": "flask.Flask", "line_number": 11, "usage_type": "call"}]} +{"seq_id": "21244071374", "text": "import collections\nimport functools\n\nfrom test_framework import generic_test\nfrom test_framework.binary_tree_utils import must_find_node, strip_parent_link\nfrom test_framework.test_failure import TestFailure\nfrom test_framework.test_utils import enable_executor_hook\n\n\"\"\"9.3 Compute the lowest common ancestor in a binary tree\n\nDesign an algorithm for computing the LCA of two nodes in a binary tree in which\nnodes do not have a parent field\n\nBasic Algorithm: Traverse thru the tree and search for node0 and node1. Return\nto the caller the result of the search and handle the cases\n1) When you traverse, the first node you find, if it's equal to n1 or n2. You\ncan always return that node consider the case:\nIf we are looking for 3,2, the first encounter with node 3, we can return early\nbecause w\n Tree\n 1\n 3 5\n2\n2) You've found both nodes, then current_node is the root\n3) You've found 1 of the nodes\n4) You've found none of the nodes\n[ ATTEMPTED ] 5/31\n[ ATTEMPTED ] 6/14\n\nWatch this video if you can't solve it and reason about it properly\nhttps://www.youtube.com/watch?v=py3R23aAPCA\n\"\"\"\n\ndef lca(tree, node0, node1):\n def search(cur_node, node0, node1):\n if cur_node is None:\n return None\n # Found the node we've been looking for, return myself\n if cur_node == node0 or cur_node == node1:\n return cur_node\n\n # Perform search, if both searches come back positive,\n # then we are sitting at the LCA\n left = search(cur_node.left, node0, node1)\n right = search(cur_node.right, node0, node1)\n if not right:\n return left\n if not left:\n return right\n return cur_node # Both children nodes have been found, this is the LCA!\n\n return search(tree, node0, node1)\n\ndef lca(tree, node0, node1):\n\n Status = collections.namedtuple('Status', ('num_target_nodes', 'ancestor'))\n\n # Returns an object consisting of an int and a node. The int field is 0,\n # 1, or 2 depending on how many of {node0, node1} are present in tree. If\n # both are present in tree, when ancestor is assigned to a non-null value,\n # it is the LCA.\n def lca_helper(tree, node0, node1):\n if not tree:\n return Status(0, None)\n\n left_result = lca_helper(tree.left, node0, node1)\n if left_result.num_target_nodes == 2:\n # Found both nodes in the left subtree.\n return left_result\n right_result = lca_helper(tree.right, node0, node1)\n if right_result.num_target_nodes == 2:\n # Found both nodes in the right subtree.\n return right_result\n num_target_nodes = (\n left_result.num_target_nodes + right_result.num_target_nodes +\n (node0, node1).count(tree))\n return Status(num_target_nodes, tree\n if num_target_nodes == 2 else None)\n\n return lca_helper(tree, node0, node1).ancestor\n\n\n@enable_executor_hook\ndef lca_wrapper(executor, tree, key1, key2):\n strip_parent_link(tree)\n result = executor.run(\n functools.partial(lca, tree, must_find_node(tree, key1),\n must_find_node(tree, key2)))\n\n if result is None:\n raise TestFailure(\"Result can't be None\")\n return result.data\n\n\nif __name__ == '__main__':\n exit(\n generic_test.generic_test_main(\"lowest_common_ancestor.py\",\n 'lowest_common_ancestor.tsv',\n lca_wrapper))\n", "repo_name": "aarboleda1/Elements-Of-Programming-Interviews", "sub_path": "epi_judge_python_solutions/lowest_common_ancestor.py", "file_name": "lowest_common_ancestor.py", "file_ext": "py", "file_size_in_byte": 3508, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "37", "api": [{"api_name": "collections.namedtuple", "line_number": 56, "usage_type": "call"}, {"api_name": "test_framework.binary_tree_utils.strip_parent_link", "line_number": 85, "usage_type": "call"}, {"api_name": "functools.partial", "line_number": 87, "usage_type": "call"}, {"api_name": "test_framework.binary_tree_utils.must_find_node", "line_number": 87, "usage_type": "call"}, {"api_name": "test_framework.binary_tree_utils.must_find_node", "line_number": 88, "usage_type": "call"}, {"api_name": "test_framework.test_failure.TestFailure", "line_number": 91, "usage_type": "call"}, {"api_name": "test_framework.test_utils.enable_executor_hook", "line_number": 83, "usage_type": "name"}, {"api_name": "test_framework.generic_test.generic_test_main", "line_number": 97, "usage_type": "call"}, {"api_name": "test_framework.generic_test", "line_number": 97, "usage_type": "name"}]} +{"seq_id": "74146140588", "text": "import json\nfrom datetime import date, timedelta\nimport urllib.parse\n\nimport requests\nfrom waste_collection_schedule import Collection # type: ignore[attr-defined]\n\nTITLE = \"Logan City Council\"\nDESCRIPTION = \"Source for Logan City Council rubbish collection.\"\nURL = \"https://www.logan.qld.gov.au\"\nTEST_CASES = {\n \"LCC ADMINISTRATION CENTRE\": {\n \"property_location\": \"LCC ADMINISTRATION CENTRE, 150 Wembley Road, LOGAN CENTRAL QLD 4114\",\n },\n \"The Family Place\": {\n \"property_location\": \"35 North Road, WOODRIDGE QLD 4114\",\n },\n \"Lee Naki's Takeaway\": {\n \"property_location\": \"2 Ashton Street, KINGSTON QLD 4114\",\n },\n}\n\nHEADERS = {\"user-agent\": \"Mozilla/5.0\"}\n\nAPI_URL = \"https://services5.arcgis.com/ZUCWDRj8F77Xo351/arcgis/rest/services/Logan_City_Bin_Collection/FeatureServer/0/query\"\n\nclass Source:\n def __init__(self, property_location):\n self.property_location = urllib.parse.quote_plus(property_location)\n\n def fetch(self):\n\n # Retrieve collection day and whether there is recycling or green waste bin\n r = requests.get(f\"{API_URL}?where=%20(Formatted_Property_Address%20%3D%20'{self.property_location}')%20&outFields=Collection_Day,Recycling_Week,Green_Waste_Week&outSR=4326&f=json\",headers=HEADERS)\n data = json.loads(r.text)\n\n if data[\"features\"] == []:\n return []\n\n collection_day = data[\"features\"][0][\"attributes\"][\"Collection_Day\"]\n recycling_week = data[\"features\"][0][\"attributes\"][\"Recycling_Week\"]\n green_waste_week = data[\"features\"][0][\"attributes\"][\"Green_Waste_Week\"]\n\n today = date.today()\n entries = []\n\n if collection_day == 'MON':\n weekday = 0\n elif collection_day == 'TUE':\n weekday = 1\n elif collection_day == 'WED':\n weekday = 2\n elif collection_day == 'THU':\n weekday = 3\n elif collection_day == 'FRI':\n weekday = 4\n elif collection_day == 'SAT':\n weekday = 5\n elif collection_day == 'SUN':\n weekday = 6\n else:\n return []\n \n next_collection_date = today + timedelta((weekday - today.weekday() + 7 )% 7)\n \n # Add next 52 collection days\n for x in range(52):\n collection_date = next_collection_date+timedelta(weeks=x)\n week = collection_date.isocalendar().week % 2\n\n entries.append(\n Collection(\n date=collection_date, t=\"Rubbish\", icon=\"mdi:trash-can\"\n )\n )\n\n # Check if Recycling Bin Collected\n if recycling_week != '':\n # Check if Recycling Week\n if (recycling_week == 'Week 1' and week == 1) or (recycling_week == 'Week 2' and week == 0):\n entries.append(\n Collection(\n date=collection_date, t=\"Recycling\", icon=\"mdi:recycle\"\n )\n )\n\n # Check if Green Waste Bin Collected\n if green_waste_week != None:\n # Check if Green Waste Week\n if (green_waste_week == 'Week 1' and week == 1) or (green_waste_week == 'Week 2' and week == 0):\n entries.append(\n Collection(\n date=collection_date, t=\"Green Waste\", icon=\"mdi:leaf\"\n )\n )\n\n return entries", "repo_name": "mampfes/hacs_waste_collection_schedule", "sub_path": "custom_components/waste_collection_schedule/waste_collection_schedule/source/logan_qld_gov_au.py", "file_name": "logan_qld_gov_au.py", "file_ext": "py", "file_size_in_byte": 3500, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 559, "dataset": "github-code", "pt": "37", "api": [{"api_name": "urllib.parse.parse.quote_plus", "line_number": 29, "usage_type": "call"}, {"api_name": "urllib.parse.parse", "line_number": 29, "usage_type": "attribute"}, {"api_name": "urllib.parse", "line_number": 29, "usage_type": "name"}, {"api_name": "requests.get", "line_number": 34, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 35, "usage_type": "call"}, {"api_name": "datetime.date.today", "line_number": 44, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 44, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 64, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 68, "usage_type": "call"}, {"api_name": "waste_collection_schedule.Collection", "line_number": 72, "usage_type": "call"}, {"api_name": "waste_collection_schedule.Collection", "line_number": 82, "usage_type": "call"}, {"api_name": "waste_collection_schedule.Collection", "line_number": 92, "usage_type": "call"}]} +{"seq_id": "15796052101", "text": "from django.shortcuts import render, redirect\nfrom blog.models import Post\nfrom blog.forms import PostForm\n\n\ndef posts(request):\n post_list = Post.objects.all()\n\n return render(request, 'posts.html', {\n 'posts': post_list\n })\n\n\ndef create_post(request):\n form = PostForm()\n\n if request.method == 'POST':\n form = PostForm(request.POST)\n if form.is_valid():\n post = form.save(commit=False)\n post.author = request.user\n post.save()\n return redirect('home')\n\n return render(request, 'create_update_form.html', {\n 'form': form,\n 'title': 'Crear nueva Noticia',\n 'submit_value': 'Publicar Noticia',\n 'button_style': 'is-primary'\n })\n\n\ndef update_post(request, post_id):\n post = Post.objects.get(id=post_id)\n form = PostForm(instance=post)\n\n if request.method == 'POST':\n form = PostForm(request.POST, instance=post)\n if form.is_valid():\n form.save()\n return redirect('home')\n\n return render(request, 'create_update_form.html', {\n 'form': form,\n 'title': f'Noticia: {post.title}',\n 'submit_value': 'Editar Noticia',\n 'button_style': 'is-warning'\n })\n\n\ndef delete_post(request, post_id):\n post = Post.objects.get(id=post_id)\n\n post.delete()\n return redirect('home')\n", "repo_name": "maxwellnewage/udemy-django-hero-game", "sub_path": "blog/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 1360, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "37", "api": [{"api_name": "blog.models.Post.objects.all", "line_number": 7, "usage_type": "call"}, {"api_name": "blog.models.Post.objects", "line_number": 7, "usage_type": "attribute"}, {"api_name": "blog.models.Post", "line_number": 7, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 9, "usage_type": "call"}, {"api_name": "blog.forms.PostForm", "line_number": 15, "usage_type": "call"}, {"api_name": "blog.forms.PostForm", "line_number": 18, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 23, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 25, "usage_type": "call"}, {"api_name": "blog.models.Post.objects.get", "line_number": 34, "usage_type": "call"}, {"api_name": "blog.models.Post.objects", "line_number": 34, "usage_type": "attribute"}, {"api_name": "blog.models.Post", "line_number": 34, "usage_type": "name"}, {"api_name": "blog.forms.PostForm", "line_number": 35, "usage_type": "call"}, {"api_name": "blog.forms.PostForm", "line_number": 38, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 41, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 43, "usage_type": "call"}, {"api_name": "blog.models.Post.objects.get", "line_number": 52, "usage_type": "call"}, {"api_name": "blog.models.Post.objects", "line_number": 52, "usage_type": "attribute"}, {"api_name": "blog.models.Post", "line_number": 52, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 55, "usage_type": "call"}]} +{"seq_id": "34184822162", "text": "from flask import Flask\nimport atexit\nfrom apscheduler.schedulers.background import BackgroundScheduler\n\nfrom send_data import tweet_data\n\napp = Flask(__name__)\n\n@app.route('/')\ndef job():\n tweet_data()\n\nscheduler = BackgroundScheduler()\n#send tweets every 24hrs\nscheduler.add_job(func=job, trigger=\"interval\", days=1)\nscheduler.start()\n\natexit.register(lambda: scheduler.shutdown())\n\n\nif __name__ == \"__main__\":\n app.run(port=5000, debug=True)\n", "repo_name": "vantage-ola/dailyNFTs-Bot", "sub_path": "tweet/web.py", "file_name": "web.py", "file_ext": "py", "file_size_in_byte": 451, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "37", "api": [{"api_name": "flask.Flask", "line_number": 7, "usage_type": "call"}, {"api_name": "send_data.tweet_data", "line_number": 11, "usage_type": "call"}, {"api_name": "apscheduler.schedulers.background.BackgroundScheduler", "line_number": 13, "usage_type": "call"}, {"api_name": "atexit.register", "line_number": 18, "usage_type": "call"}]} +{"seq_id": "71603647787", "text": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue May 26 16:59:39 2020\n\n@author: lakshmimenont\n\"\"\"\nimport tweepy\nimport json\nimport datetime\nimport pandas as pd\nimport numpy as np\n\nfrom sklearn import linear_model\nimport statsmodels.formula.api as smf\nimport statsmodels.api as sm\n\nimport nltk\nnltk.download('vader_lexicon')\n\ndef authenticate():\n consumer_key = 'WeFnehzgqqzucdamDe9yf3Kdq'\n consumer_secret = '1OawBkUxDN7oHfIdBOi77iU1JJ3iQcH4f86PyiFb9pMMZJTSTH'\n \n access_token = '43929961-GAXeegcAtzVjtsNayVA9f84NkzimmTQR65Pw1jNcO'\n access_token_secret = 'Fue1Nmw28ugvLhwpm39xSQIeH4EXmmNDemHTJRMXuwVIt'\n \n auth = tweepy.OAuthHandler(consumer_key, consumer_secret)\n auth.set_access_token(access_token, access_token_secret)\n \n api = tweepy.API(auth,wait_on_rate_limit=True)\n try:\n api.verify_credentials()\n print(\"Authentication OK\")\n except:\n print(\"Error during authentication\")\n \n return api\n\n\ndef ecommerceHandleList():\n \n ecommerceAmazonHandle = \"@amazon\"\n ecommerceWalmartHandle = \"@Walmart\"\n ecommerceAlibabaGroupHandle = \"@AlibabaGroup\" #scrapably fully \n ecommerceJDCorporateHandle = \"@JD_Corporate\" #scrapably fully\n ecommerceEBayHandle = \"@eBay\" #scrapably fully\n ecommerceFlipkartHandle = \"@Flipkart\" #scrapably fully\n ecommerceRakutenHandle = \"@Rakuten\" #scrapably fully\n ecommerceTargetHandle = \"@Target\" #scrapable 2-3 days\n \n \n ecommerceHandleList = []\n #ecommerceHandleList.append(ecommerceAmazonHandle)\n #ecommerceHandleList.append(ecommerceWalmartHandle)\n ecommerceHandleList.append(ecommerceAlibabaGroupHandle)\n ecommerceHandleList.append(ecommerceJDCorporateHandle)\n ecommerceHandleList.append(ecommerceEBayHandle)\n ecommerceHandleList.append(ecommerceFlipkartHandle)\n ecommerceHandleList.append(ecommerceRakutenHandle)\n #ecommerceHandleList.append(ecommerceTargetHandle)\n \n return ecommerceHandleList\n\ndef getEcommerceTweetsInDataFrame(ecommerceHandleList,columnNames):\n #columnNames = ['TweetText','TweetLength','HasHashtag', 'HasURL','UserFollowersCount','Retweets','Likes','CreatedAt']\n \n dfAllEcommerceTweetsRead = pd.DataFrame(columns=columnNames) \n \n for ecommerceHandle in ecommerceHandleList:\n ecommerceName = ecommerceHandle.replace(\"@\",\" \").strip()\n #dfAirlineTweets = readTweets(tweetsInJsonPath)\n dfTweetsRead = readTweets(ecommerceName,columnNames)\n dfAllEcommerceTweetsRead = dfAllEcommerceTweetsRead.append(dfTweetsRead)\n #print(dfTweetsRead)\n print(dfAllEcommerceTweetsRead)\n return dfAllEcommerceTweetsRead\n\ndef nltk_sentiment(sentence):\n \n from nltk.sentiment.vader import SentimentIntensityAnalyzer\n \n nltk_sentiment = SentimentIntensityAnalyzer()\n score = nltk_sentiment.polarity_scores(sentence)\n return score\n\ndef getCovIdStatus(createdAt):\n #Code for year\n if(createdAt.find('2020')!=-1):\n covId = 1\n \n else:\n covId = 0\n \n return covId\n\ndef readTweets(ecommerceName,columnNames): \n tweetsInJsonPath = \"/Users/lakshmimenont/Desktop/Working Python/TwitterCompetitiveStrategyProject/data/EcommerceMay26/tweetsInJsonFormat\"+ ecommerceName +\".json\"\n with open(tweetsInJsonPath,\"r\") as readFile:\n dataRead = readFile.read()\n new_dataRead = dataRead.replace('}{', '},{')\n tweetsReadList = json.loads(f'[{new_dataRead}]')\n \n #print(type(tweetsReadList))\n \n dfTweetsRead = pd.DataFrame(columns=columnNames) \n for tweetRead in tweetsReadList:\n #print(type(tweetRead))\n #print(tweetRead.get('in_reply_to_status_id'))\n #print(tweetRead.get('full_text'))\n #print(tweetRead.get('entities'))\n text = tweetRead.get('full_text')\n tweetWordCount = len(text)\n textSentimentScore = nltk_sentiment(text)\n #print(type(textSentimentScore))\n #print(textSentimentScore)\n textPosSentiment = textSentimentScore.get('pos')\n textNegSentiment = textSentimentScore.get('neg')\n textNeuSentiment = textSentimentScore.get('neu')\n textCompoundSentiment= textSentimentScore.get('compound')\n \n entities = tweetRead.get('entities')\n if(entities.get('hashtags') == []):\n #print(\"Has no Hashtag\")\n hashtag = 0\n else:\n hashtag = 1\n \n if(entities.get('urls') == []):\n #print(\"Has no URLS\")\n url = 0\n else:\n url = 1\n \n if(entities.get('media') == [] or entities.get('media') == None):\n #print(\"Has no Media\")\n media = 0\n else:\n media = 1\n \n #print(type(tweetRead.get('entities')))\n #print(tweetRead.get('user'))\n user = tweetRead.get('user')\n #print(user.get('followers_count'))\n followersCount = int(user.get('followers_count'))\n \n #print(type(tweetRead.get('user')))\n retweet = int(tweetRead.get('retweet_count'))\n likes = int(tweetRead.get('favorite_count'))\n \n \n \n \n createdAt = tweetRead.get('created_at')\n #print(type(createdAt))\n covIdStatus = getCovIdStatus(createdAt)\n \n #postCovid\n dfTweetsRead.loc[ecommerceName+str(len(dfTweetsRead))] = [text, tweetWordCount, textPosSentiment, textNegSentiment, textNeuSentiment, textCompoundSentiment, hashtag, url, media, followersCount, retweet, likes, createdAt,covIdStatus]\n \n #print(\"\\n\")\n #if(tweetRead.get('in_reply_to_status_id')=='null' or tweetRead.get('in_reply_to_status_id')==None):\n # print(tweetRead)\n return dfTweetsRead\n\ndef plotInteractionPlot(modelE):\n \n betaPreCovidPosSentCoeffE = modelE.params[\"TweetPosSentiment\"]\n print(\"PreCovidPosSent Coefficient is \" + str(betaPreCovidPosSentCoeffE))\n betaPreCovidNegSentCoeffE = modelE.params[\"TweetNegSentiment\"]\n print(\"PreCovidNegSent Coefficient is \" + str(betaPreCovidNegSentCoeffE))\n betaPostCovidPosSentCoeffE = modelE.params[\"TweetPosSentiment\"] + modelE.params[\"interactCovidPosSent\"]\n print(\"PostCovidPosSent Coefficient is \" + str(betaPostCovidPosSentCoeffE))\n betaPostCovidNegSentCoeffE = modelE.params[\"TweetNegSentiment\"] + modelE.params[\"interactCovidNegSent\"]\n print(\"PostCovidNegSent Coefficient is \" + str(betaPostCovidNegSentCoeffE))\n \n \n time = pd.Series(np.repeat(['PreCovid', 'PostCovid', 'PreCovid', 'PostCovid'], 15), name='Time')\n sentiment = pd.Series(np.repeat(['Positive_Sentiment', 'Negative_Sentiment'], 30), name='Sentiment')\n #betaCoeff = np.log(np.random.randint(1, 30, size=60))\n betaCoeff = pd.Series([betaPreCovidPosSentCoeffE,betaPostCovidPosSentCoeffE,betaPreCovidNegSentCoeffE,betaPostCovidNegSentCoeffE], name='BetaCoeff')\n \n fig, ax = plt.subplots(figsize=(6, 6))\n #fig = interaction_plot(x=time, trace=sentiment, response=betaCoeff, colors=['blue', 'red'], markers=['D', '^'], ms=10, ax=ax)\n \n \n\ndef analyseRetweetsWithinEcommerce(dfEcommerceTweetList):\n #print(dfEcommerceTweetList['interactCovidNegSent']) \n modelRetweets = smf.ols(formula ='Retweets ~ TweetLength + TweetPosSentiment + TweetNegSentiment + HasHashtag + HasURL + UserFollowersCount',data= dfEcommerceTweetList).fit()\n modelRetweets_details = modelRetweets.summary()\n #print(modelRetweets_details)\n \n #formulaRetweets = 'Retweets ~ TweetLength + TweetPosSentiment + TweetNegSentiment + HasHashtag + HasURL + UserFollowersCount'\n #formulaRetweets = 'Retweets ~ TweetPosSentiment + TweetNegSentiment + HasHashtag + HasURL + UserFollowersCount'\n formulaRetweets = 'Retweets ~ TweetLength + TweetPosSentiment + TweetNegSentiment + HasHashtag + HasURL + UserFollowersCount + interactCovidNegSent'\n glm_Nbinomial = smf.glm(formula=formulaRetweets, data=dfEcommerceTweetList,family=sm.families.NegativeBinomial())\n res_Nbinom = glm_Nbinomial.fit()\n res_Nbinom_details = res_Nbinom.summary()\n #print(res_Nbinom_details)\n \n #formulaRetweetsLogFoll = 'Retweets ~ TweetLength + TweetPosSentiment + TweetNegSentiment + HasHashtag + HasURL + logUserFollowersCount'\n #formulaRetweetsLogFoll = 'Retweets ~ TweetPosSentiment + TweetNegSentiment + HasHashtag + HasURL + logUserFollowersCount'\n #formulaRetweetsLogFoll = 'Retweets ~ TweetLength + TweetPosSentiment + TweetNegSentiment + HasHashtag + HasURL + logUserFollowersCount + interactCovidNegSent'\n #formulaRetweetsLogFoll = 'Retweets ~ TweetLength + TweetPosSentiment + TweetNegSentiment + HasHashtag + HasURL + logUserFollowersCount + interactCovidNegSent + interactCovidPosSent'\n formulaRetweetsLogFoll = 'Retweets ~ TweetLength + TweetPosSentiment + TweetNegSentiment + HasHashtag + HasURL + HasMedia + logUserFollowersCount + interactCovidNegSent + interactCovidPosSent'\n glm_NbinomialLogFoll = smf.glm(formula=formulaRetweetsLogFoll, data=dfEcommerceTweetList,family=sm.families.NegativeBinomial())\n res_NbinomLogFoll = glm_NbinomialLogFoll.fit()\n res_NbinomLogFoll_details = res_NbinomLogFoll.summary()\n print(res_NbinomLogFoll_details)\n plotInteractionPlot(res_NbinomLogFoll)\n \n modelLikes = smf.ols(formula ='Likes ~ TweetLength + TweetPosSentiment + TweetNegSentiment + HasHashtag + HasURL + UserFollowersCount',data= dfEcommerceTweetList).fit()\n \n modelLikes_details = modelLikes.summary()\n #print(modelLikes_details)\n return(res_NbinomLogFoll)\n\ndef analyseLikesWithinEcommerce(dfEcommerceTweetList):\n #print(dfEcommerceTweetList['interactCovidNegSent']) \n \n \n #formulaLikes = 'Likes ~ TweetLength + TweetPosSentiment + TweetNegSentiment + HasHashtag + HasURL + UserFollowersCount'\n #formulaLikes = 'Likes ~ TweetPosSentiment + TweetNegSentiment + HasHashtag + HasURL + UserFollowersCount'\n formulaLikes = 'Likes ~ TweetLength + TweetPosSentiment + TweetNegSentiment + HasHashtag + HasURL + UserFollowersCount + interactCovidNegSent'\n glm_Nbinomial = smf.glm(formula=formulaLikes, data=dfEcommerceTweetList,family=sm.families.NegativeBinomial())\n res_Nbinom = glm_Nbinomial.fit()\n res_Nbinom_details = res_Nbinom.summary()\n #print(res_Nbinom_details)\n \n #formulaLikesLogFoll = 'Likes ~ TweetLength + TweetPosSentiment + TweetNegSentiment + HasHashtag + HasURL + logUserFollowersCount'\n #formulaLikesLogFoll = 'Likes ~ TweetPosSentiment + TweetNegSentiment + HasHashtag + HasURL + logUserFollowersCount'\n #formulaLikesLogFoll = 'Likes ~ TweetLength + TweetPosSentiment + TweetNegSentiment + HasHashtag + HasURL + logUserFollowersCount + interactCovidNegSent'\n #formulaLikesLogFoll = 'Likes ~ TweetLength + TweetPosSentiment + TweetNegSentiment + HasHashtag + HasURL + logUserFollowersCount + interactCovidNegSent + interactCovidPosSent'\n formulaLikesLogFoll = 'Likes ~ TweetLength + TweetPosSentiment + TweetNegSentiment + HasHashtag + HasURL + HasMedia + logUserFollowersCount + interactCovidNegSent + interactCovidPosSent'\n glm_NbinomialLogFoll = smf.glm(formula=formulaLikesLogFoll, data=dfEcommerceTweetList,family=sm.families.NegativeBinomial())\n res_NbinomLogFoll = glm_NbinomialLogFoll.fit()\n res_NbinomLogFoll_details = res_NbinomLogFoll.summary()\n print(res_NbinomLogFoll_details)\n \n \n modelLikes = smf.ols(formula ='Likes ~ TweetLength + TweetPosSentiment + TweetNegSentiment + HasHashtag + HasURL + UserFollowersCount',data= dfEcommerceTweetList).fit()\n \n modelLikes_details = modelLikes.summary()\n #print(modelLikes_details)\n return(res_NbinomLogFoll)\n\ndef makeDataframeModifications(dfEcommerceTweetList):\n dfEcommerceTweetList[['TweetLength','TweetPosSentiment','TweetNegSentiment','TweetNeuSentiment','TweetCompoundSentiment','HasHashtag', 'HasURL', 'HasMedia','UserFollowersCount','Retweets','Likes','CovidStatus']] = dfEcommerceTweetList[['TweetLength','TweetPosSentiment','TweetNegSentiment','TweetNeuSentiment','TweetCompoundSentiment','HasHashtag', 'HasURL','HasMedia','UserFollowersCount','Retweets','Likes','CovidStatus']].apply(pd.to_numeric)\n dfEcommerceTweetList['logUserFollowersCount'] = np.log(dfEcommerceTweetList['UserFollowersCount'])\n dfEcommerceTweetList['interactCovidNegSent'] = dfEcommerceTweetList['TweetNegSentiment'] * dfEcommerceTweetList['CovidStatus']\n dfEcommerceTweetList['interactCovidPosSent'] = dfEcommerceTweetList['TweetPosSentiment'] * dfEcommerceTweetList['CovidStatus']\n \n return dfEcommerceTweetList\n \ndef displayDescriptiveStats(dfToDisplay,columnNames):\n for column in columnNames:\n print(column)\n if(column!='HasHashtag' and column!='HasURL'and column!='HasMedia'):\n print(\"Count is \" + str(dfToDisplay[column].count()))\n print(\"Mean is \" + str(dfToDisplay[column].mean()))\n print(\"Standard Deviation is \" + str(dfToDisplay[column].std()))\n print(\"Min is \"+str(dfToDisplay[column].min()))\n print(\"Max is \"+str(dfToDisplay[column].max()))\n print(\"\\n\")\n else:\n print(\"Count is\"+ str(dfToDisplay[column].sum()))\n print(\"\\n\")\n\ndef displayCorrelationMatrix(dfEcommerceTweetList,columnsToDisplay):\n dfCorrMatrix = pd.DataFrame(dfEcommerceTweetList,columns = columnsToDisplay)\n print(dfCorrMatrix.dtypes)\n corrMatrix = dfCorrMatrix.corr()\n with pd.option_context('display.max_rows', 11, 'display.max_columns', None): \n display(corrMatrix)\n \ndef main():\n \n \n gotEcommerceHandleList = ecommerceHandleList()\n print(gotEcommerceHandleList)\n columnNames = ['TweetText','TweetLength','TweetPosSentiment','TweetNegSentiment','TweetNeuSentiment','TweetCompoundSentiment','HasHashtag', 'HasURL','HasMedia','UserFollowersCount','Retweets','Likes','CreatedAt','CovidStatus']\n \n dfEcommerceTweetList = getEcommerceTweetsInDataFrame(gotEcommerceHandleList,columnNames)\n dfEcommerceTweetList = makeDataframeModifications(dfEcommerceTweetList)\n print(dfEcommerceTweetList.dtypes)\n columnsToDisplay = ['TweetLength','TweetPosSentiment','TweetNegSentiment','HasHashtag', 'HasURL','HasMedia','logUserFollowersCount','interactCovidNegSent','interactCovidPosSent','Retweets','Likes']\n \n #displayDescriptiveStats(dfEcommerceTweetList,columnsToDisplay)\n #displayCorrelationMatrix(dfEcommerceTweetList,columnsToDisplay)\n \n analyseRetweetsWithinEcommerce(dfEcommerceTweetList) \n #analyseLikesWithinEcommerce(dfEcommerceTweetList) \n\n\nmain()", "repo_name": "laxmenon/DataScience", "sub_path": "EcommerceTweetAnalysis.py", "file_name": "EcommerceTweetAnalysis.py", "file_ext": "py", "file_size_in_byte": 14514, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "nltk.download", "line_number": 19, "usage_type": "call"}, {"api_name": "tweepy.OAuthHandler", "line_number": 28, "usage_type": "call"}, {"api_name": "tweepy.API", "line_number": 31, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 68, "usage_type": "call"}, {"api_name": "nltk.sentiment.vader.SentimentIntensityAnalyzer", "line_number": 83, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 102, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 106, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 178, "usage_type": "call"}, {"api_name": "numpy.repeat", "line_number": 178, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 179, "usage_type": "call"}, {"api_name": "numpy.repeat", "line_number": 179, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 181, "usage_type": "call"}, {"api_name": "statsmodels.formula.api.ols", "line_number": 190, "usage_type": "call"}, {"api_name": "statsmodels.formula.api", "line_number": 190, "usage_type": "name"}, {"api_name": "statsmodels.formula.api.glm", "line_number": 197, "usage_type": "call"}, {"api_name": "statsmodels.formula.api", "line_number": 197, "usage_type": "name"}, {"api_name": "statsmodels.api.families.NegativeBinomial", "line_number": 197, "usage_type": "call"}, {"api_name": "statsmodels.api.families", "line_number": 197, "usage_type": "attribute"}, {"api_name": "statsmodels.api", "line_number": 197, "usage_type": "name"}, {"api_name": "statsmodels.formula.api.glm", "line_number": 207, "usage_type": "call"}, {"api_name": "statsmodels.formula.api", "line_number": 207, "usage_type": "name"}, {"api_name": "statsmodels.api.families.NegativeBinomial", "line_number": 207, "usage_type": "call"}, {"api_name": "statsmodels.api.families", "line_number": 207, "usage_type": "attribute"}, {"api_name": "statsmodels.api", "line_number": 207, "usage_type": "name"}, {"api_name": "statsmodels.formula.api.ols", "line_number": 213, "usage_type": "call"}, {"api_name": "statsmodels.formula.api", "line_number": 213, "usage_type": "name"}, {"api_name": "statsmodels.formula.api.glm", "line_number": 226, "usage_type": "call"}, {"api_name": "statsmodels.formula.api", "line_number": 226, "usage_type": "name"}, {"api_name": "statsmodels.api.families.NegativeBinomial", "line_number": 226, "usage_type": "call"}, {"api_name": "statsmodels.api.families", "line_number": 226, "usage_type": "attribute"}, {"api_name": "statsmodels.api", "line_number": 226, "usage_type": "name"}, {"api_name": "statsmodels.formula.api.glm", "line_number": 236, "usage_type": "call"}, {"api_name": "statsmodels.formula.api", "line_number": 236, "usage_type": "name"}, {"api_name": "statsmodels.api.families.NegativeBinomial", "line_number": 236, "usage_type": "call"}, {"api_name": "statsmodels.api.families", "line_number": 236, "usage_type": "attribute"}, {"api_name": "statsmodels.api", "line_number": 236, "usage_type": "name"}, {"api_name": "statsmodels.formula.api.ols", "line_number": 242, "usage_type": "call"}, {"api_name": "statsmodels.formula.api", "line_number": 242, "usage_type": "name"}, {"api_name": "pandas.to_numeric", "line_number": 249, "usage_type": "attribute"}, {"api_name": "numpy.log", "line_number": 250, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 271, "usage_type": "call"}, {"api_name": "pandas.option_context", "line_number": 274, "usage_type": "call"}]} +{"seq_id": "10322407829", "text": "import cv2\n\n# Load the pre-trained model for facial detection\nface_cascade = cv2.CascadeClassifier(\"haarcascade_frontalface_default.xml\")\n\n# Create a video capture object\ncap = cv2.VideoCapture(0)\n\n# Loop through video frames\nwhile True:\n # Read a frame from the video capture object\n ret, frame = cap.read()\n\n # Convert the frame to grayscale\n gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\n\n # Detect faces in the frame\n faces = face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30))\n\n # Draw rectangles around the detected faces\n for (x, y, w, h) in faces:\n cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 0, 255), 2)\n\n # Display the frame\n cv2.imshow(\"Face Detection\", frame)\n\n # Exit the loop if the 'q\n ", "repo_name": "veryFreeman/simulation-bras-robot-2D", "sub_path": "test.py", "file_name": "test.py", "file_ext": "py", "file_size_in_byte": 778, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "cv2.CascadeClassifier", "line_number": 4, "usage_type": "call"}, {"api_name": "cv2.VideoCapture", "line_number": 7, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 15, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 15, "usage_type": "attribute"}, {"api_name": "cv2.rectangle", "line_number": 22, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 25, "usage_type": "call"}]} +{"seq_id": "20282708416", "text": "\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom tensorflow.keras import Sequential\nfrom sklearn.metrics import confusion_matrix, f1_score, precision_score, recall_score, balanced_accuracy_score, accuracy_score\nfrom Utilities import APV, APV2D, FAR\nfrom tensorflow.keras.layers import Dense\n\n\n\ndef compute_conf_train_average(labelsTrained, labels_train, conf_train):\n print(\"\\n ******************** TRAIN SET ********************\")\n tempIndexer = np.arange(conf_train.shape[0]) # [0 - 59999]\n print(\"TempIndexer\", tempIndexer)\n tempConfArray = conf_train[tempIndexer, labels_train] # confidence value for every single data point, the probability that each data point will be properly classified.\n confTrain = np.average(tempConfArray) # average them out so that it give me proper (normalized value), not bias towards bigger value.\n confTrainSTD = np.std(tempConfArray) # find the standard deviation\n\n print(f\"tempIndexer: {tempIndexer} \\\n \\ntempConfArray: {tempConfArray} \\nconfTrain: {confTrain} \\\n \\nconfTrainSTD: {confTrainSTD}\")\n\n correctlyClassifiedIndex_Train = labelsTrained == labels_train # this is for thos that are correctly classified\n correctConfArray = conf_train[tempIndexer[correctlyClassifiedIndex_Train],\n labels_train[correctlyClassifiedIndex_Train]] # confidence values for correctly classified datapoints.\n correctConfTrain = np.average(correctConfArray) # find average\n correctConfTrain_STD = np.std(correctConfArray) # find standard deviation\n\n print(f\"correctly classified Index: {correctlyClassifiedIndex_Train} \\\n \\ncorrect confidence Array: {correctConfArray} \\\n \\ncorrect confidence trained: {correctConfTrain} \\\n \\ncorrect confidence trained STD: {correctConfTrain_STD}\")\n\n incorrectlyClassifiedIndex_Train = labelsTrained != labels_train # for missclassified\n incorrectConfArray = conf_train[tempIndexer[incorrectlyClassifiedIndex_Train],\n labelsTrained[incorrectlyClassifiedIndex_Train]] # confidence values of missclassification - how confidence that it will be missclassified.\n incorrectConfTrain = np.average(incorrectConfArray)\n incorrectConfTrain_STD = np.std(incorrectConfArray)\n\n\n print(f\"incorrectly classified Index: {incorrectlyClassifiedIndex_Train} \\\n \\n incorrect confidence Array: {incorrectConfArray} \\\n \\n incorrect confidence trained: {incorrectConfTrain} \\\n \\n incorrect confidence trained STD: {incorrectConfTrain_STD}\")\n\n return (confTrain, confTrainSTD, correctlyClassifiedIndex_Train, incorrectlyClassifiedIndex_Train)\n\n\ndef compute_conf_test_average(labelsTest, labels_test, conf_test):\n print(\"\\n ******************** TEST SET ********************\")\n tempIndexer = np.arange(conf_test.shape[0])\n confArray = conf_test[tempIndexer, labels_test]\n confTest = np.average(confArray)\n confTest_STD = np.std(confArray)\n\n correctlyClassifiedIndex_Test = labelsTest == labels_test\n correctConfArray = conf_test[tempIndexer[correctlyClassifiedIndex_Test],\n labels_test[correctlyClassifiedIndex_Test]]\n correctConfTest = np.average(correctConfArray)\n correctConfTest_STD = np.std(correctConfArray)\n\n print(\"************* TRAIN ***************\")\n print(f\"correctly classified Index: {correctlyClassifiedIndex_Test} \\\n \\n correct confidence Array: {correctConfArray} \\\n \\n correct confidence trained: {correctConfTest} \\\n \\n correct confidence trained STD: {correctConfTest_STD}\")\n\n print(\"************* TEST ***************\")\n incorrectlyClassifiedIndex_Test = labelsTest != labels_test\n incorrectConfArray = conf_test[tempIndexer[incorrectlyClassifiedIndex_Test],\n labelsTest[incorrectlyClassifiedIndex_Test]]\n incorrectConfTest = np.average(incorrectConfArray)\n incorrectConfArray_STD = np.std(incorrectConfArray)\n\n print(f\"correctly classified Index: {correctlyClassifiedIndex_Test} \\\n \\n correct confidence Array: {correctConfArray} \\\n \\n correct confidence trained: {correctConfTest} \\\n \\n correct confidence trained STD: {correctConfTest_STD}\")\n\n return (confTest, confTest_STD, correctlyClassifiedIndex_Test, incorrectlyClassifiedIndex_Test)\n\n\ndef getBalancedAccuracy(numTargetedClasses):\n print(\"\\n************Accuracy***************\")\n balancedAccuracy = np.zeros(numTargetedClasses) - 1\n correctlyLabeledBalancedAccuracy = np.zeros(numTargetedClasses) - 1\n incorretlyLabeledBalancedAccuracy = np.zeros(numTargetedClasses) - 1\n\n return (balancedAccuracy, correctlyLabeledBalancedAccuracy, incorretlyLabeledBalancedAccuracy)\n\n\ndef getAccuracy(numTargetedClasses):\n accuracy = np.zeros(numTargetedClasses) - 1\n return (accuracy, accuracy, accuracy)\n\n\ndef getFAR(numTargetedClasses):\n far = np.zeros(numTargetedClasses) - 1\n return(far, far, far)\n\n\ndef getPrecision(numTargetedClasses):\n precision = np.zeros((numTargetedClasses, 2)) - 1\n return (precision, precision, precision)\n\n\ndef getRecall(numTargetedClasses):\n recall = np.zeros((numTargetedClasses, 2)) - 1\n return (recall, recall, recall)\n\n\ndef getF1Score(numTargetedClasses):\n f1Score = np.zeros((numTargetedClasses, 2)) - 1\n return (f1Score, f1Score, f1Score)\n\n\n'''\n@params: numTargetedClasses\n$$ placeholder for performance measures to store.\n'''\n\n\ndef plot_graph():\n \n print(\"histogram\")\n # t = classYesX[correctlyLabeledYesX]\n # t2 = classNoX[correctlyLabeledNoX]\n\n # t = np.average(t, axis=0)\n # t2 = np.average(t2, axis=0)\n\n # plt.style.use('seaborn-deep')\n # plt.plot(np.arange(numClasses), t, 'bx', label=\"Train samples\")\n # plt.plot(np.arange(numClasses), t2, 'go', label=\"Test samples\")\n\n # plt.legend()\n # plt.xlabel(\"Class Number\")\n # plt.ylabel(\"Average Confidence\")\n # plt.savefig(\"figures/conf_histogram/\" + dataset + '/correct-' + str(j) + '.png', bbox_inches='tight')\n # plt.close()\n\n # t = classYesX[incorrectlyLabeledYesX]\n # t2 = classNoX[incorrectlyLabeledNoX]\n # t = np.average(t, axis=0)\n # t2 = np.average(t2, axis=0)\n # plt.style.use('seaborn-deep')\n # plt.plot(np.arange(numClasses), t, 'bx', label=\"Train Samples\")\n # plt.plot(np.arange(numClasses), t2, 'go', label=\"Test Samples\")\n # plt.legend()\n # plt.xlabel('Class Number')\n # plt.ylabel('Average Confidence')\n # plt.savefig('figures/conf_histogram/' + dataset + '/misclassified-' + str(j) + '.png', bbox_inches='tight')\n # plt.close()\n\n # t = classYesX[correctlyLabeledYesX]\n # t2 = classNoX[correctlyLabeledNoX]\n\n # bins = np.arange(101) / 100\n # plt.style.use('seaborn-deep')\n # n, bins, patches = plt.hist([t[:, j], t2[:, j]], bins, alpha=1, label=[\"Train Samples\", \"Test Samples\"])\n\n # plt.xlabel('Model Confidence')\n # plt.ylabel('Probability (%)')\n # plt.legend(loc=\"upper left\")\n # plt.savefig('figures/conf_histogram/' + dataset + '/' + str(j) + '.png', bbox_inches='tight')\n # plt.close()\n\n\n\ndef to_store_p_measures(numClasses, numTargetedClasses):\n\n (balancedAccuracy, correctlyLabeledBalancedAccuracy,\n incorrectlyLabeledBalancedAccuracy) = getBalancedAccuracy(numTargetedClasses)\n (accuracy, correctlyLabeledAccuracy,\n incorrectlyLabeledAccuracy) = getAccuracy(numTargetedClasses)\n (far, correctlyLabeledFar, incorrectlyLabeledFar) = getFAR(numTargetedClasses)\n\n (precision, correctlyLabeledPrecision,\n incorrectlyLabeledPrecision) = getPrecision(numTargetedClasses)\n\n (recall, correctlyLabeledRecall,\n incorrectlyLabeledRecall) = getRecall(numTargetedClasses)\n\n (f1score, correctlyLabeledF1score,\n incorrectlyLabeledF1score) = getF1Score(numTargetedClasses)\n return (\n balancedAccuracy,\n correctlyLabeledBalancedAccuracy,\n incorrectlyLabeledBalancedAccuracy,\n accuracy,\n correctlyLabeledAccuracy,\n incorrectlyLabeledAccuracy,\n far, correctlyLabeledFar, incorrectlyLabeledFar,\n precision, correctlyLabeledPrecision, incorrectlyLabeledPrecision,\n recall, correctlyLabeledRecall, incorrectlyLabeledRecall,\n f1score, correctlyLabeledF1score, incorrectlyLabeledF1score\n )\n\n\ndef attack_classwise(j, dataset, correctlyClassifiedIndex_Train, incorrectlyClassifiedIndex_Train, correctlyClassifiedIndex_Test, incorrectlyClassifiedIndex_Test, numClasses, numTargetedClasses, conf_train, conf_test, labelsTrained, labels_train, labelsTest, labels_test, attacker_knowledge, SHOW_ATTACK, attack_classifier, save_conf_histogram=True):\n print(\"XXXXXXXXXXXXXXXXXXXXXXX\")\n\n\n classYesX = conf_train[tuple([labels_train == j])] # highest at where it matches [9.9997485e-01 7.6881284e-09 7.7287825e-07 2.1447873e-07 1.6986093e-07 1.9313011e-06 5.6364697e-06 3.9415945e-06 6.9429079e-06 5.5773412e-06]\n\n classNoX = conf_test[tuple([labels_test == j])]\n \n\n #check if there is enough sample\n\n if classYesX.shape[0] < 15 or classNoX.shape[0] < 15: \n print(\n f\"Class {str(j)} doesn't have enough sample for training for attack\")\n\n\n # find the exact classified value \n correctlyLabeledYesX = correctlyClassifiedIndex_Train[tuple(\n [labels_train == j])]\n\n \n correctlyLabeledNoX = correctlyClassifiedIndex_Test[tuple(\n [labels_test == j])]\n\n incorrectlyLabeledYesX = incorrectlyClassifiedIndex_Train[tuple(\n [labels_train == j])]\n incorrectlyLabeledNoX = incorrectlyClassifiedIndex_Test[tuple(\n [labels_test == j])]\n\n # plot_graph()\n\n # multiply with what have found out and already known.\n \n classYesSize = int(classYesX.shape[0] * attacker_knowledge)\n classYesXTrain = classYesX[:classYesSize]\n classYesYTrain = np.ones(classYesXTrain.shape[0])\n\n classYesXTest = classYesX[classYesSize:]\n classYesYTest = np.ones(classYesXTest.shape[0])\n correctlyLabeledYesX = correctlyLabeledYesX[classYesSize:]\n incorrectlyLabeledYesX = incorrectlyLabeledYesX[classYesSize:]\n\n classNoSize = int(classNoX.shape[0] * attacker_knowledge)\n classNoXTrain = classNoX[:classNoSize]\n classNoYTrain = np.zeros(classNoXTrain.shape[0])\n classNoXTest = classNoX[classNoSize:]\n classNoYTest = np.zeros(classNoXTest.shape[0])\n correctlyLabeledNoX = correctlyLabeledNoX[classNoSize:]\n incorrectlyLabeledNoX = incorrectlyLabeledNoX[classYesSize:]\n\n Y_size = classYesXTrain.shape[0]\n n_size = classNoXTrain.shape[0]\n print()\n print(f\"MI attack on class:: [{j}]\")\n\n X_train = np.concatenate((classYesXTrain, classNoXTrain), axis=0)\n y_train = np.concatenate((classYesYTrain, classNoYTrain), axis=0)\n X_test = np.concatenate((classYesXTest, classNoXTest), axis=0)\n y_test = np.concatenate((classYesYTest, classNoYTest), axis=0)\n\n correctlyLabeledIndices = np.concatenate(\n (correctlyLabeledYesX, correctlyLabeledNoX), axis=0)\n incorrectlyLabeledIndices = np.concatenate(\n (incorrectlyLabeledYesX, incorrectlyLabeledNoX), axis=0)\n\n if SHOW_ATTACK:\n if attack_classifier == \"NN\":\n ATTACK_MODEL = Sequential()\n ATTACK_MODEL.add(\n Dense(128, input_dim=numClasses, activation=\"relu\"))\n ATTACK_MODEL.add(Dense(64, activation=\"relu\"))\n ATTACK_MODEL.add(Dense(1, activation=\"sigmoid\"))\n\n ATTACK_MODEL.compile(\n loss='binary_crossentropy', optimizer=\"adam\", metrics=['acc'])\n ATTACK_MODEL.fit(X_train, y_train, validation_data=(\n X_test, y_test), epochs=30, batch_size=32, verbose=False, shuffle=True)\n\n y_pred = ATTACK_MODEL.predict(X_test)\n predictions = np.where(y_pred > 0.8, 1,0) \n (\n balancedAccuracy,\n correctlyLabeledBalancedAccuracy,\n incorrectlyLabeledBalancedAccuracy,\n accuracy,\n correctlyLabeledAccuracy,\n incorrectlyLabeledAccuracy,\n far, correctlyLabeledFar, incorrectlyLabeledFar,\n precision, correctlyLabeledPrecision, incorrectlyLabeledPrecision,\n recall, correctlyLabeledRecall, incorrectlyLabeledRecall,\n f1score, correctlyLabeledF1, incorrectlyLabeledF1\n ) = to_store_p_measures(numClasses, numTargetedClasses)\n\n\n\n balancedAccuracy[j] = balanced_accuracy_score(y_test, predictions )\n accuracy[j] = accuracy_score(y_test, predictions)\n far[j] = FAR(y_test, predictions)\n precision[j] = precision_score(y_test, predictions, average=\"weighted\", labels=np.unique(predictions))\n recall[j] = recall_score(y_test, predictions)\n f1score[j] = f1_score(y_test, predictions)\n\n mi, mi_STD = APV(balancedAccuracy)\n c_mi, c_mi_STD = APV(correctlyLabeledBalancedAccuracy)\n in_mi_attack, in_mi_attack_std = APV(\n incorrectlyLabeledBalancedAccuracy)\n\n mi_acc, mi_accSTD = APV(accuracy)\n c_mi_acc, c_mi_accSTD = APV(correctlyLabeledAccuracy)\n in_mi_acc, in_mi_accSTD = APV(incorrectlyLabeledAccuracy)\n\n mi_far, mi_farSTD = APV(far)\n c_mi_far, c_mi_farSTD = APV(correctlyLabeledFar)\n in_mi_far, in_mi_farSTD = APV(incorrectlyLabeledFar)\n\n mi_prec, mi_precSTD = APV2D(precision)\n c_mi_prec, c_mi_precSTD = APV2D(correctlyLabeledPrecision)\n in_mi_prec, in_mi_precSTD = APV2D(incorrectlyLabeledPrecision)\n\n mi_rcal, mi_rcalSTD = APV2D(recall)\n c_mi_rcal, c_mi_rcalSTD = APV2D(correctlyLabeledRecall)\n in_mi_rcal, in_mi_rcalSTD = APV2D(incorrectlyLabeledRecall)\n\n mi_f1, mi_f1STD = APV2D(f1score)\n c_mi_f1, c_mi_f1STD = APV2D(correctlyLabeledF1)\n in_mi_f1, in_mi_f1STD = APV2D(incorrectlyLabeledF1)\n\n return (mi,\n mi_STD,\n c_mi, c_mi_STD, in_mi_attack,\n in_mi_attack_std, mi_acc, mi_accSTD,\n c_mi_acc, c_mi_accSTD, in_mi_acc,\n in_mi_accSTD,\n mi_far, mi_farSTD,\n c_mi_far, c_mi_farSTD,\n in_mi_far, in_mi_farSTD,\n mi_prec, mi_precSTD,\n c_mi_prec, c_mi_precSTD,\n in_mi_prec, in_mi_precSTD,\n\n mi_rcal, mi_rcalSTD,\n c_mi_rcal, c_mi_rcalSTD,\n in_mi_rcal, in_mi_rcalSTD,\n mi_f1, mi_f1STD,\n c_mi_f1, c_mi_f1STD,\n in_mi_f1, in_mi_f1STD\n )\n", "repo_name": "Abhigyan-Mishra/AI-Assignment", "sub_path": "MI/attacker_models/conf_based_utils.py", "file_name": "conf_based_utils.py", "file_ext": "py", "file_size_in_byte": 14760, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "numpy.arange", "line_number": 13, "usage_type": "call"}, {"api_name": "numpy.average", "line_number": 16, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 17, "usage_type": "call"}, {"api_name": "numpy.average", "line_number": 26, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 27, "usage_type": "call"}, {"api_name": "numpy.average", "line_number": 37, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 38, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 51, "usage_type": "call"}, {"api_name": "numpy.average", "line_number": 53, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 54, "usage_type": "call"}, {"api_name": "numpy.average", "line_number": 59, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 60, "usage_type": "call"}, {"api_name": "numpy.average", "line_number": 72, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 73, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 85, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 86, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 87, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 93, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 98, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 103, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 108, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 113, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 235, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 238, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 244, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 246, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 255, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 256, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 257, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 258, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 260, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 262, "usage_type": "call"}, {"api_name": "tensorflow.keras.Sequential", "line_number": 267, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Dense", "line_number": 269, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Dense", "line_number": 270, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Dense", "line_number": 271, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 279, "usage_type": "call"}, {"api_name": "sklearn.metrics.balanced_accuracy_score", "line_number": 295, "usage_type": "call"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 296, "usage_type": "call"}, {"api_name": "Utilities.FAR", "line_number": 297, "usage_type": "call"}, {"api_name": "sklearn.metrics.precision_score", "line_number": 298, "usage_type": "call"}, {"api_name": "numpy.unique", "line_number": 298, "usage_type": "call"}, {"api_name": "sklearn.metrics.recall_score", "line_number": 299, "usage_type": "call"}, {"api_name": "sklearn.metrics.f1_score", "line_number": 300, "usage_type": "call"}, {"api_name": "Utilities.APV", "line_number": 302, "usage_type": "call"}, {"api_name": "Utilities.APV", "line_number": 303, "usage_type": "call"}, {"api_name": "Utilities.APV", "line_number": 304, "usage_type": "call"}, {"api_name": "Utilities.APV", "line_number": 307, "usage_type": "call"}, {"api_name": "Utilities.APV", "line_number": 308, "usage_type": "call"}, {"api_name": "Utilities.APV", "line_number": 309, "usage_type": "call"}, {"api_name": "Utilities.APV", "line_number": 311, "usage_type": "call"}, {"api_name": "Utilities.APV", "line_number": 312, "usage_type": "call"}, {"api_name": "Utilities.APV", "line_number": 313, "usage_type": "call"}, {"api_name": "Utilities.APV2D", "line_number": 315, "usage_type": "call"}, {"api_name": "Utilities.APV2D", "line_number": 316, "usage_type": "call"}, {"api_name": "Utilities.APV2D", "line_number": 317, "usage_type": "call"}, {"api_name": "Utilities.APV2D", "line_number": 319, "usage_type": "call"}, {"api_name": "Utilities.APV2D", "line_number": 320, "usage_type": "call"}, {"api_name": "Utilities.APV2D", "line_number": 321, "usage_type": "call"}, {"api_name": "Utilities.APV2D", "line_number": 323, "usage_type": "call"}, {"api_name": "Utilities.APV2D", "line_number": 324, "usage_type": "call"}, {"api_name": "Utilities.APV2D", "line_number": 325, "usage_type": "call"}]} +{"seq_id": "34622660016", "text": "\"\"\"\nFormatting for text using common NLP techniques.\n\nAvailable functions:\n- `remove_stopwords(text)`: Remove stopwords from the input text using NLTK's stopwords.\n- `remove_numbers(text)`: Remove numbers from the input text.\n- `remove_whitespace(text)`: Remove excess whitespace from the input text.\n- `normalize_whitespace(text)`: Normalize multiple whitespaces into a single whitespace in the input text.\n- `seperate_symbols(text)`: Separate symbols and words with a space to ease tokenization.\n- `remove_special_characters(text)`: Remove special characters from the input text.\n- `standardize_text(text)`: Standardize the formatting of the input text.\n- `tokenize_text(text)`: Tokenize the input text into individual words.\n- `stem_words(words)`: Stem the input words using Porter stemming algorithm.\n- `lemmatize_words(words)`: Lemmatize the input words using WordNet lemmatization.\n- `pos_tag(text)`: Perform part-of-speech (POS) tagging on the input text.\n\"\"\"\n\n\nimport string\nimport re\nimport nltk\nfrom nltk.corpus import stopwords\nfrom nltk.tokenize import word_tokenize\nfrom nltk.stem import PorterStemmer, WordNetLemmatizer\n\nnltk.download('stopwords', quiet=True)\nnltk.download('punkt', quiet=True)\nnltk.download('wordnet', quiet=True)\nnltk.download('averaged_perceptron_tagger', quiet=True)\n\ndef remove_stopwords(text):\n \"\"\"\n Remove stopwords from the input text using NLTK's stopwords.\n\n Stopwords are frequently used words (e.g., 'the', 'and', 'is') that are often\n excluded from text processing to focus on more meaningful content.\n \n Parameters:\n - `text` (str): The input text from which stopwords should be removed.\n\n Returns:\n - `str`: The text without stopwords.\n \"\"\"\n try:\n stop_words = set(stopwords.words('english'))\n tokens = text.split()\n filtered_tokens = [token for token in tokens if token.lower() not in stop_words]\n filtered_text = ' '.join(filtered_tokens)\n return filtered_text\n except Exception as e:\n print(f\"An error occurred during stopwords removal: {str(e)}\")\n return text\n\ndef remove_numbers(text):\n \"\"\"\n Remove numbers from the input text.\n\n Numerical digits are removed from the text to focus on the non-numeric content.\n \n Parameters:\n - `text` (str): The input text from which numbers should be removed.\n\n Returns:\n - `str`: The text without numbers.\n \"\"\"\n try:\n text = re.sub(r'\\d+', '', text)\n return text\n except Exception as e:\n print(f\"An error occurred during number removal: {str(e)}\")\n return text\n\ndef remove_whitespace(text):\n \"\"\"\n Remove excess whitespace from the input text.\n\n Excess whitespace, including leading, trailing, and multiple consecutive spaces,\n is removed from the text to create a more standardized and readable format.\n \n Parameters:\n - `text` (str): The input text from which excess whitespace should be removed.\n\n Returns:\n - `str`: The text with the removed excess whitespace.\n \"\"\"\n try:\n text = ' '.join(text.split())\n return text\n except Exception as e:\n print(f\"An error occurred during whitespace removal: {str(e)}\")\n return text\n\ndef normalize_whitespace(text):\n \"\"\"\n Normalize multiple whitespaces into a single whitespace in the input text.\n\n Multiple consecutive whitespaces are replaced with a single whitespace to\n create a more consistent and readable text format.\n \n Parameters:\n - `text` (str): The input text from which whitespace should be normalized.\n\n Returns:\n - `str`: The text with normalized whitespace.\n \"\"\"\n try:\n text = re.sub(r'\\s+', ' ', text)\n return text\n except Exception as e:\n print(f\"An error occurred during whitespace normalization: {str(e)}\")\n return text\n\ndef separate_symbols(text):\n \"\"\"\n Separate symbols and words with a space to ease tokenization.\n\n Symbols in the input text are separated from words with a space to facilitate\n easier tokenization and analysis of the text.\n \n Parameters:\n - `text` (str): The input text from which symbols needs to be seperated.\n\n Returns:\n - `str`: The text from which symbols have been seperated.\n \"\"\"\n try:\n pattern = r\"([\\W])\"\n separated_text = re.sub(pattern, r\" \\1 \", text)\n return separated_text\n except Exception as e:\n print(f\"An error occurred during symbol separation: {str(e)}\")\n return text\n\ndef remove_special_characters(text):\n \"\"\"\n Remove special characters from the input text.\n\n Special characters, such as punctuation and user-defined symbols, are removed\n to create a text without these non-alphanumeric elements.\n \n Parameters:\n - `text` (str): The input text from which special characters should be removed.\n\n Returns:\n - `str`: The text with special characters removed.\n \"\"\"\n try:\n text = text.translate(str.maketrans(\"\", \"\", string.punctuation))\n special_characters = \"@#$%^&*\"\n text = ''.join(char for char in text if char not in special_characters)\n return text\n except Exception as e:\n print(f\"An error occurred during special character removal: {str(e)}\")\n return text\n\ndef standardize_text(text):\n \"\"\"\n Standardize the formatting of the input text.\n\n The input text is converted to lowercase and leading/trailing whitespaces are removed\n to create a standardized representation for easier comparison and analysis.\n\n Parameters:\n - `text` (str): The input text which needs to be standardized.\n\n Returns:\n - `str`: The standardized text.\n \"\"\"\n try:\n text = text.lower()\n text = text.strip()\n return text\n except Exception as e:\n print(f\"An error occurred during text standardization: {str(e)}\")\n return text\n\ndef tokenize_text(text):\n \"\"\"\n Tokenize the input text into individual words.\n\n Tokenization is the process of breaking down a text into individual words, \n facilitating further analysis, such as counting word frequencies or analyzing \n language patterns.\n \n Parameters:\n - `text` (str): The input text to be tokenized.\n\n Returns:\n - `list`: A list of tokens (words) from the input text.\n \"\"\"\n tokens = word_tokenize(text)\n return tokens\n\ndef stem_words(words):\n \"\"\"\n Stem the input words using Porter stemming algorithm.\n\n Stemming reduces words to their base or root form, helping to consolidate \n variations of words and simplify text analysis.\n\n Parameters:\n - `words` (list): A list of words to be stemmed.\n\n Returns:\n - `list`: A list of stemmed words.\n \"\"\"\n stemmer = PorterStemmer()\n stemmed_words = [stemmer.stem(word) for word in words]\n return stemmed_words\n\ndef lemmatize_words(words):\n \"\"\"\n Lemmatize the input words using WordNet lemmatization.\n\n Lemmatization reduces words to their base or dictionary form, helping to \n normalize variations and simplify text analysis.\n \n Parameters:\n - `words` (list): A list of words to be lemmatized.\n\n Returns:\n - `list`: A list of lemmatized words.\n \"\"\"\n lemmatizer = WordNetLemmatizer()\n lemmatized_words = [lemmatizer.lemmatize(word) for word in words]\n return lemmatized_words\n\ndef pos_tag(text):\n \"\"\"\n Perform part-of-speech (POS) tagging on the input text.\n\n Part-of-speech tagging assigns a grammatical category (tag) to each word \n in a text, aiding in syntactic analysis and understanding sentence structure.\n \n Parameters:\n - `text` (str): The input text to be POS tagged.\n\n Returns:\n - `list`: A list of tuples containing (word, tag) pairs.\n \"\"\"\n try:\n tokens = nltk.word_tokenize(text)\n tagged_words = nltk.pos_tag(tokens)\n return tagged_words\n except Exception as e:\n print(f\"An error occurred during POS tagging: {str(e)}\")\n return []", "repo_name": "Infinitode/duplipy", "sub_path": "duplipy/formatting.py", "file_name": "formatting.py", "file_ext": "py", "file_size_in_byte": 7962, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "nltk.download", "line_number": 26, "usage_type": "call"}, {"api_name": "nltk.download", "line_number": 27, "usage_type": "call"}, {"api_name": "nltk.download", "line_number": 28, "usage_type": "call"}, {"api_name": "nltk.download", "line_number": 29, "usage_type": "call"}, {"api_name": "nltk.corpus.stopwords.words", "line_number": 45, "usage_type": "call"}, {"api_name": "nltk.corpus.stopwords", "line_number": 45, "usage_type": "name"}, {"api_name": "re.sub", "line_number": 67, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 107, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 128, "usage_type": "call"}, {"api_name": "string.punctuation", "line_number": 148, "usage_type": "attribute"}, {"api_name": "nltk.tokenize.word_tokenize", "line_number": 191, "usage_type": "call"}, {"api_name": "nltk.stem.PorterStemmer", "line_number": 207, "usage_type": "call"}, {"api_name": "nltk.stem.WordNetLemmatizer", "line_number": 224, "usage_type": "call"}, {"api_name": "nltk.word_tokenize", "line_number": 242, "usage_type": "call"}, {"api_name": "nltk.pos_tag", "line_number": 243, "usage_type": "call"}]} +{"seq_id": "30981056313", "text": "import yaml\nimport numpy as np\nfrom typing import List, Union\nfrom monty.dev import requires\nfrom pymatgen.core.structure import Structure\nfrom randomcarbon.output.taggers.core import Tagger\nfrom randomcarbon.run.ase import get_energy, relax\nfrom randomcarbon.run.phonon import get_phonons, extract_instabilities\nfrom randomcarbon.utils.factory import Factory\nfrom randomcarbon.utils.structure import get_property, has_low_energy\nfrom randomcarbon.evolution.core import Evolver, Filter, Condition\ntry:\n import phonopy\n from phonopy.interface.phonopy_yaml import PhonopyYaml\nexcept ImportError:\n phonopy = None\n\n\nclass CalculationInfoTag(Tagger):\n\n def __init__(self, calculator: Factory, optimizer: str = None,\n constraints: list = None, fmax: float = None):\n self.calculator = calculator\n self.optimizer = optimizer\n self.constraints = constraints\n self.fmax = fmax\n\n def tag(self, doc: dict, structure: Structure = None) -> dict:\n d = {}\n dc = self.calculator.as_dict()\n dc.pop(\"@class\", None)\n dc.pop(\"@module\", None)\n\n d[\"calculator\"] = dc\n d[\"constraints\"] = self.constraints\n d[\"optimizer\"] = self.optimizer\n d[\"fmax\"] = self.fmax\n\n doc[\"calculation\"] = d\n\n return doc\n\n\nclass EvolutionInfoTag(Tagger):\n\n def __init__(self, evolvers: List[Union[Evolver, List]] = None,\n blockers: List[Condition] = None,\n filters: List[Filter] = None):\n self.evolvers = evolvers\n self.blockers = blockers\n self.filters = filters\n\n def tag(self, doc: dict, structure: Structure = None) -> dict:\n doc[\"evolution\"] = dict(\n evolvers=self.evolvers,\n blockers=self.blockers,\n filters=self.filters\n )\n\n return doc\n\n\nclass EnergyTag(Tagger):\n\n def __init__(self, calculator: Factory,\n constraints: list = None):\n self.calculator = calculator\n self.constraints = constraints\n\n def tag(self, doc: dict, structure: Structure = None) -> dict:\n\n energy = get_property(structure, \"energy\")\n\n if energy is None:\n if not self.calculator:\n return doc\n energy = get_energy(structure=structure, calculator=self.calculator,\n constraints=self.constraints, set_in_structure=True)\n\n doc[\"energy\"] = energy\n doc[\"energy_per_atom\"] = energy / len(structure)\n\n return doc\n\n\nclass RelaxTag(Tagger):\n def __init__(self, calculator: Factory, energy_threshold: float = None, constraints: list = None,\n fmax: float = 0.05, steps: int = 1000, optimizer: str = \"BFGS\",\n opt_kwargs: dict = None, prefix: str = \"\", store_structure: bool = True):\n self.calculator = calculator\n self.energy_threshold = energy_threshold\n self.constraints = constraints\n self.fmax = fmax\n self.steps = steps\n self.optimizer = optimizer\n self.opt_kwargs = opt_kwargs\n self.prefix = prefix\n self.store_structure = store_structure\n\n def tag(self, doc: dict, structure: Structure = None) -> dict:\n\n if self.energy_threshold is not None:\n if has_low_energy(structure, self.energy_threshold):\n return doc\n\n relaxed = relax(structure=structure, calculator=self.calculator,\n constraints=self.constraints, fmax=self.fmax, steps=self.steps,\n optimizer=self.optimizer, opt_kwargs=self.opt_kwargs,\n allow_not_converged=False, set_energy_in_structure= True)\n\n if not relaxed:\n return doc\n\n energy = get_property(relaxed, \"energy\")\n energy_per_atom = energy / len(structure)\n\n doc[f\"{self.prefix}energy_per_atom\"] = energy_per_atom\n if self.store_structure:\n doc[f\"{self.prefix}structure\"] = relaxed\n\n return doc\n\n\n@requires(phonopy, \"phonopy should be installed to calculate phonons\")\nclass PhononTag(Tagger):\n def __init__(self, calculator: Factory, energy_threshold: float = None, constraints: list = None,\n phfreqs_threshold: float = -0.01, store_phonopy_data: bool = False):\n self.calculator = calculator\n self.energy_threshold = energy_threshold\n self.constraints = constraints\n self.phfreqs_threshold = phfreqs_threshold\n self.store_phonopy_data = store_phonopy_data\n\n def tag(self, doc: dict, structure: Structure = None) -> dict:\n\n if self.energy_threshold is not None:\n if has_low_energy(structure, self.energy_threshold):\n return doc\n\n phonon = get_phonons(structure=structure, calculator=self.calculator,\n constraints=self.constraints, supercell_matrix=np.eys(3))\n\n info = extract_instabilities(phonon=phonon, threshold=self.phfreqs_threshold)\n\n if self.store_phonopy_data:\n phpy_yaml = PhonopyYaml(settings={})\n phpy_yaml.set_phonon_info(phonon)\n info[\"phonopy_data\"] = yaml.safe_load(str(phpy_yaml))\n\n return doc\n", "repo_name": "modl-uclouvain/randomcarbon", "sub_path": "randomcarbon/output/taggers/calc.py", "file_name": "calc.py", "file_ext": "py", "file_size_in_byte": 5182, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "37", "api": [{"api_name": "randomcarbon.output.taggers.core.Tagger", "line_number": 19, "usage_type": "name"}, {"api_name": "randomcarbon.utils.factory.Factory", "line_number": 21, "usage_type": "name"}, {"api_name": "pymatgen.core.structure.Structure", "line_number": 28, "usage_type": "name"}, {"api_name": "randomcarbon.output.taggers.core.Tagger", "line_number": 44, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 46, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 46, "usage_type": "name"}, {"api_name": "randomcarbon.evolution.core.Evolver", "line_number": 46, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 47, "usage_type": "name"}, {"api_name": "randomcarbon.evolution.core.Condition", "line_number": 47, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 48, "usage_type": "name"}, {"api_name": "randomcarbon.evolution.core.Filter", "line_number": 48, "usage_type": "name"}, {"api_name": "pymatgen.core.structure.Structure", "line_number": 53, "usage_type": "name"}, {"api_name": "randomcarbon.output.taggers.core.Tagger", "line_number": 63, "usage_type": "name"}, {"api_name": "randomcarbon.utils.factory.Factory", "line_number": 65, "usage_type": "name"}, {"api_name": "pymatgen.core.structure.Structure", "line_number": 70, "usage_type": "name"}, {"api_name": "randomcarbon.utils.structure.get_property", "line_number": 72, "usage_type": "call"}, {"api_name": "randomcarbon.run.ase.get_energy", "line_number": 77, "usage_type": "call"}, {"api_name": "randomcarbon.output.taggers.core.Tagger", "line_number": 86, "usage_type": "name"}, {"api_name": "randomcarbon.utils.factory.Factory", "line_number": 87, "usage_type": "name"}, {"api_name": "pymatgen.core.structure.Structure", "line_number": 100, "usage_type": "name"}, {"api_name": "randomcarbon.utils.structure.has_low_energy", "line_number": 103, "usage_type": "call"}, {"api_name": "randomcarbon.run.ase.relax", "line_number": 106, "usage_type": "call"}, {"api_name": "randomcarbon.utils.structure.get_property", "line_number": 114, "usage_type": "call"}, {"api_name": "randomcarbon.output.taggers.core.Tagger", "line_number": 125, "usage_type": "name"}, {"api_name": "randomcarbon.utils.factory.Factory", "line_number": 126, "usage_type": "name"}, {"api_name": "pymatgen.core.structure.Structure", "line_number": 134, "usage_type": "name"}, {"api_name": "randomcarbon.utils.structure.has_low_energy", "line_number": 137, "usage_type": "call"}, {"api_name": "randomcarbon.run.phonon.get_phonons", "line_number": 140, "usage_type": "call"}, {"api_name": "numpy.eys", "line_number": 141, "usage_type": "call"}, {"api_name": "randomcarbon.run.phonon.extract_instabilities", "line_number": 143, "usage_type": "call"}, {"api_name": "phonopy.interface.phonopy_yaml.PhonopyYaml", "line_number": 146, "usage_type": "call"}, {"api_name": "yaml.safe_load", "line_number": 148, "usage_type": "call"}, {"api_name": "monty.dev.requires", "line_number": 124, "usage_type": "call"}]} +{"seq_id": "13092091100", "text": "from flask import Flask, render_template, request, redirect, url_for, flash\nfrom DataBase_mongo import DataBase_mongo\nfrom copy import deepcopy\n\nmongo = DataBase_mongo()\n\napp = Flask(__name__)\napp.secret_key = \"mysecretkey\"\n\n\n@app.route('/')\ndef index():\n table = {'name':''}\n return render_template('landing-page.html', table = table)\n\n\n@app.route('/collection/', methods=['GET', 'POST'])\ndef index_table(collection):\n data_table = mongo.get_info(collection)\n if request.method == 'GET':\n\n data_table['data'] = mongo.find(collection)\n\n # Busqueda de registro\n elif request.method == 'POST':\n data = request.form['data']\n field = request.form['field']\n try:\n data = int(data)\n except:\n pass\n try:\n data_table['data'] = mongo.find_many(collection, field, data)\n if not data_table['data']:\n return redirect(f\"/collection/{data_table['name']}\")\n except:\n flash(f\"ERROR: Error producido en la busqueda\")\n return redirect(f\"/collection/{data_table['name']}\")\n # -----\n\n data_table['edit'] = mongo.enlist_collection(deepcopy(data_table['data']))\n return render_template('collection.html',\n table=data_table,\n data_select=mongo.get_data_select(collection))\n\n\n# INSERTS\n@app.route('/insert/', methods=['POST'])\ndef insert(collection):\n form_data = get_form_data(collection, 'insert')\n mongo.insert(collection, form_data)\n return redirect(f\"/collection/{collection}\")\n\n\n# UPDATES\n@app.route('/update//', methods=['POST'])\ndef update(collection, idx):\n try:\n idx = int(idx)\n except:\n pass\n form_data = get_form_data(collection, 'update')\n mongo.update(collection, idx, form_data)\n return redirect(f\"/collection/{collection}\")\n\n# DELETE\n@app.route(\"/delete///\")\ndef delete(collection, index, value):\n mongo.remove(collection, index, value)\n return redirect(f\"/collection/{collection}\")\n\ndef get_form_data(table_name, operation):\n data = []\n table_info = mongo.get_info(table_name)\n for field in mongo.get_fields(table_info['name']):\n if ((operation == 'update' and field != table_info['index'])\n or (operation == 'insert' and (field != 'id' or table_name == 'empleados' or table_name == 'piloto'))):\n d = request.form[field]\n try:\n d = int(d)\n except:\n pass\n data.append(d)\n return data\n\n\nif __name__ == '__main__':\n app.run(port=4000)", "repo_name": "Carlos1408/Aviacion_mongo", "sub_path": "src/App.py", "file_name": "App.py", "file_ext": "py", "file_size_in_byte": 2653, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "DataBase_mongo.DataBase_mongo", "line_number": 5, "usage_type": "call"}, {"api_name": "flask.Flask", "line_number": 7, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 14, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 20, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 20, "usage_type": "name"}, {"api_name": "flask.request.method", "line_number": 25, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 25, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 26, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 26, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 27, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 27, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 35, "usage_type": "call"}, {"api_name": "flask.flash", "line_number": 37, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 38, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 41, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 42, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 52, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 64, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 70, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 78, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 78, "usage_type": "name"}]} +{"seq_id": "6739263200", "text": "\"\"\"\nThis code was originally developed in https://github.com/NSLS-II/eiger-io\nand copied (the lazy way, without retaining git history) into this repo.\nIt has been substantially changed from the original.\n\"\"\"\nfrom glob import glob\nimport os\nfrom pathlib import Path\n\nimport dask.array\nimport h5py\n\nfrom . import HandlerBase\n\n\nclass EigerHandler(HandlerBase):\n EIGER_MD_LAYOUT = {\n 'y_pixel_size': 'entry/instrument/detector/y_pixel_size',\n 'x_pixel_size': 'entry/instrument/detector/x_pixel_size',\n 'detector_distance': 'entry/instrument/detector/detector_distance',\n 'incident_wavelength': 'entry/instrument/beam/incident_wavelength',\n 'frame_time': 'entry/instrument/detector/frame_time',\n 'beam_center_x': 'entry/instrument/detector/beam_center_x',\n 'beam_center_y': 'entry/instrument/detector/beam_center_y',\n 'count_time': 'entry/instrument/detector/count_time',\n 'pixel_mask': 'entry/instrument/detector/detectorSpecific/pixel_mask',\n }\n\n specs = {\n 'AD_EIGER2', # CHX, ISR\n 'AD_EIGER', # CHX\n 'AD_EIGER_SLICE', # CHX\n }\n\n def __init__(self, fpath, images_per_file=None, frame_per_point=None):\n '''\n Initializer for Eiger handler.\n\n Parameters\n ----------\n fpath: str\n the partial file path\n\n images_per_file: int, optional\n images per file. If not set, must set frame_per_point\n\n frame_per_point: int, optional. If not set, must set\n images_per_file\n\n This one is backwards compatible for both versions of resources\n saved in databroker. Old resources used 'frame_per_point' as a\n kwarg. Newer resources call this 'images_per_file'.\n '''\n self._file_prefix = fpath\n if images_per_file is None and frame_per_point is None:\n raise ValueError(\n \"Either images_per_file or frame_per_point must be not None.\")\n\n if images_per_file is None:\n # then grab from frame_per_point\n images_per_file = frame_per_point\n self._images_per_file = images_per_file\n self._files = {}\n\n def __call__(self, seq_id, frame_num=None):\n '''\n This returns data contained in the file.\n\n Parameters\n ----------\n seq_id: int\n The sequence id of the data\n\n frame_num: int or None\n If not None, return the frame_num'th image from this\n 3D array. Useful for when an event is one image rather\n than a stack. (Editor's note: It's not clear what this original\n docstring was supposed to mean. Is it *ever* not None?)\n\n Returns\n -------\n A dask array\n '''\n master_path = Path(f'{self._file_prefix}_{seq_id}_master.h5').absolute()\n try:\n file = self._files[master_path]\n except KeyError:\n file = h5py.File(master_path, 'r')\n self._files[master_path] = file\n\n # TODO This should be captured in documents, not extracted here.\n # This code is retained just in case, but it does not do anything other\n # than set self._md, which has no effect unless the user obtain direct\n # access to the handler instance by reaching into the Filler's cache of\n # them.\n md = {k: file[v][()] for k, v in self.EIGER_MD_LAYOUT.items()}\n # the pixel mask from the eiger contains:\n # 1 -- gap\n # 2 -- dead\n # 4 -- under-responsive\n # 8 -- over-responsive\n # 16 -- noisy\n pixel_mask = md['pixel_mask']\n # pixel_mask[pixel_mask>0] = 1\n # pixel_mask[pixel_mask==0] = 2\n # pixel_mask[pixel_mask==1] = 0\n # pixel_mask[pixel_mask==2] = 1\n md['binary_mask'] = (pixel_mask == 0)\n md['framerate'] = 1. / float(md['frame_time'])\n self._md = md\n\n try:\n # Eiger firmware v1.3.0 and onwards\n entry = file['entry']['data']\n except KeyError:\n # Older firmwares\n entry = file['entry']\n\n # Each 'master' file references multiple 'data' files.\n # We just need to know many there are, but here we make\n # a sorted list of their names because it can be handy\n # when things break and we need to debug.\n data_files = sorted([key for key in entry.keys() if key.startswith(\"data\")])\n\n to_concatenate = []\n for i in range(len(data_files)):\n dataset = entry[f'data_{1 + (i // self._images_per_file):06d}']\n da = dask.array.from_array(dataset)\n to_concatenate.append(da)\n stack = dask.array.concatenate(to_concatenate)\n if frame_num is None:\n return stack\n else:\n return stack[frame_num % self.images_per_file]\n\n def get_file_list(self, datum_kwargs_gen):\n '''\n Get the file list.\n\n Receives a list of datum_kwargs for each datum.\n '''\n filenames = []\n for dm_kw in datum_kwargs_gen:\n seq_id = dm_kw['seq_id']\n new_filenames = glob(f'{self._file_prefix}_{seq_id}*')\n filenames.extend(new_filenames)\n\n return filenames\n\n def get_file_sizes(self, datum_kwargs_gen):\n '''\n Get the file size.\n\n Returns size in bytes.\n '''\n sizes = []\n file_name = self.get_file_list(datum_kwargs_gen)\n for file in file_name:\n sizes.append(os.path.getsize(file))\n\n return sizes\n\n def close(self):\n for file in self._files.values():\n file.close()\n", "repo_name": "bluesky/area-detector-handlers", "sub_path": "area_detector_handlers/eiger.py", "file_name": "eiger.py", "file_ext": "py", "file_size_in_byte": 5641, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "pathlib.Path", "line_number": 84, "usage_type": "call"}, {"api_name": "h5py.File", "line_number": 88, "usage_type": "call"}, {"api_name": "dask.array.array.from_array", "line_number": 128, "usage_type": "call"}, {"api_name": "dask.array.array", "line_number": 128, "usage_type": "attribute"}, {"api_name": "dask.array", "line_number": 128, "usage_type": "name"}, {"api_name": "dask.array.array.concatenate", "line_number": 130, "usage_type": "call"}, {"api_name": "dask.array.array", "line_number": 130, "usage_type": "attribute"}, {"api_name": "dask.array", "line_number": 130, "usage_type": "name"}, {"api_name": "glob.glob", "line_number": 145, "usage_type": "call"}, {"api_name": "os.path.getsize", "line_number": 159, "usage_type": "call"}, {"api_name": "os.path", "line_number": 159, "usage_type": "attribute"}]} +{"seq_id": "24250415442", "text": "from django.contrib.auth.models import User\nfrom django.db import models\n\n# Create your models here.\n\nclass userInfo(models.Model):\n USER_TYPE = (\n (1,'管理员'),\n (2,'审稿员'),\n (3,'参会者')\n )\n type = models.IntegerField(verbose_name=\"用户类型\",choices=USER_TYPE)\n user = models.OneToOneField(User)\n\nclass ThesisInfo(models.Model):\n STATUS = (\n (1,'通过'),\n (2,'不通过'),\n )\n theme = models.CharField(verbose_name=\"主题\", max_length=100)\n headline = models.CharField(verbose_name=\"摘要\", max_length=100)\n keyword = models.CharField(verbose_name=\"关键词\", max_length=50)\n user = models.ForeignKey(User,verbose_name=\"用户\")\n thesisUrl = models.FileField(verbose_name=\"论文上传\", upload_to='projects/static/documents/%Y/%m/%d')\n auditStatus = models.IntegerField(verbose_name=\"审核状态\", choices=STATUS,blank=True,null=True)\n reviewStatus = models.IntegerField(verbose_name=\"评审状态\", choices=STATUS,blank=True,null=True)\n create_time = models.DateTimeField(verbose_name=\"创建时间\",default='2017-4-23')\n modifyTime = models.DateTimeField(verbose_name=\"修改时间\")\n\nclass MeetingInfo(models.Model):\n meeting_name = models.CharField(verbose_name=\"会议名称\", max_length=100)\n start_time = models.DateTimeField(verbose_name=\"开始时间\")\n end_time = models.DateTimeField(verbose_name=\"结束时间\")\n meeting_address = models.CharField(verbose_name=\"会议地址\", max_length=100)\n meeting_plan = models.CharField(verbose_name=\"会议安排\", max_length=500)\n create_time = models.DateTimeField(verbose_name=\"创建时间\",default='2017-4-23')\n create_user = models.ForeignKey(User,verbose_name=\"创建人\")\n def __str__(self):\n return self.meeting_name\n\nclass Notice(models.Model):\n message = models.CharField(verbose_name=\"信息内容\",max_length=1000)\n meetingInfo = models.ForeignKey(MeetingInfo,verbose_name=\"会议\")\n create_time = models.DateTimeField(verbose_name=\"创建时间\",default='2017-4-23')", "repo_name": "iakisme/academic", "sub_path": "projects/models.py", "file_name": "models.py", "file_ext": "py", "file_size_in_byte": 2075, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "django.db.models.Model", "line_number": 6, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 6, "usage_type": "name"}, {"api_name": "django.db.models.IntegerField", "line_number": 12, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 12, "usage_type": "name"}, {"api_name": "django.db.models.OneToOneField", "line_number": 13, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User", "line_number": 13, "usage_type": "argument"}, {"api_name": "django.db.models", "line_number": 13, "usage_type": "name"}, {"api_name": "django.db.models.Model", "line_number": 15, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 15, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 20, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 20, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 21, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 21, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 22, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 22, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 23, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User", "line_number": 23, "usage_type": "argument"}, {"api_name": "django.db.models", "line_number": 23, "usage_type": "name"}, {"api_name": "django.db.models.FileField", "line_number": 24, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 24, "usage_type": "name"}, {"api_name": "django.db.models.IntegerField", "line_number": 25, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 25, "usage_type": "name"}, {"api_name": "django.db.models.IntegerField", "line_number": 26, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 26, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 27, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 27, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 28, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 28, "usage_type": "name"}, {"api_name": "django.db.models.Model", "line_number": 30, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 30, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 31, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 31, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 32, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 32, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 33, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 33, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 34, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 34, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 35, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 35, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 36, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 36, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 37, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User", "line_number": 37, "usage_type": "argument"}, {"api_name": "django.db.models", "line_number": 37, "usage_type": "name"}, {"api_name": "django.db.models.Model", "line_number": 41, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 41, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 42, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 42, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 43, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 43, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 44, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 44, "usage_type": "name"}]} +{"seq_id": "38133860619", "text": "from flask import Flask\nfrom flask import render_template, request, jsonify\nimport re\nfrom calculator.ai_calculator import Calculator\nfrom cabbage.cabbage import Cabbage\nfrom blood.blood import Blood\n\napp = Flask(__name__)\n\n\n@app.route('/')\ndef index():\n return render_template('index.html')\n\n@app.route('/move/') #<>를 주면 변수명으로 처리됨. 해당페이지로 이동\ndef move(path):\n return render_template('{}.html'.format(path)) # move(path)에서 입력받은 해당path를 {}안에 넣음\n\n@app.route('/calculator') # 내부에서 계산하라는 URL\ndef ui_calculator(): # 인공지능없는 그냥 계산기\n stmt = request.args.get('stmt', 'NONE')\n if(stmt == 'NONE'):\n print('넘어온 값이 없음')\n else:\n print(f'넘어온 식: {stmt}')\n patt = '[0-9]+' # 숫자가 반드시 하나이상 있어야 한다는 것\n op = re.sub(patt, '', stmt) # patt에서 stmt를 빼기\n nums = stmt.split(op)\n result = 0\n n1 = int(nums[0])\n n2 = int(nums[1])\n if op == '+': result = n1 + n2\n elif op == '-': result = n1 - n2\n elif op == '*': result = n1 * n2\n elif op == '/': result = n1 / n2\n\n return jsonify(result = result) # 위에서 파이썬 값으로 만들어진 것을 자바스크립트 값으로 바꿔주어야 함\n\n@app.route('/ai_calculator', methods=['POST'])\ndef ai_calculator():\n num1 = request.form['num1']\n num2 = request.form['num2']\n opcode = request.form['opcode']\n # ai_calculator가 계산한 값을 가져와야 하므로 ai_calculator의 Calculator 클래스를 불러와서 result값에 넣어야 함\n result = Calculator.service(num1, num2, opcode)\n render_params = {}\n render_params['result'] = int(result) #텐서의 기본형은 float이므로 자연스러운 표기를 위해 int로 변경\n return render_template('ai_calculator.html', **render_params)\n # ai_calculator.html에 있는 값(num1, num2, opcode)을 가져와서 render_params에 있는 값을 뿌리는 것\n\n\n@app.route('/cabbage', methods=['POST'])\ndef cabbage():\n avg_temp = request.form['avg_temp']\n min_temp = request.form['min_temp']\n max_temp = request.form['max_temp']\n rain_fall = request.form['rain_fall']\n cabbage = Cabbage()\n cabbage.initialize(avg_temp, min_temp, max_temp, rain_fall)\n result = cabbage.service()\n render_params = {}\n render_params['result'] = result\n return render_template('cabbage.html', **render_params)\n\n\n@app.route('/blood', methods=['POST'])\ndef blood():\n weight = request.form['weight']\n age = request.form['age']\n blood = Blood()\n blood.initialize(weight, age)\n result = blood.service()\n\n render_params = {}\n render_params['result'] = result\n return render_template('blood.html', **render_params)\n\n\nif __name__ == '__main__':\n app.run()\n", "repo_name": "smile2019kr/TF_2020_day7", "sub_path": "app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 2882, "program_lang": "python", "lang": "ko", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "flask.Flask", "line_number": 8, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 13, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 17, "usage_type": "call"}, {"api_name": "flask.request.args.get", "line_number": 21, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 21, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 21, "usage_type": "name"}, {"api_name": "re.sub", "line_number": 27, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 37, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 41, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 41, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 42, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 42, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 43, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 43, "usage_type": "name"}, {"api_name": "calculator.ai_calculator.Calculator.service", "line_number": 45, "usage_type": "call"}, {"api_name": "calculator.ai_calculator.Calculator", "line_number": 45, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 48, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 54, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 54, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 55, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 55, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 56, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 56, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 57, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 57, "usage_type": "name"}, {"api_name": "cabbage.cabbage", "line_number": 58, "usage_type": "name"}, {"api_name": "cabbage.cabbage.Cabbage", "line_number": 58, "usage_type": "call"}, {"api_name": "cabbage.cabbage.initialize", "line_number": 59, "usage_type": "call"}, {"api_name": "cabbage.cabbage", "line_number": 59, "usage_type": "name"}, {"api_name": "cabbage.cabbage.service", "line_number": 60, "usage_type": "call"}, {"api_name": "cabbage.cabbage", "line_number": 60, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 63, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 68, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 68, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 69, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 69, "usage_type": "name"}, {"api_name": "blood.blood", "line_number": 70, "usage_type": "name"}, {"api_name": "blood.blood.Blood", "line_number": 70, "usage_type": "call"}, {"api_name": "blood.blood.initialize", "line_number": 71, "usage_type": "call"}, {"api_name": "blood.blood", "line_number": 71, "usage_type": "name"}, {"api_name": "blood.blood.service", "line_number": 72, "usage_type": "call"}, {"api_name": "blood.blood", "line_number": 72, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 76, "usage_type": "call"}]} +{"seq_id": "1396541378", "text": "\"\"\"Communities Bisection class.\"\"\"\r\nfrom functools import lru_cache\r\n\r\nfrom networkx.algorithms.community import kernighan_lin_bisection\r\n\r\nfrom hcga.feature_class import FeatureClass, InterpretabilityScore\r\n\r\nfeatureclass_name = \"CommunitiesBisection\"\r\n\r\n\r\n@lru_cache(maxsize=None)\r\ndef eval_bisection(graph):\r\n \"\"\"this evaluates the main function and cach it for speed up.\"\"\"\r\n communities = list(kernighan_lin_bisection(graph))\r\n communities.sort(key=len, reverse=True)\r\n\r\n return communities\r\n\r\n\r\ndef largest_commsize(graph):\r\n \"\"\"largest_commsize\"\"\"\r\n return len(eval_bisection(graph)[0])\r\n\r\n\r\nclass CommunitiesBisection(FeatureClass):\r\n \"\"\"Communities Bisection class.\r\n\r\n This algorithm partitions a network into two sets by iteratively\r\n swapping pairs of nodes to reduce the edge cut between the two sets. The\r\n pairs are chosen according to a modified form of Kernighan-Lin, which\r\n moves node individually, alternating between sides to keep the bisection\r\n balanced.\r\n\r\n References\r\n ----------\r\n .. [1] Kernighan, B. W.; Lin, Shen (1970).\r\n \"An efficient heuristic procedure for partitioning graphs.\"\r\n *Bell Systems Technical Journal* 49: 291--307.\r\n Oxford University Press 2011.\r\n\r\n \"\"\"\r\n\r\n modes = [\"medium\", \"slow\"]\r\n shortname = \"CBI\"\r\n name = \"communities_bisection\"\r\n encoding = \"networkx\"\r\n\r\n def compute_features(self):\r\n self.add_feature(\r\n \"largest_commsize\",\r\n largest_commsize,\r\n \"The ratio of the largest and second largest communities using bisection algorithm\",\r\n InterpretabilityScore(4),\r\n )\r\n\r\n self.add_feature(\r\n \"partition\",\r\n eval_bisection,\r\n \"The optimal partition for kernighan lin bisection algorithm\",\r\n InterpretabilityScore(4),\r\n statistics=\"clustering\",\r\n )\r\n", "repo_name": "barahona-research-group/hcga", "sub_path": "hcga/features/communities_bisection.py", "file_name": "communities_bisection.py", "file_ext": "py", "file_size_in_byte": 1912, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 36, "dataset": "github-code", "pt": "37", "api": [{"api_name": "networkx.algorithms.community.kernighan_lin_bisection", "line_number": 14, "usage_type": "call"}, {"api_name": "functools.lru_cache", "line_number": 11, "usage_type": "call"}, {"api_name": "hcga.feature_class.FeatureClass", "line_number": 25, "usage_type": "name"}, {"api_name": "hcga.feature_class.InterpretabilityScore", "line_number": 53, "usage_type": "call"}, {"api_name": "hcga.feature_class.InterpretabilityScore", "line_number": 60, "usage_type": "call"}]} +{"seq_id": "18080497038", "text": "import time\nimport datetime as dt\nimport threading\nimport sys\nimport argparse\n\nimport psutil\nimport firebase_util\nfrom core_data_modules.logging import Logger\n\nlog = Logger(__name__)\nfirebase_client = None\n\nCOLLECTION = 'pipeline_system_metrics' #name of the firebase collections to store metrics\nDEFAULT_INTERVAL = 600 # wait interval between each set of metric readings in seconds\n\n\ndef get_and_publish_system_metrics(interval):\n while True:\n metrics = {}\n\n # record datetime\n metrics['datetime'] = dt.datetime.now(dt.timezone.utc).isoformat()\n\n # current cpu utlization\n cpu_utilization = psutil.cpu_percent(interval=0.1)\n metrics['cpu_percent'] = cpu_utilization\n\n # cpu load over the last 1, 5 and 15 minutes in percentage\n cpu_load = [round((value / psutil.cpu_count() * 100), 2)\n for value in psutil.getloadavg()]\n metrics['cpu_load_interval_percent'] = dict(\n {\n '1min': cpu_load[0],\n '5min': cpu_load[1],\n '15min': cpu_load[2]\n }\n )\n\n # memory usage\n memory_usage = psutil.virtual_memory()\n metrics['memory_usage'] = dict(\n {\n 'available': memory_usage[1],\n 'used': memory_usage[3],\n 'percent': memory_usage[2],\n 'free': memory_usage[4]\n }\n )\n\n # disk usage\n metrics['disk_usage'] = []\n for partition in psutil.disk_partitions():\n disk_usage = dict(psutil.disk_usage(partition[0])._asdict())\n disk_usage['disk'] = partition[0]\n metrics['disk_usage'].append(disk_usage)\n\n log.info(\"Recorded metrics: {}\".format(metrics))\n\n publish_metrics_to_firestore(metrics)\n time.sleep(interval)\n\n\ndef publish_metrics_to_firestore(metrics):\n firebase_client.collection(COLLECTION).add(metrics)\n log.info(\"Successfully published metrics to firebase {} collection\".format(COLLECTION))\n\n\ndef run_system_metric_monitor(interval=DEFAULT_INTERVAL):\n parser = argparse.ArgumentParser(description='Retrieve system metrics i.e cpu utilization, memory & disk usage')\n parser.add_argument(\"crypto_token_file\", type=str, help=\"path to Firebase crypto token file\")\n args = parser.parse_args()\n\n firebase_client = firebase_util.init_firebase_client(args.crypto_token_file)\n runner = threading.Thread(target=get_and_publish_system_metrics, args=(interval,))\n runner.start()\n\n\nrun_system_metric_monitor()\n", "repo_name": "larksystems/nook", "sub_path": "tool/system_metrics_monitor.py", "file_name": "system_metrics_monitor.py", "file_ext": "py", "file_size_in_byte": 2552, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "37", "api": [{"api_name": "core_data_modules.logging.Logger", "line_number": 11, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 23, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 23, "usage_type": "attribute"}, {"api_name": "datetime.timezone", "line_number": 23, "usage_type": "attribute"}, {"api_name": "psutil.cpu_percent", "line_number": 26, "usage_type": "call"}, {"api_name": "psutil.cpu_count", "line_number": 30, "usage_type": "call"}, {"api_name": "psutil.getloadavg", "line_number": 31, "usage_type": "call"}, {"api_name": "psutil.virtual_memory", "line_number": 41, "usage_type": "call"}, {"api_name": "psutil.disk_partitions", "line_number": 53, "usage_type": "call"}, {"api_name": "psutil.disk_usage", "line_number": 54, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 61, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 70, "usage_type": "call"}, {"api_name": "firebase_util.init_firebase_client", "line_number": 74, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 75, "usage_type": "call"}]} +{"seq_id": "72654811627", "text": "import sys\r\nfrom collections import deque\r\ninput = sys.stdin.readline\r\n\r\ndeq = deque() # 대기차량 담을 deque\r\nN, M = map(int, input().split()) #주차공간 N개, 차량 M대\r\npay = [] #가격 담을 list\r\nweight = [] #무게 담을 list\r\ncnt = 0 #답\r\n\r\ncheck = [0 for _ in range(N)] #주차공간 사용중인지 check할 리스트\r\nfor _ in range(N):\r\n pay.append(int(input()))\r\nfor _ in range(M) :\r\n weight.append(int(input()))\r\nfor _ in range(2*M) :\r\n x = int(input())\r\n if 0 < x : #새로 입력받은게 양수라면\r\n if 0 in check : # 빈 자리 있는지 확인하고\r\n for i in range(N) :\r\n if check[i] == 0 :\r\n check[i] = x #넣어줌\r\n break\r\n else : deq.append(x) #빈 자리 없으면 대기석으로\r\n else : #입력받은게 음수면\r\n if abs(x) in check: #지금 주차중이면\r\n for i in range(N) :\r\n if check[i]+x == 0 :\r\n cnt += weight[check[i]-1]*pay[i] #찾아서 cnt에 더해주고\r\n if deq : #대기 리스트 확인\r\n check[i] = deq.popleft() #있으면 넣어주기\r\n else : check[i] = 0 #자리 비워주기\r\n break\r\nprint(cnt)", "repo_name": "Legitgoons/algorithm", "sub_path": "백준/Silver/5464. 주차장/주차장.py", "file_name": "주차장.py", "file_ext": "py", "file_size_in_byte": 1290, "program_lang": "python", "lang": "ko", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "sys.stdin", "line_number": 3, "usage_type": "attribute"}, {"api_name": "collections.deque", "line_number": 5, "usage_type": "call"}]} +{"seq_id": "38708899137", "text": "import pandas as pd\nimport numpy as np\nfrom datetime import datetime\n\ntickers = ['WIG', 'WIG20', 'PKN', 'KGH', 'MIL', 'TPE', 'ING', 'PGN', 'MBK', 'PKO', 'BOS']\n\ndef data_from_period(start, end):\n dict_of_tickers = dict()\n for tick in tickers:\n data = pd.read_csv(f'{tick}.csv', index_col='Date', parse_dates=['Date'],\n date_parser=lambda x: datetime.strptime(x, '%Y-%m-%d'))\n data = data[start:end]\n data['zwykla_stopa_zwrotu'] = 100 * data['Close'].pct_change()\n data['logarytmiczna_stopa_zwrotu'] = 100 * (np.log(data.Close) - np.log(data.Close.shift(1)))\n data = data.dropna()\n\n if tick == 'plopln3m':\n data = data.drop(['Open', 'High', 'Low'], axis=1)\n else:\n data = data.drop(['Open', 'High', 'Low', 'Volume'], axis=1)\n dict_of_tickers[tick] = data\n return dict_of_tickers\n\n\ndef data_from_2007_to_2011():\n\n return data_from_period(start=datetime(2006, 12, 29), end=datetime(2012, 1, 1))\n\n\ndef data_from_2015_to_now():\n return data_from_period(start=datetime(2014, 12, 30), end=datetime(2021, 6, 1))\n\n\n\n\n", "repo_name": "LukaszGrochal/Magisterka", "sub_path": "format_data.py", "file_name": "format_data.py", "file_ext": "py", "file_size_in_byte": 1116, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "pandas.read_csv", "line_number": 10, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 11, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 11, "usage_type": "name"}, {"api_name": "numpy.log", "line_number": 14, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 27, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 31, "usage_type": "call"}]} +{"seq_id": "42027059860", "text": "import torch\nimport numpy as np\nimport torch.utils.data as dataloader\nimport os\nfrom config import Config\nfrom models.segmentor_v1 import HybridUNet_single_out_consistency\nfrom miscellaneous.metrics import dice, rAVD, hd, brier, nll, ece\nimport pandas as pd\nfrom data.datagenerator import CTData_test\nfrom miscellaneous.utils import Evaluation\nfrom matplotlib import pyplot as plt\nimport h5py\nimport time\nimport cv2 as cv\nfrom sklearn.metrics import brier_score_loss\nfrom sklearn.calibration import calibration_curve\n\n\ndef online_eval(model, dataloader, txtlog, submit_path, uncertaintys_path, save_segmentation, save_uncertainty):\n txtlog.write( \"Dice_mean fg|bg|hausdorff_dist|ravd|ece|nll|sklearn_brier\\n\")\n my_evaluation = Evaluation()\n start_time = time.time()\n with torch.no_grad():\n dice_new_list = []\n data_dict_list = []\n hausdorff_dist_list = []\n ravd_list = []\n shape_list = []\n testset_list_pre = []\n testset_list_gt = []\n nll_list = []\n brier_list = []\n brier_sklearn_list = []\n ece_list = []\n for data_val in dataloader:\n images_val, targets_val, subject, slice, images_origin = data_val\n model.eval()\n images_val = images_val.to(device)\n targets_val = targets_val.to(device)\n outputs = model(images_val, test_config.lamda_sem)\n # final_out [i-1,i,i+1]\n outputs_val = outputs.final_out\n softmax = outputs.softmax_out\n # calculate predicted entropy as uncertainty\n softmax_1 = torch.unsqueeze(softmax[:,1,...],dim=1)\n softmax_2 = torch.unsqueeze(softmax[:, 3, ...], dim=1)\n softmax_3 = torch.unsqueeze(softmax[:, 5, ...], dim=1)\n softmax_fg = torch.cat((softmax_1, softmax_2, softmax_3), dim=1)\n softmax_fg_numpy = softmax_fg.data.cpu().numpy()\n softmax_fg_numpy = np.squeeze(softmax_fg_numpy, axis=0)\n mean_fg = np.mean(softmax_fg_numpy, axis=0)\n entropy = -mean_fg*np.log(mean_fg)\n\n # softmax outputs for uncertainty quantification\n softmax_final_out = softmax[:,6:8,...]\n softmax_final_out = np.squeeze(softmax_final_out.data.cpu().numpy(), axis=0)\n # 逐切片处理\n outputs_val_1 = outputs_val[:,0:2, ...]\n\n image_origin = images_origin.data.cpu().numpy()\n image_origin1 = np.squeeze(image_origin, axis=0)\n image_origin1 = image_origin1[:, :, 1]\n\n _, predicted_1 = torch.max(outputs_val_1.data, 1)\n\n # ----------Compute dice-----------\n predicted_val_1 = predicted_1.data.cpu().numpy()\n subject_val = subject.data.cpu().numpy()\n slice_val = slice.data.cpu().numpy()\n slice_val_1 = slice_val[0][1]\n targets_val = targets_val.data.cpu().numpy()\n targets_val_1 = targets_val[:,1, ...]\n\n shape_list.append(predicted_val_1.shape)\n data_dict_list.append({\"subject\": subject_val[0], \"slice\": slice_val_1, \"pre\": np.squeeze(predicted_val_1,axis=0),\n \"target\": np.squeeze(targets_val_1, axis=0), \"image\": image_origin1, \"uncertainty\": entropy, \"softmax_out\":softmax_final_out})\n\n # test the elaps of uncertainty quantification\n end_time = time.time()\n print(\"elapsed:{}\".format(end_time-start_time))\n # 利用pandas分组\n pd_data = pd.DataFrame(data_dict_list)\n for subject, volume_data in pd_data.groupby(\"subject\"):\n pre = volume_data[\"pre\"]\n tar = volume_data[\"target\"]\n slices = volume_data[\"slice\"]\n image = volume_data[\"image\"]\n uncertain = volume_data[\"uncertainty\"]\n softmax_prob = volume_data[\"softmax_out\"]\n\n pre_array = pre.values\n target_array = tar.values\n image_array = image.values\n uncertain_arr = uncertain.values\n slices_arr = slices.values\n softmax_prob_arr = softmax_prob.values\n\n pre_temp = np.zeros((len(pre_array), pre_array[0].shape[0], pre_array[0].shape[1]), dtype=\"int16\")\n target_temp = np.zeros((len(pre_array), target_array[0].shape[0], target_array[0].shape[1]), dtype=\"int16\")\n # dimentions: slices*class*width*height\n softmax_probs_temp = np.zeros((len(pre_array), softmax_prob_arr[0].shape[0], softmax_prob_arr[0].shape[1],softmax_prob_arr[0].shape[2]), dtype=\"float32\")\n for i in range(len(pre_array)):\n pre_temp[i, :, :] = pre_array[i]\n target_temp[i, :, :] = target_array[i]\n softmax_probs_temp[i,:,:,:] = softmax_prob_arr[i]\n # 保存预测结果与GT及图像\n if save_segmentation:\n image_slice = image_array[i]\n # save image and segmentation\n my_evaluation.save_contour_label(image_slice.astype(\"int16\"),\n target_array[i],save_path=submit_path, color=\"red\", file_name=str(subject)+\"_\"+\n str(slices_arr[i])+\"label\",show_mask=True)\n my_evaluation.save_contour_label(image_slice.astype(\"int16\"),\n pre_array[i], save_path=submit_path, color=\"blue\", file_name=str(subject)+\"_\"+\n str(slices_arr[i])+\"pre\", show_mask=True)\n\n orig_path = os.path.join(submit_path, str(subject)+\"_\"+str(slices_arr[i])+'.png')\n cv.imwrite(orig_path, image_slice.astype(\"uint8\"))\n if save_uncertainty:\n # Predicted error map\n error = np.abs(pre_array[i]-target_array[i])\n error_name = str(subject) + \"_\" + str(slices_arr[i]) + \"error.png\"\n error_file_path = os.path.join(uncertaintys_path, error_name)\n plt.figure()\n plt.imshow(error, cmap=plt.cm.Reds, interpolation='nearest')\n # Visulization of the uncertainty\n file_name = str(subject) + \"_\" + str(slices_arr[i]) + \".png\"\n file_path = os.path.join(uncertaintys_path, file_name)\n plt.colorbar()\n plt.xticks([])\n plt.yticks([])\n plt.savefig(error_file_path)\n plt.clf()\n plt.cla()\n plt.close()\n\n plt.figure()\n plt.imshow(uncertain_arr[i], cmap=plt.cm.rainbow, interpolation='nearest')\n plt.colorbar()\n plt.xticks([])\n plt.yticks([])\n # plt.axes('off')\n plt.savefig(file_path)\n plt.clf()\n plt.cla()\n plt.close()\n\n dsc_list1 = []\n if 0 == np.count_nonzero(pre_temp):\n print(\"zero\"+\"_\"+str(subject))\n continue\n\n # calculate the dice metric\n for i in range(0, test_config.num_classes):\n dsc_i = dice(pre_temp, target_temp, i)\n dsc_list1.append(dsc_i)\n\n # Calculate Hausdorff Distance 以及ravd\n hausdorff_dist = hd(pre_temp, target_temp, [5, 0.42, 0.42])\n # we measure the absolute volume difference\n ravd = abs(rAVD(pre_temp, target_temp))\n\n # calculate the volume of ICH for GT and predictions\n volume_gt = calculate_volume(target_temp)\n volume_pre = calculate_volume(pre_temp)\n\n # Evaluate uncertainty qualification with nll, brier, ece\n softmax_probs_temp = softmax_probs_temp.transpose(1,0,2,3)\n brier_socre = brier(torch.from_numpy(softmax_probs_temp).float(), torch.from_numpy(target_temp).long())\n ece_subject_wise,_,_= ece(softmax_probs_temp[1,:,:,:], target_temp, 10)\n # Test sklearn\n target_onehot_temp = one_hot(target_temp, 2)\n\n brier_sklearn = brier_score_loss(target_onehot_temp[0, ...].flatten(), softmax_probs_temp[0, ...].flatten())+\\\n brier_score_loss(target_onehot_temp[1,...].flatten(), softmax_probs_temp[1,...].flatten())\n\n nll_score = nll(torch.from_numpy(softmax_probs_temp).float(), torch.from_numpy(target_temp).long())\n print(\"nll_score:{} brier_socre:{}\".format(nll_score.data.numpy(), brier_socre.data.numpy()))\n print(\"dice_bg:{} dice_fg:{} Hausdorff_dist:{} ravd:{}\".format(dsc_list1[0], dsc_list1[1],hausdorff_dist, ravd))\n txtlog.write(\"ID{:30} {:3f} {:3f} {:3f} {:3f} {:3f} {:3f} {:3f} {:3f} {:3f} \\n\".format(subject, dsc_list1[0], dsc_list1[1],\n hausdorff_dist, ravd, ece_subject_wise, nll_score, brier_sklearn,volume_gt, volume_pre))\n dice_new_list.append(dsc_list1)\n hausdorff_dist_list.append(hausdorff_dist)\n ravd_list.append(ravd)\n\n brier_list.append(brier_socre.data.numpy())\n nll_list.append(nll_score.data.numpy())\n brier_sklearn_list.append(brier_sklearn)\n ece_list.append(ece_subject_wise)\n # store all the test data\n testset_list_pre.append(softmax_probs_temp[1,:,:,:])\n testset_list_gt.append(target_temp)\n\n dice_array = np.array(dice_new_list)\n dice_mean = np.mean(dice_array, axis=0)\n haus_dist_arr = np.array(hausdorff_dist_list)\n hausdorff_dist_mean = np.mean(haus_dist_arr, axis=0)\n ravd_arr = np.array(ravd_list)\n ravd_mean = np.mean(ravd_arr, axis=0)\n\n # uncertainty quantification\n brier_array = np.mean(np.array(brier_list),axis=0)\n nll_array = np.mean(np.array(nll_list), axis=0)\n brier_sklearn_mean = np.mean(np.array(brier_sklearn_list),axis=0)\n ece_subject_mean = np.mean(np.array(ece_list),axis=0)\n\n stacked_pre = merge_samples(testset_list_pre)\n stacked_gt = merge_samples(testset_list_gt)\n print(\"pre:{} gt:{}\".format(stacked_pre.shape, stacked_gt.shape))\n ece_score, confidence, accuracy = ece(stacked_pre,stacked_gt, 10)\n fraction_of_positives, mean_predicted_value = \\\n calibration_curve(stacked_gt.flatten(), stacked_pre.flatten(), n_bins=10)\n\n # Draw Reliability Diagram (binned version and curve version)\n x = np.linspace(0, 1. + 1e-8, 10)\n y3 = x\n plt.plot([0, 1], [0, 1], \"k:\")\n plt.bar(x, height=fraction_of_positives, color='b', width=-0.112, label='Outputs', linewidth=2, edgecolor=['black'] * len(x),\n align='edge')\n plt.bar(x, height=y3 - fraction_of_positives, color='g', bottom=fraction_of_positives, width=-0.112, label='Gap', linewidth=2,\n edgecolor=['black'] * len(x), align='edge')\n plt.xlim(0., 1.)\n plt.ylim(0., 1.)\n plt.xlabel(\"Confidence\")\n plt.ylabel(\"Accuracy\")\n # plt.title(\"Histogram polt\")\n plt.legend(loc=\"upper left\")\n plt.savefig('reliability_diagram_bined.png', dpi=400, bbox_inches='tight')\n\n plt.figure(figsize=(5, 5))\n ax1 = plt.subplot2grid((1, 1), (0, 0), rowspan=2)\n ax1.plot([0, 1], [0, 1], \"k:\", label=\"Perfectly calibrated\")\n ax1.plot(mean_predicted_value, fraction_of_positives, \"s-\", label=\"calibrated_sklearn\")\n ax1.set_ylabel(\"Fraction of positives\")\n ax1.set_ylim([-0.05, 1.05])\n ax1.legend(loc=\"upper left\")\n ax1.set_title('Calibration plots (reliability curve)')\n plt.savefig('reliability_diagram_sklearn.png', dpi=400, bbox_inches='tight')\n\n with h5py.File(\"reliability_se_net.h5\", \"w\") as f:\n f['condifence'] = confidence\n f['accuracy'] = accuracy\n txtlog.write(\"Dice_mean fg|bg|hausdorff_dist|ravd|ece|brier|nll|sklearn_brier|ece_sub_mea\"\\\n \"n:{:3f} ||{:3f}||{:3f}||{:3f}||{:3f}||{:3f}||{:3f}||{:3f} ||{:3f}\\n\".format(dice_mean[0],\\\n dice_mean[1], hausdorff_dist_mean, ravd_mean,ece_score,brier_array, nll_array, brier_sklearn_mean,ece_subject_mean))\n txtlog.write(\"Time Elapsed: {}\".format(end_time - start_time))\n return dice_mean\n\ndef mat2gray(I,limits):\n i = I.astype(np.float64)\n graymax = float(limits[1])\n graymin = float(limits[0])\n delta = 1 / (graymax - graymin)\n gray = delta * i - graymin * delta\n # 进行截断，对于大于最大值与小于最小值的部分，大于最大值设为1,小于最小值的设为0\n graycut = np.maximum(0, np.minimum(gray, 1))\n return graycut\n\ndef merge_samples(sample_list):\n '''\n merge slice-wise predictions or ground-truth to that of volume-vise\n :param sample_list:\n :return:\n '''\n sample = sample_list[0]\n for i in range(1,len(sample_list)):\n sample_stack = np.concatenate((sample, sample_list[i]), axis=0)\n sample = sample_stack\n return sample\n\ndef one_hot(input, class_n):\n '''\n onehot for pytorch\n :param input: N*H*W*D\n :param class_n:\n :return:N*n_class*H*W*D\n '''\n shape = input.shape\n onehot = np.zeros((class_n,)+shape)\n for i in range(class_n):\n onehot[i, ...] = (input == i)\n # onehot_trans = onehot.permute(1,0,2,3,4)\n onehot_trans = onehot\n return onehot_trans\n\ndef calculate_volume(binary_volume, pixel_spacing=(5,0.42,0.42)):\n '''\n calculate the volume of the hemorrhage\n :param binary_volume: D*W*H\n :param pixel_spacing: unit: mm\n :return:\n '''\n shape = binary_volume.shape\n volume = 0\n for i in range(shape[0]):\n binary_slice = binary_volume[i,:,:]\n volume_slice = np.sum(binary_slice)*pixel_spacing[0]*pixel_spacing[1]*pixel_spacing[2]\n volume += volume_slice\n # unit transfer to mL\n volume = volume/1000\n return volume\n\n\n\nos.environ['CUDA_VISIBLE_DEVICES'] = '3' # '1'\ndevice = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n\nif __name__ == '__main__':\n test_config = Config()\n net = HybridUNet_single_out_consistency(backbone_channel=1, backbone_class=2, SEM_channels=3, SEM_class=2,\n drop_rate=test_config.drop_rate).to(device)\n # val_path = os.path.join(test_config.data_path, \"data_val\")\n val_path = os.path.join(test_config.data_path, \"data_test\")\n ct_data_val = CTData_test(val_path, augmentation=None)\n valloader = dataloader.DataLoader(ct_data_val, batch_size=1, shuffle=False)\n save_segmentation = test_config.save_segmentation\n save_uncertain = test_config.save_uncertainty\n\n dsc_mean_list = []\n if os.path.exists(\"test_lamda_consistency.txt\"):\n os.remove(\"test_lamda_consistency.txt\")\n\n with open(\"test_lamda_consistency.txt\", \"a\") as txtlog:\n test_config.write_config(txtlog)\n # -----------------------Testing-------------------------------------\n # -----------------------Load the checkpoint (weights)---------------\n print ('Checkpoint: ', test_config.ckp_test)\n saved_state_dict = torch.load(test_config.ckp_test)\n net.load_state_dict(saved_state_dict)\n net.eval()\n submit_path = './submit'\n if not os.path.exists(submit_path):\n os.makedirs(submit_path)\n uncetainty_path = './uncertainty'\n if not os.path.exists(uncetainty_path):\n os.mkdir(uncetainty_path)\n online_eval(net, valloader, txtlog, submit_path, uncetainty_path, save_segmentation, save_uncertain)\n\n\n", "repo_name": "JohnleeHIT/SLEX-Net", "sub_path": "src/test_single_consistency.py", "file_name": "test_single_consistency.py", "file_ext": "py", "file_size_in_byte": 15722, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 24, "dataset": "github-code", "pt": "37", "api": [{"api_name": "miscellaneous.utils.Evaluation", "line_number": 21, "usage_type": "call"}, {"api_name": "time.time", "line_number": 22, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 23, "usage_type": "call"}, {"api_name": "torch.utils.data", "line_number": 35, "usage_type": "name"}, {"api_name": "torch.unsqueeze", "line_number": 45, "usage_type": "call"}, {"api_name": "torch.unsqueeze", "line_number": 46, "usage_type": "call"}, {"api_name": "torch.unsqueeze", "line_number": 47, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 48, "usage_type": "call"}, {"api_name": "numpy.squeeze", "line_number": 50, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 51, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 52, "usage_type": "call"}, {"api_name": "numpy.squeeze", "line_number": 56, "usage_type": "call"}, {"api_name": "numpy.squeeze", "line_number": 61, "usage_type": "call"}, {"api_name": "torch.max", "line_number": 64, "usage_type": "call"}, {"api_name": "numpy.squeeze", "line_number": 75, "usage_type": "call"}, {"api_name": "numpy.squeeze", "line_number": 76, "usage_type": "call"}, {"api_name": "time.time", "line_number": 79, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 82, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 98, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 99, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 101, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 117, "usage_type": "call"}, {"api_name": "os.path", "line_number": 117, "usage_type": "attribute"}, {"api_name": "cv2.imwrite", "line_number": 118, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 121, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 123, "usage_type": "call"}, {"api_name": "os.path", "line_number": 123, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 124, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 124, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 125, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 125, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.cm", "line_number": 125, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 128, "usage_type": "call"}, {"api_name": "os.path", "line_number": 128, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.colorbar", "line_number": 129, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 129, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 130, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 130, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.yticks", "line_number": 131, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 131, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 132, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 132, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.clf", "line_number": 133, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 133, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.cla", "line_number": 134, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 134, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.close", "line_number": 135, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 135, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 137, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 137, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 138, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 138, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.cm", "line_number": 138, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.colorbar", "line_number": 139, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 139, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 140, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 140, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.yticks", "line_number": 141, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 141, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 143, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 143, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.clf", "line_number": 144, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 144, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.cla", "line_number": 145, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 145, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.close", "line_number": 146, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 146, "usage_type": "name"}, {"api_name": "numpy.count_nonzero", "line_number": 149, "usage_type": "call"}, {"api_name": "miscellaneous.metrics.dice", "line_number": 155, "usage_type": "call"}, {"api_name": "miscellaneous.metrics.hd", "line_number": 159, "usage_type": "call"}, {"api_name": "miscellaneous.metrics.rAVD", "line_number": 161, "usage_type": "call"}, {"api_name": "miscellaneous.metrics.brier", "line_number": 169, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 169, "usage_type": "call"}, {"api_name": "miscellaneous.metrics.ece", "line_number": 170, "usage_type": "call"}, {"api_name": "sklearn.metrics.brier_score_loss", "line_number": 174, "usage_type": "call"}, {"api_name": "sklearn.metrics.brier_score_loss", "line_number": 175, "usage_type": "call"}, {"api_name": "miscellaneous.metrics.nll", "line_number": 177, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 177, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 194, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 195, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 196, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 197, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 198, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 199, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 202, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 202, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 203, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 203, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 204, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 204, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 205, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 205, "usage_type": "call"}, {"api_name": "miscellaneous.metrics.ece", "line_number": 210, "usage_type": "call"}, {"api_name": "sklearn.calibration.calibration_curve", "line_number": 212, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 215, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 217, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 217, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.bar", "line_number": 218, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 218, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.bar", "line_number": 220, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 220, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 222, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 222, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 223, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 223, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 224, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 224, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 225, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 225, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 227, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 227, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 228, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 228, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 230, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 230, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot2grid", "line_number": 231, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 231, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 238, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 238, "usage_type": "name"}, {"api_name": "h5py.File", "line_number": 240, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 250, "usage_type": "attribute"}, {"api_name": "numpy.maximum", "line_number": 256, "usage_type": "call"}, {"api_name": "numpy.minimum", "line_number": 256, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 267, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 279, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 297, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 305, "usage_type": "attribute"}, {"api_name": "torch.device", "line_number": 306, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 306, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 306, "usage_type": "attribute"}, {"api_name": "config.Config", "line_number": 309, "usage_type": "call"}, {"api_name": "models.segmentor_v1.HybridUNet_single_out_consistency", "line_number": 310, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 313, "usage_type": "call"}, {"api_name": "os.path", "line_number": 313, "usage_type": "attribute"}, {"api_name": "data.datagenerator.CTData_test", "line_number": 314, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 315, "usage_type": "call"}, {"api_name": "torch.utils.data", "line_number": 315, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 320, "usage_type": "call"}, {"api_name": "os.path", "line_number": 320, "usage_type": "attribute"}, {"api_name": "os.remove", "line_number": 321, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 328, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 332, "usage_type": "call"}, {"api_name": "os.path", "line_number": 332, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 333, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 335, "usage_type": "call"}, {"api_name": "os.path", "line_number": 335, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 336, "usage_type": "call"}]} +{"seq_id": "8069834200", "text": "\"\"\"Mixture model using EM\"\"\"\nfrom typing import Tuple\nimport numpy as np\nfrom common import GaussianMixture\n\n\n\ndef estep(X: np.ndarray, mixture: GaussianMixture) -> Tuple[np.ndarray, float]:\n \"\"\"E-step: Softly assigns each datapoint to a gaussian component\n\n Args:\n X: (n, d) array holding the data\n mixture: the current gaussian mixture\n\n Returns:\n np.ndarray: (n, K) array holding the soft counts\n for all components for all examples\n float: log-likelihood of the assignment\n \"\"\"\n rated = (X != 0).astype(np.float)\n d = np.sum(rated, axis=1)[:,None]\n\n likelihood = np.ones(d.shape) * mixture.p / (\n (2 * np.pi * np.ones(d.shape) * mixture.var) ** (d / 2)) * \\\n np.exp(-np.linalg.norm(X[:, None, :] - rated[:, None, :] * mixture.mu, axis=2) ** 2 / (\n 2 * mixture.var[None, :]))\n post = likelihood/np.sum(likelihood, axis=1)[:,None]\n ll = np.sum(np.log(np.sum(likelihood, axis=1)))\n\n return post, ll\n\n\ndef mstep(X: np.ndarray, post: np.ndarray) -> GaussianMixture:\n \"\"\"M-step: Updates the gaussian mixture by maximizing the log-likelihood\n of the weighted dataset\n\n Args:\n X: (n, d) array holding the data\n post: (n, K) array holding the soft counts\n for all components for all examples\n\n Returns:\n GaussianMixture: the new gaussian mixture\n \"\"\"\n rated = (X != 0).astype(np.float)[:, None, :]\n p = 1/post.shape[1] * np.ones(post.shape[1])\n\n p_j_i = p * post / np.sum(p * post, axis=1)[:,None]\n\n p = np.sum(p_j_i, axis=0) / X.shape[0]\n mu = np.sum(X[:,None,:] * rated*p_j_i[:,:,None], axis=0) / np.sum(rated*p_j_i[:,:,None], axis=0)\n var = np.sum(p_j_i * (np.linalg.norm(X[:,None,:] - rated * mu, axis=2) ** 2), axis=0) / \\\n np.sum(np.sum(rated*p_j_i[:,:,None], axis=0), axis=1)\n\n return GaussianMixture(mu, var, p)\n\n\ndef run(X: np.ndarray, mixture: GaussianMixture,\n post: np.ndarray) -> Tuple[GaussianMixture, np.ndarray, float]:\n \"\"\"Runs the mixture model\n\n Args:\n X: (n, d) array holding the data\n post: (n, K) array holding the soft counts\n for all components for all examples\n\n Returns:\n GaussianMixture: the new gaussian mixture\n np.ndarray: (n, K) array holding the soft counts\n for all components for all examples\n float: log-likelihood of the current assignment\n \"\"\"\n old_ll = None\n ll = None\n while old_ll is None or ll - old_ll >= 1e-6*np.absolute(ll):\n old_ll = ll\n post, ll = estep(X, mixture)\n mixture = mstep(X, post)\n\n return mixture, post, ll\n", "repo_name": "martlaf/MITxMachineLearningClass", "sub_path": "Project4/naive_em.py", "file_name": "naive_em.py", "file_ext": "py", "file_size_in_byte": 2654, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 11, "dataset": "github-code", "pt": "37", "api": [{"api_name": "numpy.ndarray", "line_number": 8, "usage_type": "attribute"}, {"api_name": "common.GaussianMixture", "line_number": 8, "usage_type": "name"}, {"api_name": "numpy.float", "line_number": 20, "usage_type": "attribute"}, {"api_name": "numpy.sum", "line_number": 21, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 23, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 24, "usage_type": "attribute"}, {"api_name": "numpy.ones", "line_number": 24, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 25, "usage_type": "call"}, {"api_name": "numpy.linalg.norm", "line_number": 25, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 25, "usage_type": "attribute"}, {"api_name": "numpy.sum", "line_number": 27, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 28, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 28, "usage_type": "call"}, {"api_name": "typing.Tuple", "line_number": 8, "usage_type": "name"}, {"api_name": "numpy.ndarray", "line_number": 33, "usage_type": "attribute"}, {"api_name": "numpy.float", "line_number": 45, "usage_type": "attribute"}, {"api_name": "numpy.ones", "line_number": 46, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 48, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 50, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 51, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 52, "usage_type": "call"}, {"api_name": "numpy.linalg.norm", "line_number": 52, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 52, "usage_type": "attribute"}, {"api_name": "numpy.sum", "line_number": 53, "usage_type": "call"}, {"api_name": "common.GaussianMixture", "line_number": 55, "usage_type": "call"}, {"api_name": "common.GaussianMixture", "line_number": 33, "usage_type": "name"}, {"api_name": "numpy.ndarray", "line_number": 58, "usage_type": "attribute"}, {"api_name": "common.GaussianMixture", "line_number": 58, "usage_type": "name"}, {"api_name": "numpy.ndarray", "line_number": 59, "usage_type": "attribute"}, {"api_name": "numpy.absolute", "line_number": 75, "usage_type": "call"}, {"api_name": "typing.Tuple", "line_number": 59, "usage_type": "name"}, {"api_name": "common.GaussianMixture", "line_number": 59, "usage_type": "name"}]} +{"seq_id": "25310208133", "text": "from typing import List\n\nfrom spacy.tokens.doc import Doc\nfrom spacy.tokens.span import Span\n\nfrom spacy.tokens.token import Token\n\nfrom quillnlp.grammar.constants import Dependency, Tag, POS, TokenSet, GrammarError, RELATIVE_PRONOUN_TAGS\nfrom quillnlp.grammar.myspacy import nlp\n\n# Auxiliary verb functions:\n\ndef is_negated_with_contraction(token, sentence):\n if len(sentence) > token.i+1 and sentence[token.i+1].text == \"n't\":\n return True\n return False\n\n\ndef has_noun_subject(token: Token):\n subject = get_subject(token, full=True)\n\n if subject is not None:\n for t in subject:\n if (t.dep_ == Dependency.PASS_SUBJECT.value or t.dep_ == Dependency.SUBJECT.value) \\\n and (t.pos_ == POS.NOUN.value or t.pos_ == POS.PROPER_NOUN.value):\n return True\n return False\n\n\ndef has_pronoun_subject(token: Token):\n subject = get_subject(token, full=True)\n\n if subject is not None:\n for t in subject:\n if (t.dep_ == Dependency.PASS_SUBJECT.value or t.dep_ == Dependency.SUBJECT.value) \\\n and t.pos_ == POS.PRONOUN.value:\n return True\n return False\n\n\ndef is_relative_pronoun_subject(subject: List[Token]) -> bool:\n \"\"\" Determines if the list of tokens is a subject that contains a pronoun. \"\"\"\n \n if subject is not None:\n for t in subject:\n if (t.dep_ == Dependency.PASS_SUBJECT.value or t.dep_ == Dependency.SUBJECT.value) \\\n and t.tag_ in RELATIVE_PRONOUN_TAGS:\n return True\n return False\n\n\ndef has_relative_pronoun_subject(token: Token) -> bool:\n \"\"\" Determines if the token has a pronoun subject \"\"\"\n subject = get_subject(token, full=True)\n return is_relative_pronoun_subject(subject)\n\n\ndef has_indefinite_subject(token: Token):\n subject = get_subject(token, full=False)\n\n return subject is not None and is_indefinite(subject)\n\n\ndef subject_has_neither(verb: Token):\n subject = get_subject(verb)\n\n if subject is None:\n return False\n\n for token in subject.lefts:\n if token.text.lower() == \"neither\":\n return True\n return False\n\n\ndef subject_has_either(verb: Token, sentence: Span):\n # For some reason spaCy analyzes \"either or\" differently than \"neither nor\"\n subject = get_subject(verb)\n\n if subject is None:\n return False\n\n right_tokens = list(subject.rights)\n if len(right_tokens) > 0 and right_tokens[0].text == \"or\" and \"either\" in sentence.text.lower()[:subject.idx]:\n return True\n return False\n\n\ndef has_following_subject(verb: Token):\n \"\"\" Returns True if the verb's subject comes after it in the sentence,\n and false otherwise. \"\"\"\n subject = get_subject(verb)\n if subject is not None and subject.idx > verb.idx:\n return True\n return False\n\n\ndef is_past(verb: Token) -> bool:\n \"\"\" Determines whether a verb is simple past. \"\"\"\n return verb.tag_ == Tag.SIMPLE_PAST_VERB.value\n\n\ndef is_future(verb: Token) -> bool:\n \"\"\" Determines whether a verb is future. \"\"\"\n for child in verb.lefts:\n if child.lemma_ == \"will\" and \\\n child.dep_ == Dependency.AUX.value and \\\n child.tag_ == Tag.MODAL_VERB.value:\n return True\n return False\n\n\ndef is_past_perfect(verb: Token) -> bool:\n \"\"\" Determines whether a verb is past perfect. \"\"\"\n if not verb.tag_ == Tag.PAST_PARTICIPLE_VERB.value:\n return False\n\n for child in verb.lefts:\n if child.lemma_ == \"have\" and \\\n child.dep_ == Dependency.AUX.value and \\\n child.tag_ == Tag.SIMPLE_PAST_VERB.value:\n return True\n return False\n\n\ndef is_present(verb: Token) -> bool:\n \"\"\" Determines if a verb form is present. \"\"\"\n if is_future(verb):\n return False\n return verb.tag_ == Tag.PRESENT_OTHER_VERB.value or Tag.PRESENT_SING3_VERB.value\n\n\ndef get_subject(verb: Token, full=False):\n\n # If the verb is the root, we can look up its subject in its left children\n #if verb.dep_ == Dependency.ROOT.value:\n\n for token in list(reversed(list(verb.lefts))) + list(verb.rights):\n if token.dep_ == Dependency.SUBJECT.value or \\\n token.dep_ == Dependency.PASS_SUBJECT.value or \\\n (verb.dep_ == Dependency.CCOMP.value and token.dep_ == Dependency.ATTRIBUTE.value):\n if full:\n return list(token.lefts) + [token]\n else:\n return token\n\n # cases like \"There is a man in the room.\"\n elif token.dep_ == Dependency.EXPLETIVE.value or token.dep_ == Dependency.CLAUSAL_SUBJECT.value:\n for token2 in list(reversed(list(verb.lefts))) + list(verb.rights):\n if token2.dep_ == Dependency.ATTRIBUTE.value:\n if full:\n return list(token2.lefts) + [token2]\n else:\n return token2\n\n # If we still haven't found anything, we return the attribute\n for token in list(reversed(list(verb.lefts))) + list(verb.rights):\n if token.dep_ == Dependency.ATTRIBUTE.value:\n if full:\n return list(token.lefts) + [token]\n else:\n return token\n\n # otherwise we have to look up the subject of its head.\n if verb.dep_ == Dependency.AUX.value or \\\n verb.dep_ == Dependency.PASS_AUXILIARY.value or \\\n verb.dep_ == Dependency.CONJUNCTION.value:\n return get_subject(verb.head, full=full)\n\n return None\n\n\ndef get_plural(verb: Token):\n \"\"\" Finds the plural form of a verb token. \"\"\"\n if verb.lemma_ == \"be\":\n return \"are\"\n else:\n return verb._.inflect(Tag.INFINITIVE.value)\n\n\ndef is_indefinite(noun: Token):\n \"\"\" Determines if a noun token is indefinite or not. \"\"\"\n return noun.left_edge.text.lower() in TokenSet.INDEF_PRONOUNS.value\n\n\ndef get_past_tenses(token: Token):\n \"\"\"\n This fixes a few problems with pyinflect, such as the fact that it\n only returns \"was\" as the past tense of \"were\".\n\n Args:\n token:\n\n Returns:\n\n \"\"\"\n\n PAST_TENSE_MAP = {\"be\": set([\"was\", \"were\"]),\n \"quit\": set([\"quit\"])}\n\n if token.lemma_.lower() in PAST_TENSE_MAP:\n return PAST_TENSE_MAP[token.lemma_.lower()]\n else:\n past_tense = token._.inflect(Tag.SIMPLE_PAST_VERB.value)\n if past_tense is None:\n return set()\n else:\n return set([past_tense.lower()])\n\n\ndef has_inversion(doc):\n for token in doc:\n if token.pos_ == POS.VERB.value:\n subject = get_subject(token)\n if subject is not None and subject.i > token.i:\n return True\n return False\n\n\ndef token_has_inversion(token):\n if token.tag_.startswith(\"V\"):\n subject = get_subject(token)\n if subject is not None and subject.i > token.i:\n return True\n return False\n\n# Synthetic functions\n\n\ndef replace_past_simple_with_past_perfect(sentence):\n\n doc = nlp(sentence)\n new_sentence = \"\"\n\n for token in doc:\n if token.dep_ == Dependency.ADVERBIAL_CLAUSE.value and is_past(token) and is_past_perfect(token.head):\n new_sentence += \"had \"\n new_sentence += token.text_with_ws\n\n return new_sentence\n\n\ndef get_perfect_progressives(doc: Doc) -> List[Token]:\n \"\"\" Finds all perfect progressives (e.g. 'have been working')\n in a document. \"\"\"\n perfect_progressives = []\n for token in doc:\n if token.tag_ == Tag.PRESENT_PARTICIPLE_VERB.value:\n have, been = 0, 0\n for token2 in token.lefts:\n if token2.lemma_ == \"have\":\n have = 1\n elif token2.text == \"been\" and have:\n been = 1\n if have and been:\n perfect_progressives.append(token)\n\n return perfect_progressives\n\n\ndef in_have_been_construction(token: Token) -> bool:\n \"\"\" Determines whether the token is in a 'have been' construction,\n such as 'has been found'.\n \"\"\"\n have, been = 0, 0\n for token2 in token.lefts:\n if token2.lemma_ == \"have\":\n have = 1\n elif token2.text == \"been\" and have:\n been = 1\n if have and been:\n return True\n return False\n\n\ndef is_perfect(token: Token) -> bool:\n \"\"\" Determines whether the token is in a 'have' construction.\n \"\"\"\n for token2 in token.lefts:\n if token2.lemma_ == \"have\":\n return True\n return False\n\n\ndef is_passive(verb: Token) -> bool:\n \"\"\" Determines whether a verb token is passive. \"\"\"\n for child in verb.lefts:\n if child.dep_ == Dependency.PASS_AUXILIARY.value:\n return True\n return False", "repo_name": "empirical-org/Quill-NLP-Tools-and-Datasets", "sub_path": "quillnlp/grammar/verbutils.py", "file_name": "verbutils.py", "file_ext": "py", "file_size_in_byte": 8746, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 50, "dataset": "github-code", "pt": "37", "api": [{"api_name": "spacy.tokens.token.Token", "line_number": 19, "usage_type": "name"}, {"api_name": "quillnlp.grammar.constants.Dependency.PASS_SUBJECT", "line_number": 24, "usage_type": "attribute"}, {"api_name": "quillnlp.grammar.constants.Dependency", "line_number": 24, "usage_type": "name"}, {"api_name": "quillnlp.grammar.constants.Dependency.SUBJECT", "line_number": 24, "usage_type": "attribute"}, {"api_name": "quillnlp.grammar.constants.POS.NOUN", "line_number": 25, "usage_type": "attribute"}, {"api_name": "quillnlp.grammar.constants.POS", "line_number": 25, "usage_type": "name"}, {"api_name": "quillnlp.grammar.constants.POS.PROPER_NOUN", "line_number": 25, "usage_type": "attribute"}, {"api_name": "spacy.tokens.token.Token", "line_number": 30, "usage_type": "name"}, {"api_name": "quillnlp.grammar.constants.Dependency.PASS_SUBJECT", "line_number": 35, "usage_type": "attribute"}, {"api_name": "quillnlp.grammar.constants.Dependency", "line_number": 35, "usage_type": "name"}, {"api_name": "quillnlp.grammar.constants.Dependency.SUBJECT", "line_number": 35, "usage_type": "attribute"}, {"api_name": "quillnlp.grammar.constants.POS.PRONOUN", "line_number": 36, "usage_type": "attribute"}, {"api_name": "quillnlp.grammar.constants.POS", "line_number": 36, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 41, "usage_type": "name"}, {"api_name": "spacy.tokens.token.Token", "line_number": 41, "usage_type": "name"}, {"api_name": "quillnlp.grammar.constants.Dependency.PASS_SUBJECT", "line_number": 46, "usage_type": "attribute"}, {"api_name": "quillnlp.grammar.constants.Dependency", "line_number": 46, "usage_type": "name"}, {"api_name": "quillnlp.grammar.constants.Dependency.SUBJECT", "line_number": 46, "usage_type": "attribute"}, {"api_name": "quillnlp.grammar.constants.RELATIVE_PRONOUN_TAGS", "line_number": 47, "usage_type": "name"}, {"api_name": "spacy.tokens.token.Token", "line_number": 52, "usage_type": "name"}, {"api_name": "spacy.tokens.token.Token", "line_number": 58, "usage_type": "name"}, {"api_name": "spacy.tokens.token.Token", "line_number": 64, "usage_type": "name"}, {"api_name": "spacy.tokens.token.Token", "line_number": 76, "usage_type": "name"}, {"api_name": "spacy.tokens.span.Span", "line_number": 76, "usage_type": "name"}, {"api_name": "spacy.tokens.token.Token", "line_number": 89, "usage_type": "name"}, {"api_name": "spacy.tokens.token.Token", "line_number": 98, "usage_type": "name"}, {"api_name": "quillnlp.grammar.constants.Tag.SIMPLE_PAST_VERB", "line_number": 100, "usage_type": "attribute"}, {"api_name": "quillnlp.grammar.constants.Tag", "line_number": 100, "usage_type": "name"}, {"api_name": "spacy.tokens.token.Token", "line_number": 103, "usage_type": "name"}, {"api_name": "quillnlp.grammar.constants.Dependency.AUX", "line_number": 107, "usage_type": "attribute"}, {"api_name": "quillnlp.grammar.constants.Dependency", "line_number": 107, "usage_type": "name"}, {"api_name": "quillnlp.grammar.constants.Tag.MODAL_VERB", "line_number": 108, "usage_type": "attribute"}, {"api_name": "quillnlp.grammar.constants.Tag", "line_number": 108, "usage_type": "name"}, {"api_name": "spacy.tokens.token.Token", "line_number": 113, "usage_type": "name"}, {"api_name": "quillnlp.grammar.constants.Tag.PAST_PARTICIPLE_VERB", "line_number": 115, "usage_type": "attribute"}, {"api_name": "quillnlp.grammar.constants.Tag", "line_number": 115, "usage_type": "name"}, {"api_name": "quillnlp.grammar.constants.Dependency.AUX", "line_number": 120, "usage_type": "attribute"}, {"api_name": "quillnlp.grammar.constants.Dependency", "line_number": 120, "usage_type": "name"}, {"api_name": "quillnlp.grammar.constants.Tag.SIMPLE_PAST_VERB", "line_number": 121, "usage_type": "attribute"}, {"api_name": "quillnlp.grammar.constants.Tag", "line_number": 121, "usage_type": "name"}, {"api_name": "spacy.tokens.token.Token", "line_number": 126, "usage_type": "name"}, {"api_name": "quillnlp.grammar.constants.Tag.PRESENT_OTHER_VERB", "line_number": 130, "usage_type": "attribute"}, {"api_name": "quillnlp.grammar.constants.Tag", "line_number": 130, "usage_type": "name"}, {"api_name": "quillnlp.grammar.constants.Tag.PRESENT_SING3_VERB", "line_number": 130, "usage_type": "attribute"}, {"api_name": "spacy.tokens.token.Token", "line_number": 133, "usage_type": "name"}, {"api_name": "quillnlp.grammar.constants.Dependency.SUBJECT", "line_number": 139, "usage_type": "attribute"}, {"api_name": "quillnlp.grammar.constants.Dependency", "line_number": 139, "usage_type": "name"}, {"api_name": "quillnlp.grammar.constants.Dependency.PASS_SUBJECT", "line_number": 140, "usage_type": "attribute"}, {"api_name": "quillnlp.grammar.constants.Dependency", "line_number": 140, "usage_type": "name"}, {"api_name": "quillnlp.grammar.constants.Dependency.CCOMP", "line_number": 141, "usage_type": "attribute"}, {"api_name": "quillnlp.grammar.constants.Dependency", "line_number": 141, "usage_type": "name"}, {"api_name": "quillnlp.grammar.constants.Dependency.ATTRIBUTE", "line_number": 141, "usage_type": "attribute"}, {"api_name": "quillnlp.grammar.constants.Dependency.EXPLETIVE", "line_number": 148, "usage_type": "attribute"}, {"api_name": "quillnlp.grammar.constants.Dependency", "line_number": 148, "usage_type": "name"}, {"api_name": "quillnlp.grammar.constants.Dependency.CLAUSAL_SUBJECT", "line_number": 148, "usage_type": "attribute"}, {"api_name": "quillnlp.grammar.constants.Dependency.ATTRIBUTE", "line_number": 150, "usage_type": "attribute"}, {"api_name": "quillnlp.grammar.constants.Dependency", "line_number": 150, "usage_type": "name"}, {"api_name": "quillnlp.grammar.constants.Dependency.ATTRIBUTE", "line_number": 158, "usage_type": "attribute"}, {"api_name": "quillnlp.grammar.constants.Dependency", "line_number": 158, "usage_type": "name"}, {"api_name": "quillnlp.grammar.constants.Dependency.AUX", "line_number": 165, "usage_type": "attribute"}, {"api_name": "quillnlp.grammar.constants.Dependency", "line_number": 165, "usage_type": "name"}, {"api_name": "quillnlp.grammar.constants.Dependency.PASS_AUXILIARY", "line_number": 166, "usage_type": "attribute"}, {"api_name": "quillnlp.grammar.constants.Dependency", "line_number": 166, "usage_type": "name"}, {"api_name": "quillnlp.grammar.constants.Dependency.CONJUNCTION", "line_number": 167, "usage_type": "attribute"}, {"api_name": "quillnlp.grammar.constants.Dependency", "line_number": 167, "usage_type": "name"}, {"api_name": "spacy.tokens.token.Token", "line_number": 173, "usage_type": "name"}, {"api_name": "quillnlp.grammar.constants.Tag.INFINITIVE", "line_number": 178, "usage_type": "attribute"}, {"api_name": "quillnlp.grammar.constants.Tag", "line_number": 178, "usage_type": "name"}, {"api_name": "spacy.tokens.token.Token", "line_number": 181, "usage_type": "name"}, {"api_name": "quillnlp.grammar.constants.TokenSet.INDEF_PRONOUNS", "line_number": 183, "usage_type": "attribute"}, {"api_name": "quillnlp.grammar.constants.TokenSet", "line_number": 183, "usage_type": "name"}, {"api_name": "spacy.tokens.token.Token", "line_number": 186, "usage_type": "name"}, {"api_name": "quillnlp.grammar.constants.Tag.SIMPLE_PAST_VERB", "line_number": 204, "usage_type": "attribute"}, {"api_name": "quillnlp.grammar.constants.Tag", "line_number": 204, "usage_type": "name"}, {"api_name": "quillnlp.grammar.constants.POS.VERB", "line_number": 213, "usage_type": "attribute"}, {"api_name": "quillnlp.grammar.constants.POS", "line_number": 213, "usage_type": "name"}, {"api_name": "quillnlp.grammar.myspacy.nlp", "line_number": 232, "usage_type": "call"}, {"api_name": "quillnlp.grammar.constants.Dependency.ADVERBIAL_CLAUSE", "line_number": 236, "usage_type": "attribute"}, {"api_name": "quillnlp.grammar.constants.Dependency", "line_number": 236, "usage_type": "name"}, {"api_name": "spacy.tokens.doc.Doc", "line_number": 243, "usage_type": "name"}, {"api_name": "quillnlp.grammar.constants.Tag.PRESENT_PARTICIPLE_VERB", "line_number": 248, "usage_type": "attribute"}, {"api_name": "quillnlp.grammar.constants.Tag", "line_number": 248, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 243, "usage_type": "name"}, {"api_name": "spacy.tokens.token.Token", "line_number": 243, "usage_type": "name"}, {"api_name": "spacy.tokens.token.Token", "line_number": 261, "usage_type": "name"}, {"api_name": "spacy.tokens.token.Token", "line_number": 276, "usage_type": "name"}, {"api_name": "spacy.tokens.token.Token", "line_number": 285, "usage_type": "name"}, {"api_name": "quillnlp.grammar.constants.Dependency.PASS_AUXILIARY", "line_number": 288, "usage_type": "attribute"}, {"api_name": "quillnlp.grammar.constants.Dependency", "line_number": 288, "usage_type": "name"}]} +{"seq_id": "21974266569", "text": "from django.urls import path\r\nfrom .views import *\r\nurlpatterns=[\r\n path(\"home/\",home),\r\n path('aboutus/',aboutus),\r\n path('contactus/',contactus),\r\n path(\"chefreg/\",chefreg),\r\n path(\"cheflog/\",cheflog),\r\n path(\"chefprofile/\",chefprofile),\r\n path('nfile/',nfile),\r\n path('vfile/',vfile),\r\n path('vdisplay/',vdisplay),\r\n path('ndisplay/',ndisplay),\r\n path('userreg/',userreg),\r\n path('userlog/',userlog),\r\n path('nonedit//',nonedit),\r\n path('nondelete//',nondelete)\r\n\r\n ]", "repo_name": "shilpa54/shilpaB", "sub_path": "urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 531, "program_lang": "python", "lang": "ceb", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "django.urls.path", "line_number": 4, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 5, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 6, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 7, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 8, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 9, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 10, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 11, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 12, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 13, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 14, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 15, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 16, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 17, "usage_type": "call"}]} +{"seq_id": "20449644442", "text": "from db import *\nimport regex as re\n\n\ndef extract_programming_languages(job_id, jd):\n pls_d = {'python': 'Python', 'sql': 'SQL', 'java': 'Java', 'scala': 'Scala', 'matlab': 'MATLAB', \n 'julia': 'Julia', 'c\\+\\+': 'C++', 'javascript': 'JavaScript'}\n pls_found = []\n jd_lower = jd.lower()\n for pl in pls_d:\n if re.search(pl, jd_lower):\n pls_found.append((job_id, pls_d[pl]))\n \n spec_pls = {'[\\W]R[\\W]': 'R', 'SAS': 'SAS'}\n for pl in spec_pls:\n if re.search(pl, jd):\n pls_found.append((job_id, spec_pls[pl]))\n\n return pls_found", "repo_name": "ProgressingMann/linkedin-job-data-analysis", "sub_path": "dags/modules/analyze_data.py", "file_name": "analyze_data.py", "file_ext": "py", "file_size_in_byte": 594, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "37", "api": [{"api_name": "regex.search", "line_number": 11, "usage_type": "call"}, {"api_name": "regex.search", "line_number": 16, "usage_type": "call"}]} +{"seq_id": "11645559739", "text": "from web3 import Web3\nimport json\n\n# Read the droplets' IP addresses from the terraform state file\nwith open('../terraform/terraform.tfstate') as f:\n tfstate = json.load(f)\n droplet_resource = list(filter(lambda x: x['type'] == 'digitalocean_droplet', tfstate.get('resources', [])))[0]\n droplets = droplet_resource.get('instances', [])\n\n# Attempt to connect to each anvil node hosted on each droplet\nfor droplet in droplets:\n ip = droplet['attributes']['ipv4_address']\n rpc_url = f'http://{ip}:8545'\n w3 = Web3(Web3.HTTPProvider(rpc_url))\n print(ip, f'Successful Connection: {w3.isConnected()}')\n", "repo_name": "saucepoint/anvil-instancing", "sub_path": "python/liveness.py", "file_name": "liveness.py", "file_ext": "py", "file_size_in_byte": 617, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 58, "dataset": "github-code", "pt": "37", "api": [{"api_name": "json.load", "line_number": 6, "usage_type": "call"}, {"api_name": "web3.Web3", "line_number": 14, "usage_type": "call"}, {"api_name": "web3.Web3.HTTPProvider", "line_number": 14, "usage_type": "call"}]} +{"seq_id": "42498447490", "text": "from django.conf.urls import url\nfrom Student import views\n\nurlpatterns=[\n url(r'^add$', views.add),\n url(r'^index$', views.index),\n url(r'^getinfo$', views.select),\n url(r'^sms$', views.sms),\n url(r'^del$', views.del_info),\n url(r'^edit$', views.edit_info),\n\n]", "repo_name": "xiaodage33/local_api_kubernetes", "sub_path": "Student/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 281, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "django.conf.urls.url", "line_number": 5, "usage_type": "call"}, {"api_name": "Student.views.add", "line_number": 5, "usage_type": "attribute"}, {"api_name": "Student.views", "line_number": 5, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 6, "usage_type": "call"}, {"api_name": "Student.views.index", "line_number": 6, "usage_type": "attribute"}, {"api_name": "Student.views", "line_number": 6, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 7, "usage_type": "call"}, {"api_name": "Student.views.select", "line_number": 7, "usage_type": "attribute"}, {"api_name": "Student.views", "line_number": 7, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 8, "usage_type": "call"}, {"api_name": "Student.views.sms", "line_number": 8, "usage_type": "attribute"}, {"api_name": "Student.views", "line_number": 8, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 9, "usage_type": "call"}, {"api_name": "Student.views.del_info", "line_number": 9, "usage_type": "attribute"}, {"api_name": "Student.views", "line_number": 9, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 10, "usage_type": "call"}, {"api_name": "Student.views.edit_info", "line_number": 10, "usage_type": "attribute"}, {"api_name": "Student.views", "line_number": 10, "usage_type": "name"}]} +{"seq_id": "36479238768", "text": "import sys, os\nimport setuptools\n\n# The full contents of the wx.build.config module used to be located\n# here in setup.py. They were split into a separate module so it will\n# be installed with wxPython and can then be used by the build scripts\n# of other extension modules that wish to be wxPython compatible.\n# The split is still fairly new and hasn't been tested by building\n# third-party extensions yet, so expect some things to still shift\n# back and forth, and also more stuff in config.py will get converted\n# to functions, etc.\n\n# This script imports it as just \"config\" because if wxPython doesn't\n# exist yet, then it can't be imported from wx.build.config (since\n# wx._core doesn't exist yet.) So instead we keep the main copy of\n# config .py in the same place as setup.py, and then copy it to\n# wx/build as needed below.\n\n# To fully support external builds, we need to have a build options\n# file that is created whenever a new wxPython build is performed.\n# We happen to be doing that here in this script, so make sure to\n# remove the build_options.py file, so that config.py will recreate it.\n\nfor bo_name in [\"build_options.py\", \"build_options.pyc\"]:\n if os.path.exists(bo_name):\n os.remove(bo_name)\n\nsys.setup_is_main = __name__ == \"__main__\" # an icky hack!\nfrom config import *\n\n\n#----------------------------------------------------------------------\n# Update the packaged config file.\n#----------------------------------------------------------------------\n\ncopy_file('config.py', 'wx/build', update=1, verbose=1)\ncopy_file('cfg_version.py', 'wx/build', update=1, verbose=1)\ncopy_file('build_options.py', 'wx/build', update=1, verbose=1)\nCLEANUP.append('wx/build/config.py')\nCLEANUP.append('wx/build/cfg_version.py')\nCLEANUP.append('wx/build/build_options.py')\n\n#----------------------------------------------------------------------\n# Update the version file\n#----------------------------------------------------------------------\n\n# The version file is unconditionally updated every time setup.py is\n# run since the version string can change based on the UNICODE flag\n\nopen('wx/__version__.py', 'w').write(\"\"\"\\\n# This file was generated by setup.py...\n\nVERSION_STRING = '%(VERSION)s'\nMAJOR_VERSION = %(VER_MAJOR)s\nMINOR_VERSION = %(VER_MINOR)s\nRELEASE_VERSION = %(VER_RELEASE)s\nSUBREL_VERSION = %(VER_SUBREL)s\n\nVERSION = (MAJOR_VERSION, MINOR_VERSION, RELEASE_VERSION,\n SUBREL_VERSION, '%(VER_FLAGS)s')\n\"\"\" % globals())\n\n\nopen('demo/version.py', 'w').write(\"\"\"\\\n# This file was generated by setup.py...\n\nVERSION_STRING = '%(VERSION)s'\n\"\"\" % globals())\n\n\nCLEANUP.append('wx/__version__.py')\nCLEANUP.append('demo/version.py')\n\n#----------------------------------------------------------------------\n# Write the SWIG version to a header file\n#----------------------------------------------------------------------\n\nif USE_SWIG:\n try:\n SVER = swig_version()\n open('include/wx/wxPython/swigver.h', 'w').write('''\\\n// This file was generated by setup.py\n\n#define wxPy_SWIG_VERSION \"SWIG-%s\"\n''' % SVER)\n msg('Using SWIG-' + SVER)\n except:\n msg('\\nUnable to get SWIG version number\\n')\n\n\n\n#----------------------------------------------------------------------\n# patch distutils if it can't cope with the \"classifiers\" or\n# \"download_url\" keywords\n#----------------------------------------------------------------------\n\nif sys.version < '2.2.3':\n from distutils.dist import DistributionMetadata\n DistributionMetadata.classifiers = None\n DistributionMetadata.download_url = None\n depends = {}\nelse:\n depends = {'depends' : depends}\n\n\n#----------------------------------------------------------------------\n# Define the CORE extension module\n#----------------------------------------------------------------------\n\nmsg('Preparing CORE...')\nswig_sources = run_swig(['core.i'], 'src', GENDIR, PKGDIR,\n USE_SWIG, swig_force, swig_args, swig_deps +\n [ 'src/_accel.i',\n 'src/_app.i',\n 'src/_app_ex.py',\n 'src/_constraints.i',\n 'src/_core_api.i',\n 'src/_core_ex.py',\n 'src/__core_rename.i',\n 'src/__core_reverse.txt',\n 'src/_defs.i',\n 'src/_keyboardstate.i',\n 'src/_mousestate.i',\n 'src/_event.i',\n 'src/_event_ex.py',\n 'src/_evtloop.i',\n 'src/_evthandler.i',\n 'src/_filesys.i',\n 'src/_gdicmn.i',\n 'src/_image.i',\n 'src/_menu.i',\n 'src/_obj.i',\n 'src/_sizers.i',\n 'src/_gbsizer.i',\n 'src/_streams.i',\n 'src/_validator.i',\n 'src/_window.i',\n 'src/_control.i',\n 'src/_swigtype.i',\n 'src/_headercol.i',\n 'src/_versioninfo.i',\n 'src/_withimages.i',\n 'src/_bookctrl.i',\n ],\n True)\n\ncopy_file('src/__init__.py', PKGDIR, update=1, verbose=0)\nCLEANUP.append(opj(PKGDIR, '__init__.py'))\n\n\n# update the license files\nmkpath('licence')\nfor file in ['preamble.txt', 'licence.txt', 'licendoc.txt', 'lgpl.txt']:\n copy_file(opj(WXDIR, 'docs', file), opj('licence',file), update=1, verbose=0)\n CLEANUP.append(opj('licence',file))\nCLEANUP.append('licence')\n\n\nif sys.platform in ['win32', 'darwin']:\n build_locale_dir(opj(PKGDIR, 'locale'))\n DATA_FILES += build_locale_list(opj(PKGDIR, 'locale'))\n\n\nif os.name == 'nt':\n rc_file = ['src/wxc.rc']\nelse:\n rc_file = []\n\n\next = Extension('_core_', ['src/helpers.cpp',\n ] + rc_file + swig_sources,\n\n include_dirs = includes,\n define_macros = defines,\n\n library_dirs = libdirs,\n libraries = libs,\n\n extra_compile_args = cflags,\n extra_link_args = lflags,\n\n **depends\n )\nwxpExtensions.append(ext)\n\n\n\n\n\n# Extension for the GDI module\nswig_sources = run_swig(['gdi.i'], 'src', GENDIR, PKGDIR,\n USE_SWIG, swig_force, swig_args, swig_deps +\n ['src/_bitmap.i',\n 'src/_colour.i',\n 'src/_dc.i',\n 'src/_graphics.i',\n 'src/_overlay.i',\n 'src/_gdiobj.i',\n 'src/_imaglist.i',\n 'src/_region.i',\n 'src/_stockobjs.i',\n 'src/_effects.i',\n 'src/_intl.i',\n 'src/_intl_ex.py',\n 'src/_brush.i',\n 'src/_cursor.i',\n 'src/_font.i',\n 'src/_icon.i',\n 'src/_pen.i',\n 'src/_palette.i',\n 'src/_renderer.i',\n 'src/_pseudodc.i',\n ],\n True)\next = Extension('_gdi_', ['src/drawlist.cpp',\n 'src/pseudodc.cpp'\n ] + swig_sources,\n include_dirs = includes,\n define_macros = defines,\n library_dirs = libdirs,\n libraries = libs,\n extra_compile_args = cflags,\n extra_link_args = lflags,\n **depends\n )\nwxpExtensions.append(ext)\n\n\n\n\n\n\n# Extension for the windows module\nswig_sources = run_swig(['windows.i'], 'src', GENDIR, PKGDIR,\n USE_SWIG, swig_force, swig_args, swig_deps +\n ['src/_panel.i',\n 'src/_toplvl.i',\n 'src/_statusbar.i',\n 'src/_splitter.i',\n 'src/_sashwin.i',\n 'src/_popupwin.i',\n 'src/_tipwin.i',\n 'src/_vscroll.i',\n 'src/_taskbar.i',\n 'src/_cmndlgs.i',\n 'src/_mdi.i',\n 'src/_pywindows.i',\n 'src/_printfw.i',\n ],\n True)\next = Extension('_windows_', swig_sources,\n include_dirs = includes,\n define_macros = defines,\n library_dirs = libdirs,\n libraries = libs,\n extra_compile_args = cflags,\n extra_link_args = lflags,\n **depends\n )\nwxpExtensions.append(ext)\n\n\n\n\n# Extension for the controls module\nswig_sources = run_swig(['controls.i'], 'src', GENDIR, PKGDIR,\n USE_SWIG, swig_force, swig_args, swig_deps +\n [ 'src/_toolbar.i',\n 'src/_button.i',\n 'src/_checkbox.i',\n 'src/_choice.i',\n 'src/_combobox.i',\n 'src/_gauge.i',\n 'src/_statctrls.i',\n 'src/_listbox.i',\n 'src/_textctrl.i',\n 'src/_scrolbar.i',\n 'src/_spin.i',\n 'src/_radio.i',\n 'src/_slider.i',\n 'src/_tglbtn.i',\n 'src/_notebook.i',\n 'src/_listctrl.i',\n 'src/_treectrl.i',\n 'src/_dirctrl.i',\n 'src/_pycontrol.i',\n 'src/_cshelp.i',\n 'src/_dragimg.i',\n 'src/_datectrl.i',\n 'src/_hyperlink.i',\n 'src/_picker.i',\n 'src/_collpane.i',\n 'src/_srchctrl.i',\n 'src/_axbase.i',\n 'src/_filectrl.i',\n 'src/_infobar.i',\n 'src/_cmdlinkbtn.i',\n ],\n True)\next = Extension('_controls_', swig_sources,\n include_dirs = includes,\n define_macros = defines,\n library_dirs = libdirs,\n libraries = libs,\n extra_compile_args = cflags,\n extra_link_args = lflags,\n **depends\n )\nwxpExtensions.append(ext)\n\n\n\n\n# Extension for the misc module\nswig_sources = run_swig(['misc.i'], 'src', GENDIR, PKGDIR,\n USE_SWIG, swig_force, swig_args, swig_deps +\n [ 'src/_settings.i',\n 'src/_functions.i',\n 'src/_misc.i',\n 'src/_tipdlg.i',\n 'src/_timer.i',\n 'src/_log.i',\n 'src/_process.i',\n 'src/_joystick.i',\n 'src/_sound.i',\n 'src/_mimetype.i',\n 'src/_artprov.i',\n 'src/_config.i',\n 'src/_datetime.i',\n 'src/_dataobj.i',\n 'src/_dnd.i',\n 'src/_display.i',\n 'src/_clipbrd.i',\n 'src/_stdpaths.i',\n 'src/_power.i',\n 'src/_about.i',\n 'src/_uiaction.i',\n ],\n True)\next = Extension('_misc_', swig_sources,\n include_dirs = includes,\n define_macros = defines,\n library_dirs = libdirs,\n libraries = libs,\n extra_compile_args = cflags,\n extra_link_args = lflags,\n **depends\n )\nwxpExtensions.append(ext)\n\n\n\n##\n## Core modules that are not in the \"core\" namespace start here\n##\n\nswig_sources = run_swig(['calendar.i'], 'src', GENDIR, PKGDIR,\n USE_SWIG, swig_force, swig_args, swig_deps)\next = Extension('_calendar', swig_sources,\n include_dirs = includes,\n define_macros = defines,\n library_dirs = libdirs,\n libraries = libs,\n extra_compile_args = cflags,\n extra_link_args = lflags,\n **depends\n )\nwxpExtensions.append(ext)\n\n\nswig_sources = run_swig(['combo.i'], 'src', GENDIR, PKGDIR,\n USE_SWIG, swig_force, swig_args, swig_deps)\next = Extension('_combo', swig_sources,\n include_dirs = includes,\n define_macros = defines,\n library_dirs = libdirs,\n libraries = libs,\n extra_compile_args = cflags,\n extra_link_args = lflags,\n **depends\n )\nwxpExtensions.append(ext)\n\n\nswig_sources = run_swig(['grid.i'], 'src', GENDIR, PKGDIR,\n USE_SWIG, swig_force, swig_args, swig_deps)\next = Extension('_grid', swig_sources,\n include_dirs = includes,\n define_macros = defines,\n library_dirs = libdirs,\n libraries = libs,\n extra_compile_args = cflags,\n extra_link_args = lflags,\n **depends\n )\nwxpExtensions.append(ext)\n\n\n\nswig_sources = run_swig(['html.i'], 'src', GENDIR, PKGDIR,\n USE_SWIG, swig_force, swig_args, swig_deps)\next = Extension('_html', swig_sources,\n include_dirs = includes,\n define_macros = defines,\n library_dirs = libdirs,\n libraries = libs,\n extra_compile_args = cflags,\n extra_link_args = lflags,\n **depends\n )\nwxpExtensions.append(ext)\n\n\nmediaLibs = libs[:]\nif not MONOLITHIC and findLib('media', libdirs):\n mediaLibs += makeLibName('media')\nswig_sources = run_swig(['media.i'], 'src', GENDIR, PKGDIR,\n USE_SWIG, swig_force, swig_args, swig_deps)\next = Extension('_media', swig_sources,\n include_dirs = includes,\n define_macros = defines,\n library_dirs = libdirs,\n libraries = mediaLibs,\n extra_compile_args = cflags,\n extra_link_args = lflags,\n **depends\n )\nwxpExtensions.append(ext)\n\n\nswig_sources = run_swig(['webkit.i'], 'src', GENDIR, PKGDIR,\n USE_SWIG, swig_force, swig_args, swig_deps)\next = Extension('_webkit', swig_sources,\n include_dirs = includes,\n define_macros = defines,\n library_dirs = libdirs,\n libraries = libs,\n extra_compile_args = cflags,\n extra_link_args = lflags,\n **depends\n )\nwxpExtensions.append(ext)\n\n\n\nswig_sources = run_swig(['wizard.i'], 'src', GENDIR, PKGDIR,\n USE_SWIG, swig_force, swig_args, swig_deps)\next = Extension('_wizard', swig_sources,\n include_dirs = includes,\n define_macros = defines,\n library_dirs = libdirs,\n libraries = libs,\n extra_compile_args = cflags,\n extra_link_args = lflags,\n **depends\n )\nwxpExtensions.append(ext)\n\n\nswig_sources = run_swig(['dataview.i'], 'src', GENDIR, PKGDIR,\n USE_SWIG, swig_force, swig_args, swig_deps)\next = Extension('_dataview', swig_sources,\n include_dirs = includes,\n define_macros = defines,\n library_dirs = libdirs,\n libraries = libs,\n extra_compile_args = cflags,\n extra_link_args = lflags,\n **depends\n )\nwxpExtensions.append(ext)\n\n\nswig_sources = run_swig(['xrc.i'], 'src', GENDIR, PKGDIR,\n USE_SWIG, swig_force, swig_args, swig_deps +\n [ 'src/_xrc_ex.py',\n 'src/_xmlres.i',\n 'src/_xmlsub.i',\n 'src/_xml.i',\n 'src/_xmlhandler.i',\n ])\n\nif not MONOLITHIC and findLib('xrc', libdirs):\n xrcLib = makeLibName('xrc')\nelse:\n xrcLib = []\next = Extension('_xrc',\n swig_sources,\n\n include_dirs = includes + CONTRIBS_INC,\n define_macros = defines,\n\n library_dirs = libdirs,\n libraries = libs + xrcLib,\n\n extra_compile_args = cflags,\n extra_link_args = lflags,\n )\nwxpExtensions.append(ext)\n\n\n\nswig_sources = run_swig(['richtext.i'], 'src', GENDIR, PKGDIR,\n USE_SWIG, swig_force, swig_args,\n swig_deps + [ 'src/_richtextbuffer.i',\n 'src/_richtextctrl.i',\n 'src/_richtexthtml.i',\n 'src/_richtextxml.i',\n ])\nif not MONOLITHIC and findLib('richtext', libdirs):\n richLib = makeLibName('richtext')\nelse:\n richLib = []\next = Extension('_richtext', swig_sources,\n include_dirs = includes,\n define_macros = defines,\n library_dirs = libdirs,\n libraries = libs + richLib,\n extra_compile_args = cflags,\n extra_link_args = lflags,\n **depends\n )\nwxpExtensions.append(ext)\n\n\n\nswig_sources = run_swig(['aui.i'], 'src', GENDIR, PKGDIR,\n USE_SWIG, swig_force,\n swig_args + ['-I'+opj(WXDIR, 'include/wx/aui')],\n swig_deps + ['src/_aui_docstrings.i',\n opj(WXDIR, 'include/wx/aui/framemanager.h'),\n opj(WXDIR, 'include/wx/aui/floatpane.h'),\n opj(WXDIR, 'include/wx/aui/dockart.h'),\n opj(WXDIR, 'include/wx/aui/auibook.h'),\n opj(WXDIR, 'include/wx/aui/tabmdi.h'),\n ])\nif not MONOLITHIC and findLib('aui', libdirs):\n auiLib = makeLibName('aui')\nelse:\n auiLib = []\next = Extension('_aui', swig_sources,\n include_dirs = includes,\n define_macros = defines,\n library_dirs = libdirs,\n libraries = libs + auiLib,\n extra_compile_args = cflags,\n extra_link_args = lflags,\n **depends\n )\nwxpExtensions.append(ext)\n\n\nswig_sources = run_swig(['animate.i'], 'src', GENDIR, PKGDIR,\n USE_SWIG, swig_force, swig_args, swig_deps)\next = Extension('_animate',\n swig_sources,\n\n include_dirs = includes + CONTRIBS_INC,\n define_macros = defines,\n\n library_dirs = libdirs,\n libraries = libs,\n\n extra_compile_args = cflags,\n extra_link_args = lflags,\n )\n\nwxpExtensions.append(ext)\n\n\nswig_sources = run_swig(['propgrid.i'], 'src', GENDIR, PKGDIR,\n USE_SWIG, swig_force,\n swig_args + ['-I'+opj(WXDIR, 'include/wx/propgrid')],\n swig_deps + [#'src/_propgrid_docstrings.i',\n opj(WXDIR, 'include/wx/propgrid/advprops.h'),\n opj(WXDIR, 'include/wx/propgrid/editors.h'),\n opj(WXDIR, 'include/wx/propgrid/manager.h'),\n opj(WXDIR, 'include/wx/propgrid/property.h'),\n opj(WXDIR, 'include/wx/propgrid/propgrid.h'),\n opj(WXDIR, 'include/wx/propgrid/propgriddefs.h'),\n opj(WXDIR, 'include/wx/propgrid/propgridiface.h'),\n opj(WXDIR, 'include/wx/propgrid/propgridpagestate.h'),\n opj(WXDIR, 'include/wx/propgrid/props.h'),\n ])\nif not MONOLITHIC and findLib('propgrid', libdirs):\n propgridLib = makeLibName('propgrid')\nelse:\n propgridLib = []\next = Extension('_propgrid', swig_sources,\n include_dirs = includes,\n define_macros = defines,\n library_dirs = libdirs,\n libraries = libs + propgridLib,\n extra_compile_args = cflags,\n extra_link_args = lflags,\n **depends\n )\nwxpExtensions.append(ext)\n\n\nswig_sources = run_swig(['html2.i'], 'src', GENDIR, PKGDIR,\n USE_SWIG, swig_force, swig_args, swig_deps)\nif not MONOLITHIC and findLib('webview', libdirs):\n webviewLib = makeLibName('webview')\nelse:\n webviewLib = []\next = Extension('_html2', swig_sources,\n include_dirs = includes,\n define_macros = defines,\n library_dirs = libdirs,\n libraries = libs + webviewLib,\n extra_compile_args = cflags,\n extra_link_args = lflags,\n **depends\n )\nwxpExtensions.append(ext)\n\n\n\nif BUILD_STC:\n msg('Preparing STC...')\n STC_H = opj(WXDIR, 'include/wx/stc')\n\n swig_sources = run_swig(['stc.i'], 'src', GENDIR, PKGDIR,\n USE_SWIG, swig_force, swig_args + ['-I'+STC_H],\n [opj(STC_H, 'stc.h'),\n opj(\"src/_stc_utf8_methods.py\"),\n opj(\"src/_stc_docstrings.i\"),\n opj(\"src/_stc_gendocs.i\"),\n ] + swig_deps)\n\n stcLibs = libs[:]\n if not MONOLITHIC and findLib('stc', libdirs):\n stcLibs += makeLibName('stc')\n\n ext = Extension('_stc',\n swig_sources,\n include_dirs = includes + CONTRIBS_INC,\n define_macros = defines,\n library_dirs = libdirs,\n libraries = stcLibs,\n extra_compile_args = cflags,\n extra_link_args = lflags,\n )\n\n wxpExtensions.append(ext)\n\n\nif BUILD_GLCANVAS:\n msg('Preparing GLCANVAS...')\n swig_sources = run_swig(['glcanvas.i'], 'src', GENDIR, PKGDIR,\n USE_SWIG, swig_force, swig_args, swig_deps)\n gl_libs = []\n gl_libdirs = libdirs[:]\n if os.name == 'posix':\n gl_config = os.popen(WX_CONFIG + ' --libs', 'r').read()[:-1] + \\\n os.popen(WX_CONFIG + ' --libs gl', 'r').read()[:-1]\n gl_lflags = gl_config.split()\n gl_lflags = adjustLFLAGS(gl_lflags, gl_libdirs, gl_libs)\n else:\n gl_libs = libs + ['opengl32', 'glu32'] + makeLibName('gl')\n gl_lflags = lflags\n\n if sys.platform[:6] == \"darwin\" and WXPORT == 'osx_carbon':\n if not ARCH == \"\":\n gl_lflags.append(\"-arch\")\n gl_lflags.append(ARCH)\n\n ext = Extension('_glcanvas',\n swig_sources,\n include_dirs = includes + CONTRIBS_INC,\n define_macros = defines,\n library_dirs = gl_libdirs,\n libraries = gl_libs,\n extra_compile_args = cflags,\n extra_link_args = gl_lflags,\n )\n\n wxpExtensions.append(ext)\n\n\n\n#----------------------------------------------------------------------\n# Define the ACTIVEX extension module (experimental)\n#----------------------------------------------------------------------\n\nif BUILD_ACTIVEX:\n msg('Preparing ACTIVEX...')\n location = 'contrib/activex'\n axloc = opj(location, \"wxie\")\n\n swig_files = ['activex.i', ]\n\n swig_sources = run_swig(swig_files, location, '', PKGDIR,\n USE_SWIG, swig_force, swig_args, swig_deps +\n [ '%s/_activex_ex.py' % location])\n\n\n ext = Extension('_activex', ['%s/IEHtmlWin.cpp' % axloc,\n '%s/wxactivex.cpp' % axloc,\n ] + swig_sources,\n\n include_dirs = includes + [ axloc ],\n define_macros = defines,\n\n library_dirs = libdirs,\n libraries = libs,\n\n extra_compile_args = cflags,\n extra_link_args = lflags,\n )\n\n wxpExtensions.append(ext)\n\n\n#----------------------------------------------------------------------\n# Define the GIZMOS extension module\n#----------------------------------------------------------------------\n\nif BUILD_GIZMOS:\n msg('Preparing GIZMOS...')\n location = 'contrib/gizmos'\n\n swig_sources = run_swig(['gizmos.i'], location, GENDIR, PKGDIR,\n USE_SWIG, swig_force, swig_args, swig_deps +\n [ '%s/_treelist.i' % location])\n\n ext = Extension('_gizmos',\n [ '%s/treelistctrl.cpp' % opj(location, 'wxCode/src'),\n '%s/gizmos/dynamicsash.cpp' % opj(location, 'wxCode/src'),\n #'%s/gizmos/editlbox.cpp' % opj(location, 'wxCode/src'),\n '%s/gizmos/ledctrl.cpp' % opj(location, 'wxCode/src'),\n '%s/gizmos/splittree.cpp' % opj(location, 'wxCode/src'),\n '%s/gizmos/statpict.cpp' % opj(location, 'wxCode/src'),\n ] + swig_sources,\n\n include_dirs = includes + [ location, opj(location, 'wxCode/include') ] + CONTRIBS_INC,\n define_macros = defines,\n\n library_dirs = libdirs,\n libraries = libs,\n\n extra_compile_args = cflags,\n extra_link_args = lflags,\n )\n\n wxpExtensions.append(ext)\n\n\n#----------------------------------------------------------------------\n# Define the DLLWIDGET extension module\n#----------------------------------------------------------------------\n\nif BUILD_DLLWIDGET:\n msg('Preparing DLLWIDGET...')\n location = 'contrib/dllwidget'\n swig_files = ['dllwidget_.i']\n\n swig_sources = run_swig(swig_files, location, '', PKGDIR,\n USE_SWIG, swig_force, swig_args, swig_deps)\n\n # copy a contrib project specific py module to the main package dir\n copy_file(opj(location, 'dllwidget.py'), PKGDIR, update=1, verbose=0)\n CLEANUP.append(opj(PKGDIR, 'dllwidget.py'))\n\n ext = Extension('dllwidget_c', [\n '%s/dllwidget.cpp' % location,\n ] + swig_sources,\n\n include_dirs = includes + CONTRIBS_INC,\n define_macros = defines,\n\n library_dirs = libdirs,\n libraries = libs,\n\n extra_compile_args = cflags,\n extra_link_args = lflags,\n )\n\n wxpExtensions.append(ext)\n\n\n\n#----------------------------------------------------------------------\n\nif EGGing:\n # Replace the make_zipfile function used by the bdist_egg command\n # so we can do some postprocessing of what will become the content\n # of the egg file before it is made.\n \n import setuptools.command.bdist_egg\n old_make_zipfile = setuptools.command.bdist_egg.make_zipfile\n\n def my_make_zipfile(zip_filename, base_dir, *args, **kw):\n if sys.platform == 'darwin':\n # copy wx dylibs into the egg, and set the @loader_path in\n # the wxPython .so's to find our copies.\n \n # find all wx dylibs used by the wxPython .so files, use a\n # set to collapse the duplicates.\n dylibs = set()\n soFiles = glob.glob(opj(base_dir, 'wx', '*.so'))\n for f in soFiles:\n info = os.popen('otool -L %s | grep libwx | grep -v loader_path' % f).read().strip()\n for line in info.split('\\n'):\n if not line: continue\n so = line.split()[0]\n dylibs.add(so)\n cmd = ['install_name_tool',\n '-change',\n so,\n '@loader_path/../Library/%s' % os.path.basename(so),\n f ]\n spawn(cmd)\n\n # Copy the shared library files into the egg, and fix up\n # dependency paths where needed.\n dest = opj(base_dir, 'Library')\n if not os.path.exists(dest):\n os.mkdir(dest)\n for f in dylibs:\n copy_file(f, dest)\n info = os.popen('otool -L %s | grep libwx | grep -v \":$\" | grep -v loader_path' % f).read().strip()\n for line in info.split('\\n'):\n if not line: continue\n\n so = line.split()[0]\n cmd = ['install_name_tool',\n '-change',\n so,\n '@loader_path/../Library/%s' % os.path.basename(so),\n '%s/%s' % (dest, os.path.basename(f)) ]\n spawn(cmd)\n \n \n if os.name == 'nt':\n # copy the wx DLLs and others into the wx package dir\n # inside the egg.\n dllFiles = glob.glob(opj(WXDIR, 'lib', 'vc_dll',\n 'wxmsw%s%s_*.dll' % (WXDLLVER, libFlag()))) + \\\n glob.glob(opj(WXDIR, 'lib', 'vc_dll',\n 'wxbase%s%s_*.dll' % (WXDLLVER, libFlag()))) + \\\n [ 'distrib/msw/gdiplus.dll',\n 'distrib/msw/msvcp71.dll' ]\n for f in dllFiles:\n copy_file(f, opj(base_dir, 'wx'))\n \n return old_make_zipfile(zip_filename, base_dir, *args, **kw)\n\n setuptools.command.bdist_egg.make_zipfile = my_make_zipfile\n\n\n#----------------------------------------------------------------------\n# Tools, scripts, data files, etc.\n#----------------------------------------------------------------------\n\n\nWX_PKGLIST = [ 'wx',\n 'wx.build',\n 'wx.lib',\n 'wx.lib.agw',\n 'wx.lib.agw.aui',\n 'wx.lib.agw.persist',\n 'wx.lib.agw.ribbon',\n 'wx.lib.analogclock',\n 'wx.lib.analogclock.lib_setup',\n 'wx.lib.art',\n 'wx.lib.colourchooser',\n 'wx.lib.editor',\n 'wx.lib.floatcanvas',\n 'wx.lib.floatcanvas.Utilities',\n 'wx.lib.masked',\n 'wx.lib.mixins',\n 'wx.lib.ogl',\n 'wx.lib.pdfviewer',\n 'wx.lib.pubsub',\n 'wx.lib.pubsub.core',\n 'wx.lib.pubsub.core.arg1',\n 'wx.lib.pubsub.core.kwargs',\n 'wx.lib.pubsub.utils',\n 'wx.py',\n 'wx.tools',\n 'wx.tools.XRCed',\n 'wx.tools.XRCed.plugins',\n 'wx.tools.Editra',\n 'wx.tools.Editra.src',\n 'wx.tools.Editra.src.autocomp',\n 'wx.tools.Editra.src.eclib',\n 'wx.tools.Editra.src.ebmlib',\n 'wx.tools.Editra.src.extern',\n 'wx.tools.Editra.src.extern.aui',\n 'wx.tools.Editra.src.extern.dexml',\n 'wx.tools.Editra.src.extern.pygments',\n 'wx.tools.Editra.src.extern.pygments.filters',\n 'wx.tools.Editra.src.extern.pygments.formatters',\n 'wx.tools.Editra.src.extern.pygments.lexers',\n 'wx.tools.Editra.src.extern.pygments.styles',\n 'wx.tools.Editra.src.syntax',\n ]\n\n\n# Use console_scripts so that the scripts will be native to any platform.\nENTRY_POINTS = {\n 'console_scripts': [\n 'helpviewer = wx.tools.helpviewer:main',\n 'img2png = wx.tools.img2png:main',\n 'img2py = wx.tools.img2py:main',\n 'img2xpm = wx.tools.img2xpm:main',\n 'pyalacarte = wx.py.PyAlaCarte:main',\n 'pyalamode = wx.py.PyAlaMode:main',\n 'pycrust = wx.py.PyCrust:main',\n 'pyshell = wx.py.PyShell:main',\n 'pywrap = wx.py.PyWrap:main',\n 'pywxrc = wx.tools.pywxrc:main',\n 'xrced = wx.tools.XRCed.xrced:main',\n 'editra = wx.tools.Editra.src.Editra:Main',\n ],\n}\n\n\nDATA_FILES += find_data_files('wx/lib/agw/data', '*.png', '*.html')\nDATA_FILES += find_data_files('wx/lib/editor', '*.txt')\nDATA_FILES += find_data_files('wx/py', '*.txt', '*.ico', '*.css', '*.html')\n\nDATA_FILES += find_data_files('wx/tools/XRCed', '*.txt', '*.xrc', '*.htb')\nDATA_FILES += find_data_files('wx/tools/XRCed/plugins', '*.crx')\nDATA_FILES += find_data_files('wx/tools/XRCed/plugins/bitmaps', '*.png')\n\nDATA_FILES += find_data_files('wx/tools/Editra/docs', '*.txt')\nDATA_FILES += find_data_files('wx/tools/Editra/locale', '*.mo')\nDATA_FILES += find_data_files('wx/tools/Editra/pixmaps',\n '*.png', '*.icns', '*.ico', 'README', 'AUTHORS', 'COPYING')\nDATA_FILES += find_data_files('wx/tools/Editra/plugins', '*.egg')\nDATA_FILES += find_data_files('wx/tools/Editra/src', 'README')\nDATA_FILES += find_data_files('wx/tools/Editra/styles', '*.ess')\nDATA_FILES += find_data_files('wx/tools/Editra/tests/syntax', '*')\nDATA_FILES += find_data_files('wx/tools/Editra', '[A-Z]*', recursive=False)\n\n\n## import pprint\n## pprint.pprint(DATA_FILES)\n## sys.exit()\n\n\nif NO_HEADERS or EGGing:\n HEADERS = None\nelse:\n h_files = glob.glob(opj(\"include/wx/wxPython/*.h\"))\n i_files = glob.glob(opj(\"src/*.i\")) + \\\n glob.glob(opj(\"src/_*.py\")) + \\\n glob.glob(opj(\"src/*.swg\"))\n if BUILD_GLCANVAS:\n i_files += glob.glob(opj(\"contrib/glcanvas/*.i\"))\n\n HEADERS = zip(h_files, [\"/wxPython\"]*len(h_files)) + \\\n zip(i_files, [\"/wxPython/i_files\"]*len(i_files))\n\n\nif INSTALL_MULTIVERSION:\n EXTRA_PATH = getExtraPath(addOpts=EP_ADD_OPTS, shortVer=not EP_FULL_VER)\n open(\"src/wx.pth\", \"w\").write(EXTRA_PATH + \"\\n\")\n CLEANUP.append(\"src/wx.pth\")\nelse:\n EXTRA_PATH = None\n\n\nBUILD_OPTIONS = { 'build_base' : BUILD_BASE }\nif WXPORT == 'msw':\n BUILD_OPTIONS[ 'compiler' ] = COMPILER\n\n\nother_kw = {}\nif EGGing:\n # These args are only used with setuptools, which for now is only\n # when we are building an egg.\n other_kw = dict(\n zip_safe = False,\n entry_points = {\n 'console_scripts' : [ 'img2png = wx.tools.img2png:main',\n 'img2xpm = wx.tools.img2xpm:main',\n 'img2py = wx.tools.img2py:main',\n 'pywxrc = wx.tools.pywxrc:main',\n ], \n 'gui_scripts' : [ 'pycrust = wx.py.PyCrust:main',\n 'pyshell = wx.py.PyShell:main',\n 'pywrap = wx.py.PyWrap:main',\n 'helpviewer = wx.tools.helpviewer:main',\n 'editra = wx.tools.Editra.launcher:main',\n 'xrced = wx.tools.XRCed.xrced:main',\n ], \n },\n )\n \n if os.name == 'nt':\n other_kw['entry_points']['console_scripts'].append(\n 'genaxmodule = wx.tools.genaxmodule:main')\n\n#----------------------------------------------------------------------\n# Do the Setup/Build/Install/Whatever\n#----------------------------------------------------------------------\n\nif __name__ == \"__main__\":\n if not PREP_ONLY:\n\n if not EGGing:\n if INSTALL_MULTIVERSION:\n setup(name = 'wxPython-common',\n version = VERSION,\n description = DESCRIPTION,\n long_description = LONG_DESCRIPTION,\n author = AUTHOR,\n author_email = AUTHOR_EMAIL,\n url = URL,\n download_url = DOWNLOAD_URL,\n license = LICENSE,\n platforms = PLATFORMS,\n classifiers = filter(None, CLASSIFIERS.split(\"\\n\")),\n keywords = KEYWORDS,\n\n package_dir = { '': 'wxversion' },\n py_modules = ['wxversion'],\n\n data_files = [('', ['src/wx.pth'])],\n\n options = { 'build' : BUILD_OPTIONS,\n },\n\n cmdclass = { 'install_data': wx_smart_install_data,\n },\n zip_safe = False\n )\n\n setup(name = 'wxPython',\n version = VERSION,\n description = DESCRIPTION,\n long_description = LONG_DESCRIPTION,\n author = AUTHOR,\n author_email = AUTHOR_EMAIL,\n url = URL,\n download_url = DOWNLOAD_URL,\n license = LICENSE,\n platforms = PLATFORMS,\n classifiers = filter(None, CLASSIFIERS.split(\"\\n\")),\n keywords = KEYWORDS,\n\n packages = WX_PKGLIST,\n extra_path = EXTRA_PATH,\n ext_package = PKGDIR,\n ext_modules = wxpExtensions,\n \n\n options = { 'build' : BUILD_OPTIONS, },\n\n entry_points = ENTRY_POINTS,\n data_files = DATA_FILES,\n headers = HEADERS,\n\n # Override some of the default distutils command classes with my own\n cmdclass = { 'install' : wx_install,\n 'install_data': wx_smart_install_data,\n 'install_headers': wx_install_headers,\n 'clean': wx_extra_clean,\n },\n zip_safe = False,\n\n **other_kw\n )\n\n\n\n#----------------------------------------------------------------------\n#----------------------------------------------------------------------\n", "repo_name": "wxWidgets/wxPython-Classic", "sub_path": "setup.py", "file_name": "setup.py", "file_ext": "py", "file_size_in_byte": 39526, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 297, "dataset": "github-code", "pt": "37", "api": [{"api_name": "os.path.exists", "line_number": 25, "usage_type": "call"}, {"api_name": "os.path", "line_number": 25, "usage_type": "attribute"}, {"api_name": "os.remove", "line_number": 26, "usage_type": "call"}, {"api_name": "sys.setup_is_main", "line_number": 28, "usage_type": "attribute"}, {"api_name": "sys.version", "line_number": 97, "usage_type": "attribute"}, {"api_name": "distutils.dist.DistributionMetadata.classifiers", "line_number": 99, "usage_type": "attribute"}, {"api_name": "distutils.dist.DistributionMetadata", "line_number": 99, "usage_type": "name"}, {"api_name": "distutils.dist.DistributionMetadata.download_url", "line_number": 100, "usage_type": "attribute"}, {"api_name": "distutils.dist.DistributionMetadata", "line_number": 100, "usage_type": "name"}, {"api_name": "sys.platform", "line_number": 159, "usage_type": "attribute"}, {"api_name": "os.name", "line_number": 164, "usage_type": "attribute"}, {"api_name": "os.name", "line_number": 655, "usage_type": "attribute"}, {"api_name": "os.popen", "line_number": 656, "usage_type": "call"}, {"api_name": "os.popen", "line_number": 657, "usage_type": "call"}, {"api_name": "sys.platform", "line_number": 664, "usage_type": "attribute"}, {"api_name": "setuptools.command", "line_number": 792, "usage_type": "attribute"}, {"api_name": "sys.platform", "line_number": 795, "usage_type": "attribute"}, {"api_name": "os.popen", "line_number": 804, "usage_type": "call"}, {"api_name": "os.path.basename", "line_number": 812, "usage_type": "call"}, {"api_name": "os.path", "line_number": 812, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 819, "usage_type": "call"}, {"api_name": "os.path", "line_number": 819, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 820, "usage_type": "call"}, {"api_name": "os.popen", "line_number": 823, "usage_type": "call"}, {"api_name": "os.path.basename", "line_number": 831, "usage_type": "call"}, {"api_name": "os.path", "line_number": 831, "usage_type": "attribute"}, {"api_name": "os.path.basename", "line_number": 832, "usage_type": "call"}, {"api_name": "os.path", "line_number": 832, "usage_type": "attribute"}, {"api_name": "os.name", "line_number": 836, "usage_type": "attribute"}, {"api_name": "setuptools.command", "line_number": 850, "usage_type": "attribute"}, {"api_name": "os.name", "line_number": 994, "usage_type": "attribute"}]} +{"seq_id": "36230997566", "text": "from utils.build_grid import extract_points, init_grid, coordinates_to_cover, extract_points_random\nfrom utils.math_utils import dist\nfrom utils.display_utils import circles\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport networkx as nx\n# from pylab import *\n\n\nclass Instance:\n def __init__(self, particular_points, size, Rcapt=1, Rcom=1, k=1, with_float=True):\n \"\"\"constructeur de la classe Instance\n\n Args:\n Rcapt (float, optionnal): rayon de captage. Defaults to 1.\n Rcom (float, optionnal): rayon de communication. Defaults to 1.\n particular_points (list of tuples) : may be the points to delete from a grid (with_float=False) or\n the coordinates of the targets (with_float=False)\n with_float (boolean) : tells whether we are working with randomly generated points or with discrete grid\n \"\"\"\n\n self.k = k\n self.is_float = with_float\n\n if with_float: # if the instance is cloud of randomly generated points\n self.source = (0., 0.)\n self.targets = particular_points[1:]\n\n else: \n self.source = (0, 0)\n self.n = size[0]\n self.m = size[1]\n\n self.deleted_points = particular_points\n self.n_deleted_points = len(particular_points)\n\n self.targets = [(i, j) for i in range(self.n) for j in range(self.m) if (i, j) not in particular_points\n and (i, j) != self.source]\n self.grid = init_grid(particular_points, size)\n\n #utilisés dans la classe local search\n self.indexes = {e : i+1 for i,e in enumerate(sorted(self.targets))}\n self.indexes[self.source] = 0\n\n self.reversed_indexes = {i+1 : e for i,e in enumerate(sorted(self.targets))}\n self.reversed_indexes[0] = self.source\n\n self.n_targets = len(self.targets)\n\n self.Rcapt = Rcapt\n self.Rcom = Rcom\n\n self.neighbours_Rcapt = self.neighbours_dict(Rcapt)\n self.neighbours_Rcom = self.neighbours_dict(Rcom)\n\n\n\n # construction de la matrice d'adjacence de captation\n capt_neighbours = self.neighbours(self.Rcapt, take_origin=False)\n self.E_capt = np.eye(self.n_targets+1, dtype=np.int8)\n for arc in capt_neighbours:\n self.E_capt[self.indexes[arc[0]], self.indexes[arc[1]]] = 1\n\n # construction de la matrice d'adjacence de communication\n com_neighbours = self.neighbours(self.Rcom, take_origin=True)\n self.E_com = np.zeros((self.n_targets+1, self.n_targets+1), dtype=np.int8)\n for arc in com_neighbours:\n self.E_com[self.indexes[arc[0]], self.indexes[arc[1]]] = 1\n\n @classmethod\n def from_disk(cls, data_file, Rcapt=1, Rcom=1, k=1, with_float=True):\n if with_float:\n points = extract_points_random(data_file)\n size = len(points)\n else:\n points, size = extract_points(data_file)\n return cls(points, size, Rcapt, Rcom, k, with_float)\n\n def draw_data(self):\n plt.figure(\"Instance\")\n for target in self.targets:\n plt.scatter(target[0], target[1], marker=\"+\", color='blue')\n plt.scatter(self.source[0], self.source[1], marker=\"o\", color='red')\n\n #plot a set of circles (circles in diagonal)\n ax = plt.gca()\n out = circles([t[0] for t in [(0, 0)] + self.targets], [t[1] for t in [(0, 0)] + self.targets], [self.Rcom for t in [(0, 0)] + self.targets], ax, c=\"green\", alpha=0.1, edgecolor='none')\n plt.colorbar(out)\n\n plt.show()\n\n def display(self):\n plt.figure(\"Instance\")\n self.draw_data()\n plt.show()\n\n def neighbours(self, R, take_origin):\n \"\"\"calcule l'ensemble des couples de points à couvrir distants d'au plus R\n\n Args:\n R (float): rayon\n take_origin (bool): indique si l'on doit prendre le point (0, 0, 0 dans la calcul)\n\n Returns:\n liste de tuples de tuples: chaque element est un couple de points de la liste \n \"\"\"\n list_neighbours = []\n\n if take_origin:\n v = (0, 0)\n for i in range(self.n_targets):\n u = self.targets[i]\n if dist(u, v) <= R:\n list_neighbours.append((u, v))\n list_neighbours.append((v, u))\n\n for i in range(self.n_targets):\n u = self.targets[i]\n for j in range(i):\n v = self.targets[j]\n if dist(u, v) <= R:\n list_neighbours.append((u, v))\n list_neighbours.append((v, u))\n return list_neighbours\n\n def neighbours_dict(self, R):\n \"\"\"Renvoie le dictionnaire des voisins\n\n Args:\n R (float): rayon\n take_origin (bool): indique si l'on doit prendre le point (0, 0, 0 dans le calcul)\n\n Returns:\n liste de tuples de tuples: chaque element est un couple de points de la liste\n \"\"\"\n list_neighbours = dict()\n\n targets_with_origin = self.targets + [(0, 0)]\n\n for i in range(self.n_targets + 1):\n u = targets_with_origin[i]\n if u not in list_neighbours:\n list_neighbours[u] = list()\n for j in range(i):\n v = targets_with_origin[j]\n if v not in list_neighbours:\n list_neighbours[v] = list()\n if dist(u, v) <= R:\n list_neighbours[u].append(v)\n list_neighbours[v].append(u)\n\n return list_neighbours\n", "repo_name": "Schlegen/K-cover-Metaheuristic", "sub_path": "src/instance_class.py", "file_name": "instance_class.py", "file_ext": "py", "file_size_in_byte": 5612, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "utils.build_grid.init_grid", "line_number": 39, "usage_type": "call"}, {"api_name": "numpy.eye", "line_number": 60, "usage_type": "call"}, {"api_name": "numpy.int8", "line_number": 60, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 66, "usage_type": "call"}, {"api_name": "numpy.int8", "line_number": 66, "usage_type": "attribute"}, {"api_name": "utils.build_grid.extract_points_random", "line_number": 73, "usage_type": "call"}, {"api_name": "utils.build_grid.extract_points", "line_number": 76, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 80, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 80, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 82, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 82, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 83, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 83, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.gca", "line_number": 86, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 86, "usage_type": "name"}, {"api_name": "utils.display_utils.circles", "line_number": 87, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.colorbar", "line_number": 88, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 88, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 90, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 90, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 93, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 93, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 95, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 95, "usage_type": "name"}, {"api_name": "utils.math_utils.dist", "line_number": 113, "usage_type": "call"}, {"api_name": "utils.math_utils.dist", "line_number": 121, "usage_type": "call"}, {"api_name": "utils.math_utils.dist", "line_number": 148, "usage_type": "call"}]} +{"seq_id": "33760246018", "text": "# -*- coding: utf-8 -*-\n\"\"\"\n @Time : 2020-09-06 17:14\n @Author : QDY\n @FileName: 5494. 统计所有可行路径.py\n @Software: PyCharm\n\"\"\"\n\"\"\"\n给你一个 互不相同的整数数组，其中locations[i]表示第i个城市的位置。\n同时给你start，finish和fuel分别表示出发城市、目的地城市和你初始拥有的汽油总量\n\n每一步中，如果你在城市 i，你可以选择任意一个城市 j，满足 j != i且0 <= j < locations.length，并移动到城市j。\n从城市i移动到j消耗的汽油量为|locations[i] - locations[j]|，|x|表示x的绝对值。\n\n请注意，fuel任何时刻都不能为负，且你可以经过任意城市超过一次（包括start和finish）。\n请你返回从start到finish所有可能路径的数目。\n\n由于答案可能很大， 请将它对10^9 + 7取余后返回。\n\n示例 1：\n输入：locations = [2,3,6,8,4], start = 1, finish = 3, fuel = 5\n输出：4\n解释：以下为所有可能路径，每一条都用了 5 单位的汽油：\n1 -> 3\n1 -> 2 -> 3\n1 -> 4 -> 3\n1 -> 4 -> 2 -> 3\n\n示例 2：\n输入：locations = [4,3,1], start = 1, finish = 0, fuel = 6\n输出：5\n解释：以下为所有可能的路径：\n1 -> 0，使用汽油量为 fuel = 1\n1 -> 2 -> 0，使用汽油量为 fuel = 5\n1 -> 2 -> 1 -> 0，使用汽油量为 fuel = 5\n1 -> 0 -> 1 -> 0，使用汽油量为 fuel = 3\n1 -> 0 -> 1 -> 0 -> 1 -> 0，使用汽油量为 fuel = 5\n\n示例 3：\n输入：locations = [5,2,1], start = 0, finish = 2, fuel = 3\n输出：0\n解释：没有办法只用 3 单位的汽油从 0 到达 2 。因为最短路径需要 4 单位的汽油。\n\n示例 4 ：\n输入：locations = [2,1,5], start = 0, finish = 0, fuel = 3\n输出：2\n解释：总共有两条可行路径，0 和 0 -> 1 -> 0 。\n\n示例 5：\n输入：locations = [1,2,3], start = 0, finish = 2, fuel = 40\n输出：615088286\n解释：路径总数为 2615088300 。将结果对 10^9 + 7 取余，得到 615088286 。\n\n\n提示：\n2 <= locations.length <= 100\n1 <= locations[i] <= 10^9\n所有locations中的整数 互不相同。\n0 <= start, finish int:\n mod = 10 ** 9 + 7\n N = len(locations)\n\n # dp[end][f] = start到end点'正好'花费f的油的路径数\n @lru_cache(None)\n def helper(end, f):\n if f == 0:\n if end == start:\n return 1\n else:\n return 0\n dp = 0\n for i in range(N):\n if i == end: continue\n dd = abs(locations[i] - locations[end])\n if f >= dd:\n dp += helper(i, f - dd) # start到i正好花费f-dd的路径数\n return dp\n\n res = 0\n for f in range(fuel + 1):\n res += helper(finish, f)\n return res % mod\n # dp = [[0]*(fuel+1) for _ in range(N)]\n # dp[start][0] = 1\n # for f in range(fuel+1):\n # for i in range(N):\n # for j in range(N):\n # if i==j:continue\n # dd = abs(locations[i]-locations[j])\n # if f>=dd:\n # dp[j][f] += dp[i][f-dd]\n # res = 0\n # for f in range(fuel+1):\n # res += dp[finish][f]\n # return res % (10**9+7)\n", "repo_name": "QDylan/Learning-", "sub_path": "Leetcode/5494. 统计所有���行路径.py", "file_name": "5494. 统计所有可行路径.py", "file_ext": "py", "file_size_in_byte": 3430, "program_lang": "python", "lang": "zh", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "37", "api": [{"api_name": "functools.lru_cache", "line_number": 73, "usage_type": "call"}]} +{"seq_id": "6301081185", "text": "import datetime\n\nfrom django.conf import settings\nfrom django.core.exceptions import ImproperlyConfigured\nfrom django.db import models\nfrom django.http import Http404\nfrom django.utils import timezone\nfrom django.utils.functional import cached_property\nfrom django.utils.translation import gettext as _\nfrom django.views.generic.base import View\nfrom django.views.generic.detail import (\n BaseDetailView,\n SingleObjectTemplateResponseMixin,\n)\nfrom django.views.generic.list import (\n MultipleObjectMixin,\n MultipleObjectTemplateResponseMixin,\n)\n\n\nclass YearMixin:\n \"\"\"Mixin for views manipulating year-based data.\"\"\"\n\n year_format = \"%Y\"\n year = None\n\n def get_year_format(self):\n \"\"\"\n Get a year format string in strptime syntax to be used to parse the\n year from url variables.\n \"\"\"\n return self.year_format\n\n def get_year(self):\n \"\"\"Return the year for which this view should display data.\"\"\"\n year = self.year\n if year is None:\n try:\n year = self.kwargs[\"year\"]\n except KeyError:\n try:\n year = self.request.GET[\"year\"]\n except KeyError:\n raise Http404(_(\"No year specified\"))\n return year\n\n def get_next_year(self, date):\n \"\"\"Get the next valid year.\"\"\"\n return _get_next_prev(self, date, is_previous=False, period=\"year\")\n\n def get_previous_year(self, date):\n \"\"\"Get the previous valid year.\"\"\"\n return _get_next_prev(self, date, is_previous=True, period=\"year\")\n\n def _get_next_year(self, date):\n \"\"\"\n Return the start date of the next interval.\n\n The interval is defined by start date <= item date < next start date.\n \"\"\"\n try:\n return date.replace(year=date.year + 1, month=1, day=1)\n except ValueError:\n raise Http404(_(\"Date out of range\"))\n\n def _get_current_year(self, date):\n \"\"\"Return the start date of the current interval.\"\"\"\n return date.replace(month=1, day=1)\n\n\nclass MonthMixin:\n \"\"\"Mixin for views manipulating month-based data.\"\"\"\n\n month_format = \"%b\"\n month = None\n\n def get_month_format(self):\n \"\"\"\n Get a month format string in strptime syntax to be used to parse the\n month from url variables.\n \"\"\"\n return self.month_format\n\n def get_month(self):\n \"\"\"Return the month for which this view should display data.\"\"\"\n month = self.month\n if month is None:\n try:\n month = self.kwargs[\"month\"]\n except KeyError:\n try:\n month = self.request.GET[\"month\"]\n except KeyError:\n raise Http404(_(\"No month specified\"))\n return month\n\n def get_next_month(self, date):\n \"\"\"Get the next valid month.\"\"\"\n return _get_next_prev(self, date, is_previous=False, period=\"month\")\n\n def get_previous_month(self, date):\n \"\"\"Get the previous valid month.\"\"\"\n return _get_next_prev(self, date, is_previous=True, period=\"month\")\n\n def _get_next_month(self, date):\n \"\"\"\n Return the start date of the next interval.\n\n The interval is defined by start date <= item date < next start date.\n \"\"\"\n if date.month == 12:\n try:\n return date.replace(year=date.year + 1, month=1, day=1)\n except ValueError:\n raise Http404(_(\"Date out of range\"))\n else:\n return date.replace(month=date.month + 1, day=1)\n\n def _get_current_month(self, date):\n \"\"\"Return the start date of the previous interval.\"\"\"\n return date.replace(day=1)\n\n\nclass DayMixin:\n \"\"\"Mixin for views manipulating day-based data.\"\"\"\n\n day_format = \"%d\"\n day = None\n\n def get_day_format(self):\n \"\"\"\n Get a day format string in strptime syntax to be used to parse the day\n from url variables.\n \"\"\"\n return self.day_format\n\n def get_day(self):\n \"\"\"Return the day for which this view should display data.\"\"\"\n day = self.day\n if day is None:\n try:\n day = self.kwargs[\"day\"]\n except KeyError:\n try:\n day = self.request.GET[\"day\"]\n except KeyError:\n raise Http404(_(\"No day specified\"))\n return day\n\n def get_next_day(self, date):\n \"\"\"Get the next valid day.\"\"\"\n return _get_next_prev(self, date, is_previous=False, period=\"day\")\n\n def get_previous_day(self, date):\n \"\"\"Get the previous valid day.\"\"\"\n return _get_next_prev(self, date, is_previous=True, period=\"day\")\n\n def _get_next_day(self, date):\n \"\"\"\n Return the start date of the next interval.\n\n The interval is defined by start date <= item date < next start date.\n \"\"\"\n return date + datetime.timedelta(days=1)\n\n def _get_current_day(self, date):\n \"\"\"Return the start date of the current interval.\"\"\"\n return date\n\n\nclass WeekMixin:\n \"\"\"Mixin for views manipulating week-based data.\"\"\"\n\n week_format = \"%U\"\n week = None\n\n def get_week_format(self):\n \"\"\"\n Get a week format string in strptime syntax to be used to parse the\n week from url variables.\n \"\"\"\n return self.week_format\n\n def get_week(self):\n \"\"\"Return the week for which this view should display data.\"\"\"\n week = self.week\n if week is None:\n try:\n week = self.kwargs[\"week\"]\n except KeyError:\n try:\n week = self.request.GET[\"week\"]\n except KeyError:\n raise Http404(_(\"No week specified\"))\n return week\n\n def get_next_week(self, date):\n \"\"\"Get the next valid week.\"\"\"\n return _get_next_prev(self, date, is_previous=False, period=\"week\")\n\n def get_previous_week(self, date):\n \"\"\"Get the previous valid week.\"\"\"\n return _get_next_prev(self, date, is_previous=True, period=\"week\")\n\n def _get_next_week(self, date):\n \"\"\"\n Return the start date of the next interval.\n\n The interval is defined by start date <= item date < next start date.\n \"\"\"\n try:\n return date + datetime.timedelta(days=7 - self._get_weekday(date))\n except OverflowError:\n raise Http404(_(\"Date out of range\"))\n\n def _get_current_week(self, date):\n \"\"\"Return the start date of the current interval.\"\"\"\n return date - datetime.timedelta(self._get_weekday(date))\n\n def _get_weekday(self, date):\n \"\"\"\n Return the weekday for a given date.\n\n The first day according to the week format is 0 and the last day is 6.\n \"\"\"\n week_format = self.get_week_format()\n if week_format in {\"%W\", \"%V\"}: # week starts on Monday\n return date.weekday()\n elif week_format == \"%U\": # week starts on Sunday\n return (date.weekday() + 1) % 7\n else:\n raise ValueError(\"unknown week format: %s\" % week_format)\n\n\nclass DateMixin:\n \"\"\"Mixin class for views manipulating date-based data.\"\"\"\n\n date_field = None\n allow_future = False\n\n def get_date_field(self):\n \"\"\"Get the name of the date field to be used to filter by.\"\"\"\n if self.date_field is None:\n raise ImproperlyConfigured(\n \"%s.date_field is required.\" % self.__class__.__name__\n )\n return self.date_field\n\n def get_allow_future(self):\n \"\"\"\n Return `True` if the view should be allowed to display objects from\n the future.\n \"\"\"\n return self.allow_future\n\n # Note: the following three methods only work in subclasses that also\n # inherit SingleObjectMixin or MultipleObjectMixin.\n\n @cached_property\n def uses_datetime_field(self):\n \"\"\"\n Return `True` if the date field is a `DateTimeField` and `False`\n if it's a `DateField`.\n \"\"\"\n model = self.get_queryset().model if self.model is None else self.model\n field = model._meta.get_field(self.get_date_field())\n return isinstance(field, models.DateTimeField)\n\n def _make_date_lookup_arg(self, value):\n \"\"\"\n Convert a date into a datetime when the date field is a DateTimeField.\n\n When time zone support is enabled, `date` is assumed to be in the\n current time zone, so that displayed items are consistent with the URL.\n \"\"\"\n if self.uses_datetime_field:\n value = datetime.datetime.combine(value, datetime.time.min)\n if settings.USE_TZ:\n value = timezone.make_aware(value)\n return value\n\n def _make_single_date_lookup(self, date):\n \"\"\"\n Get the lookup kwargs for filtering on a single date.\n\n If the date field is a DateTimeField, we can't just filter on\n date_field=date because that doesn't take the time into account.\n \"\"\"\n date_field = self.get_date_field()\n if self.uses_datetime_field:\n since = self._make_date_lookup_arg(date)\n until = self._make_date_lookup_arg(date + datetime.timedelta(days=1))\n return {\n \"%s__gte\" % date_field: since,\n \"%s__lt\" % date_field: until,\n }\n else:\n # Skip self._make_date_lookup_arg, it's a no-op in this branch.\n return {date_field: date}\n\n\nclass BaseDateListView(MultipleObjectMixin, DateMixin, View):\n \"\"\"Abstract base class for date-based views displaying a list of objects.\"\"\"\n\n allow_empty = False\n date_list_period = \"year\"\n\n def get(self, request, *args, **kwargs):\n self.date_list, self.object_list, extra_context = self.get_dated_items()\n context = self.get_context_data(\n object_list=self.object_list, date_list=self.date_list, **extra_context\n )\n return self.render_to_response(context)\n\n def get_dated_items(self):\n \"\"\"Obtain the list of dates and items.\"\"\"\n raise NotImplementedError(\n \"A DateView must provide an implementation of get_dated_items()\"\n )\n\n def get_ordering(self):\n \"\"\"\n Return the field or fields to use for ordering the queryset; use the\n date field by default.\n \"\"\"\n return \"-%s\" % self.get_date_field() if self.ordering is None else self.ordering\n\n def get_dated_queryset(self, **lookup):\n \"\"\"\n Get a queryset properly filtered according to `allow_future` and any\n extra lookup kwargs.\n \"\"\"\n qs = self.get_queryset().filter(**lookup)\n date_field = self.get_date_field()\n allow_future = self.get_allow_future()\n allow_empty = self.get_allow_empty()\n paginate_by = self.get_paginate_by(qs)\n\n if not allow_future:\n now = timezone.now() if self.uses_datetime_field else timezone_today()\n qs = qs.filter(**{\"%s__lte\" % date_field: now})\n\n if not allow_empty:\n # When pagination is enabled, it's better to do a cheap query\n # than to load the unpaginated queryset in memory.\n is_empty = not qs if paginate_by is None else not qs.exists()\n if is_empty:\n raise Http404(\n _(\"No %(verbose_name_plural)s available\")\n % {\n \"verbose_name_plural\": qs.model._meta.verbose_name_plural,\n }\n )\n\n return qs\n\n def get_date_list_period(self):\n \"\"\"\n Get the aggregation period for the list of dates: 'year', 'month', or\n 'day'.\n \"\"\"\n return self.date_list_period\n\n def get_date_list(self, queryset, date_type=None, ordering=\"ASC\"):\n \"\"\"\n Get a date list by calling `queryset.dates/datetimes()`, checking\n along the way for empty lists that aren't allowed.\n \"\"\"\n date_field = self.get_date_field()\n allow_empty = self.get_allow_empty()\n if date_type is None:\n date_type = self.get_date_list_period()\n\n if self.uses_datetime_field:\n date_list = queryset.datetimes(date_field, date_type, ordering)\n else:\n date_list = queryset.dates(date_field, date_type, ordering)\n if date_list is not None and not date_list and not allow_empty:\n raise Http404(\n _(\"No %(verbose_name_plural)s available\")\n % {\n \"verbose_name_plural\": queryset.model._meta.verbose_name_plural,\n }\n )\n\n return date_list\n\n\nclass BaseArchiveIndexView(BaseDateListView):\n \"\"\"\n Base class for archives of date-based items. Requires a response mixin.\n \"\"\"\n\n context_object_name = \"latest\"\n\n def get_dated_items(self):\n \"\"\"Return (date_list, items, extra_context) for this request.\"\"\"\n qs = self.get_dated_queryset()\n date_list = self.get_date_list(qs, ordering=\"DESC\")\n\n if not date_list:\n qs = qs.none()\n\n return (date_list, qs, {})\n\n\nclass ArchiveIndexView(MultipleObjectTemplateResponseMixin, BaseArchiveIndexView):\n \"\"\"Top-level archive of date-based items.\"\"\"\n\n template_name_suffix = \"_archive\"\n\n\nclass BaseYearArchiveView(YearMixin, BaseDateListView):\n \"\"\"List of objects published in a given year.\"\"\"\n\n date_list_period = \"month\"\n make_object_list = False\n\n def get_dated_items(self):\n \"\"\"Return (date_list, items, extra_context) for this request.\"\"\"\n year = self.get_year()\n\n date_field = self.get_date_field()\n date = _date_from_string(year, self.get_year_format())\n\n since = self._make_date_lookup_arg(date)\n until = self._make_date_lookup_arg(self._get_next_year(date))\n lookup_kwargs = {\n \"%s__gte\" % date_field: since,\n \"%s__lt\" % date_field: until,\n }\n\n qs = self.get_dated_queryset(**lookup_kwargs)\n date_list = self.get_date_list(qs)\n\n if not self.get_make_object_list():\n # We need this to be a queryset since parent classes introspect it\n # to find information about the model.\n qs = qs.none()\n\n return (\n date_list,\n qs,\n {\n \"year\": date,\n \"next_year\": self.get_next_year(date),\n \"previous_year\": self.get_previous_year(date),\n },\n )\n\n def get_make_object_list(self):\n \"\"\"\n Return `True` if this view should contain the full list of objects in\n the given year.\n \"\"\"\n return self.make_object_list\n\n\nclass YearArchiveView(MultipleObjectTemplateResponseMixin, BaseYearArchiveView):\n \"\"\"List of objects published in a given year.\"\"\"\n\n template_name_suffix = \"_archive_year\"\n\n\nclass BaseMonthArchiveView(YearMixin, MonthMixin, BaseDateListView):\n \"\"\"List of objects published in a given month.\"\"\"\n\n date_list_period = \"day\"\n\n def get_dated_items(self):\n \"\"\"Return (date_list, items, extra_context) for this request.\"\"\"\n year = self.get_year()\n month = self.get_month()\n\n date_field = self.get_date_field()\n date = _date_from_string(\n year, self.get_year_format(), month, self.get_month_format()\n )\n\n since = self._make_date_lookup_arg(date)\n until = self._make_date_lookup_arg(self._get_next_month(date))\n lookup_kwargs = {\n \"%s__gte\" % date_field: since,\n \"%s__lt\" % date_field: until,\n }\n\n qs = self.get_dated_queryset(**lookup_kwargs)\n date_list = self.get_date_list(qs)\n\n return (\n date_list,\n qs,\n {\n \"month\": date,\n \"next_month\": self.get_next_month(date),\n \"previous_month\": self.get_previous_month(date),\n },\n )\n\n\nclass MonthArchiveView(MultipleObjectTemplateResponseMixin, BaseMonthArchiveView):\n \"\"\"List of objects published in a given month.\"\"\"\n\n template_name_suffix = \"_archive_month\"\n\n\nclass BaseWeekArchiveView(YearMixin, WeekMixin, BaseDateListView):\n \"\"\"List of objects published in a given week.\"\"\"\n\n def get_dated_items(self):\n \"\"\"Return (date_list, items, extra_context) for this request.\"\"\"\n year = self.get_year()\n week = self.get_week()\n\n date_field = self.get_date_field()\n week_format = self.get_week_format()\n week_choices = {\"%W\": \"1\", \"%U\": \"0\", \"%V\": \"1\"}\n try:\n week_start = week_choices[week_format]\n except KeyError:\n raise ValueError(\n \"Unknown week format %r. Choices are: %s\"\n % (\n week_format,\n \", \".join(sorted(week_choices)),\n )\n )\n year_format = self.get_year_format()\n if week_format == \"%V\" and year_format != \"%G\":\n raise ValueError(\n \"ISO week directive '%s' is incompatible with the year \"\n \"directive '%s'. Use the ISO year '%%G' instead.\"\n % (\n week_format,\n year_format,\n )\n )\n date = _date_from_string(year, year_format, week_start, \"%w\", week, week_format)\n since = self._make_date_lookup_arg(date)\n until = self._make_date_lookup_arg(self._get_next_week(date))\n lookup_kwargs = {\n \"%s__gte\" % date_field: since,\n \"%s__lt\" % date_field: until,\n }\n\n qs = self.get_dated_queryset(**lookup_kwargs)\n\n return (\n None,\n qs,\n {\n \"week\": date,\n \"next_week\": self.get_next_week(date),\n \"previous_week\": self.get_previous_week(date),\n },\n )\n\n\nclass WeekArchiveView(MultipleObjectTemplateResponseMixin, BaseWeekArchiveView):\n \"\"\"List of objects published in a given week.\"\"\"\n\n template_name_suffix = \"_archive_week\"\n\n\nclass BaseDayArchiveView(YearMixin, MonthMixin, DayMixin, BaseDateListView):\n \"\"\"List of objects published on a given day.\"\"\"\n\n def get_dated_items(self):\n \"\"\"Return (date_list, items, extra_context) for this request.\"\"\"\n year = self.get_year()\n month = self.get_month()\n day = self.get_day()\n\n date = _date_from_string(\n year,\n self.get_year_format(),\n month,\n self.get_month_format(),\n day,\n self.get_day_format(),\n )\n\n return self._get_dated_items(date)\n\n def _get_dated_items(self, date):\n \"\"\"\n Do the actual heavy lifting of getting the dated items; this accepts a\n date object so that TodayArchiveView can be trivial.\n \"\"\"\n lookup_kwargs = self._make_single_date_lookup(date)\n qs = self.get_dated_queryset(**lookup_kwargs)\n\n return (\n None,\n qs,\n {\n \"day\": date,\n \"previous_day\": self.get_previous_day(date),\n \"next_day\": self.get_next_day(date),\n \"previous_month\": self.get_previous_month(date),\n \"next_month\": self.get_next_month(date),\n },\n )\n\n\nclass DayArchiveView(MultipleObjectTemplateResponseMixin, BaseDayArchiveView):\n \"\"\"List of objects published on a given day.\"\"\"\n\n template_name_suffix = \"_archive_day\"\n\n\nclass BaseTodayArchiveView(BaseDayArchiveView):\n \"\"\"List of objects published today.\"\"\"\n\n def get_dated_items(self):\n \"\"\"Return (date_list, items, extra_context) for this request.\"\"\"\n return self._get_dated_items(datetime.date.today())\n\n\nclass TodayArchiveView(MultipleObjectTemplateResponseMixin, BaseTodayArchiveView):\n \"\"\"List of objects published today.\"\"\"\n\n template_name_suffix = \"_archive_day\"\n\n\nclass BaseDateDetailView(YearMixin, MonthMixin, DayMixin, DateMixin, BaseDetailView):\n \"\"\"\n Detail view of a single object on a single date; this differs from the\n standard DetailView by accepting a year/month/day in the URL.\n \"\"\"\n\n def get_object(self, queryset=None):\n \"\"\"Get the object this request displays.\"\"\"\n year = self.get_year()\n month = self.get_month()\n day = self.get_day()\n date = _date_from_string(\n year,\n self.get_year_format(),\n month,\n self.get_month_format(),\n day,\n self.get_day_format(),\n )\n\n # Use a custom queryset if provided\n qs = self.get_queryset() if queryset is None else queryset\n\n if not self.get_allow_future() and date > datetime.date.today():\n raise Http404(\n _(\n \"Future %(verbose_name_plural)s not available because \"\n \"%(class_name)s.allow_future is False.\"\n )\n % {\n \"verbose_name_plural\": qs.model._meta.verbose_name_plural,\n \"class_name\": self.__class__.__name__,\n }\n )\n\n # Filter down a queryset from self.queryset using the date from the\n # URL. This'll get passed as the queryset to DetailView.get_object,\n # which'll handle the 404\n lookup_kwargs = self._make_single_date_lookup(date)\n qs = qs.filter(**lookup_kwargs)\n\n return super().get_object(queryset=qs)\n\n\nclass DateDetailView(SingleObjectTemplateResponseMixin, BaseDateDetailView):\n \"\"\"\n Detail view of a single object on a single date; this differs from the\n standard DetailView by accepting a year/month/day in the URL.\n \"\"\"\n\n template_name_suffix = \"_detail\"\n\n\ndef _date_from_string(\n year, year_format, month=\"\", month_format=\"\", day=\"\", day_format=\"\", delim=\"__\"\n):\n \"\"\"\n Get a datetime.date object given a format string and a year, month, and day\n (only year is mandatory). Raise a 404 for an invalid date.\n \"\"\"\n format = year_format + delim + month_format + delim + day_format\n datestr = str(year) + delim + str(month) + delim + str(day)\n try:\n return datetime.datetime.strptime(datestr, format).date()\n except ValueError:\n raise Http404(\n _(\"Invalid date string “%(datestr)s” given format “%(format)s”\")\n % {\n \"datestr\": datestr,\n \"format\": format,\n }\n )\n\n\ndef _get_next_prev(generic_view, date, is_previous, period):\n \"\"\"\n Get the next or the previous valid date. The idea is to allow links on\n month/day views to never be 404s by never providing a date that'll be\n invalid for the given view.\n\n This is a bit complicated since it handles different intervals of time,\n hence the coupling to generic_view.\n\n However in essence the logic comes down to:\n\n * If allow_empty and allow_future are both true, this is easy: just\n return the naive result (just the next/previous day/week/month,\n regardless of object existence.)\n\n * If allow_empty is true, allow_future is false, and the naive result\n isn't in the future, then return it; otherwise return None.\n\n * If allow_empty is false and allow_future is true, return the next\n date *that contains a valid object*, even if it's in the future. If\n there are no next objects, return None.\n\n * If allow_empty is false and allow_future is false, return the next\n date that contains a valid object. If that date is in the future, or\n if there are no next objects, return None.\n \"\"\"\n date_field = generic_view.get_date_field()\n allow_empty = generic_view.get_allow_empty()\n allow_future = generic_view.get_allow_future()\n\n get_current = getattr(generic_view, \"_get_current_%s\" % period)\n get_next = getattr(generic_view, \"_get_next_%s\" % period)\n\n # Bounds of the current interval\n start, end = get_current(date), get_next(date)\n\n # If allow_empty is True, the naive result will be valid\n if allow_empty:\n if is_previous:\n result = get_current(start - datetime.timedelta(days=1))\n else:\n result = end\n\n if allow_future or result <= timezone_today():\n return result\n else:\n return None\n\n # Otherwise, we'll need to go to the database to look for an object\n # whose date_field is at least (greater than/less than) the given\n # naive result\n else:\n # Construct a lookup and an ordering depending on whether we're doing\n # a previous date or a next date lookup.\n if is_previous:\n lookup = {\"%s__lt\" % date_field: generic_view._make_date_lookup_arg(start)}\n ordering = \"-%s\" % date_field\n else:\n lookup = {\"%s__gte\" % date_field: generic_view._make_date_lookup_arg(end)}\n ordering = date_field\n\n # Filter out objects in the future if appropriate.\n if not allow_future:\n # Fortunately, to match the implementation of allow_future,\n # we need __lte, which doesn't conflict with __lt above.\n if generic_view.uses_datetime_field:\n now = timezone.now()\n else:\n now = timezone_today()\n lookup[\"%s__lte\" % date_field] = now\n\n qs = generic_view.get_queryset().filter(**lookup).order_by(ordering)\n\n # Snag the first object from the queryset; if it doesn't exist that\n # means there's no next/previous link available.\n try:\n result = getattr(qs[0], date_field)\n except IndexError:\n return None\n\n # Convert datetimes to dates in the current time zone.\n if generic_view.uses_datetime_field:\n if settings.USE_TZ:\n result = timezone.localtime(result)\n result = result.date()\n\n # Return the first day of the period.\n return get_current(result)\n\n\ndef timezone_today():\n \"\"\"Return the current date in the current time zone.\"\"\"\n if settings.USE_TZ:\n return timezone.localdate()\n else:\n return datetime.date.today()\n", "repo_name": "django/django", "sub_path": "django/views/generic/dates.py", "file_name": "dates.py", "file_ext": "py", "file_size_in_byte": 26332, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 74132, "dataset": "github-code", "pt": "37", "api": [{"api_name": "django.http.Http404", "line_number": 44, "usage_type": "call"}, {"api_name": "django.utils.translation.gettext", "line_number": 44, "usage_type": "call"}, {"api_name": "django.http.Http404", "line_number": 64, "usage_type": "call"}, {"api_name": "django.utils.translation.gettext", "line_number": 64, "usage_type": "call"}, {"api_name": "django.http.Http404", "line_number": 94, "usage_type": "call"}, {"api_name": "django.utils.translation.gettext", "line_number": 94, "usage_type": "call"}, {"api_name": "django.http.Http404", "line_number": 115, "usage_type": "call"}, {"api_name": "django.utils.translation.gettext", "line_number": 115, "usage_type": "call"}, {"api_name": "django.http.Http404", "line_number": 147, "usage_type": "call"}, {"api_name": "django.utils.translation.gettext", "line_number": 147, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 164, "usage_type": "call"}, {"api_name": "django.http.Http404", "line_number": 194, "usage_type": "call"}, {"api_name": "django.utils.translation.gettext", "line_number": 194, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 212, "usage_type": "call"}, {"api_name": "django.http.Http404", "line_number": 214, "usage_type": "call"}, {"api_name": "django.utils.translation.gettext", "line_number": 214, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 218, "usage_type": "call"}, {"api_name": "django.core.exceptions.ImproperlyConfigured", "line_number": 244, "usage_type": "call"}, {"api_name": "django.db.models.DateTimeField", "line_number": 267, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 267, "usage_type": "name"}, {"api_name": "django.utils.functional.cached_property", "line_number": 259, "usage_type": "name"}, {"api_name": "datetime.datetime.combine", "line_number": 277, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 277, "usage_type": "attribute"}, {"api_name": "datetime.time", "line_number": 277, "usage_type": "attribute"}, {"api_name": "django.conf.settings.USE_TZ", "line_number": 278, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 278, "usage_type": "name"}, {"api_name": "django.utils.timezone.make_aware", "line_number": 279, "usage_type": "call"}, {"api_name": "django.utils.timezone", "line_number": 279, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 292, "usage_type": "call"}, {"api_name": "django.views.generic.list.MultipleObjectMixin", "line_number": 302, "usage_type": "name"}, {"api_name": "django.views.generic.base.View", "line_number": 302, "usage_type": "name"}, {"api_name": "django.utils.timezone.now", "line_number": 340, "usage_type": "call"}, {"api_name": "django.utils.timezone", "line_number": 340, "usage_type": "name"}, {"api_name": "django.http.Http404", "line_number": 348, "usage_type": "call"}, {"api_name": "django.utils.translation.gettext", "line_number": 349, "usage_type": "call"}, {"api_name": "django.http.Http404", "line_number": 379, "usage_type": "call"}, {"api_name": "django.utils.translation.gettext", "line_number": 380, "usage_type": "call"}, {"api_name": "django.views.generic.list.MultipleObjectTemplateResponseMixin", "line_number": 407, "usage_type": "name"}, {"api_name": "django.views.generic.list.MultipleObjectTemplateResponseMixin", "line_number": 459, "usage_type": "name"}, {"api_name": "django.views.generic.list.MultipleObjectTemplateResponseMixin", "line_number": 501, "usage_type": "name"}, {"api_name": "django.views.generic.list.MultipleObjectTemplateResponseMixin", "line_number": 559, "usage_type": "name"}, {"api_name": "django.views.generic.list.MultipleObjectTemplateResponseMixin", "line_number": 606, "usage_type": "name"}, {"api_name": "datetime.date.today", "line_number": 617, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 617, "usage_type": "attribute"}, {"api_name": "django.views.generic.list.MultipleObjectTemplateResponseMixin", "line_number": 620, "usage_type": "name"}, {"api_name": "django.views.generic.detail.BaseDetailView", "line_number": 626, "usage_type": "name"}, {"api_name": "datetime.date.today", "line_number": 649, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 649, "usage_type": "attribute"}, {"api_name": "django.http.Http404", "line_number": 650, "usage_type": "call"}, {"api_name": "django.utils.translation.gettext", "line_number": 651, "usage_type": "call"}, {"api_name": "django.views.generic.detail.SingleObjectTemplateResponseMixin", "line_number": 670, "usage_type": "name"}, {"api_name": "datetime.datetime.strptime", "line_number": 689, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 689, "usage_type": "attribute"}, {"api_name": "django.http.Http404", "line_number": 691, "usage_type": "call"}, {"api_name": "django.utils.translation.gettext", "line_number": 692, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 739, "usage_type": "call"}, {"api_name": "django.utils.timezone.now", "line_number": 766, "usage_type": "call"}, {"api_name": "django.utils.timezone", "line_number": 766, "usage_type": "name"}, {"api_name": "django.conf.settings.USE_TZ", "line_number": 782, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 782, "usage_type": "name"}, {"api_name": "django.utils.timezone.localtime", "line_number": 783, "usage_type": "call"}, {"api_name": "django.utils.timezone", "line_number": 783, "usage_type": "name"}, {"api_name": "django.conf.settings.USE_TZ", "line_number": 792, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 792, "usage_type": "name"}, {"api_name": "django.utils.timezone.localdate", "line_number": 793, "usage_type": "call"}, {"api_name": "django.utils.timezone", "line_number": 793, "usage_type": "name"}, {"api_name": "datetime.date.today", "line_number": 795, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 795, "usage_type": "attribute"}]} +{"seq_id": "40868544110", "text": "import pandas as pd\nfrom sklearn.pipeline import Pipeline as skPipeline\nfrom sklearn.compose import ColumnTransformer\nfrom sklearn.preprocessing import OneHotEncoder\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import accuracy_score\nfrom sklearn.impute import SimpleImputer\nimport joblib\nimport motor.motor_asyncio\nimport os\nimport datetime\nfrom db_models.pipeline_db_model import Pipeline\n\ndef train_pipeline( ml_models, client):\n\n pipeline= ml_models[\"ao_predictor\"]\n print(\"Training model...\")\n db = client[\"automl\"]\n collection = db[\"data_set\"]\n cursor = collection.find()\n data = list(cursor)\n # to convert data to pandas dataframe\n df = pd.DataFrame(data)\n\n # Split data into features (X) and target (y)\n X = df.drop(\"open_account_flg\", axis=1)\n y = df[\"open_account_flg\"]\n # Split data into training and testing sets\n X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\n # Model Training and Evaluation using the pipeline\n pipeline.fit(X_train, y_train)\n accuracy = accuracy_score(y_test, pipeline.predict(X_test))\n\n\n version = \"init\"\n #get the active pipeline\n active_pipeline = db[\"pipeline\"].find_one({\"active\": True})\n if active_pipeline:\n #deactivate the active pipeline\n db[\"pipeline\"].update_one({\"active\": True}, {\"$set\": {\"active\": False}})\n version = active_pipeline[\"version\"]\n if version == \"init\":\n version = \"1.0\"\n else:\n version = str(round(float(version) + 1, 2))\n #save the new pipeline\n joblib.dump(pipeline, f\"train/models/alif_aop_RF_pipeline_v{version}.pkl\")\n pipeline= Pipeline(\n algorithm=\"RandomForest\",\n name=f\"alif_aop_RF_pipeline_v{version}.pkl\",\n accuracy=accuracy,\n version=version,\n active=True,\n pipeline_path=f\"train/models/alif_aop_RF_pipeline_v{version}.pkl\",\n created_at= datetime.datetime.now(),\n updated_at= datetime.datetime.now(),\n )\n print(\"Model trained successfully!\")\n result = db[\"pipeline\"].insert_one(pipeline.dict())\n\n print(\"Loading newly trained pipeline...\")\n ml_models[\"ao_predictor\"] = joblib.load(f\"train/models/alif_aop_RF_pipeline_v{version}.pkl\")\n print(\"New pipeline loaded successfully!\")\n\n\n\n\n return pipeline\n\n", "repo_name": "KamoliddinS/AutoML", "sub_path": "backend/train/train_pipeline.py", "file_name": "train_pipeline.py", "file_ext": "py", "file_size_in_byte": 2381, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "pandas.DataFrame", "line_number": 24, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 30, "usage_type": "call"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 33, "usage_type": "call"}, {"api_name": "joblib.dump", "line_number": 48, "usage_type": "call"}, {"api_name": "db_models.pipeline_db_model.Pipeline", "line_number": 49, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 56, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 56, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 57, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 57, "usage_type": "attribute"}, {"api_name": "joblib.load", "line_number": 63, "usage_type": "call"}]} +{"seq_id": "14610429315", "text": "from orm.Base import Base\n\nfrom sqlalchemy import Column, Integer, NVARCHAR, VARBINARY, DATE\n\nclass Album(Base):\n __tablename__ = 'Albums'\n\n id = Column(Integer, primary_key=True, name='ID')\n music_brainz_id = Column(NVARCHAR(256), name='MusicBrainzID')\n name = Column(NVARCHAR(256), name='Name')\n date = Column(DATE, name='Date')\n cover_art = Column(VARBINARY, name='CoverArt')\n mime_type = Column(NVARCHAR(64), name='MimeType')", "repo_name": "clcrutch/music-org", "sub_path": "orm/Album.py", "file_name": "Album.py", "file_ext": "py", "file_size_in_byte": 450, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "orm.Base.Base", "line_number": 5, "usage_type": "name"}, {"api_name": "sqlalchemy.Column", "line_number": 8, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 8, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 9, "usage_type": "call"}, {"api_name": "sqlalchemy.NVARCHAR", "line_number": 9, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 10, "usage_type": "call"}, {"api_name": "sqlalchemy.NVARCHAR", "line_number": 10, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 11, "usage_type": "call"}, {"api_name": "sqlalchemy.DATE", "line_number": 11, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 12, "usage_type": "call"}, {"api_name": "sqlalchemy.VARBINARY", "line_number": 12, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 13, "usage_type": "call"}, {"api_name": "sqlalchemy.NVARCHAR", "line_number": 13, "usage_type": "call"}]} +{"seq_id": "17395176499", "text": "import pytest\nimport requests\nfrom app.apis.vpic import getVin, VpicApiError\nfrom unittest.mock import MagicMock, patch\nfrom ..data import REAL_VINS, FAKE_VALID_FORMAT_VIN\n\n@pytest.mark.parametrize(\"vin\", [\"xxxxxxxxxxxxxxxxxx\", \"xxxxxxxxxxxxxxxx;\", \"123\"])\ndef test_getVin_bad_format_vin(vin: str):\n with pytest.raises(ValueError):\n getVin(vin)\n\n@pytest.mark.parametrize(\"errorStatusCode\", [500, 400])\ndef test_getVin_vpic_api_failed(errorStatusCode: int):\n with patch(\"requests.get\") as mockRequestsGet:\n dummyResponse = requests.Response()\n dummyResponse.status_code = errorStatusCode\n dummyResponse.raise_for_status = MagicMock(side_effect=requests.HTTPError(\"Dummy HTTP error\"))\n mockRequestsGet.return_value = dummyResponse\n\n with pytest.raises(VpicApiError) as ex:\n getVin(FAKE_VALID_FORMAT_VIN)\n assert ex.value.errorStatusCode == errorStatusCode\n\ndef test_getVin_returns_vin_object():\n vpicVin = getVin(REAL_VINS[0])\n assert vpicVin.vin != \"\"\n assert vpicVin.make != \"\"\n assert vpicVin.model != \"\"\n assert vpicVin.modelYear != \"\"\n assert vpicVin.bodyClass != \"\"\n", "repo_name": "pandabytes/vin-lookup", "sub_path": "backend/tests/apis/test_vpic.py", "file_name": "test_vpic.py", "file_ext": "py", "file_size_in_byte": 1104, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "pytest.raises", "line_number": 9, "usage_type": "call"}, {"api_name": "app.apis.vpic.getVin", "line_number": 10, "usage_type": "call"}, {"api_name": "pytest.mark.parametrize", "line_number": 7, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 7, "usage_type": "attribute"}, {"api_name": "unittest.mock.patch", "line_number": 14, "usage_type": "call"}, {"api_name": "requests.Response", "line_number": 15, "usage_type": "call"}, {"api_name": "unittest.mock.MagicMock", "line_number": 17, "usage_type": "call"}, {"api_name": "requests.HTTPError", "line_number": 17, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 20, "usage_type": "call"}, {"api_name": "app.apis.vpic.VpicApiError", "line_number": 20, "usage_type": "argument"}, {"api_name": "app.apis.vpic.getVin", "line_number": 21, "usage_type": "call"}, {"api_name": "data.FAKE_VALID_FORMAT_VIN", "line_number": 21, "usage_type": "argument"}, {"api_name": "pytest.mark.parametrize", "line_number": 12, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 12, "usage_type": "attribute"}, {"api_name": "app.apis.vpic.getVin", "line_number": 25, "usage_type": "call"}, {"api_name": "data.REAL_VINS", "line_number": 25, "usage_type": "name"}]} +{"seq_id": "40124342260", "text": "from typing import List\r\n\r\n\r\nclass Solution:\r\n def maximumProduct(self, nums: List[int]) -> int:\r\n nums.sort()\r\n val = nums[-3] * nums[-2] * nums[-1]\r\n if nums[0] < 0 < nums[-1] and nums[1] < 0:\r\n temp = nums[0] * nums[1] * nums[-1]\r\n val = max(val, temp)\r\n return val\r\n", "repo_name": "pangyouzhen/data-structure", "sub_path": "other/628 maximumProduct.py", "file_name": "628 maximumProduct.py", "file_ext": "py", "file_size_in_byte": 323, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "typing.List", "line_number": 5, "usage_type": "name"}]} +{"seq_id": "10256287628", "text": "import math, collections\nimport homing, chelper\nimport extras.driver as driver_base\n\n\n\n######################################################################\n# Stepper enable pins\n######################################################################\n\n# Tracking of shared stepper enable pins\nclass StepperEnablePin:\n def __init__(self, mcu_enable, enable_count=0):\n self.mcu_enable = mcu_enable\n self.enable_count = enable_count\n def set_enable(self, print_time, enable):\n if enable:\n if not self.enable_count:\n self.mcu_enable.set_digital(print_time, 1)\n self.enable_count += 1\n else:\n self.enable_count -= 1\n if not self.enable_count:\n self.mcu_enable.set_digital(print_time, 0)\n\n\ndef lookup_enable_pin(ppins, pin):\n if pin is None:\n return StepperEnablePin(None, 9999)\n pin_params = ppins.lookup_pin('digital_out', pin, 'stepper_enable')\n enable = pin_params.get('class')\n if enable is None:\n mcu_enable = pin_params['chip'].setup_pin(pin_params)\n mcu_enable.setup_max_duration(0.)\n pin_params['class'] = enable = StepperEnablePin(mcu_enable)\n return enable\n\n\ndef calculate_steps(config, microsteps=None):\n # Read config and send to driver\n step_dist = config.getfloat('step_distance', default=None, above=0.)\n steps_per_mm = config.getfloat('steps_per_mm', default=None, above=0.)\n microsteps = config.getfloat('microsteps', default=microsteps, above=0.)\n if step_dist is None and steps_per_mm is None and microsteps is not None:\n motor_deg = config.getfloat('motor_step_angle', above=0.)\n # Calculate base on settings\n pitch = config.getfloat('pitch', above=0.)\n teeth = config.getfloat('teeths', above=0.)\n ratio = config.getfloat('gear_ratio', above=0., default=1.0)\n motor_rev = 360. / motor_deg\n steps_per_mm = motor_rev * microsteps / (pitch * teeth) * ratio\n if steps_per_mm is not None:\n inv_step_dist = steps_per_mm\n step_dist = 1.0 / inv_step_dist\n else:\n inv_step_dist = 1. / step_dist\n return step_dist, inv_step_dist\n\n######################################################################\n# Steppers\n######################################################################\n\n# Code storing the definitions for a stepper motor\nclass PrinterStepper:\n driver = mcu_stepper = None\n step_driver = None\n max_velocity = max_accel = 0\n def __init__(self, config, logger=None):\n printer = config.get_printer()\n self.name = config.get_name()\n if logger is None:\n self.logger = printer.logger.getChild(self.name)\n else:\n self.logger = logger.getChild(self.name)\n self.need_motor_enable = True\n step_dist = inv_step_dist = None\n # get a driver\n driver = microsteps = None\n driver_name = config.get('driver', None)\n if driver_name is not None:\n driver_section = 'driver %s' % driver_name\n driver = driver_base.load_driver(config.getsection(driver_section))\n self.driver = driver\n if driver is not None:\n microsteps = driver.microsteps\n step_dist = driver.step_dist\n inv_step_dist = driver.inv_step_dist\n if not driver.has_step_dir_pins:\n self.mcu_stepper = driver\n if step_dist is None or inv_step_dist is None:\n step_dist, inv_step_dist = calculate_steps(config, microsteps)\n if driver is not None:\n driver.step_dist = step_dist\n # Stepper definition\n ppins = printer.lookup_object('pins')\n if self.mcu_stepper is None:\n self.mcu_stepper = ppins.setup_pin('stepper', config.get('step_pin'))\n dir_pin_params = ppins.lookup_pin('digital_out', config.get('dir_pin'))\n self.mcu_stepper.setup_dir_pin(dir_pin_params)\n self.mcu_stepper.setup_step_distance(step_dist)\n # Wrappers\n self.step_itersolve = self.mcu_stepper.step_itersolve\n self.setup_itersolve = self.mcu_stepper.setup_itersolve\n self.set_ignore_move = self.mcu_stepper.set_ignore_move\n self.set_position = self.mcu_stepper.set_position\n self.get_mcu_position = self.mcu_stepper.get_mcu_position\n self.get_commanded_position = self.mcu_stepper.get_commanded_position\n self.get_step_dist = self.mcu_stepper.get_step_dist\n self.enable = lookup_enable_pin(ppins, config.get('enable_pin', None))\n # Register STEPPER_BUZZ command\n stepper_buzz = printer.try_load_module(config, 'stepper_buzz')\n stepper_buzz.register_stepper(self, self.name)\n self.logger.info(\"steps per mm {} , step in mm {}\".\n format(inv_step_dist, step_dist))\n printer.add_object(self.name, self) # to get printer_state called\n def get_name(self, short=False):\n if short and self.name.startswith('stepper_'):\n return self.name[8:]\n return self.name\n def add_to_endstop(self, mcu_endstop):\n mcu_endstop.add_stepper(self.mcu_stepper)\n @staticmethod\n def _dist_to_time(dist, start_velocity, accel):\n # Calculate the time it takes to travel a distance with constant accel\n time_offset = start_velocity / accel\n return math.sqrt(2. * dist / accel + time_offset**2) - time_offset\n def set_max_jerk(self, max_halt_velocity, max_accel, max_velocity=0):\n if max_velocity > 0:\n self.max_velocity = max_velocity\n self.max_accel = max_accel\n # Calculate the firmware's maximum halt interval time\n step_dist = self.get_step_dist()\n last_step_time = self._dist_to_time(\n step_dist, max_halt_velocity, max_accel)\n second_last_step_time = self._dist_to_time(\n 2. * step_dist, max_halt_velocity, max_accel)\n min_stop_interval = second_last_step_time - last_step_time\n self.mcu_stepper.setup_min_stop_interval(min_stop_interval)\n def motor_enable(self, print_time, enable=0):\n if self.need_motor_enable != (not enable):\n self.enable.set_enable(print_time, enable)\n self.need_motor_enable = not enable\n def is_motor_enabled(self):\n return not self.need_motor_enable\n def get_max_velocity(self):\n return self.max_velocity, self.max_accel\n def get_driver(self):\n return self.driver\n def has_driver_endstop(self):\n return getattr(self.driver, \"has_endstop\", False)\n\n\n######################################################################\n# Stepper controlled rails\n######################################################################\n\n# A motor control \"rail\" with one (or more) steppers and one (or more)\n# endstops.\nclass PrinterRail:\n def __init__(self, config, need_position_minmax=True,\n default_position_endstop=None):\n # Primary stepper\n stepper = PrinterStepper(config)\n self.logger = stepper.logger\n self.steppers = [stepper]\n self.name = stepper.get_name(short=True)\n self.step_itersolve = stepper.step_itersolve\n self.setup_itersolve = stepper.setup_itersolve\n self.step_driver = stepper.step_driver\n self.get_commanded_position = stepper.get_commanded_position\n self.is_motor_enabled = stepper.is_motor_enabled\n if default_position_endstop is None:\n self.position_endstop = config.getfloat('position_endstop')\n else:\n self.position_endstop = config.getfloat(\n 'position_endstop', default_position_endstop)\n # Homing offset will be substracted from homed position\n self.homing_offset = config.getfloat('homing_offset', None)\n if self.homing_offset is None:\n # Try in steps and convert steps to mm\n self.homing_offset = (config.getfloat('homing_offset_steps', 0.) *\n stepper.get_step_dist())\n # Homing finetune after enstop hit (mainly for deltas)\n self.tune_after_homing = \\\n config.getfloat('endstop_correction', None) # in mm\n if self.tune_after_homing is None:\n self.tune_after_homing = (config.getfloat('endstop_correction_steps', 0.) *\n stepper.get_step_dist())\n # Axis range\n if need_position_minmax:\n self.position_min = config.getfloat('position_min', 0.)\n self.position_max = config.getfloat(\n 'position_max', above=self.position_min)\n else:\n self.position_min = 0.\n self.position_max = self.position_endstop\n if (self.position_endstop < self.position_min\n or self.position_endstop > self.position_max):\n raise config.error(\n \"position_endstop in section '%s' must be between\"\n \" position_min and position_max\" % config.get_name())\n # Homing mechanics\n self.homing_slowdown = config.getfloat('homing_slowdown', 5.0)\n self.homing_speed = config.getfloat('homing_speed', 5.0, above=0.)\n self.homing_retract_dist = config.getfloat(\n 'homing_retract_dist', 5., minval=0.)\n homing_dirs = { 'min' : False, 'max' : True, 'NA' : None}\n homing_dir = config.getchoice('homing_direction',\n homing_dirs, 'NA')\n if homing_dir is not None:\n self.homing_positive_dir = homing_dir\n else:\n self.homing_positive_dir = config.getboolean('homing_positive_dir', None)\n if self.homing_positive_dir is None:\n axis_len = self.position_max - self.position_min\n if self.position_endstop <= self.position_min + axis_len / 4.:\n self.homing_positive_dir = False\n elif self.position_endstop >= self.position_max - axis_len / 4.:\n self.homing_positive_dir = True\n else:\n raise config.error(\n \"Unable to infer homing_positive_dir in section '%s'\" % (\n self.name,))\n # Endstop\n if stepper.has_driver_endstop():\n homedir = ['min', 'max'][self.homing_positive_dir]\n stepper.mcu_stepper.set_homing_speed(self.homing_speed)\n stepper.mcu_stepper.set_homing_dir(homedir)\n self.endstops = [(stepper.get_driver(), self.name)]\n self.homing_stepper_phases = None\n else:\n # Primary endstop and its position\n endstop_pin = config.get('endstop_pin', None)\n if endstop_pin is None:\n endstop_pin = config.get(\n ['endstop_min_pin',\n 'endstop_max_pin'][self.homing_positive_dir])\n ppins = config.get_printer().lookup_object('pins')\n mcu_endstop = ppins.setup_pin('endstop', endstop_pin)\n self.endstops = [(mcu_endstop, self.name)]\n stepper.add_to_endstop(mcu_endstop)\n # Endstop stepper phase position tracking\n self.homing_stepper_phases = config.getint(\n 'homing_stepper_phases', None, minval=0)\n endstop_accuracy = config.getfloat(\n 'homing_endstop_accuracy', None, above=0.)\n self.homing_endstop_accuracy = self.homing_endstop_phase = None\n if self.homing_stepper_phases:\n self.homing_step_dist = step_dist = stepper.get_step_dist()\n self.homing_endstop_phase = config.getint(\n 'homing_endstop_phase', None, minval=0\n , maxval=self.homing_stepper_phases-1)\n if (self.homing_endstop_phase is not None\n and config.getboolean('homing_endstop_align_zero', False)):\n # Adjust the endstop position so 0.0 is always at a full step\n micro_steps = self.homing_stepper_phases // 4\n phase_offset = (\n ((self.homing_endstop_phase + micro_steps // 2) % micro_steps)\n - micro_steps // 2) * step_dist\n full_step = micro_steps * step_dist\n es_pos = (int(self.position_endstop / full_step + .5) * full_step\n + phase_offset)\n if es_pos != self.position_endstop:\n self.logger.info(\"Changing %s endstop position to %.3f\"\n \" (from %.3f)\", self.name,\n es_pos, self.position_endstop)\n self.position_endstop = es_pos\n if endstop_accuracy is None:\n self.homing_endstop_accuracy = self.homing_stepper_phases//2 - 1\n elif self.homing_endstop_phase is not None:\n self.homing_endstop_accuracy = int(math.ceil(\n endstop_accuracy * .5 / step_dist))\n else:\n self.homing_endstop_accuracy = int(math.ceil(\n endstop_accuracy / step_dist))\n if self.homing_endstop_accuracy >= self.homing_stepper_phases // 2:\n self.logger.info(\"Endstop for %s is not accurate enough for stepper\"\n \" phase adjustment\", self.name)\n self.homing_stepper_phases = None\n if mcu_endstop.get_mcu().is_fileoutput():\n self.homing_endstop_accuracy = self.homing_stepper_phases\n # Valid for CoreXY and Cartesian Z axis\n if 'Z' in self.name.upper():\n self.homing_pos_x = config.getfloat('homing_pos_x', default=None,\n minval=self.position_min,\n maxval=self.position_max)\n self.homing_pos_y = config.getfloat('homing_pos_y', default=None,\n minval=self.position_min,\n maxval=self.position_max)\n else:\n # None for X and Y axis\n self.homing_pos_x = None\n self.homing_pos_y = None\n self.homing_travel_speed = config.getfloat(\n 'homing_travel_speed', default=60, minval=0)\n self.retract_after_home = config.getfloat(\n 'homing_retract_dist_after', 0., minval=0.)\n def get_tune_after_homing(self):\n return self.tune_after_homing\n def set_homing_offset(self, offset):\n self.homing_offset = offset\n def get_homed_offset(self):\n if not self.homing_stepper_phases:\n return 0. - self.homing_offset\n pos = self.steppers[0].get_mcu_position()\n pos %= self.homing_stepper_phases\n if self.homing_endstop_phase is None:\n self.logger.info(\"Setting %s endstop phase to %d\", self.name, pos)\n self.homing_endstop_phase = pos\n return 0. - self.homing_offset\n delta = (pos - self.homing_endstop_phase) % self.homing_stepper_phases\n if delta >= self.homing_stepper_phases - self.homing_endstop_accuracy:\n delta -= self.homing_stepper_phases\n elif delta > self.homing_endstop_accuracy:\n raise homing.EndstopError(\n \"Endstop %s incorrect phase (got %d vs %d)\" % (\n self.name, pos, self.homing_endstop_phase))\n return (delta * self.homing_step_dist) - self.homing_offset\n def get_range(self):\n return self.position_min, self.position_max\n def get_homing_info(self):\n homing_info = collections.namedtuple('homing_info', [\n 'speed', 'speed_slow', 'position_endstop',\n 'retract_dist', 'positive_dir',\n 'homing_pos', 'travel_speed',\n 'retract_after_home', 'init_home_funcs'])(\n self.homing_speed, (self.homing_speed / self.homing_slowdown),\n self.position_endstop,\n self.homing_retract_dist, self.homing_positive_dir,\n [self.homing_pos_x, self.homing_pos_y, None, None],\n self.homing_travel_speed,\n self.retract_after_home,\n [getattr(s.driver, 'init_home', None) for s in self.steppers])\n return homing_info\n def get_steppers(self):\n return list(self.steppers)\n def get_endstops(self):\n return list(self.endstops)\n def add_extra_stepper(self, config):\n stepper = PrinterStepper(config)\n self.steppers.append(stepper)\n self.step_itersolve = self.step_multi_itersolve\n mcu_endstop = self.endstops[0][0]\n endstop_pin = config.get('endstop_pin', None)\n if endstop_pin is not None:\n ppins = config.get_printer().lookup_object('pins')\n mcu_endstop = ppins.setup_pin('endstop', endstop_pin)\n self.endstops.append((mcu_endstop, stepper.get_name(short=True)))\n stepper.add_to_endstop(mcu_endstop)\n def add_to_endstop(self, mcu_endstop):\n for stepper in self.steppers:\n stepper.add_to_endstop(mcu_endstop)\n def step_multi_itersolve(self, cmove):\n for stepper in self.steppers:\n stepper.step_itersolve(cmove)\n def setup_cartesian_itersolve(self, axis):\n ffi_main, ffi_lib = chelper.get_ffi()\n for stepper in self.steppers:\n stepper.setup_itersolve(ffi_main.gc(\n ffi_lib.cartesian_stepper_alloc(axis), ffi_lib.free))\n def set_max_jerk(self, max_halt_velocity, max_accel, max_velocity=0):\n for stepper in self.steppers:\n stepper.set_max_jerk(max_halt_velocity, max_accel, max_velocity)\n def set_position(self, newpos):\n for stepper in self.steppers:\n stepper.set_position(newpos)\n def motor_enable(self, print_time, enable=0):\n for stepper in self.steppers:\n stepper.motor_enable(print_time, enable)\n\n\n# Wrapper for dual stepper motor support\ndef LookupMultiRail(config):\n rail = PrinterRail(config)\n for i in range(1, 99):\n if not config.has_section(config.get_name() + str(i)):\n break\n rail.add_extra_stepper(config.getsection(config.get_name() + str(i)))\n return rail\n", "repo_name": "tuwwe/klipper", "sub_path": "klippy/stepper.py", "file_name": "stepper.py", "file_ext": "py", "file_size_in_byte": 18329, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "github-code", "pt": "37", "api": [{"api_name": "extras.driver.load_driver", "line_number": 82, "usage_type": "call"}, {"api_name": "extras.driver", "line_number": 82, "usage_type": "name"}, {"api_name": "math.sqrt", "line_number": 126, "usage_type": "call"}, {"api_name": "math.ceil", "line_number": 271, "usage_type": "call"}, {"api_name": "math.ceil", "line_number": 274, "usage_type": "call"}, {"api_name": "homing.EndstopError", "line_number": 315, "usage_type": "call"}, {"api_name": "collections.namedtuple", "line_number": 322, "usage_type": "call"}, {"api_name": "chelper.get_ffi", "line_number": 357, "usage_type": "call"}]} +{"seq_id": "35508702908", "text": "import os.path\nimport json\nfrom PyQt5.QtWidgets import QWizardPage, QHBoxLayout, QLabel, QLineEdit\nfrom PyQt5.QtWidgets import QPushButton, QFileDialog, QVBoxLayout, QRadioButton\nfrom PyQt5.QtWidgets import QWidget, QMessageBox, QGroupBox, QCheckBox, QWizard\n\nclass Project(object):\n \"\"\"\n Represents an Informatic project.\n \"\"\"\n def __init__(self, mainSourcePath='', sourceDir='', projectFilePath='',\n compilerOptions={'path': 'inform', 'version': 'v5', 'switches': ['S']},\n terpOptions={'terpPath': 'sfrotz', 'storyFile': ''}):\n \"\"\"\n Takes five optional keyword arguments representing different\n project options: mainSourcePath, a relative filepath from the\n project file to the main source file, an empty string by\n default; sourceDir, a relative filepath from the project file to\n the main source directory, an empty string by default;\n projectFilePath, an absolute filepath to the project file, an\n empty string by default; and compilerOptions, a dictionary for\n the project's compiler options, currently containing only one\n key-value pair: \"path\", representing the absolute filepath to\n the compiler, \"inform\" by default.\n \"\"\"\n self.mainSourcePath = mainSourcePath\n self.sourceDir = sourceDir\n self.projectFilePath = projectFilePath\n self.compilerOptions = compilerOptions\n self.terpOptions = terpOptions\n def absSourceDir(self):\n \"\"\"\n Returns an absolute filepath to the project source directory.\n \"\"\"\n fullPath = os.path.dirname(self.projectFilePath)\n fullPath = os.path.join(fullPath, self.sourceDir)\n fullPath = os.path.normpath(fullPath)\n return fullPath\n def absMainSource(self):\n \"\"\"\n Returns an absolute filepath to the project's main source file.\n \"\"\"\n fullPath = os.path.dirname(self.projectFilePath)\n fullPath = os.path.join(fullPath, self.mainSourcePath)\n fullPath = os.path.normpath(fullPath)\n return fullPath\n def dump(self, fp):\n \"\"\"\n Takes one argument, fp, an write-opened file or file-like\n object. Prepares a dictionary containing the Informatic\n project's options and writes it to fp as a JSON object with\n indenting for human-readability.\n \"\"\"\n attrDict = {}\n for attr in ['mainSourcePath', 'sourceDir', 'compilerOptions',\n 'terpOptions']:\n attrDict[attr] = getattr(self, attr)\n json.dump(attrDict, fp, indent=2)\n def load(self, fp):\n \"\"\"\n Takes one argument, fp, a read-opened file or file-like object\n containing a JSON object. Loads that JSON object as a dictionary\n containing project options and applies those options to the\n project object.\n \"\"\"\n attrDict = json.load(fp)\n for attr in ['mainSourcePath', 'sourceDir', 'compilerOptions',\n 'terpOptions']:\n setattr(self, attr, attrDict[attr])\n \n # Only load the compilerOptions and terpOptions elements if they are\n # present in the file, so Informatic can load older project files that\n # don't contain those elements\n for attr in ['compilerOptions', 'terpOptions']:\n if attr in attrDict:\n setattr(self, attr, attrDict[attr])\n else:\n setattr(self, attr, {})\n\nclass SourceDirPage(QWizardPage):\n \"\"\"\n Page for the new project wizard that allows the user to choose the\n source directory for the project.\n \"\"\"\n def __init__(self, *args, **kwargs):\n \"\"\"\n Passes all arguments to the QWizardPage constructor.\n \"\"\"\n super().__init__(*args, **kwargs)\n \n # The rest of this function sets up the layout of the wizard page,\n # connecting widgets to fields and functions as it does.\n self.setTitle(self.tr('Source directory'))\n layout = QHBoxLayout()\n label = QLabel()\n label.setText(self.tr('Source directory:'))\n layout.addWidget(label)\n self.lineEdit = QLineEdit()\n self.registerField('sourceDir*', self.lineEdit)\n self.lineEdit.textChanged.connect(self.completeChanged)\n layout.addWidget(self.lineEdit)\n button = QPushButton(self.tr('Choose...'))\n button.clicked.connect(self.chooseSourceDir)\n layout.addWidget(button)\n self.setLayout(layout)\n def chooseSourceDir(self):\n \"\"\"\n Change the line edit to a directory path chosen through a File\n Dialog.\n \"\"\"\n self.lineEdit.setText(QFileDialog.getExistingDirectory())\n def isComplete(self):\n \"\"\"\n Allow the user to move on to the next step in creating a new\n project if the line edit contains a valid path to an existing\n directory.\n \"\"\"\n if os.path.isdir(self.lineEdit.text()):\n return True\n else:\n return False\n\nclass MainSourceFilePage(QWizardPage):\n \"\"\"\n Page for the new project wizard that allows the user to choose the\n main source file for the project.\n \"\"\"\n def __init__(self, *args, **kwargs):\n \"\"\"\n Passes all arguments to the QWizardPage constructor.\n \"\"\"\n super().__init__(*args, **kwargs)\n \n # The rest of the function sets up the layout of the wizard page.\n self.setTitle(self.tr('Main source file'))\n layout = QVBoxLayout()\n \n # The widgets for choosing the path of a new main source file.\n self.newFileButton = QRadioButton(\n self.tr('Create new file as main source file:'))\n # This radio button is connected to a function that re-evaluates which\n # widgets should be active based on user choices.\n self.newFileButton.toggled.connect(self.reEnableWidgets)\n self.registerField('newFile', self.newFileButton)\n layout.addWidget(self.newFileButton)\n newFileHBox = QHBoxLayout()\n self.newFileLineEdit = QLineEdit()\n self.newFileLineEdit.setEnabled(False)\n self.newFileLineEdit.textChanged.connect(self.completeChanged)\n self.registerField('newFilePath', self.newFileLineEdit)\n newFileHBox.addWidget(self.newFileLineEdit)\n self.newFileChooser = QPushButton(self.tr('Choose...'))\n self.newFileChooser.setEnabled(False)\n self.newFileChooser.clicked.connect(self.chooseNewFile)\n newFileHBox.addWidget(self.newFileChooser)\n newFileWidget = QWidget()\n newFileWidget.setLayout(newFileHBox)\n layout.addWidget(newFileWidget)\n \n # The widgets for choosing the path of an existing main source file.\n self.oldFileButton = QRadioButton(\n self.tr('Use existing file as main source file:'))\n # Again, a radio button is connected to the function that re-evaluates\n # which widgets should be active.\n self.oldFileButton.toggled.connect(self.reEnableWidgets)\n self.registerField('oldFile', self.oldFileButton)\n layout.addWidget(self.oldFileButton)\n oldFileHBox = QHBoxLayout()\n self.oldFileLineEdit = QLineEdit()\n self.oldFileLineEdit.setEnabled(False)\n self.oldFileLineEdit.textChanged.connect(self.completeChanged)\n self.registerField('oldFilePath', self.oldFileLineEdit)\n oldFileHBox.addWidget(self.oldFileLineEdit)\n self.oldFileChooser = QPushButton(self.tr('Choose...'))\n self.oldFileChooser.setEnabled(False)\n self.oldFileChooser.clicked.connect(self.chooseOldFile)\n oldFileHBox.addWidget(self.oldFileChooser)\n oldFileWidget = QWidget()\n oldFileWidget.setLayout(oldFileHBox)\n layout.addWidget(oldFileWidget)\n self.setLayout(layout)\n def reEnableWidgets(self):\n \"\"\"\n Re-evaluate which line-edit/push-button set for choosing a\n filepath is enabled, based on which radio button the user has\n selected.\n \"\"\"\n if self.newFileButton.isChecked():\n self.newFileLineEdit.setEnabled(True)\n self.newFileChooser.setEnabled(True)\n else:\n self.newFileLineEdit.setEnabled(False)\n self.newFileChooser.setEnabled(False)\n if self.oldFileButton.isChecked():\n self.oldFileLineEdit.setEnabled(True)\n self.oldFileChooser.setEnabled(True)\n else:\n self.oldFileLineEdit.setEnabled(False)\n self.oldFileChooser.setEnabled(False)\n self.completeChanged.emit()\n def chooseNewFile(self):\n \"\"\"\n Retrieves the filepath for an empty main source file to be\n created for the new Informatic project, and displays that\n filepath in the appropriate line-edit widget.\n \"\"\"\n self.newFileLineEdit.setText(QFileDialog.getSaveFileName(\n directory=os.path.join(self.field('sourceDir'), 'main.inf'),\n filter=self.tr('Inform 6 source files (*.inf)'))[0])\n def chooseOldFile(self):\n \"\"\"\n Retrieves the filepath for an existing main source file for the\n new Informatic project, and displays that filepath in the\n appropriate line-edit widget.\n \"\"\"\n self.oldFileLineEdit.setText(QFileDialog.getOpenFileName(\n directory=self.field('sourceDir'),\n filter=self.tr('Inform 6 source files (*.inf *.i6)'))[0])\n def isComplete(self):\n \"\"\"\n Checks whether the the wizard page is complete. Allows the user\n to continue if a radio button is checked and the corresponding\n line-edit widget is appropriately filled in.\n \"\"\"\n if self.oldFileButton.isChecked():\n if os.path.isfile(self.oldFileLineEdit.text()):\n return True\n else:\n return False\n if self.newFileButton.isChecked():\n if self.newFileLineEdit.text():\n return True\n else:\n return False\n else:\n return False\n def validatePage(self):\n \"\"\"\n Attempts to create the new source file before the user continues\n to the next wizard page, if the user has chosen to create a new\n source file. Stops the user if the file cannot be created.\n \"\"\"\n if self.newFileButton.isChecked():\n if os.path.exists(self.newFileLineEdit.text()):\n overwriteResponse = QMessageBox.question(self,\n self.tr('File overwrite'),\n self.tr('A file already exists at the filepath you have '\n 'given. Is it okay to overwrite this with a blank file?'))\n if overwriteResponse != QMessageBox.Yes:\n return False\n try:\n with open(self.newFileLineEdit.text(), 'w') as newFile:\n newFile.write('')\n return True\n except Exception as err:\n QMessageBox.critical(self, self.tr('Filesystem error'),\n self.tr('Informatic encountered an error while creating a new '\n 'source file:\\n\\n') + str(err))\n return False\n else:\n return True\n\nclass CompilerPage(QWizardPage):\n \"\"\"\n Wizard page that allows the user to set the project's compiler\n options. Used by both the new project wizard and the compiler\n options wizard.\n \"\"\"\n def __init__(self, compilerOptions, *args, **kwargs):\n \"\"\"\n The positional argument compilerOptions is processed as a\n dictionary containing compiler Options for an Informatic\n project. All remaining arguments are passed directly to the\n QWizardPage constructor.\n \"\"\"\n self.compilerOptions = compilerOptions\n super().__init__(*args, **kwargs)\n \n # The rest of this function sets up the wizard page's layout,\n # connecting widgets to functions and wizard fields as necessary. \n mainLayout = QVBoxLayout()\n \n pathLayout = QHBoxLayout()\n pathLayout.addWidget(QLabel(self.tr('Compiler path:')))\n self.lineEdit = QLineEdit()\n self.registerField('compilerPath*', self.lineEdit)\n self.lineEdit.setText(self.compilerOptions.get('path', 'inform'))\n self.lineEdit.textChanged.connect(self.completeChanged)\n pathLayout.addWidget(self.lineEdit)\n chooser = QPushButton(self.tr('Choose...'))\n chooser.clicked.connect(self.chooseCompilerPath)\n pathLayout.addWidget(chooser)\n mainLayout.addLayout(pathLayout)\n \n versionGroupBox = QGroupBox(self.tr('Story file version:'))\n versionLayout = QHBoxLayout()\n versionLeftLayout = QVBoxLayout()\n radio_v3 = QRadioButton(self.tr('Z-code version 3 \"Standard\"'))\n versionLeftLayout.addWidget(radio_v3)\n radio_v4 = QRadioButton(self.tr('Z-code version 4 \"Plus\"'))\n versionLeftLayout.addWidget(radio_v4)\n radio_v5 = QRadioButton(\n self.tr('Z-code version 5 \"Advanced\" (default)'))\n versionLeftLayout.addWidget(radio_v5)\n versionRightLayout = QVBoxLayout()\n radio_v6 = QRadioButton(self.tr('Z-code version 6 graphical'))\n versionRightLayout.addWidget(radio_v6)\n radio_v8 = QRadioButton(\n self.tr('Z-code version 8 expanded \"Advanced\"'))\n versionRightLayout.addWidget(radio_v8)\n radio_G = QRadioButton(self.tr('Glulx'))\n versionRightLayout.addWidget(radio_G)\n versionLayout.addLayout(versionLeftLayout)\n versionLayout.addLayout(versionRightLayout)\n versionGroupBox.setLayout(versionLayout) \n mainLayout.addWidget(versionGroupBox)\n \n self.storyFileVersions = {\n 'v3': radio_v3,\n 'v4': radio_v4,\n 'v5': radio_v5,\n 'v6': radio_v6,\n 'v8': radio_v8,\n 'G': radio_G}\n \n for key in self.storyFileVersions:\n self.registerField(key, self.storyFileVersions[key])\n if self.compilerOptions.get('version', 'v5') == key:\n self.storyFileVersions[key].setChecked(True)\n \n switchesGroupBox = QGroupBox(self.tr('Popular compiler switches:'))\n switchesLayout = QHBoxLayout()\n switchesLeftLayout = QVBoxLayout()\n check_c = QCheckBox(self.tr('c: more concise error messages'))\n switchesLeftLayout.addWidget(check_c)\n check_d = QCheckBox(self.tr('d: contract double spaces after full '\n 'stops in text'))\n switchesLeftLayout.addWidget(check_d)\n check_d2 = QCheckBox(self.tr('d2: contract double spaces after '\n 'exclamation and question marks, too'))\n switchesLeftLayout.addWidget(check_d2)\n check_e = QCheckBox(self.tr('e: economy mode; use declared '\n 'abbreviations'))\n switchesLeftLayout.addWidget(check_e)\n check_i = QCheckBox(self.tr('i: ignore default switches set in source '\n 'file'))\n switchesLeftLayout.addWidget(check_i)\n check_k = QCheckBox(self.tr('k: output Infix debugging information to '\n '\"gameinfo.dbg\" (and switch -D on)'))\n switchesLeftLayout.addWidget(check_k)\n check_n = QCheckBox(self.tr('n: print numbers of properties, '\n 'attributes and actions'))\n switchesLeftLayout.addWidget(check_n)\n check_r = QCheckBox(self.tr('r: record all the text to '\n '\"gametext.txt\"'))\n switchesLeftLayout.addWidget(check_r)\n switchesLayout.addLayout(switchesLeftLayout)\n switchesRightLayout = QVBoxLayout()\n check_s = QCheckBox(self.tr('s: give statistics'))\n switchesRightLayout.addWidget(check_s)\n check_u = QCheckBox(self.tr('u: work out most useful abbreviations'))\n switchesRightLayout.addWidget(check_u)\n check_w = QCheckBox(self.tr('w: disable warning messages'))\n switchesRightLayout.addWidget(check_w)\n check_B = QCheckBox(self.tr('B: use big memory model (for large V6/V7 '\n 'files)'))\n switchesRightLayout.addWidget(check_B)\n check_D = QCheckBox(self.tr('D: insert \"Constant DEBUG;\" '\n 'automatically'))\n switchesRightLayout.addWidget(check_D)\n check_H = QCheckBox(self.tr('H: use Huffman encoding to compress '\n 'Glulx strings'))\n switchesRightLayout.addWidget(check_H)\n check_S = QCheckBox(self.tr('S: compile strict error-checking at '\n 'run-time (on by default)'))\n switchesRightLayout.addWidget(check_S)\n check_X = QCheckBox(self.tr('X: compile with INFIX debugging '\n 'facilities present'))\n switchesRightLayout.addWidget(check_X)\n switchesLayout.addLayout(switchesRightLayout)\n switchesGroupBox.setLayout(switchesLayout)\n mainLayout.addWidget(switchesGroupBox)\n \n self.switchesDict = {\n 'c': check_c, 'd': check_d, 'd2': check_d2, 'd': check_d,\n 'e': check_e, 'i': check_i, 'k': check_k, 'n': check_n,\n 'r': check_r, 's': check_s, 'u': check_u, 'w': check_w,\n 'B': check_B, 'D': check_D, 'H': check_H, 'S': check_S,\n 'X': check_X}\n \n for key in self.switchesDict:\n self.registerField(key, self.switchesDict[key])\n if key in self.compilerOptions.get('switches', ['S']):\n self.switchesDict[key].setChecked(True)\n \n self.setLayout(mainLayout)\n def chooseCompilerPath(self):\n \"\"\"\n Sets the contents of the page's compiler path line-edit widget\n using the output of a file dialog.\n \"\"\"\n self.lineEdit.setText(QFileDialog.getOpenFileName()[0])\n def isComplete(self):\n \"\"\"\n Checks whether the wizard page is complete. Allows the user to\n continue if the page's line-edit widget contains text.\n \"\"\"\n if self.lineEdit.text():\n return True\n else:\n return False\n def storyFileVersion(self):\n \"\"\"\n Returns a string representing the story file version switch\n that should be passed to the Inform 6 compiler, based on\n user-selected options. Default is 'v5'.\n \"\"\"\n for key in self.storyFileVersions:\n if self.field(key):\n return key\n return 'v5'\n def switches(self):\n \"\"\"\n Returns a list of strings representing compiler switches that\n should be passed to the Inform 6 compiler, based on\n user-selected options.\n \"\"\"\n switches = []\n for key in self.switchesDict:\n if self.field(key):\n switches += [key]\n return switches\n\nclass ProjectFilePage(QWizardPage):\n \"\"\"\n Page for the new project wizard that prompts the user for a name for\n the project file.\n \"\"\"\n def __init__(self, *args, **kwargs):\n \"\"\"\n Passes all arguments directly to the QWizardPage constructor.\n \"\"\"\n super().__init__(*args, **kwargs)\n \n # The rest of this function sets up the wizard page's layout,\n # connecting widgets to functions and wizard fields as necessary.\n self.setTitle(self.tr('Project file'))\n layout = QHBoxLayout()\n layout.addWidget(QLabel(self.tr('Project file path:')))\n self.lineEdit = QLineEdit()\n self.registerField('projectFilePath*', self.lineEdit)\n self.lineEdit.textChanged.connect(self.completeChanged)\n layout.addWidget(self.lineEdit)\n chooser = QPushButton(self.tr('Choose...'))\n chooser.clicked.connect(self.chooseProjectFile)\n layout.addWidget(chooser)\n self.setLayout(layout)\n def chooseProjectFile(self):\n \"\"\"\n Displays a file dialog, then fills the wizard page's line-edit\n widget with the chosen filename.\n \"\"\"\n \n # The default project file name is based on the main source file name,\n # so first it is necessary to determine which field contains that\n # filename.\n if self.field('oldFile'):\n mainSourcePath = self.field('oldFilePath')\n if self.field('newFile'):\n mainSourcePath = self.field('newFilePath')\n \n # Now the default filename for the project file can be constructed.\n defaultPath = os.path.splitext(\n os.path.basename(mainSourcePath))[0] + '.informatic'\n defaultPath = os.path.join(self.field('sourceDir'), defaultPath)\n \n # Finally, launch the file dialog and use its output to fill the\n # line-edit widget.\n self.lineEdit.setText(\n QFileDialog.getSaveFileName(directory=defaultPath)[0])\n def isComplete(self):\n \"\"\"\n Checks whether the wizard page is complete. Allows the user to\n continue if the page's line-edit widget contains text.\n \"\"\"\n if self.lineEdit.text():\n return True\n else:\n return False\n\nclass NewProjectWizard(QWizard):\n \"\"\"\n Wizard for creating a new Informatic project.\n \"\"\"\n def __init__(self, *args, **kwargs):\n \"\"\"\n The project keyword argument, if given, is an Informatic Project\n object that will be configured to represent the new project. All\n other arguments are passed directly to the QWizard constructor.\n \"\"\"\n if 'project' in kwargs:\n self.project = kwargs.pop('project')\n else:\n self.project = Project()\n super().__init__(*args, **kwargs)\n \n # The rest of this function sets up the wizard's layout.\n self.setWindowTitle(self.tr('New project'))\n self.addPage(SourceDirPage())\n self.addPage(MainSourceFilePage())\n self.compilerPage = CompilerPage(self.project.compilerOptions)\n self.compilerPage.setTitle(self.tr('Compiler options'))\n self.addPage(self.compilerPage)\n self.addPage(ProjectFilePage())\n def accept(self):\n \"\"\"\n Performs the final actions of setting up the Project object and\n writing the project file. Finishes the wizard if the project\n file is successfully written.\n \"\"\"\n absSourceDir = self.field('sourceDir')\n projectFilePath = self.field('projectFilePath')\n projectFileDir = os.path.dirname(projectFilePath)\n self.project.sourceDir = os.path.relpath(absSourceDir,\n start=projectFileDir)\n if self.field('oldFile'):\n absMainSourcePath = self.field('oldFilePath')\n self.project.mainSourcePath = os.path.relpath(absMainSourcePath,\n start=projectFileDir)\n if self.field('newFile'):\n absMainSourcePath = self.field('newFilePath')\n self.project.mainSourcePath = os.path.relpath(absMainSourcePath,\n start=projectFileDir)\n self.project.compilerOptions = {'path': self.field('compilerPath')}\n self.project.compilerOptions['version'] = \\\n self.compilerPage.storyFileVersion()\n self.project.compilerOptions['switches'] = self.compilerPage.switches()\n try:\n with open(projectFilePath, 'w', encoding='utf_8') as projectFile:\n self.project.dump(projectFile)\n except Exception as err:\n QMessageBox.critical(self, self.tr('Filesystem error'),\n self.tr('Informatic encountered an error while writing a new '\n 'project file:\\n\\n' + str(err)))\n else:\n self.parent().displayProjectFile(projectFilePath)\n return super().accept()\n\nclass CompilerOptionsWizard(QWizard):\n \"\"\"\n Wizard for editing the compiler options of an Informatic project.\n \"\"\"\n def __init__(self, project, *args, **kwargs):\n \"\"\"\n The positional object project is interpeted as a Project object.\n All other arguments are passed directly to the QWizard\n constructor.\n \"\"\"\n self.project = project\n super().__init__(*args, **kwargs)\n self.setWindowTitle(self.tr('Compiler options'))\n # In tests, the following line changed the stretch behavior of the\n # window, so it's commented out for now.\n # self.setOption(QWizard.NoBackButtonOnLastPage)\n self.compilerPage = CompilerPage(project.compilerOptions)\n self.addPage(self.compilerPage)\n def accept(self):\n \"\"\"\n Processes Inform 6 compiler options and saves the project file\n when the user finishes the compiler options wizard.\n \"\"\"\n \n self.project.compilerOptions['path'] = self.field('compilerPath')\n self.project.compilerOptions['version'] = \\\n self.compilerPage.storyFileVersion()\n self.project.compilerOptions['switches'] = self.compilerPage.switches()\n \n try:\n with open(self.project.projectFilePath, 'w', encoding='utf_8') \\\n as projectFile:\n self.project.dump(projectFile)\n except Exception as err:\n QMessageBox.critical(self, self.tr('Filesystem error'),\n self.tr('Informatic encountered an error while updating the '\n 'project file:\\n\\n') + str(err))\n else:\n return super().accept()\n", "repo_name": "ddelabru/informatic", "sub_path": "informatic/project.py", "file_name": "project.py", "file_ext": "py", "file_size_in_byte": 25201, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "os.path.path.dirname", "line_number": 35, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 35, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 35, "usage_type": "name"}, {"api_name": "os.path.path.join", "line_number": 36, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 36, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 36, "usage_type": "name"}, {"api_name": "os.path.path.normpath", "line_number": 37, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 37, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 37, "usage_type": "name"}, {"api_name": "os.path.path.dirname", "line_number": 43, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 43, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 43, "usage_type": "name"}, {"api_name": "os.path.path.join", "line_number": 44, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 44, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 44, "usage_type": "name"}, {"api_name": "os.path.path.normpath", "line_number": 45, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 45, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 45, "usage_type": "name"}, {"api_name": "json.dump", "line_number": 58, "usage_type": "call"}, {"api_name": "json.load", "line_number": 66, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QWizardPage", "line_number": 80, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QHBoxLayout", "line_number": 94, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 95, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QLineEdit", "line_number": 98, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QPushButton", "line_number": 102, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QFileDialog.getExistingDirectory", "line_number": 111, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QFileDialog", "line_number": 111, "usage_type": "name"}, {"api_name": "os.path.path.isdir", "line_number": 118, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 118, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 118, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QWizardPage", "line_number": 123, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QVBoxLayout", "line_number": 136, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QRadioButton", "line_number": 139, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QHBoxLayout", "line_number": 146, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QLineEdit", "line_number": 147, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QPushButton", "line_number": 152, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QWidget", "line_number": 156, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QRadioButton", "line_number": 161, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QHBoxLayout", "line_number": 168, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QLineEdit", "line_number": 169, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QPushButton", "line_number": 174, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QWidget", "line_number": 178, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QFileDialog.getSaveFileName", "line_number": 207, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QFileDialog", "line_number": 207, "usage_type": "name"}, {"api_name": "os.path.path.join", "line_number": 208, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 208, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 208, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QFileDialog.getOpenFileName", "line_number": 216, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QFileDialog", "line_number": 216, "usage_type": "name"}, {"api_name": "os.path.path.isfile", "line_number": 226, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 226, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 226, "usage_type": "name"}, {"api_name": "os.path.path.exists", "line_number": 244, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 244, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 244, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.question", "line_number": 245, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 245, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.Yes", "line_number": 249, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 249, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.critical", "line_number": 256, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 256, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QWizardPage", "line_number": 263, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QVBoxLayout", "line_number": 281, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QHBoxLayout", "line_number": 283, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 284, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QLineEdit", "line_number": 285, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QPushButton", "line_number": 290, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QGroupBox", "line_number": 295, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QHBoxLayout", "line_number": 296, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QVBoxLayout", "line_number": 297, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QRadioButton", "line_number": 298, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QRadioButton", "line_number": 300, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QRadioButton", "line_number": 302, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QVBoxLayout", "line_number": 305, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QRadioButton", "line_number": 306, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QRadioButton", "line_number": 308, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QRadioButton", "line_number": 311, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QGroupBox", "line_number": 331, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QHBoxLayout", "line_number": 332, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QVBoxLayout", "line_number": 333, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QCheckBox", "line_number": 334, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QCheckBox", "line_number": 336, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QCheckBox", "line_number": 339, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QCheckBox", "line_number": 342, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QCheckBox", "line_number": 345, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QCheckBox", "line_number": 348, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QCheckBox", "line_number": 351, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QCheckBox", "line_number": 354, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QVBoxLayout", "line_number": 358, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QCheckBox", "line_number": 359, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QCheckBox", "line_number": 361, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QCheckBox", "line_number": 363, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QCheckBox", "line_number": 365, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QCheckBox", "line_number": 368, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QCheckBox", "line_number": 371, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QCheckBox", "line_number": 374, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QCheckBox", "line_number": 377, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QFileDialog.getOpenFileName", "line_number": 402, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QFileDialog", "line_number": 402, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QWizardPage", "line_number": 434, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QHBoxLayout", "line_number": 448, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 449, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QLineEdit", "line_number": 450, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QPushButton", "line_number": 454, "usage_type": "call"}, {"api_name": "os.path.path.splitext", "line_number": 473, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 473, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 473, "usage_type": "name"}, {"api_name": "os.path.path.basename", "line_number": 474, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 474, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 474, "usage_type": "name"}, {"api_name": "os.path.path.join", "line_number": 475, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 475, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 475, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QFileDialog.getSaveFileName", "line_number": 480, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QFileDialog", "line_number": 480, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QWizard", "line_number": 491, "usage_type": "name"}, {"api_name": "os.path.path.dirname", "line_number": 523, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 523, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 523, "usage_type": "name"}, {"api_name": "os.path.path.relpath", "line_number": 524, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 524, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 524, "usage_type": "name"}, {"api_name": "os.path.path.relpath", "line_number": 528, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 528, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 528, "usage_type": "name"}, {"api_name": "os.path.path.relpath", "line_number": 532, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 532, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 532, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.critical", "line_number": 542, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 542, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QWizard", "line_number": 549, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.critical", "line_number": 583, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 583, "usage_type": "name"}]} +{"seq_id": "9197463372", "text": "import zss\nimport os\nimport re\nimport sqlite3\nimport hashlib\nimport json\nfrom sqlite3 import Error\nfrom graphviz import Digraph as Di\n\n\n# Returns the children of the process\ndef get_the_child_processes(process_cursor, pid):\n process_id_query = '''\n SELECT id\n FROM processes\n WHERE parent = %s\n '''\n process_cursor.execute(process_id_query % pid)\n child_list = process_cursor.fetchall()\n chlst = []\n for child2 in child_list:\n chlst.append(child2[0])\n return chlst\n\n\n# Returns the process name\ndef get_the_processes_name(executed_cursor, pid):\n process_name_query = '''\n SELECT name, argv\n FROM executed_files\n WHERE process = %s\n '''\n executed_cursor.execute(process_name_query % pid)\n process_name = executed_cursor.fetchall()\n if process_name != []:\n process_name = str(process_name[0][0]) #.split('/')[-1:][0]\n else:\n process_name = \"\"\n return process_name\n\n\n# Returns all opened files (both W/R modes)\ndef get_opened_files_list(openfile_cursor, pid):\n opened_files_query = '''\n SELECT process, name, mode, id\n FROM opened_files\n WHERE process = %s AND mode <= 2\n '''\n openfile_cursor.execute(opened_files_query % pid)\n return openfile_cursor.fetchall()\n\n\ndef add_edge(graph_, from_, to_, mode):\n if mode == 'file':\n graph_.attr('edge', style='solid',color='black', penwidth='3')\n elif mode == 'ptree':\n graph_.attr('edge', style='dashed', color='grey', penwidth='3')\n graph_.edge(str(from_), str(to_))\n return graph_\n\n\ndef add_node(graph_, id, name, mode, color):\n # with graph_.subgraph(name='cluster_{}'.format(cluster)) as ngraph_:\n if mode == 'process':\n if name == 'remove_ext':\n name = 'remove\\next'\n if color == 'red':\n if name == 'fslmaths':\n name = 'fsl\\nmaths'\n if name == 'new_invwarp':\n name = 'new\\ninvwarp'\n graph_.node(str(id), name.upper(), width='2.3', fontsize='26',fontname=\"Helvetica bold\",\n fontcolor='black', shape='circle',\n style='filled', fillcolor='#EDB9B8', penwidth='.5')\n elif color == 'green':\n graph_.node(str(id), name.upper(), width='2.3', penwidth='.5', fontsize='26', shape='circle',\n style='filled', fillcolor='#7FCDB1', fontname=\"Helvetica bold\")\n elif color == 'subgreen':\n graph_.node(str(id), name.upper(), width='2.3', fontsize='26',fontname=\"Helvetica bold\",\n shape='circle', style='filled', fillcolor='#7FCDB1')\n elif color == 'white':\n graph_.node(str(id), name.upper(), width='2.3', shape='circle', style='filled', fontsize='26',\n fontname=\"Helvetica bold\", fillcolor='white')\n elif mode == 'file':\n if name != '':\n graph_.node(str(id), name, shape='box', style='filled', fillcolor=color, penwidth='1',\n fontsize='26', fontname=\"Helvetica bold\")\n else:\n graph_.node(str(id), name, shape='box', style='filled', fillcolor=color, penwidth='1',\n fontsize='26')\n # graph_.node(str(id), name) \n return graph_\n\n\ndef create_provenance_graph(db_path, pid, graph_, pipe_list=[], multi_list={},\n tmp_list=[], total_proc_diff=[], dag=False):\n try:\n db = sqlite3.connect(db_path)\n except Error as e:\n print(e)\n process_cursor = db.cursor()\n executed_cursor = db.cursor()\n openfile_cursor = db.cursor()\n # Get the list of child process from pid\n child_list = get_the_child_processes(process_cursor, pid)\n # Get the process name\n process_name = get_the_processes_name(executed_cursor, pid)\n\n short_name = {}\n short_name['ACPCAlignment.sh'] = 'ACPC-A'\n short_name['BrainExtraction_FNIRTbased.sh'] = 'BExt'\n short_name['AnatomicalAverage.sh'] = 'AAve'\n short_name['BiasFieldCorrection_sqrtT1wXT1w.sh'] = 'BFC'\n short_name['T2wToT1wDistortionCorrectAndReg.sh'] = 'DC'\n short_name['AtlasRegistrationToMNI152_FLIRTandFNIRT.sh'] = 'AR'\n\n filter_ = re.match('(/usr/bin/)|(/bin/)', process_name)\n exclude_list = [\"\"]\n # exclude_list = [\"\", \"imtest\", \"imcp\", \"remove_ext\",\n # \"fslval\", \"avscale\", \"fslhd\"]\n if filter_ is None and process_name.split('/')[-1:][0] not in exclude_list:\n # Get the list of opened files (w/r) from pid\n flag_ = False\n process_ofiles = get_opened_files_list(openfile_cursor, pid)\n p_name = process_name.split('/')[-1:][0]\n if pid in total_proc_diff:\n graph_ = add_node(graph_, pid, p_name, 'process', 'red')\n flag_ = True\n elif total_proc_diff != []:\n graph_ = add_node(graph_, pid, p_name, 'process', 'green')\n if p_name in short_name.keys():\n graph_ = add_node(graph_, pid, short_name[p_name], 'process', 'subgreen')\n else:\n graph_ = add_node(graph_, pid, p_name, 'process', 'white')\n\n for file in process_ofiles:\n fname_ = str(os.path.basename(file[1]))\n file_code = str(file[1])\n if dag and fname_ in multi_list.keys():\n if file[2] == 2:\n file_code = str(file[1]) + str(pid)\n else:\n sorted_ = sorted(multi_list[fname_])\n set_pid = pid\n for pid_lst in sorted_:\n if pid > pid_lst:\n set_pid = pid_lst\n continue\n file_code = str(file[1]) + str(set_pid)\n\n hash_object = hashlib.sha1(file_code.encode('utf-8'))\n hex_dig_file = hash_object.hexdigest()\n # Read files\n if file[2] == 1 and str(hex_dig_file) in pipe_list:\n graph_ = add_edge(graph_, hex_dig_file, pid, 'file')\n\n # Write files\n elif file[2] == 2 and (fname_ in pipe_list or fname_ in tmp_list):\n if str(hex_dig_file) not in pipe_list:\n pipe_list.append(str(hex_dig_file))\n color = 'white'\n if dag and (fname_ in tmp_list or fname_ in multi_list.keys()):\n color = 'grey'\n elif fname_ in tmp_list or fname_ in multi_list.keys():\n color = 'grey'\n if flag_:\n graph_ = add_node(graph_, hex_dig_file, fname_, 'file', color)\n else:\n graph_ = add_node(graph_, hex_dig_file, fname_, 'file', color)\n graph_ = add_edge(graph_, pid, hex_dig_file, 'file')\n\n for child in child_list:\n p_name_child = get_the_processes_name(executed_cursor, child)\n # find node in graph\n filter_ = re.match('(/usr/bin/)|(/bin/)', p_name_child)\n x_node = [True for v in graph_.body if '\\t{} '.format(pid) in v]\n if filter_ is None and x_node and p_name_child.split('/')[-1:][0] not in exclude_list:\n graph_ = add_edge(graph_, pid, child, 'ptree')\n create_provenance_graph(db_path, child, graph_, pipe_list,\n multi_list, tmp_list, total_proc_diff, dag)\n\ndef parse_transient(tr_file):\n try:\n with open(tr_file, 'r') as cfile:\n data = json.load(cfile)\n if \"total_temp_proc\" not in data:\n tmp_write = []\n else:\n tmp_write = []\n tmp_write_dic = data[\"total_temp_proc\"]\n for key, file_ in tmp_write_dic.items():\n for tmp in file_['files']:\n # ntmp = tmp.encode('utf8', 'ignore') \\\n # .replace('\\x00', ' ').strip()\n tmp_write.append(os.path.basename(tmp))\n\n if \"total_multi_write_proc\" not in data:\n multi_write = {}\n else:\n multi_write = {}\n multi_write_t = data[\"total_multi_write_proc\"]\n for key, file_ in multi_write_t.items():\n for mw in file_['files']:\n fname = os.path.basename(mw)\n # (mw.encode('utf8',\n # 'ignore').strip())\n if fname not in multi_write.keys():\n multi_write[fname] = [file_['id']]\n else:\n tmp_list = multi_write[fname]\n tmp_list.append(file_['id'])\n multi_write[fname] = tmp_list\n except Exception:\n multi_write = {}\n tmp_write = []\n return multi_write, tmp_write\n\n\ndef parse_labelling(diff_processes):\n try:\n with open(diff_processes, 'r') as cfile:\n data = json.load(cfile)\n if \"total_commands\" not in data:\n list_p = []\n else:\n list_p = []\n tmp_write_dic = data[\"total_commands\"]\n for key, file_ in tmp_write_dic.items():\n list_p.append(file_['id'])\n\n if \"total_commands_multi\" in data:\n multi_write_t = data[\"total_commands_multi\"]\n for key, file_ in multi_write_t.items():\n list_p.append(file_['id'])\n\n except Exception:\n list_p = []\n return list_p\n\n\ndef main(args=None):\n input_folder = 'data/example/input'\n db_file = 'data/example/trace.sqlite3'\n diff_processes = 'data/example/nonreproducible_captured.json'\n tr_file = 'data/example/transient_captured.json'\n multi_list, tmp_list = parse_transient(tr_file)\n total_proc_diff = parse_labelling(diff_processes)\n\n # Get the list of output files\n lst_files = []\n for file in os.walk(input_folder):\n for elem in file[2]:\n lst_files.append(elem)\n # Create provenance graphs\n graph = Di('Graph',\n filename='figures/p-graph',\n format='pdf',\n strict=True)\n # graph.attr(compound=True)\n create_provenance_graph(db_file, 1, graph, lst_files, multi_list,\n tmp_list, [], False)\n graph.render()\n\n graph = Di('Graph',\n filename='figures/p-graph-dag',\n format='pdf',\n strict=True)\n create_provenance_graph(db_file, 1, graph, lst_files, multi_list,\n tmp_list, [], True)\n graph.render()\n\n graph = Di('Graph',\n filename='figures/p-graph-dag-labelled',\n format='pdf',\n strict=True)\n create_provenance_graph(db_file, 1, graph, lst_files, multi_list,\n tmp_list, total_proc_diff, True)\n graph.render()\n\n \n\n\nif __name__ == '__main__':\n main()\n", "repo_name": "big-data-lab-team/HCP-reproducibility-paper", "sub_path": "bin/code/provenance_graph.py", "file_name": "provenance_graph.py", "file_ext": "py", "file_size_in_byte": 10987, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "sqlite3.connect", "line_number": 98, "usage_type": "call"}, {"api_name": "sqlite3.Error", "line_number": 99, "usage_type": "name"}, {"api_name": "re.match", "line_number": 117, "usage_type": "call"}, {"api_name": "os.path.basename", "line_number": 137, "usage_type": "call"}, {"api_name": "os.path", "line_number": 137, "usage_type": "attribute"}, {"api_name": "hashlib.sha1", "line_number": 151, "usage_type": "call"}, {"api_name": "re.match", "line_number": 175, "usage_type": "call"}, {"api_name": "json.load", "line_number": 185, "usage_type": "call"}, {"api_name": "os.path.basename", "line_number": 195, "usage_type": "call"}, {"api_name": "os.path", "line_number": 195, "usage_type": "attribute"}, {"api_name": "os.path.basename", "line_number": 204, "usage_type": "call"}, {"api_name": "os.path", "line_number": 204, "usage_type": "attribute"}, {"api_name": "json.load", "line_number": 222, "usage_type": "call"}, {"api_name": "os.walk", "line_number": 251, "usage_type": "call"}, {"api_name": "graphviz.Digraph", "line_number": 255, "usage_type": "call"}, {"api_name": "graphviz.Digraph", "line_number": 264, "usage_type": "call"}, {"api_name": "graphviz.Digraph", "line_number": 272, "usage_type": "call"}]} +{"seq_id": "72379839787", "text": "import functools\nfrom typing import Any, Iterable, List, Mapping, Optional, Tuple\n\nimport numpy as np\n\nfrom open_spiel.python.games import dynamic_routing_data\nfrom open_spiel.python.games import dynamic_routing_utils\nfrom open_spiel.python.observation import IIGObserverForPublicInfoGame\nimport pyspiel\n\n_DEFAULT_PARAMS = {\n \"max_num_time_step\": 10,\n \"time_step_length\": 0.5,\n \"players\": -1\n}\n_GAME_TYPE = pyspiel.GameType(\n short_name=\"python_mfg_dynamic_routing\",\n long_name=\"Python Mean Field Routing Game\",\n dynamics=pyspiel.GameType.Dynamics.MEAN_FIELD,\n chance_mode=pyspiel.GameType.ChanceMode.EXPLICIT_STOCHASTIC,\n information=pyspiel.GameType.Information.PERFECT_INFORMATION,\n utility=pyspiel.GameType.Utility.GENERAL_SUM,\n reward_model=pyspiel.GameType.RewardModel.REWARDS,\n max_num_players=1,\n min_num_players=1,\n provides_information_state_string=True,\n provides_information_state_tensor=True,\n provides_observation_string=True,\n provides_observation_tensor=True,\n default_loadable=True,\n provides_factored_observation_string=True,\n parameter_specification=_DEFAULT_PARAMS)\n\nWAITING_TIME_NOT_ASSIGNED = -1\n\n\n@functools.lru_cache(maxsize=None)\ndef _state_to_str(\n is_chance_init: bool,\n location: str,\n time_step: int,\n player_id: int,\n waiting_time: int,\n destination: str,\n final_arrival_time: float,\n) -> str:\n \"\"\"Convert the state to a string representation.\n\n As the string representation will be used in dictionaries for various\n algorithms that computes the state value, expected return, best response or\n find the mean field Nash equilibrium.\n The state is uniquely define by the current time, the type of node\n (decision, mean field or chance), the vehicle location, its destination and\n its waiting time.\n Args:\n is_chance_init: True if at chance initialization.\n location: the location of the representative player.\n time_step: the current time step.\n player_id: the current node type as a player id.\n waiting_time: the representative player waiting time.\n destination: the destination of the representative player.\n final_arrival_time: time of arrival.\n\n Returns:\n state_string: string representing uniquely the mean field game.\n \"\"\"\n if is_chance_init:\n return \"initial chance node\"\n if player_id == pyspiel.PlayerId.DEFAULT_PLAYER_ID:\n time = str(time_step)\n elif player_id == pyspiel.PlayerId.MEAN_FIELD:\n time = f\"{time_step}_mean_field\"\n elif player_id == pyspiel.PlayerId.CHANCE:\n time = f\"{time_step}_chance\"\n else:\n raise ValueError(\n \"Player id should be DEFAULT_PLAYER_ID, MEAN_FIELD or CHANCE\")\n if final_arrival_time:\n return (f\"Arrived at {location}, with arrival time \"\n f\"{final_arrival_time}, t={time}\")\n return (f\"Location={location}, waiting_time={waiting_time},\"\n f\" t={time}, destination='{destination}'\")\n\n\nclass MeanFieldRoutingGame(pyspiel.Game):\n \"\"\"Implementation of mean field routing game.\n\n The representative vehicle/player is represented as a tuple current location,\n current waiting time and destination. When the waiting time is negative, the\n vehicle choose on with successor link it would like to go. When arriving on\n the link, a waiting time is assigned to the player based on the distribution\n of players on the link. The vehicle arrival time is equal to the time step\n when they first reach their destination. See module docstring for more\n information.\n\n Attributes inherited from GameInfo:\n max_chance_outcomes: maximum number of chance actions. Set to the length of\n od_demand, i.e. the number of `OriginDestinationDemand`s.\n max_game_length: maximum number of time step played. Passed during\n construction.\n max_utility: maximum utility is the opposite of the minimum arrival\n time. Set to 0.\n min_utility: minimum utility is the opposite of the maximum arrival\n time. Set to - max_game_length - 1.\n num_distinct_actions: maximum number of possible actions. This is\n equal to the number of links + 1 (corresponding to having no\n possible action _NO_POSSIBLE_ACTION).\n num_players: the number of vehicles. Should be 1 as this mean field\n game is a one population game.\n Attributes:\n network: the network of the game.\n od_demand: a list of the vehicle. Their origin and their destination should\n be road sections of the game.\n time_step_length: size of the time step, used to convert travel times into\n number of game time steps.\n perform_sanity_checks: if true, sanity checks are done during the game,\n should be set to false to speed up the game.\n total_num_vehicle: total number of vehicles as the sum of the od_demand.\n chance_outcomes: chance outcomes based on the initial probability\n distribution and their probabilities.\n \"\"\"\n network: dynamic_routing_utils.Network\n od_demand: List[dynamic_routing_utils.OriginDestinationDemand]\n perform_sanity_checks: bool\n time_step_length: float\n\n def __init__(self,\n params: Mapping[str, Any],\n network: Optional[dynamic_routing_utils.Network] = None,\n od_demand: Optional[List[\n dynamic_routing_utils.OriginDestinationDemand]] = None,\n perform_sanity_checks: bool = True):\n \"\"\"Initiliaze the game.\n\n Args:\n params: game parameters. It should define max_num_time_step and\n time_step_length.\n network: the network of the game.\n od_demand: a list of the vehicle. Their origin and their destination\n should be road sections of the game.\n perform_sanity_checks: set the perform_sanity_checks attribute.\n \"\"\"\n max_num_time_step = params[\"max_num_time_step\"]\n time_step_length = params[\"time_step_length\"]\n self.network = network if network else dynamic_routing_data.BRAESS_NETWORK\n self.od_demand = (\n od_demand\n if od_demand else dynamic_routing_data.BRAESS_NETWORK_OD_DEMAND)\n self.network.check_list_of_od_demand_is_correct(self.od_demand)\n self.perform_sanity_checks = perform_sanity_checks\n self.time_step_length = time_step_length\n self.total_num_vehicle = sum(\n [od_demand_item.counts for od_demand_item in self.od_demand])\n self.chance_outcomes = [(i, od_demand_item.counts / self.total_num_vehicle)\n for i, od_demand_item in enumerate(self.od_demand)]\n game_info = pyspiel.GameInfo(\n num_distinct_actions=self.network.num_actions(),\n max_chance_outcomes=len(self.od_demand),\n num_players=1,\n min_utility=-max_num_time_step - 1,\n max_utility=0,\n max_game_length=max_num_time_step)\n super().__init__(_GAME_TYPE, game_info, params if params else {})\n\n def new_initial_state(self) -> \"MeanFieldRoutingGameState\":\n \"\"\"Returns the state corresponding to the start of a game.\"\"\"\n return MeanFieldRoutingGameState(self, self.time_step_length)\n\n def make_py_observer(self, iig_obs_type=None, params=None):\n \"\"\"Returns a NetworkObserver object used for observing game state.\"\"\"\n if ((iig_obs_type is None) or\n (iig_obs_type.public_info and not iig_obs_type.perfect_recall)):\n return NetworkObserver(self.network.num_actions(), self.max_game_length())\n return IIGObserverForPublicInfoGame(iig_obs_type, params)\n\n def max_chance_nodes_in_history(self):\n \"\"\"Maximun chance nodes in game history.\"\"\"\n return self.max_game_length() + 1\n\n def get_road_section_as_int(self, section: Optional[str]) -> int:\n \"\"\"Returns the integer representation of the road section.\"\"\"\n if section is None:\n return 0\n start_node, end_node = (\n dynamic_routing_utils._nodes_from_road_section(section)) # pylint:disable=protected-access\n return self.network.get_action_id_from_movement(start_node, end_node)\n\n\nclass MeanFieldRoutingGameState(pyspiel.State):\n \"\"\"State of the DynamicRoutingGame.\n\n One player is equal to one vehicle.\n See docstring of the game class and of the file for more information.\n Attributes:\n _current_time_step: current time step of the game.\n _is_chance_init: boolean that encodes weither the current node is the\n initial chance node.\n _is_terminal: boolean that encodes weither the game is over.\n _max_arrival_time: int that encodes maximum arrival time on any link in\n number of time steps. Needed to enumerate all the possible state of a\n vehicle being on a link to compute volume of cars on the link.\n _max_waiting_time: maximum time a vehicle can wait on a time. This is done\n in order to limit the number of possible state with a vehicle on a\n specific link.\n _normed_density_on_vehicle_link: density of vehicles on the link that is\n used by the representative vehicle. This is given by the mean field\n distribution.\n _time_step_length: size of the time step, used to convert travel times into\n number of game time steps.\n _vehicle_at_destination: boolean that encodes if the representative vehicle\n has reached its destination.\n _vehicle_destination: the destination of the representative vehicle\n corresponding to this state. It is associated to the representative\n vehicle after the initial chance node according to the od_demand\n distribution.\n _vehicle_final_arrival_time: the arrival time of the representative vehicle,\n the arrival is either 0 if the vehicle is still in the network or its\n arrival time if the vehicle has reached its destination.\n _vehicle_location: current location of the vehicle as a network road\n section.\n _vehicle_without_legal_action: boolean that encodes if the representative\n vehicle has reach a sink node, meaning that it will not be able to move\n anymore.\n _waiting_time: time that the vehicle has to wait before moving to the next\n link (equal to the link travel time when the vehicle just reached the\n link).\n \"\"\"\n _current_time_step: int\n _is_chance_init: bool\n _is_terminal: bool\n _max_arrival_time: int\n _max_waiting_time: int\n _normed_density_on_vehicle_link: float\n _time_step_length: float\n _vehicle_at_destination: bool\n _vehicle_destination: Optional[str]\n _vehicle_final_arrival_time: float\n _vehicle_location: Optional[str]\n _vehicle_without_legal_action: bool\n _waiting_time: int\n\n def __init__(self, game: MeanFieldRoutingGame, time_step_length: float):\n \"\"\"Constructor; should only be called by Game.new_initial_state.\"\"\"\n super().__init__(game)\n self._current_time_step = 0\n self._is_chance_init = True # is true for the first state of the game.\n self._is_terminal = False\n if self.get_game().perform_sanity_checks:\n assert game.num_players() == 1, (\n \"This mean field routing game should have a unique player.\")\n self._player_id = pyspiel.PlayerId.CHANCE\n self._time_step_length = time_step_length\n self._vehicle_at_destination = False\n self._vehicle_final_arrival_time = 0.0\n self._vehicle_without_legal_action = False\n self._vehicle_location = None\n self._vehicle_destination = None\n self._max_arrival_time = self.get_game().max_game_length()\n # Cap maximum link waiting time to faster simulations.\n self._max_waiting_time = self._max_arrival_time\n self._waiting_time = WAITING_TIME_NOT_ASSIGNED\n\n @property\n def current_time_step(self) -> int:\n \"\"\"Return current time step.\"\"\"\n return self._current_time_step\n\n def current_player(self) -> pyspiel.PlayerId:\n \"\"\"Returns the current player.\"\"\"\n if self._is_terminal:\n return pyspiel.PlayerId.TERMINAL\n return self._player_id\n\n def state_to_str(self,\n location: str,\n time_step: int,\n player_id: int = pyspiel.PlayerId.DEFAULT_PLAYER_ID,\n waiting_time: int = 0,\n destination: str = \"\"):\n \"\"\"Convert the state to a string representation.\"\"\"\n return _state_to_str(\n self._is_chance_init,\n location,\n time_step,\n player_id,\n waiting_time,\n destination or self._vehicle_destination,\n self._vehicle_final_arrival_time,\n )\n\n def distribution_support(self) -> List[str]:\n \"\"\"Returns the state that should be used for update_distribution.\n\n The distribution of the vehicle is used to determined the number of\n cars on the same link of the representative vehicle in order to define\n the waiting time of the representative vehicle when joining a link.\n Therefore, only the states corresponding to be on the link of the\n representative vehicle at this current time are useful.\n Returns:\n list of the two state: being on the link of the representative vehicle at\n the current time and being stuck in traffic or not.\n \"\"\"\n if self._vehicle_without_legal_action:\n return []\n od_demand = self.get_game().od_demand\n dist = [\n self.state_to_str( # pylint:disable=g-complex-comprehension\n self._vehicle_location,\n self._current_time_step,\n player_id=pyspiel.PlayerId.MEAN_FIELD,\n waiting_time=waiting_time,\n destination=destination)\n for waiting_time in range(WAITING_TIME_NOT_ASSIGNED,\n self._max_arrival_time)\n for destination in {od._destination for od in od_demand} # pylint:disable=protected-access\n ]\n assert len(set(dist)) == len(dist), (\n f\"Distribution should not have duplicated states: {dist}.\")\n return dist\n\n def update_distribution(self, distribution: List[float]):\n \"\"\"Get the number of cars on the same link as the representative player.\n\n _normed_density_on_vehicle_link stores the number of cars on the link\n where the representative player is.\n Args:\n distribution: the probability for a vehicle to be in the states in\n distribution_support. The distribution is a list of probabilities.\n \"\"\"\n game = self.get_game()\n if game.perform_sanity_checks:\n if self._player_id != pyspiel.PlayerId.MEAN_FIELD:\n raise ValueError((\"update_distribution should only be called at\"\n \" a MEAN_FIELD state.\"))\n self._player_id = pyspiel.PlayerId.DEFAULT_PLAYER_ID\n if not self._vehicle_without_legal_action:\n self._normed_density_on_vehicle_link = sum(distribution)\n if game.perform_sanity_checks:\n assert 0 <= self._normed_density_on_vehicle_link <= 1 + 1e-4, (\n f\"{self._normed_density_on_vehicle_link} is not in [0, 1].\")\n if self._waiting_time == WAITING_TIME_NOT_ASSIGNED:\n volume = (game.total_num_vehicle * self._normed_density_on_vehicle_link)\n self._waiting_time = int(\n game.network.get_travel_time(self._vehicle_location, volume) /\n self._time_step_length) - 1\n self._waiting_time = max(0, self._waiting_time)\n\n def chance_outcomes(self) -> List[Tuple[int, float]]:\n \"\"\"Returns the initial probability distribution is returned.\n\n One chance outcome correspond to each possible OD pair with a departure\n time, the probability of each chance outcome is the proportion of vehicle in\n each OD pair with a departure time.\n Returns:\n list_tuple_outcome_probabilities: chance outcomes and their probability.\n \"\"\"\n game = self.get_game()\n if game.perform_sanity_checks:\n assert self._player_id == pyspiel.PlayerId.CHANCE\n assert self._is_chance_init\n return game.chance_outcomes\n\n def _legal_actions(self, player: pyspiel.PlayerId) -> List[int]:\n \"\"\"Return the legal actions of the vehicle.\n\n Legal actions are the succesor road section of the vehicle current road\n section.\n Args:\n player: the vehicle id.\n\n Returns:\n list_legal_actions: a list of legal actions. If the game is finished then\n the list is empty. If the vehicle is at its destination, has a positive\n waiting time or if it is on a node without successors then an empty list\n is returned. Otherwise the list of successors nodes of the current\n vehicle location is returned.\n \"\"\"\n if self._is_terminal:\n return []\n if self.get_game().perform_sanity_checks:\n assert player == pyspiel.PlayerId.DEFAULT_PLAYER_ID, str(player)\n if self._vehicle_without_legal_action:\n # If the vehicle is at destination it cannot do anything.\n return [dynamic_routing_utils.NO_POSSIBLE_ACTION]\n if self._waiting_time > 0:\n return [dynamic_routing_utils.NO_POSSIBLE_ACTION]\n _, end_section_node = dynamic_routing_utils._nodes_from_road_section( # pylint:disable=protected-access\n self._vehicle_location)\n successors = self.get_game().network.get_successors(end_section_node)\n if self.get_game().perform_sanity_checks:\n if not successors:\n raise ValueError((\"If a vehicle is not without legal action, it\"\n \" should have an action.\"))\n assert isinstance(successors, Iterable)\n actions = [\n self.get_game().network.get_action_id_from_movement(\n end_section_node, d) for d in successors\n ]\n map(self.get_game().network.assert_valid_action, actions)\n return sorted(actions)\n\n def _apply_action(self, action: int):\n \"\"\"Apply the action to the state.\n\n This function can be either called on a chance node or on a decision\n node. If called on the initial chance node, the action gives in which OD\n demand the representative vehicle belongs too (it put the vehicle at\n this location and define its destination).\n If called on decision node, the action defines on which link the vehicle\n will move (if it is not stuck in traffic) and assign a waiting time to the\n vehicle.\n Args:\n action: the action to apply.\n \"\"\"\n if self._player_id == pyspiel.PlayerId.CHANCE:\n self._player_id = pyspiel.PlayerId.DEFAULT_PLAYER_ID\n assert self._is_chance_init\n # Apply action is called on initial chance node to initialized\n # the vehicle position based on the initial location\n # distribution.\n od_demand = self.get_game().od_demand\n self._vehicle_destination = od_demand[action].destination\n self._vehicle_location = od_demand[action].origin\n self._waiting_time = int(od_demand[action].departure_time /\n self._time_step_length)\n self._is_chance_init = False\n self._normed_density_on_vehicle_link = 0\n elif self._player_id == pyspiel.PlayerId.DEFAULT_PLAYER_ID:\n self._player_id = pyspiel.PlayerId.MEAN_FIELD\n # Apply action is called on a descision node. If the vehicle can\n # move, then it will move to the next road section.\n # Has the vehicle already reached a sink node?\n if not self._vehicle_without_legal_action:\n # If the vehicle is stuck in traffic it cannot move.\n if self._waiting_time > 0:\n self._waiting_time -= 1\n else:\n if self.get_game().perform_sanity_checks:\n self.get_game().network.assert_valid_action(action,\n self._vehicle_location)\n self._vehicle_location = (\n self.get_game().network.get_road_section_from_action_id(action))\n # Has the vehicle just reached its destination?\n if self._vehicle_location == self._vehicle_destination:\n self._vehicle_final_arrival_time = self._current_time_step\n self._vehicle_at_destination = True\n self._vehicle_without_legal_action = True\n # Will the vehicle have a legal action for next time step?\n elif self.get_game().network.is_location_at_sink_node(\n self._vehicle_location):\n self._vehicle_without_legal_action = True\n self._vehicle_final_arrival_time = -self.get_game().min_utility()\n else:\n self._waiting_time = WAITING_TIME_NOT_ASSIGNED\n self._current_time_step += 1\n elif self.get_game().perform_sanity_checks:\n if self._is_terminal:\n raise ValueError(\n \"_apply_action should not be called at a end of the game.\")\n if self._player_id == pyspiel.PlayerId.MEAN_FIELD:\n raise ValueError(\n \"_apply_action should not be called at a MEAN_FIELD state.\")\n # Is the game finished?\n if self._current_time_step >= self.get_game().max_game_length():\n self._is_terminal = True\n if not self._vehicle_at_destination:\n self._vehicle_final_arrival_time = -self.get_game().min_utility()\n\n def _action_to_string(self, player, action) -> str:\n \"\"\"Action -> string.\"\"\"\n if player == pyspiel.PlayerId.CHANCE:\n if self._is_chance_init:\n return f\"Vehicle is assigned to population {action}.\"\n return f\"Change node; the vehicle movement is {bool(action)}.\"\n if self.get_game().perform_sanity_checks:\n assert player == pyspiel.PlayerId.DEFAULT_PLAYER_ID\n if action == dynamic_routing_utils.NO_POSSIBLE_ACTION:\n return f\"Vehicle {player} reach a sink node or its destination.\"\n if self.get_game().perform_sanity_checks:\n self.get_game().network.assert_valid_action(action)\n return (f\"Vehicle {player} would like to move to \" + str(\n self.get_game().network.get_road_section_from_action_id(action)) + \".\")\n\n def is_terminal(self) -> bool:\n \"\"\"Returns True if the game is over.\"\"\"\n return self._is_terminal\n\n def is_waiting(self) -> bool:\n \"\"\"Returns True if the wait time is non-zero.\"\"\"\n return self._waiting_time > 0\n\n def returns(self) -> List[float]:\n \"\"\"Total reward for each player over the course of the game so far.\"\"\"\n if not self._is_terminal:\n return [0]\n return [-self._vehicle_final_arrival_time * self._time_step_length]\n\n def get_location_as_int(self) -> int:\n \"\"\"Returns the vehicle location.\n\n This will be 1-based action index of the location, or 0 when the location is\n None before the initial chance node.\n \"\"\"\n return self.get_game().get_road_section_as_int(self._vehicle_location)\n\n def get_destination_as_int(self) -> int:\n \"\"\"Returns the vehicle destination.\n\n\n This will be 1-based action index of the destination, or 0 when the\n destination is None before the initial chance node.\n \"\"\"\n return self.get_game().get_road_section_as_int(self._vehicle_destination)\n\n def __str__(self) -> str:\n \"\"\"String for debug purposes. No particular semantics are required.\"\"\"\n if self._vehicle_location is not None:\n return self.state_to_str(\n self._vehicle_location,\n self._current_time_step,\n player_id=self._player_id,\n waiting_time=self._waiting_time)\n assert self._current_time_step == 0\n return \"Before initial chance node\"\n\n\nclass NetworkObserver:\n \"\"\"Network observer used by the learning algorithm.\n\n The state string is the state history string. The state tensor is an array\n of size number of locations * 2 + maximum number of time steps + 2, which is\n the concatenation of one-hot encodings of the location, destination (1-based;\n if location or destination is None, then the 0th element will be set to 1) and\n the current time (0-based). The last element of the array will be set to 1 if\n waiting time is positive, or 0 otherwise.\n\n Attributes:\n dict: Dictionary of tensors for the components of the observation\n corresponding to the location, destination and time.\n tensor: The concatenated form of the observation.\n \"\"\"\n\n def __init__(self, num_locations: int, max_num_time_step: int):\n \"\"\"Initializes an empty observation tensor.\"\"\"\n self.tensor = np.zeros(num_locations * 2 + max_num_time_step + 1 + 1,\n np.float32)\n self.dict = {\n \"location\": self.tensor[:num_locations],\n \"destination\": self.tensor[num_locations:num_locations * 2],\n \"time\": self.tensor[num_locations * 2:-1],\n \"waiting\": self.tensor[-1:]\n }\n\n def set_from(self, state, player):\n \"\"\"Sets the state tensor based on the specified state.\n\n Note that the function may be called with arbitrary states of the game, e.g.\n from different runs, and therefore the tensor should be cleared and updated\n instead of preserving any earlier values.\n\n Args:\n state: state of the game.\n player: player id that should play.\n \"\"\"\n assert player == pyspiel.PlayerId.DEFAULT_PLAYER_ID\n self.tensor.fill(0)\n self.dict[\"location\"][state.get_location_as_int()] = 1\n self.dict[\"destination\"][state.get_destination_as_int()] = 1\n self.dict[\"time\"][state.current_time_step] = 1\n self.dict[\"waiting\"][0] = state.is_waiting()\n\n def string_from(self, state, player):\n \"\"\"Return the state history string.\"\"\"\n assert player == pyspiel.PlayerId.DEFAULT_PLAYER_ID\n return str(state)\n\n\n# Register the game with the OpenSpiel library\npyspiel.register_game(_GAME_TYPE, MeanFieldRoutingGame)\n", "repo_name": "deepmind/open_spiel", "sub_path": "open_spiel/python/mfg/games/dynamic_routing.py", "file_name": "dynamic_routing.py", "file_ext": "py", "file_size_in_byte": 24927, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3700, "dataset": "github-code", "pt": "37", "api": [{"api_name": "pyspiel.GameType", "line_number": 16, "usage_type": "call"}, {"api_name": "pyspiel.GameType", "line_number": 19, "usage_type": "attribute"}, {"api_name": "pyspiel.GameType", "line_number": 20, "usage_type": "attribute"}, {"api_name": "pyspiel.GameType", "line_number": 21, "usage_type": "attribute"}, {"api_name": "pyspiel.GameType", "line_number": 22, "usage_type": "attribute"}, {"api_name": "pyspiel.GameType", "line_number": 23, "usage_type": "attribute"}, {"api_name": "pyspiel.PlayerId", "line_number": 69, "usage_type": "attribute"}, {"api_name": "pyspiel.PlayerId", "line_number": 71, "usage_type": "attribute"}, {"api_name": "pyspiel.PlayerId", "line_number": 73, "usage_type": "attribute"}, {"api_name": "functools.lru_cache", "line_number": 37, "usage_type": "call"}, {"api_name": "pyspiel.Game", "line_number": 85, "usage_type": "attribute"}, {"api_name": "open_spiel.python.games.dynamic_routing_utils.Network", "line_number": 122, "usage_type": "attribute"}, {"api_name": "open_spiel.python.games.dynamic_routing_utils", "line_number": 122, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 123, "usage_type": "name"}, {"api_name": "open_spiel.python.games.dynamic_routing_utils.OriginDestinationDemand", "line_number": 123, "usage_type": "attribute"}, {"api_name": "open_spiel.python.games.dynamic_routing_utils", "line_number": 123, "usage_type": "name"}, {"api_name": "typing.Mapping", "line_number": 128, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 128, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 129, "usage_type": "name"}, {"api_name": "open_spiel.python.games.dynamic_routing_utils.Network", "line_number": 129, "usage_type": "attribute"}, {"api_name": "open_spiel.python.games.dynamic_routing_utils", "line_number": 129, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 130, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 130, "usage_type": "name"}, {"api_name": "open_spiel.python.games.dynamic_routing_utils.OriginDestinationDemand", "line_number": 131, "usage_type": "attribute"}, {"api_name": "open_spiel.python.games.dynamic_routing_utils", "line_number": 131, "usage_type": "name"}, {"api_name": "open_spiel.python.games.dynamic_routing_data.BRAESS_NETWORK", "line_number": 145, "usage_type": "attribute"}, {"api_name": "open_spiel.python.games.dynamic_routing_data", "line_number": 145, "usage_type": "name"}, {"api_name": "open_spiel.python.games.dynamic_routing_data.BRAESS_NETWORK_OD_DEMAND", "line_number": 148, "usage_type": "attribute"}, {"api_name": "open_spiel.python.games.dynamic_routing_data", "line_number": 148, "usage_type": "name"}, {"api_name": "pyspiel.GameInfo", "line_number": 156, "usage_type": "call"}, {"api_name": "open_spiel.python.observation.IIGObserverForPublicInfoGame", "line_number": 174, "usage_type": "call"}, {"api_name": "typing.Optional", "line_number": 180, "usage_type": "name"}, {"api_name": "open_spiel.python.games.dynamic_routing_utils._nodes_from_road_section", "line_number": 185, "usage_type": "call"}, {"api_name": "open_spiel.python.games.dynamic_routing_utils", "line_number": 185, "usage_type": "name"}, {"api_name": "pyspiel.State", "line_number": 189, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 236, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 238, "usage_type": "name"}, {"api_name": "pyspiel.PlayerId", "line_number": 251, "usage_type": "attribute"}, {"api_name": "pyspiel.PlayerId", "line_number": 271, "usage_type": "attribute"}, {"api_name": "pyspiel.PlayerId", "line_number": 268, "usage_type": "attribute"}, {"api_name": "pyspiel.PlayerId", "line_number": 277, "usage_type": "attribute"}, {"api_name": "pyspiel.PlayerId", "line_number": 310, "usage_type": "attribute"}, {"api_name": "typing.List", "line_number": 291, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 321, "usage_type": "name"}, {"api_name": "pyspiel.PlayerId", "line_number": 332, "usage_type": "attribute"}, {"api_name": "pyspiel.PlayerId", "line_number": 335, "usage_type": "attribute"}, {"api_name": "pyspiel.PlayerId", "line_number": 359, "usage_type": "attribute"}, {"api_name": "typing.List", "line_number": 348, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 348, "usage_type": "name"}, {"api_name": "pyspiel.PlayerId", "line_number": 363, "usage_type": "attribute"}, {"api_name": "pyspiel.PlayerId", "line_number": 381, "usage_type": "attribute"}, {"api_name": "open_spiel.python.games.dynamic_routing_utils.NO_POSSIBLE_ACTION", "line_number": 384, "usage_type": "attribute"}, {"api_name": "open_spiel.python.games.dynamic_routing_utils", "line_number": 384, "usage_type": "name"}, {"api_name": "open_spiel.python.games.dynamic_routing_utils.NO_POSSIBLE_ACTION", "line_number": 386, "usage_type": "attribute"}, {"api_name": "open_spiel.python.games.dynamic_routing_utils", "line_number": 386, "usage_type": "name"}, {"api_name": "open_spiel.python.games.dynamic_routing_utils._nodes_from_road_section", "line_number": 387, "usage_type": "call"}, {"api_name": "open_spiel.python.games.dynamic_routing_utils", "line_number": 387, "usage_type": "name"}, {"api_name": "typing.Iterable", "line_number": 394, "usage_type": "argument"}, {"api_name": "typing.List", "line_number": 363, "usage_type": "name"}, {"api_name": "pyspiel.PlayerId", "line_number": 415, "usage_type": "attribute"}, {"api_name": "pyspiel.PlayerId", "line_number": 416, "usage_type": "attribute"}, {"api_name": "pyspiel.PlayerId", "line_number": 428, "usage_type": "attribute"}, {"api_name": "pyspiel.PlayerId", "line_number": 429, "usage_type": "attribute"}, {"api_name": "pyspiel.PlayerId", "line_number": 460, "usage_type": "attribute"}, {"api_name": "pyspiel.PlayerId", "line_number": 471, "usage_type": "attribute"}, {"api_name": "pyspiel.PlayerId", "line_number": 476, "usage_type": "attribute"}, {"api_name": "open_spiel.python.games.dynamic_routing_utils.NO_POSSIBLE_ACTION", "line_number": 477, "usage_type": "attribute"}, {"api_name": "open_spiel.python.games.dynamic_routing_utils", "line_number": 477, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 492, "usage_type": "name"}, {"api_name": "numpy.zeros", "line_number": 545, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 546, "usage_type": "attribute"}, {"api_name": "pyspiel.PlayerId", "line_number": 565, "usage_type": "attribute"}, {"api_name": "pyspiel.PlayerId", "line_number": 574, "usage_type": "attribute"}, {"api_name": "pyspiel.register_game", "line_number": 579, "usage_type": "call"}]} +{"seq_id": "27215613137", "text": "import multiprocessing as mp\nimport subprocess as sp\nimport shutil\nimport subprocess\nimport requests\nimport config\nimport os\nfrom os.path import join\nimport json\nfrom requests.adapters import HTTPAdapter\nfrom loguru import logger\nfrom languages import extensions\nfrom builder import build\n\n\ndef run(queue: mp.Queue):\n pool = mp.Pool(initializer=init_process)\n logger.info(\"Starting process poll...\")\n while True:\n data = queue.get()\n # command task from flask\n if isinstance(data, str):\n\n if data == 'DIE':\n logger.info('time to go out with a bang!')\n return\n continue\n\n pool.apply_async(run_fight, (data,), callback=res_callback, error_callback=err_callback)\n\n\ndef send_as_json(invocation_id, session, file_name):\n with open(file_name) as file:\n response = session.post(config.INVOCATION_RESULT_ENDPOINT, json=json.load(file))\n if response.status_code == 200:\n logger.success(f'ID: {invocation_id}. {file_name} was sent successfully pid:{os.getpid()}')\n else:\n logger.critical(f'ID: {invocation_id}. {file_name} could not have been sent.')\n\n\ndef send_as_file(invocation_id, session, file_name):\n with open(file_name) as file:\n response = session.post(config.INVOCATION_RESULT_ENDPOINT, files={\n file_name: file\n })\n if response.status_code == 200:\n logger.success(f'ID: {invocation_id}. {file_name} was sent successfully pid:{os.getpid()}')\n else:\n logger.critical(f'ID: {invocation_id}. {file_name} could not have been sent.')\n\n\n@logger.catch\ndef run_fight(data, *args, **kwargs):\n _run_fight(data['problem'], data['solutions'], data['tests'])\n session = requests.session()\n session.mount(config.RAILS_HOST, HTTPAdapter(max_retries=10))\n send_as_json(data['invocation_id'], session, 'logs/result.json')\n send_as_file(data['invocation_id'], session, 'logs/checker.log')\n send_as_file(data['invocation_id'], session, 'logs/game.json')\n send_as_file(data['invocation_id'], session, 'logs/streams.json')\n\n\ndef _run_fight(problem, solutions, tests):\n calls = []\n files = []\n for id, solution in enumerate(solutions):\n filename = f\"solution_{id}.{extensions[solution['lang']]}\"\n filepath = f'sources/{filename}'\n with open(filepath, \"w\") as sol:\n sol.write(solution['source'])\n calls.append(build(filename, filepath, solution['lang']))\n files.append('')\n problem_folder = os.path.join(config.PROBLEM_FOLDER, problem)\n problem_config = None\n with open(os.path.join(problem_folder, 'config/problem.json')) as file:\n problem_config = json.load(file)\n test_files = [f'{problem_folder}/tests/{x[\"filename\"]}' for x in\n filter(lambda x: x['id'] in tests, problem_config['tests'])]\n for file in test_files:\n return_code = sp.call([\"bash\", os.path.join(problem_folder, \"scripts/check.sh\"), '--players_cmd'] + calls +\n ['--players_file'] + files + ['--test_file'] + [file])\n if return_code != 0:\n logger.critical(f'Checker failed with exit code {return_code}.')\n\n\ndef err_callback(exc):\n logger.warning(f'ERROR at Judge \\n{exc}')\n # TODO: implement errors check\n pass\n\n\ndef res_callback(res):\n \"\"\"\n it states here just for fun\n :param res: must be None\n :return:\n \"\"\"\n pass\n\n\ndef init_process():\n my_wd = f'tmp/{os.getpid()}'\n if os.path.exists(my_wd):\n shutil.rmtree(my_wd)\n os.mkdir(my_wd)\n os.chdir(my_wd)\n\n os.mkdir('sources')\n os.mkdir('bin')\n os.mkdir('logs')\n\n code = subprocess.call(['python3', '-m', 'venv', 'py3_venv'])\n if code != 0:\n logger.critical(f\"Error while creating python3 venv {os.getpid()}\")\n exit(1)\n\n code = subprocess.call(['python', '-m', 'venv', 'py2_venv'])\n if code != 0:\n logger.critical(f\"Error while creating python venv {os.getpid()}\")\n exit(1)\n logger.success(f\"init new worker {os.getpid()}\")\n\n # code = subprocess.call(['pypy', '-m', 'venv', 'pypy2_venv'])\n # if code != 0:\n # logger.critical(f\"Error while creating pypy2 venv {os.getpid()}\")\n # exit(1)\n #\n # code = subprocess.call(['pypy3', '-m', 'venv', 'pypy3_venv'])\n # if code != 0:\n # logger.critical(f\"Error while creating pypy3 venv {os.getpid()}\")\n # exit(1)\n\n logger.success(f\"init new worker {os.getpid()}\")\n\n", "repo_name": "AIForces/AIforcesJudge", "sub_path": "worker.py", "file_name": "worker.py", "file_ext": "py", "file_size_in_byte": 4474, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "37", "api": [{"api_name": "multiprocessing.Queue", "line_number": 16, "usage_type": "attribute"}, {"api_name": "multiprocessing.Pool", "line_number": 17, "usage_type": "call"}, {"api_name": "loguru.logger.info", "line_number": 18, "usage_type": "call"}, {"api_name": "loguru.logger", "line_number": 18, "usage_type": "name"}, {"api_name": "loguru.logger.info", "line_number": 25, "usage_type": "call"}, {"api_name": "loguru.logger", "line_number": 25, "usage_type": "name"}, {"api_name": "config.INVOCATION_RESULT_ENDPOINT", "line_number": 34, "usage_type": "attribute"}, {"api_name": "json.load", "line_number": 34, "usage_type": "call"}, {"api_name": "loguru.logger.success", "line_number": 36, "usage_type": "call"}, {"api_name": "loguru.logger", "line_number": 36, "usage_type": "name"}, {"api_name": "os.getpid", "line_number": 36, "usage_type": "call"}, {"api_name": "loguru.logger.critical", "line_number": 38, "usage_type": "call"}, {"api_name": "loguru.logger", "line_number": 38, "usage_type": "name"}, {"api_name": "config.INVOCATION_RESULT_ENDPOINT", "line_number": 43, "usage_type": "attribute"}, {"api_name": "loguru.logger.success", "line_number": 47, "usage_type": "call"}, {"api_name": "loguru.logger", "line_number": 47, "usage_type": "name"}, {"api_name": "os.getpid", "line_number": 47, "usage_type": "call"}, {"api_name": "loguru.logger.critical", "line_number": 49, "usage_type": "call"}, {"api_name": "loguru.logger", "line_number": 49, "usage_type": "name"}, {"api_name": "requests.session", "line_number": 55, "usage_type": "call"}, {"api_name": "config.RAILS_HOST", "line_number": 56, "usage_type": "attribute"}, {"api_name": "requests.adapters.HTTPAdapter", "line_number": 56, "usage_type": "call"}, {"api_name": "loguru.logger.catch", "line_number": 52, "usage_type": "attribute"}, {"api_name": "loguru.logger", "line_number": 52, "usage_type": "name"}, {"api_name": "languages.extensions", "line_number": 67, "usage_type": "name"}, {"api_name": "builder.build", "line_number": 71, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 73, "usage_type": "call"}, {"api_name": "os.path", "line_number": 73, "usage_type": "attribute"}, {"api_name": "config.PROBLEM_FOLDER", "line_number": 73, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 75, "usage_type": "call"}, {"api_name": "os.path", "line_number": 75, "usage_type": "attribute"}, {"api_name": "json.load", "line_number": 76, "usage_type": "call"}, {"api_name": "subprocess.call", "line_number": 80, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 80, "usage_type": "call"}, {"api_name": "os.path", "line_number": 80, "usage_type": "attribute"}, {"api_name": "loguru.logger.critical", "line_number": 83, "usage_type": "call"}, {"api_name": "loguru.logger", "line_number": 83, "usage_type": "name"}, {"api_name": "loguru.logger.warning", "line_number": 87, "usage_type": "call"}, {"api_name": "loguru.logger", "line_number": 87, "usage_type": "name"}, {"api_name": "os.getpid", "line_number": 102, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 103, "usage_type": "call"}, {"api_name": "os.path", "line_number": 103, "usage_type": "attribute"}, {"api_name": "shutil.rmtree", "line_number": 104, "usage_type": "call"}, {"api_name": "os.mkdir", "line_number": 105, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 106, "usage_type": "call"}, {"api_name": "os.mkdir", "line_number": 108, "usage_type": "call"}, {"api_name": "os.mkdir", "line_number": 109, "usage_type": "call"}, {"api_name": "os.mkdir", "line_number": 110, "usage_type": "call"}, {"api_name": "subprocess.call", "line_number": 112, "usage_type": "call"}, {"api_name": "loguru.logger.critical", "line_number": 114, "usage_type": "call"}, {"api_name": "loguru.logger", "line_number": 114, "usage_type": "name"}, {"api_name": "os.getpid", "line_number": 114, "usage_type": "call"}, {"api_name": "subprocess.call", "line_number": 117, "usage_type": "call"}, {"api_name": "loguru.logger.critical", "line_number": 119, "usage_type": "call"}, {"api_name": "loguru.logger", "line_number": 119, "usage_type": "name"}, {"api_name": "os.getpid", "line_number": 119, "usage_type": "call"}, {"api_name": "loguru.logger.success", "line_number": 121, "usage_type": "call"}, {"api_name": "loguru.logger", "line_number": 121, "usage_type": "name"}, {"api_name": "os.getpid", "line_number": 121, "usage_type": "call"}, {"api_name": "loguru.logger.success", "line_number": 133, "usage_type": "call"}, {"api_name": "loguru.logger", "line_number": 133, "usage_type": "name"}, {"api_name": "os.getpid", "line_number": 133, "usage_type": "call"}]} +{"seq_id": "2010248741", "text": "# vim: ft=python fileencoding=utf-8 sw=4 et sts=4\n\"\"\"Slideshow for vimiv.\"\"\"\n\nfrom gi.repository import GLib, GObject\nfrom vimiv.settings import settings\n\n\nclass Slideshow(GObject.Object):\n \"\"\"Handle everything related to slideshow for vimiv.\n\n Attributes:\n running: If True the slideshow is running.\n\n _app: The main application class to interact with.\n _start_index: Index of the image when slideshow was started. Saved to\n display a message when slideshow is back at the beginning.\n _timer_id: ID of the currently running GLib.Timeout.\n \"\"\"\n\n def __init__(self, app):\n \"\"\"Create the necessary objects and settings.\n\n Args:\n app: The main vimiv application to interact with.\n \"\"\"\n super(Slideshow, self).__init__()\n self._app = app\n\n self._start_index = 0\n self.running = False\n self._timer_id = GLib.Timeout\n settings.connect(\"changed\", self._on_settings_changed)\n\n def toggle(self):\n \"\"\"Toggle the slideshow or update the delay.\"\"\"\n if not self._app.get_paths():\n message = \"No valid paths, starting slideshow failed\"\n self._app[\"statusbar\"].message(message, \"error\")\n return\n if self._app[\"thumbnail\"].toggled:\n message = \"Slideshow makes no sense in thumbnail mode\"\n self._app[\"statusbar\"].message(message, \"warning\")\n return\n # Delay changed via vimiv[\"eventhandler\"].num_str?\n number = self._app[\"eventhandler\"].get_num_str()\n if number:\n settings.override(\"slideshow_delay\", number)\n self._app[\"eventhandler\"].num_clear()\n # If the delay wasn't changed in any way just toggle the slideshow\n else:\n self.running = not self.running\n if self.running:\n delay = 1000 * settings[\"slideshow_delay\"].get_value()\n self._start_index = self._app.get_index()\n self._timer_id = GLib.timeout_add(delay, self._next)\n else:\n self._app[\"statusbar\"].lock = False\n GLib.source_remove(self._timer_id)\n self._app[\"statusbar\"].update_info()\n\n def _next(self):\n \"\"\"Command to run in the GLib.timeout moving to the next image.\"\"\"\n self._app[\"image\"].move_index()\n # Info if slideshow returns to beginning\n if self._app.get_index() == self._start_index:\n message = \"Back at beginning of slideshow\"\n self._app[\"statusbar\"].lock = True\n self._app[\"statusbar\"].message(message, \"info\")\n else:\n self._app[\"statusbar\"].lock = False\n return True # So we continue running\n\n def get_formatted_delay(self):\n \"\"\"Return the delay formatted neatly for the statusbar.\"\"\"\n delay = settings[\"slideshow_delay\"].get_value()\n return \"[slideshow - {0:.1f}s]\".format(delay)\n\n def _on_settings_changed(self, new_settings, setting):\n if setting == \"slideshow_delay\":\n delay = settings[\"slideshow_delay\"].get_value()\n # Set a minimum\n if delay < 0.5:\n settings.override(\"slideshow_delay\", \"0.5\")\n self._app[\"statusbar\"].message(\n \"Delays shorter than 0.5 s are not allowed\", \"warning\")\n return\n # If slideshow was running reload it\n if self.running:\n GLib.source_remove(self._timer_id)\n self._timer_id = GLib.timeout_add(1000 * delay, self._next)\n self._app[\"statusbar\"].update_info()\n", "repo_name": "karlch/vimiv", "sub_path": "vimiv/slideshow.py", "file_name": "slideshow.py", "file_ext": "py", "file_size_in_byte": 3621, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 259, "dataset": "github-code", "pt": "37", "api": [{"api_name": "gi.repository.GObject.Object", "line_number": 8, "usage_type": "attribute"}, {"api_name": "gi.repository.GObject", "line_number": 8, "usage_type": "name"}, {"api_name": "gi.repository.GLib.Timeout", "line_number": 31, "usage_type": "attribute"}, {"api_name": "gi.repository.GLib", "line_number": 31, "usage_type": "name"}, {"api_name": "vimiv.settings.settings.connect", "line_number": 32, "usage_type": "call"}, {"api_name": "vimiv.settings.settings", "line_number": 32, "usage_type": "name"}, {"api_name": "vimiv.settings.settings.override", "line_number": 47, "usage_type": "call"}, {"api_name": "vimiv.settings.settings", "line_number": 47, "usage_type": "name"}, {"api_name": "vimiv.settings.settings", "line_number": 53, "usage_type": "name"}, {"api_name": "gi.repository.GLib.timeout_add", "line_number": 55, "usage_type": "call"}, {"api_name": "gi.repository.GLib", "line_number": 55, "usage_type": "name"}, {"api_name": "gi.repository.GLib.source_remove", "line_number": 58, "usage_type": "call"}, {"api_name": "gi.repository.GLib", "line_number": 58, "usage_type": "name"}, {"api_name": "vimiv.settings.settings", "line_number": 75, "usage_type": "name"}, {"api_name": "vimiv.settings.settings", "line_number": 80, "usage_type": "name"}, {"api_name": "vimiv.settings.settings.override", "line_number": 83, "usage_type": "call"}, {"api_name": "vimiv.settings.settings", "line_number": 83, "usage_type": "name"}, {"api_name": "gi.repository.GLib.source_remove", "line_number": 89, "usage_type": "call"}, {"api_name": "gi.repository.GLib", "line_number": 89, "usage_type": "name"}, {"api_name": "gi.repository.GLib.timeout_add", "line_number": 90, "usage_type": "call"}, {"api_name": "gi.repository.GLib", "line_number": 90, "usage_type": "name"}]} +{"seq_id": "28994179330", "text": "import time\nimport sys\nimport serial\nfrom serial.threaded import ReaderThread, LineReader\nimport traceback\n\nimport time\nimport io\n\nimport serial.rs485\nimport keyboard\n# запуск двигателя с клавиатуры после нажатия 0\nimport libscrc\n\n\n\ndef append_crc(values) -> bytearray:\n \"\"\"values ->bytearray\"\"\"\n crc16b = libscrc.modbus(values).to_bytes(2, byteorder='little')\n values.append(crc16b[0])\n values.append(crc16b[1])\n return values\n\n\ndef write_to_serial(ser, message):\n cmd = append_crc(message)\n ser.write(cmd)\n print('ok')\n\n\ndef read_from_serial(ser):\n print(\"11!\")\n\n\ndef start_servo(self):\n with Servo(port=self.port, baudrate=self.br, timeout=self.timeout) as serial:\n serial.write_msg(\n bytearray([0x01, 0x06, 0x01, 0x04, 0x00, 0x01]))\n response = serial.write_msg(\n bytearray([0x01, 0x03, 0x01, 0x04, 0x00, 0x01]))\n print(response)\n\n\ndef stop_servo(self):\n with Servo(port=self.port, baudrate=self.br, timeout=self.timeout) as serial:\n serial.write_msg(\n bytearray([0x01, 0x06, 0x01, 0x04, 0x00, 0x00]))\n response = serial.write_msg(\n bytearray([0x01, 0x03, 0x01, 0x04, 0x00, 0x01]))\n print(response)\n\n\ndef read_register_p(self):\n self.ui.lineEdit.clear()\n p_number = int(self.ui.spinBox.value())\n p_value = int(self.ui.spinBox_2.value())\n with Servo(port=self.port, baudrate=self.br, timeout=self.timeout) as serial:\n response_raw = serial.write_msg(\n bytearray([0x01, 0x03, p_number, p_value, 0x00, 0x01]))\n response_raw_list = [hex(i) for i in list(response_raw)]\n response = (' ').join(response_raw_list)\n self.ui.lineEdit.setText(response)\n if len(response_raw_list) == 7:\n response_raw_list = bytearray(response_raw)\n print(len(response_raw_list))\n first_byte, second_byte = serial.bytes_to_high_low(\n response_raw_list, 3, 4)\n val = (first_byte << 8) | second_byte\n\n self.ui.spinBox_4.setValue(val)\n else:\n self.ui.plainTextEdit.insertPlainText(\n f'\\nResponse error register P \\n{response_raw_list}')\n print(f'\\nResponse error register P \\n{response_raw_list}')\n\n\ndef read_register_f(self):\n\n self.ui.lineEdit_2.clear()\n index = self.ui.comboBox.currentIndex()\n val = self.fregisters[index]\n # из полученного значения выделяем ст и мл байт\n high = (val >> 8) & 0xff\n low = val & 0xff\n # формирование команды согласно документации\n cmd = bytearray([0x01, 0x03])\n cmd.append(high)\n cmd.append(low)\n cmd = cmd+bytearray([0x00, 0x01])\n with Servo(port=self.port, baudrate=self.br, timeout=self.timeout) as serial:\n response_raw = serial.write_msg(cmd)\n response_raw_list = bytearray(response_raw)\n response_raw_bytes = [hex(i) for i in list(response_raw)]\n # Исключаем возможные ошибки при передачу\n if len(response_raw_list) == 7:\n print(response_raw_list)\n first_byte, second_byte = serial.bytes_to_high_low(\n response_raw_list, 3, 4)\n val = (first_byte << 8) | second_byte\n\n self.ui.lineEdit_2.setText((\" \").join(\n [hex(i) for i in response_raw_list]))\n self.ui.spinBox_5.setValue(val)\n else:\n self.ui.plainTextEdit.insertPlainText(\n f'\\nResponse error register F \\n{response_raw_bytes}')\n print(f'\\nResponse error register f \\n{response_raw_bytes}')\n\n\ndef write_value_to_p(self):\n \"\"\"Отрицательные значения. 65535-х \"\"\"\n write_value = self.ui.spinBox_3.value()\n p_number = int(self.ui.spinBox.value())\n p_value = int(self.ui.spinBox_2.value())\n cmd = bytearray([0x01, 0x06, p_number, p_value])\n if write_value < 0xff and write_value >= 0:\n cmd = cmd + bytearray([0x00, write_value])\n else:\n if write_value > 65535:\n print(\"Use big number\")\n return\n b1 = write_value >> 8\n b2 = write_value & 0xFF\n cmd.append(b1)\n cmd.append(b2)\n with Servo(port=self.port, baudrate=self.br, timeout=self.timeout) as serial:\n response_raw = serial.write_msg(cmd)\n\n\nclass Servo:\n def __init__(self, port, baudrate, timeout):\n self.port = port\n self.baudrate = baudrate\n self.timeout = timeout\n self.ser = None\n self.is_ok = False\n\n def __enter__(self):\n try:\n self.ser = serial.rs485.RS485(\n port=self.port, baudrate=self.baudrate, timeout=self.timeout)\n self.is_ok = True\n return self\n except:\n self.is_ok = False\n\n def __exit__(self, type, value, traceback):\n if self.is_ok:\n self.ser.close()\n\n def append_crc(self, values) -> bytearray:\n \"\"\"values ->bytearray\"\"\"\n crc16b = libscrc.modbus(values).to_bytes(2, byteorder='little')\n values.append(crc16b[0])\n values.append(crc16b[1])\n return values\n\n def write_msg(self, msg) -> bytearray:\n cmd = self.append_crc(msg)\n self.ser.write(cmd)\n time.sleep(0.1)\n response = self.ser.readline()\n return response\n\n def bytes_to_high_low(self, b_arr, b1, b2) -> tuple:\n first_byte = b_arr[b1]\n second_byte = b_arr[b2]\n return (first_byte, second_byte)\n\n # def read_msg(self, msg) -> bytearray:\n # response = self.ser.readline()\n # return response\n\n\n# start\n# values = bytearray([0x01, 0x06, 0x01, 0x04, 0x00, 0x01, 0x08, 0x37])\n# stop\n# values = bytearray([0x01, 0x06, 0x01, 0x04, 0x00, 0x00, 0xC9, 0xF7])\n\n\n# serw = serial.Serial('COM4', 38400, serial.EIGHTBITS,\n# serial.PARITY_NONE, serial.STOPBITS_ONE)\n# serr = serial.Serial('COM4', 38400, serial.EIGHTBITS,\n# serial.PARITY_NONE, serial.STOPBITS_ONE)\n# time.sleep(2)\n\n\nif __name__ == \"__main__\":\n\n start = bytearray([0x01, 0x06, 0x01, 0x04, 0x00, 0x01])\n stop = bytearray([0x01, 0x06, 0x01, 0x04, 0x00, 0x00])\n test_response = bytearray([0x01, 0x03, 0x02, 0x00, 0x01])\n wr = bytearray([0x01, 0x03, 0x01, 0x04, 0x00, 0x01])\n cmdstart = append_crc(start)\n cmdstop = append_crc(stop)\n cmd = append_crc(wr)\n ser = serial.rs485.RS485(port='COM4', baudrate=38400, timeout=0)\n #ser = serial.rs485.RS485(port='COM4', baudrate=38400, timeout=1)\n # ser.rs485_mode = serial.rs485.RS485Settings(False,True)\n # ser.write(cmdstart)\n # ser.write(cmdstop)\n count = 0\n while 1:\n if keyboard.is_pressed('0'):\n print(\"press\")\n isWork_without = bytearray([0x01, 0x03, 0x01, 0x04, 0x00, 0x01])\n isWork = append_crc(isWork_without)\n ser.write(isWork)\n time.sleep(0.1)\n response = ser.readline()\n print(response)\n if response == b'\\x01\\x03\\x02\\x00\\x00\\xb8D' or response == b'\\x01\\x06\\x01\\x04\\x00\\x00\\xc9\\xf7\\x01\\x03\\x02\\x00\\x00\\xb8D':\n ser.write(cmdstart)\n print(\"start\")\n ser.readline()\n elif response == b'\\x01\\x03\\x02\\x00\\x01y\\x84' or response == b'\\x01\\x06\\x01\\x04\\x00\\x01\\x087\\x01\\x03\\x02\\x00\\x01y\\x84':\n ser.write(cmdstop)\n print(\"stop\")\n ser.readline()\n", "repo_name": "sirinnrus/servo_uart_usb", "sub_path": "control.py", "file_name": "control.py", "file_ext": "py", "file_size_in_byte": 7512, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "libscrc.modbus", "line_number": 19, "usage_type": "call"}, {"api_name": "serial.write_msg", "line_number": 37, "usage_type": "call"}, {"api_name": "serial.write_msg", "line_number": 39, "usage_type": "call"}, {"api_name": "serial.write_msg", "line_number": 46, "usage_type": "call"}, {"api_name": "serial.write_msg", "line_number": 48, "usage_type": "call"}, {"api_name": "serial.write_msg", "line_number": 58, "usage_type": "call"}, {"api_name": "serial.bytes_to_high_low", "line_number": 66, "usage_type": "call"}, {"api_name": "serial.write_msg", "line_number": 91, "usage_type": "call"}, {"api_name": "serial.bytes_to_high_low", "line_number": 97, "usage_type": "call"}, {"api_name": "serial.write_msg", "line_number": 127, "usage_type": "call"}, {"api_name": "serial.rs485.RS485", "line_number": 140, "usage_type": "call"}, {"api_name": "serial.rs485", "line_number": 140, "usage_type": "attribute"}, {"api_name": "libscrc.modbus", "line_number": 153, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 161, "usage_type": "call"}, {"api_name": "serial.rs485.RS485", "line_number": 197, "usage_type": "call"}, {"api_name": "serial.rs485", "line_number": 197, "usage_type": "attribute"}, {"api_name": "keyboard.is_pressed", "line_number": 204, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 209, "usage_type": "call"}]} +{"seq_id": "40169208674", "text": "from cx_Oracle import DatabaseError, OperationalError\nfrom watch import app, task_pool, notification_pool, lock, unsent_pool\nfrom watch.utils.chat_bot import send_message\nfrom watch.utils.parse_args import get_offset\nfrom watch.utils.manage_message import t_italic\nimport threading\nfrom time import sleep\nfrom datetime import datetime\n\n\ndef prepare_and_send(chat_id, reply_to_message_id, message):\n message_parameters = {'chat_id': chat_id\n , 'text': message\n , 'parse_mode': 'HTML'\n , 'disable_web_page_preview': 'true'}\n if reply_to_message_id:\n message_parameters['reply_to_message_id'] = reply_to_message_id\n return send_message(message_parameters)\n\n\ndef check_dnd_time():\n if not app.config['DND_HOURS']:\n return False\n start_dnd_hour = app.config['DND_HOURS'][0]\n end_dnd_hour = app.config['DND_HOURS'][1]\n now_hour = datetime.now().hour\n if start_dnd_hour < end_dnd_hour and start_dnd_hour <= now_hour < end_dnd_hour:\n return True\n if start_dnd_hour >= end_dnd_hour and (now_hour >= start_dnd_hour or now_hour < end_dnd_hour):\n return True\n return False\n\n\nclass Worker(threading.Thread):\n def __init__(self):\n super(Worker, self).__init__()\n self.active = True\n\n def run(self):\n while self.active:\n if check_dnd_time():\n sleep(app.config['WORKER_FREQ_SEC'])\n continue\n\n with lock:\n active_tasks = tuple(t for t in sorted(task_pool.values()\n , key=lambda x: x.priority) if t.state == 'wait')\n for task in active_tasks:\n with lock:\n if not task_pool.get(task.uuid):\n continue\n if task.last_call:\n pt = task.period[-1:]\n pv = task.period[:-1]\n next_call = task.last_call + get_offset(pv, pt)\n if next_call > datetime.now():\n continue\n task.state = 'run'\n try:\n message = app.view_functions[task.endpoint](task)\n r = 0\n if message:\n if task.text:\n message = f'{t_italic(task.text)}\\n{message}'\n notification_pool.appendleft((datetime.now()\n , task.uuid\n , task.name\n , message))\n if task.chat_id and not app.config['MUTE_MESSAGES']:\n r = prepare_and_send(task.chat_id, task.reply_to_message_id, message)\n if r != 0:\n unsent_pool.appendleft((datetime.now()\n , task.uuid\n , task.name\n , task.chat_id\n , task.reply_to_message_id\n , message))\n if task.finished:\n del task_pool[task.uuid]\n else:\n task.last_call = datetime.now()\n task.execs += 1\n task.state = 'wait' if r == 0 or not app.config['FAIL_TASK_ON_MSG_ERROR'] else 'msg error'\n # retry sending even if prev msg had no recipient\n if r == 0 and message and not app.config['MUTE_MESSAGES']:\n while r == 0 and len(unsent_pool) > 0:\n m = unsent_pool.popleft()\n r = prepare_and_send(m[3]\n , m[4]\n , f'{t_italic(\"This message was postponed due to network problem\")}'\n f'\\n{m[5]}')\n if r == 0 and task_pool.get(m[1], None):\n task_pool[m[1]].state = 'wait'\n if r != 0:\n unsent_pool.appendleft(m)\n except (DatabaseError, OperationalError) as e:\n app.logger.error(f'{task.uuid} {e.args[0].message}')\n task.state = 'db error'\n sleep(app.config['WORKER_FREQ_SEC'])\n\n def shutdown(self):\n self.active = False\n", "repo_name": "kvshi/watch", "sub_path": "watch/utils/task_worker.py", "file_name": "task_worker.py", "file_ext": "py", "file_size_in_byte": 4700, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "watch.utils.chat_bot.send_message", "line_number": 18, "usage_type": "call"}, {"api_name": "watch.app.config", "line_number": 22, "usage_type": "attribute"}, {"api_name": "watch.app", "line_number": 22, "usage_type": "name"}, {"api_name": "watch.app.config", "line_number": 24, "usage_type": "attribute"}, {"api_name": "watch.app", "line_number": 24, "usage_type": "name"}, {"api_name": "watch.app.config", "line_number": 25, "usage_type": "attribute"}, {"api_name": "watch.app", "line_number": 25, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 26, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 26, "usage_type": "name"}, {"api_name": "threading.Thread", "line_number": 34, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 42, "usage_type": "call"}, {"api_name": "watch.app.config", "line_number": 42, "usage_type": "attribute"}, {"api_name": "watch.app", "line_number": 42, "usage_type": "name"}, {"api_name": "watch.lock", "line_number": 45, "usage_type": "name"}, {"api_name": "watch.task_pool.values", "line_number": 46, "usage_type": "call"}, {"api_name": "watch.task_pool", "line_number": 46, "usage_type": "name"}, {"api_name": "watch.lock", "line_number": 49, "usage_type": "name"}, {"api_name": "watch.task_pool.get", "line_number": 50, "usage_type": "call"}, {"api_name": "watch.task_pool", "line_number": 50, "usage_type": "name"}, {"api_name": "watch.utils.parse_args.get_offset", "line_number": 55, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 56, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 56, "usage_type": "name"}, {"api_name": "watch.app.view_functions", "line_number": 60, "usage_type": "attribute"}, {"api_name": "watch.app", "line_number": 60, "usage_type": "name"}, {"api_name": "watch.utils.manage_message.t_italic", "line_number": 64, "usage_type": "call"}, {"api_name": "watch.notification_pool.appendleft", "line_number": 65, "usage_type": "call"}, {"api_name": "watch.notification_pool", "line_number": 65, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 65, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 65, "usage_type": "name"}, {"api_name": "watch.app.config", "line_number": 69, "usage_type": "attribute"}, {"api_name": "watch.app", "line_number": 69, "usage_type": "name"}, {"api_name": "watch.unsent_pool.appendleft", "line_number": 72, "usage_type": "call"}, {"api_name": "watch.unsent_pool", "line_number": 72, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 72, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 72, "usage_type": "name"}, {"api_name": "watch.task_pool", "line_number": 79, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 81, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 81, "usage_type": "name"}, {"api_name": "watch.app.config", "line_number": 83, "usage_type": "attribute"}, {"api_name": "watch.app", "line_number": 83, "usage_type": "name"}, {"api_name": "watch.app.config", "line_number": 85, "usage_type": "attribute"}, {"api_name": "watch.app", "line_number": 85, "usage_type": "name"}, {"api_name": "watch.unsent_pool", "line_number": 86, "usage_type": "argument"}, {"api_name": "watch.unsent_pool.popleft", "line_number": 87, "usage_type": "call"}, {"api_name": "watch.unsent_pool", "line_number": 87, "usage_type": "name"}, {"api_name": "watch.utils.manage_message.t_italic", "line_number": 90, "usage_type": "call"}, {"api_name": "watch.task_pool.get", "line_number": 92, "usage_type": "call"}, {"api_name": "watch.task_pool", "line_number": 92, "usage_type": "name"}, {"api_name": "watch.task_pool", "line_number": 93, "usage_type": "name"}, {"api_name": "watch.unsent_pool.appendleft", "line_number": 95, "usage_type": "call"}, {"api_name": "watch.unsent_pool", "line_number": 95, "usage_type": "name"}, {"api_name": "cx_Oracle.DatabaseError", "line_number": 96, "usage_type": "name"}, {"api_name": "cx_Oracle.OperationalError", "line_number": 96, "usage_type": "name"}, {"api_name": "watch.app.logger.error", "line_number": 97, "usage_type": "call"}, {"api_name": "watch.app.logger", "line_number": 97, "usage_type": "attribute"}, {"api_name": "watch.app", "line_number": 97, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 99, "usage_type": "call"}, {"api_name": "watch.app.config", "line_number": 99, "usage_type": "attribute"}, {"api_name": "watch.app", "line_number": 99, "usage_type": "name"}]} +{"seq_id": "42344197352", "text": "import tkinter\nfrom urllib.request import urlopen\nimport json\n\ndef print_author():\n info = '''\n Author: Jakub Jonczyk\n Created at: 8.09.2019\n '''\n print(info)\n\ndef load_location(location):\n try:\n with urlopen(\"https://www.metaweather.com/api/location/search/?query={}\".format(location)) as file:\n text = file.read().decode(\"utf-8\")\n object = json.loads(text)\n woeid = object[0]['woeid']\n return woeid\n except:\n print(\"I cannot find this location, sorry...\")\n\ndef set_temperature():\n woeid = load_location(pole1.get())\n date = pole0.get()\n with urlopen(\"https://www.metaweather.com/api/location/{}/{}\".format(woeid, date)) as file:\n napis = file.read().decode(\"utf-8\")\n obiekt = json.loads(napis)\n temp = obiekt[0][\"the_temp\"]\n pole2.configure(text = temp)\n\ndef set_air_pressure():\n woeid = load_location(pole1.get())\n date = pole0.get()\n with urlopen(\"https://www.metaweather.com/api/location/{}/{}\".format(woeid, date)) as file:\n napis = file.read().decode(\"utf-8\")\n obiekt = json.loads(napis)#[\"consolidated_weather\"]\n airp = obiekt[0][\"air_pressure\"]\n pole3.configure(text=airp)\n\ndef set_weather_conditions():\n woeid = load_location(pole1.get())\n date = pole0.get()\n with urlopen(\"https://www.metaweather.com/api/location/{}/{}\".format(woeid, date)) as file:\n napis = file.read().decode(\"utf-8\")\n obiekt = json.loads(napis)#[\"consolidated_weather\"]\n weather = obiekt[0]['weather_state_name']\n #return weather\n pole4.configure(text=weather)\n\nprint_author()\nroot = tkinter.Tk()\n\npole0 = tkinter.Entry(master=root)\npole0.grid(row=0, column=1)\npole1 = tkinter.Entry(master=root)\npole1.grid(row=1, column=1)\npole2 = tkinter.Label(master=root, text=\"Set date and location to continue...\")\npole2.grid(row=2, column=1)\npole3 = tkinter.Label(master=root, text=\"Set date and location to continue...\")\npole3.grid(row=3, column=1)\npole4 = tkinter.Label(master=root, text=\"Set date and location to continue...\")\npole4.grid(row=4, column=1)\n\nnapis0 = tkinter.Label(master=root, text=\"Date [yyyy/mm/dd]: \")\nnapis0.grid(row=0, column=0)\nnapis1 = tkinter.Label(master=root, text=\"Location: \")\nnapis1.grid(row=1, column=0)\nnapis2 = tkinter.Button(master=root, text=\"Check temperature [st. C]\", command=set_temperature)\nnapis2.grid(row=2, column=0)\nnapis3 = tkinter.Button(master=root, text=\"Check air pressure [hPa]\", command=set_air_pressure)\nnapis3.grid(row=3, column=0)\nnapis4 = tkinter.Button(master=root, text=\"Check weather conditions\", command=set_weather_conditions)\nnapis4.grid(row=4, column=0)\n\nroot.mainloop()", "repo_name": "jjonczyk/WeatherAPI", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 2706, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "urllib.request.urlopen", "line_number": 14, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 16, "usage_type": "call"}, {"api_name": "urllib.request.urlopen", "line_number": 25, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 27, "usage_type": "call"}, {"api_name": "urllib.request.urlopen", "line_number": 34, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 36, "usage_type": "call"}, {"api_name": "urllib.request.urlopen", "line_number": 43, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 45, "usage_type": "call"}, {"api_name": "tkinter.Tk", "line_number": 51, "usage_type": "call"}, {"api_name": "tkinter.Entry", "line_number": 53, "usage_type": "call"}, {"api_name": "tkinter.Entry", "line_number": 55, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 57, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 59, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 61, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 64, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 66, "usage_type": "call"}, {"api_name": "tkinter.Button", "line_number": 68, "usage_type": "call"}, {"api_name": "tkinter.Button", "line_number": 70, "usage_type": "call"}, {"api_name": "tkinter.Button", "line_number": 72, "usage_type": "call"}]} +{"seq_id": "5548833358", "text": "# -*- coding: utf-8 -*-\n\n__author__ = \"Leidinice Silva\"\n__email__ = \"leidinicesilva@gmail.com\"\n__date__ = \"Dec 04, 2023\"\n__description__ = \"This script plot bias maps\"\n\nimport os\nimport netCDF4\nimport numpy as np\nimport matplotlib.cm as cm\nimport matplotlib.pyplot as plt\n\nfrom matplotlib.path import Path\nfrom matplotlib.patches import PathPatch\nfrom mpl_toolkits.basemap import Basemap\n\n\t\ndef basemap(lat, lon):\n\t\n\tmap = Basemap(projection='cyl', llcrnrlon=-80., llcrnrlat=-38., urcrnrlon=-34.,urcrnrlat=-8., resolution='c')\n\tmap.drawmeridians(np.arange(-80., -34., 6.), size=8, labels=[0,0,0,1], linewidth=0.4, color='black')\n\tmap.drawparallels(np.arange(-38., -8., 6.), size=8, labels=[1,0,0,0], linewidth=0.4, color='black')\n\t\t\n\tlons, lats = np.meshgrid(lon, lat)\n\txx, yy = map(lons,lats)\n\n\t# Import shapefile \t\n\tmap.readshapefile('{0}/github_projects/shp/shp_america_sul/america_sul'.format(path), 'america_sul', drawbounds=True, color='black')\n\t\n\treturn map, xx, yy\n\n\n# Import model and obs database \nvar_rcm = 'clt'\n\nif var_rcm == 'pr':\n\tvar_ref = 'pre'\nelif var_rcm == 'tas':\n\tvar_ref = 'tmp'\nelif var_rcm == 'tasmax':\n\tvar_ref = 'tmx'\nelif var_rcm == 'tasmin':\n\tvar_ref = 'tmn'\nelif var_rcm == 'clt':\n\tvar_ref = 'cld'\nelif var_rcm == 'cl':\n\tvar_ref = 'cl'\nelif var_rcm == 'clw':\n\tvar_ref = 'clw'\nelif var_rcm == 'cli':\n\tvar_ref = 'cli'\nelif var_rcm == 'hus':\n\tvar_ref = 'q'\nelif var_rcm == 'ua':\n\tvar_ref = 'u'\nelse:\n\tvar_ref = 'v'\n\t\nlat, lon, clim_rcm = import_rcm(var_rcm)\nlat, lon, clim_ref = import_ref(var_ref)\n\nbias_rcm_ref = clim_rcm - clim_ref\n\n# Plot figure \nfig = plt.figure(figsize=(10, 4))\nfont_size = 8\n\nif var_rcm == 'pr':\n\tlegend = 'Bias of precipitation (mm d⁻¹)'\n\tlevs0 = [0, 0.5, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12]\n\tcolor0 = cm.Blues\n\tlevs1 = [-6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6]\n\tcolor1 = cm.BrBG\nelif var_rcm == 'clt':\n\tlegend = 'Bias of total cloud cover (%)'\n\tlevs0 = [0, 5, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100]\n\tcolor0 = cm.Greys\n\tlevs1 = [-50, -40, -30, -20, -10, -5, 0, 5, 10, 20, 30, 40, 50]\n\tcolor1 = cm.RdGy\nelif var_rcm == 'tas':\n\tlegend = 'Bias of air temperature (°C)'\n\tlevs0 = [10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32, 34]\n\tcolor0 = cm.Reds\n\tlevs1 = [-6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6]\n\tcolor1 = cm.bwr\nelif var_rcm == 'tasmax':\n\tlegend = 'Bias of maximum air temperature (°C)'\n\tlevs0 = [10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32, 34]\n\tcolor0 = cm.Reds\n\tlevs1 = [-6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6]\n\tcolor1 = cm.bwr\nelse:\n\tlegend = 'Bias of minimum air temperature (°C)'\n\tlevs0 = [10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32, 34]\n\tcolor0 = cm.Reds\n\tlevs1 = [-6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6]\n\tcolor1 = cm.bwr\n\nax = fig.add_subplot(1, 3, 1) \nmap, xx, yy = basemap(lat, lon)\nplt_map = map.contourf(xx, yy, clim_ref, levels=levs0, latlon=True, cmap=color0, extend='max') \nplt.title(u'(a) CRU', loc='left', fontsize=font_size, fontweight='bold')\nplt.ylabel(u'Latitude', labelpad=25, fontsize=font_size, fontweight='bold')\nplt.xlabel(u'Longitude', labelpad=15, fontsize=font_size, fontweight='bold')\n\nax = fig.add_subplot(1, 3, 2) \nmap, xx, yy = basemap(lat, lon)\nplt_map = map.contourf(xx, yy, clim_rcm, levels=levs0, latlon=True, cmap=color0, extend='max') \nplt.title(u'(b) Reg-3km', loc='left', fontsize=font_size, fontweight='bold')\nplt.xlabel(u'Longitude', labelpad=15, fontsize=font_size, fontweight='bold')\ncbar = plt.colorbar(plt_map, cax=fig.add_axes([0.91, 0.3, 0.015, 0.4]))\ncbar.ax.tick_params(labelsize=font_size)\n\nax = fig.add_subplot(1, 3, 3) \nmap, xx, yy = basemap(lat, lon)\nplt_map = map.contourf(xx, yy, bias_rcm_ref, levels=levs1, latlon=True, cmap=color1, extend='both') \nplt.title(u'(c) Reg-3km - CRU', loc='left', fontsize=font_size, fontweight='bold')\nplt.xlabel(u'Longitude', labelpad=15, fontsize=font_size, fontweight='bold')\ncbar = plt.colorbar(plt_map, cax=fig.add_axes([0.96, 0.3, 0.015, 0.4]))\ncbar.set_label('{0}'.format(legend), fontsize=font_size, fontweight='bold')\ncbar.ax.tick_params(labelsize=font_size)\n\n# Path out to save figure\npath_out = '{0}/ICTP/figs/sam_3km'.format(path)\nname_out = 'pyplt_maps_{0}_SAM-3km_RegCM5_cru_ts4.07_2018-2021.png'.format(var_rcm)\nplt.savefig(os.path.join(path_out, name_out), dpi=400, bbox_inches='tight')\nplt.show()\nexit()\n", "repo_name": "LeidiniceSilva/pypostdoc", "sub_path": "sam_3km/plot_maps_bias_atm.py", "file_name": "plot_maps_bias_atm.py", "file_ext": "py", "file_size_in_byte": 4319, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "mpl_toolkits.basemap.Basemap", "line_number": 21, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 22, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 23, "usage_type": "call"}, {"api_name": "numpy.meshgrid", "line_number": 25, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 66, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 66, "usage_type": "name"}, {"api_name": "matplotlib.cm.Blues", "line_number": 72, "usage_type": "attribute"}, {"api_name": "matplotlib.cm", "line_number": 72, "usage_type": "name"}, {"api_name": "matplotlib.cm.BrBG", "line_number": 74, "usage_type": "attribute"}, {"api_name": "matplotlib.cm", "line_number": 74, "usage_type": "name"}, {"api_name": "matplotlib.cm.Greys", "line_number": 78, "usage_type": "attribute"}, {"api_name": "matplotlib.cm", "line_number": 78, "usage_type": "name"}, {"api_name": "matplotlib.cm.RdGy", "line_number": 80, "usage_type": "attribute"}, {"api_name": "matplotlib.cm", "line_number": 80, "usage_type": "name"}, {"api_name": "matplotlib.cm.Reds", "line_number": 84, "usage_type": "attribute"}, {"api_name": "matplotlib.cm", "line_number": 84, "usage_type": "name"}, {"api_name": "matplotlib.cm.bwr", "line_number": 86, "usage_type": "attribute"}, {"api_name": "matplotlib.cm", "line_number": 86, "usage_type": "name"}, {"api_name": "matplotlib.cm.Reds", "line_number": 90, "usage_type": "attribute"}, {"api_name": "matplotlib.cm", "line_number": 90, "usage_type": "name"}, {"api_name": "matplotlib.cm.bwr", "line_number": 92, "usage_type": "attribute"}, {"api_name": "matplotlib.cm", "line_number": 92, "usage_type": "name"}, {"api_name": "matplotlib.cm.Reds", "line_number": 96, "usage_type": "attribute"}, {"api_name": "matplotlib.cm", "line_number": 96, "usage_type": "name"}, {"api_name": "matplotlib.cm.bwr", "line_number": 98, "usage_type": "attribute"}, {"api_name": "matplotlib.cm", "line_number": 98, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 103, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 103, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 104, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 104, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 105, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 105, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 110, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 110, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 111, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 111, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.colorbar", "line_number": 112, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 112, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 118, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 118, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 119, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 119, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.colorbar", "line_number": 120, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 120, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 127, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 127, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 127, "usage_type": "call"}, {"api_name": "os.path", "line_number": 127, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.show", "line_number": 128, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 128, "usage_type": "name"}]} +{"seq_id": "13039391218", "text": "import requests\nfrom datetime import datetime\nimport matplotlib\nmatplotlib.use('Agg')\nimport matplotlib.pyplot as plt\nimport io\nimport base64\nimport os\nimport matplotlib.dates as mdates\nfrom dotenv import load_dotenv\nfrom flask import Flask, render_template, request\nimport pandas\n\nload_dotenv()\n\nclass SensorData:\n def __init__(self, api_key, api_secret, url):\n self.api_key = api_key\n self.api_secret = api_secret\n self.url = url\n\n def get_sensor_data(self, start_date, end_date, sensor_id):\n headers = {\n \"APIKeyID\": self.api_key,\n \"APISecretKey\": self.api_secret\n }\n \n num_calls = 5\n \n delta = (end_date - start_date) / num_calls\n\n # Make the API calls and combine the data into a single dictionary\n data = {}\n i = 0\n for j in range(num_calls):\n call_start = start_date + j * delta\n call_end = call_start + delta\n call_from_date = call_start.strftime(\"%Y-%m-%d %H:%M:%S\")\n call_to_date = call_end.strftime(\"%Y-%m-%d %H:%M:%S\")\n\n params = {\n \"sensorID\": sensor_id,\n \"fromDate\": call_from_date,\n \"toDate\": call_to_date\n }\n \n try:\n response = requests.post(self.url, headers=headers, data=params)\n response_dict = response.json()\n except requests.exceptions.RequestException as e:\n print(f\"An error occurred while making the API call: {e}\")\n return {}\n if \"Result\" not in response_dict:\n print(f\"Unexpected response format: {response_dict}\")\n return {}\n for item in response_dict['Result']:\n try:\n timestamp = int(item['Date'][6:-2]) / 1000 # Extract timestamp value and convert to seconds\n date = datetime.fromtimestamp(timestamp)\n\n # format the datetime object as a string in the desired format\n formatted_date = date.strftime(\"%Y-%m-%d %H:%M:%S\")\n\n data[i] = {formatted_date: item['Value']}\n i += 1\n except Exception as e:\n print(f\"Error processing response item: {e}\")\n\n return data\n\n\nclass ChartGenerator:\n def __init__(self):\n pass\n\n def generate_chart(self, x_data, y_data, start_date, end_date):\n fig, ax = plt.subplots()\n fig.subplots_adjust(bottom=0.33)\n \n # Convert x_data to datetime objects\n x_data = [datetime.strptime(date, \"%Y-%m-%d %H:%M:%S\") for date in x_data]\n\n # Plot the data\n ax.plot(x_data, y_data)\n\n # Set the x-axis limits to the given start and end dates\n ax.set_xlim(start_date, end_date)\n\n # Set the x-axis tick formatter to display dates in the desired format\n ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d %H:%M:%S'))\n \n ax.tick_params(axis='x', labelrotation=45)\n \n ax.set_title(\"Temperatures Over Time\")\n ax.set_xlabel(\"Date and Time\")\n ax.set_ylabel(\"Temperature (F)\")\n\n # Encode the chart image as base64 and embed it in the HTML response\n chart = io.BytesIO()\n fig.tight_layout()\n fig.savefig(chart, format='png')\n chart.seek(0)\n chart_b64 = base64.b64encode(chart.getvalue()).decode('utf-8')\n chart_html = ''.format(chart_b64)\n\n return chart_html\n \n\napp = Flask(__name__, static_folder='static')\napi_key = os.environ.get('API_KEY')\napi_secret = os.environ.get('API_SECRET')\nurl = \"https://www.imonnit.com/json/SensorChartMessages\"\n\nsensor_data = SensorData(api_key, api_secret, url)\nchart_generator = ChartGenerator()\n\n\n\n@app.route('/', methods=['GET', 'POST'])\ndef home():\n if request.method == 'POST':\n from_date = request.form['from_date']\n to_date = request.form['to_date']\n sensor_id = request.form['sensor']\n\n # Calculate the time range for each API call\n start_date = datetime.strptime(from_date, \"%Y-%m-%d\")\n end_date = datetime.strptime(to_date, \"%Y-%m-%d\")\n \n data = sensor_data.get_sensor_data(start_date, end_date, sensor_id)\n\n x_data = []\n y_data = []\n\n for x in data:\n if len(data[x]) > 0: \n date = list(data[x].keys())[0]\n x_data.append(date)\n y_data.append(data[x].get(date))\n \n x_data, y_data = zip(*sorted(zip(x_data, y_data)))\n chart_html = chart_generator.generate_chart(x_data, y_data, start_date, end_date)\n\n # Render the HTML template with the chart image embedded\n return render_template('index.html', chart_html=chart_html)\n\n # Render the initial HTML template with no chart image\n return render_template('index.html', chart_html='')\n\n@app.route('/select_date')\ndef select_date():\n return render_template('select_date.html')\n\nif __name__ == '__main__':\n app.run()\n", "repo_name": "DavidBrynnHouse/TempTracker", "sub_path": "app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 5101, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "matplotlib.use", "line_number": 4, "usage_type": "call"}, {"api_name": "dotenv.load_dotenv", "line_number": 14, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 48, "usage_type": "call"}, {"api_name": "requests.exceptions", "line_number": 50, "usage_type": "attribute"}, {"api_name": "datetime.datetime.fromtimestamp", "line_number": 59, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 59, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 77, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 77, "usage_type": "name"}, {"api_name": "datetime.datetime.strptime", "line_number": 81, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 81, "usage_type": "name"}, {"api_name": "matplotlib.dates.DateFormatter", "line_number": 90, "usage_type": "call"}, {"api_name": "matplotlib.dates", "line_number": 90, "usage_type": "name"}, {"api_name": "io.BytesIO", "line_number": 99, "usage_type": "call"}, {"api_name": "base64.b64encode", "line_number": 103, "usage_type": "call"}, {"api_name": "flask.Flask", "line_number": 109, "usage_type": "call"}, {"api_name": "os.environ.get", "line_number": 110, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 110, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 111, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 111, "usage_type": "attribute"}, {"api_name": "flask.request.method", "line_number": 121, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 121, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 122, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 122, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 123, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 123, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 124, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 124, "usage_type": "name"}, {"api_name": "datetime.datetime.strptime", "line_number": 127, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 127, "usage_type": "name"}, {"api_name": "datetime.datetime.strptime", "line_number": 128, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 128, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 145, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 148, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 152, "usage_type": "call"}]} +{"seq_id": "38404665610", "text": "import pygame\nimport sys\nimport os\nimport math\n\nfrom pygame.locals import K_ESCAPE, K_RETURN, K_w, K_a, K_s, K_d\n\nfrom ui import UI\nfrom constants import *\n\npygame.init()\npygame.font.init()\npygame.display.set_caption('Money')\nwindow = pygame.display.set_mode(window_dims)\nclock = pygame.time.Clock()\n\nmusicFile = \"resources/FreeJump.mp3\"\ncoinClickNoise = pygame.mixer.Sound(\"resources/marioCoinNoise.wav\")\n\npygame.mixer.music.load(musicFile)\npygame.mixer.music.play(-1)\n\n# font_big = pygame.font.SysFont('arial.ttf', int(window_dims[1] / 20))\n# text_big = font_big.render(\"GAME JAM\", True, (255, 255, 255), background)\n# textpos = [window_dims[0] // 2, window_dims[1] // 2]\n\nui = UI(pygame)\n\ni = 0\n\n# GAME EVENT LOOP\ngame = True\nwhile game:\n i += 1\n if i >= 30:\n i = 0\n ui.add_money()\n # EVENT LOOP\n for event in pygame.event.get():\n if event.type == pygame.KEYDOWN:\n if event.key == K_ESCAPE:\n sys.exit()\n if event.type == pygame.MOUSEBUTTONDOWN:\n if event.button == 1: # LEFT CLICK\n ui.handle_event(pygame, event)\n\n ui.hover_check(pygame.mouse.get_pos())\n\n pygame.display.flip()\n clock.tick(FPS)\n # DRAW BACKGROUND\n window.fill(background)\n # DRAW UI\n ui.draw(pygame, window)\n", "repo_name": "DeanShin/pygamejam", "sub_path": "game/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 1225, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "37", "api": [{"api_name": "pygame.init", "line_number": 11, "usage_type": "call"}, {"api_name": "pygame.font.init", "line_number": 12, "usage_type": "call"}, {"api_name": "pygame.font", "line_number": 12, "usage_type": "attribute"}, {"api_name": "pygame.display.set_caption", "line_number": 13, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 13, "usage_type": "attribute"}, {"api_name": "pygame.display.set_mode", "line_number": 14, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 14, "usage_type": "attribute"}, {"api_name": "pygame.time.Clock", "line_number": 15, "usage_type": "call"}, {"api_name": "pygame.time", "line_number": 15, "usage_type": "attribute"}, {"api_name": "pygame.mixer.Sound", "line_number": 18, "usage_type": "call"}, {"api_name": "pygame.mixer", "line_number": 18, "usage_type": "attribute"}, {"api_name": "pygame.mixer.music.load", "line_number": 20, "usage_type": "call"}, {"api_name": "pygame.mixer", "line_number": 20, "usage_type": "attribute"}, {"api_name": "pygame.mixer.music.play", "line_number": 21, "usage_type": "call"}, {"api_name": "pygame.mixer", "line_number": 21, "usage_type": "attribute"}, {"api_name": "ui.UI", "line_number": 27, "usage_type": "call"}, {"api_name": "ui.add_money", "line_number": 37, "usage_type": "call"}, {"api_name": "pygame.event.get", "line_number": 39, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 39, "usage_type": "attribute"}, {"api_name": "pygame.KEYDOWN", "line_number": 40, "usage_type": "attribute"}, {"api_name": "pygame.locals.K_ESCAPE", "line_number": 41, "usage_type": "name"}, {"api_name": "sys.exit", "line_number": 42, "usage_type": "call"}, {"api_name": "pygame.MOUSEBUTTONDOWN", "line_number": 43, "usage_type": "attribute"}, {"api_name": "ui.handle_event", "line_number": 45, "usage_type": "call"}, {"api_name": "ui.hover_check", "line_number": 47, "usage_type": "call"}, {"api_name": "pygame.mouse.get_pos", "line_number": 47, "usage_type": "call"}, {"api_name": "pygame.mouse", "line_number": 47, "usage_type": "attribute"}, {"api_name": "pygame.display.flip", "line_number": 49, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 49, "usage_type": "attribute"}, {"api_name": "ui.draw", "line_number": 54, "usage_type": "call"}]} +{"seq_id": "74783759468", "text": "from fal_serverless import isolated, cached\n\nimport streamlit as st\nimport io\nfrom PIL import Image\nimport base64\nimport time\n\nst.title(\"Welcome to deepfloyd on fal-serverless\")\n\nrequirements = [\n \"deepfloyd_if==1.0.0\",\n \"xformers==0.0.16\",\n \"git+https://github.com/openai/CLIP.git\",\n]\n\n\n@cached\ndef setup():\n return\n\n\n@isolated(requirements=requirements, machine_type=\"M\")\ndef generate_image(text_input):\n a = setup()\n return a\n\n\nwith st.form(key=\"my_form\"):\n print(\"Starting\", time.time())\n text_input = st.text_input(\n label=\"Enter a prompt here to generate an image with Deep Floyd\"\n )\n submit_button = st.form_submit_button(label=\"Generate\")\n\nif submit_button:\n img64 = generate_image(text_input)\n # image = Image.open(io.BytesIO(base64.b64decode(img64)))\n st.text(\"test\")\n # st.image(image)\n", "repo_name": "fal-ai/serverless-floyd", "sub_path": "deep.py", "file_name": "deep.py", "file_ext": "py", "file_size_in_byte": 846, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "streamlit.title", "line_number": 9, "usage_type": "call"}, {"api_name": "fal_serverless.cached", "line_number": 18, "usage_type": "name"}, {"api_name": "fal_serverless.isolated", "line_number": 23, "usage_type": "call"}, {"api_name": "streamlit.form", "line_number": 29, "usage_type": "call"}, {"api_name": "time.time", "line_number": 30, "usage_type": "call"}, {"api_name": "streamlit.text_input", "line_number": 31, "usage_type": "call"}, {"api_name": "streamlit.form_submit_button", "line_number": 34, "usage_type": "call"}, {"api_name": "streamlit.text", "line_number": 39, "usage_type": "call"}]} +{"seq_id": "36460734329", "text": "# args.py\n# CTES Optimization Processor\n# Populate parser for command line arguments\n# Karl Heine, kheine@mines.edu, heinek@erau.edu\n# July 2021\n\nimport argparse\n\ndef args():\n # Create parser\n parser = argparse.ArgumentParser(description=\"CTES Optimization Processor.\")\n\n # Create arguments\n parser.add_argument('-i', '--input_path', type=str, action='append',\n help=('specify source directory for input building energy simulation ' \\\n 'files; may be used multiple times'))\n parser.add_argument('-o', '--overwrite', action='store_const', const=True,\n help='overwrite existing project')\n parser.add_argument('-p', '--project_name', type=str,\n default='ctes_project', help='specify name of project directory')\n parser.add_argument('-r', '--run', action='store_const', const=True,\n help='run project pre-optimization processor; use with -p')\n parser.add_argument('-s', '--setup', action='store_const', const=True,\n help='set up initial project structure; use with -i and -p')\n parser.add_argument('-u', '--utility', action='store_const', const=True,\n help='update utility rate only')\n\n return parser\n", "repo_name": "x92499/ctes_opt", "sub_path": "ctes_resources/scripts/args.py", "file_name": "args.py", "file_ext": "py", "file_size_in_byte": 1180, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 11, "usage_type": "call"}]} +{"seq_id": "70121660908", "text": "import numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch.utils.data import DataLoader\nfrom torch.nn import Linear\nfrom data_generator import get_training_data, dataset_test, dataset_train, cat_dict\nfrom sklearn.metrics import confusion_matrix, classification_report\nfrom sklearn.cluster import KMeans\nfrom sklearn.tree import DecisionTreeClassifier\nimport os, glob\nimport pickle\n\nnp.random.seed(12345)\ntorch.manual_seed(12345)\nimport random\n\n# 0.01: trainstop 0.005, cluster /25, min_imp_dec: 0.01, (80,100,150) , 5:15PM 26/11/20\n\n\nrandom.seed(12345)\ndevice = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\ntotal_dataset, labeled_dataset, unlabeled_dataset = get_training_data(label_ratio=0.1)\ntest_dataset = dataset_test()\ntest_dataset_neg = dataset_test(test_neg=True)\n\nae_epoch = 80\npretrain_epoch = 200\ntrain_epoch = 150\n\nnum_data = total_dataset.get_x()\nlabels = total_dataset.get_y()\n\ntotal_original_label_counts = dict()\ndistinct_labels, distinct_label_counts = np.unique(labels, return_counts=True)\nfor i in range(len(distinct_labels)):\n if distinct_labels[i] != -1:\n total_original_label_counts[distinct_labels[i]] = distinct_label_counts[i]\n\n\n# print(total_original_label_counts)\n\n\ndef tree_work(load_cluster_from_file=False):\n if load_cluster_from_file:\n clustering = pickle.load(file=open('models/clustering.pkl', 'rb'))\n else:\n clustering = KMeans(n_clusters=int(total_dataset.__len__() / 175), random_state=0)\n print(\"Clustering Started.\")\n clustering.fit(num_data)\n print(\"Clustering ended.\")\n\n cluster_assignment = clustering.labels_\n all_clusters = dict()\n for j in range(len(cluster_assignment)):\n all_clusters.setdefault(cluster_assignment[j], []).append(num_data[j])\n\n cluster_to_label_dict = dict()\n for j in range(len(cluster_assignment)):\n if labels[j] != -1:\n cluster_to_label_dict.setdefault(cluster_assignment[j], []).append(labels[j])\n\n #print(\"Clusters:\")\n label_to_cluster_dict = dict()\n for k, v in cluster_to_label_dict.items():\n cl_labels, cl_label_counts = np.unique(np.array(v), return_counts=True) # labeled member count in each cluster\n # print(k)\n # print(cl_labels)\n # print(cl_label_counts)\n # print(\"\\n\")\n total_labeled_counts = np.sum(cl_label_counts)\n\n max_label = np.argmax(cl_label_counts)\n\n '''\n If a cluster contains more than 10% of all samples of a label present in the training dataset, it is considered \n important for this label.\n If the majority label is not the normal label and there are no other labels for which this cluster is important,\n having more than 50% of the labeled members would be enough for soft labeling\n If the majority label is the normal label, then all labeled members must be normal for soft labeling.\n In any other case, we do not soft label.\n '''\n\n imp_for_label = []\n for label, total_label_count in total_original_label_counts.items():\n for j in range(len(cl_labels)):\n if cl_labels[j] == label and cl_label_counts[j] > 0.1 * total_label_count:\n imp_for_label.append(label)\n\n if (cl_label_counts[max_label] / total_labeled_counts) > 0.5:\n selected_label = cl_labels[max_label]\n if len(imp_for_label) == 1:\n if imp_for_label[0] == selected_label:\n if selected_label != int(cat_dict['Normal']):\n label = selected_label\n size = len(v)\n label_to_cluster_dict.setdefault(label, []).append([k, size])\n else:\n if len(cl_labels) == 1:\n label = selected_label\n size = len(v)\n label_to_cluster_dict.setdefault(label, []).append([k, size])\n elif len(imp_for_label) == 0:\n if selected_label != int(cat_dict['Normal']):\n label = selected_label\n size = len(v)\n label_to_cluster_dict.setdefault(label, []).append([k, size])\n else:\n if len(cl_labels) == 1:\n label = selected_label\n size = len(v)\n label_to_cluster_dict.setdefault(label, []).append([k, size])\n\n '''\n clusters that belong to a particular label, after soft labeling.\n '''\n soft_label_mapping = dict()\n for k, v in label_to_cluster_dict.items():\n for cluster_index in v:\n soft_label_mapping[cluster_index[0]] = k\n\n '''\n soft labeling particular unlabeled samples.\n Also add this to labeled dataset.\n '''\n\n for j in range(len(labels)):\n if labels[j] == -1 and (int(cluster_assignment[j]) in soft_label_mapping.keys()):\n labels[j] = soft_label_mapping[cluster_assignment[j]]\n labeled_dataset.add_sample(num_data[j], labels[j])\n\n\n '''\n checking total labeled and soft labeled members.\n '''\n\n total_soft_label_counts = dict()\n distinct_slabels, distinct_slabel_counts = np.unique(labels, return_counts=True)\n for j in range(len(distinct_slabels)):\n if distinct_slabels[j] != -1:\n total_soft_label_counts[distinct_slabels[j]] = distinct_slabel_counts[j]\n\n print(total_soft_label_counts)\n\n cluster_to_labels_dict = dict()\n for k, v in cluster_to_label_dict.items():\n cl_labels = np.unique(np.array(v))\n cl_labels = sorted(list(cl_labels))\n if cl_labels[0] == -1:\n cl_labels = cl_labels[1:]\n cluster_to_labels_dict[k] = cl_labels\n\n total_dataset.set_y(labels)\n\n dt_X = labeled_dataset.get_x()\n dt_Y = labeled_dataset.get_y()\n\n print(\"labeled members dimensions:\")\n print(dt_X.shape)\n print(dt_Y.shape)\n\n clf = DecisionTreeClassifier(random_state=0, max_leaf_nodes=7)\n clf.fit(dt_X, dt_Y)\n\n print(\"No. of leaves of decision tree:\")\n print(clf.get_n_leaves())\n\n '''\n saving decision tree, cluster membership info (this is just to skip the clustering step for our faster use) and soft\n labeling info.\n '''\n\n file = open('models/tree.pkl', 'wb')\n pickle.dump(clf, file)\n file.close()\n\n for k in list(all_clusters.keys()):\n if int(k) not in soft_label_mapping.keys():\n soft_label_mapping[int(k)] = int(cat_dict['Normal'])\n\n file = open('models/soft_label_mapping.pkl', 'wb')\n pickle.dump(soft_label_mapping, file)\n file.close()\n\n if not load_cluster_from_file:\n file = open('models/clustering.pkl', 'wb')\n pickle.dump(clustering, file)\n file.close()\n\n leaf_dataset_X = dict()\n leaf_dataset_Y = dict()\n\n '''\n finding out corresponding leaf for each training sample.\n '''\n\n for j in range(len(num_data)):\n leaf = clf.apply([num_data[j]])[0]\n if labels[j] != -1:\n leaf_dataset_X.setdefault(leaf, []).append(num_data[j])\n leaf_dataset_Y.setdefault(leaf, []).append(labels[j])\n\n for k, v in leaf_dataset_X.items():\n leaf_dataset_X[k] = np.array(leaf_dataset_X[k])\n leaf_dataset_Y[k] = np.array(leaf_dataset_Y[k])\n\n return leaf_dataset_X, leaf_dataset_Y\n\n'''\nThe dictionary of X-Y training dataset for each individual leaf.\n'''\n\nleaf_dataset_X, leaf_dataset_Y = tree_work(load_cluster_from_file=False)\n\n'''\nundercomplete autoencoder for embedding.\n'''\n\nclass AE(nn.Module):\n\n def __init__(self, n_input, n_z):\n super(AE, self).__init__()\n # encoder\n self.enc_1 = Linear(n_input, 80)\n self.enc_2 = Linear(80, 50)\n self.z_layer = Linear(50, n_z)\n\n # decoder\n self.dec_1 = Linear(n_z, 50)\n self.dec_2 = Linear(50, 80)\n self.x_bar_layer = Linear(80, n_input)\n\n def forward(self, x):\n # encoder\n enc_h1 = F.relu(self.enc_1(x))\n enc_h2 = F.relu(self.enc_2(enc_h1))\n z = self.z_layer(enc_h2)\n\n # decoder\n dec_h1 = F.relu(self.dec_1(z))\n dec_h2 = F.relu(self.dec_2(dec_h1))\n x_bar = self.x_bar_layer(dec_h2)\n\n return x_bar, z\n\n\ndef train_ae(epochs, load_from_file=False, save_path='models/train_ae'):\n '''\n train autoencoder\n '''\n\n model = AE(total_dataset.get_feature_shape(), 32)\n model.to(device)\n\n ae_train_ds = total_dataset\n training_data_length = int(0.7 * ae_train_ds.__len__())\n validation_data_length = ae_train_ds.__len__() - training_data_length\n training_data, validation_data = torch.utils.data.random_split(ae_train_ds,\n [training_data_length, validation_data_length])\n\n train_loader = DataLoader(training_data, batch_size=32, shuffle=True)\n validation_loader = DataLoader(validation_data, batch_size=32, shuffle=True)\n\n optimizer = torch.optim.Adam(model.parameters(), lr=0.001)\n min_val_loss = 1000000\n\n for epoch in range(epochs):\n training_loss = 0.\n validation_loss = 0.\n train_batch_num = 0\n val_batch_num = 0\n\n model.train()\n for batch_idx, (x, _, idx) in enumerate(train_loader):\n x = x.float()\n x = x.to(device)\n\n train_batch_num = batch_idx\n\n optimizer.zero_grad()\n x_bar, z = model(x)\n loss = F.mse_loss(x_bar, x)\n training_loss += loss.item()\n\n loss.backward()\n optimizer.step()\n\n training_loss /= (train_batch_num + 1)\n\n model.eval()\n for batch_idx, (x, _, idx) in enumerate(validation_loader):\n x = x.float()\n x = x.to(device)\n\n val_batch_num = batch_idx\n\n x_bar, z = model(x)\n loss = F.mse_loss(x_bar, x)\n validation_loss += loss.item()\n\n validation_loss /= (val_batch_num + 1)\n\n if epoch % 1 == 0:\n print(\n \"epoch {} , Training loss={:.4f}, Validation loss={:.4f}\".format(epoch, training_loss, validation_loss))\n\n if epoch == 0 or min_val_loss > validation_loss:\n min_val_loss = validation_loss\n torch.save(model.state_dict(), save_path)\n\n print(\"model saved to {}.\".format(save_path))\n\n return model\n\n\n# train_ae(ae_epoch, False)\n\n\nclass leaf_dnn(nn.Module):\n\n def __init__(self, n_input, n_output):\n super(leaf_dnn, self).__init__()\n self.fc1 = nn.Linear(n_input, 8)\n self.fc2 = nn.Linear(8, n_output)\n\n def forward(self, x):\n out_1 = torch.relu(self.fc1(x))\n out_2 = self.fc2(out_1)\n\n return out_2\n\n'''\npretraining using labeled and soft labeled dataset.\n'''\n\ndef pretrain_leaf_dnn(save_path, epochs):\n ae_model = AE(total_dataset.get_feature_shape(), 32)\n ae_model.load_state_dict(torch.load('models/train_ae'))\n ae_model.to(device)\n\n model = leaf_dnn(32, int(max(labels)) + 1)\n model.to(device)\n\n weights = torch.FloatTensor(labeled_dataset.get_weight()).to(device)\n\n train_loader = DataLoader(labeled_dataset, batch_size=32, shuffle=True) # soft label must be assigned\n\n optimizer = torch.optim.Adam(model.parameters(), lr=0.001)\n min_train_loss = 1000000\n\n for epoch in range(epochs):\n train_loss = 0.0\n train_batch_num = 0\n train_num_correct = 0\n train_num_examples = 0\n\n model.train()\n for batch_idx, (x, y_t, idx) in enumerate(train_loader):\n x = x.float()\n x = x.to(device)\n train_batch_num = batch_idx\n\n optimizer.zero_grad()\n\n x_emb = ae_model(x)[1]\n y_pred = model(x_emb)\n\n y_t = y_t.clone().detach().to(device)\n\n loss = torch.nn.CrossEntropyLoss(weight=weights)(y_pred, y_t)\n train_loss += loss.item()\n\n loss.backward()\n optimizer.step()\n\n correct = torch.eq(torch.max(torch.softmax(y_pred, dim=-1), dim=1)[1], y_t).view(-1)\n train_num_correct += torch.sum(correct).item()\n train_num_examples += correct.shape[0]\n\n train_loss /= (train_batch_num + 1)\n train_acc = train_num_correct / train_num_examples\n\n if epoch % 1 == 0:\n print(\"epoch {}; T loss={:.4f} T Accuracy={:.4f}\".\n format(epoch, train_loss, train_num_correct / train_num_examples))\n\n if epoch == 0 or min_train_loss > train_loss:\n min_train_loss = train_loss\n torch.save(model.state_dict(), save_path)\n\n print(\"model saved to {}.\".format(save_path))\n\n return model\n\n\n# pretrain_leaf_dnn('models/pretrain_leaf_dnn', pretrain_epoch)\n\n'''\ntraining dataset of an individual leaf.\n'''\n\ndef train_leaf_dnn(model, dataset, save_path, epochs):\n ae_model = AE(total_dataset.get_feature_shape(), 32)\n ae_model.load_state_dict(torch.load('models/train_ae'))\n ae_model.to(device)\n\n weights = torch.FloatTensor(dataset.get_weight()).to(device)\n\n train_loader = DataLoader(dataset, batch_size=32, shuffle=True) # soft label must be assigned\n\n optimizer = torch.optim.Adam(model.parameters(), lr=0.0005)\n min_train_loss = 1000000\n prev_train_acc = 0\n stop_flag = 1\n\n for epoch in range(epochs):\n train_loss = 0.0\n train_batch_num = 0\n train_num_correct = 0\n train_num_examples = 0\n\n model.train()\n for batch_idx, (x, y_t, idx) in enumerate(train_loader):\n x = x.float()\n x = x.to(device)\n train_batch_num = batch_idx\n\n optimizer.zero_grad()\n\n x_emb = ae_model(x)[1]\n y_pred = model(x_emb)\n\n y_t = y_t.clone().detach().to(device)\n\n loss = torch.nn.CrossEntropyLoss(weight=weights)(y_pred, y_t)\n train_loss += loss.item()\n\n loss.backward()\n optimizer.step()\n\n correct = torch.eq(torch.max(torch.softmax(y_pred, dim=-1), dim=1)[1], y_t).view(-1)\n train_num_correct += torch.sum(correct).item()\n train_num_examples += correct.shape[0]\n\n train_loss /= (train_batch_num + 1)\n train_acc = train_num_correct / train_num_examples\n\n if epoch % 1 == 0:\n print(\"epoch {}; T loss={:.4f} T Accuracy={:.4f}\".\n format(epoch, train_loss, train_num_correct / train_num_examples))\n\n if train_acc - prev_train_acc > 0.005:\n stop_flag = 0\n\n if epoch == 0 or min_train_loss > train_loss:\n min_train_loss = train_loss\n torch.save(model.state_dict(), save_path)\n\n if train_acc == 1.0:\n break\n\n if epoch % 30 == 0:\n if stop_flag == 1:\n break\n stop_flag = 1\n prev_train_acc = train_acc\n\n print(\"model saved to {}.\".format(save_path))\n\n return model\n\n'''\ntraining all leaves.\n'''\n\ndef create_leaf_dnns():\n filelist = glob.glob(os.path.join('models/leaf_models', \"*\"))\n for f in filelist:\n os.remove(f)\n\n for key in leaf_dataset_Y.keys():\n dataset_X = leaf_dataset_X[key]\n dataset_Y = leaf_dataset_Y[key]\n\n print(key)\n print(dataset_X.shape)\n print(dataset_Y.shape)\n print(\"\\n\")\n\n data = dataset_X, dataset_Y\n dataset = dataset_train(data)\n\n model = leaf_dnn(32, int(max(labels)) + 1)\n model.load_state_dict(torch.load('models/pretrain_leaf_dnn'))\n model.to(device)\n\n save_path = \"models/leaf_models/leaf_\" + str(key)\n\n train_leaf_dnn(model, dataset, save_path, train_epoch)\n\n\ncreate_leaf_dnns()\n\n\ndef generate_result():\n clf = pickle.load(file=open('models/tree.pkl', 'rb'))\n soft_label_mapping = pickle.load(file=open('models/soft_label_mapping.pkl', 'rb'))\n clustering = pickle.load(file=open('models/clustering.pkl', 'rb'))\n\n test_X = test_dataset.get_x()\n test_Y = test_dataset.get_y()\n\n leaf_nodes = clf.apply(test_X)\n cluster_assignment = clustering.predict(test_X)\n\n print(\"done\")\n\n ae_model = AE(total_dataset.get_feature_shape(), 32)\n ae_model.load_state_dict(torch.load('models/train_ae'))\n ae_model.to(device)\n\n '''\n If model does not exist for a particular leaf, use the default pretrained model.\n '''\n\n model_dict = dict()\n leaf_model_files = os.listdir('models/leaf_models')\n for file in leaf_model_files:\n spl = str(file).split(\"_\")\n\n leaf_model = leaf_dnn(32, int(max(labels)) + 1)\n\n if not os.path.exists('models/leaf_models/leaf_' + spl[1]):\n leaf_model.load_state_dict(torch.load('models/pretrain_leaf_dnn'))\n else:\n leaf_model.load_state_dict(torch.load('models/leaf_models/leaf_' + spl[1]))\n\n leaf_model.to(device)\n\n model_dict[int(spl[1])] = leaf_model\n\n leaf_pred_dict = dict()\n for k, v in model_dict.items():\n leaf_model = v\n X_emb = ae_model(torch.FloatTensor(test_X).to(device))[1]\n Y = torch.softmax(leaf_model(X_emb), dim=-1)\n Y_ = Y.cpu().detach().numpy()\n leaf_pred_dict[k] = Y_\n\n test_Y_pred = np.zeros(test_Y.shape)\n\n for j in range(len(leaf_nodes)):\n y_ = leaf_pred_dict[leaf_nodes[j]][j]\n if soft_label_mapping[cluster_assignment[j]] != cat_dict['Normal']:\n y_[int(cat_dict['Normal'])] = 0\n y_pred = np.argmax(y_)\n test_Y_pred[j] = y_pred\n\n print(confusion_matrix(test_Y, test_Y_pred))\n print(classification_report(test_Y, test_Y_pred))\n\n\ngenerate_result()\n", "repo_name": "tzpranto/NIDS_NSYSS_23", "sub_path": "training_file.py", "file_name": "training_file.py", "file_ext": "py", "file_size_in_byte": 17584, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "37", "api": [{"api_name": "numpy.random.seed", "line_number": 14, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 14, "usage_type": "attribute"}, {"api_name": "torch.manual_seed", "line_number": 15, "usage_type": "call"}, {"api_name": "random.seed", "line_number": 21, "usage_type": "call"}, {"api_name": "torch.device", "line_number": 22, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 22, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 22, "usage_type": "attribute"}, {"api_name": "data_generator.get_training_data", "line_number": 23, "usage_type": "call"}, {"api_name": "data_generator.dataset_test", "line_number": 24, "usage_type": "call"}, {"api_name": "data_generator.dataset_test", "line_number": 25, "usage_type": "call"}, {"api_name": "numpy.unique", "line_number": 35, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 46, "usage_type": "call"}, {"api_name": "sklearn.cluster.KMeans", "line_number": 48, "usage_type": "call"}, {"api_name": "numpy.unique", "line_number": 66, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 66, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 71, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 73, "usage_type": "call"}, {"api_name": "data_generator.cat_dict", "line_number": 94, "usage_type": "name"}, {"api_name": "data_generator.cat_dict", "line_number": 104, "usage_type": "name"}, {"api_name": "numpy.unique", "line_number": 138, "usage_type": "call"}, {"api_name": "numpy.unique", "line_number": 147, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 147, "usage_type": "call"}, {"api_name": "sklearn.tree.DecisionTreeClassifier", "line_number": 162, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 174, "usage_type": "call"}, {"api_name": "data_generator.cat_dict", "line_number": 179, "usage_type": "name"}, {"api_name": "pickle.dump", "line_number": 182, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 187, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 204, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 205, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 219, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 219, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 224, "usage_type": "call"}, {"api_name": "torch.nn.Linear", "line_number": 225, "usage_type": "call"}, {"api_name": "torch.nn.Linear", "line_number": 226, "usage_type": "call"}, {"api_name": "torch.nn.Linear", "line_number": 229, "usage_type": "call"}, {"api_name": "torch.nn.Linear", "line_number": 230, "usage_type": "call"}, {"api_name": "torch.nn.Linear", "line_number": 231, "usage_type": "call"}, {"api_name": "torch.nn.functional.relu", "line_number": 235, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 235, "usage_type": "name"}, {"api_name": "torch.nn.functional.relu", "line_number": 236, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 236, "usage_type": "name"}, {"api_name": "torch.nn.functional.relu", "line_number": 240, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 240, "usage_type": "name"}, {"api_name": "torch.nn.functional.relu", "line_number": 241, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 241, "usage_type": "name"}, {"api_name": "torch.utils.data.random_split", "line_number": 258, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 258, "usage_type": "attribute"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 261, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 262, "usage_type": "call"}, {"api_name": "torch.optim.Adam", "line_number": 264, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 264, "usage_type": "attribute"}, {"api_name": "torch.nn.functional.mse_loss", "line_number": 282, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 282, "usage_type": "name"}, {"api_name": "torch.nn.functional.mse_loss", "line_number": 298, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 298, "usage_type": "name"}, {"api_name": "torch.save", "line_number": 309, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 319, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 319, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 323, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 323, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 324, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 324, "usage_type": "name"}, {"api_name": "torch.relu", "line_number": 327, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 338, "usage_type": "call"}, {"api_name": "torch.FloatTensor", "line_number": 344, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 346, "usage_type": "call"}, {"api_name": "torch.optim.Adam", "line_number": 348, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 348, "usage_type": "attribute"}, {"api_name": "torch.nn.CrossEntropyLoss", "line_number": 370, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 370, "usage_type": "attribute"}, {"api_name": "torch.eq", "line_number": 376, "usage_type": "call"}, {"api_name": "torch.max", "line_number": 376, "usage_type": "call"}, {"api_name": "torch.softmax", "line_number": 376, "usage_type": "call"}, {"api_name": "torch.sum", "line_number": 377, "usage_type": "call"}, {"api_name": "torch.save", "line_number": 389, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 404, "usage_type": "call"}, {"api_name": "torch.FloatTensor", "line_number": 407, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 409, "usage_type": "call"}, {"api_name": "torch.optim.Adam", "line_number": 411, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 411, "usage_type": "attribute"}, {"api_name": "torch.nn.CrossEntropyLoss", "line_number": 435, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 435, "usage_type": "attribute"}, {"api_name": "torch.eq", "line_number": 441, "usage_type": "call"}, {"api_name": "torch.max", "line_number": 441, "usage_type": "call"}, {"api_name": "torch.softmax", "line_number": 441, "usage_type": "call"}, {"api_name": "torch.sum", "line_number": 442, "usage_type": "call"}, {"api_name": "torch.save", "line_number": 457, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 477, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 477, "usage_type": "call"}, {"api_name": "os.path", "line_number": 477, "usage_type": "attribute"}, {"api_name": "os.remove", "line_number": 479, "usage_type": "call"}, {"api_name": "data_generator.dataset_train", "line_number": 491, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 494, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 506, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 507, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 508, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 519, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 527, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 533, "usage_type": "call"}, {"api_name": "os.path", "line_number": 533, "usage_type": "attribute"}, {"api_name": "torch.load", "line_number": 534, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 536, "usage_type": "call"}, {"api_name": "torch.FloatTensor", "line_number": 545, "usage_type": "call"}, {"api_name": "torch.softmax", "line_number": 546, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 550, "usage_type": "call"}, {"api_name": "data_generator.cat_dict", "line_number": 554, "usage_type": "name"}, {"api_name": "data_generator.cat_dict", "line_number": 555, "usage_type": "name"}, {"api_name": "numpy.argmax", "line_number": 556, "usage_type": "call"}, {"api_name": "sklearn.metrics.confusion_matrix", "line_number": 559, "usage_type": "call"}, {"api_name": "sklearn.metrics.classification_report", "line_number": 560, "usage_type": "call"}]} +{"seq_id": "10221204582", "text": "from torch.utils.data.dataset import Dataset\nfrom Utils_Bashivan import *\n\nimport torch\n\nimport scipy.io as sio\nimport torch.optim as optim\nimport torch.nn as nn\nimport numpy as np\n\ndef kfold(length, n_fold):\n tot_id = np.arange(length)\n np.random.shuffle(tot_id)\n len_fold = int(length/n_fold)\n train_id = []\n test_id = []\n for i in range(n_fold):\n test_id.append(tot_id[i*len_fold:(i+1)*len_fold])\n train_id.append(np.hstack([tot_id[0:i*len_fold],tot_id[(i+1)*len_fold:-1]]))\n return train_id, test_id\n\n\nclass EEGImagesDataset(Dataset):\n \"\"\"EEGLearn Images Dataset from EEG.\"\"\"\n \n def __init__(self, label, image):\n self.label = label\n self.Images = image\n \n def __len__(self):\n return len(self.label)\n \n def __getitem__(self, idx):\n if torch.is_tensor(idx):\n idx = idx.tolist()\n image = self.Images[idx]\n label = self.label[idx]\n sample = (image, label)\n \n return sample\n\n\n\ndef Test_Model(net, Testloader, criterion, is_cuda=True):\n running_loss = 0.0 \n evaluation = []\n for i, data in enumerate(Testloader, 0):\n input_img, labels = data\n input_img = input_img.to(torch.float32)\n if is_cuda:\n input_img = input_img.cuda()\n outputs = net(input_img)\n _, predicted = torch.max(outputs.cpu().data, 1)\n evaluation.append((predicted==labels).tolist())\n loss = criterion(outputs, labels.cuda())\n running_loss += loss.item()\n running_loss = running_loss/(i+1)\n evaluation = [item for sublist in evaluation for item in sublist]\n running_acc = sum(evaluation)/len(evaluation)\n return running_loss, running_acc\n\n\ndef TrainTest_Model(model, trainloader, testloader, n_epoch=30, opti='SGD', learning_rate=0.0001, is_cuda=True, print_epoch =5, verbose=False):\n if is_cuda:\n net = model().cuda()\n else :\n net = model()\n \n criterion = nn.CrossEntropyLoss()\n \n if opti=='SGD':\n optimizer = optim.SGD(net.parameters(), lr=learning_rate)\n elif opti =='Adam':\n optimizer = optim.Adam(net.parameters(), lr=learning_rate)\n else: \n print(\"Optimizer: \"+optim+\" not implemented.\")\n \n for epoch in range(n_epoch):\n running_loss = 0.0\n evaluation = []\n for i, data in enumerate(trainloader, 0):\n # get the inputs; data is a list of [inputs, labels]\n inputs, labels = data\n # zero the parameter gradients\n optimizer.zero_grad()\n\n # forward + backward + optimize\n outputs = net(inputs.to(torch.float32).cuda())\n _, predicted = torch.max(outputs.cpu().data, 1)\n evaluation.append((predicted==labels).tolist())\n loss = criterion(outputs, labels.cuda())\n loss.backward()\n optimizer.step()\n\n running_loss += loss.item()\n\n running_loss = running_loss/(i+1)\n evaluation = [item for sublist in evaluation for item in sublist]\n running_acc = sum(evaluation)/len(evaluation)\n validation_loss, validation_acc = Test_Model(net, testloader, criterion,True)\n \n if epoch%print_epoch==(print_epoch-1):\n print('[%d, %3d]\\tloss: %.3f\\tAccuracy : %.3f\\t\\tval-loss: %.3f\\tval-Accuracy : %.3f' %\n (epoch+1, n_epoch, running_loss, running_acc, validation_loss, validation_acc))\n if verbose:\n print('Finished Training \\n loss: %.3f\\tAccuracy : %.3f\\t\\tval-loss: %.3f\\tval-Accuracy : %.3f' %\n (running_loss, running_acc, validation_loss,validation_acc))\n \n return (running_loss, running_acc, validation_loss,validation_acc)\n\n\n\ndef create_img():\n feats = sio.loadmat('Sample Data/FeatureMat_timeWin.mat')['features']\n locs = sio.loadmat('Sample Data/Neuroscan_locs_orig.mat')\n locs_3d = locs['A']\n locs_2d = []\n # Convert to 2D\n for e in locs_3d:\n locs_2d.append(azim_proj(e))\n\n images_timewin = np.array([gen_images(np.array(locs_2d),\n feats[:, i * 192:(i + 1) * 192], 32, normalize=True) for i in\n range(int(feats.shape[1] / 192))\n ])\n\n sio.savemat(\"Sample Data/images_time.mat\",{\"img\":images_timewin})\n print(\"Images Created and Save in Sample Dat/images_time\")", "repo_name": "numediart/EEGLearn-Pytorch", "sub_path": "Utils.py", "file_name": "Utils.py", "file_ext": "py", "file_size_in_byte": 4390, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 103, "dataset": "github-code", "pt": "37", "api": [{"api_name": "numpy.arange", "line_number": 12, "usage_type": "call"}, {"api_name": "numpy.random.shuffle", "line_number": 13, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 13, "usage_type": "attribute"}, {"api_name": "numpy.hstack", "line_number": 19, "usage_type": "call"}, {"api_name": "torch.utils.data.dataset.Dataset", "line_number": 23, "usage_type": "name"}, {"api_name": "torch.is_tensor", "line_number": 34, "usage_type": "call"}, {"api_name": "torch.float32", "line_number": 49, "usage_type": "attribute"}, {"api_name": "torch.max", "line_number": 53, "usage_type": "call"}, {"api_name": "torch.nn.CrossEntropyLoss", "line_number": 69, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 69, "usage_type": "name"}, {"api_name": "torch.optim.SGD", "line_number": 72, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 72, "usage_type": "name"}, {"api_name": "torch.optim.Adam", "line_number": 74, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 74, "usage_type": "name"}, {"api_name": "torch.optim", "line_number": 76, "usage_type": "name"}, {"api_name": "torch.float32", "line_number": 88, "usage_type": "attribute"}, {"api_name": "torch.max", "line_number": 89, "usage_type": "call"}, {"api_name": "scipy.io.loadmat", "line_number": 114, "usage_type": "call"}, {"api_name": "scipy.io", "line_number": 114, "usage_type": "name"}, {"api_name": "scipy.io.loadmat", "line_number": 115, "usage_type": "call"}, {"api_name": "scipy.io", "line_number": 115, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 122, "usage_type": "call"}, {"api_name": "scipy.io.savemat", "line_number": 127, "usage_type": "call"}, {"api_name": "scipy.io", "line_number": 127, "usage_type": "name"}]} +{"seq_id": "43027244908", "text": "# [445] Add Two Numbers 2\n\nimport random\nfrom typing import Optional, Union, Any, List, Tuple, Set, Dict\n\nfrom definitions import debug_mode\nfrom solutions.interface import ProblemInterface, Difficulty\n\n\nclass Problem445(ProblemInterface):\n difficulty = Difficulty.Medium\n name = 'add-two-numbers-2'\n\n class ListNode:\n def __init__(self, val=0, next=None):\n self.val = val\n self.next = next\n if not 0 <= val <= 9:\n raise ValueError\n\n def __eq__(self, other: 'ListNode') -> bool:\n if self.val != other.val:\n return False\n else:\n if self.next is None and other.next is None:\n return True\n elif self.next is None or other.next is None:\n return False\n else:\n return self.next.__eq__(other.next)\n\n def __repr__(self) -> str:\n return str(self.val) + self.next.__repr__() if self.next else ''\n\n def sum_ListNode(self, n1: ListNode, n2: ListNode, carry=0) -> (ListNode, int):\n u\"\"\" time complexity: O(1) \"\"\"\n sum_val = (n1.val + n2.val + carry)\n return self.ListNode(val=sum_val%10), sum_val//10\n\n @ProblemInterface.time_check(debug_mode)\n def solution(self, l1: Optional[ListNode], l2: Optional[ListNode]) -> Optional[ListNode]:\n u\"\"\" time complexity: O(n) \"\"\"\n s1, s2 = [], []\n curr_node1, curr_node2 = l1, l2\n while curr_node1 is not None or curr_node2 is not None:\n if curr_node1 is not None:\n s1.append(curr_node1)\n curr_node1 = curr_node1.next\n if curr_node2 is not None:\n s2.append(curr_node2)\n curr_node2 = curr_node2.next\n next_node, carry = None, 0\n while s1 and s2:\n prev_node, carry = self.sum_ListNode(s1.pop(), s2.pop(), carry=carry)\n prev_node.next = next_node\n next_node = prev_node\n while s1:\n prev_node, carry = self.sum_ListNode(s1.pop(), self.ListNode(val=0), carry=carry)\n prev_node.next = next_node\n next_node = prev_node\n while s2:\n prev_node, carry = self.sum_ListNode(s2.pop(), self.ListNode(val=0), carry=carry)\n prev_node.next = next_node\n next_node = prev_node\n if carry != 0:\n prev_node, carry = self.sum_ListNode(self.ListNode(val=0), self.ListNode(val=0), carry=carry)\n prev_node.next = next_node\n next_node = prev_node\n return next_node\n\n def int_to_ListNode(self, val: int) -> ListNode:\n u\"\"\" time complexity: O(n) \"\"\"\n next_node = None\n while True:\n prev_node = self.ListNode(val=val%10)\n prev_node.next = next_node\n next_node = prev_node\n val //= 10\n if val <= 0:\n break\n return next_node\n\n def ListNode_to_int(self, curr_node: ListNode) -> int:\n u\"\"\" time complexity: O(n) \"\"\"\n sum_val = curr_node.val\n next_node = curr_node.next\n while next_node:\n sum_val = sum_val * 10 + next_node.val\n next_node = next_node.next\n return sum_val\n\n @ProblemInterface.time_check(debug_mode)\n def comparison_solution(self, l1: Optional[ListNode], l2: Optional[ListNode]) -> Optional[ListNode]:\n u\"\"\" time complexity: O(n) \"\"\"\n return self.int_to_ListNode((self.ListNode_to_int(l1) + self.ListNode_to_int(l2)))\n\n def test_one_random(self, max_list_length=10):\n l1 = self.int_to_ListNode(random.randint(1, 10**random.randint(1, max_list_length + 1) + 1))\n l2 = self.int_to_ListNode(random.randint(1, 10**random.randint(1, max_list_length + 1) + 1))\n\n answer1 = self.solution(l1, l2)\n answer2 = self.comparison_solution(l1, l2)\n\n assert answer1 == answer2\n", "repo_name": "kjunm000n/LeetCode", "sub_path": "solutions/problem445.py", "file_name": "problem445.py", "file_ext": "py", "file_size_in_byte": 3925, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "solutions.interface.ProblemInterface", "line_number": 10, "usage_type": "name"}, {"api_name": "solutions.interface.Difficulty.Medium", "line_number": 11, "usage_type": "attribute"}, {"api_name": "solutions.interface.Difficulty", "line_number": 11, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 41, "usage_type": "name"}, {"api_name": "solutions.interface.ProblemInterface.time_check", "line_number": 40, "usage_type": "call"}, {"api_name": "definitions.debug_mode", "line_number": 40, "usage_type": "argument"}, {"api_name": "solutions.interface.ProblemInterface", "line_number": 40, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 93, "usage_type": "name"}, {"api_name": "solutions.interface.ProblemInterface.time_check", "line_number": 92, "usage_type": "call"}, {"api_name": "definitions.debug_mode", "line_number": 92, "usage_type": "argument"}, {"api_name": "solutions.interface.ProblemInterface", "line_number": 92, "usage_type": "name"}, {"api_name": "random.randint", "line_number": 98, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 99, "usage_type": "call"}]} +{"seq_id": "23824491698", "text": "from enum import auto\nfrom enum import IntEnum\n\nfrom furl import Path as UrlPath\nfrom furl.furl import furl as Url\nfrom requests.models import Response\nfrom testlodge.api.base import BaseAPI\nfrom testlodge.typing.suite import SuiteJSON\nfrom testlodge.typing.suite import SuiteListJSON\n\n\nclass SortSuiteOrder(IntEnum):\n \"\"\"Method to sort by.\"\"\"\n\n CREATED_AT = auto()\n UPDATED_AT = auto()\n NAME = auto()\n\n\nclass SuiteAPI(BaseAPI):\n \"\"\"API for test suites.\n\n Endpoints\n ---------\n * List\n * Show\n * Create\n * Update\n * Delete\n \"\"\"\n\n name: str = 'suite'\n\n def _list(\n self,\n *,\n project_id: int,\n page: int = 1,\n order: SortSuiteOrder = SortSuiteOrder.CREATED_AT,\n ) -> SuiteListJSON:\n \"\"\"Paginated list of all suites in a project.\n\n Parameters\n ----------\n project_id: int\n The ID of the project.\n page: int, default=1\n Default: 1\n The number of the page to return.\n order: SortSuiteOrder, default=SortSuiteOrder.CREATED_AT\n Default: SortSuiteOrder.CREATED_AT\n Method to sort the list of suites.\n \"\"\"\n\n method = 'GET'\n url: Url = self.client.base_url / UrlPath(\n f'/projects/{project_id}' '/suites.json'\n )\n params: dict = {}\n if page != 1:\n params['page'] = page\n if order != SortSuiteOrder.CREATED_AT:\n params['order'] = int(order)\n\n response: Response = self.client._request(\n method=method, url=url, params=params\n )\n suite_list: SuiteListJSON = response.json()\n\n return suite_list\n\n def _show(\n self,\n *,\n project_id: int,\n suite_id: int,\n ) -> SuiteJSON:\n \"\"\"Get the details for a suite.\n\n Parameters\n ----------\n project_id: int\n The ID of the project.\n suite_id: int\n The ID of the suite.\n \"\"\"\n\n method = 'GET'\n url: Url = self.client.base_url / UrlPath(\n f'/projects/{project_id}' f'/suites/{suite_id}.json'\n )\n\n response: Response = self.client._request(\n method=method,\n url=url,\n )\n suite_json: SuiteJSON = response.json()\n\n return suite_json\n\n def _create(\n self,\n *,\n project_id: int,\n suite: SuiteJSON,\n ) -> SuiteJSON:\n \"\"\"Create a suite.\n\n Parameters\n ----------\n project_id: int\n The ID of the project.\n suite: SuiteJSON\n\n name: str\n Name of the suite.\n plan_id: int, optional\n Associated test plan.\n \"\"\"\n\n method = 'POST'\n url: Url = self.client.base_url / UrlPath(\n f'/projects/{project_id}' '/suites.json'\n )\n\n data = dict(suite=suite)\n\n response: Response = self.client._request(\n method=method, url=url, json=data\n )\n suite_json: SuiteJSON = response.json()\n\n return suite_json\n\n def _update(\n self,\n *,\n project_id: int,\n suite_id: int,\n suite: SuiteJSON,\n ) -> SuiteJSON:\n \"\"\"Update a suite.\n\n Parameters\n ----------\n project_id: int\n The ID of the project.\n suite_id: int\n The ID of the suite.\n suite: SuiteJSON\n\n name: str\n Name of the suite.\n plan_id: int\n Associated test plan.\n \"\"\"\n\n method = 'PATCH'\n url: Url = self.client.base_url / UrlPath(\n f'/projects/{project_id}' f'/suites/{suite_id}.json'\n )\n data = dict(suite=suite)\n\n response: Response = self.client._request(\n method=method,\n url=url,\n json=data,\n )\n suite_json: SuiteJSON = response.json()\n\n return suite_json\n\n def _delete(\n self,\n *,\n project_id: int,\n suite_id: int,\n ) -> None:\n \"\"\"Delete a suite.\n\n Parameters\n ----------\n project_id: int\n The ID of the project.\n suite_id: int\n The ID of the suite.\n \"\"\"\n\n method = 'DELETE'\n url: Url = self.client.base_url / UrlPath(\n f'/projects/{project_id}' f'/suites/{suite_id}.json'\n )\n\n response: Response = self.client._request(method=method, url=url)\n\n status_code: int = response.status_code\n if status_code != 204:\n print(f'Unexpected response code: {status_code}')\n\n return None\n", "repo_name": "AceofSpades5757/testlodge", "sub_path": "src/testlodge/api/suite.py", "file_name": "suite.py", "file_ext": "py", "file_size_in_byte": 4653, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "enum.IntEnum", "line_number": 12, "usage_type": "name"}, {"api_name": "enum.auto", "line_number": 15, "usage_type": "call"}, {"api_name": "enum.auto", "line_number": 16, "usage_type": "call"}, {"api_name": "enum.auto", "line_number": 17, "usage_type": "call"}, {"api_name": "testlodge.api.base.BaseAPI", "line_number": 20, "usage_type": "name"}, {"api_name": "furl.furl.furl", "line_number": 56, "usage_type": "name"}, {"api_name": "furl.Path", "line_number": 56, "usage_type": "call"}, {"api_name": "requests.models.Response", "line_number": 65, "usage_type": "name"}, {"api_name": "testlodge.typing.suite.SuiteListJSON", "line_number": 68, "usage_type": "name"}, {"api_name": "testlodge.typing.suite.SuiteListJSON", "line_number": 40, "usage_type": "name"}, {"api_name": "furl.furl.furl", "line_number": 89, "usage_type": "name"}, {"api_name": "furl.Path", "line_number": 89, "usage_type": "call"}, {"api_name": "requests.models.Response", "line_number": 93, "usage_type": "name"}, {"api_name": "testlodge.typing.suite.SuiteJSON", "line_number": 97, "usage_type": "name"}, {"api_name": "testlodge.typing.suite.SuiteJSON", "line_number": 77, "usage_type": "name"}, {"api_name": "testlodge.typing.suite.SuiteJSON", "line_number": 105, "usage_type": "name"}, {"api_name": "furl.furl.furl", "line_number": 122, "usage_type": "name"}, {"api_name": "furl.Path", "line_number": 122, "usage_type": "call"}, {"api_name": "requests.models.Response", "line_number": 128, "usage_type": "name"}, {"api_name": "testlodge.typing.suite.SuiteJSON", "line_number": 131, "usage_type": "name"}, {"api_name": "testlodge.typing.suite.SuiteJSON", "line_number": 106, "usage_type": "name"}, {"api_name": "testlodge.typing.suite.SuiteJSON", "line_number": 140, "usage_type": "name"}, {"api_name": "furl.furl.furl", "line_number": 159, "usage_type": "name"}, {"api_name": "furl.Path", "line_number": 159, "usage_type": "call"}, {"api_name": "requests.models.Response", "line_number": 164, "usage_type": "name"}, {"api_name": "testlodge.typing.suite.SuiteJSON", "line_number": 169, "usage_type": "name"}, {"api_name": "testlodge.typing.suite.SuiteJSON", "line_number": 141, "usage_type": "name"}, {"api_name": "furl.furl.furl", "line_number": 190, "usage_type": "name"}, {"api_name": "furl.Path", "line_number": 190, "usage_type": "call"}, {"api_name": "requests.models.Response", "line_number": 194, "usage_type": "name"}]} +{"seq_id": "10476784331", "text": "# encoding: utf-8\nimport datetime\nfrom south.db import db\nfrom south.v2 import SchemaMigration\nfrom django.db import models\n\nclass Migration(SchemaMigration):\n\n def forwards(self, orm):\n \n # Deleting field 'SummaryAttribute.source_url'\n db.delete_column('h1ds_summary_summaryattribute', 'source_url')\n\n # Adding field 'SummaryAttribute.source'\n db.add_column('h1ds_summary_summaryattribute', 'source', self.gf('django.db.models.fields.CharField')(default='changeme', max_length=4096), keep_default=False)\n\n\n def backwards(self, orm):\n \n # User chose to not deal with backwards NULL issues for 'SummaryAttribute.source_url'\n raise RuntimeError(\"Cannot reverse this migration. 'SummaryAttribute.source_url' and its values cannot be restored.\")\n\n # Deleting field 'SummaryAttribute.source'\n db.delete_column('h1ds_summary_summaryattribute', 'source')\n\n\n models = {\n 'h1ds_summary.summaryattribute': {\n 'Meta': {'ordering': \"['display_order']\", 'object_name': 'SummaryAttribute'},\n 'description': ('django.db.models.fields.TextField', [], {}),\n 'display_order': ('django.db.models.fields.IntegerField', [], {'default': '1000'}),\n 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),\n 'is_default': ('django.db.models.fields.BooleanField', [], {'default': 'False'}),\n 'name': ('django.db.models.fields.CharField', [], {'max_length': '500'}),\n 'slug': ('django.db.models.fields.SlugField', [], {'unique': 'True', 'max_length': '100', 'db_index': 'True'}),\n 'source': ('django.db.models.fields.CharField', [], {'max_length': '4096'})\n }\n }\n\n complete_apps = ['h1ds_summary']\n", "repo_name": "h1ds/h1ds", "sub_path": "h1ds/h1ds_summary/migrations/0016_auto__del_field_summaryattribute_source_url__add_field_summaryattribut.py", "file_name": "0016_auto__del_field_summaryattribute_source_url__add_field_summaryattribut.py", "file_ext": "py", "file_size_in_byte": 1776, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "south.v2.SchemaMigration", "line_number": 7, "usage_type": "name"}, {"api_name": "south.db.db.delete_column", "line_number": 12, "usage_type": "call"}, {"api_name": "south.db.db", "line_number": 12, "usage_type": "name"}, {"api_name": "south.db.db.add_column", "line_number": 15, "usage_type": "call"}, {"api_name": "south.db.db", "line_number": 15, "usage_type": "name"}, {"api_name": "south.db.db.delete_column", "line_number": 24, "usage_type": "call"}, {"api_name": "south.db.db", "line_number": 24, "usage_type": "name"}, {"api_name": "django.db.models", "line_number": 27, "usage_type": "name"}]} +{"seq_id": "74105226668", "text": "from flask_wtf import FlaskForm\nfrom wtforms import IntegerField, StringField, HiddenField, validators\nfrom wtforms.ext.sqlalchemy.fields import QuerySelectField\n\nfrom application import db\n\nfrom flask_login import current_user\n\nfrom application.worktypes.models import WorkType\nfrom application.projects.models import Project\n\ndef work_type_choices():\n return WorkType.query.all()\n\ndef project_choices():\n return Project.find_projects_user_is_assigned_to(current_user.id)\n\nclass TimeLogForm(FlaskForm):\n hours = IntegerField(\"Hours\")\n project = QuerySelectField(\"Project\", query_factory = project_choices, get_label = \"name\")\n work_type = QuerySelectField(\"Work type\", query_factory = work_type_choices, get_label = \"name\")\n description = StringField(\"Log description\", [validators.Length(min=2, max=144, message=\"Log description must be between 2 and 144 characters.\")])\n\n class Meta:\n csrf = False\n\nclass TimeLogEditForm(FlaskForm):\n id = HiddenField()\n hours = IntegerField(\"Hours\")\n project = QuerySelectField(\"Project\", query_factory = project_choices, get_label = \"name\")\n work_type = QuerySelectField(\"Work type\", query_factory = work_type_choices, get_label = \"name\")\n description = StringField(\"Log description\", [validators.Length(min=2, max=144, message=\"Log description must be between 2 and 144 characters.\")])\n\n class Meta:\n csrf = False", "repo_name": "emmalait/project-time-mgmt", "sub_path": "application/timelogs/forms.py", "file_name": "forms.py", "file_ext": "py", "file_size_in_byte": 1405, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "37", "api": [{"api_name": "application.worktypes.models.WorkType.query.all", "line_number": 13, "usage_type": "call"}, {"api_name": "application.worktypes.models.WorkType.query", "line_number": 13, "usage_type": "attribute"}, {"api_name": "application.worktypes.models.WorkType", "line_number": 13, "usage_type": "name"}, {"api_name": "application.projects.models.Project.find_projects_user_is_assigned_to", "line_number": 16, "usage_type": "call"}, {"api_name": "application.projects.models.Project", "line_number": 16, "usage_type": "name"}, {"api_name": "flask_login.current_user.id", "line_number": 16, "usage_type": "attribute"}, {"api_name": "flask_login.current_user", "line_number": 16, "usage_type": "name"}, {"api_name": "flask_wtf.FlaskForm", "line_number": 18, "usage_type": "name"}, {"api_name": "wtforms.IntegerField", "line_number": 19, "usage_type": "call"}, {"api_name": "wtforms.ext.sqlalchemy.fields.QuerySelectField", "line_number": 20, "usage_type": "call"}, {"api_name": "wtforms.ext.sqlalchemy.fields.QuerySelectField", "line_number": 21, "usage_type": "call"}, {"api_name": "wtforms.StringField", "line_number": 22, "usage_type": "call"}, {"api_name": "wtforms.validators.Length", "line_number": 22, "usage_type": "call"}, {"api_name": "wtforms.validators", "line_number": 22, "usage_type": "name"}, {"api_name": "flask_wtf.FlaskForm", "line_number": 27, "usage_type": "name"}, {"api_name": "wtforms.HiddenField", "line_number": 28, "usage_type": "call"}, {"api_name": "wtforms.IntegerField", "line_number": 29, "usage_type": "call"}, {"api_name": "wtforms.ext.sqlalchemy.fields.QuerySelectField", "line_number": 30, "usage_type": "call"}, {"api_name": "wtforms.ext.sqlalchemy.fields.QuerySelectField", "line_number": 31, "usage_type": "call"}, {"api_name": "wtforms.StringField", "line_number": 32, "usage_type": "call"}, {"api_name": "wtforms.validators.Length", "line_number": 32, "usage_type": "call"}, {"api_name": "wtforms.validators", "line_number": 32, "usage_type": "name"}]} +{"seq_id": "72697354347", "text": "#!/usr/bin/env python\n# coding: utf-8\n\nimport pickle as pickle\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\nimport os\nimport io\nimport requests\nimport zipfile\n\n# # Load Data\n#\n\n# We download the AFRC radial forge data from the link specified.\n#\n# Credits to Christos Tachtatzis for the code to download & extract.\n\n# In[2]:\n\nafrc_data_url = \"https://zenodo.org/record/3405265/files/STRATH%20radial%20forge%20dataset%20v2.zip?download=1\"\n\ndata_path = \"Data_v2\" # folder for dataset\n\n# In[3]:\n\n\ndef download_and_extract(url, destination, force=True):\n if not os.path.exists(data_path):\n os.mkdir(data_path)\n\n response = requests.get(url)\n zipDocument = zipfile.ZipFile(io.BytesIO(response.content))\n\n # Attempt to see if we are going to overwrite anything\n if not force:\n abort = False\n for file in zipDocument.filelist:\n if os.path.isfile(os.path.join(destination, file.filename)):\n print(\n file.filename,\n \"already exists. If you want to overwrite the file call the method with force=True\",\n )\n abort = True\n if abort:\n print(\"Zip file was not extracted.\")\n return\n\n zipDocument.extractall(destination)\n\n\n# In[4]:\n\ndownload_and_extract(afrc_data_url, data_path)\n\n\n# The data is downloaded into the folder 'Data' , now we transform the data into a list of dataframes.\n#\n# Each dataframe in list represents the time-series measurements of all sensors for a part.\n\n\n# ## Load sensor data into dataframes\n\ndata_inputs_list = []\nall_files = [\n file\n for file in os.listdir(\n os.path.join(data_path, \"STRATH radial forge dataset 11Sep19\")\n )\n if (\"Scope\" in file and \"csv\" in file)\n]\nall_files.sort()\n\n# load each part's data as a dataframe to a list\nfor filename in all_files:\n if \"Scope\" in filename and \"csv\" in filename:\n file_csv = pd.read_csv(\n os.path.join(data_path, \"STRATH radial forge dataset 11Sep19\", filename),\n encoding=\"cp1252\",\n )\n data_inputs_list.append(file_csv)\n\n# data_inputs_list = []\n#\n# # load each part's data as a dataframe to a list\n# for filename in os.listdir(\n# os.path.join(data_path, \"STRATH radial forge dataset 11Sep19\")\n# ):\n# if \"Scope\" in filename and \"csv\" in filename:\n# file_csv = pd.read_csv(\n# os.path.join(data_path, \"STRATH radial forge dataset 11Sep19\", filename),\n# encoding=\"ISO-8859-1\",\n# # encoding='cp1252'\n# )\n#\n# data_inputs_list.append(file_csv)\n\n\n# In[8]:\n\n\nlen(data_inputs_list)\n\n\n### Load CMM data into dataframe\n#\n# 1. Read data\n# 2. Subtract the CMM measurements from the \"base value\"\n# 3. Save into a dataframe\n\n# In[4]:\n\n\ndata_path = \"Data_v2\" # folder for dataset\noutput_pd = pd.read_excel(\n os.path.join(data_path, \"STRATH radial forge dataset 11Sep19\", \"CMMData.xlsx\")\n)\n\n# extract necessary output values\noutput_headers = output_pd.columns[4:]\nbase_val = output_pd.values[0, 4:]\noutput_val = output_pd.values[3:, 4:]\n\nnp_data_outputs = np.copy(output_val)\nnp_data_abs_err = np.copy(np_data_outputs)\n\n# extract abs error from expected base values\nfor output in range(np_data_abs_err.shape[1]):\n np_data_abs_err[:, output] -= base_val[output]\nnp_data_abs_err = np.abs(np_data_abs_err)\n\n\n# In[5]:\noutput_df = {}\nfor i, value in enumerate(output_headers):\n new_df = {value: np_data_outputs[:, i]}\n output_df.update(new_df)\noutput_df = pd.DataFrame(output_df)\n\noutput_df_abs_err = {}\nfor i, value in enumerate(output_headers):\n new_df = {value: np_data_abs_err[:, i]}\n output_df_abs_err.update(new_df)\noutput_df_abs_err = pd.DataFrame(output_df_abs_err)\n\n\n# ## Pickle Data\n#\n# Pickle the input & output data for ease of future use\n\n# In[13]:\n\npickle_path = \"pickles\"\ninput_file_name = \"strath_inputs.p\"\noutput_file_name = \"strath_outputs.p\"\noutput_file_name_abs_err = \"strath_outputs_abs_err.p\"\n\n\nif pickle_path not in os.listdir():\n os.mkdir(pickle_path)\n\n# save into pickle file\npickle.dump(data_inputs_list, open(pickle_path + \"/\" + input_file_name, \"wb\"))\npickle.dump(output_df, open(pickle_path + \"/\" + output_file_name, \"wb\"))\npickle.dump(output_df_abs_err, open(pickle_path + \"/\" + output_file_name_abs_err, \"wb\"))\n\nprint(\n \"Data preparation from Zenodo completed as \"\n + input_file_name\n + \" and \"\n + output_file_name\n)\n\n## Preproc STRATH\n\n# In[3]:\nsensor_data = data_inputs_list\n\n# split into forging, heating, transfer phases\nstitched_data = sensor_data[0:]\n\nstitched_data = np.concatenate(stitched_data, axis=0)\n\ncolumn_names = sensor_data[0].columns\n\n# segment based on digital signals of Heat and Force\ndigital_heat = np.diff(stitched_data[:, -1])\ndigital_forge = np.diff((stitched_data[:, 3] > 0).astype(\"int\"))\n\nprint(np.argwhere(column_names == \"$U_GH_HEATON_1 (U25S0)\"))\nprint(np.argwhere(column_names == \"Force [kN]\"))\n\ndigital_heat_diff_index = np.argwhere(digital_heat > 0)\ndigital_forge_start_index = np.argwhere(digital_forge == 1)\ndigital_forge_end_index = np.argwhere(digital_forge == -1)\n\n# for\nheating_traces = [\n stitched_data[digital_heat_diff_index[i][0] : digital_heat_diff_index[i + 1][0]]\n for i in range(digital_heat_diff_index.shape[0])\n if i < (digital_heat_diff_index.shape[0] - 1)\n]\nforging_traces = [\n stitched_data[digital_forge_start[0] : digital_forge_end[0]]\n for digital_forge_start, digital_forge_end in zip(\n digital_forge_start_index, digital_forge_end_index\n )\n]\n\n# verify the number of parts segmented\nif len(heating_traces) != len(sensor_data):\n print(\"STITCHING ERROR IN HEATING PHASE\")\nif len(forging_traces) != len(sensor_data):\n print(\"STITCHING ERROR IN FORGING PHASE\")\n\n# =============PICKLE THEM========\npickle_path = \"pickles\"\n\nif pickle_path not in os.listdir():\n os.mkdir(pickle_path)\n\n# truncate to shortest length\n# cut to smallest trace length\nmin_heat_length = np.array([len(trace) for trace in heating_traces]).min()\nmin_forge_length = np.array([len(trace) for trace in forging_traces]).min()\n\nx_heating = np.array(\n [heating_trace[:min_heat_length] for heating_trace in heating_traces]\n)\nx_forging = np.array(\n [forging_trace[:min_forge_length] for forging_trace in forging_traces]\n)\n\n# build sensor metadata\n# Prepare sensor units and names for labelling\nsensor_units = []\nsensor_names = []\nfor sensor_full_name in column_names:\n temp_ = sensor_full_name.split(\"[\")\n if len(temp_) > 1:\n sensor_units.append(temp_[1][:-1])\n else:\n sensor_units.append(\"\")\n sensor_names.append(temp_[0].strip().replace(\"_\", \"-\").replace(\" \", \"-\"))\nsensor_metadata = pd.DataFrame(\n {\n \"sensor_id\": sensor_names,\n \"sensor_name\": sensor_names,\n \"unit\": sensor_units,\n \"freq\": 100,\n }\n)\n\n# save into pickle file\nfinal_dict = {\n \"heating\": x_heating,\n \"forging\": x_forging,\n \"cmm_data\": np_data_outputs,\n \"cmm_data_abs_err\": np_data_abs_err,\n \"sensor_names\": sensor_names,\n \"cmm_header\": output_headers,\n \"sensor_metadata\": sensor_metadata,\n}\npickle.dump(final_dict, open(pickle_path + \"/\" + \"strath_inputs_outputs.p\", \"wb\"))\n", "repo_name": "bangxiangyong/bottleneck_ae", "sub_path": "case_study/01-Download-STRATH.py", "file_name": "01-Download-STRATH.py", "file_ext": "py", "file_size_in_byte": 7199, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "37", "api": [{"api_name": "os.path.exists", "line_number": 30, "usage_type": "call"}, {"api_name": "os.path", "line_number": 30, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 31, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 33, "usage_type": "call"}, {"api_name": "zipfile.ZipFile", "line_number": 34, "usage_type": "call"}, {"api_name": "io.BytesIO", "line_number": 34, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 40, "usage_type": "call"}, {"api_name": "os.path", "line_number": 40, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 40, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 68, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 69, "usage_type": "call"}, {"api_name": "os.path", "line_number": 69, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 78, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 79, "usage_type": "call"}, {"api_name": "os.path", "line_number": 79, "usage_type": "attribute"}, {"api_name": "pandas.read_excel", "line_number": 116, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 117, "usage_type": "call"}, {"api_name": "os.path", "line_number": 117, "usage_type": "attribute"}, {"api_name": "numpy.copy", "line_number": 125, "usage_type": "call"}, {"api_name": "numpy.copy", "line_number": 126, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 131, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 139, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 145, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 160, "usage_type": "call"}, {"api_name": "os.mkdir", "line_number": 161, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 164, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 165, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 166, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 183, "usage_type": "call"}, {"api_name": "numpy.diff", "line_number": 188, "usage_type": "call"}, {"api_name": "numpy.diff", "line_number": 189, "usage_type": "call"}, {"api_name": "numpy.argwhere", "line_number": 191, "usage_type": "call"}, {"api_name": "numpy.argwhere", "line_number": 192, "usage_type": "call"}, {"api_name": "numpy.argwhere", "line_number": 194, "usage_type": "call"}, {"api_name": "numpy.argwhere", "line_number": 195, "usage_type": "call"}, {"api_name": "numpy.argwhere", "line_number": 196, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 220, "usage_type": "call"}, {"api_name": "os.mkdir", "line_number": 221, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 225, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 226, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 228, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 231, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 246, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 265, "usage_type": "call"}]} +{"seq_id": "39420700128", "text": "import itertools\nimport logging\nfrom typing import *\n\nimport torch\nimport torch.nn as nn\nimport torch.utils.tensorboard as tb\nfrom einops import rearrange\nfrom torch.distributions import Bernoulli\n\nfrom .mnist import HomogenousBinaryMNIST\n\n\nlogger = logging.getLogger()\n\n\ndef gen_all_binary_vectors(length: int) -> torch.Tensor:\n return (\n (torch.arange(2 ** length).unsqueeze(1) >> torch.arange(length - 1, -1, -1)) & 1\n ).float()\n\n\ndef log_1_plus_exp(x: torch.Tensor) -> torch.Tensor:\n \"\"\"Applies elementwise the function f(x) = ln(1 + exp(x_i)).\"\"\"\n result = torch.logsumexp(torch.stack((x, torch.zeros_like(x)), dim=-1), dim=-1)\n assert result.shape == x.shape\n return result\n\n\nclass HomogenousBinaryRBM(nn.Module):\n def __init__(\n self,\n visible_num_vars: int,\n hidden_num_vars: int,\n visible_bias_init: Optional[torch.Tensor] = None,\n dataset_for_visible_bias_init: Optional[HomogenousBinaryMNIST] = None,\n ):\n super().__init__()\n self.visible_num_vars = visible_num_vars\n self.hidden_num_vars = hidden_num_vars\n self.W = nn.Parameter(torch.randn(visible_num_vars, hidden_num_vars) * 1e-4)\n self.visible_bias = nn.Parameter(torch.randn(self.visible_num_vars) * 1e-4)\n self.hidden_bias = nn.Parameter(torch.randn(self.hidden_num_vars) * 1e-4)\n if dataset_for_visible_bias_init is not None:\n assert visible_bias_init is None\n visible_bias_init = self.calc_visible_bias_init(\n dataset_for_visible_bias_init, 1e-30\n )\n if visible_bias_init is not None:\n self.visible_bias.data = visible_bias_init\n\n def unnormalized_log_likelihood(\n self, visible: torch.Tensor, hidden: torch.Tensor\n ) -> torch.Tensor:\n return (\n torch.einsum(\"nm,bn,bm->b\", self.W, visible, hidden)\n + torch.einsum(\"n,bn->b\", self.visible_bias, visible)\n + torch.einsum(\"m,bm->b\", self.hidden_bias, hidden)\n )\n\n def log_conditional_visible_likelihood(\n self,\n what: torch.Tensor,\n condition: torch.Tensor,\n all_what: Optional[torch.Tensor] = None,\n ) -> torch.Tensor:\n \"\"\"Calculates ln p(what|condition) = ln p(what, condition) - ln \\sum_{what'} p(what', condition). The sum is\n performed over all_what. If all_what is not passed, the sum is performed over all binary vectors of\n matching length. all_what must be binary and contain what.\n It is assumed that condition is the first part of visible, and\n what is the last part of visible. Sure it can be done any other way in principle,\n but fuck writing additional code.\"\"\"\n # how to calculate it\n # concat all_what with condition while respecting the batch - get a \"visible\"\n # calculate unnormalized log likelihood of \"visible\"\n # calculate the result using logsumexp\n batch_size, what_size = what.shape\n condition_size = condition.shape[1]\n assert condition_size + what_size == self.visible_num_vars\n if all_what is None:\n all_what = gen_all_binary_vectors(what_size)\n num_all_what = all_what.shape[0]\n all_what_duplicated = torch.stack(tuple(itertools.repeat(all_what, batch_size)))\n assert all_what_duplicated.shape == (batch_size, num_all_what, what_size)\n condition_duplicated = rearrange(\n torch.stack(tuple(itertools.repeat(condition, num_all_what))),\n \"a b n -> b a n\",\n a=num_all_what,\n b=batch_size,\n )\n visible = torch.cat((condition_duplicated, all_what_duplicated), dim=2)\n assert visible.shape == (batch_size, num_all_what, self.visible_num_vars)\n unnormalized_ll_of_all = rearrange(\n self.log_unnormalized_marginal_likelihood_of_visible(\n rearrange(visible, \"b a n -> (b a) n\")\n ),\n \"(b a)-> b a\",\n b=batch_size,\n )\n # now we look for index of each what[b] in all_what\n what_match = torch.all(what.unsqueeze(0) == all_what.unsqueeze(1), dim=-1)\n # what_match[a, b] is True iff all_what[a] is the same as what[b]\n assert torch.all(what_match.sum(dim=0) == 1)\n unnormalized_ll = torch.einsum(\n \"ab,ba->b\", what_match.float(), unnormalized_ll_of_all\n )\n # unnormalized_ll[b] equals unnormalized_ll(condition[b], what[b])\n unnormalized_marginal_ll_of_condition = unnormalized_ll_of_all.logsumexp(dim=1)\n assert unnormalized_ll.shape == unnormalized_marginal_ll_of_condition.shape\n return unnormalized_ll - unnormalized_marginal_ll_of_condition\n\n def unnormalized_likelihood(\n self, visible: torch.Tensor, hidden: torch.Tensor\n ) -> torch.Tensor:\n return torch.exp(self.unnormalized_log_likelihood(visible, hidden))\n\n def log_unnormalized_marginal_likelihood_of_visible(\n self, visible: torch.Tensor\n ) -> torch.Tensor:\n foo = torch.einsum(\"bn,n->b\", visible, self.visible_bias)\n bar = self.hidden_bias + torch.einsum(\"nm,bn->bm\", self.W, visible)\n buzz = torch.sum(log_1_plus_exp(bar), dim=1)\n assert buzz.shape == foo.shape\n result = foo + buzz\n assert torch.all(torch.isfinite(result))\n return result\n\n def log_unnormalized_marginal_likelihood_of_hidden(\n self, hidden: torch.Tensor\n ) -> torch.Tensor:\n \"\"\"This method and log_unnormalized_marginal_likelihood_of_visible are\n mirrored copy pastes of each other.\"\"\"\n foo = torch.einsum(\"bm,m->b\", hidden, self.hidden_bias)\n bar = self.visible_bias + torch.einsum(\"nm,bm->bn\", self.W, hidden)\n buzz = torch.sum(log_1_plus_exp(bar), dim=1)\n assert buzz.shape == foo.shape\n result = foo + buzz\n assert torch.all(torch.isfinite(result))\n return result\n\n def log_normalization_constant(self) -> torch.Tensor:\n \"\"\"returns ln(Z), such that unnormalized_likelihood / Z = likelihood.\"\"\"\n assert self.hidden_num_vars <= 20\n hidden = gen_all_binary_vectors(self.hidden_num_vars)\n return torch.logsumexp(\n self.log_unnormalized_marginal_likelihood_of_hidden(hidden), dim=0\n )\n\n def log_marginal_likelihood_of_visible(\n self,\n visible: torch.Tensor,\n log_normalization_constant: Optional[torch.Tensor] = None,\n ) -> torch.Tensor:\n if log_normalization_constant is None:\n log_normalization_constant = self.log_normalization_constant()\n assert log_normalization_constant.shape == ()\n return (\n self.log_unnormalized_marginal_likelihood_of_visible(visible)\n - log_normalization_constant\n )\n\n def gibbs_sample(\n self,\n batch_size: int,\n num_steps: int,\n tb_writer: Optional[tb.SummaryWriter] = None,\n tb_tag_many_records: Optional[str] = None,\n tb_tag_one_record: Optional[str] = None,\n hidden_base_rate: float = 0.5,\n ) -> torch.Tensor:\n assert batch_size > 0\n assert num_steps >= 2\n with torch.no_grad():\n hidden = Bernoulli(hidden_base_rate).sample(\n (batch_size, self.hidden_num_vars)\n )\n hidden_history = []\n visible_history = []\n for step in range(num_steps):\n # calculate the conditional distribution p(visible|hidden)\n # it'll be a multivariate bernoulli distribution\n p_vis_given_hid = Bernoulli(\n torch.sigmoid(\n self.visible_bias + torch.einsum(\"nm,bm->bn\", self.W, hidden)\n )\n )\n visible = p_vis_given_hid.sample()\n if tb_tag_many_records is not None:\n tb_writer.add_histogram(\n f\"{tb_tag_many_records}_expectation_of_visible\",\n p_vis_given_hid.mean,\n step,\n )\n tb_writer.add_scalar(\n f\"{tb_tag_many_records}_mean_of_expectation_of_visible\",\n p_vis_given_hid.mean.mean(),\n step,\n )\n tb_writer.add_image(\n f\"{tb_tag_many_records}_expectation_of_visible_of_image_part\",\n HomogenousBinaryMNIST.extract_images(\n p_vis_given_hid.mean[0]\n ).squeeze(),\n step,\n dataformats=\"HW\",\n )\n if step > 0:\n tb_writer.add_histogram(\n f\"{tb_tag_many_records}_expectation_of_hidden\",\n p_hid_given_vis.mean,\n step,\n )\n tb_writer.add_scalar(\n f\"{tb_tag_many_records}_mean of expectation of hidden\",\n p_hid_given_vis.mean.mean(),\n step,\n )\n tb_writer.add_scalar(\n f\"{tb_tag_many_records}_hidden at 0,0\", hidden[0, 0], step\n )\n hidden_history.append(hidden)\n visible_history.append(visible)\n p_hid_given_vis = Bernoulli(\n torch.sigmoid(\n self.hidden_bias + torch.einsum(\"nm,bn->bm\", self.W, visible)\n )\n )\n hidden = p_hid_given_vis.sample()\n # do Rubin-Gelman convergence diagnostic\n # https://stats.stackexchange.com/questions/99375/gelman-and-rubin-convergence-diagnostic-how-to-generalise-to-work-with-vectors\n # and BMoML Sk lecture 12 slides\n last_half = torch.cat(\n (torch.stack(hidden_history), torch.stack(visible_history)), dim=2\n )[round(len(hidden_history) / 2) :]\n # shape: chain length × batch size × (visible+hidden) size\n\n half_chain_len = last_half.shape[0]\n variables_size = last_half.shape[-1]\n within_chain_var = torch.mean(torch.var(last_half, dim=0), dim=0) + 1e-15\n assert within_chain_var.shape == (variables_size,)\n between_chain_var = (\n torch.var(torch.mean(last_half, dim=0), dim=0) * half_chain_len\n )\n assert between_chain_var.shape == within_chain_var.shape\n weighted_sum_of_vars = (\n within_chain_var * (half_chain_len - 1) / half_chain_len\n + between_chain_var / half_chain_len\n )\n gelman_rubin_statistic = torch.sqrt(weighted_sum_of_vars / within_chain_var)\n assert torch.all(torch.isfinite(gelman_rubin_statistic))\n threshold = 1.2\n num_unconverged_components = torch.sum(gelman_rubin_statistic > 1.2)\n log_str = f\"num_unconverged_components / variables_size = {num_unconverged_components} / {variables_size}\"\n if tb_tag_one_record is not None:\n tb_writer.add_text(f\"{tb_tag_one_record}\", log_str, step)\n tb_writer.add_histogram(\n f\"{tb_tag_one_record}_gelman_rubin_statistic\",\n gelman_rubin_statistic,\n step,\n )\n tb_writer.add_histogram(\n f\"{tb_tag_one_record}_within_chain_var\", within_chain_var, step\n )\n tb_writer.add_histogram(\n f\"{tb_tag_one_record}_between_chain_var\", between_chain_var, step\n )\n tb_writer.add_histogram(\n f\"{tb_tag_one_record}_weighted_sum_of_vars\",\n weighted_sum_of_vars,\n step,\n )\n if num_unconverged_components > 0:\n logger.warning(log_str)\n return visible, hidden\n\n @staticmethod\n def calc_visible_bias_init(\n train_dataset: HomogenousBinaryMNIST, eps: float\n ) -> torch.Tensor:\n visible_base_rate = train_dataset.data.mean(dim=0).clamp(eps, 1 - eps)\n return torch.log(visible_base_rate) - torch.log(1 - visible_base_rate)\n\n\n# TODO: add visualization (as an image) of hidden during gibbs sampling\n# TODO: add visualization (as an image) of what each hidden unit does\n", "repo_name": "philip-bl/tensor_decompositions", "sub_path": "rbm/rbm/rbm.py", "file_name": "rbm.py", "file_ext": "py", "file_size_in_byte": 12519, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "37", "api": [{"api_name": "logging.getLogger", "line_number": 14, "usage_type": "call"}, {"api_name": "torch.arange", "line_number": 19, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 17, "usage_type": "attribute"}, {"api_name": "torch.Tensor", "line_number": 23, "usage_type": "attribute"}, {"api_name": "torch.logsumexp", "line_number": 25, "usage_type": "call"}, {"api_name": "torch.stack", "line_number": 25, "usage_type": "call"}, {"api_name": "torch.zeros_like", "line_number": 25, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 30, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 30, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 35, "usage_type": "attribute"}, {"api_name": "mnist.HomogenousBinaryMNIST", "line_number": 36, "usage_type": "name"}, {"api_name": "torch.nn.Parameter", "line_number": 41, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 41, "usage_type": "name"}, {"api_name": "torch.randn", "line_number": 41, "usage_type": "call"}, {"api_name": "torch.nn.Parameter", "line_number": 42, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 42, "usage_type": "name"}, {"api_name": "torch.randn", "line_number": 42, "usage_type": "call"}, {"api_name": "torch.nn.Parameter", "line_number": 43, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 43, "usage_type": "name"}, {"api_name": "torch.randn", "line_number": 43, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 53, "usage_type": "attribute"}, {"api_name": "torch.einsum", "line_number": 56, "usage_type": "call"}, {"api_name": "torch.einsum", "line_number": 57, "usage_type": "call"}, {"api_name": "torch.einsum", "line_number": 58, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 54, "usage_type": "attribute"}, {"api_name": "torch.Tensor", "line_number": 63, "usage_type": "attribute"}, {"api_name": "torch.Tensor", "line_number": 64, "usage_type": "attribute"}, {"api_name": "torch.Tensor", "line_number": 65, "usage_type": "attribute"}, {"api_name": "torch.stack", "line_number": 83, "usage_type": "call"}, {"api_name": "itertools.repeat", "line_number": 83, "usage_type": "call"}, {"api_name": "einops.rearrange", "line_number": 85, "usage_type": "call"}, {"api_name": "torch.stack", "line_number": 86, "usage_type": "call"}, {"api_name": "itertools.repeat", "line_number": 86, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 91, "usage_type": "call"}, {"api_name": "einops.rearrange", "line_number": 93, "usage_type": "call"}, {"api_name": "einops.rearrange", "line_number": 95, "usage_type": "call"}, {"api_name": "torch.all", "line_number": 101, "usage_type": "call"}, {"api_name": "torch.all", "line_number": 103, "usage_type": "call"}, {"api_name": "torch.einsum", "line_number": 104, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 66, "usage_type": "attribute"}, {"api_name": "torch.Tensor", "line_number": 113, "usage_type": "attribute"}, {"api_name": "torch.exp", "line_number": 115, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 114, "usage_type": "attribute"}, {"api_name": "torch.Tensor", "line_number": 118, "usage_type": "attribute"}, {"api_name": "torch.einsum", "line_number": 120, "usage_type": "call"}, {"api_name": "torch.einsum", "line_number": 121, "usage_type": "call"}, {"api_name": "torch.sum", "line_number": 122, "usage_type": "call"}, {"api_name": "torch.all", "line_number": 125, "usage_type": "call"}, {"api_name": "torch.isfinite", "line_number": 125, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 119, "usage_type": "attribute"}, {"api_name": "torch.Tensor", "line_number": 129, "usage_type": "attribute"}, {"api_name": "torch.einsum", "line_number": 133, "usage_type": "call"}, {"api_name": "torch.einsum", "line_number": 134, "usage_type": "call"}, {"api_name": "torch.sum", "line_number": 135, "usage_type": "call"}, {"api_name": "torch.all", "line_number": 138, "usage_type": "call"}, {"api_name": "torch.isfinite", "line_number": 138, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 130, "usage_type": "attribute"}, {"api_name": "torch.logsumexp", "line_number": 145, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 141, "usage_type": "attribute"}, {"api_name": "torch.Tensor", "line_number": 151, "usage_type": "attribute"}, {"api_name": "torch.Tensor", "line_number": 152, "usage_type": "attribute"}, {"api_name": "torch.Tensor", "line_number": 153, "usage_type": "attribute"}, {"api_name": "torch.utils.tensorboard.SummaryWriter", "line_number": 166, "usage_type": "attribute"}, {"api_name": "torch.utils.tensorboard", "line_number": 166, "usage_type": "name"}, {"api_name": "torch.no_grad", "line_number": 173, "usage_type": "call"}, {"api_name": "torch.distributions.Bernoulli", "line_number": 174, "usage_type": "call"}, {"api_name": "torch.distributions.Bernoulli", "line_number": 182, "usage_type": "call"}, {"api_name": "torch.sigmoid", "line_number": 183, "usage_type": "call"}, {"api_name": "torch.einsum", "line_number": 184, "usage_type": "call"}, {"api_name": "mnist.HomogenousBinaryMNIST.extract_images", "line_number": 201, "usage_type": "call"}, {"api_name": "mnist.HomogenousBinaryMNIST", "line_number": 201, "usage_type": "name"}, {"api_name": "torch.distributions.Bernoulli", "line_number": 223, "usage_type": "call"}, {"api_name": "torch.sigmoid", "line_number": 224, "usage_type": "call"}, {"api_name": "torch.einsum", "line_number": 225, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 232, "usage_type": "call"}, {"api_name": "torch.stack", "line_number": 233, "usage_type": "call"}, {"api_name": "torch.mean", "line_number": 239, "usage_type": "call"}, {"api_name": "torch.var", "line_number": 239, "usage_type": "call"}, {"api_name": "torch.var", "line_number": 242, "usage_type": "call"}, {"api_name": "torch.mean", "line_number": 242, "usage_type": "call"}, {"api_name": "torch.sqrt", "line_number": 249, "usage_type": "call"}, {"api_name": "torch.all", "line_number": 250, "usage_type": "call"}, {"api_name": "torch.isfinite", "line_number": 250, "usage_type": "call"}, {"api_name": "torch.sum", "line_number": 252, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 170, "usage_type": "attribute"}, {"api_name": "mnist.HomogenousBinaryMNIST", "line_number": 278, "usage_type": "name"}, {"api_name": "torch.log", "line_number": 281, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 279, "usage_type": "attribute"}]} +{"seq_id": "72243330027", "text": "# -*- coding:utf-8 -*-\nimport os\nimport cv2\nimport csv\nimport argparse\nfrom scipy.io import loadmat\nimport gen_density_map\n\n\ndef main(args):\n dataset = args.dataset\n path = ''.join(['./data/original/shanghaitech/part_', dataset, '_final/test_data/images/'])\n gt_path = ''.join(['./data/original/shanghaitech/part_', dataset, '_final/test_data/ground_truth/'])\n gt_path_csv = ''.join(['./data/original/shanghaitech/part_', dataset, '_final/test_data/ground_truth_csv/'])\n if not os.path.exists(gt_path_csv):\n os.makedirs(gt_path_csv)\n if dataset == 'A':\n num_images = 182\n else:\n num_images = 316\n\n for i in range(1, num_images+1):\n if i % 10 == 0:\n print('Processing {}/{} files'.format(i, num_images), '\\nwriting to {}'.format(''.join([gt_path_csv, 'IMG_', str(i), '.csv'])))\n image_info = loadmat(''.join((gt_path, 'GT_IMG_', str(i), '.mat')))['image_info']\n input_img_name = ''.join((path, 'IMG_', str(i), '.jpg'))\n im = cv2.imread(input_img_name, 0)\n annPoints = image_info[0][0][0][0][0] - 1\n im_density = gen_density_map.gen_density_map(im, annPoints)\n with open(''.join([gt_path_csv, 'IMG_', str(i), '.csv']), 'w', newline='') as fout:\n writer = csv.writer(fout)\n writer.writerows(im_density)\n\n\nif __name__ == '__main__':\n parser = argparse.ArgumentParser()\n parser.add_argument(\"dataset\", help=\"the dataset you want to create\", choices=['A', 'B'])\n args = parser.parse_args()\n main(args)\n", "repo_name": "ybcc2015/MCNN_in_Keras", "sub_path": "data_preparation/create_gt_test_set_shtech.py", "file_name": "create_gt_test_set_shtech.py", "file_ext": "py", "file_size_in_byte": 1535, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "37", "api": [{"api_name": "os.path.exists", "line_number": 15, "usage_type": "call"}, {"api_name": "os.path", "line_number": 15, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 16, "usage_type": "call"}, {"api_name": "scipy.io.loadmat", "line_number": 25, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 27, "usage_type": "call"}, {"api_name": "gen_density_map.gen_density_map", "line_number": 29, "usage_type": "call"}, {"api_name": "csv.writer", "line_number": 31, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 36, "usage_type": "call"}]} +{"seq_id": "12637072221", "text": "import rcssmin\nimport rjsmin\nimport sass\n\n\"\"\"\nThis script does 3 things:\n - Compiles scss to css\n - Minifies css\n - Minifies JavaScript\n\"\"\"\n\n# Map scss source files to css destination files\nsass_map = {\"static/css/screen.scss\": \"static/css/screen.css\"}\n\n# Map un-minified css source files to minified css destination files\ncss_map = {\"static/css/screen.css\": \"static/css/screen.min.css\"}\n\n# Map un-minified JavaScript source files to minified JavaScript destination files\njs_map = {\"static/javascript/app.js\": \"static/javascript/app.min.js\"}\n\n\ndef compile_sass_to_css(sass_map):\n\n print(\"Compiling scss to css:\")\n\n for source, dest in sass_map.items():\n with open(dest, \"w\") as outfile:\n outfile.write(sass.compile(filename=source))\n print(f\"{source} compiled to {dest}\")\n\n\ndef minify_css(css_map):\n\n print(\"Minifying css files:\")\n\n for source, dest in css_map.items():\n with open(source, \"r\") as infile:\n with open(dest, \"w\") as outfile:\n outfile.write(rcssmin.cssmin(infile.read()))\n print(f\"{source} minified to {dest}\")\n\n\ndef minify_javascript(js_map):\n\n print(\"Minifying JavaScript files:\")\n\n for source, dest in js_map.items():\n with open(source, \"r\") as infile:\n with open(dest, \"w\") as outfile:\n outfile.write(rjsmin.jsmin(infile.read()))\n print(f\"{source} minified to {dest}\")\n\n\nif __name__ == \"__main__\":\n print()\n print(\"Starting runner\")\n print(\"--------------------\")\n compile_sass_to_css(sass_map)\n print(\"--------------------\")\n minify_css(css_map)\n print(\"--------------------\")\n minify_javascript(js_map)\n print(\"--------------------\")\n print(\"Done\")\n print()", "repo_name": "dr-spaceman/axnh", "sub_path": "runner.py", "file_name": "runner.py", "file_ext": "py", "file_size_in_byte": 1741, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "37", "api": [{"api_name": "sass.compile", "line_number": 28, "usage_type": "call"}, {"api_name": "rcssmin.cssmin", "line_number": 39, "usage_type": "call"}, {"api_name": "rjsmin.jsmin", "line_number": 50, "usage_type": "call"}]} +{"seq_id": "15780474318", "text": "from __future__ import annotations\nfrom dataclasses import dataclass, field\nfrom kiota_abstractions.serialization import AdditionalDataHolder, Parsable, ParseNode, SerializationWriter\nfrom kiota_abstractions.store import BackedModel, BackingStore, BackingStoreFactorySingleton\nfrom typing import Any, Callable, Dict, List, Optional, TYPE_CHECKING, Union\n\nif TYPE_CHECKING:\n from ........models.attribute_definition import AttributeDefinition\n from ........models.expression_input_object import ExpressionInputObject\n\n@dataclass\nclass ParseExpressionPostRequestBody(AdditionalDataHolder, BackedModel, Parsable):\n # Stores model information.\n backing_store: BackingStore = field(default_factory=BackingStoreFactorySingleton(backing_store_factory=None).backing_store_factory.create_backing_store, repr=False)\n\n # Stores additional data not described in the OpenAPI description found when deserializing. Can be used for serialization as well.\n additional_data: Dict[str, Any] = field(default_factory=dict)\n # The expression property\n expression: Optional[str] = None\n # The targetAttributeDefinition property\n target_attribute_definition: Optional[AttributeDefinition] = None\n # The testInputObject property\n test_input_object: Optional[ExpressionInputObject] = None\n \n @staticmethod\n def create_from_discriminator_value(parse_node: Optional[ParseNode] = None) -> ParseExpressionPostRequestBody:\n \"\"\"\n Creates a new instance of the appropriate class based on discriminator value\n param parse_node: The parse node to use to read the discriminator value and create the object\n Returns: ParseExpressionPostRequestBody\n \"\"\"\n if not parse_node:\n raise TypeError(\"parse_node cannot be null.\")\n return ParseExpressionPostRequestBody()\n \n def get_field_deserializers(self,) -> Dict[str, Callable[[ParseNode], None]]:\n \"\"\"\n The deserialization information for the current model\n Returns: Dict[str, Callable[[ParseNode], None]]\n \"\"\"\n from ........models.attribute_definition import AttributeDefinition\n from ........models.expression_input_object import ExpressionInputObject\n\n from ........models.attribute_definition import AttributeDefinition\n from ........models.expression_input_object import ExpressionInputObject\n\n fields: Dict[str, Callable[[Any], None]] = {\n \"expression\": lambda n : setattr(self, 'expression', n.get_str_value()),\n \"targetAttributeDefinition\": lambda n : setattr(self, 'target_attribute_definition', n.get_object_value(AttributeDefinition)),\n \"testInputObject\": lambda n : setattr(self, 'test_input_object', n.get_object_value(ExpressionInputObject)),\n }\n return fields\n \n def serialize(self,writer: SerializationWriter) -> None:\n \"\"\"\n Serializes information the current object\n param writer: Serialization writer to use to serialize this model\n Returns: None\n \"\"\"\n if not writer:\n raise TypeError(\"writer cannot be null.\")\n writer.write_str_value(\"expression\", self.expression)\n writer.write_object_value(\"targetAttributeDefinition\", self.target_attribute_definition)\n writer.write_object_value(\"testInputObject\", self.test_input_object)\n writer.write_additional_data_value(self.additional_data)\n \n\n", "repo_name": "microsoftgraph/msgraph-sdk-python", "sub_path": "msgraph/generated/applications/item/synchronization/templates/item/schema/parse_expression/parse_expression_post_request_body.py", "file_name": "parse_expression_post_request_body.py", "file_ext": "py", "file_size_in_byte": 3424, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 186, "dataset": "github-code", "pt": "37", "api": [{"api_name": "typing.TYPE_CHECKING", "line_number": 7, "usage_type": "name"}, {"api_name": "kiota_abstractions.serialization.AdditionalDataHolder", "line_number": 12, "usage_type": "name"}, {"api_name": "kiota_abstractions.store.BackedModel", "line_number": 12, "usage_type": "name"}, {"api_name": "kiota_abstractions.serialization.Parsable", "line_number": 12, "usage_type": "name"}, {"api_name": "kiota_abstractions.store.BackingStore", "line_number": 14, "usage_type": "name"}, {"api_name": "dataclasses.field", "line_number": 14, "usage_type": "call"}, {"api_name": "kiota_abstractions.store.BackingStoreFactorySingleton", "line_number": 14, "usage_type": "call"}, {"api_name": "typing.Dict", "line_number": 17, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 17, "usage_type": "name"}, {"api_name": "dataclasses.field", "line_number": 17, "usage_type": "call"}, {"api_name": "typing.Optional", "line_number": 19, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 21, "usage_type": "name"}, {"api_name": "models.attribute_definition.AttributeDefinition", "line_number": 21, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 23, "usage_type": "name"}, {"api_name": "models.expression_input_object.ExpressionInputObject", "line_number": 23, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 26, "usage_type": "name"}, {"api_name": "kiota_abstractions.serialization.ParseNode", "line_number": 26, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 47, "usage_type": "name"}, {"api_name": "typing.Callable", "line_number": 47, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 47, "usage_type": "name"}, {"api_name": "models.attribute_definition.AttributeDefinition", "line_number": 49, "usage_type": "argument"}, {"api_name": "models.expression_input_object.ExpressionInputObject", "line_number": 50, "usage_type": "argument"}, {"api_name": "typing.Dict", "line_number": 36, "usage_type": "name"}, {"api_name": "typing.Callable", "line_number": 36, "usage_type": "name"}, {"api_name": "kiota_abstractions.serialization.ParseNode", "line_number": 36, "usage_type": "name"}, {"api_name": "kiota_abstractions.serialization.SerializationWriter", "line_number": 54, "usage_type": "name"}, {"api_name": "dataclasses.dataclass", "line_number": 11, "usage_type": "name"}]} +{"seq_id": "72276965867", "text": "from django.http import JsonResponse\nfrom rest_framework.decorators import api_view\nfrom rest_framework.response import Response\nfrom rest_framework import status\nfrom .models import CikisSebebi,EskiKupeNo,AsiBilgi,Cinsiyet,Pazarlikci,AlisBilgisi,Ticaret,Isletme,Cikis,Asi,Irk,Hayvan\nimport json\n\n@api_view([\"POST\"])\ndef isletmeEkle(jsonDosyasi):\n try:\n veriler = json.loads(jsonDosyasi.body)\n ad = str(veriler.get('ad'))\n isletme = Isletme(ad = ad)\n isletme.save()\n return JsonResponse(\"Kayit yapildi\", safe = False)\n except ValueError as e:\n return Response(e.args[0], status.HTTP_400_BAD_REQUEST)\n except Exception as e:\n return Response(e.args[0], status.HTTP_204_NO_CONTENT)\n\n@api_view([\"POST\"])\ndef asiEkle(jsonDosyasi):\n try:\n veriler = json.loads(jsonDosyasi.body)\n ad = str(veriler.get('ad'))\n aciklama = str(veriler.get('aciklama'))\n asi = Asi(ad = ad, aciklama = aciklama)\n asi.save()\n return JsonResponse(\"Kayit yapildi\", safe = False)\n except ValueError as e:\n return Response(e.args[0], status.HTTP_400_BAD_REQUEST)\n except Exception as e:\n return Response(e.args[0], status.HTTP_204_NO_CONTENT)\n\n@api_view([\"POST\"])\ndef cikisSebebiEkle(jsonDosyasi):\n try:\n veriler = json.loads(jsonDosyasi.body)\n ad = str(veriler.get('ad'))\n cikisSebebi = CikisSebebi(ad = ad)\n cikisSebebi.save()\n return JsonResponse(\"Kayit yapildi\", safe = False)\n except ValueError as e:\n return Response(e.args[0], status.HTTP_400_BAD_REQUEST)\n except Exception as e:\n return Response(e.args[0], status.HTTP_204_NO_CONTENT)\n\n@api_view([\"POST\"])\ndef cinsiyetEkle(jsonDosyasi):\n try:\n veriler = json.loads(jsonDosyasi.body)\n ad = str(veriler.get('ad'))\n cinsiyet = Cinsiyet(ad = ad)\n cinsiyet.save()\n return JsonResponse(\"Kayit yapildi\", safe = False)\n except ValueError as e:\n return Response(e.args[0], status.HTTP_400_BAD_REQUEST)\n except Exception as e:\n return Response(e.args[0], status.HTTP_204_NO_CONTENT)\n\n\n@api_view([\"POST\"])\ndef irkEkle(jsonDosyasi):\n try:\n veriler = json.loads(jsonDosyasi.body)\n ad = str(veriler.get('ad'))\n irk = Irk(ad = ad)\n irk.save()\n return JsonResponse(\"Kayit yapildi\", safe = False)\n except ValueError as e:\n return Response(e.args[0], status.HTTP_400_BAD_REQUEST)\n except Exception as e:\n return Response(e.args[0], status.HTTP_204_NO_CONTENT)\n\n@api_view([\"POST\"])\ndef pazarlikciEkle(jsonDosyasi):\n try:\n veriler = json.loads(jsonDosyasi.body)\n ad = str(veriler.get('ad'))\n soyad = str(veriler.get('soyad'))\n telefon = str(veriler.get('telefon'))\n adres = str(veriler.get('adres'))\n pazarlikci = Pazarlikci(ad = ad, soyad = soyad, telefon = telefon, adres = adres)\n pazarlikci.save()\n return JsonResponse(\"Pazarlikci eklendi\", safe = False)\n except ValueError as e:\n return Response(e.args[0], status.HTTP_400_BAD_REQUEST)\n except Exception as e:\n return Response(e.args[0], status.HTTP_204_NO_CONTENT)\n\n@api_view([\"POST\"])\ndef alisBilgisiEkle(jsonDosyasi):\n try:\n veriler = json.loads(jsonDosyasi.body)\n fiyatTL = int(veriler.get('fiyatTL'))\n tarih = str(veriler.get('tarih'))\n kilo = int(veriler.get('kilo'))\n pazarlikciNo = int(veriler.get('pazarlikci'))\n pazarlikci = Pazarlikci.objects.get(pk = pazarlikciNo)\n alisBilgisi = AlisBilgisi(fiyatTL = fiyatTL, tarih = tarih, kilo = kilo, pazarlikci = pazarlikci)\n alisBilgisi.save()\n return JsonResponse(\"Alis bilgisi eklendi\", safe = False)\n except ValueError as e:\n return Response(e.args[0], status.HTTP_400_BAD_REQUEST)\n except Exception as e:\n return Response(e.args[0], status.HTTP_204_NO_CONTENT)\n\n@api_view([\"POST\"])\ndef hayvanEkle(jsonDosyasi):\n try:\n veriler = json.loads(jsonDosyasi.body)\n kupeNo = str(veriler.get('kupeNo'))\n padokNo = str(veriler.get('padokNo'))\n aciklama = str(veriler.get('aciklama'))\n aktif = bool(veriler.get('aktif'))\n alisBilgisiNo = int(veriler.get('alisBilgisi'))\n alisBilgisi = AlisBilgisi.objects.get(pk = alisBilgisiNo)\n irkNo = int(veriler.get('irk'))\n irk = Irk.objects.get(pk = irkNo)\n cinsiyetNo = int(veriler.get('cinsiyet'))\n cinsiyet = Cinsiyet.objects.get(pk = cinsiyetNo)\n isletmeNo = int(veriler.get('isletme'))\n isletme = Isletme.objects.get(pk = isletmeNo)\n hayvan = Hayvan(kupeNo = kupeNo, padokNo = padokNo, aciklama = aciklama, aktif = aktif, alisBilgisi = alisBilgisi, irk = irk, cinsiyet = cinsiyet, isletme = isletme)\n hayvan.save()\n return JsonResponse(\"Hayvan bilgisi eklendi\", safe = False)\n except ValueError as e:\n return Response(e.args[0], status.HTTP_400_BAD_REQUEST)\n except Exception as e:\n return Response(e.args[0], status.HTTP_204_NO_CONTENT)\n\n@api_view([\"POST\"])\ndef asiBilgisiEkle(jsonDosyasi):\n try:\n veriler = json.loads(jsonDosyasi.body)\n hayvanNo = int(veriler.get('hayvan'))\n hayvan = Hayvan.objects.get(pk = hayvanNo)\n asiNo = int(veriler.get('asi'))\n asi = Asi.objects.get(pk = asiNo)\n tarih = str(veriler.get('tarih'))\n asiBilgisi = AsiBilgi(hayvan = hayvan, asi = asi, tarih = tarih)\n asiBilgisi.save()\n return JsonResponse(\"AsiBilgisi bilgisi eklendi\", safe = False)\n except ValueError as e:\n return Response(e.args[0], status.HTTP_400_BAD_REQUEST)\n except Exception as e:\n return Response(e.args[0], status.HTTP_204_NO_CONTENT)\n\n@api_view([\"POST\"])\ndef cikisEkle(jsonDosyasi):\n try:\n veriler = json.loads(jsonDosyasi.body)\n sebepNo = int(veriler.get('sebep'))\n sebep = CikisSebebi.objects.get(pk = sebepNo)\n hayvanNo = int(veriler.get('hayvan'))\n hayvan = Hayvan.objects.get(pk = hayvanNo)\n tarih = str(veriler.get('tarih'))\n cikis = Cikis(sebep = sebep, hayvan = hayvan, tarih = tarih)\n cikis.save()\n return JsonResponse(\"Cikis bilgisi eklendi\", safe = False)\n except ValueError as e:\n return Response(e.args[0], status.HTTP_400_BAD_REQUEST)\n except Exception as e:\n return Response(e.args[0], status.HTTP_204_NO_CONTENT)\n\n@api_view([\"POST\"])\ndef ticaretEkle(jsonDosyasi):\n try:\n veriler = json.loads(jsonDosyasi.body)\n cikisNo = int(veriler.get('cikis'))\n cikis = Cikis.objects.get(pk = cikisNo)\n musteriNo = int(veriler.get('musteri'))\n musteri = Pazarlikci.objects.get(pk = musteriNo)\n fiyatTL = int(veriler.get('fiyatTL'))\n tarih = str(veriler.get('tarih'))\n ticaret = Ticaret(cikis = cikis, musteri = musteri, fiyatTL = fiyatTL, tarih = tarih)\n ticaret.save()\n return JsonResponse(\"Ticaret bilgisi eklendi\", safe = False)\n except ValueError as e:\n return Response(e.args[0], status.HTTP_400_BAD_REQUEST)\n except Exception as e:\n return Response(e.args[0], status.HTTP_204_NO_CONTENT)\n\n@api_view([\"POST\"])\ndef eskiKupeNoEkle(jsonDosyasi):\n try:\n veriler = json.loads(jsonDosyasi.body)\n hayvanNo = int(veriler.get('hayvan'))\n hayvan = Hayvan.objects.get(pk = hayvanNo)\n eskiKupe = str(veriler.get('eskiKupe'))\n yeniKupe= str(veriler.get('yeniKupe'))\n tarih = str(veriler.get('tarih'))\n eskiKupeNo = EskiKupeNo(hayvan = hayvan, eskiKupe = eskiKupe, yeniKupe = yeniKupe, tarih = tarih)\n eskiKupeNo.save()\n return JsonResponse(\"Eski kupe bilgisi eklendi\", safe = False)\n except ValueError as e:\n return Response(e.args[0], status.HTTP_400_BAD_REQUEST)\n except Exception as e:\n return Response(e.args[0], status.HTTP_204_NO_CONTENT)\n\n@api_view([\"POST\"])\ndef isletmeSil(jsonDosyasi):\n try:\n veriler = json.loads(jsonDosyasi.body)\n id = int(veriler.get('id'))\n isletme = Isletme.objects.get(pk = id)\n isletme.delete()\n return JsonResponse(\"Isletme silindi\", safe = False)\n except ValueError as e:\n return Response(e.args[0], status.HTTP_400_BAD_REQUEST)\n except Exception as e:\n return Response(e.args[0], status.HTTP_204_NO_CONTENT)\n\n@api_view([\"POST\"])\ndef asiSil(jsonDosyasi):\n try:\n veriler = json.loads(jsonDosyasi.body)\n id = int(veriler.get('id'))\n asi = Asi.objects.get(pk = id)\n asi.delete()\n return JsonResponse(\"Asi silindi\", safe = False)\n except ValueError as e:\n return Response(e.args[0], status.HTTP_400_BAD_REQUEST)\n except Exception as e:\n return Response(e.args[0], status.HTTP_204_NO_CONTENT)\n\n@api_view([\"POST\"])\ndef cikisSebebiSil(jsonDosyasi):\n try:\n veriler = json.loads(jsonDosyasi.body)\n id = int(veriler.get('id'))\n cikisSebebi = CikisSebebi.objects.get(pk=id)\n cikisSebebi.delete()\n return JsonResponse(\"Cikis sebebi silindi\", safe=False)\n except ValueError as e:\n return Response(e.args[0], status.HTTP_400_BAD_REQUEST)\n except Exception as e:\n return Response(e.args[0], status.HTTP_204_NO_CONTENT)\n\n@api_view([\"POST\"])\ndef cinsiyetSil(jsonDosyasi):\n try:\n veriler = json.loads(jsonDosyasi.body)\n id = int(veriler.get('id'))\n cinsiyet = Cinsiyet.objects.get(pk=id)\n cinsiyet.delete()\n return JsonResponse(\"Cinsiyet silindi\", safe=False)\n except ValueError as e:\n return Response(e.args[0], status.HTTP_400_BAD_REQUEST)\n except Exception as e:\n return Response(e.args[0], status.HTTP_204_NO_CONTENT)\n\n\n@api_view([\"POST\"])\ndef irkSil(jsonDosyasi):\n try:\n veriler = json.loads(jsonDosyasi.body)\n id = int(veriler.get('id'))\n irk = Irk.objects.get(pk=id)\n irk.delete()\n return JsonResponse(\"Irk silindi\", safe=False)\n except ValueError as e:\n return Response(e.args[0], status.HTTP_400_BAD_REQUEST)\n except Exception as e:\n return Response(e.args[0], status.HTTP_204_NO_CONTENT)\n\n@api_view([\"POST\"])\ndef pazarlikciSil(jsonDosyasi):\n try:\n veriler = json.loads(jsonDosyasi.body)\n id = int(veriler.get('id'))\n pazarlikci = Pazarlikci.objects.get(pk=id)\n pazarlikci.delete()\n return JsonResponse(\"Pazarlikci silindi\", safe=False)\n except ValueError as e:\n return Response(e.args[0], status.HTTP_400_BAD_REQUEST)\n except Exception as e:\n return Response(e.args[0], status.HTTP_204_NO_CONTENT)\n\n@api_view([\"POST\"])\ndef alisBilgisiSil(jsonDosyasi):\n try:\n veriler = json.loads(jsonDosyasi.body)\n id = int(veriler.get('id'))\n alisBilgisi = AlisBilgisi.objects.get(pk=id)\n alisBilgisi.delete()\n return JsonResponse(\"Alis bilgisi silindi\", safe=False)\n except ValueError as e:\n return Response(e.args[0], status.HTTP_400_BAD_REQUEST)\n except Exception as e:\n return Response(e.args[0], status.HTTP_204_NO_CONTENT)\n\n@api_view([\"POST\"])\ndef hayvanSil(jsonDosyasi):\n try:\n veriler = json.loads(jsonDosyasi.body)\n id = int(veriler.get('id'))\n hayvan = Hayvan.objects.get(pk=id)\n hayvan.delete()\n return JsonResponse(\"Hayvan silindi\", safe=False)\n except ValueError as e:\n return Response(e.args[0], status.HTTP_400_BAD_REQUEST)\n except Exception as e:\n return Response(e.args[0], status.HTTP_204_NO_CONTENT)\n\n@api_view([\"POST\"])\ndef asiBilgisiSil(jsonDosyasi):\n try:\n veriler = json.loads(jsonDosyasi.body)\n id = int(veriler.get('id'))\n asiBilgisi = AsiBilgi.objects.get(pk=id)\n asiBilgisi.delete()\n return JsonResponse(\"Asi bilgisi silindi\", safe=False)\n except ValueError as e:\n return Response(e.args[0], status.HTTP_400_BAD_REQUEST)\n except Exception as e:\n return Response(e.args[0], status.HTTP_204_NO_CONTENT)\n\n@api_view([\"POST\"])\ndef cikisSil(jsonDosyasi):\n try:\n veriler = json.loads(jsonDosyasi.body)\n id = int(veriler.get('id'))\n cikis = Cikis.objects.get(pk=id)\n cikis.delete()\n return JsonResponse(\"Cikis silindi\", safe=False)\n except ValueError as e:\n return Response(e.args[0], status.HTTP_400_BAD_REQUEST)\n except Exception as e:\n return Response(e.args[0], status.HTTP_204_NO_CONTENT)\n\n@api_view([\"POST\"])\ndef ticaretSil(jsonDosyasi):\n try:\n veriler = json.loads(jsonDosyasi.body)\n id = int(veriler.get('id'))\n ticaret = Ticaret.objects.get(pk=id)\n ticaret.delete()\n return JsonResponse(\"Ticaret silindi\", safe=False)\n except ValueError as e:\n return Response(e.args[0], status.HTTP_400_BAD_REQUEST)\n except Exception as e:\n return Response(e.args[0], status.HTTP_204_NO_CONTENT)\n\n@api_view([\"POST\"])\ndef eskiKupeNoSil(jsonDosyasi):\n try:\n veriler = json.loads(jsonDosyasi.body)\n id = int(veriler.get('id'))\n eskiKupeNo = EskiKupeNo.objects.get(pk=id)\n eskiKupeNo.delete()\n return JsonResponse(\"Eski kupe silindi\", safe=False)\n except ValueError as e:\n return Response(e.args[0], status.HTTP_400_BAD_REQUEST)\n except Exception as e:\n return Response(e.args[0], status.HTTP_204_NO_CONTENT)\n\n@api_view([\"POST\"])\ndef isletmeGuncelle(jsonDosyasi):\n try:\n veriler = json.loads(jsonDosyasi.body)\n id = int(veriler.get('id'))\n isletme = Isletme.objects.get(pk = id)\n guncellenecekAd = str(veriler.get('ad'))\n if not guncellenecekAd == \"None\" and not guncellenecekAd == \"\":\n isletme.ad = guncellenecekAd\n isletme.save()\n return JsonResponse(\"Isletme guncellendi\", safe = False)\n else:\n return JsonResponse(\"Duzgun deger gir\", safe=False)\n except ValueError as e:\n return Response(e.args[0], status.HTTP_400_BAD_REQUEST)\n except Exception as e:\n return Response(e.args[0], status.HTTP_204_NO_CONTENT)\n\n@api_view([\"POST\"])\ndef asiGuncelle(jsonDosyasi):\n try:\n veriler = json.loads(jsonDosyasi.body)\n id = int(veriler.get('id'))\n asi = Asi.objects.get(pk = id)\n guncellenecekAd = str(veriler.get('ad'))\n if not guncellenecekAd == \"None\" and not guncellenecekAd == \"\":\n asi.ad = guncellenecekAd\n asi.save()\n return JsonResponse(\"Asi guncellendi\", safe = False)\n else:\n return JsonResponse(\"Duzgun deger gir\", safe=False)\n except ValueError as e:\n return Response(e.args[0], status.HTTP_400_BAD_REQUEST)\n except Exception as e:\n return Response(e.args[0], status.HTTP_204_NO_CONTENT)\n\n@api_view([\"POST\"])\ndef cikisSebebiGuncelle(jsonDosyasi):\n try:\n veriler = json.loads(jsonDosyasi.body)\n id = int(veriler.get('id'))\n cikisSebebi = CikisSebebi.objects.get(pk = id)\n guncellenecekAd = str(veriler.get('ad'))\n if not guncellenecekAd == \"None\" and not guncellenecekAd == \"\":\n cikisSebebi.ad = guncellenecekAd\n cikisSebebi.save()\n return JsonResponse(\"Cikis Sebebi guncellendi\", safe = False)\n else:\n return JsonResponse(\"Duzgun deger gir\", safe=False)\n except ValueError as e:\n return Response(e.args[0], status.HTTP_400_BAD_REQUEST)\n except Exception as e:\n return Response(e.args[0], status.HTTP_204_NO_CONTENT)\n\n@api_view([\"POST\"])\ndef cinsiyetGuncelle(jsonDosyasi):\n try:\n veriler = json.loads(jsonDosyasi.body)\n id = int(veriler.get('id'))\n cinsiyet = Cinsiyet.objects.get(pk = id)\n guncellenecekAd = str(veriler.get('ad'))\n if not guncellenecekAd == \"None\" and not guncellenecekAd == \"\":\n cinsiyet.ad = guncellenecekAd\n cinsiyet.save()\n return JsonResponse(\"Cinsiyet guncellendi\", safe = False)\n else:\n return JsonResponse(\"Duzgun deger gir\", safe=False)\n except ValueError as e:\n return Response(e.args[0], status.HTTP_400_BAD_REQUEST)\n except Exception as e:\n return Response(e.args[0], status.HTTP_204_NO_CONTENT)\n\n@api_view([\"POST\"])\ndef irkGuncelle(jsonDosyasi):\n try:\n veriler = json.loads(jsonDosyasi.body)\n id = int(veriler.get('id'))\n irk = Irk.objects.get(pk = id)\n guncellenecekAd = str(veriler.get('ad'))\n if not guncellenecekAd == \"None\" and not guncellenecekAd == \"\":\n irk.ad = guncellenecekAd\n irk.save()\n return JsonResponse(\"Irk guncellendi\", safe = False)\n else:\n return JsonResponse(\"Duzgun deger gir\", safe=False)\n except ValueError as e:\n return Response(e.args[0], status.HTTP_400_BAD_REQUEST)\n except Exception as e:\n return Response(e.args[0], status.HTTP_204_NO_CONTENT)\n\n@api_view([\"POST\"])\ndef pazarlikciGuncelle(jsonDosyasi):\n try:\n veriler = json.loads(jsonDosyasi.body)\n id = int(veriler.get('id'))\n pazarlikci = Pazarlikci.objects.get(pk = id)\n guncellenecekAd = str(veriler.get('ad'))\n guncellenecekSoyad = str(veriler.get('soyad'))\n guncellenecekAdres = str(veriler.get('adres'))\n guncellenecekTelefon = str(veriler.get('telefon'))\n kontrol = False\n if not guncellenecekAd == \"None\" and not guncellenecekAd == \"\":\n pazarlikci.ad = guncellenecekAd\n kontrol = True\n if not guncellenecekSoyad == \"None\" and not guncellenecekSoyad == \"\":\n pazarlikci.soyad = guncellenecekSoyad\n kontrol = True\n if not guncellenecekAdres == \"None\" and not guncellenecekAdres == \"\":\n pazarlikci.adres = guncellenecekAdres\n kontrol = True\n if not guncellenecekTelefon == \"None\" and not guncellenecekTelefon == \"\":\n pazarlikci.telefon = guncellenecekTelefon\n kontrol = True\n if kontrol:\n pazarlikci.save()\n return JsonResponse(\"Pazarlikci guncellendi\", safe=False)\n else:\n return JsonResponse(\"Duzgun deger gir\", safe=False)\n except ValueError as e:\n return Response(e.args[0], status.HTTP_400_BAD_REQUEST)\n except Exception as e:\n return Response(e.args[0], status.HTTP_204_NO_CONTENT)\n\n@api_view([\"POST\"])\ndef alisBilgisiGuncelle(jsonDosyasi):\n try:\n veriler = json.loads(jsonDosyasi.body)\n id = int(veriler.get('id'))\n alisBilgisi = AlisBilgisi.objects.get(pk = id)\n guncellenecekFiyatTL = str(veriler.get('fiyatTL'))\n guncellenecekTarih = str(veriler.get('tarih'))\n guncellenecekKilo = str(veriler.get('kilo'))\n guncellenecekPazarlikciNo = str(veriler.get('pazarlikci'))\n kontrol = False\n if not guncellenecekFiyatTL == \"None\" and not guncellenecekFiyatTL == \"\":\n guncellenecekFiyatTL = int(guncellenecekFiyatTL)\n alisBilgisi.fiyatTL = guncellenecekFiyatTL\n kontrol = True\n if not guncellenecekTarih == \"None\" and not guncellenecekTarih == \"\":\n alisBilgisi.tarih = guncellenecekTarih\n kontrol = True\n if not guncellenecekKilo == \"None\" and not guncellenecekKilo == \"\":\n guncellenecekKilo = int(guncellenecekKilo)\n alisBilgisi.kilo = guncellenecekKilo\n kontrol = True\n if not guncellenecekPazarlikciNo == \"None\" and not guncellenecekPazarlikciNo == \"\":\n guncellenecekPazarlikciNo = int(guncellenecekPazarlikciNo)\n guncellenecekPazarlikci = Pazarlikci.objects.get(pk = guncellenecekPazarlikciNo)\n alisBilgisi.pazarlikci = guncellenecekPazarlikci\n kontrol = True\n if kontrol:\n alisBilgisi.save()\n return JsonResponse(\"Alis Bilgisi guncellendi\", safe=False)\n else:\n return JsonResponse(\"Duzgun deger gir\", safe=False)\n except ValueError as e:\n return Response(e.args[0], status.HTTP_400_BAD_REQUEST)\n except Exception as e:\n return Response(e.args[0], status.HTTP_204_NO_CONTENT)\n\n@api_view([\"POST\"])\ndef hayvanGuncelle(jsonDosyasi):\n try:\n veriler = json.loads(jsonDosyasi.body)\n id = int(veriler.get('id'))\n hayvan = Hayvan.objects.get(pk = id)\n kupeNo = str(veriler.get('kupeno'))\n padokNo = str(veriler.get('padakno'))\n aciklama = str(veriler.get('aciklama'))\n aktif = str(veriler.get('aktif'))\n alisBilgisiNo = str(veriler.get('alisbilgisi'))\n irkNo = str(veriler.get('irk'))\n cinsiyetNo = str(veriler.get('cinsiyet'))\n isletmeNo = str(veriler.get('isletme'))\n kontrol = False\n if not kupeNo == \"None\" and not kupeNo == \"\":\n hayvan.kupeNo = kupeNo\n kontrol = True\n if not padokNo == \"None\" and not padokNo == \"\":\n hayvan.padokNo = padokNo\n kontrol = True\n if not aciklama == \"None\" and not aciklama == \"\":\n hayvan.aciklama = aciklama\n kontrol = True\n if not aktif == \"None\" and not aktif == \"\":\n aktif = bool(aktif)\n hayvan.aktif = aktif\n kontrol = True\n if not alisBilgisiNo == \"None\" and not alisBilgisiNo == \"\":\n alisBilgisiNo = int(alisBilgisiNo)\n alisBilgisi = AlisBilgisi.objects.get(pk = alisBilgisiNo)\n hayvan.alisBilgisi = alisBilgisi\n kontrol = True\n if not irkNo == \"None\" and not irkNo == \"\":\n irkNo = int(irkNo)\n irk = Irk.objects.get(pk = irkNo)\n hayvan.irk = irk\n kontrol = True\n if not cinsiyetNo == \"None\" and not cinsiyetNo == \"\":\n cinsiyetNo = int(cinsiyetNo)\n cinsiyet = Cinsiyet.objects.get(pk = cinsiyetNo)\n hayvan.cinsiyet = cinsiyet\n kontrol = True\n if not isletmeNo == \"None\" and not isletmeNo == \"\":\n isletmeNo = int(isletmeNo)\n isletme = Isletme.objects.get(pk = isletmeNo)\n hayvan.isletme = isletme\n kontrol = True\n if kontrol:\n hayvan.save()\n return JsonResponse(\"Hayvan guncellendi\", safe=False)\n else:\n return JsonResponse(\"Duzgun deger gir\", safe=False)\n\n except ValueError as e:\n return Response(e.args[0], status.HTTP_400_BAD_REQUEST)\n except Exception as e:\n return Response(e.args[0], status.HTTP_204_NO_CONTENT)\n\n@api_view([\"POST\"])\ndef asiBilgisiGuncelle(jsonDosyasi):\n try:\n veriler = json.loads(jsonDosyasi.body)\n id = int(veriler.get('id'))\n asiBilgisi = AsiBilgi.objects.get(pk = id)\n hayvanNo = str(veriler.get('hayvan'))\n asiNo = str(veriler.get('asi'))\n tarih = str(veriler.get('tarih'))\n kontrol = False\n if not hayvanNo == \"None\" and not hayvanNo == \"\":\n hayvanNo = int(hayvanNo)\n hayvan = Hayvan.objects.get(pk = hayvanNo)\n asiBilgisi.hayvan = hayvan\n kontrol = True\n if not asiNo == \"None\" and not asiNo == \"\":\n asiNo = int(asiNo)\n asi = Asi.objects.get(pk = asiNo)\n asiBilgisi.asi = asi\n kontrol = True\n if not tarih == \"None\" and not tarih == \"\":\n asiBilgisi.tarih = tarih\n kontrol = True\n if kontrol:\n asiBilgisi.save()\n return JsonResponse(\"Asi Bilgisi guncellendi\", safe=False)\n else:\n return JsonResponse(\"Duzgun deger gir\", safe=False)\n except ValueError as e:\n return Response(e.args[0], status.HTTP_400_BAD_REQUEST)\n except Exception as e:\n return Response(e.args[0], status.HTTP_204_NO_CONTENT)\n\n@api_view([\"POST\"])\ndef cikisGuncelle(jsonDosyasi):\n try:\n veriler = json.loads(jsonDosyasi.body)\n id = int(veriler.get('id'))\n cikis = Cikis.objects.get(pk = id)\n sebepNo = str(veriler.get('sebep'))\n hayvanNo = str(veriler.get('hayvan'))\n tarih = str(veriler.get('tarih'))\n kontrol = False\n if not sebepNo == \"None\" and not sebepNo == \"\":\n sebepNo = int(sebepNo)\n sebep = CikisSebebi.objects.get(pk = sebepNo)\n cikis.sebep = sebep\n kontrol = True\n if not hayvanNo == \"None\" and not hayvanNo == \"\":\n hayvanNo = int(hayvanNo)\n hayvan = Hayvan.objects.get(pk = hayvanNo)\n cikis.hayvan = hayvan\n kontrol = True\n if not tarih == \"None\" and not tarih == \"\":\n cikis.tarih = tarih\n kontrol = True\n if kontrol:\n cikis.save()\n return JsonResponse(\"Cikis guncellendi\", safe=False)\n else:\n return JsonResponse(\"Duzgun deger gir\", safe=False)\n except ValueError as e:\n return Response(e.args[0], status.HTTP_400_BAD_REQUEST)\n except Exception as e:\n return Response(e.args[0], status.HTTP_204_NO_CONTENT)\n\n@api_view([\"POST\"])\ndef ticaretGuncelle(jsonDosyasi):\n try:\n veriler = json.loads(jsonDosyasi.body)\n id = int(veriler.get('id'))\n ticaret = Ticaret.objects.get(pk = id)\n cikisNo = str(veriler.get('cikis'))\n pazarlikciNo = str(veriler.get('musteri'))\n fiyatTL = str(veriler.get('fiyatTL'))\n tarih = str(veriler.get('tarih'))\n kontrol = False\n if not cikisNo == \"None\" and not cikisNo == \"\":\n cikisNo = int(cikisNo)\n cikis = Cikis.objects.get(pk = cikisNo)\n ticaret.cikis = cikis\n kontrol = True\n if not pazarlikciNo == \"None\" and not pazarlikciNo == \"\":\n pazarlikciNo = int(pazarlikciNo)\n pazarlikci = Pazarlikci.objects.get(pk = pazarlikciNo)\n ticaret.musteri = pazarlikci\n kontrol = True\n if not fiyatTL == \"None\" and not fiyatTL == \"\":\n fiyatTL = int(fiyatTL)\n ticaret.fiyatTL = fiyatTL\n kontrol = True\n if not tarih == \"None\" and not tarih == \"\":\n cikis.tarih = tarih\n kontrol = True\n if kontrol:\n cikis.save()\n return JsonResponse(\"Ticaret guncellendi\", safe=False)\n else:\n return JsonResponse(\"Duzgun deger gir\", safe=False)\n except ValueError as e:\n return Response(e.args[0], status.HTTP_400_BAD_REQUEST)\n except Exception as e:\n return Response(e.args[0], status.HTTP_204_NO_CONTENT)\n\n@api_view([\"POST\"])\ndef eskiKupeNoGuncelle(jsonDosyasi):\n try:\n veriler = json.loads(jsonDosyasi.body)\n id = int(veriler.get('id'))\n eskiKupeNo = EskiKupeNo.objects.get(pk = id)\n eskiKupe = str(veriler.get('cikis'))\n yeniKupe = str(veriler.get('musteri'))\n tarih = str(veriler.get('tarih'))\n kontrol = False\n if not eskiKupe == \"None\" and not eskiKupe == \"\":\n eskiKupeNo.eskiKupe = eskiKupe\n kontrol = True\n if not yeniKupe == \"None\" and not yeniKupe == \"\":\n eskiKupeNo.yeniKupe = yeniKupe\n kontrol = True\n if not tarih == \"None\" and not tarih == \"\":\n eskiKupeNo.tarih = tarih\n kontrol = True\n if kontrol:\n eskiKupeNo.save()\n return JsonResponse(\"Eski kupe numarasi guncellendi\", safe=False)\n else:\n return JsonResponse(\"Duzgun deger gir\", safe=False)\n except ValueError as e:\n return Response(e.args[0], status.HTTP_400_BAD_REQUEST)\n except Exception as e:\n return Response(e.args[0], status.HTTP_204_NO_CONTENT)\n", "repo_name": "emogooo/Buyukbas-Android-IOS-Uygulamasinin-Sunucusu", "sub_path": "DjangoSunucu/Proje/Sunucu/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 27651, "program_lang": "python", "lang": "tr", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "json.loads", "line_number": 11, "usage_type": "call"}, {"api_name": "models.Isletme", "line_number": 13, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 15, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 17, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_400_BAD_REQUEST", "line_number": 17, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 17, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 19, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_204_NO_CONTENT", "line_number": 19, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 19, "usage_type": "name"}, {"api_name": "rest_framework.decorators.api_view", "line_number": 8, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 24, "usage_type": "call"}, {"api_name": "models.Asi", "line_number": 27, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 29, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 31, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_400_BAD_REQUEST", "line_number": 31, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 31, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 33, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_204_NO_CONTENT", "line_number": 33, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 33, "usage_type": "name"}, {"api_name": "rest_framework.decorators.api_view", "line_number": 21, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 38, "usage_type": "call"}, {"api_name": "models.CikisSebebi", "line_number": 40, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 42, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 44, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_400_BAD_REQUEST", "line_number": 44, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 44, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 46, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_204_NO_CONTENT", "line_number": 46, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 46, "usage_type": "name"}, {"api_name": "rest_framework.decorators.api_view", "line_number": 35, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 51, "usage_type": "call"}, {"api_name": "models.Cinsiyet", "line_number": 53, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 55, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 57, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_400_BAD_REQUEST", "line_number": 57, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 57, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 59, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_204_NO_CONTENT", "line_number": 59, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 59, "usage_type": "name"}, {"api_name": "rest_framework.decorators.api_view", "line_number": 48, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 65, "usage_type": "call"}, {"api_name": "models.Irk", "line_number": 67, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 69, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 71, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_400_BAD_REQUEST", "line_number": 71, "usage_type": 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"rest_framework.status", "line_number": 354, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 356, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_204_NO_CONTENT", "line_number": 356, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 356, "usage_type": "name"}, {"api_name": "rest_framework.decorators.api_view", "line_number": 345, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 361, "usage_type": "call"}, {"api_name": "models.Isletme.objects.get", "line_number": 363, "usage_type": "call"}, {"api_name": "models.Isletme.objects", "line_number": 363, "usage_type": "attribute"}, {"api_name": "models.Isletme", "line_number": 363, "usage_type": "name"}, {"api_name": "django.http.JsonResponse", "line_number": 368, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 370, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 372, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_400_BAD_REQUEST", "line_number": 372, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 372, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 374, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_204_NO_CONTENT", "line_number": 374, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 374, "usage_type": "name"}, {"api_name": "rest_framework.decorators.api_view", "line_number": 358, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 379, "usage_type": "call"}, {"api_name": "models.Asi.objects.get", "line_number": 381, "usage_type": "call"}, {"api_name": "models.Asi.objects", "line_number": 381, "usage_type": "attribute"}, {"api_name": "models.Asi", "line_number": 381, "usage_type": "name"}, {"api_name": "django.http.JsonResponse", "line_number": 386, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 388, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 390, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_400_BAD_REQUEST", "line_number": 390, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 390, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 392, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_204_NO_CONTENT", "line_number": 392, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 392, "usage_type": "name"}, {"api_name": "rest_framework.decorators.api_view", "line_number": 376, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 397, "usage_type": "call"}, {"api_name": "models.CikisSebebi.objects.get", "line_number": 399, "usage_type": "call"}, {"api_name": "models.CikisSebebi.objects", "line_number": 399, "usage_type": "attribute"}, {"api_name": "models.CikisSebebi", "line_number": 399, "usage_type": "name"}, {"api_name": "django.http.JsonResponse", "line_number": 404, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 406, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 408, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_400_BAD_REQUEST", "line_number": 408, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 408, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 410, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_204_NO_CONTENT", "line_number": 410, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 410, "usage_type": "name"}, {"api_name": "rest_framework.decorators.api_view", "line_number": 394, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 415, "usage_type": "call"}, {"api_name": "models.Cinsiyet.objects.get", "line_number": 417, "usage_type": "call"}, {"api_name": "models.Cinsiyet.objects", "line_number": 417, "usage_type": "attribute"}, {"api_name": "models.Cinsiyet", "line_number": 417, "usage_type": "name"}, {"api_name": "django.http.JsonResponse", "line_number": 422, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 424, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 426, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_400_BAD_REQUEST", "line_number": 426, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 426, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 428, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_204_NO_CONTENT", "line_number": 428, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 428, "usage_type": "name"}, {"api_name": "rest_framework.decorators.api_view", "line_number": 412, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 433, "usage_type": "call"}, {"api_name": "models.Irk.objects.get", "line_number": 435, "usage_type": "call"}, {"api_name": "models.Irk.objects", "line_number": 435, "usage_type": "attribute"}, {"api_name": "models.Irk", "line_number": 435, "usage_type": "name"}, {"api_name": "django.http.JsonResponse", "line_number": 440, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 442, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 444, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_400_BAD_REQUEST", "line_number": 444, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 444, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 446, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_204_NO_CONTENT", "line_number": 446, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 446, "usage_type": "name"}, {"api_name": "rest_framework.decorators.api_view", "line_number": 430, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 451, "usage_type": "call"}, {"api_name": "models.Pazarlikci.objects.get", "line_number": 453, "usage_type": "call"}, {"api_name": "models.Pazarlikci.objects", "line_number": 453, "usage_type": "attribute"}, {"api_name": "models.Pazarlikci", "line_number": 453, "usage_type": "name"}, {"api_name": "django.http.JsonResponse", "line_number": 473, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 475, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 477, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_400_BAD_REQUEST", "line_number": 477, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 477, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 479, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_204_NO_CONTENT", "line_number": 479, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 479, "usage_type": "name"}, {"api_name": "rest_framework.decorators.api_view", "line_number": 448, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 484, "usage_type": "call"}, {"api_name": "models.AlisBilgisi.objects.get", "line_number": 486, "usage_type": "call"}, {"api_name": "models.AlisBilgisi.objects", "line_number": 486, "usage_type": "attribute"}, {"api_name": "models.AlisBilgisi", "line_number": 486, "usage_type": "name"}, {"api_name": "models.Pazarlikci.objects.get", "line_number": 505, "usage_type": "call"}, {"api_name": "models.Pazarlikci.objects", "line_number": 505, "usage_type": "attribute"}, {"api_name": "models.Pazarlikci", "line_number": 505, "usage_type": "name"}, {"api_name": "django.http.JsonResponse", "line_number": 510, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 512, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 514, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_400_BAD_REQUEST", "line_number": 514, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 514, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 516, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_204_NO_CONTENT", "line_number": 516, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 516, "usage_type": "name"}, {"api_name": "rest_framework.decorators.api_view", "line_number": 481, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 521, "usage_type": "call"}, {"api_name": "models.Hayvan.objects.get", "line_number": 523, "usage_type": "call"}, {"api_name": "models.Hayvan.objects", "line_number": 523, "usage_type": "attribute"}, {"api_name": "models.Hayvan", "line_number": 523, "usage_type": "name"}, {"api_name": "models.AlisBilgisi.objects.get", "line_number": 548, "usage_type": "call"}, {"api_name": "models.AlisBilgisi.objects", "line_number": 548, "usage_type": "attribute"}, {"api_name": "models.AlisBilgisi", "line_number": 548, "usage_type": "name"}, {"api_name": "models.Irk.objects.get", "line_number": 553, "usage_type": "call"}, {"api_name": "models.Irk.objects", "line_number": 553, "usage_type": "attribute"}, {"api_name": "models.Irk", "line_number": 553, "usage_type": "name"}, {"api_name": "models.Cinsiyet.objects.get", "line_number": 558, "usage_type": "call"}, {"api_name": "models.Cinsiyet.objects", "line_number": 558, "usage_type": "attribute"}, {"api_name": "models.Cinsiyet", "line_number": 558, "usage_type": "name"}, {"api_name": "models.Isletme.objects.get", "line_number": 563, "usage_type": "call"}, {"api_name": "models.Isletme.objects", "line_number": 563, "usage_type": "attribute"}, {"api_name": "models.Isletme", "line_number": 563, "usage_type": "name"}, {"api_name": "django.http.JsonResponse", "line_number": 568, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 570, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 573, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_400_BAD_REQUEST", "line_number": 573, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 573, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 575, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_204_NO_CONTENT", "line_number": 575, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 575, "usage_type": "name"}, {"api_name": "rest_framework.decorators.api_view", "line_number": 518, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 580, "usage_type": "call"}, {"api_name": "models.AsiBilgi.objects.get", "line_number": 582, "usage_type": "call"}, {"api_name": "models.AsiBilgi.objects", "line_number": 582, "usage_type": "attribute"}, {"api_name": "models.AsiBilgi", "line_number": 582, "usage_type": "name"}, {"api_name": "models.Hayvan.objects.get", "line_number": 589, "usage_type": "call"}, {"api_name": "models.Hayvan.objects", "line_number": 589, "usage_type": "attribute"}, {"api_name": "models.Hayvan", "line_number": 589, "usage_type": "name"}, {"api_name": "models.Asi.objects.get", "line_number": 594, "usage_type": "call"}, {"api_name": "models.Asi.objects", "line_number": 594, "usage_type": "attribute"}, {"api_name": "models.Asi", "line_number": 594, "usage_type": "name"}, {"api_name": "django.http.JsonResponse", "line_number": 602, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 604, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 606, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_400_BAD_REQUEST", "line_number": 606, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 606, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 608, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_204_NO_CONTENT", "line_number": 608, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 608, "usage_type": "name"}, {"api_name": "rest_framework.decorators.api_view", "line_number": 577, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 613, "usage_type": "call"}, {"api_name": "models.Cikis.objects.get", "line_number": 615, "usage_type": "call"}, {"api_name": "models.Cikis.objects", "line_number": 615, "usage_type": "attribute"}, {"api_name": "models.Cikis", "line_number": 615, "usage_type": "name"}, {"api_name": "models.CikisSebebi.objects.get", "line_number": 622, "usage_type": "call"}, {"api_name": "models.CikisSebebi.objects", "line_number": 622, "usage_type": "attribute"}, {"api_name": "models.CikisSebebi", "line_number": 622, "usage_type": "name"}, {"api_name": "models.Hayvan.objects.get", "line_number": 627, "usage_type": "call"}, {"api_name": "models.Hayvan.objects", "line_number": 627, "usage_type": "attribute"}, {"api_name": "models.Hayvan", "line_number": 627, "usage_type": "name"}, {"api_name": "django.http.JsonResponse", "line_number": 635, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 637, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 639, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_400_BAD_REQUEST", "line_number": 639, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 639, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 641, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_204_NO_CONTENT", "line_number": 641, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 641, "usage_type": "name"}, {"api_name": "rest_framework.decorators.api_view", "line_number": 610, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 646, "usage_type": "call"}, {"api_name": "models.Ticaret.objects.get", "line_number": 648, "usage_type": "call"}, {"api_name": "models.Ticaret.objects", "line_number": 648, "usage_type": "attribute"}, {"api_name": "models.Ticaret", "line_number": 648, "usage_type": "name"}, {"api_name": "models.Cikis.objects.get", "line_number": 656, "usage_type": "call"}, {"api_name": "models.Cikis.objects", "line_number": 656, "usage_type": "attribute"}, {"api_name": "models.Cikis", "line_number": 656, "usage_type": "name"}, {"api_name": "models.Pazarlikci.objects.get", "line_number": 661, "usage_type": "call"}, {"api_name": "models.Pazarlikci.objects", "line_number": 661, "usage_type": "attribute"}, {"api_name": "models.Pazarlikci", "line_number": 661, "usage_type": "name"}, {"api_name": "django.http.JsonResponse", "line_number": 673, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 675, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 677, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_400_BAD_REQUEST", "line_number": 677, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 677, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 679, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_204_NO_CONTENT", "line_number": 679, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 679, "usage_type": "name"}, {"api_name": "rest_framework.decorators.api_view", "line_number": 643, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 684, "usage_type": "call"}, {"api_name": "models.EskiKupeNo.objects.get", "line_number": 686, "usage_type": "call"}, {"api_name": "models.EskiKupeNo.objects", "line_number": 686, "usage_type": "attribute"}, {"api_name": "models.EskiKupeNo", "line_number": 686, "usage_type": "name"}, {"api_name": "django.http.JsonResponse", "line_number": 702, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 704, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 706, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_400_BAD_REQUEST", "line_number": 706, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 706, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 708, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_204_NO_CONTENT", "line_number": 708, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 708, "usage_type": "name"}, {"api_name": "rest_framework.decorators.api_view", "line_number": 681, "usage_type": "call"}]} +{"seq_id": "22184336739", "text": "import numpy as np\nimport pandas as pd\nfrom pylab import plt\nfrom sklearn.preprocessing import MinMaxScaler\nimport torch\nimport torch.nn as nn\ndef load_data(stock, look_back, look_forward, batchsize =1):\n\n data_raw = stock.values # convert to numpy array\n data = []\n\n # create all possible sequences of length look_back\n for index in range(len(data_raw) - look_back):\n data.append(data_raw[index: index + look_back])\n\n data = np.array(data)\n\n test_set_size = int(np.round(0.25 * data.shape[0]))\n if(test_set_size<2*look_back+10 ):\n test_set_size = 2*look_back+10\n train_set_size = data.shape[0] - (test_set_size)\n\n\n\n\n\n x_train = data[:train_set_size-look_back, :-look_forward, :]\n y_train = data[:train_set_size-look_back, -look_forward:, :]\n indices = torch.randperm(x_train.shape[0])\n\n x_train_temp = x_train[indices]\n y_train = y_train[indices]\n y_train_temp = np.zeros((y_train.shape[0],1))\n\n for i in range(y_train_temp.shape[0]):\n y_train_temp[i][0] = y_train[i].mean()\n\n y_train = []\n x_train=[]\n for i in range(0,x_train_temp.shape[0], batchsize):\n x_train.append(x_train_temp[i:i+batchsize])\n y_train.append( y_train_temp[i:i+batchsize])\n\n y_train = np.array(y_train[:-1])\n x_train = np.array(x_train[:-1])\n\n\n x_validation = data[train_set_size :-2*look_back, :-look_forward, :]\n y_validation_temp = data[train_set_size :-2*look_back, -look_forward:, :]\n y_validation = np.zeros((y_validation_temp.shape[0], 1))\n\n for i in range(y_validation_temp.shape[0]):\n y_validation[i][0] = y_validation_temp[i].mean()\n\n\n\n\n x_test = data[-look_back:, :-look_forward, :]\n y_test_temp = data[-look_back:, -look_forward:, :]\n\n y_test = np.zeros((y_test_temp.shape[0], 1))\n for i in range(y_test_temp.shape[0]):\n y_test[i][0] = y_test_temp[i].mean()\n\n\n return [x_train, y_train, x_test, y_test, x_validation, y_validation, y_test_temp]\n\n\nclass LSTM(nn.Module):\n def __init__(self, input_dim, hidden_dim, num_layers, output_dim):\n super(LSTM, self).__init__()\n # Hidden dimensions\n self.hidden_dim = hidden_dim\n\n # Number of hidden layers\n self.num_layers = num_layers\n\n # batch_first=True causes input/output tensors to be of shape\n # (batch_dim, seq_dim, feature_dim)\n self.lstm = nn.LSTM(input_dim, hidden_dim, num_layers, batch_first=True).cuda()\n\n # Readout layer\n self.fc = nn.Linear(hidden_dim, output_dim).cuda()\n\n def forward(self, x):\n cuda0 = torch.device(0)\n # Initialize hidden state with zeros\n h0 = torch.zeros(self.num_layers, x.size(0), self.hidden_dim).to(cuda0).requires_grad_()\n\n # Initialize cell state\n c0 = torch.zeros(self.num_layers, x.size(0), self.hidden_dim).to(cuda0).requires_grad_()\n\n # We need to detach as we are doing truncated backpropagation through time (BPTT)\n # If we don't, we'll backprop all the way to the start even after going through another batch\n out, (hn, cn) = self.lstm(x, (h0.detach(), c0.detach()),)\n\n # Index hidden state of last time step\n # out.size() --> 100, 32, 100\n # out[:, -1, :] --> 100, 100 --> just want last time step hidden states!\n out = self.fc(out[:, -1, :])\n # out.size() --> 100, 10\n return out\n\n\nif __name__ == \"__main__\":\n\n dates = pd.date_range('2018-01-04', '2023-10-01')\n df1 = pd.DataFrame(index=dates)\n df_stockIndex = pd.read_csv(\"./kaggle/Data/Stocks/us100.us.txt\", parse_dates=True, index_col=0)\n df_stockIndex = df1.join(df_stockIndex)\n # df_stockIndex[['Close']].plot(figsize=(15, 6))\n # plt.ylabel(\"stock_price\")\n # plt.title(\"NASDAQ Stock\")\n # plt.show()\n df_stockIndex = df_stockIndex[['Close']]\n df_stockIndex = df_stockIndex.ffill()\n scaler = MinMaxScaler(feature_range=(-1, 1))\n\n df_stockIndex['Close'] = scaler.fit_transform(df_stockIndex['Close'].values.reshape(-1, 1))\n\n look_back = 365 # choose sequence length\n look_forward = 5\n x_train, y_train , x_test, y_test , x_validation, y_validation , y_real= load_data(df_stockIndex, look_back, look_forward)\n print('x_train.shape = ', x_train.shape)\n print('y_train.shape = ', y_train.shape)\n print('x_test.shape = ', x_test.shape)\n print('y_test.shape = ', y_test.shape)\n print('x_validation.shape = ', x_validation.shape)\n print('y_validation.shape = ', y_validation.shape)\n\n x_train = torch.from_numpy(x_train).type(torch.Tensor)\n y_train = torch.from_numpy(y_train).type(torch.Tensor)\n y_test = torch.from_numpy(y_test).type(torch.Tensor)\n x_test = torch.from_numpy(x_test).type(torch.Tensor)\n x_validation = torch.from_numpy(x_validation).type(torch.Tensor)\n y_validation = torch.from_numpy(y_validation).type(torch.Tensor)\n input_dim = 1\n hidden_dim = 128\n num_layers = 2\n output_dim = 1\n model = LSTM(input_dim=input_dim, hidden_dim=hidden_dim, output_dim=output_dim, num_layers=num_layers)\n loss_fn = torch.nn.MSELoss()\n optimiser = torch.optim.Adam(model.parameters(), lr=0.01)\n\n print(x_train.shape, x_test.shape)\n num_epochs =10\n\n hist = np.zeros(num_epochs*len(x_train))\n\n validation = np.zeros(num_epochs*len(x_train))\n\n # Number of steps to unroll\n cuda0 = torch.device(0)\n for t in range(num_epochs):\n for i in range(len(x_train)):\n # Initialise hidden state\n # Don't do this if you want your LSTM to be stateful\n y_train_pred = model(x_train[i].to(cuda0))\n\n loss = loss_fn(torch.unsqueeze(y_train_pred.cpu(), -1),y_train[i])\n\n hist[t*len(x_train)+i] = loss.item()\n\n optimiser.zero_grad()\n\n # Backward pass\n loss.backward()\n\n # Update parameters\n optimiser.step()\n\n y_train_pred = model(x_validation.to(cuda0))\n validation[t*len(x_train)+i] = loss_fn(torch.unsqueeze(y_train_pred.cpu(), -1), y_validation).item()\n\n print(\"Epoch \", t, \"MSE: \", hist[t*len(x_train)], \"Validation: \", validation[t].item())\n\n # Zero out gradient, else they will accumulate between epochs\n\n plt.plot(validation, label=\"Validation loss\", color='red')\n plt.plot(hist, label=\"Training loss\")\n plt.legend()\n plt.show()\n\n\n # make predictions\n\n print(x_test.shape)\n y_test_pred = model(torch.unsqueeze(x_test.to(cuda0),0))\n\n\n\n\n y_test_pred = scaler.inverse_transform(y_test_pred.cpu().detach().numpy().reshape(-1, 1))\n y_test = scaler.inverse_transform(y_test.detach().numpy().reshape(-1, 1))\n\n\n # Visualising the results\n figure, axes = plt.subplots(figsize=(15, 6))\n axes.xaxis_date()\n print(y_test_pred.shape)\n print(y_test.shape)\n print(df_stockIndex[:].index.shape)\n\n axes.plot(df_stockIndex[-y_test.shape[0]:].index, y_test_pred, color='blue',\n label='Predicted mean NASDAQ Index Price')\n\n df_stockIndex['Close'] = scaler.inverse_transform(df_stockIndex['Close'].values.reshape(-1, 1))\n y_test = scaler.inverse_transform(y_test.detach().numpy().reshape(-1, 1))\n y_real = scaler.inverse_transform(y_real.detach().numpy().reshape(-1, 1))\n axes.plot(df_stockIndex[-y_test.shape[0]:].index, y_test, color='red', label='Real mean NASDQ Index Price')\n axes.plot(df_stockIndex[-y_test.shape[0]:].index, df_stockIndex['Close'].values[-y_test.shape[0]:], color='green', label='Real NASDQ Index Price')\n\n # axes.xticks(np.arange(0,394,50))\n plt.title('NASDAQ Index Price Prediction')\n plt.xlabel('Time')\n plt.ylabel('NASDAQ Index Price')\n plt.legend()\n plt.savefig('ibm_pred.png')\n plt.show()\n", "repo_name": "BrunoMartelli01/Lstm-IndexMarket", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 7708, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "numpy.array", "line_number": 16, "usage_type": "call"}, {"api_name": "numpy.round", "line_number": 18, "usage_type": "call"}, {"api_name": "torch.randperm", "line_number": 29, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 33, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 44, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 45, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 50, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 61, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 69, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 69, "usage_type": "name"}, {"api_name": "torch.nn.LSTM", "line_number": 80, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 80, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 83, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 83, "usage_type": "name"}, {"api_name": "torch.device", "line_number": 86, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 88, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 91, "usage_type": "call"}, {"api_name": "pandas.date_range", "line_number": 107, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 108, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 109, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.MinMaxScaler", "line_number": 117, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 131, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 131, "usage_type": "attribute"}, {"api_name": "torch.from_numpy", "line_number": 132, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 132, "usage_type": "attribute"}, {"api_name": "torch.from_numpy", "line_number": 133, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 133, "usage_type": "attribute"}, {"api_name": "torch.from_numpy", "line_number": 134, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 134, "usage_type": "attribute"}, {"api_name": "torch.from_numpy", "line_number": 135, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 135, "usage_type": "attribute"}, {"api_name": "torch.from_numpy", "line_number": 136, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 136, "usage_type": "attribute"}, {"api_name": "torch.nn.MSELoss", "line_number": 142, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 142, "usage_type": "attribute"}, {"api_name": "torch.optim.Adam", "line_number": 143, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 143, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 148, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 150, "usage_type": "call"}, {"api_name": "torch.device", "line_number": 153, "usage_type": "call"}, {"api_name": "torch.unsqueeze", "line_number": 160, "usage_type": "call"}, {"api_name": "torch.unsqueeze", "line_number": 173, "usage_type": "call"}, {"api_name": "pylab.plt.plot", "line_number": 179, "usage_type": "call"}, {"api_name": "pylab.plt", "line_number": 179, "usage_type": "name"}, {"api_name": "pylab.plt.plot", "line_number": 180, "usage_type": "call"}, {"api_name": "pylab.plt", "line_number": 180, "usage_type": "name"}, {"api_name": "pylab.plt.legend", "line_number": 181, "usage_type": "call"}, {"api_name": "pylab.plt", "line_number": 181, "usage_type": "name"}, {"api_name": "pylab.plt.show", "line_number": 182, "usage_type": "call"}, {"api_name": "pylab.plt", "line_number": 182, "usage_type": "name"}, {"api_name": "torch.unsqueeze", "line_number": 188, "usage_type": "call"}, {"api_name": "pylab.plt.subplots", "line_number": 198, "usage_type": "call"}, {"api_name": "pylab.plt", "line_number": 198, "usage_type": "name"}, {"api_name": "pylab.plt.title", "line_number": 214, "usage_type": "call"}, {"api_name": "pylab.plt", "line_number": 214, "usage_type": "name"}, {"api_name": "pylab.plt.xlabel", "line_number": 215, "usage_type": "call"}, {"api_name": "pylab.plt", "line_number": 215, "usage_type": "name"}, {"api_name": "pylab.plt.ylabel", "line_number": 216, "usage_type": "call"}, {"api_name": "pylab.plt", "line_number": 216, "usage_type": "name"}, {"api_name": "pylab.plt.legend", "line_number": 217, "usage_type": "call"}, {"api_name": "pylab.plt", "line_number": 217, "usage_type": "name"}, {"api_name": "pylab.plt.savefig", "line_number": 218, "usage_type": "call"}, {"api_name": "pylab.plt", "line_number": 218, "usage_type": "name"}, {"api_name": "pylab.plt.show", "line_number": 219, "usage_type": "call"}, {"api_name": "pylab.plt", "line_number": 219, "usage_type": "name"}]} +{"seq_id": "38935531635", "text": "import pickle\nimport argparse\n\nimport numpy as np\nimport torch\nimport torch.autograd as autograd\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch.optim as optim\nfrom IPython.display import clear_output\nfrom torch.utils.data import DataLoader, Dataset\nfrom tqdm import tqdm\n\nfrom sched_solver import Solver\nfrom sched import SchedT1Dataset\nfrom sched_heuristic import get_rm_solution\nfrom sched_heuristic import liu_test\nimport sched_heuristic as heu\nfrom sched_heuristic import scores_to_priority\nfrom sklearn.utils import shuffle\nparser = argparse.ArgumentParser()\n\nparser.add_argument(\"--num_tasks\", type=int, default=8)\nparser.add_argument(\"--num_procs\", type=int, default=2)\nparser.add_argument(\"--num_epochs\", type=int, default=10000)\nparser.add_argument(\"--num_train_dataset\", type=int, default=1000)\nparser.add_argument(\"--num_test_dataset\", type=int, default=100)\nparser.add_argument(\"--embedding_size\", type=int, default=256)\nparser.add_argument(\"--hidden_size\", type=int, default=256)\nparser.add_argument(\"--batch_size\", type=int, default=256)\nparser.add_argument(\"--grad_clip\", type=float, default=1.5)\nparser.add_argument(\"--use_cuda\", type=bool, default=False)\nparser.add_argument(\"--beta\", type=float, default=0.7)\nparser.add_argument(\"--lr\", type=float, default=1.0 * 1e-4)\nargs = parser.parse_args()\nuse_deadline = True\n\nclass Datasets(Dataset):\n def __init__(self, l):\n super(Datasets, self).__init__()\n ret = []\n le = []\n for dd in l:\n ret.append(dd.data_set)\n self.data_set = np.vstack(ret)\n\n def setlen(self, newlen):\n self.data_set = shuffle(self.data_set)\n self.data_set = self.data_set[:newlen]\n\n def __len__(self):\n return self.data_set.shape[0]\n\n def __getitem__(self, idx):\n return idx, self.data_set[idx]\n\nif __name__ ==\"__main__\":\n if args.use_cuda:\n use_pin_memory = True\n else:\n use_pin_memory = False\n with open(\"tr/%d-%d/0.80\" % (args.num_procs, args.num_tasks), 'rb') as f:\n t1 = pickle.load(f)\n with open(\"tr/%d-%d/1.00\" % (args.num_procs, args.num_tasks), 'rb') as f:\n train_dataset = t2 = pickle.load(f)\n with open(\"tr/%d-%d/1.20\" % (args.num_procs, args.num_tasks), 'rb') as f:\n t3 = pickle.load(f)\n\n with open(\"te/%d-%d/1.00\" % (args.num_procs, args.num_tasks), 'rb') as f:\n test_dataset = pickle.load(f)\n\n #train_dataset = Datasets([t1, t2, t3])\n train_dataset.setlen(args.num_train_dataset)\n test_dataset.setlen(args.num_test_dataset)\n train_dataset = test_dataset\n\n train_data_loader = DataLoader(\n train_dataset,\n batch_size=args.batch_size,\n shuffle=True,\n pin_memory=use_pin_memory)\n\n test_data_loader = DataLoader(\n test_dataset,\n batch_size=args.batch_size,\n shuffle=True,\n pin_memory=use_pin_memory)\n\n eval_loader = DataLoader(test_dataset, batch_size=args.num_test_dataset, shuffle=False)\n\n # Calculating heuristics\n\n model = Solver(args.num_procs,\n args.embedding_size,\n args.hidden_size,\n args.num_tasks,\n use_deadline=use_deadline)\n\n if args.use_cuda:\n model = model.cuda()\n\n\n # Train loop\n moving_avg = torch.zeros(args.num_train_dataset)\n if args.use_cuda:\n moving_avg = moving_avg.cuda()\n #generating first baseline\n cc = 1\n for (indices, sample_batch) in tqdm(train_data_loader):\n if args.use_cuda:\n sample_batch = sample_batch.cuda()\n rewards, _, _ = model(sample_batch)\n print(rewards)\n moving_avg[indices] = rewards.float()\n model.eval()\n ret = []\n res_tkc = []\n res_rm = []\n res_opa = []\n for i, sample in tqdm(test_dataset):\n\n scores = heu.get_DkC_scores(sample, args.num_procs)\n priority = scores_to_priority(scores)\n res_tkc.append(heu.test_DA(sample, args.num_procs, priority, use_deadline))\n\n scores = heu.get_DM_scores(sample, args.num_procs)\n priority = scores_to_priority(scores)\n res_rm.append(heu.test_DA(sample, args.num_procs, priority, use_deadline))\n\n ret, _ = heu.OPA(sample, args.num_procs, heu.test_DA, use_deadline)\n res_opa.append(ret)\n\n for i, batch in eval_loader:\n if args.use_cuda:\n batch = batch.cuda()\n R, _, _ = model(batch, argmax=True)\n\n print(\"[before training][RL model generates %d][rm generates %d][DkC generates %d][OPA generates %d]\" %(\n (R > 1.0).sum().detach().numpy(),\n np.sum(res_rm),\n np.sum(res_tkc),\n np.sum(res_opa)))\n\n optimizer = optim.Adam(model.parameters(), lr=args.lr)\n\n for epoch in range(args.num_epochs):\n loss_ = 0\n avg_hit = 0\n for batch_idx, (indices, sample_batch) in enumerate(train_data_loader):\n if args.use_cuda:\n sample_batch.cuda()\n rewards, log_probs, action = model(sample_batch)\n #print(action[0])\n advantage = rewards - moving_avg[indices]\n moving_avg[indices] = moving_avg[indices] * args.beta + rewards * (1.0 - args.beta)\n\n #advantage = rewards\n\n\n avg_hit += (rewards).mean()\n log_probs = torch.sum(log_probs, dim=-1)\n log_probs[log_probs < -100] = -100\n #print(log_probs[:5])\n loss = -(advantage * log_probs).mean()\n\n optimizer.zero_grad()\n loss.backward()\n torch.nn.utils.clip_grad_norm_(model.parameters(), args.grad_clip)\n optimizer.step()\n loss_ += loss.detach().numpy()\n print(\"loss\", loss_, \"avg_hit\", avg_hit.detach().numpy())\n\n model.eval()\n ret = []\n for i, batch in eval_loader:\n if args.use_cuda:\n batch = batch.cuda()\n R, log_prob, actions = model(batch, argmax=True)\n for j, chosen in enumerate(actions.numpy()):\n order = np.zeros_like(chosen)\n for i in range(args.num_tasks):\n order[chosen[i]] = args.num_tasks - i\n ret.append(heu.test_DA(batch[j], args.num_procs, order, use_deadline, False))\n print(\"log_probability\\t\", log_prob.detach().numpy().mean())\n print(\"[at epoch %d][RL model generates %d][rm generates %d][DkC generates %d][OPA generates %d]\" % (\n epoch,\n np.sum(ret),\n np.sum(res_rm),\n np.sum(res_tkc),\n np.sum(res_opa)))\n torch.save(model, \"blah2\")\n model.train()\n", "repo_name": "ita9naiwa/PandaSchedulingModel", "sub_path": "t1.py", "file_name": "t1.py", "file_ext": "py", "file_size_in_byte": 6588, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 5, "dataset": "github-code", "pt": "37", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 21, "usage_type": "call"}, {"api_name": "torch.utils.data.Dataset", "line_number": 38, "usage_type": "name"}, {"api_name": "numpy.vstack", "line_number": 45, "usage_type": "call"}, {"api_name": "sklearn.utils.shuffle", "line_number": 48, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 63, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 65, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 67, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 70, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 77, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 83, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 89, "usage_type": "call"}, {"api_name": "sched_solver.Solver", "line_number": 93, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 104, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 109, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 120, "usage_type": "call"}, {"api_name": "sched_heuristic.get_DkC_scores", "line_number": 122, "usage_type": "call"}, {"api_name": "sched_heuristic.scores_to_priority", "line_number": 123, "usage_type": "call"}, {"api_name": "sched_heuristic.test_DA", "line_number": 124, "usage_type": "call"}, {"api_name": "sched_heuristic.get_DM_scores", "line_number": 126, "usage_type": "call"}, {"api_name": "sched_heuristic.scores_to_priority", "line_number": 127, "usage_type": "call"}, {"api_name": "sched_heuristic.test_DA", "line_number": 128, "usage_type": "call"}, {"api_name": "sched_heuristic.OPA", "line_number": 130, "usage_type": "call"}, {"api_name": "sched_heuristic.test_DA", "line_number": 130, "usage_type": "attribute"}, {"api_name": "numpy.sum", "line_number": 140, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 141, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 142, "usage_type": "call"}, {"api_name": "torch.optim.Adam", "line_number": 144, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 144, "usage_type": "name"}, {"api_name": "torch.sum", "line_number": 161, "usage_type": "call"}, {"api_name": "torch.nn.utils.clip_grad_norm_", "line_number": 168, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 168, "usage_type": "attribute"}, {"api_name": "numpy.zeros_like", "line_number": 180, "usage_type": "call"}, {"api_name": "sched_heuristic.test_DA", "line_number": 183, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 187, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 188, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 189, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 190, "usage_type": "call"}, {"api_name": "torch.save", "line_number": 191, "usage_type": "call"}]} +{"seq_id": "43365297142", "text": "import sys\nimport os\nfrom termcolor import colored\nimport re\nclass ArgValidator:\n def validate(self):\n regex_pattern = r'\\[.*\\]'\n log_file = \"\"\n data = {}\n\n if sys.argv[1] == \"-h\" or sys.argv[1] == \"-H\" or sys.argv[1] == \"--help\":\n print(\n colored(\"[^] python3 main.py -f [log file] -r [/,/login,/dashboard]\", 'yellow'))\n exit()\n if len(sys.argv) != 5:\n print(colored(\"[!] Arg Error ... [-h]\",'red'))\n exit()\n\n if sys.argv[1] == \"-f\":\n if os.path.exists(sys.argv[2]):\n print(colored(\"[^] file is found ...\", 'green'))\n log_file = sys.argv[2]\n else:\n print(colored(\"[404] file is not found ...\", 'red'))\n exit()\n if sys.argv[3] == \"-r\":\n if re.match(regex_pattern, sys.argv[4]):\n print(colored(\"[^] match pattern ...\", 'green'))\n root_argv = sys.argv[4]\n root_argv = root_argv.strip(\"[]\")\n if root_argv == \"\":\n print(\n colored(\"[!] pattern Error sample : [/index]\", 'red'))\n exit()\n else:\n data = {\n 'file': log_file,\n 'root': root_argv.split(',')\n }\n return data\n else:\n print(colored(\"[!] pattern Error sample : [/index]\" ,'red'))\n exit()\n\n else:\n print(\n colored(\"[^] python3 main.py -f [log file] -r [/,/login,/dashboard]\", 'yellow'))\n exit()\n", "repo_name": "localho3t/logRouting", "sub_path": "utils/arg_validator.py", "file_name": "arg_validator.py", "file_ext": "py", "file_size_in_byte": 1763, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "37", "api": [{"api_name": "sys.argv", "line_number": 11, "usage_type": "attribute"}, {"api_name": "termcolor.colored", "line_number": 13, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 15, "usage_type": "attribute"}, {"api_name": "termcolor.colored", "line_number": 16, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 19, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 20, "usage_type": "call"}, {"api_name": "os.path", "line_number": 20, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 20, "usage_type": "attribute"}, {"api_name": "termcolor.colored", "line_number": 21, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 22, "usage_type": "attribute"}, {"api_name": "termcolor.colored", "line_number": 24, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 26, "usage_type": "attribute"}, {"api_name": "re.match", "line_number": 27, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 27, "usage_type": "attribute"}, {"api_name": "termcolor.colored", "line_number": 28, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 29, "usage_type": "attribute"}, {"api_name": "termcolor.colored", "line_number": 33, "usage_type": "call"}, {"api_name": "termcolor.colored", "line_number": 42, "usage_type": "call"}, {"api_name": "termcolor.colored", "line_number": 47, "usage_type": "call"}]} +{"seq_id": "15129055842", "text": "import numpy as np\nimport torch\nfrom torch.autograd import Variable\nfrom matplotlib import cm\nfrom matplotlib import pylab\nfrom matplotlib.animation import FuncAnimation\nfrom sklearn.neighbors import KernelDensity\n\n\ndef load_problem():\n \n N, D_in, D_out = 64, 1, 1\n \n def train(model, train_op):\n x = Variable(torch.randn(N, 1)*3)\n y = Variable(torch.randn(N, 1)*.5+5*torch.sin(x).data)\n \n fig = pylab.figure()\n fig.set_tight_layout(True)\n \n def ani(t):\n train_op(t, x, y)\n fig.clf()\n ax = fig.add_subplot(111)\n pts = 300\n pylab.plot(x.data.numpy().ravel(), y.data.numpy().ravel(), 'r.')\n x_plot = Variable(torch.linspace(torch.min(x.data)-1.,\n torch.max(x.data)+1., pts)[:, None])\n y_plot = np.linspace(torch.min(y.data)-1.,\n torch.max(y.data)+1., pts)[:, None]\n y_pred = torch.Tensor(0, 1)\n for _ in range(100):\n y_pred = torch.cat([y_pred, model(x_plot).data], 1)\n y_mu = torch.mean(y_pred, 1).numpy()\n y_std = torch.std(y_pred, 1).numpy()\n kde_skl = KernelDensity(bandwidth=0.5)\n grid = []\n for i in range(pts):\n kde_skl.fit(y_pred.numpy()[i][:, None])\n log_pdf = kde_skl.score_samples(y_plot)\n grid.append(np.exp(log_pdf)[:, None])\n grid = np.asarray(grid).reshape((pts, pts)).T\n ax.imshow(grid, extent=(\n x_plot.data.numpy().min(), x_plot.data.numpy().max(),\n y_plot.max(), y_plot.min()),\n interpolation='bicubic', cmap=cm.Blues)\n ax.set_ylim([torch.min(y.data)-1, torch.max(y.data)+1])\n ax.set_xlim([torch.min(x.data)-1, torch.max(x.data)+1])\n pylab.pause(.5)\n return ax\n \n anim = FuncAnimation(fig, ani, frames=np.arange(0, 200), interval=300)\n anim.save('demo_1d_reg/demo_1d_reg_kde.gif', writer='imagemagick')\n \n return (N, D_in, D_out), train, None", "repo_name": "MaxInGaussian/SGPA-in-PyTorch", "sub_path": "demo/1d_reg_kde.py", "file_name": "1d_reg_kde.py", "file_ext": "py", "file_size_in_byte": 2106, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "torch.autograd.Variable", "line_number": 15, "usage_type": "call"}, {"api_name": "torch.randn", "line_number": 15, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 16, "usage_type": "call"}, {"api_name": "torch.randn", "line_number": 16, "usage_type": "call"}, {"api_name": "torch.sin", "line_number": 16, "usage_type": "call"}, {"api_name": "matplotlib.pylab.figure", "line_number": 18, "usage_type": "call"}, {"api_name": "matplotlib.pylab", "line_number": 18, "usage_type": "name"}, {"api_name": "matplotlib.pylab.plot", "line_number": 26, "usage_type": "call"}, {"api_name": "matplotlib.pylab", "line_number": 26, "usage_type": "name"}, {"api_name": "torch.autograd.Variable", "line_number": 27, "usage_type": "call"}, {"api_name": "torch.linspace", "line_number": 27, "usage_type": "call"}, {"api_name": "torch.min", "line_number": 27, "usage_type": "call"}, {"api_name": "torch.max", "line_number": 28, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 29, "usage_type": "call"}, {"api_name": "torch.min", "line_number": 29, "usage_type": "call"}, {"api_name": "torch.max", "line_number": 30, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 31, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 33, "usage_type": "call"}, {"api_name": "torch.mean", "line_number": 34, "usage_type": "call"}, {"api_name": "torch.std", "line_number": 35, "usage_type": "call"}, {"api_name": "sklearn.neighbors.KernelDensity", "line_number": 36, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 41, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 42, "usage_type": "call"}, {"api_name": "matplotlib.cm.Blues", "line_number": 46, "usage_type": "attribute"}, {"api_name": "matplotlib.cm", "line_number": 46, "usage_type": "name"}, {"api_name": "torch.min", "line_number": 47, "usage_type": "call"}, {"api_name": "torch.max", "line_number": 47, "usage_type": "call"}, {"api_name": "torch.min", "line_number": 48, "usage_type": "call"}, {"api_name": "torch.max", "line_number": 48, "usage_type": "call"}, {"api_name": "matplotlib.pylab.pause", "line_number": 49, "usage_type": "call"}, {"api_name": "matplotlib.pylab", "line_number": 49, "usage_type": "name"}, {"api_name": "matplotlib.animation.FuncAnimation", "line_number": 52, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 52, "usage_type": "call"}]} +{"seq_id": "6369224265", "text": "from setuptools import find_packages, setup\n\n\nDESCRIPTION = 'App Engine backends for Django-nonrel'\nLONG_DESCRIPTION = None\ntry:\n LONG_DESCRIPTION = open('README.rst').read()\nexcept:\n pass\n\nsetup(name='djangoappengine',\n version='1.7.1',\n description=DESCRIPTION,\n long_description=LONG_DESCRIPTION,\n author='Waldemar Kornewald',\n author_email='wkornewald@gmail.com',\n url='https://github.com/django-nonrel/djangoappengine',\n packages=find_packages(exclude=['docs']),\n include_package_data=True,\n install_requires=['djangotoolbox>=1.6.0'],\n zip_safe=False,\n license='3-clause BSD',\n test_suite='djangoappengine.tests',\n classifiers=[\n 'Development Status :: 5 - Production/Stable',\n 'Environment :: Web Environment',\n 'Framework :: Django',\n 'Intended Audience :: Developers',\n 'License :: OSI Approved :: BSD License',\n 'Operating System :: OS Independent',\n 'Programming Language :: Python',\n 'Programming Language :: Python :: 2.5',\n 'Programming Language :: Python :: 2.6',\n 'Programming Language :: Python :: 2.7',\n 'Topic :: Database',\n 'Topic :: Software Development :: Libraries :: Application Frameworks',\n 'Topic :: Software Development :: Libraries :: Python Modules',\n ],\n)\n", "repo_name": "django-nonrel/djangoappengine", "sub_path": "setup.py", "file_name": "setup.py", "file_ext": "py", "file_size_in_byte": 1379, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 267, "dataset": "github-code", "pt": "37", "api": [{"api_name": "setuptools.setup", "line_number": 11, "usage_type": "call"}, {"api_name": "setuptools.find_packages", "line_number": 18, "usage_type": "call"}]} +{"seq_id": "1294323905", "text": "import os\nimport json\n\ndef parse_directory(base_dir, exclude_dir='build'):\n result = []\n subdirs = []\n for name in os.listdir(base_dir):\n path = os.path.join(base_dir, name)\n if os.path.isdir(path) and name != exclude_dir:\n if name in ['src', 'include']:\n subdirs.append(name)\n else:\n subdir_content = parse_directory(path)\n if subdir_content:\n subdirs.append({name: subdir_content})\n if subdirs:\n result.append({\n os.path.basename(base_dir): subdirs\n })\n return result\n\ndef write_to_json_file(data, filename):\n with open(filename, 'w') as f:\n json.dump(data, f, indent=4)\n\nbase_dir = './game/flecs-3.2.3/examples/c/' # replace with your directory\noutput_file = 'examples.json' # replace with your output file\nmax_depth = 1\nexclude_dir = 'build'\n\nparsed_data = parse_directory(base_dir, exclude_dir)\nwrite_to_json_file(parsed_data, output_file)\n", "repo_name": "Wesxdz/booksim", "sub_path": "examplefinder.py", "file_name": "examplefinder.py", "file_ext": "py", "file_size_in_byte": 997, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "os.listdir", "line_number": 7, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 8, "usage_type": "call"}, {"api_name": "os.path", "line_number": 8, "usage_type": "attribute"}, {"api_name": "os.path.isdir", "line_number": 9, "usage_type": "call"}, {"api_name": "os.path", "line_number": 9, "usage_type": "attribute"}, {"api_name": "os.path.basename", "line_number": 18, "usage_type": "call"}, {"api_name": "os.path", "line_number": 18, "usage_type": "attribute"}, {"api_name": "json.dump", "line_number": 24, "usage_type": "call"}]} +{"seq_id": "36906919335", "text": "import os\r\nimport json\r\nimport requests\r\n\r\nwith open('face_detection.json', 'r') as handle:\r\n\tdata_info = [json.loads(line) for line in handle]\r\n\r\nimage_links = [line['content'] for line in data_info]\r\n\r\nIMG_DOWNLOAD_DIR = 'raw_face_images'\r\n\r\nimg_count = len(image_links)\r\nfor i, link in enumerate(image_links):\r\n\tprint(\"Downloading image {}/{}\".format(i+1, img_count))\r\n\twith open(os.path.join(IMG_DOWNLOAD_DIR, 'img'+str(i)+'.png'), 'wb') as handle:\r\n\t\tresponse = requests.get(link, stream=True)\r\n\r\n\t\tif not response.ok:\r\n\t\t\tprint(response)\r\n\r\n\t\tfor block in response.iter_content(1024):\r\n\t\t\tif not block:\r\n\t\t\t break\r\n\t\t\thandle.write(block)\r\n", "repo_name": "miguel-rodrigo/dot-csv-pix2pix", "sub_path": "download_face_images.py", "file_name": "download_face_images.py", "file_ext": "py", "file_size_in_byte": 648, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "37", "api": [{"api_name": "json.loads", "line_number": 6, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 15, "usage_type": "call"}, {"api_name": "os.path", "line_number": 15, "usage_type": "attribute"}, {"api_name": "requests.get", "line_number": 16, "usage_type": "call"}]} +{"seq_id": "38566826877", "text": "import argparse\n\nfrom src.graph_classification import graph_classifier\n\n\ndef main(args):\n graph_classifier(args.root_dataset,\n args.parameters_edit_cost,\n args.alphas,\n args.ks,\n args.n_trials,\n args.n_outer_cv,\n args.n_inner_cv,\n args.n_cores,\n args.folder_results,\n args.save_gt_labels,\n args.save_predictions,\n args.verbose,\n args)\n\n\nDEFAULT_ALPHAS = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]\nDEFAULT_KS = [3, 5, 7]\n\nif __name__ == '__main__':\n\n args_parser = argparse.ArgumentParser(description='Graph Classification Using KNN with GED')\n subparser = args_parser.add_subparsers()\n\n args_parser.add_argument('--root_dataset',\n type=str,\n required=True,\n default='./data',\n help='Root of the dataset')\n\n args_parser.add_argument('--parameters_edit_cost',\n nargs='+',\n default=(1., 1., 1., 1., 'euclidean'),\n help='Tuple with the cost for the edit operations')\n\n # Hyperparameters to test\n args_parser.add_argument('--alphas',\n nargs='*',\n default=DEFAULT_ALPHAS,\n type=float,\n help='List of alphas to test')\n args_parser.add_argument('--ks',\n nargs='*',\n default=DEFAULT_KS,\n type=int,\n help='List of ks to test (k being the number of neighbors for the KNN)')\n\n # Parameters used during the optimization process\n args_parser.add_argument('--n_trials',\n default=10,\n type=int,\n help='Number of cross-validation to perform')\n args_parser.add_argument('--n_outer_cv',\n default=10,\n type=int,\n help='Number of outer loops in the cross-validation')\n args_parser.add_argument('--n_inner_cv',\n default=5,\n type=int,\n help='Number of inner loops in the cross-validation')\n args_parser.add_argument('--n_cores',\n default=0,\n type=int,\n help='Set the number of cores to use.'\n 'If n_cores == 0 then it is run without parallelization.'\n 'If n_cores > 0 then use this number of cores')\n\n args_parser.add_argument('--save_gt_labels',\n action='store_true',\n help='save the ground truth classes if activated')\n args_parser.add_argument('--save_predictions',\n action='store_true',\n help='save the predicted classes if activated')\n\n args_parser.add_argument('--folder_results',\n type=str,\n required=True,\n help='Folder where to save the reduced graphs')\n\n args_parser.add_argument('-v',\n '--verbose',\n action='store_true',\n help='Activate verbose print')\n\n parse_args = args_parser.parse_args()\n\n if parse_args.alphas == [0] and parse_args.ks == [0]:\n parse_args.alphas = DEFAULT_ALPHAS\n parse_args.ks = DEFAULT_KS\n\n main(parse_args)\n", "repo_name": "CheshireCat12/graph-classification-ged", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 3839, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "src.graph_classification.graph_classifier", "line_number": 7, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 27, "usage_type": "call"}]} +{"seq_id": "25754362169", "text": "import wrapt\n\nfrom thundra.config import config_names\nfrom thundra.config.config_provider import ConfigProvider\nfrom thundra.integrations.sqlalchemy import SqlAlchemyIntegration\n\n\ndef _wrapper(wrapped, instance, args, kwargs):\n engine = wrapped(*args, **kwargs)\n SqlAlchemyIntegration(engine)\n return engine\n\n\ndef patch():\n if not ConfigProvider.get(config_names.THUNDRA_TRACE_INTEGRATIONS_SQLALCHEMY_DISABLE):\n try:\n from sqlalchemy.event import listen\n from sqlalchemy.engine.interfaces import ExecutionContext\n wrapt.wrap_function_wrapper(\n 'sqlalchemy',\n 'create_engine',\n _wrapper\n )\n\n wrapt.wrap_function_wrapper(\n 'sqlalchemy.engine',\n 'create_engine',\n _wrapper\n )\n except:\n pass\n", "repo_name": "thundra-io/thundra-agent-python", "sub_path": "thundra/integrations/modules/sqlalchemy.py", "file_name": "sqlalchemy.py", "file_ext": "py", "file_size_in_byte": 881, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 38, "dataset": "github-code", "pt": "37", "api": [{"api_name": "thundra.integrations.sqlalchemy.SqlAlchemyIntegration", "line_number": 10, "usage_type": "call"}, {"api_name": "thundra.config.config_provider.ConfigProvider.get", "line_number": 15, "usage_type": "call"}, {"api_name": "thundra.config.config_provider.ConfigProvider", "line_number": 15, "usage_type": "name"}, {"api_name": "thundra.config.config_names.THUNDRA_TRACE_INTEGRATIONS_SQLALCHEMY_DISABLE", "line_number": 15, "usage_type": "attribute"}, {"api_name": "thundra.config.config_names", "line_number": 15, "usage_type": "name"}, {"api_name": "wrapt.wrap_function_wrapper", "line_number": 19, "usage_type": "call"}, {"api_name": "wrapt.wrap_function_wrapper", "line_number": 25, "usage_type": "call"}]} +{"seq_id": "43456797554", "text": "import json\nimport ssl\nimport urllib.request\n\nimport twurl\n\n# Ignore SSL certificate errors\nctx = ssl.create_default_context()\nctx.check_hostname = False\nctx.verify_mode = ssl.CERT_NONE\n\nTWITTER_URL = 'https://api.twitter.com/1.1/statuses/user_timeline.json'\n\nwhile True:\n print('')\n acct = input('Enter a Twitter account: ')\n if len(acct) < 1:\n break\n\n url = twurl.augment(TWITTER_URL, {'screen_name': acct, 'count': '5'})\n print('Retrieving URL', url)\n connection = urllib.request.urlopen(url, context=ctx)\n data = connection.read().decode()\n # print(data[:250])\n\n # Print headers\n headers = dict(connection.getheaders())\n print('Remaining', headers['x-rate-limit-remaining'])\n\n js = json.loads(data)\n print(json.dumps(js, indent=4))\n\n for u in js['users']:\n print(u['screen_name'])\n if 'status' not in u:\n print(' *** No status found...')\n continue\n\n s = u['status']['text']\n print(' ', s[:50])\n", "repo_name": "mehdi-ahmed/python_access_web_data", "sub_path": "week6/twitter_api/twitter_account.py", "file_name": "twitter_account.py", "file_ext": "py", "file_size_in_byte": 1001, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "ssl.create_default_context", "line_number": 8, "usage_type": "call"}, {"api_name": "ssl.CERT_NONE", "line_number": 10, "usage_type": "attribute"}, {"api_name": "twurl.augment", "line_number": 20, "usage_type": "call"}, {"api_name": "urllib.request.request.urlopen", "line_number": 22, "usage_type": "call"}, {"api_name": "urllib.request.request", "line_number": 22, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 22, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 30, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 31, "usage_type": "call"}]} +{"seq_id": "16462136457", "text": "# encode=utf-8\nimport requests\nimport urllib.request\nimport os\nimport sys\n\n\ndef getSogouImage(category, length, path):\n request = requests.get(\n 'https://pic.sogou.com/pics/channel/getAllRecomPicByTag.jsp?category=' + category + '&tag=%E5%85%A8%E9%83%A8&&start=0&len=' + str(\n length) + '&width=1920&height=1080')\n json = request.json()\n m = 0\n print(\"全部的json\" + str(json['all_items']))\n print(len(json['all_items']))\n if not os.path.exists(path):\n os.makedirs(path)\n for j in json['all_items']:\n try:\n print('正在下载。。。' + path + str(m) + '.jpg')\n urllib.request.urlretrieve(j['pic_url'], path + str(m) + '.jpg')\n m = m + 1\n except Exception:\n print('程序出现了问题。。。' + j['pic_url'] + path + str(m) + '.jpg')\n\n\n\nif __name__ == '__main__':\n getSogouImage('壁纸', 1079, 'D:\\\\壁纸\\\\')\n", "repo_name": "WangShaoBin28/python-demo", "sub_path": "demo.py", "file_name": "demo.py", "file_ext": "py", "file_size_in_byte": 926, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "requests.get", "line_number": 9, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 16, "usage_type": "call"}, {"api_name": "os.path", "line_number": 16, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 17, "usage_type": "call"}, {"api_name": "urllib.request.request.urlretrieve", "line_number": 21, "usage_type": "call"}, {"api_name": "urllib.request.request", "line_number": 21, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 21, "usage_type": "name"}]} +{"seq_id": "10457744353", "text": "import logging\nimport json\n\n__author__ = '''Sayantan Khanra '''\n__docformat__ = '''google'''\n__date__ = '''22-04-2022'''\n__copyright__ = '''Copyright 2022, Sayantan Khanra'''\n__credits__ = [\"Sayantan Khanra\"]\n__license__ = '''MIT'''\n__maintainer__ = '''Sayantan Khanra'''\n__email__ = ''''''\n__status__ = '''Development''' # \"Prototype\", \"Development\", \"Production\".\n\n# This is the main prefix used for logging\nLOGGER_BASENAME = '''datamodels'''\nLOGGER = logging.getLogger(LOGGER_BASENAME)\nLOGGER.addHandler(logging.NullHandler())\n\n\nclass TenantEnergyLabelingData:\n \"\"\"Models the data for energy labeling to export.\"\"\"\n\n def __init__(self, # pylint: disable= too-many-arguments\n filename,\n id, # pylint: disable= redefined-builtin\n energy_label,\n labeled_subscriptions,\n defender_for_cloud_findings):\n self.filename = filename\n self._id = id\n self._energy_label = energy_label\n self._labeled_subscriptions = labeled_subscriptions\n self._defender_for_cloud_findings = defender_for_cloud_findings\n\n @property\n def json(self):\n \"\"\"Data to json.\"\"\"\n subscription_metrics = []\n for subscription in self._labeled_subscriptions:\n energy_label = subscription.get_energy_label(self._defender_for_cloud_findings)\n subscription_metrics.append({\n 'Subscription ID': subscription.subscription_id,\n 'Subscription Display Name': subscription.display_name,\n 'Number of high findings': energy_label.number_of_high_findings,\n 'Number of medium findings': energy_label.number_of_medium_findings,\n 'Number of low findings': energy_label.number_of_low_findings,\n 'Number of exempted findings': len(subscription.exempted_policies),\n 'Number of maximum days open': energy_label.max_days_open,\n 'Energy Label': energy_label.label\n })\n return json.dumps(\n [{\n 'Tenant ID': self._id,\n 'Tenant Energy Label': self._energy_label,\n 'Labeled subscriptions': subscription_metrics\n }], indent=2, default=str)\n\n\nclass DefenderForCloudFindingsData:\n \"\"\"Models the data for energy labeling to export.\"\"\"\n\n def __init__(self, filename, defender_for_cloud_findings):\n self.filename = filename\n self._defender_for_cloud_findings = defender_for_cloud_findings\n\n @property\n def json(self):\n \"\"\"Data to json.\"\"\"\n return json.dumps([{'Compliance Standard ID': finding.compliance_standard_id,\n 'Compliance Control ID': finding.compliance_control_id,\n 'Compliance State': finding.compliance_state,\n 'Subscription ID': finding.subscription_id,\n 'Resource Group': finding.resource_group,\n 'Resource Type': finding.resource_type,\n 'Resource Name': finding.resource_name,\n 'Resource ID': finding.resource_id,\n 'Severity': finding.severity,\n 'State': finding.state,\n 'Recommendation ID': finding.recommendation_id,\n 'Recommendation Name': finding.recommendation_name,\n 'Recommendation Display Name': finding.recommendation_display_name,\n 'Description': finding.description,\n 'Remediation Steps': finding.remediation_steps,\n 'Azure Portal Recommendation Link': finding.azure_portal_recommendation_link,\n 'Control Name': finding.control_name,\n 'Days Open': finding.days_open\n }\n for finding in self._defender_for_cloud_findings if not finding.is_skipped],\n indent=2, default=str)\n\n\nclass SubscriptionExemptedPolicies:\n \"\"\"Models the data for exempted policies to export.\"\"\"\n\n def __init__(self, filename, labeled_subscriptions):\n self.filename = filename\n self._labeled_subscriptions = labeled_subscriptions\n\n @property\n def data(self):\n \"\"\"Data of an subscription exempted policies to export.\"\"\"\n exempted_policies = []\n for subscription in self._labeled_subscriptions:\n for exempted_policy in subscription.exempted_policies:\n exempted_policies.append({'Subscription ID': subscription.subscription_id,\n 'Created At': exempted_policy.system_data.created_at,\n 'Created By': exempted_policy.system_data.created_by,\n 'Description': exempted_policy.description,\n 'Display Name': exempted_policy.display_name,\n 'Exemption Category': exempted_policy.exemption_category,\n 'Last Modified By': exempted_policy.system_data.last_modified_by,\n 'Last Modified At': exempted_policy.system_data.last_modified_at,\n 'Name': exempted_policy.name,\n 'Expires On': exempted_policy.expires_on\n })\n return exempted_policies\n\n @property\n def json(self):\n \"\"\"Data to json.\"\"\"\n return json.dumps(self.data, indent=2, default=str)\n\n\nclass LabeledSubscriptionData:\n \"\"\"Models the data for energy labeling to export.\"\"\"\n\n def __init__(self, filename, labeled_subscription, defender_for_cloud_findings):\n self.filename = filename\n self._labeled_subscription = labeled_subscription\n self._defender_for_cloud_findings = defender_for_cloud_findings\n\n @property\n def data(self):\n \"\"\"Data of an subscription to export.\"\"\"\n energy_label = self._labeled_subscription.get_energy_label(self._defender_for_cloud_findings)\n return {'Subscription ID': self._labeled_subscription.subscription_id,\n 'Subscription Display Name': self._labeled_subscription.display_name,\n 'Number of high findings': energy_label.number_of_high_findings,\n 'Number of medium findings': energy_label.number_of_medium_findings,\n 'Number of low findings': energy_label.number_of_low_findings,\n 'Number of exempted findings': len(self._labeled_subscription.exempted_policies),\n 'Number of maximum days open': energy_label.max_days_open,\n 'Energy Label': energy_label.label}\n\n @property\n def json(self):\n \"\"\"Data to json.\"\"\"\n return json.dumps(self.data, indent=2, default=str)\n\n\nclass LabeledResourceGroupData:\n \"\"\"Models the data for energy labeling to export.\"\"\"\n\n def __init__(self, filename, labeled_resource_group_data, defender_for_cloud_findings):\n self.filename = filename\n self._subscription_id = labeled_resource_group_data.get('subscription_id')\n self._labeled_resource_group = labeled_resource_group_data.get('labeled_resource_group')\n self._defender_for_cloud_findings = defender_for_cloud_findings\n\n @property\n def data(self):\n \"\"\"Data of an subscription to export.\"\"\"\n energy_label = self._labeled_resource_group.get_energy_label(self._defender_for_cloud_findings)\n return {'Subscription ID': self._subscription_id,\n 'ResourceGroup Name': self._labeled_resource_group.name,\n 'Number of high findings':\n energy_label.number_of_high_findings,\n 'Number of medium findings': energy_label.number_of_medium_findings,\n 'Number of low findings': energy_label.number_of_low_findings,\n 'Number of maximum days open': energy_label.max_days_open,\n 'Energy Label': energy_label.label}\n\n @property\n def json(self):\n \"\"\"Data to json.\"\"\"\n return json.dumps(self.data, indent=2, default=str)\n\n\nclass LabeledResourceGroupsData:\n \"\"\"Models the data for energy labeling to export.\"\"\"\n\n def __init__(self, filename, labeled_subscriptions, defender_for_cloud_findings):\n self.filename = filename\n self._labeled_subscriptions = labeled_subscriptions\n self._defender_for_cloud_findings = defender_for_cloud_findings\n\n @property\n def json(self):\n \"\"\"Data to json.\"\"\"\n labeled_resource_groups = []\n for subscription in self._labeled_subscriptions:\n for resource_group in subscription.resource_groups:\n labeled_resource_groups.append({\n 'subscription_id': subscription.subscription_id,\n 'labeled_resource_group': resource_group\n })\n return json.dumps([LabeledResourceGroupData(self.filename,\n resource_group,\n self._defender_for_cloud_findings).data\n for resource_group in labeled_resource_groups], indent=2, default=str)\n\n\nclass LabeledSubscriptionsData:\n \"\"\"Models the data for energy labeling to export.\"\"\"\n\n def __init__(self, filename, labeled_subscriptions, defender_for_cloud_findings):\n self.filename = filename\n self._labeled_subscriptions = labeled_subscriptions\n self.defender_for_cloud_findings = defender_for_cloud_findings\n\n @property\n def json(self):\n \"\"\"Data to json.\"\"\"\n return json.dumps([LabeledSubscriptionData(self.filename, subscription, self.defender_for_cloud_findings).data\n for subscription in self._labeled_subscriptions], indent=2, default=str)\n", "repo_name": "schubergphilis/azureenergylabelerlib", "sub_path": "azureenergylabelerlib/datamodels.py", "file_name": "datamodels.py", "file_ext": "py", "file_size_in_byte": 10071, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "logging.getLogger", "line_number": 16, "usage_type": "call"}, {"api_name": "logging.NullHandler", "line_number": 17, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 51, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 69, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 121, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 148, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 176, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 197, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 214, "usage_type": "call"}]} +{"seq_id": "20492558074", "text": "import os \r\nimport xlsxwriter \r\nimport openpyxl\r\nimport pprint\r\nfrom pathlib import Path\r\n\r\n \r\n\r\ndef group_wavs():\r\n\t# print(os.listdir(\"all_tracks\"))\r\n\tall_tracks = os.listdir(\"all_tracks\")\r\n\ttrack_list_list = []\r\n\ttrack_list = []\r\n\tprevious_track =\"\"\r\n\tc = 0\r\n\tfor track in all_tracks:\r\n\t\tif \"-\" not in track:\r\n\t\t\ttrack_list.append([track])\r\n\tc = 0\t\r\n\tfor base_track in track_list:\r\n\t\tfor track in all_tracks:\r\n\t\t\tif base_track[0][:-4] in track and track not in track_list[c]:\r\n\t\t\t\ttrack_list[c].append(track)\r\n\t\tc = c+1\r\n\treturn track_list\r\n\r\ndef read_isrc(filename):\r\n\t# returns a dict with the track names as keys and isrc codes and values\r\n\txlsx_file = Path(filename)\r\n\twb_obj = openpyxl.load_workbook(xlsx_file)\r\n\tsheet = wb_obj.active\t\r\n\r\n\trow_count = sheet.max_row + 1\r\n\tcurrent_product_name = \"\"\r\n\tcategories = \"\"\r\n\tisrc_dic = {}\r\n\tfor row in sheet.iter_rows(max_row = row_count):\r\n\t\tif row[0].value != None:\r\n\t\t\tisrc_dic[row[0].value.lower()] = row[1].value\r\n\treturn isrc_dic\r\n\r\ndef read_iswc_and_tunecode(filename):\r\n\txlsx_file = Path(filename)\r\n\twb_obj = openpyxl.load_workbook(xlsx_file)\r\n\tsheet = wb_obj.active\t\r\n\r\n\trow_count = sheet.max_row + 1\r\n\tcurrent_product_name = \"\"\r\n\tcategories = \"\"\r\n\tiswc_and_tunecode_dic = {}\r\n\tfor row in sheet.iter_rows(max_row = row_count):\r\n\t\tif row[0].value != None:\r\n\t\t\tiswc_and_tunecode_dic[row[0].value.lower()] = [row[3].value, row[4].value]\r\n\tpprint.pprint(iswc_and_tunecode_dic)\r\n\treturn iswc_and_tunecode_dic\r\n\r\ndef add_borders(letter, c, cell_format,worksheet):\r\n\tworksheet.write(letter + str(c), None,cell_format)\r\n\r\ndef write_excel(grouped_wavs):\r\n\tworkbook = xlsxwriter.Workbook('test_track_information_to_add.xlsx') \r\n\r\n\t# The workbook object is then used to add new \r\n\t# worksheet via the add_worksheet() method. \r\n\tworksheet = workbook.add_worksheet() \r\n\t \r\n\tletters = \"BCDEFGHIJKLMNOPQRSTUVWXYZ\"\r\n\theadings = [\"Name\", \"Categories\", \"Regular price\", \"Type\", \"Download 1 name\", \"Choose value(s)\", \"ISRC Code\", \"PRS Tunecode\", \"ISWC Code\", \"Mins value(s)\", \"BPM value(s)\", \"Genre value(s)\", \"Instrument value(s)\", \"Mood value(s)\", \"Price value(s)\", \"Purpose value(s)\", \"Tempo value(s)\", \"Rating\", \"Tags\", \"Download 1 URL\", \"Artist\", \"Year\", \"Genre\", \"Comment\", \"CAE number\", \"Composer\"]\r\n\tc = 0 \r\n\tfor l in letters:\r\n\t\tworksheet.write(l + \"1\", headings[c])\t\r\n\t\tc = c + 1\r\n\r\n\tcell_format = workbook.add_format()\r\n\t\r\n\tcell_format.set_top(1) # This is optional when using a solid fill.\r\n\tcell_format.set_top_color(\"#\")\r\n\r\n\tend = False\r\n\tc = 2\r\n\tfor track_group in grouped_wavs:\r\n\t\tif len(track_group) == 1:\r\n\t\t\tfor letter in letters:\r\n\t\t\t\tadd_borders(letter,c,cell_format, worksheet)\t\r\n\r\n\t\t\tworksheet.write('A' + str(c), \"Product\",cell_format)\t\r\n\t\t\tworksheet.write('B' + str(c), track_group[0][:-4],cell_format )\t\r\n\t\t\tworksheet.write('C' + str(c), \"Track Only\",cell_format)\r\n\t\t\tworksheet.write('D' + str(c), 10,cell_format)\r\n\t\t\tworksheet.write('E' + str(c), \"Simple\",cell_format)\t\r\n\t\t\tworksheet.write('F' + str(c), track_group[0][:-4] + \".zip\",cell_format)\r\n\r\n\t\t\tisrc_dic = read_isrc(\"isrc.xlsx\")\r\n\t\t\ttrack_name = track_group[0][:-4].lower()\r\n\t\t\ttry:\r\n\t\t\t\tworksheet.write('H' + str(c), isrc_dic[track_name],cell_format)\t\r\n\t\t\texcept:\r\n\t\t\t\tprint(\"No ISRC code given for: \" + track_name)\r\n\r\n\t\t\tiswc_and_tunecode_dic = read_iswc_and_tunecode(\"iswc_+_tunecode.xlsx\")\r\n\t\t\ttry:\r\n\t\t\t\tworksheet.write('I' + str(c), iswc_and_tunecode_dic[track_name][0],cell_format)\t\r\n\t\t\t\tworksheet.write('J' + str(c), iswc_and_tunecode_dic[track_name][1],cell_format)\t\r\n\t\t\texcept:\r\n\t\t\t\tprint(\"No ISWC or PRS Tunecode given for: \" + track_group[0][:-4])\r\n\r\n\r\n\t\t\tc = c+1\r\n\t\t\tworksheet.write('A' + str(c), \"Variation 1\")\r\n\t\t\tc = c+1\r\n\t\t\tworksheet.write('A' + str(c), \"Variation 2\")\r\n\r\n\t\telif len(track_group)>1:\r\n\t\t\tfor letter in letters:\r\n\t\t\t\tadd_borders(letter,c,cell_format, worksheet)\t\r\n\r\n\t\t\tworksheet.write('A' + str(c), \"Product\",cell_format)\r\n\t\t\tworksheet.write('B' + str(c), track_group[0][:-4],cell_format)\r\n\r\n\t\t\tworksheet.write('E' + str(c), \"variable\",cell_format)\r\n\t\t\tworksheet.write('F' + str(c), None,cell_format)\r\n\t\t\tworksheet.write('G' + str(c), \"Track Only $10, Track+Stems $15\",cell_format)\r\n\t\t\tisrc_dic = read_isrc(\"isrc.xlsx\")\r\n\t\t\ttrack_name = track_group[0][:-4].lower()\r\n\t\t\ttry:\r\n\t\t\t\tworksheet.write('H' + str(c), isrc_dic[track_name],cell_format)\t\r\n\t\t\texcept:\r\n\t\t\t\tprint(\"No ISRC code given for: \" + track_name)\t\t\t\r\n\r\n\t\t\tiswc_and_tunecode_dic = read_iswc_and_tunecode(\"iswc_+_tunecode.xlsx\")\r\n\t\t\ttry:\r\n\t\t\t\tworksheet.write('I' + str(c), iswc_and_tunecode_dic[track_name][0],cell_format)\t\r\n\t\t\t\tworksheet.write('J' + str(c), iswc_and_tunecode_dic[track_name][1],cell_format)\t\r\n\t\t\texcept:\r\n\t\t\t\tprint(\"No ISWC or PRS Tunecode given for: \" + track_group[0][:-4])\r\n\r\n\r\n\r\n\t\t\tc +=1\r\n\t\t\tworksheet.write('A' + str(c), \"Variation 1\")\r\n\t\t\tworksheet.write('B' + str(c), track_group[0][:-4] + \" - Track Only $10\")\r\n\t\t\tworksheet.write('D' + str(c), 10)\r\n\t\t\tworksheet.write('E' + str(c), \"variation, downloadable, virtual\")\r\n\t\t\tworksheet.write('F' + str(c), track_group[0][:-4] + \".zip\")\r\n\t\t\tworksheet.write('G' + str(c), \"Track Only $10\")\r\n\r\n\r\n\t\t\tc+=1\r\n\t\t\tworksheet.write('A' + str(c), \"Variation 2\")\r\n\t\t\tworksheet.write('B' + str(c), track_group[0][:-4] + \" - Track+Stems $15\" )\r\n\t\t\tworksheet.write('E' + str(c), \"variation, downloadable, virtual\")\r\n\t\t\tworksheet.write('D' + str(c), 15)\r\n\t\t\tworksheet.write('F' + str(c), track_group[0][:-4] + \" - Stems.zip\")\r\n\t\t\tworksheet.write('G' + str(c), \"Track+Stems $15\")\r\n\r\n\r\n\r\n\t\t\tfor track in track_group:\r\n\t\t\t\tif \"Guide\" in track or \"Section\" in track:\r\n\t\t\t\t\tc+=1\r\n\t\t\t\t\tworksheet.write('A' + str(c), \"Variation 3\")\r\n\t\t\t\t\tworksheet.write('B' + str(c), track_group[0][:-4] + \" - Track+Sections $15\")\r\n\t\t\t\t\tworksheet.write('D' + str(c), 15)\r\n\t\t\t\t\tworksheet.write('E' + str(c), \"variation, downloadable, virtual\")\r\n\t\t\t\t\tworksheet.write('C' + str(c-3), \"Stems+Sections Available\",cell_format)\r\n\t\t\t\t\tworksheet.write('F' + str(c), track_group[0][:-4] + \" - Sections.zip\")\r\n\t\t\t\t\tworksheet.write('G' + str(c), \"Track+Sections $15\")\r\n\t\t\t\t\tworksheet.write('G' + str(c-3), \"Track Only $10, Track+Stems $15, Track+Sections $15\",cell_format)\r\n\r\n\t\t\t\t\tbreak\r\n\t\t\t\telse:\r\n\t\t\t\t\tworksheet.write('C' + str(c-2), \"Stems Available\",cell_format)\r\n\r\n\t\tif c == 20:\r\n\t\t\tend = True \r\n\t\tc = c+1\r\n\t\r\n\r\n\r\n\t# Finally, close the Excel file \r\n\t# via the close() method. \r\n\tworkbook.close()\r\n\r\n\r\ngrouped_wavs = group_wavs()\r\n# # pprint.pprint(grouped_wavs)\r\nwrite_excel(grouped_wavs)\r\n\r\n\r\n", "repo_name": "LeoGardner12/soundtrackable_track_upload_automation", "sub_path": "write_excel.py", "file_name": "write_excel.py", "file_ext": "py", "file_size_in_byte": 6437, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "os.listdir", "line_number": 11, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 29, "usage_type": "call"}, {"api_name": "openpyxl.load_workbook", "line_number": 30, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 43, "usage_type": "call"}, {"api_name": "openpyxl.load_workbook", "line_number": 44, "usage_type": "call"}, {"api_name": "pprint.pprint", "line_number": 54, "usage_type": "call"}, {"api_name": "xlsxwriter.Workbook", "line_number": 61, "usage_type": "call"}]} +{"seq_id": "18847835679", "text": "import pandas as pd\nimport numpy as np\nimport pandas as pd\nimport pdb\nfrom vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer\nimport nltk\nnltk.download('stopwords', quiet=True)\nfrom nltk.corpus import stopwords \nfrom nltk.tokenize import TweetTokenizer\nimport sys\n\n\n\ndef get_files(directory,file_ext):\n file_list = []\n for root,dirs,files in os.walk(directory):\n for file in files:\n if file.endswith(file_ext):\n file_list.append(file)\n file_list.sort()\n return file_list\n\ndef get_mentions(status):\n mentionid,mentionsn = [],[]\n if len(status['entities']['user_mentions']) > 0:\n for i in range(len(status['entities']['user_mentions'])):\n mentionid.append(status['entities']['user_mentions'][i]['id'])\n mentionsn.append(status['entities']['user_mentions'][i]['screen_name'])\n return(mentionid,mentionsn)\n\ndef get_media_urls(status, tweet_type, extended_text): \n media_urls = []\n rt_media_urls = []\n q_media_urls = []\n \n try: \n if \"extended_tweet\" in status: \n for url in status[\"extended_tweet\"][\"entities\"]['media']: \n media_urls.append(url['media_url'])\n else: \n urls = status[\"entities\"][\"urls\"]\n for url in status[\"entities\"]['media']: \n media_urls.append(url['media_url'])\n except: \n pass\n\n try: \n if 'retweeted_status' in status: \n if \"extended_tweet\" in status[\"retweeted_status\"]: \n for url in status[\"retweeted_status\"][\"extended_tweet\"][\"entities\"]['media']: \n rt_media_urls.append(url['media_url'])\n else: \n for url in status[\"retweeted_status\"][\"entities\"]['media']: \n rt_media_urls.append(url['media_url'])\n except: \n pass\n\n try: \n if 'quoted_status' in status: \n if \"extended_tweet\" in status['quoted_status']: \n for url in status[\"quoted_status\"][\"extended_tweet\"][\"entities\"]['media']: \n q_media_urls.append(url['media_url'])\n else: \n for url in status[\"quoted_status\"][\"entities\"]['media']: \n q_media_urls.append(url['media_url'])\n except: \n pass\n\n return (media_urls, rt_media_urls, q_media_urls)\n\ndef get_urls(status, tweet_type, extended_text):\n\n urls = []\n rt_urls = []\n q_urls = []\n\n if \"extended_tweet\" in status: \n urls = status[\"extended_tweet\"][\"entities\"][\"urls\"]\n else: \n urls = status[\"entities\"][\"urls\"]\n\n if 'retweeted_status' in status: \n if \"extended_tweet\" in status[\"retweeted_status\"]: \n rt_urls = status[\"retweeted_status\"][\"extended_tweet\"][\"entities\"][\"urls\"]\n else: \n rt_urls = status[\"retweeted_status\"][\"entities\"][\"urls\"]\n if 'quoted_status' in status: \n if \"extended_tweet\" in status['quoted_status']: \n q_urls = status[\"quoted_status\"][\"extended_tweet\"][\"entities\"]['urls']\n else: \n q_urls = status[\"quoted_status\"][\"entities\"][\"urls\"]\n\n return (urls, rt_urls, q_urls)\n\ndef get_rt_urls(status,tweet_type,extended_text):\n #pdb.set_trace()\n # look through entities from the root \n rt_urls_list = []\n\n if tweet_type == 'retweeted_tweet_without_comment' and extended_text == 'yes':\n try: \n# print(count)\n rt_urls_list=status[\"retweeted_status\"][\"extended_tweet\"][\"entities\"][\"urls\"]\n\n except:\n pass\n \n if tweet_type == 'retweeted_tweet_without_comment' and extended_text == 'no':\n try: \n# print(count)\n rt_urls_list=status[\"retweeted_status\"][\"entities\"][\"urls\"]\n\n except:\n pass\n return rt_urls_list\n\ndef get_tweet_text(status):\n rt_text = \"\"\n qtd_text = \"\"\n text = \"\"\n extended_text = \"no\"\n tweet_type = \"original\"\n reply_userid = None\n reply_screen = None\n reply_statusid = None\n\n # rt\n rt_qtd_count = 0\n rt_rt_count = 0\n rt_reply_count = 0\n rt_fav_count = 0\n rt_tweetid = None\n\n # quoted\n qtd_qtd_count = 0\n qtd_rt_count = 0\n qtd_reply_count = 0\n qtd_fav_count = 0\n qtd_tweetid = None\n\n if \"extended_tweet\" in status:\n text=status['extended_tweet']['full_text']\n extended_text = \"yes\"\n elif \"text\" in status: \n text = status['text']\n extended_text = \"no\"\n\n # take care of retweets\n if 'retweeted_status' in status: \n rt_tweetid = status['retweeted_status']['id_str']\n if 'extended_tweet' in status['retweeted_status']: \n rt_text = status['retweeted_status']['extended_tweet']['full_text']\n extended_text = \"yes\"\n tweet_type = \"retweeted_tweet_without_comment\"\n elif 'text' in status: \n rt_text = status['retweeted_status']['text']\n extended_text = \"no\"\n tweet_type = \"retweeted_tweet_without_comment\"\n\n if 'quote_count' in status['retweeted_status']:\n rt_qtd_count = status['retweeted_status']['quote_count']\n\n if 'retweet_count' in status['retweeted_status']:\n rt_rt_count = status['retweeted_status']['retweet_count']\n\n if 'reply_count' in status['retweeted_status']:\n rt_reply_count = status['retweeted_status']['reply_count']\n\n if 'favorite_count' in status['retweeted_status']:\n rt_fav_count = status['retweeted_status']['favorite_count']\n\n\n # take care of quoted texts\n if 'quoted_status' in status:\n qtd_tweetid = status['quoted_status']['id_str']\n if 'extended_tweet' in status['quoted_status']:\n qtd_text = status['quoted_status']['extended_tweet']['full_text']\n extended_text = \"yes\"\n tweet_type = \"quoted_tweet\"\n elif 'text' in status['quoted_status']:\n qtd_text = status['quoted_status']['text']\n extended_text = \"no\"\n tweet_type = \"quoted_tweet\"\n\n if 'quote_count' in status['quoted_status']:\n qtd_qtd_count = status['quoted_status']['quote_count']\n\n if 'retweet_count' in status['quoted_status']:\n qtd_rt_count = status['quoted_status']['retweet_count']\n\n if 'reply_count' in status['quoted_status']:\n qtd_reply_count = status['quoted_status']['reply_count']\n\n if 'favorite_count' in status['quoted_status']:\n qtd_fav_count = status['quoted_status']['favorite_count']\n\n\n if status['in_reply_to_status_id_str'] is not None and not status['truncated']:\n tweet_type = \"reply\"\n reply_userid = status['in_reply_to_user_id']\n reply_screen = status['in_reply_to_screen_name']\n reply_statusid = status['in_reply_to_status_id']\n\n elif status['in_reply_to_status_id_str'] is not None and status['truncated']:\n tweet_type = \"reply\"\n reply_userid = status['in_reply_to_user_id']\n reply_screen = status['in_reply_to_screen_name']\n reply_statusid = status['in_reply_to_status_id']\n\n return(tweet_type,text.replace(\"\\n\", \" \"),extended_text.replace(\"\\n\", \" \"), rt_text.replace(\"\\n\", \" \"), qtd_text.replace(\"\\n\", \" \"), reply_userid, reply_screen, reply_statusid, \n rt_qtd_count, rt_rt_count, rt_reply_count, rt_fav_count, rt_tweetid, qtd_qtd_count, qtd_rt_count, \n qtd_reply_count, qtd_fav_count, qtd_tweetid)\n\ndef get_hashtags(status,tweet_type,extended_text):\n rt_screen=''\n rt_userid=''\n q_screen=''\n q_userid=''\n rt_loc = ''\n q_loc = ''\n\n hashtag = []\n rt_hashtag = []\n q_hashtag = []\n\n if \"extended_tweet\" in status: \n hashtag_list = status[\"extended_tweet\"][\"entities\"][\"hashtags\"]\n else: \n hashtag_list = status[\"entities\"][\"hashtags\"]\n\n \n if len(hashtag_list)>=1:\n for i in hashtag_list:\n hashtag.append(i[\"text\"])\n else:\n pass\n \n\n if tweet_type==\"retweeted_tweet_without_comment\":\n rt_screen=status['retweeted_status']['user']['screen_name']\n rt_userid=status['retweeted_status']['user']['id_str']\n rt_loc = status['retweeted_status']['user']['location']\n\n if \"extended_tweet\" in status[\"retweeted_status\"]: \n hashtag_list = status[\"retweeted_status\"][\"extended_tweet\"][\"entities\"][\"hashtags\"]\n else: \n hashtag_list = status[\"retweeted_status\"][\"entities\"][\"hashtags\"]\n\n if len(hashtag_list)>=1:\n for i in hashtag_list:\n rt_hashtag.append(i[\"text\"])\n else:\n pass\n \n\n elif tweet_type==\"quoted_tweet\":\n try: \n q_screen=status['quoted_status']['user']['screen_name']\n q_userid=status['quoted_status']['user']['id_str']\n q_loc = status['quoted_status']['user']['location']\n except: \n pass\n try:\n if \"extended_tweet\" in status[\"quoted_status\"]: \n hashtag_list = status[\"quoted_status\"][\"extended_tweet\"][\"entities\"][\"hashtags\"]\n else: \n hashtag_list = status[\"quoted_status\"][\"entities\"][\"hashtags\"]\n\n \n if len(hashtag_list)>=1:\n for i in hashtag_list:\n q_hashtag.append(i[\"text\"])\n else:\n pass\n \n except:\n pass\n\n return(hashtag,rt_userid,rt_screen, rt_hashtag, rt_loc, q_userid,q_screen, q_hashtag, q_loc)\n\n\ndef get_profile_image(status):\n try:\n profile_pic_url = status['user']['profile_image_url']\n profile_banner_url = status['user']['profile_banner_url']\n except:\n profile_pic_url = ''\n profile_banner_url = ''\n return(profile_pic_url,profile_banner_url)\n\ndef main(list_of_dicts):\n\n fields=['tweetid','userid','screen_name','date','lang','location', \"place_id\", \"place_url\", \"place_type\", \\\n \"place_name\", \"place_full_name\", \"place_country_code\", \"place_country\", \"place_bounding_box\", 'text','extended','coord', 'reply_userid', 'reply_screen', 'reply_statusid',\\\n 'tweet_type', \"friends_count\", \"listed_count\", \"followers_count\", \"favourites_count\", \\\n \"statuses_count\", \"verified\", \"hashtag\", 'urls_list','profile_pic_url', 'profile_banner_url', \\\n 'display_name', 'date_first_tweet', 'account_creation_date', 'rt_urls_list','mentionid',\\\n 'mentionsn','rt_screen','rt_userid', 'rt_text', 'rt_hashtag', 'rt_qtd_count', 'rt_rt_count', \\\n 'rt_reply_count', 'rt_fav_count', 'rt_tweetid', 'rt_location', 'qtd_screen','qtd_userid', 'qtd_text', 'qtd_hashtag', \\\n 'qtd_qtd_count', 'qtd_rt_count', 'qtd_reply_count', 'qtd_fav_count', 'qtd_tweetid', 'qtd_urls_list', 'qtd_location', 'sent_vader', \\\n \"token\", \"media_urls\", \"rt_media_urls\", \"q_media_urls\"]\n \n data_frame = []\n\n stop_words = set(stopwords.words('english')) \n stop_words.add(\"&\")\n stop_words.add(\"…\")\n\n count = 0\n language_dist = {}\n curr_ids = []\n analyser = SentimentIntensityAnalyzer()\n tknzr = TweetTokenizer(preserve_case=False, reduce_len=False, strip_handles=False)\n\n for status in list_of_dicts:\n \n tweetid=status['id_str']\n\n screen_name=status['user']['screen_name']\n userid=status['user']['id_str']\n date=status['created_at']\n lang=status['lang']\n\n place_id = None\n place_url = None\n place_type = None\n place_name = None\n place_full_name = None\n place_country_code = None\n place_country = None\n place_bounding_box = None\n place = status['place']\n if place is not None: \n place_id = place['id']\n place_url = place['url']\n place_type = place['place_type']\n place_name = place['name']\n place_full_name = place['full_name']\n place_country_code = place['country_code']\n place_country = place['country']\n place_bounding_box = place['bounding_box']\n\n location=status['user']['location']\n coord=status['coordinates']\n friends_count=status['user']['friends_count']\n listed_count=status['user']['listed_count']\n followers_count=status['user']['followers_count']\n favourites_count=status['user']['favourites_count']\n statuses_count=status['user']['statuses_count']\n verified=status['user']['verified']\n display_name = status['user']['name']\n date_first_tweet = status['created_at']\n account_creation_date = status['user']['created_at']\n profile_pic_url,profile_banner_url=get_profile_image(status)\n\n # add quoted and retweet tweet ids \n\n tweet_type,text,extended_text, rt_text, qtd_text, reply_userid, reply_screen, \\\n reply_statusid, rt_qtd_count, rt_rt_count, rt_reply_count, rt_fav_count, rt_tweetid,\\\n qtd_qtd_count, qtd_rt_count, qtd_reply_count, qtd_fav_count, qtd_tweetid = get_tweet_text(status)\n\n urls_list, rt_urls_list, qtd_urls_list = get_urls(status,tweet_type,extended_text)\n #rt_urls_list=get_rt_urls(status,tweet_type,extended_text)\n mentionid,mentionsn=get_mentions(status)\n hashtag, rt_userid,rt_screen, rt_hashtag, rt_loc, qtd_userid, qtd_screen, qtd_hashtag, qtd_loc = get_hashtags(status, tweet_type, extended_text) \n\n media_urls, rt_media_urls, q_media_urls = get_media_urls(status, tweet_type, extended_text)\n\n # vader sentiment analysis here\n comp_tweet = \"\" \n if tweet_type == \"quoted_tweet\": \n # do retweet text first\n\n try: \n if \"RT @\" not in text: \n comp_tweet += text \n else: \n pass\n except: \n pdb.set_trace()\n\n try: \n if rt_text: \n comp_tweet += (\" \" + rt_text)\n\n comp_tweet += (\" \" + qtd_text)\n\n except:\n pdb.set_trace()\n\n elif tweet_type == \"retweeted_tweet_without_comment\":\n comp_tweet += rt_text\n\n\n else: \n if tweet_type != \"original\" and tweet_type != \"reply\":\n print (\"something may be wrong here\")\n pdb.set_trace()\n comp_tweet += text\n\n\n sent_vader = analyser.polarity_scores(comp_tweet)\n\n # tokenize the comp_tweet\n\n token_tweet = comp_tweet.replace(\"RT\", \"\")\n token_tweet = token_tweet.replace(\"#\", \"\")\n token_tweet = token_tweet.replace(\"&\", \"\")\n token_tweet = token_tweet.replace(\"…\", \"\")\n token_tweet = token_tweet.replace (\",\", \" \")\n token_tweet = token_tweet.replace(\"\\n\", \" \")\n token_tweet = token_tweet.replace(\"!\", \"\")\n token_tweet = token_tweet.replace(\":\", \"\")\n token_tweet = token_tweet.replace(\"(\", \" \")\n token_tweet = token_tweet.replace(\")\", \" \")\n tokens = token_tweet.split()\n\n filtered_tweet = [w for w in tokens if not w in stop_words] \n tokenized_tweet = []\n for w in filtered_tweet: \n if \"https//t.co\" not in w: \n tokenized_tweet.append(w)\n tokenized_tweet = (' '.join(tokenized_tweet) ).lower()\n tokenized_tweet = tokenized_tweet.replace(\"/\", \" \")\n #tokenized_tweet = tokenized_tweet.replace(\".\", \" \")\n tokenized_tweet = tokenized_tweet.replace('\"', \" \")\n tokenized_tweet = tokenized_tweet.replace('?', \" \")\n #tokenized_tweet = tknzr.tokenize(' '.join(tokenized_tweet))\n\n row=[tweetid,userid,screen_name,date,lang,location, place_id, place_url, place_type, place_name, place_full_name, \n place_country_code, place_country, place_bounding_box, text,extended_text,coord, reply_userid, reply_screen, reply_statusid, \n tweet_type,friends_count,listed_count,followers_count,favourites_count,statuses_count,verified,hashtag, urls_list, profile_pic_url,profile_banner_url, \n display_name, date_first_tweet,account_creation_date,rt_urls_list,mentionid,mentionsn,rt_screen,rt_userid, rt_text, rt_hashtag, rt_qtd_count, rt_rt_count,\n rt_reply_count, rt_fav_count, rt_tweetid, rt_loc, qtd_screen,qtd_userid, qtd_text, qtd_hashtag, \n qtd_qtd_count, qtd_rt_count, qtd_reply_count, qtd_fav_count, qtd_tweetid ,qtd_urls_list, qtd_loc, sent_vader['compound'], tokenized_tweet, media_urls, rt_media_urls, q_media_urls] \n\n data_frame.append(row)\n\n count +=1\n\n print(f\"Successfully parsed {count} tweets.\")\n df = pd.DataFrame(data_frame, columns=fields)\n #print(df)\n return df", "repo_name": "Matheus-Schmitz/avaxtar", "sub_path": "avaxtar/DF_from_DICT.py", "file_name": "DF_from_DICT.py", "file_ext": "py", "file_size_in_byte": 16581, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 10, "dataset": "github-code", "pt": "37", "api": [{"api_name": "nltk.download", "line_number": 7, "usage_type": "call"}, {"api_name": "nltk.corpus.stopwords.words", "line_number": 305, "usage_type": "call"}, {"api_name": "nltk.corpus.stopwords", "line_number": 305, "usage_type": "name"}, {"api_name": "vaderSentiment.vaderSentiment.SentimentIntensityAnalyzer", "line_number": 312, "usage_type": "call"}, {"api_name": "nltk.tokenize.TweetTokenizer", "line_number": 313, "usage_type": "call"}, {"api_name": "pdb.set_trace", "line_number": 380, "usage_type": "call"}, {"api_name": "pdb.set_trace", "line_number": 389, "usage_type": "call"}, {"api_name": "pdb.set_trace", "line_number": 398, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 442, "usage_type": "call"}]} +{"seq_id": "24645361244", "text": "import json\nimport xml.etree.ElementTree as ET\n\nfrom cafe.engine.models.base import \\\n AutoMarshallingModel, AutoMarshallingListModel\nfrom cloudcafe.networking.lbaas.common.constants import Constants\n\n\nclass Members(AutoMarshallingListModel):\n \"\"\"Members Response Model\n @summary: Response Model for a List of Member Objects\n @note: Returns a list of elements of type \"Member\"\n\n json ex:\n {\n \"members\": [\n {\n \"id\": \"8992a43f-83af-4b49-9afd-c2bfbd82d7d7\",\n \"subnet_id\": \"SUBNET_ID\",\n \"tenant_id\": \"453105b9-1754-413f-aab1-55f1af620750\",\n \"address\": \"192.0.2.14\",\n \"protocol_port\": 8080,\n \"weight\": 7,\n \"admin_state_up\": false,\n \"status\": \"ACTIVE\"\n }\n ]\n }\n\n xml ex:\n \n \n \n \"\"\"\n ROOT_TAG = 'members'\n\n @classmethod\n def _json_to_obj(cls, serialized_string):\n json_dict = json.loads(serialized_string)\n return cls._list_to_obj(json_dict.get(cls.ROOT_TAG))\n\n @classmethod\n def _list_to_obj(cls, members_dict_list):\n members = Members()\n members.extend([Member._dict_to_obj(member) for\n member in members_dict_list])\n return members\n\n @classmethod\n def _xml_to_obj(cls, serialized_string):\n element = ET.fromstring(serialized_string)\n if element.tag != cls.ROOT_TAG:\n return None\n return cls._xml_list_to_obj(element.findall(Member.ROOT_TAG))\n\n @classmethod\n def _xml_list_to_obj(cls, xml_list):\n members = Members()\n members.extend(\n [Member._xml_ele_to_obj(members_ele)\n for members_ele in xml_list])\n return members\n\n\nclass Member(AutoMarshallingModel):\n \"\"\"Member Response Model\n @summary: Response Model for a Member\n @note: Represents a single Member object\n\n json ex:\n {\n \"member\": {\n \"id\": \"8992a43f-83af-4b49-9afd-c2bfbd82d7d7\",\n \"subnet_id\": \"SUBNET_ID\",\n \"tenant_id\": \"453105b9-1754-413f-aab1-55f1af620750\",\n \"address\": \"192.0.2.14\",\n \"protocol_port\": 8080,\n \"weight\": 7,\n \"admin_state_up\": false\n \"status\": \"ACTIVE\"\n }\n }\n\n xml ex:\n \n \"\"\"\n\n ROOT_TAG = 'member'\n\n def __init__(self, id_=None, subnet_id=None, tenant_id=None,\n address=None, protocol_port=None, weight=None,\n admin_state_up=None, status=None):\n super(Member, self).__init__()\n self.id_ = id_\n self.subnet_id = subnet_id\n self.tenant_id = tenant_id\n self.address = address\n self.protocol_port = protocol_port\n self.weight = weight\n self.admin_state_up = admin_state_up\n self.status = status\n\n @classmethod\n def _json_to_obj(cls, serialized_string):\n json_dict = json.loads(serialized_string)\n return cls._dict_to_obj(json_dict[cls.ROOT_TAG])\n\n @classmethod\n def _dict_to_obj(cls, member_dict):\n member = Member(\n id_=member_dict.get('id'),\n subnet_id=member_dict.get('subnet_id'),\n tenant_id=member_dict.get('tenant_id'),\n address=member_dict.get('address'),\n protocol_port=member_dict.get('protocol_port'),\n weight=member_dict.get('weight'),\n admin_state_up=member_dict.get('admin_state_up'),\n status=member_dict.get('status'))\n return member\n\n @classmethod\n def _xml_to_obj(cls, serialized_string):\n element = ET.fromstring(serialized_string)\n if element.tag != cls.ROOT_TAG:\n return None\n cls._remove_xml_etree_namespace(element, Constants.XML_API_NAMESPACE)\n member = cls._xml_ele_to_obj(element)\n return member\n\n @classmethod\n def _xml_ele_to_obj(cls, element):\n member_dict = element.attrib\n # Cast Integers\n if 'protocol_port' in member_dict:\n member_dict['protocol_port'] = (\n member_dict.get('protocol_port') and\n int(member_dict.get('protocol_port')))\n if 'weight' in member_dict:\n member_dict['weight'] = (\n member_dict.get('weight') and\n int(member_dict.get('weight')))\n # Cast boolean\n if 'admin_state_up' in member_dict:\n member_dict['admin_state_up'] = cls._string_to_bool(\n member_dict.get('admin_state_up'))\n member = Member(\n id_=member_dict.get('id'),\n subnet_id=member_dict.get('subnet_id'),\n tenant_id=member_dict.get('tenant_id'),\n address=member_dict.get('address'),\n protocol_port=member_dict.get('protocol_port'),\n weight=member_dict.get('weight'),\n admin_state_up=member_dict.get('admin_state_up'),\n status=member_dict.get('status'))\n return member\n", "repo_name": "jcourtois/rpc9_cloudcafe", "sub_path": "cloudcafe/networking/lbaas/lbaas_api/member/response.py", "file_name": "response.py", "file_ext": "py", "file_size_in_byte": 5817, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "cafe.engine.models.base.AutoMarshallingListModel", "line_number": 9, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 48, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree.fromstring", "line_number": 60, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 60, "usage_type": "name"}, {"api_name": "cafe.engine.models.base.AutoMarshallingModel", "line_number": 74, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 123, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree.fromstring", "line_number": 141, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 141, "usage_type": "name"}, {"api_name": "cloudcafe.networking.lbaas.common.constants.Constants.XML_API_NAMESPACE", "line_number": 144, "usage_type": "attribute"}, {"api_name": "cloudcafe.networking.lbaas.common.constants.Constants", "line_number": 144, "usage_type": "name"}]} +{"seq_id": "5848959277", "text": "import collections.abc\nimport os\nimport warnings\n\nimport h5py\nimport numpy\n\nfrom ..adapters.utils import IndexersMixin\nfrom ..iterviews import ItemsView, KeysView, ValuesView\nfrom ..structures.core import StructureFamily\nfrom ..utils import node_repr\nfrom .array import ArrayAdapter\n\nSWMR_DEFAULT = bool(int(os.getenv(\"TILED_HDF5_SWMR_DEFAULT\", \"0\")))\nINLINED_DEPTH = int(os.getenv(\"TILED_HDF5_INLINED_CONTENTS_MAX_DEPTH\", \"7\"))\n\n\ndef from_dataset(dataset):\n return ArrayAdapter.from_array(dataset, metadata=getattr(dataset, \"attrs\", {}))\n\n\nclass HDF5Adapter(collections.abc.Mapping, IndexersMixin):\n \"\"\"\n Read an HDF5 file or a group within one.\n\n This map the structure of an HDF5 file onto a \"Tree\" of array structures.\n\n Examples\n --------\n\n From the root node of a file given a filepath\n\n >>> import h5py\n >>> HDF5Adapter.from_file(\"path/to/file.h5\")\n\n From the root node of a file given an h5py.File object\n\n >>> import h5py\n >>> file = h5py.File(\"path/to/file.h5\")\n >>> HDF5Adapter.from_file(file)\n\n From a group within a file\n\n >>> import h5py\n >>> file = h5py.File(\"path/to/file.h5\")\n >>> HDF5Adapter(file[\"some_group'][\"some_sub_group\"])\n\n \"\"\"\n\n structure_family = StructureFamily.container\n\n def __init__(\n self, node, *, structure=None, metadata=None, specs=None, access_policy=None\n ):\n self._node = node\n self._access_policy = access_policy\n self.specs = specs or []\n self._provided_metadata = metadata or {}\n super().__init__()\n\n @classmethod\n def from_file(\n cls,\n file,\n *,\n structure=None,\n metadata=None,\n swmr=SWMR_DEFAULT,\n libver=\"latest\",\n specs=None,\n access_policy=None,\n ):\n if not isinstance(file, h5py.File):\n file = h5py.File(file, \"r\", swmr=swmr, libver=libver)\n return cls(file, metadata=metadata, specs=specs, access_policy=access_policy)\n\n def __repr__(self):\n return node_repr(self, list(self))\n\n @property\n def access_policy(self):\n return self._access_policy\n\n def structure(self):\n return None\n\n def metadata(self):\n d = dict(self._node.attrs)\n for k, v in list(d.items()):\n # Convert any bytes to str.\n if isinstance(v, bytes):\n d[k] = v.decode()\n d.update(self._provided_metadata)\n return d\n\n def __iter__(self):\n yield from self._node\n\n def __getitem__(self, key):\n value = self._node[key]\n if isinstance(value, h5py.Group):\n return HDF5Adapter(value)\n else:\n if value.dtype == numpy.dtype(\"O\"):\n warnings.warn(\n f\"The dataset {key} is of object type, using a \"\n \"Python-only feature of h5py that is not supported by \"\n \"HDF5 in general. Read more about that feature at \"\n \"https://docs.h5py.org/en/stable/special.html. \"\n \"Consider using a fixed-length field instead. \"\n \"Tiled will serve an empty placeholder, unless the \"\n \"object is of size 1, where it will attempt to repackage \"\n \"the data into a numpy array.\"\n )\n\n check_str_dtype = h5py.check_string_dtype(value.dtype)\n if check_str_dtype.length is None:\n dataset_names = value.file[self._node.name + \"/\" + key][...][()]\n if value.size == 1:\n arr = numpy.array(dataset_names)\n return from_dataset(arr)\n return from_dataset(numpy.array([]))\n return from_dataset(value)\n\n def __len__(self):\n return len(self._node)\n\n def keys(self):\n return KeysView(lambda: len(self), self._keys_slice)\n\n def values(self):\n return ValuesView(lambda: len(self), self._items_slice)\n\n def items(self):\n return ItemsView(lambda: len(self), self._items_slice)\n\n def search(self, query):\n \"\"\"\n Return a Tree with a subset of the mapping.\n \"\"\"\n raise NotImplementedError\n\n def read(self, fields=None):\n if fields is not None:\n raise NotImplementedError\n return self\n\n # The following two methods are used by keys(), values(), items().\n\n def _keys_slice(self, start, stop, direction):\n keys = list(self._node)\n if direction < 0:\n keys = reversed(keys)\n return keys[start:stop]\n\n def _items_slice(self, start, stop, direction):\n items = [(key, self[key]) for key in list(self)]\n if direction < 0:\n items = reversed(items)\n return items[start:stop]\n\n def inlined_contents_enabled(self, depth):\n return depth <= INLINED_DEPTH\n", "repo_name": "bluesky/tiled", "sub_path": "tiled/adapters/hdf5.py", "file_name": "hdf5.py", "file_ext": "py", "file_size_in_byte": 4858, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 48, "dataset": "github-code", "pt": "37", "api": [{"api_name": "os.getenv", "line_number": 14, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 15, "usage_type": "call"}, {"api_name": "array.ArrayAdapter.from_array", "line_number": 19, "usage_type": "call"}, {"api_name": "array.ArrayAdapter", "line_number": 19, "usage_type": "name"}, {"api_name": "collections.abc.abc", "line_number": 22, "usage_type": "attribute"}, {"api_name": "collections.abc", "line_number": 22, "usage_type": "name"}, {"api_name": "adapters.utils.IndexersMixin", "line_number": 22, "usage_type": "name"}, {"api_name": "structures.core.StructureFamily.container", "line_number": 50, "usage_type": "attribute"}, {"api_name": "structures.core.StructureFamily", "line_number": 50, "usage_type": "name"}, {"api_name": "h5py.File", "line_number": 73, "usage_type": "attribute"}, {"api_name": "h5py.File", "line_number": 74, "usage_type": "call"}, {"api_name": "utils.node_repr", "line_number": 78, "usage_type": "call"}, {"api_name": "h5py.Group", "line_number": 101, "usage_type": "attribute"}, {"api_name": "numpy.dtype", "line_number": 104, "usage_type": "call"}, {"api_name": "warnings.warn", "line_number": 105, "usage_type": "call"}, {"api_name": "h5py.check_string_dtype", "line_number": 116, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 120, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 122, "usage_type": "call"}, {"api_name": "iterviews.KeysView", "line_number": 129, "usage_type": "call"}, {"api_name": "iterviews.ValuesView", "line_number": 132, "usage_type": "call"}, {"api_name": "iterviews.ItemsView", "line_number": 135, "usage_type": "call"}]} +{"seq_id": "9938332317", "text": "import discord\nfrom answer_config import answers\nfrom message import Message\nfrom util import get_motivational_quote\nfrom discord_util import get_message, get_my_user_id\nfrom config import TOKEN\n\nclient = discord.Client()\n\n\n@client.event\nasync def on_ready():\n # Print all the guilds the bot is active in\n print(f'Active on: ')\n for guild in client.guilds:\n print(f'- {guild.name}')\n\n # Change the bot status to Watching ...\n await client.change_presence(\n activity=discord.Activity(type=discord.ActivityType.watching, name=\"everyone study :)\"))\n\n\n@client.event\nasync def on_message(message):\n message = Message(message)\n\n if message.from_me(client):\n return\n\n if message.is_dm():\n await message.log(client)\n\n for answer in answers:\n await answer.answer_message(message, client)\n\n\n@client.event\nasync def on_reaction_add(reaction, user):\n if not user.bot and reaction.message.content == \"try me\":\n await reaction.remove(user)\n await reaction.message.add_reaction(reaction.emoji)\n\n\n@client.event\nasync def on_raw_reaction_add(reaction):\n if reaction.emoji.name == '🚣‍♂️' and reaction.user_id == get_my_user_id(): # emoji is man rowing boat\n await get_message(reaction.channel_id, reaction.message_id, client).reply(get_motivational_quote())\n\n@client.event\nasync def on_voice_state_update(member, before, after):\n\n bibvriendjes_voice = 1055831367495196722\n bibvriendjes_chat = 1091843831768555570\n\n bottest_voice = 802509903540912192\n bottest_chat = 802509903540912191\n\n channels = {bottest_voice: bottest_chat, bibvriendjes_voice: bibvriendjes_chat}\n\n # if a user joins voice channel 802509903540912192 and is alone there, send a message in text channel 802509903540912191\n if after.channel is not None and len(after.channel.members) == 1 and after.channel.id in channels:\n text_channel = client.get_channel(channels[after.channel.id])\n await text_channel.send(f\"{member.display_name} is studying. Feel free to join!\")\n\n\nclient.run(TOKEN)\n", "repo_name": "arnodeceuninck/Bollekebot", "sub_path": "bot.py", "file_name": "bot.py", "file_ext": "py", "file_size_in_byte": 2072, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "discord.Client", "line_number": 8, "usage_type": "call"}, {"api_name": "discord.Activity", "line_number": 20, "usage_type": "call"}, {"api_name": "discord.ActivityType", "line_number": 20, "usage_type": "attribute"}, {"api_name": "message.Message", "line_number": 25, "usage_type": "call"}, {"api_name": "message.from_me", "line_number": 27, "usage_type": "call"}, {"api_name": "message.is_dm", "line_number": 30, "usage_type": "call"}, {"api_name": "message.log", "line_number": 31, "usage_type": "call"}, {"api_name": "answer_config.answers", "line_number": 33, "usage_type": "name"}, {"api_name": "discord_util.get_my_user_id", "line_number": 46, "usage_type": "call"}, {"api_name": "discord_util.get_message", "line_number": 47, "usage_type": "call"}, {"api_name": "util.get_motivational_quote", "line_number": 47, "usage_type": "call"}, {"api_name": "config.TOKEN", "line_number": 66, "usage_type": "argument"}]} +{"seq_id": "37316504120", "text": "import argparse\nimport numpy as np\nimport tensorflow.compat.v1 as tf\nfrom tensorflow.python import ipu\nfrom tensorflow.python.ipu import horovod as hvd\nfrom tensorflow.python.ipu.horovod import ipu_horovod_strategy\n\nBATCH_SIZE = 64\n\n\ndef input_fn(mode): # pylint: disable=unused-argument\n train_data, _ = tf.keras.datasets.mnist.load_data()\n\n def normalise(image, label):\n image = image.astype(np.float32) / 255.0\n image = np.expand_dims(image, axis=-1)\n label = label.astype(np.int32)\n return image, label\n\n x_train, y_train = normalise(*train_data)\n\n def generator():\n return zip(x_train, y_train)\n\n types = (x_train.dtype, y_train.dtype)\n shapes = (x_train.shape[1:], y_train.shape[1:])\n mnist_dataset = tf.data.Dataset.from_generator(generator, types, shapes)\n mnist_dataset = mnist_dataset.shard(hvd.size(), hvd.rank())\n mnist_dataset = mnist_dataset.shuffle(len(y_train)) \\\n .cache().batch(BATCH_SIZE, drop_remainder=True).repeat()\n return mnist_dataset\n\n\ndef model_fn(features, labels, mode):\n model = tf.keras.Sequential([\n tf.keras.layers.Conv2D(32, 3, activation=\"relu\"),\n tf.keras.layers.MaxPooling2D(),\n tf.keras.layers.Flatten(),\n tf.keras.layers.Dense(64, activation=\"relu\"),\n tf.keras.layers.Dense(10)\n ])\n logits = model(features, training=mode == tf.estimator.ModeKeys.TRAIN)\n\n if mode == tf.estimator.ModeKeys.PREDICT:\n predictions = {\"logits\": logits}\n return tf.estimator.EstimatorSpec(labels=labels, predictions=predictions)\n\n optimizer = tf.compat.v1.train.AdamOptimizer()\n loss = tf.keras.losses.SparseCategoricalCrossentropy(\n from_logits=True, reduction=tf.compat.v1.losses.Reduction.NONE)(labels,\n logits)\n loss = tf.reduce_sum(loss) * (1. / BATCH_SIZE)\n\n variables = model.trainable_variables\n\n def host_model_fn(*host_gradients):\n # This will allreduce the gradients and update the weights on the host.\n return optimizer.apply_gradients(zip(host_gradients, variables))\n\n train_op = tf.identity(loss)\n grads_and_vars = optimizer.compute_gradients(loss, var_list=variables)\n gradients = [g for (g, _) in grads_and_vars]\n host_call = (host_model_fn, gradients)\n\n return ipu.ipu_estimator.IPUEstimatorSpec(mode=mode,\n loss=loss,\n train_op=train_op,\n host_call=host_call)\n\n\n# Initialise the Horovod runtime.\nhvd.init()\n\n# Create a Horovod strategy that places variables on the host.\nstrategy = ipu_horovod_strategy.IPUHorovodStrategy(variables_on_host=True)\n\nipu_options = ipu.utils.create_ipu_config()\nipu.utils.auto_select_ipus(ipu_options, num_ipus=1)\nipu_run_config = ipu.ipu_run_config.IPURunConfig(ipu_options=ipu_options)\n\nconfig = ipu.ipu_run_config.RunConfig(\n session_config=tf.ConfigProto(allow_soft_placement=False),\n ipu_run_config=ipu_run_config,\n train_distribute=strategy,\n)\n\nparser = argparse.ArgumentParser()\nparser.add_argument(\"--num-steps\", type=int, default=10000)\nparser.add_argument(\"--model-dir\")\nargs = parser.parse_args()\n\nclassifier = ipu.ipu_estimator.IPUEstimator(\n config=config,\n model_fn=model_fn,\n model_dir=args.model_dir,\n)\n\n# Training progress is logged as INFO, so enable that logging level.\ntf.logging.set_verbosity(tf.logging.INFO)\nclassifier.train(input_fn=input_fn, max_steps=args.num_steps)\n", "repo_name": "hephaex/tensorflow-1", "sub_path": "tensorflow/compiler/plugin/poplar/docs/distributed_training_horovod_example.py", "file_name": "distributed_training_horovod_example.py", "file_ext": "py", "file_size_in_byte": 3460, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "github-code", "pt": "37", "api": [{"api_name": "tensorflow.compat.v1.keras.datasets.mnist.load_data", "line_number": 12, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1.keras", "line_number": 12, "usage_type": "attribute"}, {"api_name": "tensorflow.compat.v1", "line_number": 12, "usage_type": "name"}, {"api_name": "numpy.float32", "line_number": 15, "usage_type": "attribute"}, {"api_name": "numpy.expand_dims", "line_number": 16, "usage_type": "call"}, {"api_name": "numpy.int32", "line_number": 17, "usage_type": "attribute"}, {"api_name": "tensorflow.compat.v1.data.Dataset.from_generator", "line_number": 27, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1.data", "line_number": 27, "usage_type": "attribute"}, {"api_name": "tensorflow.compat.v1", "line_number": 27, "usage_type": "name"}, {"api_name": "tensorflow.python.ipu.horovod.size", "line_number": 28, "usage_type": "call"}, {"api_name": "tensorflow.python.ipu.horovod", "line_number": 28, "usage_type": "name"}, {"api_name": "tensorflow.python.ipu.horovod.rank", "line_number": 28, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1.keras.Sequential", "line_number": 35, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1.keras", "line_number": 35, "usage_type": "attribute"}, {"api_name": "tensorflow.compat.v1", "line_number": 35, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.keras.layers.Conv2D", "line_number": 36, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1.keras", "line_number": 36, "usage_type": "attribute"}, {"api_name": "tensorflow.compat.v1", "line_number": 36, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.keras.layers.MaxPooling2D", "line_number": 37, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1.keras", "line_number": 37, "usage_type": "attribute"}, {"api_name": "tensorflow.compat.v1", "line_number": 37, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.keras.layers.Flatten", "line_number": 38, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1.keras", "line_number": 38, "usage_type": "attribute"}, {"api_name": "tensorflow.compat.v1", "line_number": 38, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.keras.layers.Dense", "line_number": 39, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1.keras", "line_number": 39, "usage_type": "attribute"}, {"api_name": "tensorflow.compat.v1", "line_number": 39, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.keras.layers.Dense", "line_number": 40, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1.keras", "line_number": 40, "usage_type": "attribute"}, {"api_name": "tensorflow.compat.v1", "line_number": 40, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.estimator", "line_number": 42, "usage_type": "attribute"}, {"api_name": "tensorflow.compat.v1", "line_number": 42, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.estimator", "line_number": 44, "usage_type": "attribute"}, {"api_name": "tensorflow.compat.v1", "line_number": 44, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.estimator.EstimatorSpec", "line_number": 46, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1.estimator", "line_number": 46, "usage_type": "attribute"}, {"api_name": "tensorflow.compat.v1", "line_number": 46, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.compat.v1.train.AdamOptimizer", "line_number": 48, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1.compat", "line_number": 48, "usage_type": "attribute"}, {"api_name": "tensorflow.compat.v1", "line_number": 48, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.keras.losses.SparseCategoricalCrossentropy", "line_number": 49, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1.keras", "line_number": 49, "usage_type": "attribute"}, {"api_name": "tensorflow.compat.v1", "line_number": 49, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.compat", "line_number": 50, "usage_type": "attribute"}, {"api_name": "tensorflow.compat.v1", "line_number": 50, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.reduce_sum", "line_number": 52, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1", "line_number": 52, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.identity", "line_number": 60, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1", "line_number": 60, "usage_type": "name"}, {"api_name": "tensorflow.python.ipu.ipu_estimator.IPUEstimatorSpec", "line_number": 65, "usage_type": "call"}, {"api_name": "tensorflow.python.ipu.ipu_estimator", "line_number": 65, "usage_type": "attribute"}, {"api_name": "tensorflow.python.ipu", "line_number": 65, "usage_type": "name"}, {"api_name": "tensorflow.python.ipu.horovod.init", "line_number": 72, "usage_type": "call"}, {"api_name": "tensorflow.python.ipu.horovod", "line_number": 72, "usage_type": "name"}, {"api_name": "tensorflow.python.ipu.horovod.ipu_horovod_strategy.IPUHorovodStrategy", "line_number": 75, "usage_type": "call"}, {"api_name": "tensorflow.python.ipu.horovod.ipu_horovod_strategy", "line_number": 75, "usage_type": "name"}, {"api_name": "tensorflow.python.ipu.utils.create_ipu_config", "line_number": 77, "usage_type": "call"}, {"api_name": "tensorflow.python.ipu.utils", "line_number": 77, "usage_type": "attribute"}, {"api_name": "tensorflow.python.ipu", "line_number": 77, "usage_type": "name"}, {"api_name": "tensorflow.python.ipu.utils.auto_select_ipus", "line_number": 78, "usage_type": "call"}, {"api_name": "tensorflow.python.ipu.utils", "line_number": 78, "usage_type": "attribute"}, {"api_name": "tensorflow.python.ipu", "line_number": 78, "usage_type": "name"}, {"api_name": "tensorflow.python.ipu.ipu_run_config.IPURunConfig", "line_number": 79, "usage_type": "call"}, {"api_name": "tensorflow.python.ipu.ipu_run_config", "line_number": 79, "usage_type": "attribute"}, {"api_name": "tensorflow.python.ipu", "line_number": 79, "usage_type": "name"}, {"api_name": "tensorflow.python.ipu.ipu_run_config.RunConfig", "line_number": 81, "usage_type": "call"}, {"api_name": "tensorflow.python.ipu.ipu_run_config", "line_number": 81, "usage_type": "attribute"}, {"api_name": "tensorflow.python.ipu", "line_number": 81, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.ConfigProto", "line_number": 82, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1", "line_number": 82, "usage_type": "name"}, {"api_name": "argparse.ArgumentParser", "line_number": 87, "usage_type": "call"}, {"api_name": "tensorflow.python.ipu.ipu_estimator.IPUEstimator", "line_number": 92, "usage_type": "call"}, {"api_name": "tensorflow.python.ipu.ipu_estimator", "line_number": 92, "usage_type": "attribute"}, {"api_name": "tensorflow.python.ipu", "line_number": 92, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.logging.set_verbosity", "line_number": 99, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1.logging", "line_number": 99, "usage_type": "attribute"}, {"api_name": "tensorflow.compat.v1", "line_number": 99, "usage_type": "name"}]} +{"seq_id": "36389266335", "text": "# coding: utf-8\n\"\"\"\n ╦╔═╗╦═╗╦ ╦╔╗╔╔═╗╔═╗╦ ╦\n ║║ ║╠╦╝║║║║║║╠═╝╠═╣╚╦╝\n╚╝╚═╝╩╚═╚╩╝╝╚╝╩ ╩ ╩ ╩ \n@time: 2022/04/02\n@file: analyse_result.py \n@author: Jorwnpay \n@contact: jwp@mail.nankai.edu.cn \n\"\"\" \nimport os\nimport torch\nimport torch.nn.functional as F\nfrom torch.utils.data import dataset\nfrom my_utils import *\nimport numpy as np\nfrom sklearn.metrics import f1_score\nfrom sklearn.metrics import PrecisionRecallDisplay, RocCurveDisplay\nfrom sklearn.metrics import precision_recall_curve, average_precision_score, auc, roc_curve, roc_auc_score\nfrom scipy import interpolate\nimport matplotlib.pyplot as plt\nimport argparse\n\ndevice = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n\n'''\nGet 10-trail 5-fold results from the saved files. \n @ dataset : name of dataset, can be KLSG or FLSMDD\n @ backbone : name of backbone, can be resnet18, resnet34, resnet50, vgg16 or vgg19\n @ method : name of method, can be baseline or betl\n @ if_get_logits : if you want to get logits output, default is false\n @ trails : the number of trails results you want to get, default is 10\n @ folds : the number of folds results you want to get, default is 5\n'''\ndef get_y_and_logits_results(dataset, backbone, method='baseline', if_get_logits=False, trails=10, folds=5):\n curr_dir = os.path.dirname(__file__)\n data_dir = os.path.join(curr_dir, '../output/result', dataset, method, backbone) \n y_hat = []\n y_true = []\n logits = []\n for p in range(trails):\n for k in range(folds):\n if if_get_logits:\n y_h, y_t, lo = read_result_from_file(data_dir, p=p, k=k, if_get_logits=if_get_logits)\n logits = logits + lo\n else:\n y_h, y_t = read_result_from_file(data_dir, p=p, k=k, if_get_logits=if_get_logits)\n y_hat = y_hat + y_h\n y_true = y_true + y_t\n if if_get_logits:\n return y_true, y_hat, logits\n return y_true, y_hat\n\n'''\nCalculate Gmean result of each trail\n @ dataset : name of dataset, can be KLSG or FLSMDD\n @ backbone : name of backbone, can be resnet18, resnet34, resnet50, vgg16 or vgg19\n @ method : name of method, can be baseline or betl\n @ trails : the number of trails results you want to get, default is 10\n @ folds : the number of folds results you want to get, default is 5\n'''\ndef get_gmean_each_trial(dataset, backbone, method='baseline', trails=10, folds=5):\n curr_dir = os.path.dirname(__file__)\n data_dir = os.path.join(curr_dir, '../output/result', dataset, method, backbone)\n gmean = [] \n for p in range(trails):\n y_hat = []\n y_true = []\n for k in range(folds):\n y_h, y_t = read_result_from_file(data_dir, p=p, k=k)\n y_hat = y_hat + y_h\n y_true = y_true + y_t\n gmean.append(cal_gmean(y_true, y_hat))\n return gmean \n\n'''\nDraw confusion matrix\n @ class_list : class name list, e.g., ['Plane', 'Wreck']\n @ y_true : the ground truth labels\n @ y_hat : the predict labels\n @ tail_idxes : indexes of tail classes, e.g., [0]\n @ pic_dir : confusion matrix picture save direction\n'''\ndef draw_conf_matrix(class_list, y_true, y_hat, tail_idxes, pic_dir):\n conf_matrix = confusion_matrix(y_true, y_hat)\n prob_matrix = np.around((conf_matrix.T/np.sum(conf_matrix, 1)).T, 3)\n print('--------- Drawing confusion matrix ... ----------')\n fig = plt.figure(figsize=(10, 8))\n plt.imshow(prob_matrix, interpolation='nearest', cmap=plt.cm.Blues, vmin=0., vmax=1.)\n plt.colorbar()\n tick_marks = np.arange(len(class_list))\n plt.xticks(tick_marks, class_list, rotation=45, horizontalalignment='right', family='Times New Roman', fontsize=25)\n plt.yticks(tick_marks, class_list, family='Times New Roman', fontsize=25)\n for i in range(len(prob_matrix)):\n for j in range(len(prob_matrix)):\n if i in tail_idxes and j == i:\n plt.annotate(prob_matrix[i, j], xy=(j, i), horizontalalignment='center', verticalalignment='center', family='Times New Roman', color='white', fontsize=20, fontweight='bold')\n elif j == i:\n plt.annotate(prob_matrix[i, j], xy=(j, i), horizontalalignment='center', verticalalignment='center', family='Times New Roman', color='white', fontsize=20, fontweight='bold')\n else:\n plt.annotate(prob_matrix[i, j], xy=(j, i), horizontalalignment='center', verticalalignment='center', family='Times New Roman', fontsize=17)\n for idx in tail_idxes:\n plt.gca().get_xticklabels()[idx].set_color('red')\n plt.gca().get_yticklabels()[idx].set_color('red')\n plt.tight_layout()\n plt.ylabel('True label', family='Times New Roman', fontsize=30, fontweight='bold')\n plt.xlabel('Predicted label', family='Times New Roman', fontsize=30, fontweight='bold')\n fig.savefig(pic_dir, format='pdf', bbox_inches='tight')\n print(f'Confusion matrix has been saved to {pic_dir}.')\n\n'''\nTransform label from [1, 2 ...] to one-hot format\n @ label : labels with original format, i.e., [1, 2, 3 ...]\n @ num_class : number of classes\n'''\ndef label2onehot(label, num_class):\n label_tensor = torch.tensor(label)\n label_tensor = label_tensor.reshape((label_tensor.shape[0], 1))\n label_onehot = torch.zeros(label_tensor.shape[0], num_class)\n label_onehot.scatter_(dim=1, index=label_tensor, value=1)\n label_onehot = np.array(label_onehot)\n return label_onehot\n\n'''\nDraw F1 scale for Precision-Recall curves\n'''\ndef draw_f1_scale():\n f_scores = np.linspace(0.2, 0.8, num=4)\n for f_score in f_scores:\n x = np.linspace(0.01, 1)\n y = f_score * x / (2 * x - f_score)\n (l,) = plt.plot(x[y >= 0], y[y >= 0], color=\"gray\", alpha=0.2)\n plt.annotate(\"F1={0:0.1f}\".format(f_score), xy=(0.9, y[45] + 0.02))\n\n'''\nDraw macro average Precision-Recall curves of tail classes among different methods\n @ dataset : name of dataset, can be KLSG or FLSMDD\n @ num_class : number of classes\n @ tail_idxes : indexes of tail classes, e.g., [0]\n @ pic_dir : Precision-Recall curves picture save direction\n'''\ndef draw_tail_classes_pr_curve(dataset, num_class, tail_idxes, pic_dir):\n print(f'--------- Drawing macro average P-R curves of tail classes ... ----------')\n # setup plot details \n fig, ax = plt.subplots(figsize=(10, 8))\n colors = ['lightseagreen', 'red']\n\n # draw f1 scale\n draw_f1_scale()\n\n # set backbone and method list\n backbone = 'resnet18'\n method_list = ['baseline', 'betl']\n show_method_list = ['DTL', 'BETL(Ours)']\n \n for i in range(len(method_list)):\n y_true, y_hat, logits = get_y_and_logits_results(dataset, backbone, method_list[i], if_get_logits=True)\n if 'svm' in method_list[i]: \n probs = logits\n else:\n probs = F.softmax(torch.tensor(logits), dim=1).numpy()\n y_true = np.array(y_true)\n y_score = np.array(probs)\n score_array = np.array(y_score)\n\n # transform label to onehot format\n label_onehot = label2onehot(y_true, num_class)\n\n # calculate precision and recall corresponding to each class via sklearn\n precision_dict = dict()\n recall_dict = dict()\n average_precision_dict = dict()\n inter_func_dict = dict()\n for j in range(num_class):\n precision_dict[j], recall_dict[j], _ = precision_recall_curve(label_onehot[:, j], score_array[:, j])\n inter_func_dict[j] = interpolate.interp1d(recall_dict[j], precision_dict[j], kind='linear')\n average_precision_dict[j] = average_precision_score(label_onehot[:, j], score_array[:, j])\n\n # calculate macro average precision and recall of tail classes\n all_r = np.unique(np.concatenate([recall_dict[k] for k in range(num_class)]))\n mean_p = np.zeros_like(all_r)\n for l in tail_idxes:\n mean_p += inter_func_dict[l](all_r)\n mean_p /= len(tail_idxes)\n recall_dict[f'macro_{method_list[i]}'] = all_r\n precision_dict[f'macro_{method_list[i]}'] = mean_p\n average_precision_dict[f'macro_{method_list[i]}'] = auc(all_r, mean_p)\n \n # draw macro average P-R curves of tail classes\n display = PrecisionRecallDisplay(\n recall=recall_dict[f'macro_{method_list[i]}'],\n precision=precision_dict[f'macro_{method_list[i]}'],\n average_precision=average_precision_dict[f'macro_{method_list[i]}'],\n )\n display.plot(ax=ax, name=f'{show_method_list[i]}', color=colors[i])\n\n # add the legend for the iso-f1 curves\n handles, labels = display.ax_.get_legend_handles_labels()\n handles.extend([l])\n\n # set the legend and the axes\n ax.set_xlim([0.0, 1.0])\n ax.set_ylim([0.0, 1.05])\n ax.legend(handles=handles, labels=labels, loc=\"best\")\n ax.set_xlabel('Recall', fontsize=30, fontweight='bold')\n ax.set_ylabel('Precision', fontsize=30, fontweight='bold')\n fig.savefig(pic_dir, format='pdf', bbox_inches='tight')\n print(f'Macro average P-R curves of tail classes have been saved to {pic_dir}.')\n\n'''\nDraw macro average Precision-Recall curves of all classes among different methods\n @ dataset : name of dataset, can be KLSG or FLSMDD\n @ num_class : number of classes\n @ pic_dir : Precision-Recall curves picture save direction\n'''\ndef draw_macro_pr_curve(dataset, num_class, pic_dir):\n print(f'--------- Drawing macro average P-R curves of all classes ... ----------')\n # setup plot details \n fig, ax = plt.subplots(figsize=(10, 8))\n colors = ['lightseagreen', 'red']\n\n # draw f1 scale\n draw_f1_scale()\n\n # set backbone and method list\n backbone = 'resnet18'\n method_list = ['baseline', 'betl']\n show_method_list = ['DTL', 'BETL(Ours)']\n for i in range(len(method_list)):\n y_true, y_hat, logits = get_y_and_logits_results(dataset, backbone, method_list[i], if_get_logits=True)\n if 'svm' in method_list[i]: \n probs = logits\n else:\n probs = F.softmax(torch.tensor(logits), dim=1).numpy()\n y_true = np.array(y_true)\n y_score = np.array(probs)\n score_array = np.array(y_score)\n\n # transform label to onehot format\n label_onehot = label2onehot(y_true, num_class)\n\n # calculate macro average precision and recall of all classes\n precision_dict = dict()\n recall_dict = dict()\n average_precision_dict = dict()\n inter_func_dict = dict()\n for j in range(num_class):\n precision_dict[j], recall_dict[j], _ = precision_recall_curve(label_onehot[:, j], score_array[:, j])\n inter_func_dict[j] = interpolate.interp1d(recall_dict[j], precision_dict[j], kind='linear')\n average_precision_dict[j] = average_precision_score(label_onehot[:, j], score_array[:, j])\n \n # draw macro average P-R curves of all classes\n all_r = np.unique(np.concatenate([recall_dict[k] for k in range(num_class)]))\n mean_p = np.zeros_like(all_r)\n for l in range(num_class):\n mean_p += inter_func_dict[l](all_r)\n mean_p /= num_class\n recall_dict[f'macro_{method_list[i]}'] = all_r\n precision_dict[f'macro_{method_list[i]}'] = mean_p\n average_precision_dict[f'macro_{method_list[i]}'] = average_precision_score(label_onehot, score_array, average=\"macro\")\n \n # draw macro average P-R curves of all classes\n display = PrecisionRecallDisplay(\n recall=recall_dict[f'macro_{method_list[i]}'],\n precision=precision_dict[f'macro_{method_list[i]}'],\n average_precision=average_precision_dict[f'macro_{method_list[i]}'],\n )\n display.plot(ax=ax, name=f'{show_method_list[i]}', color=colors[i])\n\n # add the legend for the iso-f1 curves\n handles, labels = display.ax_.get_legend_handles_labels()\n handles.extend([l])\n # set the legend and the axes\n ax.set_xlim([0.0, 1.0])\n ax.set_ylim([0.0, 1.05])\n ax.legend(handles=handles, labels=labels, loc=\"best\")\n ax.set_xlabel('Recall', fontsize=30, fontweight='bold')\n ax.set_ylabel('Precision', fontsize=30, fontweight='bold')\n fig.savefig(pic_dir, format='pdf', bbox_inches='tight')\n print(f'Macro average P-R curves of all classes have been saved to {pic_dir}.')\n\n'''\nDraw Precision-Recall curves of all classes \n @ y_true : the ground truth labels\n @ logits: the predict scores of classification model\n @ class_list : class name list, e.g., ['Plane', 'Wreck']\n @ colors : color name list, e.g., ['orangered', 'lightseagreen']\n @ pic_dir : Precision-Recall curves picture save direction\n'''\ndef draw_pr_curve(y_true, logits, class_list, colors, pic_dir):\n print(f'--------- Drawing P-R curves ... ----------')\n # setup plot details \n fig, ax = plt.subplots(figsize=(10, 8))\n\n # draw f1 scale\n f_scores = np.linspace(0.2, 0.8, num=4)\n lines, labels = [], []\n for f_score in f_scores:\n x = np.linspace(0.01, 1)\n y = f_score * x / (2 * x - f_score)\n (l,) = plt.plot(x[y >= 0], y[y >= 0], color=\"gray\", alpha=0.2)\n plt.annotate(\"F1={0:0.1f}\".format(f_score), xy=(0.9, y[45] + 0.02))\n\n y_true = np.array(y_true)\n y_score = np.array(logits)\n score_array = np.array(y_score)\n\n # transform label to onehot format\n label_onehot = label2onehot(y_true, num_class)\n\n # calculate precision and recall corresponding to each class\n precision_dict = dict()\n recall_dict = dict()\n average_precision_dict = dict()\n for i in range(num_class):\n precision_dict[i], recall_dict[i], _ = precision_recall_curve(label_onehot[:, i], score_array[:, i])\n average_precision_dict[i] = average_precision_score(label_onehot[:, i], score_array[:, i])\n\n # draw P-R curves of each class\n for i, color in zip(range(num_class), colors):\n display = PrecisionRecallDisplay(\n recall=recall_dict[i],\n precision=precision_dict[i],\n average_precision=average_precision_dict[i],\n )\n display.plot(ax=ax, name=f\"{class_list[i]}\", color=color)\n\n # add the legend for the iso-f1 curves\n handles, labels = display.ax_.get_legend_handles_labels()\n handles.extend([l])\n # set the legend and the axes\n ax.set_xlim([0.0, 1.0])\n ax.set_ylim([0.0, 1.05])\n ax.legend(handles=handles, labels=labels, loc=\"best\")\n ax.set_xlabel('Recall', fontsize=30, fontweight='bold')\n ax.set_ylabel('Precision', fontsize=30, fontweight='bold')\n fig.savefig(pic_dir, format='pdf', bbox_inches='tight')\n print(f'P-R curves have been saved to {pic_dir}.')\n\n'''\nWrite Gmean result to file \n @ gmean : Gmean result\n @ gmean_dir: Gmean save direction\n @ dataset : name of dataset, can be KLSG or FLSMDD\n @ method : name of method, can be baseline or betl\n @ backbone : name of backbone, can be resnet18, resnet34, resnet50, vgg16 or vgg19\n'''\ndef write_gmean(gmean, gmean_dir, dataset, method, backbone):\n print(f'--------- Writing Gmean results ... ----------')\n gmean_file = open(gmean_dir, 'a')\n gmean_file.write(f'[Time: {time.asctime()}] Gmean of {dataset}_{method}_{backbone} is : {gmean}\\n')\n print(f'Gmean result: {gmean} have been saved to {gmean_dir}.')\n\n'''\nWrite macro-F1 result to file \n @ f1 : macro-F1 result\n @ f1_dir: macro-F1 save direction\n @ dataset : name of dataset, can be KLSG or FLSMDD\n @ method : name of method, can be baseline or betl\n @ backbone : name of backbone, can be resnet18, resnet34, resnet50, vgg16 or vgg19\n'''\ndef write_f1(f1, f1_dir, dataset, method, backbone):\n print(f'--------- Writing Macro-F1 results ... ----------')\n f1_file = open(f1_dir, 'a')\n f1_file.write(f'[Time: {time.asctime()}] F1 of {dataset}_{method}_{backbone} is : {f1}\\n')\n print(f'Macro-F1 result: {f1} have been saved to {f1_dir}.')\n\ndef parse_args():\n # set arg parser\n parser = argparse.ArgumentParser(description='analyse result')\n parser.add_argument('--dataset', type=str, default='KLSG')\n parser.add_argument('--method', type=str, default='betl')\n parser.add_argument('--backbone', type=str, default='resnet18')\n parser.add_argument('--get_gmean', type=str, default='True', help='If you want to get Gmean result, default is True.')\n parser.add_argument('--get_f1', type=str, default='True', help='If you want to get Macro-F1 result, default is True.')\n parser.add_argument('--get_conf_matrix', type=str, default='False', help='If you want to get confusion matrix result, default is False.')\n parser.add_argument('--get_pr', type=str, default='False', help='If you want to get Precision-Recall curves result, default is False.')\n parser.add_argument('--get_macro_pr_all', type=str, default='False', help='If you want to get macro average Precision-Recall curves result of all classes, default is False.')\n parser.add_argument('--get_macro_pr_tail', type=str, default='False', help='If you want to get macro average Precision-Recall curves result of tail classes, default is False.')\n parser.add_argument('--show_pic', type=str, default='False', help='If you want to show confusion matrix or Precision-Recall curves, default is False.')\n args = parser.parse_args()\n return args\n\nif __name__ == '__main__':\n # get args\n args = parse_args()\n\n # set params\n dataset = args.dataset\n method = args.method\n backbone = args.backbone\n plt.rc('font', family='Times New Roman', size=19) # size=19\n curr_dir = os.path.dirname(__file__)\n y_true, y_hat, logits = get_y_and_logits_results(dataset, backbone, method, if_get_logits=True)\n if dataset == 'KLSG':\n num_class = 2\n class_list = ['Plane', 'Wreck']\n tail_idxes = [0]\n colors = ['orangered', 'lightseagreen']\n elif dataset == 'LTSID':\n num_class = 8\n class_list = ['C_Seabed', 'D_Victim', 'Plane', 'G_Seabed', \n 'S_Seabed', 'Tire', 'Valve', 'Wreck']\n tail_idxes = [0, 1, 2, 3, 6]\n colors = ['orangered', 'lightpink', 'coral', 'deeppink', \n 'lightseagreen', 'steelblue', 'magenta', 'cyan']\n elif dataset == 'FLSMDD':\n num_class = 10\n class_list = ['Bottle', 'Can', 'Chain', 'D_Carton', 'Hook',\n 'Propeller', 'Sh_Bottle', 'St_Bottle', 'Tire', 'Valve']\n tail_idxes = [2, 4, 5, 6, 7, 9]\n colors = ['lightseagreen', 'steelblue', 'deeppink', 'blue', 'orangered', \n 'lightpink', 'coral', 'magenta', 'cyan', 'red']\n else:\n print(f'ERROR! DATASET {dataset} IS NOT EXIST!')\n\n # write gmean\n if args.get_gmean in ['True', 'true']:\n gmean = cal_gmean(y_true, y_hat)\n gmean_dir = os.path.join(curr_dir, f'../output/display/gmean.txt') \n write_gmean(gmean, gmean_dir, dataset, method, backbone)\n # gmean_list = get_gmean_each_trial(dataset, backbone, method)\n # print(f'gmean list is : {gmean_list}')\n\n # write f1\n if args.get_f1 in ['True', 'true']:\n f1 = f1_score(y_true, y_hat, average='macro')\n f1_dir = os.path.join(curr_dir, f'../output/display/f1.txt') \n write_f1(f1, f1_dir, dataset, method, backbone)\n\n # save confusion matrix\n if args.get_conf_matrix in ['True', 'true']:\n save_folder = os.path.join(curr_dir, f'../output/display/cm')\n cm_dir = os.path.join(save_folder, f'{dataset}_{method}_{backbone}.pdf') \n if not os.path.exists(save_folder):\n os.makedirs(save_folder)\n draw_conf_matrix(class_list, y_true, y_hat, tail_idxes, cm_dir)\n\n # save pr curve\n if args.get_pr in ['True', 'true']:\n save_folder = os.path.join(curr_dir, f'../output/display/pr')\n pr_dir = os.path.join(save_folder, f'{dataset}_{method}_{backbone}.pdf')\n if not os.path.exists(save_folder):\n os.makedirs(save_folder)\n if 'svm' in method:\n probs = logits\n else:\n probs = F.softmax(torch.tensor(logits), dim=1).numpy()\n draw_pr_curve(y_true, probs, class_list, colors, pr_dir)\n\n # save macro-pr curve\n if args.get_macro_pr_all in ['True', 'true']:\n save_folder = os.path.join(curr_dir, f'../output/display/macro_pr')\n macro_pr_dir = os.path.join(save_folder, f'all_classes_{dataset}_{backbone}.pdf')\n if not os.path.exists(save_folder):\n os.makedirs(save_folder)\n draw_macro_pr_curve(dataset, num_class, macro_pr_dir)\n\n # save tail-classes macro-pr curve\n if args.get_macro_pr_tail in ['True', 'true']:\n save_folder = os.path.join(curr_dir, f'../output/display/macro_pr')\n tail_macro_pr_dir = os.path.join(save_folder, f'tail_classes_{dataset}_{backbone}.pdf')\n if not os.path.exists(save_folder):\n os.makedirs(save_folder)\n draw_tail_classes_pr_curve(dataset, num_class, tail_idxes, tail_macro_pr_dir)\n \n if args.show_pic in ['True', 'true']:\n plt.show()", "repo_name": "Jorwnpay/TGRS_BETL", "sub_path": "code/analyse_result.py", "file_name": "analyse_result.py", "file_ext": "py", "file_size_in_byte": 21154, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 6, "dataset": "github-code", "pt": "37", "api": [{"api_name": "torch.device", "line_number": 24, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 24, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 24, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 36, "usage_type": "call"}, {"api_name": "os.path", "line_number": 36, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 37, "usage_type": "call"}, {"api_name": "torch.utils.data.dataset", "line_number": 37, "usage_type": "argument"}, {"api_name": "os.path", "line_number": 37, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 63, "usage_type": "call"}, {"api_name": "os.path", "line_number": 63, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 64, "usage_type": "call"}, {"api_name": "torch.utils.data.dataset", "line_number": 64, "usage_type": "argument"}, {"api_name": "os.path", "line_number": 64, "usage_type": "attribute"}, {"api_name": "numpy.around", "line_number": 86, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 86, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 88, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 88, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 89, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 89, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.cm", "line_number": 89, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.colorbar", "line_number": 90, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 90, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 91, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 92, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 92, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.yticks", "line_number": 93, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 93, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.annotate", "line_number": 97, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 97, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.annotate", "line_number": 99, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 99, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.annotate", "line_number": 101, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 101, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.gca", "line_number": 103, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 103, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.gca", "line_number": 104, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 104, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tight_layout", "line_number": 105, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 105, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 106, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 106, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 107, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 107, "usage_type": "name"}, {"api_name": "torch.tensor", "line_number": 117, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 119, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 121, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 128, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 130, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 132, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 132, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.annotate", "line_number": 133, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 133, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 145, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 145, "usage_type": "name"}, {"api_name": "torch.utils.data.dataset", "line_number": 157, "usage_type": "argument"}, {"api_name": "torch.nn.functional.softmax", "line_number": 161, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 161, "usage_type": "name"}, {"api_name": "torch.tensor", "line_number": 161, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 162, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 163, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 164, "usage_type": "call"}, {"api_name": "sklearn.metrics.precision_recall_curve", "line_number": 175, "usage_type": "call"}, {"api_name": "scipy.interpolate.interp1d", "line_number": 176, "usage_type": "call"}, {"api_name": "scipy.interpolate", "line_number": 176, "usage_type": "name"}, {"api_name": "sklearn.metrics.average_precision_score", "line_number": 177, "usage_type": "call"}, {"api_name": "numpy.unique", "line_number": 180, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 180, "usage_type": "call"}, {"api_name": "numpy.zeros_like", "line_number": 181, "usage_type": "call"}, {"api_name": "sklearn.metrics.auc", "line_number": 187, "usage_type": "call"}, {"api_name": "sklearn.metrics.PrecisionRecallDisplay", "line_number": 190, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 219, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 219, "usage_type": "name"}, {"api_name": "torch.utils.data.dataset", "line_number": 230, "usage_type": "argument"}, {"api_name": "torch.nn.functional.softmax", "line_number": 234, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 234, "usage_type": "name"}, {"api_name": "torch.tensor", "line_number": 234, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 235, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 236, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 237, "usage_type": "call"}, {"api_name": "sklearn.metrics.precision_recall_curve", "line_number": 248, "usage_type": "call"}, {"api_name": "scipy.interpolate.interp1d", "line_number": 249, "usage_type": "call"}, {"api_name": "scipy.interpolate", "line_number": 249, "usage_type": "name"}, {"api_name": "sklearn.metrics.average_precision_score", "line_number": 250, "usage_type": "call"}, {"api_name": "numpy.unique", "line_number": 253, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 253, "usage_type": "call"}, {"api_name": "numpy.zeros_like", "line_number": 254, "usage_type": "call"}, {"api_name": "sklearn.metrics.average_precision_score", "line_number": 260, "usage_type": "call"}, {"api_name": "sklearn.metrics.PrecisionRecallDisplay", "line_number": 263, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 293, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 293, "usage_type": "name"}, {"api_name": "numpy.linspace", "line_number": 296, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 299, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 301, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 301, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.annotate", "line_number": 302, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 302, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 304, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 305, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 306, "usage_type": "call"}, {"api_name": "sklearn.metrics.precision_recall_curve", "line_number": 316, "usage_type": "call"}, {"api_name": "sklearn.metrics.average_precision_score", "line_number": 317, "usage_type": "call"}, {"api_name": "sklearn.metrics.PrecisionRecallDisplay", "line_number": 321, "usage_type": "call"}, {"api_name": "torch.utils.data.dataset", "line_number": 351, "usage_type": "name"}, {"api_name": "torch.utils.data.dataset", "line_number": 365, "usage_type": "name"}, {"api_name": "argparse.ArgumentParser", "line_number": 370, "usage_type": "call"}, {"api_name": "torch.utils.data.dataset", "line_number": 389, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.rc", "line_number": 392, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 392, "usage_type": "name"}, {"api_name": "os.path.dirname", "line_number": 393, "usage_type": "call"}, {"api_name": "os.path", "line_number": 393, "usage_type": "attribute"}, {"api_name": "torch.utils.data.dataset", "line_number": 394, "usage_type": "argument"}, {"api_name": "torch.utils.data.dataset", "line_number": 395, "usage_type": "name"}, {"api_name": "torch.utils.data.dataset", "line_number": 400, "usage_type": "name"}, {"api_name": "torch.utils.data.dataset", "line_number": 407, "usage_type": "name"}, {"api_name": "torch.utils.data.dataset", "line_number": 415, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 420, "usage_type": "call"}, {"api_name": "os.path", "line_number": 420, "usage_type": "attribute"}, {"api_name": "torch.utils.data.dataset", "line_number": 421, "usage_type": "argument"}, {"api_name": "sklearn.metrics.f1_score", "line_number": 427, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 428, "usage_type": "call"}, {"api_name": "os.path", "line_number": 428, "usage_type": "attribute"}, {"api_name": "torch.utils.data.dataset", "line_number": 429, "usage_type": "argument"}, {"api_name": "os.path.join", "line_number": 433, "usage_type": "call"}, {"api_name": "os.path", "line_number": 433, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 434, "usage_type": "call"}, {"api_name": "os.path", "line_number": 434, "usage_type": "attribute"}, {"api_name": "torch.utils.data.dataset", "line_number": 434, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 435, "usage_type": "call"}, {"api_name": "os.path", "line_number": 435, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 436, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 441, "usage_type": "call"}, {"api_name": "os.path", "line_number": 441, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 442, "usage_type": "call"}, {"api_name": "os.path", "line_number": 442, "usage_type": "attribute"}, {"api_name": "torch.utils.data.dataset", "line_number": 442, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 443, "usage_type": "call"}, {"api_name": "os.path", "line_number": 443, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 444, "usage_type": "call"}, {"api_name": "torch.nn.functional.softmax", "line_number": 448, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 448, "usage_type": "name"}, {"api_name": "torch.tensor", "line_number": 448, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 453, "usage_type": "call"}, {"api_name": "os.path", "line_number": 453, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 454, "usage_type": "call"}, {"api_name": "os.path", "line_number": 454, "usage_type": "attribute"}, {"api_name": "torch.utils.data.dataset", "line_number": 454, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 455, "usage_type": "call"}, {"api_name": "os.path", "line_number": 455, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 456, "usage_type": "call"}, {"api_name": "torch.utils.data.dataset", "line_number": 457, "usage_type": "argument"}, {"api_name": "os.path.join", "line_number": 461, "usage_type": "call"}, {"api_name": "os.path", "line_number": 461, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 462, "usage_type": "call"}, {"api_name": "os.path", "line_number": 462, "usage_type": "attribute"}, {"api_name": "torch.utils.data.dataset", "line_number": 462, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 463, "usage_type": "call"}, {"api_name": "os.path", "line_number": 463, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 464, "usage_type": "call"}, {"api_name": "torch.utils.data.dataset", "line_number": 465, "usage_type": "argument"}, {"api_name": "matplotlib.pyplot.show", "line_number": 468, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 468, "usage_type": "name"}]} +{"seq_id": "74054562347", "text": "from django.urls import include, path\nfrom rest_framework import routers\n\nfrom api.views import AlbumViewSet, ArtistViewSet, SongViewSet\n\napp_name = 'api'\nrouter = routers.DefaultRouter()\n\nrouter.register(r'albums', AlbumViewSet, basename='albums')\nrouter.register(r'artists', ArtistViewSet, basename='artists')\nrouter.register(r'songs', SongViewSet, basename='songs')\n\nurlpatterns = [\n path('api/', include(router.urls)),\n]\n", "repo_name": "sntchweb/albums_and_songs", "sub_path": "backend/api/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 428, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "rest_framework.routers.DefaultRouter", "line_number": 7, "usage_type": "call"}, {"api_name": "rest_framework.routers", "line_number": 7, "usage_type": "name"}, {"api_name": "api.views.AlbumViewSet", "line_number": 9, "usage_type": "argument"}, {"api_name": "api.views.ArtistViewSet", "line_number": 10, "usage_type": "argument"}, {"api_name": "api.views.SongViewSet", "line_number": 11, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 14, "usage_type": "call"}, {"api_name": "django.urls.include", "line_number": 14, "usage_type": "call"}]} +{"seq_id": "2575939441", "text": "import numpy as np\nfrom shapely.geometry.polygon import Polygon\nfrom shapely.geometry import Point\nimport math, random\nfrom sympy import Ray as sRay, Segment as sSegment\nfrom sympy import Point as sPoint\nfrom sympy import intersection\nimport matplotlib.pyplot as plt\nfrom IPython.display import display\n\n\ndef turn(points, p):\n return np.sign(np.linalg.det(np.array(points) - p))\n\n\ndef check(points, point):\n p = Polygon(points)\n point = Point(point)\n return p.contains(point)\n\n\ndef generate_test(ctr_x, ctr_y, aveRadius, irregularity, spikeyness, numVerts):\n irregularity = clip(irregularity, 0, 1) * 2 * math.pi / numVerts\n spikeyness = clip(spikeyness, 0, 1) * aveRadius\n\n # generate n angle steps\n angle_steps = []\n lower = (2 * math.pi / numVerts) - irregularity\n upper = (2 * math.pi / numVerts) + irregularity\n sum = 0\n for i in range(numVerts):\n tmp = random.uniform(lower, upper)\n angle_steps.append(tmp)\n sum = sum + tmp\n\n # normalize the steps so that point 0 and point n+1 are the same\n k = sum / (2 * math.pi)\n for i in range(numVerts):\n angle_steps[i] = angle_steps[i] / k\n\n # now generate the points\n points = []\n angle = random.uniform(0, 2 * math.pi)\n for i in range(numVerts):\n r_i = clip(random.gauss(aveRadius, spikeyness), 0, 2 * aveRadius)\n x = ctr_x + r_i * math.cos(angle)\n y = ctr_y + r_i * math.sin(angle)\n points.append((int(x), int(y)))\n angle = angle + angle_steps[i]\n\n return points\n\n\ndef clip(x, min, max):\n if min > max:\n return x\n elif x < min:\n return min\n elif x > max:\n return max\n else:\n return x\n\n\ndef intersect(ray, segment):\n ray = sRay(sPoint(*ray[0]), sPoint(*ray[1]))\n segment = sSegment(segment[0], segment[1])\n return len(intersection(ray, segment)) != 0\n\n\ndef draw(points, point, title):\n fig = plt.figure(figsize=(6, 6))\n ax1 = plt.subplot(111, aspect='equal')\n ax1.plot(point[0], point[1], 'o', color='g')\n points.insert(len(points), [points[0][0], points[0][1]])\n points_t = np.array(points).T\n ax1.plot(points_t[0,], points_t[1,], '--', c='r')\n ax1.scatter(points_t[0,], points_t[1,], c='r')\n ax1.set_xlim(0 - 1, 30 + 1)\n ax1.set_ylim(0 - 1, 30 + 1)\n if title != \"\":\n ax1.set_title(title)\n display(fig)\n plt.close()\n\n\ndef test(f, n=200):\n for i in range(0, n):\n points = generate_test(15, 15, 10, 0.35, 0.35, 30)\n if i % 50 == 0 and i != 0:\n print('passed {} tests'.format(i))\n for j in range(0, 200):\n point = np.random.randint(0, 25, size=(2))\n answer = check(points, point)\n result = f(points, point)\n if result is answer:\n continue\n print(\"Test №{} failed\".format(i + 1))\n print(\"Expected {}, result {}\".format(answer, result))\n print(\"points={}\".format(points))\n print(\"point={}\".format(point))\n draw(points, point, \"\")\n return\n print(\"All tests passed\")\n\n\ndef show_test(test, f):\n res = f(test[0], test[1])\n draw(test[0], test[1], \"inside\" if res else \"outside\")\n\n\ndef draw_segment(ax, segment, color):\n segment = np.array(segment).T\n ax.plot(segment[0,], segment[1,], c=color)\n ax.scatter(segment[0,], segment[1,], c=color)\n\n\ndef draw_ray(ax, ray, maxX, color):\n a = ray[0][1] - ray[1][1]\n b = ray[1][0] - ray[0][0]\n c = ray[0][0] * ray[1][1] - ray[1][0] * ray[0][1]\n y = (-c - a * maxX) / b\n ray[1] = [maxX, y]\n draw_segment(ax, ray, color)\n\n\ndef test_intersect(f, n=200):\n for i in range(1, n):\n if i % 50 == 0:\n print('passed {} tests'.format(i))\n ray = np.random.randint(0, 25, size=(2, 2))\n segment = np.random.randint(0, 25, size=(2, 2))\n answer = intersect(ray, segment)\n result = f(ray, segment)\n if f(ray, segment) is intersect(ray, segment):\n continue\n fig = plt.figure(figsize=(6, 6))\n ax1 = plt.subplot(111, aspect='equal')\n draw_ray(ax1, ray, 27, 'r')\n draw_segment(ax1, segment, 'g')\n ax1.set_xlim(0 - 1, 25 + 1)\n ax1.set_ylim(0 - 1, 25 + 1)\n ax1.set_title(\"expected {}, result {}\".format(answer, result))\n display(fig)\n plt.close()\n return\n print(\"All tests passed\")\n\n\ndef test_turn(f, n=200):\n for i in range(1, n):\n if i % 50 == 0:\n print('passed {} tests'.format(i))\n while True:\n points = np.random.randint(0, 25, size=(2, 2))\n point = np.random.randint(0, 25, size=(1, 2))\n if points[0] is points[1]:\n continue\n break\n answer = f(points, point)\n result = turn(points, point)\n if int(answer) == int(result):\n continue\n print(\"Test №{} failed\".format(i + 1))\n print(\"Expected {}, result {}\".format(answer, result))\n print(\"points={}\".format(points))\n print(\"point={}\".format(point))\n print(\"All tests passed\")\n\n\ndef show_examples(tests, f):\n lines = len(tests) // 3 + (1 if len(tests) % 3 != 0 else 0)\n fig, axes = plt.subplots(lines, 3, figsize=(9, 3 * lines))\n\n while lines * 3 != len(tests):\n tests.insert(len(tests), [[-5, -5], [-5, -5], [-5, -5], [-5, -5]])\n\n for (a, b, c, d), axis in zip(tests, axes.reshape(len(tests))):\n if a[0] == a[1] == b[0] == b[1] == c[0] == c[1] == d[0] == d[1] == -5:\n continue\n draw_ray(axis, [a, b], 4, 'r')\n draw_segment(axis, [c, d], 'g')\n axis.set_xlim(-1, 3)\n axis.set_ylim(-1, 3)\n ray = [a, b]\n segment = [c, d]\n if f(ray, segment):\n title = \"intersect\"\n else:\n title = \"not intersect\"\n\n axis.set_title(title)\n plt.show()\n plt.close()\n", "repo_name": "CT-18/cg", "sub_path": "20_PSLG_&_localization/inside_not_convex/solutions.py", "file_name": "solutions.py", "file_ext": "py", "file_size_in_byte": 5882, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 13, "dataset": "github-code", "pt": "37", "api": [{"api_name": "numpy.sign", "line_number": 13, "usage_type": "call"}, {"api_name": "numpy.linalg.det", "line_number": 13, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 13, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 13, "usage_type": "call"}, {"api_name": "shapely.geometry.polygon.Polygon", "line_number": 17, "usage_type": "call"}, {"api_name": "shapely.geometry.Point", "line_number": 18, "usage_type": "call"}, {"api_name": "math.pi", "line_number": 23, "usage_type": "attribute"}, {"api_name": "math.pi", "line_number": 28, "usage_type": "attribute"}, {"api_name": "math.pi", "line_number": 29, "usage_type": "attribute"}, {"api_name": "random.uniform", "line_number": 32, "usage_type": "call"}, {"api_name": "math.pi", "line_number": 37, "usage_type": "attribute"}, {"api_name": "random.uniform", "line_number": 43, "usage_type": "call"}, {"api_name": "math.pi", "line_number": 43, "usage_type": "attribute"}, {"api_name": "random.gauss", "line_number": 45, "usage_type": "call"}, {"api_name": "math.cos", "line_number": 46, "usage_type": "call"}, {"api_name": "math.sin", "line_number": 47, "usage_type": "call"}, {"api_name": "sympy.Ray", "line_number": 66, "usage_type": "call"}, {"api_name": "sympy.Point", "line_number": 66, "usage_type": "call"}, {"api_name": "sympy.Segment", "line_number": 67, "usage_type": "call"}, {"api_name": "sympy.intersection", "line_number": 68, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 72, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 72, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 73, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 73, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 76, "usage_type": "call"}, {"api_name": "IPython.display.display", "line_number": 83, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.close", "line_number": 84, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 84, "usage_type": "name"}, {"api_name": "numpy.random.randint", "line_number": 93, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 93, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 113, "usage_type": "call"}, {"api_name": "numpy.random.randint", "line_number": 131, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 131, "usage_type": "attribute"}, {"api_name": "numpy.random.randint", "line_number": 132, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 132, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 137, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 137, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 138, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 138, "usage_type": "name"}, {"api_name": "IPython.display.display", "line_number": 144, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.close", "line_number": 145, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 145, "usage_type": "name"}, {"api_name": "numpy.random.randint", "line_number": 155, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 155, "usage_type": "attribute"}, {"api_name": "numpy.random.randint", "line_number": 156, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 156, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 173, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 173, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 193, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 193, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.close", "line_number": 194, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 194, "usage_type": "name"}]} +{"seq_id": "1359039932", "text": "from pyspark.sql import SparkSession\r\nfrom pyspark.sql.functions import udf\r\nfrom pyspark.sql.types import StringType\r\n\r\n\r\n#create a spark session\r\nspark=SparkSession.builder.appName(\"Example1\").getOrCreate()\r\n\r\n\r\n#read the ccsv file\r\ndf=spark.read.csv(\"emp.csv\",header=True, inferSchema=True)\r\ndf.printSchema()\r\n\r\n\r\n#define a funtion that we need to use as udf : user define function\r\ndef greet(name):\r\n return f\"Hello,{name}\"\r\n\r\n\r\ngreet_udf=udf(greet,StringType())\r\ndf_greeting=df.withColumn(\"Greeting\",greet_udf(df[\"Name\"]))\r\ndf_greeting.show()\r\n# spark.stop()\r\n", "repo_name": "jess-web/hadoop", "sub_path": "user_defined_function.py", "file_name": "user_defined_function.py", "file_ext": "py", "file_size_in_byte": 566, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "pyspark.sql.SparkSession.builder.appName", "line_number": 7, "usage_type": "call"}, {"api_name": "pyspark.sql.SparkSession.builder", "line_number": 7, "usage_type": "attribute"}, {"api_name": "pyspark.sql.SparkSession", "line_number": 7, "usage_type": "name"}, {"api_name": "pyspark.sql.functions.udf", "line_number": 20, "usage_type": "call"}, {"api_name": "pyspark.sql.types.StringType", "line_number": 20, "usage_type": "call"}]} +{"seq_id": "30711660948", "text": "import numpy as np\nimport pandas as pd\nimport os\nimport h5py\nfrom pathlib import Path\nfrom datetime import datetime\nfrom .serdeutils import set_metadata_without_warning\nfrom .utilities import ProjectException\nfrom .utilities import TooManyBinomialValuesError\nfrom .utilities import ValueConversionError\nfrom .connections import Connections\n\n\n\ndef decode(x):\n if isinstance(x, str):\n return x\n return x.decode(\"utf-8\")\n\n\nclass Project():\n \"\"\"\n Class for using a project from RapidMiner Server that has been cloned locally. Use git for cloning the repository, then read and write calls can work on local resources. You need to use git commands to push changes that you make locally.\n \"\"\"\n\n __NANOSECONDS_IN_A_DAY = 86400000000000\n _RM_HDF5_EXTENSION = \".rmhdf5table\"\n _RM_RMP_EXTENSION = \".rmp\"\n _METADATA_TYPES = {\"NOMINAL\": np.int8(1),\n \"INTEGER\": np.int8(3),\n \"REAL\": np.int8(4),\n \"TEXT\": np.int8(5),\n \"BINOMINAL\": np.int8(6),\n \"POLYNOMINAL\": np.int8(7),\n \"FILE_PATH\": np.int8(8),\n \"DATE_TIME\": np.int8(9),\n \"DATE\": np.int8(10),\n \"TIME\": np.int8(11)}\n _LEGACY_TYPES = [\"TEXT\", \"BINOMINAL\", \"FILE_PATH\", \"DATE\"] # POLYNOMIAL handled separatly\n _METADATA_ROLES = [\"BATCH\", \"CLUSTER\", \"ID\", \"LABEL\", \"OUTLIER\", \"PREDICTION\", \"WEIGHT\"]\n\n __RM_MISSING_DATETIME_OR_TIME = 9223372036854775807 # constant value is coming from com.rapidminer.storage.hdf5.ExampleSetHdf5Writer.java#writeDateData/writeTimeData as of Long.MAX_VALUE/TimeColumn.MISSING_VALUE\n __PANDAS_MISSING_DATE = pd.to_numeric(pd.Series([datetime.utcfromtimestamp(0), None]), downcast=\"integer\")[1]\n\n def __init__(self, path=\".\"):\n \"\"\"\n Initializes a reference to a locally cloned project. You need to clone a project from RapidMiner Server first (e.g. via git commands) to be able to use the methods of this instance.\n \n :param path: path to the local project repository root folder. It can be a relative path from the current working directory or an absolute path, . The default value points to the working directory.\n \"\"\"\n if not os.path.exists(path):\n if path == \"\":\n msg_dir_part = \"in the current directory\"\n else:\n msg_dir_part = \"at the specified path '%s'\" % (os.path.dirname(path))\n raise ProjectException(\"Project '%s' does not exist %s. Please make sure you have a local copy of the project.\" % (os.path.basename(path), msg_dir_part))\n self.path = os.path.abspath(path)\n\n def read(self, path_or_buffer):\n \"\"\"\n Reads a dataset from the local project repository into a pandas DataFrame. Note that only the new HDF5 format is supported, earlier RapidMiner Server data formats are not supported.\n \n :param path_or_buffer: this can either be a relative path inside the project (e.g. subfolder and file name), an absolute path, or a io.BytesIO stream. If a path is specified, the RapidMiner-specific HDF5 file extension can be omitted.\n \"\"\"\n if isinstance(path_or_buffer, str):\n path_or_buffer = os.path.join(self.path, path_or_buffer)\n if not os.path.isfile(path_or_buffer):\n if (os.path.isfile(path_or_buffer + Project._RM_HDF5_EXTENSION)):\n path_or_buffer = path_or_buffer + Project._RM_HDF5_EXTENSION\n else:\n raise FileNotFoundError(\"File '%s' not found in the project.\" % (path_or_buffer))\n return Project.__read_data_safe(path_or_buffer)\n\n def write(self, df, path):\n \"\"\"\n Writes a pandas DataFrame into the RapidMiner-specific HDF5 file format that the rest of the RapidMiner platform uses. Note that you need to explicitly commit and push your local changes to the remote project repository (e.g. via git commands) to make them available to the platform.\n \n :param path: relative path inside the project (e.g. subfolder and file name) specifying the target location or an absolute path. The RapidMiner-specific HDF5 file extension is automatically added to the filename, if it is missing.\n \"\"\"\n path = os.path.join(self.path, path)\n if len(Path(path).suffix) == 0:\n path = path + Project._RM_HDF5_EXTENSION\n Project.__write_data_safe(df, path)\n\n def get_connections(self):\n \"\"\"\n Returns the connection in that this project contains.\n \"\"\"\n return Connections(self.path)\n \n#####################\n# Private functions #\n#####################\n\n __from_ts_nanos = np.vectorize(lambda x: datetime.utcfromtimestamp(x/1e9) if x != Project.__RM_MISSING_DATETIME_OR_TIME else None)\n __from_ts_seconds_and_nanos = np.vectorize(lambda x,y: datetime.utcfromtimestamp(x+y/1e9) if x != Project.__RM_MISSING_DATETIME_OR_TIME else None)\n __hyp5_string_dtype = h5py.string_dtype()\n __h5py_reference_dtype = h5py.special_dtype(ref=h5py.Reference)\n \n def __get_numerical(x, typestr):\n if typestr == 'Integer':\n # NaN values are not allowed in int64\n if not np.isnan(np.min(x[:])):\n with x.astype('int64'):\n return x[:]\n else:\n return x\n elif typestr in ('Date-Time', 'Date'):\n return Project.__from_ts_seconds_and_nanos(x[:],0)\n elif typestr == \"Time\":\n return Project.__from_ts_nanos(x[:])\n else:\n return x\n\n def __get_data(f, x):\n r = f[x]\n mapping = r.attrs.get('dictionary')\n if \"additional\" in r.attrs and r.attrs.get(\"type\") == \"Date-Time\":\n additional = r.attrs.get('additional')\n additional = f[additional]\n return Project.__from_ts_seconds_and_nanos(r[:], additional[:])\n if mapping is None:\n return Project.__get_numerical(r, decode(r.attrs.get('type')))\n if isinstance(mapping,h5py.Reference):\n mapping = f[mapping]\n mapping = mapping[1:]\n g = lambda x:x-1\n return pd.Categorical.from_codes(g(r[()]), [decode(s) if not isinstance(s, str) else s for s in mapping], ordered=False)\n\n def __get_type(df_kind_char, hdf_attrs):\n \"\"\"\n Decides type based on the pandas DataFrame type, the explicit HDF5 type and whether there is a positive nominal type that indicates binominal.\n \"\"\"\n if \"legacy_type\" in hdf_attrs:\n for k in Project._METADATA_TYPES.keys():\n if Project._METADATA_TYPES[k] == hdf_attrs.get(\"legacy_type\"):\n return k.lower()\n hdf_type = decode(hdf_attrs.get(\"type\"))\n if hdf_type in (\"Date-Time\", \"Date\"):\n meta_type = \"date_time\"\n elif hdf_type == \"Time\":\n meta_type = \"time\"\n elif df_kind_char in ('i', 'u'):\n meta_type = 'integer'\n elif df_kind_char in ('f'):\n meta_type = 'real'\n elif df_kind_char in ('M'):\n meta_type = 'date_time'\n elif df_kind_char in ('b') or \"positive_index\" in hdf_attrs:\n meta_type = 'binominal'\n else:\n meta_type = 'polynominal'\n return meta_type\n\n def __get_role(hdf_column):\n if \"role\" in hdf_column.attrs:\n r = decode(hdf_column.attrs.get(\"role\"))\n if r in Project._METADATA_ROLES:\n return r.lower()\n elif r in [\"SCORE\", \"METADATA\"]:\n return decode(hdf_column.attrs.get(\"legacy_role\"))\n else:\n raise ProjectException(\"Role '\" + r + \"' not recognized. (Column '\" + decode(hdf_column.attrs.get('name')) + \"'.)\" )\n return None\n \n def __read_data_from_input(f):\n numberOfColumns = f.attrs.get('columns')\n keys = [\"a\" + str(i) for i in range(0, numberOfColumns)]\n names = [decode(f[x].attrs.get('name')) for x in keys]\n data = [Project.__get_data(f, x) for x in keys]\n df = pd.DataFrame.from_dict(dict(zip(names, data)))\n metadata = dict(zip(names,\n [((Project.__get_type(df.dtypes[decode(f[x].attrs.get('name'))].kind,\n f[x].attrs)),\n Project.__get_role(f[x])) for x in keys]))\n set_metadata_without_warning(df, metadata)\n return df\n\n def __read_data_safe(filename_or_buffer):\n try:\n return Project.__read_data(filename_or_buffer)\n except OSError:\n raise ProjectException(\"Cannot read file. Not a valid rmhdf5table file format.\")\n\n\n def __read_data(filename_or_buffer):\n with h5py.File(filename_or_buffer, 'r') as f:\n return Project.__read_data_from_input(f)\n\n def __get_desired_type(frame, column, name):\n desired_type = None\n if hasattr(frame, \"rm_metadata\") and name in frame.rm_metadata:\n desired_type = frame.rm_metadata[name][0].upper()\n else:\n if column.dtype.kind == 'O':\n desired_type = \"NOMINAL\"\n elif np.issubdtype(column.dtype, np.integer):\n desired_type = \"INTEGER\"\n elif column.dtype.kind == 'M':\n desired_type = \"DATE_TIME\"\n else:\n desired_type = \"REAL\"\n return desired_type\n\n def __create_dataset(f, column, desired_type, index):\n shortname = 'a'+str(index)\n if desired_type in [\"NOMINAL\", \"BINOMINAL\", \"POLYNOMINAL\", \"TEXT\", \"FILE_PATH\"]:\n cat = column.astype(\"category\").cat\n dset = f.create_dataset(shortname, data = pd.to_numeric(cat.codes.apply(lambda x:x+1), downcast='integer'))\n mappingname = 'd'+str(index)\n mappingvals = cat.categories.values.astype(object)\n replacement = \"NULL\"\n while replacement in mappingvals:\n replacement = \"\\x00\" + replacement\n mappingvals = np.concatenate([[replacement], mappingvals])\n if desired_type == \"BINOMINAL\" and len(mappingvals) > 3:\n raise TooManyBinomialValuesError(\"Column '%s' marked as binomial column in rm_metadata attribute, but has more there is more then two distinct values present.\", )\n if len(mappingvals) <= 3:\n dset.attrs[\"dictionary\"] = [str(v) for v in mappingvals]\n if desired_type == \"BINOMINAL\":\n # positive index may change after read and write\n dset.attrs['positive_index'] = np.int8(len(mappingvals) - 1)\n else:\n mset = f.create_dataset(mappingname, (len(mappingvals), ), dtype=Project.__hyp5_string_dtype)\n try:\n mset[()] = mappingvals\n except TypeError:\n # fixes error: TypeError: Can't implicitly convert non-string objects to strings (caused by some Python environments)\n mset[()] = [str(v) for v in mappingvals]\n dset.attrs.create(\"dictionary\", mset.ref, dtype=Project.__h5py_reference_dtype)\n dset.attrs['type'] = \"Nominal\"\n elif desired_type == \"INTEGER\":\n dset = f.create_dataset(shortname, data = column.astype(\"int64\"))\n dset.attrs['type'] = \"Integer\"\n elif desired_type in [\"DATE\", \"TIME\", \"DATE_TIME\"]:\n if column.dtype.kind == \"M\":\n nanoseconds = pd.to_numeric(column.dt.tz_localize(None), downcast=\"integer\")\n else:\n nanoseconds = pd.to_numeric(column, downcast=\"integer\")\n if desired_type == \"TIME\":\n missing = nanoseconds==Project.__PANDAS_MISSING_DATE\n nanoseconds.loc[missing] = Project.__RM_MISSING_DATETIME_OR_TIME\n nanoseconds.loc[~missing] = nanoseconds.loc[~missing] % int(Project.__NANOSECONDS_IN_A_DAY)\n dset = f.create_dataset(shortname, data = nanoseconds)\n dset.attrs['type'] = \"Time\"\n else:\n seconds = nanoseconds.apply(lambda x: x // int(1e9) if x != Project.__PANDAS_MISSING_DATE else Project.__RM_MISSING_DATETIME_OR_TIME)\n dset = f.create_dataset(shortname, data = seconds )\n additionalname = shortname+\"a\"\n aset = f.create_dataset(additionalname, data = (nanoseconds % int(1e9)).astype(\"int32\"))\n dset.attrs.create(\"additional\", aset.ref, dtype=Project.__h5py_reference_dtype)\n dset.attrs['type'] = \"Date-Time\"\n else:\n dset = f.create_dataset(shortname, data = column.astype(\"float64\"))\n dset.attrs['type'] = \"Real\"\n return dset\n\n def __to_column_role(role):\n if role.startswith(\"confidence\"):\n return (\"SCORE\", role)\n elif role.upper() in Project._METADATA_ROLES:\n return (role.upper(), None)\n else:\n return (\"METADATA\", role)\n\n def __set_common_column_attributes(frame, dset, desired_type, name):\n dset.attrs['name'] = name\n if desired_type in Project._LEGACY_TYPES:\n dset.attrs['legacy_type'] = Project._METADATA_TYPES[desired_type]\n if hasattr(frame, \"rm_metadata\") and name in frame.rm_metadata:\n md = frame.rm_metadata[name]\n if len(md) > 1 and md[1]:\n role, legacy_role = Project.__to_column_role(md[1])\n dset.attrs['role'] = role\n if legacy_role is not None:\n dset.attrs['legacy_role'] = legacy_role\n\n def __write_column(f, frame, column, name, index):\n desired_type = Project.__get_desired_type(frame, column, name)\n try:\n dset = Project.__create_dataset(f, column, desired_type, index)\n except ValueError as e:\n raise ValueConversionError(\"Cannot write output file with the desired format. Please review rm_metadata of the pandas DataFrame. Cause: \" + str(e))\n Project.__set_common_column_attributes(frame, dset, desired_type, name)\n\n def __write_data_safe(data, filename):\n if not isinstance(data, pd.DataFrame):\n raise TypeError(\"'data' attribute of write_data is not pandas DataFrame.\")\n if not os.path.exists(os.path.dirname(filename)):\n raise ProjectException(\"Cannot write file. Parent directory '%s' does not exists.\" % (os.path.dirname(filename)))\n if hasattr(data, \"rm_metadata\"):\n md = data.rm_metadata\n for key in md.keys():\n if md[key][0] is not None and md[key][0].upper() not in Project._METADATA_TYPES.keys():\n raise ProjectException(\"%s is not a valid type in rm_metadata.\" % (md[key][0]))\n Project.__write_data(data, filename)\n\n def __write_data(data, filename): #todo: error handling\n frame = data\n # without this call you get fixed length strings which can only be ascii\n with h5py.File(filename, 'w') as f:\n shape = frame.shape\n f.attrs['rows'] = np.int32(shape[0])\n f.attrs['columns'] = np.int32(shape[1])\n for i in range(0, len(frame.columns)):\n name = str(frame.columns[i])\n column = frame.iloc[:,i]\n Project.__write_column(f, frame, column, name, i)\n", "repo_name": "rapidminer/python-rapidminer", "sub_path": "rapidminer/core/project.py", "file_name": "project.py", "file_ext": "py", "file_size_in_byte": 15346, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 37, "dataset": "github-code", "pt": "37", "api": [{"api_name": "numpy.int8", "line_number": 29, "usage_type": "call"}, {"api_name": "numpy.int8", "line_number": 30, "usage_type": "call"}, {"api_name": "numpy.int8", "line_number": 31, "usage_type": "call"}, {"api_name": "numpy.int8", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.int8", "line_number": 33, "usage_type": "call"}, {"api_name": "numpy.int8", "line_number": 34, "usage_type": "call"}, {"api_name": "numpy.int8", "line_number": 35, "usage_type": "call"}, {"api_name": "numpy.int8", "line_number": 36, "usage_type": "call"}, {"api_name": "numpy.int8", "line_number": 37, "usage_type": "call"}, {"api_name": "numpy.int8", "line_number": 38, "usage_type": "call"}, {"api_name": "pandas.to_numeric", "line_number": 43, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 43, "usage_type": "call"}, {"api_name": "datetime.datetime.utcfromtimestamp", "line_number": 43, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 43, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 51, "usage_type": "call"}, {"api_name": "os.path", "line_number": 51, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 55, "usage_type": "call"}, {"api_name": "os.path", "line_number": 55, "usage_type": "attribute"}, {"api_name": "utilities.ProjectException", "line_number": 56, "usage_type": "call"}, {"api_name": "os.path.basename", "line_number": 56, "usage_type": "call"}, {"api_name": "os.path", "line_number": 56, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 57, "usage_type": "call"}, {"api_name": "os.path", "line_number": 57, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 66, "usage_type": "call"}, {"api_name": "os.path", "line_number": 66, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 67, "usage_type": "call"}, {"api_name": "os.path", "line_number": 67, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 68, "usage_type": "call"}, {"api_name": "os.path", "line_number": 68, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 80, "usage_type": "call"}, {"api_name": "os.path", "line_number": 80, "usage_type": "attribute"}, {"api_name": "pathlib.Path", "line_number": 81, "usage_type": "call"}, {"api_name": "connections.Connections", "line_number": 89, "usage_type": "call"}, {"api_name": "numpy.vectorize", "line_number": 95, "usage_type": "call"}, {"api_name": "datetime.datetime.utcfromtimestamp", "line_number": 95, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 95, "usage_type": "name"}, {"api_name": "numpy.vectorize", "line_number": 96, "usage_type": "call"}, {"api_name": "datetime.datetime.utcfromtimestamp", "line_number": 96, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 96, "usage_type": "name"}, {"api_name": "h5py.string_dtype", "line_number": 97, "usage_type": "call"}, {"api_name": "h5py.special_dtype", "line_number": 98, "usage_type": "call"}, {"api_name": "h5py.Reference", "line_number": 98, "usage_type": "attribute"}, {"api_name": "numpy.isnan", "line_number": 103, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 103, "usage_type": "call"}, {"api_name": "h5py.Reference", "line_number": 124, "usage_type": "attribute"}, {"api_name": "pandas.Categorical.from_codes", "line_number": 128, "usage_type": "call"}, {"api_name": "pandas.Categorical", "line_number": 128, "usage_type": "attribute"}, {"api_name": "utilities.ProjectException", "line_number": 163, "usage_type": "call"}, {"api_name": "pandas.DataFrame.from_dict", "line_number": 171, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 171, "usage_type": "attribute"}, {"api_name": "serdeutils.set_metadata_without_warning", "line_number": 176, "usage_type": "call"}, {"api_name": "utilities.ProjectException", "line_number": 183, "usage_type": "call"}, {"api_name": "h5py.File", "line_number": 187, "usage_type": "call"}, {"api_name": "numpy.issubdtype", "line_number": 197, "usage_type": "call"}, {"api_name": "numpy.integer", "line_number": 197, "usage_type": "attribute"}, {"api_name": "pandas.to_numeric", "line_number": 209, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 215, "usage_type": "call"}, {"api_name": "utilities.TooManyBinomialValuesError", "line_number": 217, "usage_type": "call"}, {"api_name": "numpy.int8", "line_number": 222, "usage_type": "call"}, {"api_name": "pandas.to_numeric", "line_number": 237, "usage_type": "call"}, {"api_name": "pandas.to_numeric", "line_number": 239, "usage_type": "call"}, {"api_name": "utilities.ValueConversionError", "line_number": 283, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 287, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 289, "usage_type": "call"}, {"api_name": "os.path", "line_number": 289, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 289, "usage_type": "call"}, {"api_name": "utilities.ProjectException", "line_number": 290, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 290, "usage_type": "call"}, {"api_name": "os.path", "line_number": 290, "usage_type": "attribute"}, {"api_name": "utilities.ProjectException", "line_number": 295, "usage_type": "call"}, {"api_name": "h5py.File", "line_number": 301, "usage_type": "call"}, {"api_name": "numpy.int32", "line_number": 303, "usage_type": "call"}, {"api_name": "numpy.int32", "line_number": 304, "usage_type": "call"}]} +{"seq_id": "29271222372", "text": "import os\nimport json\nimport random\nimport socket\nimport time\n\nfrom requests_oauthlib import requests\nimport xml.etree.ElementTree as ET\n\nfrom TwitterAPI import TwitterAPI\n\n# pip install pygeocoder\nfrom pygeocoder import Geocoder\nimport pandas as pd\nimport numpy as np\n\nimport putils.darksky as darksky\n\n#pip install wikipedia\nimport putils.uwikipedia as wikipedia\n\nimport putils.waze as waze\n\nheaders = {'User-Agent':'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_5) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/50.0.2661.102 Safari/537.36'}\n\nspotUrl=\"https://api.findmespot.com/spot-main-web/consumer/rest-api/2.0/public/feed/\"\n\nspotId = \"0kGtoQJ7kRRE4Wz5lKhofJjsXf95t27LK\"\n\nremoveId = [741276466,741263971,741254154,741245153,741245060]\nhashtags=['oya','ironman','triathlon','expedition',\"run\",\"marathon\"]\n\n# debug on local\nprint(\"Running on \"+socket.gethostname())\nif \"digital-gf.local\" in socket.gethostname() :\n\tprint(\"Local testing...\")\n\tlocal = True\nelse :\n local = False\n\ndef loadxml(params_file) :\n tree = ET.parse(params_file)\n return tree.getroot()\n\ndef loadjson(json_file) :\n if os.path.exists(json_file) :\n with open(json_file) as f:\n return json.load(f)\n else :\n return {}\n\ndef createFirstSentence(ville, meteo, disday) :\n locationSentence=[\n \"C'est reparti pour une nouvelle journée ! Passage par VILLE\",\n \"Première ville traversée aujourd'hui : VILLE\",\n \"Pour bien commencer votre journée, passez par VILLE\",\n \"Dommage que je vienne juste de commencer la journée, j'aurais bien pris un café à VILLE\",\n \"Je suis passé admirer le lever de soleil à VILLE\"\n ]\n\ndef createSentence(ville,meteo,disday) :\n locationSentence=[\n \"Je suis vers VILLE\",\n \"Je pédale à VILLE\",\n \"J'ai visité VILLE\",\n \"Je suis à VILLE\",\n \"Je visite VILLE\",\n \"Rejoignez moi, je suis à VILLE\",\n \"Passage par VILLE\",\n \"Connaissez vous VILLE ? J'y suis en ce moment !\",\n \"Si vous me rattrapez à VILLE, je vous offre une bière\",\n \"De Porspoder à VILLE, la digue la digue..\",\n \"Si vous passez par VILLE et que vous voyez un cycliste..\",\n \"Sur les routes de VILLE\",\n \"Je viens de croiser un panneau qui souhaite la bienvenue à VILLE\",\n \"VILLE, son troquet, sa poste et sa mairie\",\n \"Le village de VILLE souhaite la bienvenue aux cyclistes\",\n \"VILLE : le bitume de ses routes, et ses paysages\",\n \"Si j'avais un peu plus de temps je visiterais bien VILLE\",\n \"Je pédale donc je suis.. à VILLE\"\n ]\n\n if disday < 20 :\n locationSentence.append(\"Je visiterais bien VILLE, mais je n'ai fait que {} km ce matin\".format(disday))\n\n if disday > 50 :\n locationSentence.append(\"Déja {} km aujourd'hui et je suis enfin à VILLE\".format(disday))\n locationSentence.append(\"{} km depuis mon réveil, et me voici à VILLE\".format(disday))\n\n weet_text = random.choice(locationSentence).replace('VILLE',results.city)\n\n return weet_text\n\ndef computeDistance(lilianjson) :\n distToday = 0\n distTotal = 0\n for id in lilianjson :\n d=0\n today=False\n for s in lilianjson[id] :\n if 'distance' in s :\n d = s['distance']\n distTotal = distTotal + d\n\n if 'time' in s and s['time'].startswith(time.strftime(\"%Y-%m-%d\")) :\n today=True\n\n if today and d > 0 :\n distToday = distToday + d\n d=0\n return distToday,distTotal\n\nif __name__ == \"__main__\":\n\n auth_settings = loadxml(\"auth.xml\")\n\n for service in auth_settings.findall('service') :\n \tif service.get(\"name\") == \"twitter\" :\n \t\tconsumer_key=service.find(\"consumer_key\").text\n \t\tconsumer_secret=service.find(\"consumer_secret\").text\n \t\taccess_token_key=service.find(\"access_token_key\").text\n \t\taccess_token_secret=service.find(\"access_token_secret\").text\n \telif service.get(\"name\") == \"darksky\" :\n darksky_token=service.find(\"token\").text\n darksky_url=service.find(\"url\").text\n\n # Local record\n lilianjsonfile=\"lilian.json\"\n lilianjson = loadjson(lilianjsonfile)\n print(\"+-[Loaded json] : {}\".format(lilianjsonfile))\n\n lastPointId = max(lilianjson)\n lastPoint= lilianjson[lastPointId]\n for s in lastPoint :\n if 'latitude' in s : lastLat = s['latitude']\n if 'longitude' in s : lastLong = s['longitude']\n\n # Get Spot data\n client = requests.session()\n url = spotUrl+spotId+\"/message.json\"\n print(\"+-[Load spot url] : {} \".format(url))\n r = client.get(url, headers=headers)\n response = r.content.decode(\"utf-8\")\n json_decode=json.loads(response)\n messages = json_decode[\"response\"][\"feedMessageResponse\"][\"messages\"][\"message\"]\n print(\"+-[Load spot data] with {} entries \".format(len(messages)))\n\n for message in messages :\n msg_id=message[\"id\"]\n msg_time=message[\"dateTime\"]\n msg_lat=message[\"latitude\"]\n msg_long=message[\"longitude\"]\n # if \"messageContent\" in message :\n # print(message[\"messageContent\"])\n\n # New entry\n if (str(msg_id) not in lilianjson) :\n print(\"+-[{}] : {} ({},{})\".format(msg_id,msg_time,msg_lat, msg_long))\n results = Geocoder.reverse_geocode(msg_lat,msg_long)\n\n link = \"https://www.google.com/maps/@{},{},12z\".format(msg_lat, msg_long)\n\n #link = \"http://maps.google.com/?q={},France/@{},{},12z\".format(results.city,msg_lat, msg_long)\n\n dark_decode=darksky.getDarkWeather(darksky_url,darksky_token,msg_lat, msg_long)\n dark_icon = dark_decode[\"currently\"][\"icon\"]\n dark_weather = dark_decode[\"currently\"]\n print(dark_icon)\n\n # Distance\n route_distance=0\n if lastLat and lastLong :\n route = waze.WazeRouteCalculator(lastLat, lastLong, msg_lat, msg_long)\n route_time, route_distance = route.calc_route_info()\n print(\"+-[{} km] depuis le dernier point\" .format(route_distance))\n\n tweet_text = createSentence(results.city, \"none\",route_distance)\n\n tweet_text += \" \"+ link\n tweet_text += \" #oya\"\n\n lilianjson[msg_id] = []\n lilianjson[msg_id].append({\n 'id': msg_id,\n 'time': msg_time,\n 'city': results.city,\n 'latitude' : msg_lat,\n 'longitude' : msg_long,\n 'weather' : dark_weather,\n 'distance' : route_distance\n })\n\n if not local and route_distance >= 1 :\n twitterapi = TwitterAPI(consumer_key=consumer_key, consumer_secret=consumer_secret, access_token_key=access_token_key, access_token_secret=access_token_secret)\n\n twitterapi.request('statuses/update', {'status':tweet_text, 'lat':msg_lat,'long':msg_long})\n\n print(\"+-[Tweet] {}\".format(tweet_text))\n\n #img = wikipedia.getImage(results.city)\n #if (img) :\n # print(\"Pic : \"+img)\n # response = requests.get(img, headers=headers, allow_redirects=True)\n # data = response.content\n # twitterapi.request('statuses/update_with_media', {'status':tweet_text}, {'media[]':data})\n # else :\n\n distToday,distTotal = computeDistance(lilianjson)\n\n print(\"+-[Distance] Today : {} km, Total : {} km\".format(int(distToday),int(distTotal)))\n\n with open(lilianjsonfile, 'w') as jsonfile:\n json.dump(lilianjson, jsonfile)\n", "repo_name": "iero/PythonMisc", "sub_path": "followLilian.py", "file_name": "followLilian.py", "file_ext": "py", "file_size_in_byte": 7640, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "socket.gethostname", "line_number": 34, "usage_type": "call"}, {"api_name": "socket.gethostname", "line_number": 35, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree.parse", "line_number": 42, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 42, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 46, "usage_type": "call"}, {"api_name": "os.path", "line_number": 46, "usage_type": "attribute"}, {"api_name": "json.load", "line_number": 48, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 90, "usage_type": "call"}, {"api_name": "time.strftime", "line_number": 105, "usage_type": "call"}, {"api_name": "requests_oauthlib.requests.session", "line_number": 139, "usage_type": "call"}, {"api_name": "requests_oauthlib.requests", "line_number": 139, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 144, "usage_type": "call"}, {"api_name": "pygeocoder.Geocoder.reverse_geocode", "line_number": 159, "usage_type": "call"}, {"api_name": "pygeocoder.Geocoder", "line_number": 159, "usage_type": "name"}, {"api_name": "putils.darksky.getDarkWeather", "line_number": 165, "usage_type": "call"}, {"api_name": "putils.darksky", "line_number": 165, "usage_type": "name"}, {"api_name": "putils.waze.WazeRouteCalculator", "line_number": 173, "usage_type": "call"}, {"api_name": "putils.waze", "line_number": 173, "usage_type": "name"}, {"api_name": "TwitterAPI.TwitterAPI", "line_number": 194, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 213, "usage_type": "call"}]} +{"seq_id": "13846539258", "text": "import struct\nimport os\nimport urllib\nimport zipfile\nimport pickle\nimport PIL\nimport numpy as np\nimport tensorflow as tf\nimport matplotlib.pyplot as plt\nimport torch\nfrom torch.utils.data import Dataset, DataLoader\nfrom torchvision import transforms, utils\n\n\ndef jpg_reader(filename):\n img = PIL.Image.open(filename)\n return np.asarray(img)\n\n\nclass Normalize:\n def __call__(self, sample):\n image, label = sample['image'], sample['label']\n image = image.transpose((2,0,1))\n image = 2*(image/255) - 1\n label = label/32\n\n return {'image': torch.from_numpy(image),\n 'label': torch.from_numpy(label)}\n\n\nclass HomographyDataset(Dataset):\n def __init__(self, val_frac=0.05, mode='train', img_dir='data/synth_data'): \n assert os.path.isdir(img_dir), 'Download the MSCOCO dataset and prepare it' \n self.transforms = transforms.Compose([\n Normalize()])\n \n with open('data/label_file.txt', 'r') as f:\n num_and_label = [line.rstrip().rstrip(',').split(';') for line in f]\n\n L = len(num_and_label)\n idx = int(val_frac*L)\n \n if mode == 'train':\n self.num_and_label = num_and_label[idx:]\n elif mode == 'eval':\n self.num_and_label = num_and_label[:idx]\n else:\n raise ValueError('no such mode')\n \n def __len__(self):\n return len(self.num_and_label)\n\n def __getitem__(self, idx):\n num = self.num_and_label[idx][0]\n input_file_orig = 'data/synth_data/{:s}_orig.jpg'.format(num)\n input_file_warp = 'data/synth_data/{:s}_warp.jpg'.format(num)\n img_orig = jpg_reader(input_file_orig)\n img_warp = jpg_reader(input_file_warp)\n img = np.concatenate([img_orig[:,:,None], img_warp[:,:,None]], axis=2).astype(np.float32)\n \n label_str = self.num_and_label[idx][1] \n label = np.array([float(el) for el in label_str.split(',')]).astype(np.float32)\n sample = {\n 'image': img,\n 'label': label}\n sample = self.transforms(sample)\n return sample\n\n\nif __name__ == '__main__':\n dataset = HomographyDataset()\n dataloader = DataLoader(\n dataset,\n batch_size=4,\n shuffle=True,\n num_workers=4)\n\n for i_batch, sample_batch in enumerate(dataloader):\n print(sample_batch['image'].size())\n print(sample_batch['label'].size())\n", "repo_name": "ekrim/deep-homography", "sub_path": "pipeline.py", "file_name": "pipeline.py", "file_ext": "py", "file_size_in_byte": 2239, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 22, "dataset": "github-code", "pt": "37", "api": [{"api_name": "PIL.Image.open", "line_number": 16, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 16, "usage_type": "attribute"}, {"api_name": "numpy.asarray", "line_number": 17, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 27, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 28, "usage_type": "call"}, {"api_name": "torch.utils.data.Dataset", "line_number": 31, "usage_type": "name"}, {"api_name": "os.path.isdir", "line_number": 33, "usage_type": "call"}, {"api_name": "os.path", "line_number": 33, "usage_type": "attribute"}, {"api_name": "torchvision.transforms.Compose", "line_number": 34, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 34, "usage_type": "name"}, {"api_name": "numpy.concatenate", "line_number": 59, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 59, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 62, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 62, "usage_type": "attribute"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 72, "usage_type": "call"}]} +{"seq_id": "8395994011", "text": "\"\"\"keras init.\"\"\"\n\nimport logging\nimport operator\nimport os\nimport shutil\nimport sys\nfrom itertools import chain\n\nimport numpy as np\nimport tensorflow as tf\nimport tensorflow.keras.backend as K # noqa: N812\n\nimport wandb\nfrom wandb.sdk.integration_utils.data_logging import ValidationDataLogger\nfrom wandb.sdk.lib.deprecate import Deprecated, deprecate\nfrom wandb.util import add_import_hook\n\n\ndef _check_keras_version():\n from keras import __version__ as keras_version\n from pkg_resources import parse_version\n\n if parse_version(keras_version) < parse_version(\"2.4.0\"):\n wandb.termwarn(\n f\"Keras version {keras_version} is not fully supported. Required keras >= 2.4.0\"\n )\n\n\ndef _can_compute_flops() -> bool:\n \"\"\"FLOPS computation is restricted to TF 2.x as it requires tf.compat.v1.\"\"\"\n from pkg_resources import parse_version\n\n if parse_version(tf.__version__) >= parse_version(\"2.0.0\"):\n return True\n\n return False\n\n\nif \"keras\" in sys.modules:\n _check_keras_version()\nelse:\n add_import_hook(\"keras\", _check_keras_version)\n\n\nlogger = logging.getLogger(__name__)\n\n\ndef is_dataset(data):\n dataset_ops = wandb.util.get_module(\"tensorflow.python.data.ops.dataset_ops\")\n if dataset_ops and hasattr(dataset_ops, \"DatasetV2\"):\n dataset_types = (dataset_ops.DatasetV2,)\n if hasattr(dataset_ops, \"DatasetV1\"):\n dataset_types = dataset_types + (dataset_ops.DatasetV1,)\n return isinstance(data, dataset_types)\n else:\n return False\n\n\ndef is_generator_like(data):\n # Checks if data is a generator, Sequence, or Iterator.\n\n types = (tf.keras.utils.Sequence,)\n iterator_ops = wandb.util.get_module(\"tensorflow.python.data.ops.iterator_ops\")\n if iterator_ops:\n types = types + (iterator_ops.Iterator,)\n # EagerIterator was in tensorflow < 2\n if hasattr(iterator_ops, \"EagerIterator\"):\n types = types + (iterator_ops.EagerIterator,)\n elif hasattr(iterator_ops, \"IteratorV2\"):\n types = types + (iterator_ops.IteratorV2,)\n return hasattr(data, \"next\") or hasattr(data, \"__next__\") or isinstance(data, types)\n\n\ndef patch_tf_keras(): # noqa: C901\n from pkg_resources import parse_version\n from tensorflow.python.eager import context\n\n if (\n parse_version(\"2.6.0\")\n <= parse_version(tf.__version__)\n < parse_version(\"2.13.0\")\n ):\n keras_engine = \"keras.engine\"\n try:\n from keras.engine import training\n from keras.engine import training_arrays_v1 as training_arrays\n from keras.engine import training_generator_v1 as training_generator\n except (ImportError, AttributeError):\n wandb.termerror(\"Unable to patch Tensorflow/Keras\")\n logger.exception(\"exception while trying to patch_tf_keras\")\n return\n else:\n keras_engine = \"tensorflow.python.keras.engine\"\n\n from tensorflow.python.keras.engine import training\n\n try:\n from tensorflow.python.keras.engine import (\n training_arrays_v1 as training_arrays,\n )\n from tensorflow.python.keras.engine import (\n training_generator_v1 as training_generator,\n )\n except (ImportError, AttributeError):\n try:\n from tensorflow.python.keras.engine import (\n training_arrays,\n training_generator,\n )\n except (ImportError, AttributeError):\n wandb.termerror(\"Unable to patch Tensorflow/Keras\")\n logger.exception(\"exception while trying to patch_tf_keras\")\n return\n\n # Tensorflow 2.1\n training_v2_1 = wandb.util.get_module(\"tensorflow.python.keras.engine.training_v2\")\n # Tensorflow 2.2\n training_v2_2 = wandb.util.get_module(f\"{keras_engine}.training_v1\")\n\n if training_v2_1:\n old_v2 = training_v2_1.Loop.fit\n elif training_v2_2:\n old_v2 = training.Model.fit\n\n old_arrays = training_arrays.fit_loop\n old_generator = training_generator.fit_generator\n\n def set_wandb_attrs(cbk, val_data):\n if isinstance(cbk, WandbCallback):\n if is_generator_like(val_data):\n cbk.generator = val_data\n elif is_dataset(val_data):\n if context.executing_eagerly():\n cbk.generator = iter(val_data)\n else:\n wandb.termwarn(\n \"Found a validation dataset in graph mode, can't patch Keras.\"\n )\n elif isinstance(val_data, tuple) and isinstance(val_data[0], tf.Tensor):\n # Graph mode dataset generator\n def gen():\n while True:\n yield K.get_session().run(val_data)\n\n cbk.generator = gen()\n else:\n cbk.validation_data = val_data\n\n def new_arrays(*args, **kwargs):\n cbks = kwargs.get(\"callbacks\", [])\n val_inputs = kwargs.get(\"val_inputs\")\n val_targets = kwargs.get(\"val_targets\")\n # TODO: these could be generators, why index 0?\n if val_inputs and val_targets:\n for cbk in cbks:\n set_wandb_attrs(cbk, (val_inputs[0], val_targets[0]))\n return old_arrays(*args, **kwargs)\n\n def new_generator(*args, **kwargs):\n cbks = kwargs.get(\"callbacks\", [])\n val_data = kwargs.get(\"validation_data\")\n if val_data:\n for cbk in cbks:\n set_wandb_attrs(cbk, val_data)\n return old_generator(*args, **kwargs)\n\n def new_v2(*args, **kwargs):\n cbks = kwargs.get(\"callbacks\", [])\n val_data = kwargs.get(\"validation_data\")\n if val_data:\n for cbk in cbks:\n set_wandb_attrs(cbk, val_data)\n return old_v2(*args, **kwargs)\n\n training_arrays.orig_fit_loop = old_arrays\n training_arrays.fit_loop = new_arrays\n training_generator.orig_fit_generator = old_generator\n training_generator.fit_generator = new_generator\n wandb.patched[\"keras\"].append([f\"{keras_engine}.training_arrays\", \"fit_loop\"])\n wandb.patched[\"keras\"].append(\n [f\"{keras_engine}.training_generator\", \"fit_generator\"]\n )\n\n if training_v2_1:\n training_v2_1.Loop.fit = new_v2\n wandb.patched[\"keras\"].append(\n [\"tensorflow.python.keras.engine.training_v2.Loop\", \"fit\"]\n )\n elif training_v2_2:\n training.Model.fit = new_v2\n wandb.patched[\"keras\"].append([f\"{keras_engine}.training.Model\", \"fit\"])\n\n\ndef _array_has_dtype(array):\n return hasattr(array, \"dtype\")\n\n\ndef _update_if_numeric(metrics, key, values):\n if not _array_has_dtype(values):\n _warn_not_logging(key)\n return\n\n if not is_numeric_array(values):\n _warn_not_logging_non_numeric(key)\n return\n\n metrics[key] = wandb.Histogram(values)\n\n\ndef is_numeric_array(array):\n return np.issubdtype(array.dtype, np.number)\n\n\ndef _warn_not_logging_non_numeric(name):\n wandb.termwarn(\n f\"Non-numeric values found in layer: {name}, not logging this layer\",\n repeat=False,\n )\n\n\ndef _warn_not_logging(name):\n wandb.termwarn(\n f\"Layer {name} has undetermined datatype not logging this layer\",\n repeat=False,\n )\n\n\ntf_logger = tf.get_logger()\n\npatch_tf_keras()\n\n\n### For gradient logging ###\n\n\ndef _get_custom_optimizer_parent_class():\n from pkg_resources import parse_version\n\n if parse_version(tf.__version__) >= parse_version(\"2.9.0\"):\n custom_optimizer_parent_class = tf.keras.optimizers.legacy.Optimizer\n else:\n custom_optimizer_parent_class = tf.keras.optimizers.Optimizer\n\n return custom_optimizer_parent_class\n\n\n_custom_optimizer_parent_class = _get_custom_optimizer_parent_class()\n\n\nclass _CustomOptimizer(_custom_optimizer_parent_class):\n def __init__(self):\n super().__init__(name=\"CustomOptimizer\")\n self._resource_apply_dense = tf.function(self._resource_apply_dense)\n self._resource_apply_sparse = tf.function(self._resource_apply_sparse)\n\n def _resource_apply_dense(self, grad, var):\n var.assign(grad)\n\n # this needs to be implemented to prevent a NotImplementedError when\n # using Lookup layers.\n def _resource_apply_sparse(self, grad, var, indices):\n pass\n\n def get_config(self):\n return super().get_config()\n\n\nclass _GradAccumulatorCallback(tf.keras.callbacks.Callback):\n \"\"\"Accumulates gradients during a fit() call when used in conjunction with the CustomOptimizer above.\"\"\"\n\n def set_model(self, model):\n super().set_model(model)\n self.og_weights = model.get_weights()\n self.grads = [np.zeros(tuple(w.shape)) for w in model.trainable_weights]\n\n def on_batch_end(self, batch, logs=None):\n for g, w in zip(self.grads, self.model.trainable_weights):\n g += w.numpy()\n self.model.set_weights(self.og_weights)\n\n def get_grads(self):\n return [g.copy() for g in self.grads]\n\n\n###\n\n\nclass WandbCallback(tf.keras.callbacks.Callback):\n \"\"\"`WandbCallback` automatically integrates keras with wandb.\n\n Example:\n ```python\n model.fit(\n X_train,\n y_train,\n validation_data=(X_test, y_test),\n callbacks=[WandbCallback()],\n )\n ```\n\n `WandbCallback` will automatically log history data from any\n metrics collected by keras: loss and anything passed into `keras_model.compile()`.\n\n `WandbCallback` will set summary metrics for the run associated with the \"best\" training\n step, where \"best\" is defined by the `monitor` and `mode` attributes. This defaults\n to the epoch with the minimum `val_loss`. `WandbCallback` will by default save the model\n associated with the best `epoch`.\n\n `WandbCallback` can optionally log gradient and parameter histograms.\n\n `WandbCallback` can optionally save training and validation data for wandb to visualize.\n\n Arguments:\n monitor: (str) name of metric to monitor. Defaults to `val_loss`.\n mode: (str) one of {`auto`, `min`, `max`}.\n `min` - save model when monitor is minimized\n `max` - save model when monitor is maximized\n `auto` - try to guess when to save the model (default).\n save_model:\n True - save a model when monitor beats all previous epochs\n False - don't save models\n save_graph: (boolean) if True save model graph to wandb (default to True).\n save_weights_only: (boolean) if True, then only the model's weights will be\n saved (`model.save_weights(filepath)`), else the full model\n is saved (`model.save(filepath)`).\n log_weights: (boolean) if True save histograms of the model's layer's weights.\n log_gradients: (boolean) if True log histograms of the training gradients\n training_data: (tuple) Same format `(X,y)` as passed to `model.fit`. This is needed\n for calculating gradients - this is mandatory if `log_gradients` is `True`.\n validation_data: (tuple) Same format `(X,y)` as passed to `model.fit`. A set of data\n for wandb to visualize. If this is set, every epoch, wandb will\n make a small number of predictions and save the results for later visualization. In case\n you are working with image data, please also set `input_type` and `output_type` in order\n to log correctly.\n generator: (generator) a generator that returns validation data for wandb to visualize. This\n generator should return tuples `(X,y)`. Either `validate_data` or generator should\n be set for wandb to visualize specific data examples. In case you are working with image data,\n please also set `input_type` and `output_type` in order to log correctly.\n validation_steps: (int) if `validation_data` is a generator, how many\n steps to run the generator for the full validation set.\n labels: (list) If you are visualizing your data with wandb this list of labels\n will convert numeric output to understandable string if you are building a\n multiclass classifier. If you are making a binary classifier you can pass in\n a list of two labels [\"label for false\", \"label for true\"]. If `validate_data`\n and generator are both false, this won't do anything.\n predictions: (int) the number of predictions to make for visualization each epoch, max\n is 100.\n input_type: (string) type of the model input to help visualization. can be one of:\n (`image`, `images`, `segmentation_mask`, `auto`).\n output_type: (string) type of the model output to help visualization. can be one of:\n (`image`, `images`, `segmentation_mask`, `label`).\n log_evaluation: (boolean) if True, save a Table containing validation data and the\n model's predictions at each epoch. See `validation_indexes`,\n `validation_row_processor`, and `output_row_processor` for additional details.\n class_colors: ([float, float, float]) if the input or output is a segmentation mask,\n an array containing an rgb tuple (range 0-1) for each class.\n log_batch_frequency: (integer) if None, callback will log every epoch.\n If set to integer, callback will log training metrics every `log_batch_frequency`\n batches.\n log_best_prefix: (string) if None, no extra summary metrics will be saved.\n If set to a string, the monitored metric and epoch will be prepended with this value\n and stored as summary metrics.\n validation_indexes: ([wandb.data_types._TableLinkMixin]) an ordered list of index keys to associate\n with each validation example. If log_evaluation is True and `validation_indexes` is provided,\n then a Table of validation data will not be created and instead each prediction will\n be associated with the row represented by the `TableLinkMixin`. The most common way to obtain\n such keys are is use `Table.get_index()` which will return a list of row keys.\n validation_row_processor: (Callable) a function to apply to the validation data, commonly used to visualize the data.\n The function will receive an `ndx` (int) and a `row` (dict). If your model has a single input,\n then `row[\"input\"]` will be the input data for the row. Else, it will be keyed based on the name of the\n input slot. If your fit function takes a single target, then `row[\"target\"]` will be the target data for the row. Else,\n it will be keyed based on the name of the output slots. For example, if your input data is a single ndarray,\n but you wish to visualize the data as an Image, then you can provide `lambda ndx, row: {\"img\": wandb.Image(row[\"input\"])}`\n as the processor. Ignored if log_evaluation is False or `validation_indexes` are present.\n output_row_processor: (Callable) same as `validation_row_processor`, but applied to the model's output. `row[\"output\"]` will contain\n the results of the model output.\n infer_missing_processors: (bool) Determines if `validation_row_processor` and `output_row_processor`\n should be inferred if missing. Defaults to True. If `labels` are provided, we will attempt to infer classification-type\n processors where appropriate.\n log_evaluation_frequency: (int) Determines the frequency which evaluation results will be logged. Default 0 (only at the end of training).\n Set to 1 to log every epoch, 2 to log every other epoch, and so on. Has no effect when log_evaluation is False.\n compute_flops: (bool) Compute the FLOPs of your Keras Sequential or Functional model in GigaFLOPs unit.\n \"\"\"\n\n def __init__(\n self,\n monitor=\"val_loss\",\n verbose=0,\n mode=\"auto\",\n save_weights_only=False,\n log_weights=False,\n log_gradients=False,\n save_model=True,\n training_data=None,\n validation_data=None,\n labels=None,\n predictions=36,\n generator=None,\n input_type=None,\n output_type=None,\n log_evaluation=False,\n validation_steps=None,\n class_colors=None,\n log_batch_frequency=None,\n log_best_prefix=\"best_\",\n save_graph=True,\n validation_indexes=None,\n validation_row_processor=None,\n prediction_row_processor=None,\n infer_missing_processors=True,\n log_evaluation_frequency=0,\n compute_flops=False,\n **kwargs,\n ):\n if wandb.run is None:\n raise wandb.Error(\"You must call wandb.init() before WandbCallback()\")\n with wandb.wandb_lib.telemetry.context(run=wandb.run) as tel:\n tel.feature.keras = True\n self.validation_data = None\n # This is kept around for legacy reasons\n if validation_data is not None:\n if is_generator_like(validation_data):\n generator = validation_data\n else:\n self.validation_data = validation_data\n if labels is None:\n labels = []\n self.labels = labels\n self.predictions = min(predictions, 100)\n\n self.monitor = monitor\n self.verbose = verbose\n self.save_weights_only = save_weights_only\n self.save_graph = save_graph\n\n wandb.save(\"model-best.h5\")\n self.filepath = os.path.join(wandb.run.dir, \"model-best.h5\")\n self.save_model = save_model\n if save_model:\n deprecate(\n field_name=Deprecated.keras_callback__save_model,\n warning_message=(\n \"The save_model argument by default saves the model in the HDF5 format that cannot save \"\n \"custom objects like subclassed models and custom layers. This behavior will be deprecated \"\n \"in a future release in favor of the SavedModel format. Meanwhile, the HDF5 model is saved \"\n \"as W&B files and the SavedModel as W&B Artifacts.\"\n ),\n )\n\n self.save_model_as_artifact = True\n self.log_weights = log_weights\n self.log_gradients = log_gradients\n self.training_data = training_data\n self.generator = generator\n self._graph_rendered = False\n\n data_type = kwargs.get(\"data_type\", None)\n if data_type is not None:\n deprecate(\n field_name=Deprecated.keras_callback__data_type,\n warning_message=(\n \"The data_type argument of wandb.keras.WandbCallback is deprecated \"\n \"and will be removed in a future release. Please use input_type instead.\\n\"\n \"Setting input_type = data_type.\"\n ),\n )\n input_type = data_type\n self.input_type = input_type\n self.output_type = output_type\n self.log_evaluation = log_evaluation\n self.validation_steps = validation_steps\n self.class_colors = np.array(class_colors) if class_colors is not None else None\n self.log_batch_frequency = log_batch_frequency\n self.log_best_prefix = log_best_prefix\n self.compute_flops = compute_flops\n\n self._prediction_batch_size = None\n\n if self.log_gradients:\n if int(tf.__version__.split(\".\")[0]) < 2:\n raise Exception(\"Gradient logging requires tensorflow 2.0 or higher.\")\n if self.training_data is None:\n raise ValueError(\n \"training_data argument is required for gradient logging.\"\n )\n if isinstance(self.training_data, (list, tuple)):\n if len(self.training_data) != 2:\n raise ValueError(\"training data must be a tuple of length two\")\n self._training_data_x, self._training_data_y = self.training_data\n else:\n self._training_data_x = (\n self.training_data\n ) # generator, tf.data.Dataset etc\n self._training_data_y = None\n\n # From Keras\n if mode not in [\"auto\", \"min\", \"max\"]:\n print(f\"WandbCallback mode {mode} is unknown, fallback to auto mode.\")\n mode = \"auto\"\n\n if mode == \"min\":\n self.monitor_op = operator.lt\n self.best = float(\"inf\")\n elif mode == \"max\":\n self.monitor_op = operator.gt\n self.best = float(\"-inf\")\n else:\n if \"acc\" in self.monitor or self.monitor.startswith(\"fmeasure\"):\n self.monitor_op = operator.gt\n self.best = float(\"-inf\")\n else:\n self.monitor_op = operator.lt\n self.best = float(\"inf\")\n # Get the previous best metric for resumed runs\n previous_best = wandb.run.summary.get(f\"{self.log_best_prefix}{self.monitor}\")\n if previous_best is not None:\n self.best = previous_best\n\n self._validation_data_logger = None\n self._validation_indexes = validation_indexes\n self._validation_row_processor = validation_row_processor\n self._prediction_row_processor = prediction_row_processor\n self._infer_missing_processors = infer_missing_processors\n self._log_evaluation_frequency = log_evaluation_frequency\n self._model_trained_since_last_eval = False\n\n def _build_grad_accumulator_model(self):\n inputs = self.model.inputs\n outputs = self.model(inputs)\n grad_acc_model = tf.keras.models.Model(inputs, outputs)\n grad_acc_model.compile(loss=self.model.loss, optimizer=_CustomOptimizer())\n\n # make sure magic doesn't think this is a user model\n grad_acc_model._wandb_internal_model = True\n\n self._grad_accumulator_model = grad_acc_model\n self._grad_accumulator_callback = _GradAccumulatorCallback()\n\n def _implements_train_batch_hooks(self):\n return self.log_batch_frequency is not None\n\n def _implements_test_batch_hooks(self):\n return self.log_batch_frequency is not None\n\n def _implements_predict_batch_hooks(self):\n return self.log_batch_frequency is not None\n\n def set_params(self, params):\n self.params = params\n\n def set_model(self, model):\n self.model = model\n if self.input_type == \"auto\" and len(model.inputs) == 1:\n self.input_type = wandb.util.guess_data_type(\n model.inputs[0].shape, risky=True\n )\n if self.input_type and self.output_type is None and len(model.outputs) == 1:\n self.output_type = wandb.util.guess_data_type(model.outputs[0].shape)\n if self.log_gradients:\n self._build_grad_accumulator_model()\n\n def _attempt_evaluation_log(self, commit=True):\n if self.log_evaluation and self._validation_data_logger:\n try:\n if not self.model:\n wandb.termwarn(\"WandbCallback unable to read model from trainer\")\n else:\n self._validation_data_logger.log_predictions(\n predictions=self._validation_data_logger.make_predictions(\n self.model.predict\n ),\n commit=commit,\n )\n self._model_trained_since_last_eval = False\n except Exception as e:\n wandb.termwarn(\"Error durring prediction logging for epoch: \" + str(e))\n\n def on_epoch_end(self, epoch, logs=None):\n if logs is None:\n logs = {}\n if self.log_weights:\n wandb.log(self._log_weights(), commit=False)\n\n if self.log_gradients:\n wandb.log(self._log_gradients(), commit=False)\n\n if self.input_type in (\n \"image\",\n \"images\",\n \"segmentation_mask\",\n ) or self.output_type in (\"image\", \"images\", \"segmentation_mask\"):\n if self.generator:\n self.validation_data = next(self.generator)\n if self.validation_data is None:\n wandb.termwarn(\n \"No validation_data set, pass a generator to the callback.\"\n )\n elif self.validation_data and len(self.validation_data) > 0:\n wandb.log(\n {\"examples\": self._log_images(num_images=self.predictions)},\n commit=False,\n )\n\n if (\n self._log_evaluation_frequency > 0\n and epoch % self._log_evaluation_frequency == 0\n ):\n self._attempt_evaluation_log(commit=False)\n\n wandb.log({\"epoch\": epoch}, commit=False)\n wandb.log(logs, commit=True)\n\n self.current = logs.get(self.monitor)\n if self.current and self.monitor_op(self.current, self.best):\n if self.log_best_prefix:\n wandb.run.summary[\n f\"{self.log_best_prefix}{self.monitor}\"\n ] = self.current\n wandb.run.summary[\"{}{}\".format(self.log_best_prefix, \"epoch\")] = epoch\n if self.verbose and not self.save_model:\n print(\n \"Epoch %05d: %s improved from %0.5f to %0.5f\"\n % (epoch, self.monitor, self.best, self.current)\n )\n if self.save_model:\n self._save_model(epoch)\n\n if self.save_model and self.save_model_as_artifact:\n self._save_model_as_artifact(epoch)\n\n self.best = self.current\n\n # This is what keras used pre tensorflow.keras\n def on_batch_begin(self, batch, logs=None):\n pass\n\n # This is what keras used pre tensorflow.keras\n def on_batch_end(self, batch, logs=None):\n if self.save_graph and not self._graph_rendered:\n # Couldn't do this in train_begin because keras may still not be built\n wandb.run.summary[\"graph\"] = wandb.Graph.from_keras(self.model)\n self._graph_rendered = True\n\n if self.log_batch_frequency and batch % self.log_batch_frequency == 0:\n wandb.log(logs, commit=True)\n\n def on_train_batch_begin(self, batch, logs=None):\n self._model_trained_since_last_eval = True\n\n def on_train_batch_end(self, batch, logs=None):\n if self.save_graph and not self._graph_rendered:\n # Couldn't do this in train_begin because keras may still not be built\n wandb.run.summary[\"graph\"] = wandb.Graph.from_keras(self.model)\n self._graph_rendered = True\n\n if self.log_batch_frequency and batch % self.log_batch_frequency == 0:\n wandb.log(logs, commit=True)\n\n def on_test_begin(self, logs=None):\n pass\n\n def on_test_end(self, logs=None):\n pass\n\n def on_test_batch_begin(self, batch, logs=None):\n pass\n\n def on_test_batch_end(self, batch, logs=None):\n pass\n\n def on_train_begin(self, logs=None):\n if self.log_evaluation:\n try:\n validation_data = None\n if self.validation_data:\n validation_data = self.validation_data\n elif self.generator:\n if not self.validation_steps:\n wandb.termwarn(\n \"WandbCallback is unable to log validation data. \"\n \"When using a generator for validation_data, you must pass validation_steps\"\n )\n else:\n x = None\n y_true = None\n for _ in range(self.validation_steps):\n bx, by_true = next(self.generator)\n if x is None:\n x, y_true = bx, by_true\n else:\n x, y_true = (\n np.append(x, bx, axis=0),\n np.append(y_true, by_true, axis=0),\n )\n validation_data = (x, y_true)\n else:\n wandb.termwarn(\n \"WandbCallback is unable to read validation_data from trainer \"\n \"and therefore cannot log validation data. Ensure Keras is properly \"\n \"patched by calling `from wandb.keras import WandbCallback` at the top of your script.\"\n )\n if validation_data:\n self._validation_data_logger = ValidationDataLogger(\n inputs=validation_data[0],\n targets=validation_data[1],\n indexes=self._validation_indexes,\n validation_row_processor=self._validation_row_processor,\n prediction_row_processor=self._prediction_row_processor,\n class_labels=self.labels,\n infer_missing_processors=self._infer_missing_processors,\n )\n except Exception as e:\n wandb.termwarn(\n \"Error initializing ValidationDataLogger in WandbCallback. \"\n f\"Skipping logging validation data. Error: {str(e)}\"\n )\n\n if self.compute_flops and _can_compute_flops():\n try:\n wandb.summary[\"GFLOPs\"] = self.get_flops()\n except Exception as e:\n wandb.termwarn(\"Unable to compute FLOPs for this model.\")\n logger.exception(e)\n\n def on_train_end(self, logs=None):\n if self._model_trained_since_last_eval:\n self._attempt_evaluation_log()\n\n def on_predict_begin(self, logs=None):\n pass\n\n def on_predict_end(self, logs=None):\n pass\n\n def on_predict_batch_begin(self, batch, logs=None):\n pass\n\n def on_predict_batch_end(self, batch, logs=None):\n pass\n\n def _logits_to_captions(self, logits):\n if logits[0].shape[-1] == 1:\n # Scalar output from the model\n # TODO: handle validation_y\n if len(self.labels) == 2:\n # User has named true and false\n captions = [\n self.labels[1] if logits[0] > 0.5 else self.labels[0]\n for logit in logits\n ]\n else:\n if len(self.labels) != 0:\n wandb.termwarn(\n \"keras model is producing a single output, \"\n 'so labels should be a length two array: [\"False label\", \"True label\"].'\n )\n captions = [logit[0] for logit in logits]\n else:\n # Vector output from the model\n # TODO: handle validation_y\n labels = np.argmax(np.stack(logits), axis=1)\n\n if len(self.labels) > 0:\n # User has named the categories in self.labels\n captions = []\n for label in labels:\n try:\n captions.append(self.labels[label])\n except IndexError:\n captions.append(label)\n else:\n captions = labels\n return captions\n\n def _masks_to_pixels(self, masks):\n # if its a binary mask, just return it as grayscale instead of picking the argmax\n if len(masks[0].shape) == 2 or masks[0].shape[-1] == 1:\n return masks\n class_colors = (\n self.class_colors\n if self.class_colors is not None\n else np.array(wandb.util.class_colors(masks[0].shape[2]))\n )\n imgs = class_colors[np.argmax(masks, axis=-1)]\n return imgs\n\n def _log_images(self, num_images=36):\n validation_X = self.validation_data[0] # noqa: N806\n validation_y = self.validation_data[1]\n\n validation_length = len(validation_X)\n\n if validation_length > num_images:\n # pick some data at random\n indices = np.random.choice(validation_length, num_images, replace=False)\n else:\n indices = range(validation_length)\n\n test_data = []\n test_output = []\n for i in indices:\n test_example = validation_X[i]\n test_data.append(test_example)\n test_output.append(validation_y[i])\n\n if self.model.stateful:\n predictions = self.model.predict(np.stack(test_data), batch_size=1)\n self.model.reset_states()\n else:\n predictions = self.model.predict(\n np.stack(test_data), batch_size=self._prediction_batch_size\n )\n if len(predictions) != len(test_data):\n self._prediction_batch_size = 1\n predictions = self.model.predict(\n np.stack(test_data), batch_size=self._prediction_batch_size\n )\n\n if self.input_type == \"label\":\n if self.output_type in (\"image\", \"images\", \"segmentation_mask\"):\n captions = self._logits_to_captions(test_data)\n output_image_data = (\n self._masks_to_pixels(predictions)\n if self.output_type == \"segmentation_mask\"\n else predictions\n )\n reference_image_data = (\n self._masks_to_pixels(test_output)\n if self.output_type == \"segmentation_mask\"\n else test_output\n )\n output_images = [\n wandb.Image(data, caption=captions[i], grouping=2)\n for i, data in enumerate(output_image_data)\n ]\n reference_images = [\n wandb.Image(data, caption=captions[i])\n for i, data in enumerate(reference_image_data)\n ]\n return list(chain.from_iterable(zip(output_images, reference_images)))\n elif self.input_type in (\"image\", \"images\", \"segmentation_mask\"):\n input_image_data = (\n self._masks_to_pixels(test_data)\n if self.input_type == \"segmentation_mask\"\n else test_data\n )\n if self.output_type == \"label\":\n # we just use the predicted label as the caption for now\n captions = self._logits_to_captions(predictions)\n return [\n wandb.Image(data, caption=captions[i])\n for i, data in enumerate(test_data)\n ]\n elif self.output_type in (\"image\", \"images\", \"segmentation_mask\"):\n output_image_data = (\n self._masks_to_pixels(predictions)\n if self.output_type == \"segmentation_mask\"\n else predictions\n )\n reference_image_data = (\n self._masks_to_pixels(test_output)\n if self.output_type == \"segmentation_mask\"\n else test_output\n )\n input_images = [\n wandb.Image(data, grouping=3)\n for i, data in enumerate(input_image_data)\n ]\n output_images = [\n wandb.Image(data) for i, data in enumerate(output_image_data)\n ]\n reference_images = [\n wandb.Image(data) for i, data in enumerate(reference_image_data)\n ]\n return list(\n chain.from_iterable(\n zip(input_images, output_images, reference_images)\n )\n )\n else:\n # unknown output, just log the input images\n return [wandb.Image(img) for img in test_data]\n elif self.output_type in (\"image\", \"images\", \"segmentation_mask\"):\n # unknown input, just log the predicted and reference outputs without captions\n output_image_data = (\n self._masks_to_pixels(predictions)\n if self.output_type == \"segmentation_mask\"\n else predictions\n )\n reference_image_data = (\n self._masks_to_pixels(test_output)\n if self.output_type == \"segmentation_mask\"\n else test_output\n )\n output_images = [\n wandb.Image(data, grouping=2)\n for i, data in enumerate(output_image_data)\n ]\n reference_images = [\n wandb.Image(data) for i, data in enumerate(reference_image_data)\n ]\n return list(chain.from_iterable(zip(output_images, reference_images)))\n\n def _log_weights(self):\n metrics = {}\n for layer in self.model.layers:\n weights = layer.get_weights()\n if len(weights) == 1:\n _update_if_numeric(\n metrics, \"parameters/\" + layer.name + \".weights\", weights[0]\n )\n elif len(weights) == 2:\n _update_if_numeric(\n metrics, \"parameters/\" + layer.name + \".weights\", weights[0]\n )\n _update_if_numeric(\n metrics, \"parameters/\" + layer.name + \".bias\", weights[1]\n )\n return metrics\n\n def _log_gradients(self):\n # Suppress callback warnings grad accumulator\n og_level = tf_logger.level\n tf_logger.setLevel(\"ERROR\")\n\n self._grad_accumulator_model.fit(\n self._training_data_x,\n self._training_data_y,\n verbose=0,\n callbacks=[self._grad_accumulator_callback],\n )\n tf_logger.setLevel(og_level)\n weights = self.model.trainable_weights\n grads = self._grad_accumulator_callback.grads\n metrics = {}\n for weight, grad in zip(weights, grads):\n metrics[\n \"gradients/\" + weight.name.split(\":\")[0] + \".gradient\"\n ] = wandb.Histogram(grad)\n return metrics\n\n def _log_dataframe(self):\n x, y_true, y_pred = None, None, None\n\n if self.validation_data:\n x, y_true = self.validation_data[0], self.validation_data[1]\n y_pred = self.model.predict(x)\n elif self.generator:\n if not self.validation_steps:\n wandb.termwarn(\n \"when using a generator for validation data with dataframes, \"\n \"you must pass validation_steps. skipping\"\n )\n return None\n\n for _ in range(self.validation_steps):\n bx, by_true = next(self.generator)\n by_pred = self.model.predict(bx)\n if x is None:\n x, y_true, y_pred = bx, by_true, by_pred\n else:\n x, y_true, y_pred = (\n np.append(x, bx, axis=0),\n np.append(y_true, by_true, axis=0),\n np.append(y_pred, by_pred, axis=0),\n )\n\n if self.input_type in (\"image\", \"images\") and self.output_type == \"label\":\n return wandb.image_categorizer_dataframe(\n x=x, y_true=y_true, y_pred=y_pred, labels=self.labels\n )\n elif (\n self.input_type in (\"image\", \"images\")\n and self.output_type == \"segmentation_mask\"\n ):\n return wandb.image_segmentation_dataframe(\n x=x,\n y_true=y_true,\n y_pred=y_pred,\n labels=self.labels,\n class_colors=self.class_colors,\n )\n else:\n wandb.termwarn(\n f\"unknown dataframe type for input_type={self.input_type} and output_type={self.output_type}\"\n )\n return None\n\n def _save_model(self, epoch):\n if wandb.run.disabled:\n return\n if self.verbose > 0:\n print(\n \"Epoch %05d: %s improved from %0.5f to %0.5f,\"\n \" saving model to %s\"\n % (epoch, self.monitor, self.best, self.current, self.filepath)\n )\n\n try:\n if self.save_weights_only:\n self.model.save_weights(self.filepath, overwrite=True)\n else:\n self.model.save(self.filepath, overwrite=True)\n # Was getting `RuntimeError: Unable to create link` in TF 1.13.1\n # also saw `TypeError: can't pickle _thread.RLock objects`\n except (ImportError, RuntimeError, TypeError, AttributeError) as e:\n wandb.termerror(\n \"Can't save model in the h5py format. The model will be saved as \"\n \"as an W&B Artifact in the 'tf' format.\"\n )\n logger.exception(e)\n\n def _save_model_as_artifact(self, epoch):\n if wandb.run.disabled:\n return\n\n # Save the model in the SavedModel format.\n # TODO: Replace this manual artifact creation with the `log_model` method\n # after `log_model` is released from beta.\n self.model.save(self.filepath[:-3], overwrite=True, save_format=\"tf\")\n\n # Log the model as artifact.\n name = wandb.util.make_artifact_name_safe(f\"model-{wandb.run.name}\")\n model_artifact = wandb.Artifact(name, type=\"model\")\n model_artifact.add_dir(self.filepath[:-3])\n wandb.run.log_artifact(model_artifact, aliases=[\"latest\", f\"epoch_{epoch}\"])\n\n # Remove the SavedModel from wandb dir as we don't want to log it to save memory.\n shutil.rmtree(self.filepath[:-3])\n\n def get_flops(self) -> float:\n \"\"\"Calculate FLOPS [GFLOPs] for a tf.keras.Model or tf.keras.Sequential model in inference mode.\n\n It uses tf.compat.v1.profiler under the hood.\n \"\"\"\n if not hasattr(self, \"model\"):\n raise wandb.Error(\"self.model must be set before using this method.\")\n\n if not isinstance(\n self.model, (tf.keras.models.Sequential, tf.keras.models.Model)\n ):\n raise ValueError(\n \"Calculating FLOPS is only supported for \"\n \"`tf.keras.Model` and `tf.keras.Sequential` instances.\"\n )\n\n from tensorflow.python.framework.convert_to_constants import (\n convert_variables_to_constants_v2_as_graph,\n )\n\n # Compute FLOPs for one sample\n batch_size = 1\n inputs = [\n tf.TensorSpec([batch_size] + inp.shape[1:], inp.dtype)\n for inp in self.model.inputs\n ]\n\n # convert tf.keras model into frozen graph to count FLOPs about operations used at inference\n real_model = tf.function(self.model).get_concrete_function(inputs)\n frozen_func, _ = convert_variables_to_constants_v2_as_graph(real_model)\n\n # Calculate FLOPs with tf.profiler\n run_meta = tf.compat.v1.RunMetadata()\n opts = (\n tf.compat.v1.profiler.ProfileOptionBuilder(\n tf.compat.v1.profiler.ProfileOptionBuilder().float_operation()\n )\n .with_empty_output()\n .build()\n )\n\n flops = tf.compat.v1.profiler.profile(\n graph=frozen_func.graph, run_meta=run_meta, cmd=\"scope\", options=opts\n )\n\n # convert to GFLOPs\n return (flops.total_float_ops / 1e9) / 2\n", "repo_name": "wandb/wandb", "sub_path": "wandb/integration/keras/keras.py", "file_name": "keras.py", "file_ext": "py", "file_size_in_byte": 43714, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 7479, "dataset": "github-code", "pt": "37", "api": [{"api_name": "pkg_resources.parse_version", "line_number": 24, "usage_type": "call"}, {"api_name": "keras.__version__", "line_number": 24, "usage_type": "argument"}, {"api_name": "wandb.termwarn", "line_number": 25, "usage_type": "call"}, {"api_name": "keras.__version__", "line_number": 26, "usage_type": "name"}, {"api_name": "pkg_resources.parse_version", "line_number": 34, "usage_type": "call"}, {"api_name": "tensorflow.__version__", "line_number": 34, "usage_type": "attribute"}, {"api_name": "sys.modules", "line_number": 40, "usage_type": "attribute"}, {"api_name": "wandb.util.add_import_hook", "line_number": 43, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 46, "usage_type": "call"}, {"api_name": "wandb.util.get_module", "line_number": 50, "usage_type": "call"}, {"api_name": "wandb.util", "line_number": 50, "usage_type": "attribute"}, {"api_name": "tensorflow.keras", "line_number": 63, "usage_type": "attribute"}, {"api_name": "wandb.util.get_module", "line_number": 64, "usage_type": "call"}, {"api_name": "wandb.util", "line_number": 64, "usage_type": "attribute"}, {"api_name": "pkg_resources.parse_version", "line_number": 80, "usage_type": "call"}, {"api_name": "pkg_resources.parse_version", "line_number": 81, "usage_type": "call"}, {"api_name": "tensorflow.__version__", "line_number": 81, "usage_type": "attribute"}, {"api_name": "pkg_resources.parse_version", "line_number": 82, "usage_type": "call"}, {"api_name": "wandb.termerror", "line_number": 90, "usage_type": "call"}, {"api_name": "wandb.termerror", "line_number": 112, "usage_type": "call"}, {"api_name": "wandb.util.get_module", "line_number": 117, "usage_type": "call"}, {"api_name": "wandb.util", "line_number": 117, "usage_type": "attribute"}, {"api_name": "wandb.util.get_module", "line_number": 119, "usage_type": "call"}, {"api_name": "wandb.util", "line_number": 119, "usage_type": "attribute"}, {"api_name": "tensorflow.python.keras.engine.training.Model", "line_number": 124, "usage_type": "attribute"}, {"api_name": "tensorflow.python.keras.engine.training", "line_number": 124, "usage_type": "name"}, {"api_name": "tensorflow.python.keras.engine.training_arrays.fit_loop", "line_number": 126, "usage_type": "attribute"}, {"api_name": "tensorflow.python.keras.engine.training_arrays", "line_number": 126, "usage_type": "name"}, {"api_name": "tensorflow.python.keras.engine.training_generator.fit_generator", "line_number": 127, "usage_type": "attribute"}, {"api_name": "tensorflow.python.keras.engine.training_generator", "line_number": 127, "usage_type": "name"}, {"api_name": "tensorflow.python.eager.context.executing_eagerly", "line_number": 134, "usage_type": "call"}, {"api_name": "tensorflow.python.eager.context", "line_number": 134, "usage_type": "name"}, {"api_name": "wandb.termwarn", "line_number": 137, "usage_type": "call"}, {"api_name": "tensorflow.Tensor", "line_number": 140, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.backend.get_session", "line_number": 144, "usage_type": "call"}, {"api_name": "tensorflow.keras.backend", "line_number": 144, "usage_type": "name"}, {"api_name": "tensorflow.python.keras.engine.training_arrays.orig_fit_loop", "line_number": 176, "usage_type": "attribute"}, {"api_name": "tensorflow.python.keras.engine.training_arrays", "line_number": 176, "usage_type": "name"}, {"api_name": "tensorflow.python.keras.engine.training_arrays.fit_loop", "line_number": 177, "usage_type": "attribute"}, {"api_name": "tensorflow.python.keras.engine.training_arrays", "line_number": 177, "usage_type": "name"}, {"api_name": "tensorflow.python.keras.engine.training_generator.orig_fit_generator", "line_number": 178, "usage_type": "attribute"}, {"api_name": "tensorflow.python.keras.engine.training_generator", "line_number": 178, "usage_type": "name"}, {"api_name": "tensorflow.python.keras.engine.training_generator.fit_generator", "line_number": 179, "usage_type": "attribute"}, {"api_name": "tensorflow.python.keras.engine.training_generator", "line_number": 179, "usage_type": "name"}, {"api_name": "wandb.patched", "line_number": 180, "usage_type": "attribute"}, {"api_name": "wandb.patched", "line_number": 181, "usage_type": "attribute"}, {"api_name": "wandb.patched", "line_number": 187, "usage_type": "attribute"}, {"api_name": "tensorflow.python.keras.engine.training.Model", "line_number": 191, "usage_type": "attribute"}, {"api_name": "tensorflow.python.keras.engine.training", "line_number": 191, "usage_type": "name"}, {"api_name": "wandb.patched", "line_number": 192, "usage_type": "attribute"}, {"api_name": "wandb.Histogram", "line_number": 208, "usage_type": "call"}, {"api_name": "numpy.issubdtype", "line_number": 212, "usage_type": "call"}, {"api_name": "numpy.number", "line_number": 212, "usage_type": "attribute"}, {"api_name": "wandb.termwarn", "line_number": 216, "usage_type": "call"}, {"api_name": "wandb.termwarn", "line_number": 223, "usage_type": "call"}, {"api_name": "tensorflow.get_logger", "line_number": 229, "usage_type": "call"}, {"api_name": "pkg_resources.parse_version", "line_number": 240, "usage_type": "call"}, {"api_name": "tensorflow.__version__", "line_number": 240, "usage_type": "attribute"}, {"api_name": "tensorflow.keras", "line_number": 241, "usage_type": "attribute"}, {"api_name": "tensorflow.keras", "line_number": 243, "usage_type": "attribute"}, {"api_name": "tensorflow.function", "line_number": 254, "usage_type": "call"}, {"api_name": "tensorflow.function", "line_number": 255, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 269, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 275, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 289, "usage_type": "attribute"}, {"api_name": "wandb.run", "line_number": 416, "usage_type": "attribute"}, {"api_name": "wandb.Error", "line_number": 417, "usage_type": "call"}, {"api_name": "wandb.wandb_lib.telemetry.context", "line_number": 418, "usage_type": "call"}, {"api_name": "wandb.wandb_lib", "line_number": 418, "usage_type": "attribute"}, {"api_name": "wandb.run", "line_number": 418, "usage_type": "attribute"}, {"api_name": "wandb.save", "line_number": 437, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 438, "usage_type": "call"}, {"api_name": "os.path", "line_number": 438, "usage_type": "attribute"}, {"api_name": "wandb.run", "line_number": 438, "usage_type": "attribute"}, {"api_name": "wandb.sdk.lib.deprecate.deprecate", "line_number": 441, "usage_type": "call"}, {"api_name": "wandb.sdk.lib.deprecate.Deprecated.keras_callback__save_model", "line_number": 442, "usage_type": "attribute"}, {"api_name": "wandb.sdk.lib.deprecate.Deprecated", "line_number": 442, "usage_type": "name"}, {"api_name": "wandb.sdk.lib.deprecate.deprecate", "line_number": 460, "usage_type": "call"}, {"api_name": "wandb.sdk.lib.deprecate.Deprecated.keras_callback__data_type", "line_number": 461, "usage_type": "attribute"}, {"api_name": "wandb.sdk.lib.deprecate.Deprecated", "line_number": 461, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 473, "usage_type": "call"}, {"api_name": "tensorflow.__version__.split", "line_number": 481, "usage_type": "call"}, {"api_name": "tensorflow.__version__", "line_number": 481, "usage_type": "attribute"}, {"api_name": "operator.lt", "line_number": 503, "usage_type": "attribute"}, {"api_name": "operator.gt", "line_number": 506, "usage_type": "attribute"}, {"api_name": "operator.gt", "line_number": 510, "usage_type": "attribute"}, {"api_name": "operator.lt", "line_number": 513, "usage_type": "attribute"}, {"api_name": "wandb.run.summary.get", "line_number": 516, "usage_type": "call"}, {"api_name": "wandb.run", "line_number": 516, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.models.Model", "line_number": 531, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 531, "usage_type": "attribute"}, {"api_name": "wandb.util.guess_data_type", "line_number": 555, "usage_type": "call"}, {"api_name": "wandb.util", "line_number": 555, "usage_type": "attribute"}, {"api_name": "wandb.util.guess_data_type", "line_number": 559, "usage_type": "call"}, {"api_name": "wandb.util", "line_number": 559, "usage_type": "attribute"}, {"api_name": "wandb.termwarn", "line_number": 567, "usage_type": "call"}, {"api_name": "wandb.termwarn", "line_number": 577, "usage_type": "call"}, {"api_name": "wandb.log", "line_number": 583, "usage_type": "call"}, {"api_name": "wandb.log", "line_number": 586, "usage_type": "call"}, {"api_name": "wandb.termwarn", "line_number": 596, "usage_type": "call"}, {"api_name": "wandb.log", "line_number": 600, "usage_type": "call"}, {"api_name": "wandb.log", "line_number": 611, "usage_type": "call"}, {"api_name": "wandb.log", "line_number": 612, "usage_type": "call"}, {"api_name": "wandb.run", "line_number": 617, "usage_type": "attribute"}, {"api_name": "wandb.run", "line_number": 620, "usage_type": "attribute"}, {"api_name": "wandb.run", "line_number": 642, "usage_type": "attribute"}, {"api_name": "wandb.Graph.from_keras", "line_number": 642, "usage_type": "call"}, {"api_name": "wandb.Graph", "line_number": 642, "usage_type": "attribute"}, {"api_name": "wandb.log", "line_number": 646, "usage_type": "call"}, {"api_name": "wandb.run", "line_number": 654, "usage_type": "attribute"}, {"api_name": "wandb.Graph.from_keras", "line_number": 654, "usage_type": "call"}, {"api_name": "wandb.Graph", "line_number": 654, "usage_type": "attribute"}, {"api_name": "wandb.log", "line_number": 658, "usage_type": "call"}, {"api_name": "wandb.termwarn", "line_number": 680, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 693, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 694, "usage_type": "call"}, {"api_name": "wandb.termwarn", "line_number": 698, "usage_type": "call"}, {"api_name": "wandb.sdk.integration_utils.data_logging.ValidationDataLogger", "line_number": 704, "usage_type": "call"}, {"api_name": "wandb.termwarn", "line_number": 714, "usage_type": "call"}, {"api_name": "wandb.summary", "line_number": 721, "usage_type": "attribute"}, {"api_name": "wandb.termwarn", "line_number": 723, "usage_type": "call"}, {"api_name": "wandb.termwarn", "line_number": 754, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 762, "usage_type": "call"}, {"api_name": "numpy.stack", "line_number": 762, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 783, "usage_type": "call"}, {"api_name": "wandb.util.class_colors", "line_number": 783, "usage_type": "call"}, {"api_name": "wandb.util", "line_number": 783, "usage_type": "attribute"}, {"api_name": "numpy.argmax", "line_number": 785, "usage_type": "call"}, {"api_name": "numpy.random.choice", "line_number": 796, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 796, "usage_type": "attribute"}, {"api_name": "numpy.stack", "line_number": 808, "usage_type": "call"}, {"api_name": "numpy.stack", "line_number": 812, "usage_type": "call"}, {"api_name": "numpy.stack", "line_number": 817, "usage_type": "call"}, {"api_name": "wandb.Image", "line_number": 834, "usage_type": "call"}, {"api_name": "wandb.Image", "line_number": 838, "usage_type": "call"}, {"api_name": "itertools.chain.from_iterable", "line_number": 841, "usage_type": "call"}, {"api_name": "itertools.chain", "line_number": 841, "usage_type": "name"}, {"api_name": "wandb.Image", "line_number": 852, "usage_type": "call"}, {"api_name": "wandb.Image", "line_number": 867, "usage_type": "call"}, {"api_name": "wandb.Image", "line_number": 871, "usage_type": "call"}, {"api_name": "wandb.Image", "line_number": 874, "usage_type": "call"}, {"api_name": "itertools.chain.from_iterable", "line_number": 877, "usage_type": "call"}, {"api_name": "itertools.chain", "line_number": 877, "usage_type": "name"}, {"api_name": "wandb.Image", "line_number": 883, "usage_type": "call"}, {"api_name": "wandb.Image", "line_number": 897, "usage_type": "call"}, {"api_name": "wandb.Image", "line_number": 901, "usage_type": "call"}, {"api_name": "itertools.chain.from_iterable", "line_number": 903, "usage_type": "call"}, {"api_name": "itertools.chain", "line_number": 903, "usage_type": "name"}, {"api_name": "wandb.Histogram", "line_number": 940, "usage_type": "call"}, {"api_name": "wandb.termwarn", "line_number": 951, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 964, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 965, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 966, "usage_type": "call"}, {"api_name": "wandb.image_categorizer_dataframe", "line_number": 970, "usage_type": "call"}, {"api_name": "wandb.image_segmentation_dataframe", "line_number": 977, "usage_type": "call"}, {"api_name": "wandb.termwarn", "line_number": 985, "usage_type": "call"}, {"api_name": "wandb.run", "line_number": 991, "usage_type": "attribute"}, {"api_name": "wandb.termerror", "line_number": 1008, "usage_type": "call"}, {"api_name": "wandb.run", "line_number": 1015, "usage_type": "attribute"}, {"api_name": "wandb.util.make_artifact_name_safe", "line_number": 1024, "usage_type": "call"}, {"api_name": "wandb.util", "line_number": 1024, "usage_type": "attribute"}, {"api_name": "wandb.run", "line_number": 1024, "usage_type": "attribute"}, {"api_name": "wandb.Artifact", "line_number": 1025, "usage_type": "call"}, {"api_name": "wandb.run.log_artifact", "line_number": 1027, "usage_type": "call"}, {"api_name": "wandb.run", "line_number": 1027, "usage_type": "attribute"}, {"api_name": "shutil.rmtree", "line_number": 1030, "usage_type": "call"}, {"api_name": "wandb.Error", "line_number": 1038, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 1041, "usage_type": "attribute"}, {"api_name": "tensorflow.TensorSpec", "line_number": 1055, "usage_type": "call"}, {"api_name": "tensorflow.function", "line_number": 1060, "usage_type": "call"}, {"api_name": "tensorflow.python.framework.convert_to_constants.convert_variables_to_constants_v2_as_graph", "line_number": 1061, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1.RunMetadata", "line_number": 1064, "usage_type": "call"}, {"api_name": "tensorflow.compat", "line_number": 1064, "usage_type": "attribute"}, {"api_name": "tensorflow.compat.v1.profiler.ProfileOptionBuilder", "line_number": 1066, "usage_type": "call"}, {"api_name": "tensorflow.compat", "line_number": 1066, "usage_type": "attribute"}, {"api_name": "tensorflow.compat.v1.profiler.ProfileOptionBuilder", "line_number": 1067, "usage_type": "call"}, {"api_name": "tensorflow.compat", "line_number": 1067, "usage_type": "attribute"}, {"api_name": "tensorflow.compat.v1.profiler.profile", "line_number": 1073, "usage_type": "call"}, {"api_name": "tensorflow.compat", "line_number": 1073, "usage_type": "attribute"}]} +{"seq_id": "17557719587", "text": "import cv2 as cv\n\ndef threshold(frame, options):\n thresh = options['thresh']\n maxValue = options['maxValue']\n\n thresh_frame = cv.threshold(frame, thresh, maxValue, cv.THRESH_BINARY)[1]\n return thresh_frame\n\n\nfilter = {\n \"name\": \"threshold\",\n \"filter\": threshold,\n \"options\": {\n \"thresh\": 20,\n \"maxValue\": 255,\n }\n}", "repo_name": "miguel-martinr/deepviewcore", "sub_path": "deepviewcore/filters/threshold.py", "file_name": "threshold.py", "file_ext": "py", "file_size_in_byte": 328, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "37", "api": [{"api_name": "cv2.threshold", "line_number": 7, "usage_type": "call"}, {"api_name": "cv2.THRESH_BINARY", "line_number": 7, "usage_type": "attribute"}]} +{"seq_id": "32118539018", "text": "import cv2\nimport numpy as np\n\n\n###############################################################################################\n# Create a function for Resizing Direct Mapping: 1-Order algorithm\n# First parameter: Input image that will be resized\n# Second parameter: resizing factor along the horizontal and the vertical axes\ndef direct_mapping_1Order(old_image, factor):\n # Saving the shape width (row) and height (column) and channels\n # of the image using shape attribute.\n [row, column, channel] = old_image.shape\n\n # calculate the new dimensions of the resized image using `factor`.\n new_row = row * factor\n new_column = column * factor\n\n # Creating a new matrix of zeros with the new dimensions of the resized image.\n # We use the zeros function of NumPy to create this new matrix.\n # We also specify the data type of the matrix as unsigned 8-bit integers using the dtype argument.\n resized_image = np.zeros([new_row, new_column, channel], dtype=np.uint8)\n\n # Copying an old values of the old image into the new image that will be resized\n # We iterate over each channel of the image using k\n # Then for each pixel in the image using i and j multiplying by the factor\n for k in range(channel):\n for i in range(row):\n for j in range(column):\n resized_image[i * factor, j * factor, k] = old_image[i, j, k]\n\n # We iterate throw each row to fill the gaps between the pixels in the resized image.\n # Filling in the rows by comparing adjacent pixel values in each row using linear interpolation\n for k in range(channel):\n for i in range(0, new_row, factor):\n for j in range(0, new_column - factor - 1, factor):\n # Saving the maximum and minimum pixels from the resized image\n minimum = resized_image[i, j, k]\n maximum = resized_image[i, j + factor, k]\n # Case of from top (minimum) to bottom (maximum)\n if maximum > minimum:\n for pixel in range(1, factor):\n # Pixel(i) = Round(((Max - Min)/Fact)*i + Min))\n resized_image[i, j + pixel, k] = round(((maximum - minimum) / factor) * pixel + minimum)\n # Case of from bottom (minimum) to top (maximum)\n else:\n for pixel in range(1, factor):\n # Pixel(i) = Round(((Min - Max)/Fact)*i + Max))\n resized_image[i, j + factor - pixel, k] = round(\n ((minimum - maximum) / factor) * pixel + maximum\n )\n # The rest of pixels that not between min and max pixels\n resized_image[i, new_column - factor + 1:new_column, k] = resized_image[i, new_column - factor, k]\n\n # We iterate throw each column to fill the gaps between the pixels in the resized image.\n # Filling in the column by comparing adjacent pixel values in each column using linear interpolation\n for k in range(channel):\n for j in range(0, new_column):\n for i in range(0, new_row - factor - 1, factor):\n # Saving the maximum and minimum pixels from the resized image\n minimum = resized_image[i, j, k]\n maximum = resized_image[i + factor, j, k]\n # Case of from right (minimum) to left (maximum)\n if maximum > minimum:\n for pixel in range(1, factor):\n # Pixel(i)= Round(((Max - Min)/Fact)*i + Min))\n resized_image[i + pixel, j, k] = int(round(((maximum - minimum) / factor) * pixel + minimum))\n # Case of from left (minimum) to right (maximum)\n else:\n for pixel in range(1, factor):\n # Pixel(i)= Round(((Min - Max)/Fact)*i + Max))\n resized_image[i + factor - pixel, j, k] = round(\n ((minimum - maximum) / factor) * pixel + maximum\n )\n # The rest of pixels that not between min and max pixels\n resized_image[new_row - factor:new_row, j, k] = resized_image[new_row - factor, j, k]\n\n return resized_image\n\n\n###############################################################################################\n\n# imread(): this method loads an image from the specified file\nimage = cv2.imread(\"/home/m7md43ban/Image Processing Assignments/pyramid.jpg\")\nnew_image = direct_mapping_1Order(image, 2)\n\n# Displaying the original and resized images using the imshow function of OpenCV\ncv2.imshow('Original Image', image)\nprint(image.shape)\ncv2.imshow('Resized', new_image)\nprint(new_image.shape)\n\n# hold the screen until user close it.\ncv2.waitKey(0)\n\n# Deleting created GUI window from screen and memory\ncv2.destroyAllWindows()\n", "repo_name": "M7mdSh3banX/Image-Processing-Assignments", "sub_path": "direct-mapping-1-order-algorithm.py", "file_name": "direct-mapping-1-order-algorithm.py", "file_ext": "py", "file_size_in_byte": 4850, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "numpy.zeros", "line_number": 21, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 21, "usage_type": "attribute"}, {"api_name": "cv2.imread", "line_number": 83, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 87, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 89, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 93, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 96, "usage_type": "call"}]} +{"seq_id": "41048755285", "text": "# -*- coding: utf-8 -*-\nimport random\n\nfrom faker import Faker\nfrom sqlalchemy.exc import IntegrityError\n\nfrom nativ import db\nfrom nativ.models import User, Post, Comment\n\nfake = Faker()\n\n\ndef fake_user(count=30):\n for i in range(count):\n user = User(\n email=fake.email(),\n username=fake.name(),\n confirmed=True,\n about_me=fake.sentence(),\n last_seen=fake.date_time_this_year()\n )\n user.set_password('Xx3015783')\n db.session.add(user)\n try:\n db.session.commit()\n except IntegrityError:\n db.session.rollback()\n\n\ndef fake_posts(count=300):\n for i in range(count):\n post = Post(\n title=fake.sentence(),\n body=fake.text(2000),\n author=User.query.get(random.randint(1, User.query.count())),\n timestamp=fake.date_time_this_year()\n )\n db.session.add(post)\n db.session.commit()\n\n\ndef fake_comments(count=5000):\n origin = int(count - 0.5)\n for i in range(origin):\n post = Post.query.get(random.randint(1, Post.query.count()))\n replier = User.query.get(random.randint(1, User.query.count()))\n comment = Comment(\n body=fake.sentence(),\n timestamp=fake.date_time_this_year(),\n replier=replier,\n post=post,\n replierd=post.author\n )\n db.session.add(comment)\n\n replied = count - origin\n for i in range(replied):\n tmpComment = Comment.query.get_or_404(random.randint(1, Comment.query.count()))\n comment = Comment(\n body=fake.sentence(),\n user_id=User.query.get(random.randint(1, User.query.count())).id,\n post=Post.query.get(random.randint(1, Post.query.count())),\n replied=tmpComment,\n replierd=tmpComment.replier,\n timestamp=fake.date_time_this_year()\n )\n db.session.add(comment)\n db.session.commit()", "repo_name": "hxdarrowbbom/nativ", "sub_path": "nativ/fakers.py", "file_name": "fakers.py", "file_ext": "py", "file_size_in_byte": 1983, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "faker.Faker", "line_number": 10, "usage_type": "call"}, {"api_name": "nativ.models.User", "line_number": 15, "usage_type": "call"}, {"api_name": "nativ.db.session.add", "line_number": 23, "usage_type": "call"}, {"api_name": "nativ.db.session", "line_number": 23, "usage_type": "attribute"}, {"api_name": "nativ.db", "line_number": 23, "usage_type": "name"}, {"api_name": "nativ.db.session.commit", "line_number": 25, "usage_type": "call"}, {"api_name": "nativ.db.session", "line_number": 25, "usage_type": "attribute"}, {"api_name": "nativ.db", "line_number": 25, "usage_type": "name"}, {"api_name": "sqlalchemy.exc.IntegrityError", "line_number": 26, "usage_type": "name"}, {"api_name": "nativ.db.session.rollback", "line_number": 27, "usage_type": "call"}, {"api_name": "nativ.db.session", "line_number": 27, "usage_type": "attribute"}, {"api_name": "nativ.db", "line_number": 27, "usage_type": "name"}, {"api_name": "nativ.models.Post", "line_number": 32, "usage_type": "call"}, {"api_name": "nativ.models.User.query.get", "line_number": 35, "usage_type": "call"}, {"api_name": "nativ.models.User.query", "line_number": 35, "usage_type": "attribute"}, {"api_name": "nativ.models.User", "line_number": 35, "usage_type": "name"}, {"api_name": "random.randint", "line_number": 35, "usage_type": "call"}, {"api_name": "nativ.models.User.query.count", "line_number": 35, "usage_type": "call"}, {"api_name": "nativ.db.session.add", "line_number": 38, "usage_type": "call"}, {"api_name": "nativ.db.session", "line_number": 38, "usage_type": "attribute"}, {"api_name": "nativ.db", "line_number": 38, "usage_type": "name"}, {"api_name": "nativ.db.session.commit", "line_number": 39, "usage_type": "call"}, {"api_name": "nativ.db.session", "line_number": 39, "usage_type": "attribute"}, {"api_name": "nativ.db", "line_number": 39, "usage_type": "name"}, {"api_name": "nativ.models.Post.query.get", "line_number": 45, "usage_type": "call"}, {"api_name": "nativ.models.Post.query", "line_number": 45, "usage_type": "attribute"}, {"api_name": "nativ.models.Post", "line_number": 45, "usage_type": "name"}, {"api_name": "random.randint", "line_number": 45, "usage_type": "call"}, {"api_name": "nativ.models.Post.query.count", "line_number": 45, "usage_type": "call"}, {"api_name": "nativ.models.User.query.get", "line_number": 46, "usage_type": "call"}, {"api_name": "nativ.models.User.query", "line_number": 46, "usage_type": "attribute"}, {"api_name": "nativ.models.User", "line_number": 46, "usage_type": "name"}, {"api_name": "random.randint", "line_number": 46, "usage_type": "call"}, {"api_name": "nativ.models.User.query.count", "line_number": 46, "usage_type": "call"}, {"api_name": "nativ.models.Comment", "line_number": 47, "usage_type": "call"}, {"api_name": "nativ.db.session.add", "line_number": 54, "usage_type": "call"}, {"api_name": "nativ.db.session", "line_number": 54, "usage_type": "attribute"}, {"api_name": "nativ.db", "line_number": 54, "usage_type": "name"}, {"api_name": "nativ.models.Comment.query.get_or_404", "line_number": 58, "usage_type": "call"}, {"api_name": "nativ.models.Comment.query", "line_number": 58, "usage_type": "attribute"}, {"api_name": "nativ.models.Comment", "line_number": 58, "usage_type": "name"}, {"api_name": "random.randint", "line_number": 58, "usage_type": "call"}, {"api_name": "nativ.models.Comment.query.count", "line_number": 58, "usage_type": "call"}, {"api_name": "nativ.models.Comment", "line_number": 59, "usage_type": "call"}, {"api_name": "nativ.models.User.query.get", "line_number": 61, "usage_type": "call"}, {"api_name": "nativ.models.User.query", "line_number": 61, "usage_type": "attribute"}, {"api_name": "nativ.models.User", "line_number": 61, "usage_type": "name"}, {"api_name": "random.randint", "line_number": 61, "usage_type": "call"}, {"api_name": "nativ.models.User.query.count", "line_number": 61, "usage_type": "call"}, {"api_name": "nativ.models.Post.query.get", "line_number": 62, "usage_type": "call"}, {"api_name": "nativ.models.Post.query", "line_number": 62, "usage_type": "attribute"}, {"api_name": "nativ.models.Post", "line_number": 62, "usage_type": "name"}, {"api_name": "random.randint", "line_number": 62, "usage_type": "call"}, {"api_name": "nativ.models.Post.query.count", "line_number": 62, "usage_type": "call"}, {"api_name": "nativ.db.session.add", "line_number": 67, "usage_type": "call"}, {"api_name": "nativ.db.session", "line_number": 67, "usage_type": "attribute"}, {"api_name": "nativ.db", "line_number": 67, "usage_type": "name"}, {"api_name": "nativ.db.session.commit", "line_number": 68, "usage_type": "call"}, {"api_name": "nativ.db.session", "line_number": 68, "usage_type": "attribute"}, {"api_name": "nativ.db", "line_number": 68, "usage_type": "name"}]} +{"seq_id": "24325972161", "text": "import psycopg2\nimport emoji\n# from pprint import pprint\n\n\ndef create_db(cur):\n \"\"\"\n 1. Функция, создающая структуру БД (таблицы)\n \"\"\"\n cur.execute(\"\"\"\n CREATE TABLE IF NOT EXISTS clients(\n id SERIAL PRIMARY KEY,\n name VARCHAR(20) NOT NULL,\n lastname VARCHAR(30) NOT NULL,\n email VARCHAR(50) NOT NULL UNIQUE\n );\n \"\"\")\n cur.execute(\"\"\"\n CREATE TABLE IF NOT EXISTS phones(\n number CHAR(12) PRIMARY KEY,\n client_id INTEGER REFERENCES clients(id)\n );\n \"\"\")\n # print(\"Tables have been created successfully\")\n return \"Tables have been created successfully\"\n\ndef delete_db(cur):\n \"\"\"\n Функция, удаляющая таблицы при запуске\n \"\"\"\n cur.execute(\"\"\"\n DROP TABLE clients, phones CASCADE;\n \"\"\")\n # print(\"Tables successfully deleted\")\n return \"Tables successfully deleted\"\n\n\ndef add_phone(cur, client_id, number):\n \"\"\"\n 3. Функция, позволяющая добавить телефон для существующего клиента\n \"\"\"\n cur.execute(\"\"\"\n INSERT INTO phones(number, client_id)\n VALUES (%s, %s)\n \"\"\", (number, client_id))\n # print(f\"A phone number {number} has been added to the client {client_id}\")\n return f\"A phone number {number} has been added to the client {client_id}\"\n\ndef add_client(cur, name, lastname, email, number=None):\n \"\"\"\n 2. Функция, позволяющая добавить нового клиента\n \"\"\"\n cur.execute(\"\"\"\n INSERT INTO clients(name, lastname, email)\n VALUES (%s, %s, %s)\n \"\"\", (name, lastname, email))\n cur.execute(\"\"\"\n SELECT id from clients\n ORDER BY id DESC\n LIMIT 1\n \"\"\")\n client_id = cur.fetchone()[0]\n if number is None:\n # print(f\"The client {name, lastname, email, number} was added without a phone number\")\n return f\"The client {name, lastname, email, number} was added without a phone number\"\n else:\n add_phone(cur, client_id, number)\n # print(f\"The client {name, lastname, email, number} added with phone number\")\n return f\"The client {name, lastname, email, number} added with phone number\"\n\ndef change_client(cur, id, name=None, lastname=None, email=None):\n \"\"\"\n 4. Функция, позволяющая изменить данные о клиенте\n \"\"\"\n cur.execute(\"\"\"\n SELECT * FROM clients\n WHERE id = %s\n \"\"\", (id, ))\n info = cur.fetchone()\n if name is None:\n name = info[1]\n if lastname is None:\n lastname = info[2]\n if email is None:\n email = info[3]\n cur.execute(\"\"\"\n UPDATE clients\n SET name = %s, lastname = %s, email = %s\n WHERE id = %s\n \"\"\", (name, lastname, email, id))\n return \"The clients data changed\"\n\ndef delete_phone(cur, number):\n \"\"\"\n 5. Функция, позволяющая удалить телефон для существующего клиента\n \"\"\"\n cur.execute(\"\"\"\n DELETE FROM phones\n WHERE number = %s\n \"\"\", (number, ))\n return f\"Phone number {number} deleted\"\n\ndef delete_clients(cur, id):\n \"\"\"\n 6. Функция, позволяющая удалить существующего клиента\n \"\"\"\n cur.execute(\"\"\"\n DELETE FROM phones\n WHERE client_id = %s\n \"\"\", (id,))\n cur.execute(\"\"\"\n DELETE FROM clients\n WHERE id = %s\n \"\"\", (id, ))\n return f\"Client number {id} has been successfully deleted\"\n\ndef client_search(cur, name=None, lastname=None, email=None, number=None):\n \"\"\"\n 7. Функция, позволяющая найти клиента по его данным: имени, фамилии, email или телефону\n \"\"\"\n if name is None:\n name = '%'\n else:\n name = '%' + name + '%'\n if lastname is None:\n surname = '%'\n else:\n surname = '%' + lastname + '%'\n if email is None:\n email = '%'\n else:\n email = '%' + email + '%'\n if number is None:\n cur.execute(\"\"\"\n SELECT cl.id, cl.name, cl.lastname, cl.email, ph.number FROM clients cl\n JOIN phones ph ON cl.id = ph.client_id\n WHERE cl.name LIKE %s AND cl.lastname LIKE %s\n AND cl.email LIKE %s\n \"\"\", (name, surname, email))\n else:\n cur.execute(\"\"\"\n SELECT cl.id, cl.name, cl.lastname, cl.email, ph.number FROM clients cl\n LEFT JOIN phones ph ON cl.id = ph.client_id\n WHERE cl.name LIKE %s AND cl.lastname LIKE %s\n AND cl.email LIKE %s AND ph.number like %s\n \"\"\", (name, surname, email, number))\n return cur.fetchall()\n\n\nwith psycopg2.connect(database=\"psypost_db\", user=\"postgres\", password=\"PASSWORD\") as conn:\n with conn.cursor() as curs:\n\n # Удаляем таблицы при запуске\n print(delete_db(curs))\n print(emoji.emojize(':cross_mark:'))\n\n # Создаём структуру БД\n print(create_db(curs))\n print(emoji.emojize(':thumbs_up:'))\n\n # Добавляем новых клиентов:\n\n print(add_client(curs, \"Константин\", \"Хабеников\", \"konsthab@mail.ru\", 89219212131))\n print(add_client(curs, \"Марат\", \"Башкаров\", \"martbash@mail.ru\", 89998554050))\n print(add_client(curs, \"Светлана\", \"Ходчекова\", \"svethod@mail.ru\"))\n print(add_client(curs, \"Павел\", \"Волен\", \"pavwol@mail.ru\"))\n\n # Добавляем телефон для существующего клиента:\n print(add_phone(curs, 3, 89554554535))\n print(add_phone(curs, 2, 89453859525))\n\n # Изменение данных о клиенте:\n print(change_client(curs, 2, \"Матвей\", \"Коротков\", \"matvcor@mail.ru\"))\n\n # Удаление телефона для существующего клиента:\n print(delete_phone(curs, \"89554554535\"))\n\n # Удаление существующего клиента:\n print(delete_clients(curs, 1))\n\n # Поиск клиента по его данным: имени, фамилии, email или телефону:\n print(client_search(curs, \"Павел\", \"Волен\", \"pavwol@mail.ru\", None))\n print(client_search(curs, \"Марат\", None, None, None))\n print(client_search(curs, \"Константин\", \"Хабеников\", \"konsthab@mail.ru\", \"89219212131\"))\n\nconn.close()", "repo_name": "pr0100smile/PostDB_psycopg2", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 6663, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "psycopg2.connect", "line_number": 150, "usage_type": "call"}, {"api_name": "emoji.emojize", "line_number": 155, "usage_type": "call"}, {"api_name": "emoji.emojize", "line_number": 159, "usage_type": "call"}]} +{"seq_id": "40720198614", "text": "from django.shortcuts import render\nfrom django.core.paginator import Paginator\nfrom django.db.models import Q\nfrom HR.models import p_info, departments\n\ndef index(request):\n context = {}\n department_list_all = departments.objects.all()\n context['department_list_all'] = department_list_all\n return render(request,'index.html',context)\n\n\n\ndef search(request):\n search_words = request.GET.get('wd', '').strip()\n department_list_all = departments.objects.all()\n # 分词\n condition = None\n for word in search_words.split(' '):\n if condition is None:\n condition = Q(name__icontains=word)\n else:\n condition = condition | Q(name__icontains=word)\n\n search_blogs = []\n if condition is not None:\n # 搜索\n search_blogs = p_info.objects.filter(condition)\n\n # 分页\n paginator = Paginator(search_blogs, 5) # 5\n page_num = request.GET.get('page', 1) # 得参\n page_of_blogs = paginator.get_page(page_num)\n\n context = {}\n context['search_words'] = search_words\n context['page_of_people'] = page_of_blogs\n context['search_people_count'] = search_blogs.count()\n context['department_list_all'] = department_list_all\n return render(request, 'search.html', context)\n\ndef my_notifications(request):\n context = {}\n return render(request,'my_notifications.html',context)\n\n", "repo_name": "weihuan213/SPC", "sub_path": "spc/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 1376, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "HR.models.departments.objects.all", "line_number": 8, "usage_type": "call"}, {"api_name": "HR.models.departments.objects", "line_number": 8, "usage_type": "attribute"}, {"api_name": "HR.models.departments", "line_number": 8, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 10, "usage_type": "call"}, {"api_name": "HR.models.departments.objects.all", "line_number": 16, "usage_type": "call"}, {"api_name": "HR.models.departments.objects", "line_number": 16, "usage_type": "attribute"}, {"api_name": "HR.models.departments", "line_number": 16, "usage_type": "name"}, {"api_name": "django.db.models.Q", "line_number": 21, "usage_type": "call"}, {"api_name": "django.db.models.Q", "line_number": 23, "usage_type": "call"}, {"api_name": "HR.models.p_info.objects.filter", "line_number": 28, "usage_type": "call"}, {"api_name": "HR.models.p_info.objects", "line_number": 28, "usage_type": "attribute"}, {"api_name": "HR.models.p_info", "line_number": 28, "usage_type": "name"}, {"api_name": "django.core.paginator.Paginator", "line_number": 31, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 40, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 44, "usage_type": "call"}]} +{"seq_id": "8657397359", "text": "import numpy\nfrom scipy.special import jn_zeros\nfrom fzp_simulator.hankel_transform_MGS import Hankel_Transform_MGS\nfrom fzp_simulator.refractive_index import RefractiveIndex\nfrom fzp_simulator.efficiency_MGS import Efficiency_MGS\n\n## FZP, CS, OSA and simulation parameters\n#--------------------------------------------------------------------------\n \nwith_CS = 1 # equal to 0 --> without CS - CENTRAL STOP\n # equal to 1 --> with CS\n \nwith_OSA = 0 # equal to 0 --> without OSA - ORDER SORTING APERTURE\n # equal to 1 --> with OSA\n \nFZP_TYPE = 0 # equal to 0 --> Ordinary FZP\n # equal to 1 --> Zone-Doubled FZP\n # equal to 2 --> Zone-Filled FZP\n # equal to 3 --> Two-Level FZP\n # equal to 4 --> Three-Level FZP\n # equal to 5 --> ALD Multideposition FZP 1\n # equal to 6 --> ALD Multideposition FZP 2\n # equal to 7 --> Zone-Edge Slanted FZP\n \nwith_Range = 1 # equal to 0 --> plot to focal length\n # equal to 1 --> plot in a given range\n \nwith_MultiSlicing = 0 # equal to 1 --> apply multisilcing of the element \n # equal to 0 --> no multislicing\n\nwith_Complex = 0 # equal to 0 --> Complex Wavefront is not stored\n # equal to 1 --> Complex Wavefront is storedk\n\nwith_MultiPool = 1 # equal to 1 --> activate multipool (n cores - 1)\n # equal to 0 --> no multipool\n\nenergy = 8 # photon energy [keV]\nwavelength = 12.398/energy*1e-10 # wavelength [m]\nk = 2*numpy.pi/wavelength # wavevector [m-1]\nheight = 20000e-9 # zone thickness or height [m]\ndiam = 50e-6 # FZP diameter [m]\nbmin = 50e-9 # outermost zone width [m] / outermost period for ZD [m]\nf = diam*bmin/wavelength # focal distance [m]\n\nRange_i = f-2e-6 # distance to FZP\nRange_f = f+2e-6 # distance to FZP\n\nCS_diam = 10e-6 # beamstop diameter [m]\nOSA_position = 0.01 # distance FZP-OSA [m]\nOSA_diam = 30e-6 # OSA diameter [m]\n\nN = 5000 # Number of sampling point in radial direction\nR = diam # Radius of the simulation\nstep = R/N\nNzeros = int(numpy.floor(1.25*diam/2/R*N)) # Parameter to speed up the Hankel Transform\n # when the function has zeros for N > Nzero\nNz = 3 # Number of sampling points along the z axis\nfactor_z = 1.6 # Z axis range up to factor_z*f\n\n#% FZP Material\n#--------------------------------------------------------------------------\n\nFZP_Material = 'Au'\nTemplate_Material = 'SiO2'\n\ndelta_FZP, beta_FZP = RefractiveIndex(energy, FZP_Material)\ndelta_template, beta_template = RefractiveIndex(energy, Template_Material)\n\nif with_MultiSlicing == 1: NSlices = 100 # Number of slices of the element\nelse: NSlices = 1\n\nwidth_coating = 20e-9 # Width of the coated material for \n # for a zone-filled FZP.\n## Parameters for Stair Case Profiles \n# Currently parameters for maximum at 6.2 keV using Au as FZP material \n \n#Two-level profile\nheight1 = (2/3)*1000e-9\nheight2 = (4/3)*1000e-9\n\n## Contruction of the FZP profile\n#--------------------------------------------------------------------------\n\ndef run_simulation():\n\n Nzones = int(numpy.floor(1.0/4.0*(diam/bmin)))\n multipool = with_MultiPool == 1\n\n radia = numpy.sqrt(numpy.arange(0, Nzones+1)*wavelength*f + ((numpy.arange(0, Nzones+1)*wavelength)**2)/4)\n profile = numpy.full(N, 1 + 0j)\n profile[int(numpy.floor(radia[Nzones]/step)):N] = 0\n\n # Ordinary FZP\n if FZP_TYPE == 0:\n for i in range (1, Nzones, 2):\n position_i = int(numpy.floor(radia[i]/step))\n position_f = int(numpy.floor(radia[i+1]/step)) # N.B. the index is excluded\n profile[position_i:position_f] = numpy.exp(-1j*(-2*numpy.pi*delta_FZP/wavelength*height-1j*2*numpy.pi*beta_FZP/wavelength*height))\n\n Membrane_Transmission = 1\n\n # Zone-doubled FZP\n if FZP_TYPE == 1:\n for i in range (1, Nzones, 2):\n position_i = int(numpy.floor((radia[i]+bmin/4)/step))\n position_f = int(numpy.floor((radia[i+1]-bmin/4)/step))\n profile[position_i:position_f] = numpy.exp(-1j*(-2*numpy.pi*delta_template/wavelength*height-1j*2*numpy.pi*beta_template/wavelength*height))\n\n position_i = int(numpy.floor((radia[i]-width_coating/2)/step))\n position_f = int(numpy.floor((radia[i]+width_coating/2)/step))\n profile[position_i:position_f] = numpy.exp(-1j*(-2*numpy.pi*delta_FZP/wavelength*height-1j*2*numpy.pi*beta_FZP/wavelength*height))\n\n position_i = int(numpy.floor((radia[i+1]-width_coating/2)/step))\n position_f = int(numpy.floor((radia[i+1]+width_coating/2)/step))\n profile[position_i:position_f] = numpy.exp(-1j*(-2*numpy.pi*delta_FZP/wavelength*height-1j*2*numpy.pi*beta_FZP/wavelength*height))\n\n #including absorption of coating material\n Membrane_Transmission = numpy.exp(-1j*(-1j*2*numpy.pi*beta_FZP/wavelength*width_coating/2))\n\n # Zone-filled FZP\n if FZP_TYPE == 2:\n for i in range (1, Nzones, 2):\n\n position_i = int(numpy.floor(radia[i]/step))\n position_f = int(numpy.floor(radia[i+1]/step))\n\n width = numpy.abs(int(numpy.floor((radia[i+1]-radia[i])/step)))\n width_coating_step = numpy.abs(int(numpy.floor(width_coating/step/2)))\n\n if width_coating < width:\n profile[position_i:position_f] = numpy.exp(-1j*(-2*numpy.pi*delta_template/wavelength*height-1j*2*numpy.pi*beta_template/wavelength*height))\n\n position_i = int(numpy.floor((radia[i]-width_coating)/step))\n position_f = int(numpy.floor(radia[i]/step))\n profile[position_i:position_f] = numpy.exp(-1j*(-2*numpy.pi*delta_FZP/wavelength*height-1j*2*numpy.pi*beta_FZP/wavelength*height))\n\n position_i = int(numpy.floor(radia[i+1]/step))\n position_f = int(numpy.floor((radia[i+1]+width_coating)/step))\n profile[position_i:position_f] = numpy.exp(-1j*(-2*numpy.pi*delta_FZP/wavelength*height-1j*2*numpy.pi*beta_FZP/wavelength*height))\n else:\n profile[position_i:position_f] = numpy.exp(-1j*(-2*numpy.pi*delta_template/wavelength*height-1j*2*numpy.pi*beta_template/wavelength*height))\n\n position_i = int(numpy.floor((radia[i]-width_coating)/step))\n position_f = int(numpy.floor(radia[i]/step))\n profile[position_i:position_f] = numpy.exp(-1j*(-2*numpy.pi*delta_FZP/wavelength*height-1j*2*numpy.pi*beta_FZP/wavelength*height))\n\n position_i = int(numpy.floor(radia[i+1]/step))\n position_f = int(numpy.floor((radia[i+1]-width_coating)/step))\n profile[position_i:position_f] = numpy.exp(-1j*(-2*numpy.pi*delta_FZP/wavelength*height-1j*2*numpy.pi*beta_FZP/wavelength*height))\n\n #including absorption of coating material\n Membrane_Transmission = numpy.exp(-1j*(-1j*2*numpy.pi*beta_FZP/wavelength*width_coating))\n\n\n # Two-Level FZP - stop here refactoring\n if FZP_TYPE == 3:\n for i in range (1, Nzones, 2):\n position_i = int(numpy.floor((2*radia[i-1]/3+radia[i+1]/3)/step))\n position_f = int(numpy.floor((radia[i-1]/3+2*radia[i+1]/3)/step))\n profile[position_i:position_f] = numpy.exp(-1j*(-2*numpy.pi*delta_FZP/wavelength*height1-1j*2*numpy.pi*beta_FZP/wavelength*height1))\n\n position_i = int(numpy.floor((radia[i-1]/3+2*radia[i+1]/3)/step))\n position_f = int(numpy.floor((radia[i+1])/step))\n profile[position_i:position_f] = numpy.exp(-1j*(-2*numpy.pi*delta_FZP/wavelength*height2-1j*2*numpy.pi*beta_FZP/wavelength*height2))\n\n Membrane_Transmission = 1\n\n\n # Inserting the CS\n # --------------------------------------------------------------------------\n CS_pix = numpy.floor(CS_diam / step)\n\n if with_CS == 1: profile[0: int(numpy.floor(CS_pix / 2))] = 0\n\n #% Propagation of the wavefield\n # The routine performing the 0th order Hankel tranform is based in a\n # algorithm that calculates the function at positision related to the zeros\n # of the 0th order Bessel function.\n #--------------------------------------------------------------------------\n\n # Loading the position of the zeros, as much position as N+1. The file\n # c.mat contains up to 200000 zeros of the 1st order Bessel function.\n c = jn_zeros(0, N+1)\n\n # Definition of the position where the calculated input and transformated\n # funtions are evaluated. We define also the maximum frequency in the\n # angular domain.\n\n Q = c[N]/(2*numpy.pi*R) # Maximum frequency\n r = c[0:N]*R/c[N] # Radius vector\n q = c[0:N]/(2*numpy.pi*R) # Frequency vector\n\n ## Recalculation of the position where the initial profile is defined.\n # Originally the profile is defined in position r0, that are linear for all\n # the values of position. Now we need to define the function in a new\n # coordinates that are by r. The next loop is interpolating the values of\n # the profile from the coordinates in r0 to the coordinates in r.\n # The output intensity profiles will be defined in r coordinates.\n #--------------------------------------------------------------------------\n\n r0 = numpy.arange(0, R, step)\n profile_h = numpy.full(N, 0j)\n for i in range(0, N-1):\n profile_h[i] = profile[i]+(profile[i+1]-profile[i])/(r0[i+1]-r0[i])*(r[i]-r0[i])\n profile_h[N-1] = profile[N-1]\n\n ## Calculation of the first angular spectrum\n #--------------------------------------------------------------------------\n map_int=numpy.zeros((NSlices+Nz, N))\n if with_Complex == 1: map_complex = numpy.zeros((NSlices+Nz, N))\n\n # The function 'Hankel_Transform_MGS' needs as input the field 'field0',\n # the maximum radius R and the zeros of the 0th Bessel function. In case of\n # the inverse transformation from the angular domain back to spatial domain\n # the function needs as a input the angular specturm 'fun', the maximum\n # frequency Q and the zeros of the Bessel function.\n\n field0 = profile_h*Membrane_Transmission\n map_int[0, :] = numpy.multiply(numpy.abs(field0), numpy.abs(field0))\n if with_Complex == 1: map_complex[0, :] = field0[0:N]\n\n four0 = Hankel_Transform_MGS(field0, R, c, multipool=multipool)\n field0 = profile_h\n\n ## Multi-Slicing of the FZP\n #--------------------------------------------------------------------------\n # Propagation of the wavefield inside the FZP is performed by slicing the\n # element in pieces and considering free propagation between them. The\n # volume effects in this way are considered.\n #\n\n if with_MultiSlicing == 1:\n Step_Slice = height\n\n for n in range(NSlices-1):\n proj = numpy.exp(-1j*Step_Slice*((2*numpy.pi*q)**2)/(2*k))\n fun = numpy.multiply(proj, four0)\n field = Hankel_Transform_MGS(fun, Q, c, multipool=multipool)\n\n fun = numpy.multiply(field0, field)\n map_int[1+n, :] = numpy.multiply(numpy.abs(fun), numpy.abs(fun))\n if with_Complex == 1: map_complex[1+n, :] = fun\n\n four0 = Hankel_Transform_MGS(fun, R, c, Nzeros, multipool=multipool)\n\n # Calculation from the FZP position to full z range\n if with_Range == 0:\n stepz = factor_z*f/Nz\n z = numpy.arange(1, Nz+1)*stepz\n\n if with_OSA == 0:\n for o in range(Nz):\n print(\"Z position nr. \", o + 1, \": \", z[o])\n proj = numpy.exp(-1j*z[o]*((2*numpy.pi*q)**2)/(2*k))\n fun = numpy.multiply(proj, four0)\n four11 = Hankel_Transform_MGS(fun, Q, c, multipool=multipool)\n map_int[o + NSlices, :] = numpy.multiply(numpy.abs(four11), numpy.abs(four11))\n if with_Complex == 1: map_complex[o + NSlices, :] = four11\n\n elif with_OSA == 1:\n OSA_pos = int(numpy.floor(OSA_position/stepz) - 1)\n\n for o in range(OSA_pos-1):\n print(\"(OSA) Z position nr. \", o + 1, \": \", z[o])\n proj = numpy.exp(-1j * z[o] * ((2 * numpy.pi * q)**2) / (2 * k))\n fun = numpy.multiply(proj, four0)\n four11 = Hankel_Transform_MGS(fun, Q, c, multipool=multipool)\n map_int[o + NSlices, :] = numpy.multiply(numpy.abs(four11), numpy.abs(four11))\n if with_Complex == 1: map_complex[o + NSlices, :] = four11\n\n # Propagation at the OSA position\n #--------------------------------------------------------------------------\n\n proj_OSA = numpy.exp(-1j * z[OSA_pos] * ((2 * numpy.pi * q)**2) / (2 * k))\n fun = numpy.multiply(proj_OSA, four0)\n field_OSA = Hankel_Transform_MGS(fun, Q, c, multipool=multipool)\n\n # Inserting OSA\n #--------------------------------------------------------------------------\n\n OSA_pix = int(numpy.floor(OSA_diam/step) - 1)\n field_OSA[int(OSA_pix/2)+1:N] = 0\n\n map_int[OSA_pos+NSlices,:] = numpy.multiply(numpy.abs(field_OSA), numpy.abs(field_OSA))\n four_OSA = Hankel_Transform_MGS(field_OSA, R, c, multipool=multipool)\n if with_Complex == 1: map_complex[OSA_pos+NSlices,:] = field_OSA\n\n # Continue the propagation from OSA to focus\n #--------------------------------------------------------------------------\n for o in range(OSA_pos+1, Nz):\n print(\"(OSA) Z position nr. \", o + 1, \": \", z[o] - z[OSA_pos])\n proj = numpy.exp(-1j * (z[o] - z[OSA_pos]) * ((2 * numpy.pi * q)**2) / (2 * k))\n fun = numpy.multiply(proj, four_OSA)\n four11 = Hankel_Transform_MGS(fun, Q, c, multipool=multipool)\n map_int[o + NSlices, :] = numpy.multiply(numpy.abs(four11), numpy.abs(four11))\n if with_Complex == 1: map_complex[o + NSlices, :] = four11\n\n elif with_Range == 1:\n stepz = (Range_f-Range_i)/(Nz-1)\n z = (Range_i + numpy.arange(Nz)*stepz)\n\n if (with_OSA == 0) or (OSA_position > Range_f):\n for o in range(Nz):\n print(\"Z position nr. \", o+1, \": \", z[o])\n proj = numpy.exp(-1j*z[o]*((2*numpy.pi*q)**2)/(2*k))\n fun = numpy.multiply(proj, four0)\n four11 = Hankel_Transform_MGS(fun, Q, c, multipool=multipool)\n map_int[o + NSlices, :] = numpy.multiply(numpy.abs(four11), numpy.abs(four11))\n if with_Complex == 1: map_complex[o + NSlices, :] = four11\n elif with_OSA == 1:\n if OSA_position < Range_i:\n proj_OSA = numpy.exp(-1j*OSA_position*((2*numpy.pi*q)**2)/(2*k))\n fun = numpy.multiply(proj_OSA, four0)\n field_OSA = Hankel_Transform_MGS(fun, Q, c, multipool=multipool)\n\n # Inserting OSA\n #--------------------------------------------------------------------------\n\n OSA_pix = int(numpy.floor(OSA_diam/step) - 1)\n field_OSA[int(OSA_pix/2)+1:N] = 0\n four_OSA = Hankel_Transform_MGS(field_OSA, R, c, multipool=multipool)\n\n for o in range(Nz):\n print(\"(OSA) Z position nr. \", o+1, \": \", z[o] - OSA_position)\n proj = numpy.exp(-1j*(z[o] - OSA_position)*((2*numpy.pi*q)**2)/(2*k))\n fun = numpy.multiply(proj, four_OSA)\n four11 = Hankel_Transform_MGS(fun, Q, c, multipool=multipool)\n map_int[o + NSlices, :] = numpy.multiply(numpy.abs(four11), numpy.abs(four11))\n if with_Complex == 1: map_complex[o + NSlices, :] = four11\n else:\n # Propagation from Range_i to OSA\n #------------------------------------------------------------------\n OSA_pos = int(numpy.floor((OSA_position-Range_i)/stepz) - 1)\n\n for o in range(OSA_pos-1):\n print(\"(OSA) Z position nr. \", o+1, \": \", z[o])\n proj = numpy.exp(-1j*z[o]*((2*numpy.pi*q)**2)/(2*k))\n fun = numpy.multiply(proj, four0)\n four11 = Hankel_Transform_MGS(fun, Q, c, multipool=multipool)\n map_int[o + NSlices, :] = numpy.multiply(numpy.abs(four11), numpy.abs(four11))\n if with_Complex == 1: map_complex[o + NSlices, :] = four11\n\n # Propagation at the OSA position\n #--------------------------------------------------------------------------\n\n proj_OSA = numpy.exp(-1j * z[OSA_pos] * ((2 * numpy.pi * q)**2) / (2 * k))\n fun = numpy.multiply(proj_OSA, four0)\n field_OSA = Hankel_Transform_MGS(fun, Q, c, multipool=multipool)\n\n # Inserting OSA\n #--------------------------------------------------------------------------\n\n OSA_pix = int(numpy.floor(OSA_diam/step) - 1)\n field_OSA[int(OSA_pix/2)+1:N] = 0\n\n map_int[OSA_pos+NSlices,:] = numpy.multiply(numpy.abs(field_OSA), numpy.abs(field_OSA))\n four_OSA = Hankel_Transform_MGS(field_OSA, R, c, multipool=multipool)\n if with_Complex == 1: map_complex[OSA_pos+NSlices, :] = field_OSA\n\n # Continue the propagation from OSA to focus\n #--------------------------------------------------------------------------\n for o in range(OSA_pos+1, Nz):\n print(\"(OSA) Z position nr. \", o + 1, \": \", z[o] - z[OSA_pos])\n proj = numpy.exp(-1j * (z[o] - z[OSA_pos]) * ((2 * numpy.pi * q)**2) / (2 * k))\n fun = numpy.multiply(proj, four_OSA)\n four11 = Hankel_Transform_MGS(fun, Q, c, multipool=multipool)\n map_int[o + NSlices, :] = numpy.multiply(numpy.abs(four11), numpy.abs(four11))\n if with_Complex == 1: map_complex[o + NSlices, :] = four11\n\n shape = map_int.shape\n\n map = numpy.full((2*shape[1], shape[0]), None)\n\n if with_Complex == 1:\n map[0:N,:] = numpy.flipud(map_complex[:, 0:N].T)\n map[N:2*N,:] = map_complex[:, 0:N].T\n else:\n map[0:N,:] = numpy.flipud(map_int[:, 0:N].T)\n map[N:2*N,:] = map_int[:, 0:N].T\n\n\n DE=Efficiency_MGS(3, map, profile_h, r, N, int(numpy.floor(10*bmin/step)))\n\n print(\"Efficiency:\", DE)\n\n ###################################################\n #\n # Plotting\n #\n ###################################################\n\n from matplotlib import pyplot as plt\n\n print(\"Shape:\", map_int.shape)\n\n for i in range(1, map_int.shape[0]):\n plt.plot(r0[:100], map_int[i, :100])\n\n plt.show()\n\n return 0\n\n\nimport sys\n\nif __name__ == \"__main__\":\n sys.exit(run_simulation())\n", "repo_name": "APS-XSD-OPT-Group/FZP-Simulator", "sub_path": "fzp_simulator/FZP_Simulation_in_2D.py", "file_name": "FZP_Simulation_in_2D.py", "file_ext": "py", "file_size_in_byte": 19225, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "numpy.pi", "line_number": 39, "usage_type": "attribute"}, {"api_name": "numpy.floor", "line_number": 55, "usage_type": "call"}, {"api_name": "fzp_simulator.refractive_index.RefractiveIndex", "line_number": 66, "usage_type": "call"}, {"api_name": "fzp_simulator.refractive_index.RefractiveIndex", "line_number": 67, "usage_type": "call"}, {"api_name": "numpy.floor", "line_number": 86, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 89, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 89, "usage_type": "call"}, {"api_name": "numpy.full", "line_number": 90, "usage_type": "call"}, {"api_name": "numpy.floor", "line_number": 91, "usage_type": "call"}, {"api_name": "numpy.floor", "line_number": 96, "usage_type": "call"}, {"api_name": "numpy.floor", "line_number": 97, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 98, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 98, "usage_type": "attribute"}, {"api_name": "numpy.floor", "line_number": 105, "usage_type": "call"}, {"api_name": "numpy.floor", "line_number": 106, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 107, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 107, "usage_type": "attribute"}, {"api_name": "numpy.floor", "line_number": 109, "usage_type": "call"}, {"api_name": "numpy.floor", "line_number": 110, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 111, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 111, "usage_type": "attribute"}, {"api_name": "numpy.floor", "line_number": 113, "usage_type": "call"}, {"api_name": "numpy.floor", "line_number": 114, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 115, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 115, "usage_type": "attribute"}, {"api_name": "numpy.exp", "line_number": 118, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 118, "usage_type": "attribute"}, {"api_name": "numpy.floor", "line_number": 124, "usage_type": "call"}, {"api_name": "numpy.floor", "line_number": 125, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 127, "usage_type": "call"}, {"api_name": "numpy.floor", "line_number": 127, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 128, "usage_type": "call"}, {"api_name": "numpy.floor", "line_number": 128, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 131, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 131, "usage_type": "attribute"}, {"api_name": "numpy.floor", "line_number": 133, "usage_type": "call"}, {"api_name": "numpy.floor", "line_number": 134, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 135, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 135, "usage_type": "attribute"}, {"api_name": "numpy.floor", "line_number": 137, "usage_type": "call"}, {"api_name": "numpy.floor", "line_number": 138, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 139, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 139, "usage_type": "attribute"}, {"api_name": "numpy.exp", "line_number": 141, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 141, "usage_type": "attribute"}, {"api_name": "numpy.floor", "line_number": 143, "usage_type": "call"}, {"api_name": "numpy.floor", "line_number": 144, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 145, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 145, "usage_type": "attribute"}, {"api_name": "numpy.floor", "line_number": 147, "usage_type": "call"}, {"api_name": "numpy.floor", "line_number": 148, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 149, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 149, "usage_type": "attribute"}, {"api_name": "numpy.exp", "line_number": 152, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 152, "usage_type": "attribute"}, {"api_name": "numpy.floor", "line_number": 158, "usage_type": "call"}, {"api_name": "numpy.floor", "line_number": 159, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 160, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 160, "usage_type": "attribute"}, {"api_name": "numpy.floor", "line_number": 162, "usage_type": "call"}, {"api_name": "numpy.floor", "line_number": 163, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 164, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 164, "usage_type": "attribute"}, {"api_name": "numpy.floor", "line_number": 171, "usage_type": "call"}, {"api_name": "numpy.floor", "line_number": 173, "usage_type": "call"}, {"api_name": "scipy.special.jn_zeros", "line_number": 183, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 189, "usage_type": "attribute"}, {"api_name": "numpy.pi", "line_number": 191, "usage_type": "attribute"}, {"api_name": "numpy.arange", "line_number": 201, "usage_type": "call"}, {"api_name": "numpy.full", "line_number": 202, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 209, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 210, "usage_type": "call"}, {"api_name": "numpy.multiply", "line_number": 219, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 219, "usage_type": "call"}, {"api_name": "fzp_simulator.hankel_transform_MGS.Hankel_Transform_MGS", "line_number": 222, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 236, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 236, "usage_type": "attribute"}, {"api_name": "numpy.multiply", "line_number": 237, "usage_type": "call"}, {"api_name": "fzp_simulator.hankel_transform_MGS.Hankel_Transform_MGS", "line_number": 238, "usage_type": "call"}, {"api_name": "numpy.multiply", "line_number": 240, "usage_type": "call"}, {"api_name": "numpy.multiply", "line_number": 241, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 241, "usage_type": "call"}, {"api_name": "fzp_simulator.hankel_transform_MGS.Hankel_Transform_MGS", "line_number": 244, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 249, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 254, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 254, "usage_type": "attribute"}, {"api_name": "numpy.multiply", "line_number": 255, "usage_type": "call"}, {"api_name": "fzp_simulator.hankel_transform_MGS.Hankel_Transform_MGS", "line_number": 256, "usage_type": "call"}, {"api_name": "numpy.multiply", "line_number": 257, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 257, "usage_type": "call"}, {"api_name": "numpy.floor", "line_number": 261, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 265, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 265, "usage_type": "attribute"}, {"api_name": "numpy.multiply", "line_number": 266, "usage_type": "call"}, {"api_name": "fzp_simulator.hankel_transform_MGS.Hankel_Transform_MGS", "line_number": 267, "usage_type": "call"}, {"api_name": "numpy.multiply", "line_number": 268, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 268, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 274, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 274, "usage_type": "attribute"}, {"api_name": "numpy.multiply", "line_number": 275, "usage_type": "call"}, {"api_name": "fzp_simulator.hankel_transform_MGS.Hankel_Transform_MGS", "line_number": 276, "usage_type": "call"}, {"api_name": "numpy.floor", "line_number": 281, "usage_type": "call"}, {"api_name": "numpy.multiply", "line_number": 284, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 284, "usage_type": "call"}, {"api_name": "fzp_simulator.hankel_transform_MGS.Hankel_Transform_MGS", "line_number": 285, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 292, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 292, "usage_type": "attribute"}, {"api_name": "numpy.multiply", "line_number": 293, "usage_type": "call"}, {"api_name": "fzp_simulator.hankel_transform_MGS.Hankel_Transform_MGS", "line_number": 294, "usage_type": "call"}, {"api_name": "numpy.multiply", "line_number": 295, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 295, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 300, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 305, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 305, "usage_type": "attribute"}, {"api_name": "numpy.multiply", "line_number": 306, "usage_type": "call"}, {"api_name": "fzp_simulator.hankel_transform_MGS.Hankel_Transform_MGS", "line_number": 307, "usage_type": "call"}, {"api_name": "numpy.multiply", "line_number": 308, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 308, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 312, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 312, "usage_type": "attribute"}, {"api_name": "numpy.multiply", "line_number": 313, "usage_type": "call"}, {"api_name": "fzp_simulator.hankel_transform_MGS.Hankel_Transform_MGS", "line_number": 314, "usage_type": "call"}, {"api_name": "numpy.floor", "line_number": 319, "usage_type": "call"}, {"api_name": "fzp_simulator.hankel_transform_MGS.Hankel_Transform_MGS", "line_number": 321, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 325, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 325, "usage_type": "attribute"}, {"api_name": "numpy.multiply", "line_number": 326, "usage_type": "call"}, {"api_name": "fzp_simulator.hankel_transform_MGS.Hankel_Transform_MGS", "line_number": 327, "usage_type": "call"}, {"api_name": "numpy.multiply", "line_number": 328, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 328, "usage_type": "call"}, {"api_name": "numpy.floor", "line_number": 333, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 337, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 337, "usage_type": "attribute"}, {"api_name": "numpy.multiply", "line_number": 338, "usage_type": "call"}, {"api_name": "fzp_simulator.hankel_transform_MGS.Hankel_Transform_MGS", "line_number": 339, "usage_type": "call"}, {"api_name": "numpy.multiply", "line_number": 340, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 340, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 346, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 346, "usage_type": "attribute"}, {"api_name": "numpy.multiply", "line_number": 347, "usage_type": "call"}, {"api_name": "fzp_simulator.hankel_transform_MGS.Hankel_Transform_MGS", "line_number": 348, "usage_type": "call"}, {"api_name": "numpy.floor", "line_number": 353, "usage_type": "call"}, {"api_name": "numpy.multiply", "line_number": 356, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 356, "usage_type": "call"}, {"api_name": "fzp_simulator.hankel_transform_MGS.Hankel_Transform_MGS", "line_number": 357, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 364, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 364, "usage_type": "attribute"}, {"api_name": "numpy.multiply", "line_number": 365, "usage_type": "call"}, {"api_name": "fzp_simulator.hankel_transform_MGS.Hankel_Transform_MGS", "line_number": 366, "usage_type": "call"}, {"api_name": "numpy.multiply", "line_number": 367, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 367, "usage_type": "call"}, {"api_name": "numpy.full", "line_number": 372, "usage_type": "call"}, {"api_name": "numpy.flipud", "line_number": 375, "usage_type": "call"}, {"api_name": "numpy.flipud", "line_number": 378, "usage_type": "call"}, {"api_name": "fzp_simulator.efficiency_MGS.Efficiency_MGS", "line_number": 382, "usage_type": "call"}, {"api_name": "numpy.floor", "line_number": 382, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 397, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 397, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 399, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 399, "usage_type": "name"}, {"api_name": "sys.exit", "line_number": 407, "usage_type": "call"}]} +{"seq_id": "14141876887", "text": "\"\"\"Utility file to seed ratings database from MovieLens data in seed_data/\"\"\"\n\nfrom sqlalchemy import func\n\nfrom model import User, Post, Comment, connect_to_db, db\nfrom server import app\nimport time\nfrom datetime import datetime\n\n\ndef load_users():\n \"\"\"Load users into database.\"\"\"\n\n\n grace = User(email=\"gracelee.durham@gmail.com\", password=\"password\")\n db.session.add(grace)\n db.session.commit()\n\n## commenting out load posts because users are going to be inputting this info via Flask\ndef load_posts():\n \"\"\"Load posts into database.\"\"\"\n #deletes duplicates from the Post database when seed.py is ran\n\n img_url = \"https://www.hautelookcdn.com/resizer/434x650/products/JS2005/large/6156640.jpg\"\n \n added_at = datetime.now()\n title = \"Online Nordstrom Rack\"\n text = \"Jessica Simpson Madison ballet flats only $29.99 usually $69.99\"\n user = User.query.filter_by(email=\"gracelee.durham@gmail.com\").one()\n user_id = user.user_id\n\n post1 = Post(img_url=img_url, added_at=added_at, title=title, text=text, user_id=user_id)\n\n db.session.add(post1)\n db.session.commit()\n\n img_url = \"http://scene7.targetimg1.com/is/image/Target/14354227?wid=360&hei=360&qlt=80&fmt=pjpeg\"\n\n added_at = datetime.now()\n title = \"test post 2\"\n text = \"Great shoes at Nordstrom\"\n user = User.query.filter_by(email=\"gracelee.durham@gmail.com\").one()\n user_id = user.user_id\n\n post2 = Post(img_url=img_url, added_at=added_at, title=title, text=text, user_id=user_id)\n\n db.session.add(post2)\n db.session.commit()\n\n\n img_url = \"https://s-media-cache-ak0.pinimg.com/originals/e7/fd/65/e7fd6591a37ebd61ac6e19d454df8d45.jpg\"\n added_at = datetime.now()\n title = \"test post 3\"\n text = \"Great shoes at Target\"\n user = User.query.filter_by(email=\"gracelee.durham@gmail.com\").one()\n user_id = user.user_id\n\n post3 = Post(img_url=img_url, added_at=added_at, title=title, text=text, user_id=user_id)\n\n db.session.add(post3)\n db.session.commit()\n\nif __name__ == \"__main__\":\n connect_to_db(app)\n db.create_all()\n\n grace = User.query.filter_by(email=\"gracelee.durham@gmail.com\").first()\n \n #If grace is not there then we know we need to seed the database\n if(grace == None):\n load_users()\n load_posts()\n \n", "repo_name": "GraceDurham/Shoe-Spotting", "sub_path": "seed.py", "file_name": "seed.py", "file_ext": "py", "file_size_in_byte": 2295, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "37", "api": [{"api_name": "model.User", "line_number": 15, "usage_type": "call"}, {"api_name": "model.db.session.add", "line_number": 16, "usage_type": "call"}, {"api_name": "model.db.session", "line_number": 16, "usage_type": "attribute"}, {"api_name": "model.db", "line_number": 16, "usage_type": "name"}, {"api_name": "model.db.session.commit", "line_number": 17, "usage_type": "call"}, {"api_name": "model.db.session", "line_number": 17, "usage_type": "attribute"}, {"api_name": "model.db", "line_number": 17, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 26, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 26, "usage_type": "name"}, {"api_name": "model.User.query.filter_by", "line_number": 29, "usage_type": "call"}, {"api_name": "model.User.query", "line_number": 29, "usage_type": "attribute"}, {"api_name": "model.User", "line_number": 29, "usage_type": "name"}, {"api_name": "model.Post", "line_number": 32, "usage_type": "call"}, {"api_name": "model.db.session.add", "line_number": 34, "usage_type": "call"}, {"api_name": "model.db.session", "line_number": 34, "usage_type": "attribute"}, {"api_name": "model.db", "line_number": 34, "usage_type": "name"}, {"api_name": "model.db.session.commit", "line_number": 35, "usage_type": "call"}, {"api_name": "model.db.session", "line_number": 35, "usage_type": "attribute"}, {"api_name": "model.db", "line_number": 35, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 39, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 39, "usage_type": "name"}, {"api_name": "model.User.query.filter_by", "line_number": 42, "usage_type": "call"}, {"api_name": "model.User.query", "line_number": 42, "usage_type": "attribute"}, {"api_name": "model.User", "line_number": 42, "usage_type": "name"}, {"api_name": "model.Post", "line_number": 45, "usage_type": "call"}, {"api_name": "model.db.session.add", "line_number": 47, "usage_type": "call"}, {"api_name": "model.db.session", "line_number": 47, "usage_type": "attribute"}, {"api_name": "model.db", "line_number": 47, "usage_type": "name"}, {"api_name": "model.db.session.commit", "line_number": 48, "usage_type": "call"}, {"api_name": "model.db.session", "line_number": 48, "usage_type": "attribute"}, {"api_name": "model.db", "line_number": 48, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 52, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 52, "usage_type": "name"}, {"api_name": "model.User.query.filter_by", "line_number": 55, "usage_type": "call"}, {"api_name": "model.User.query", "line_number": 55, "usage_type": "attribute"}, {"api_name": "model.User", "line_number": 55, "usage_type": "name"}, {"api_name": "model.Post", "line_number": 58, "usage_type": "call"}, {"api_name": "model.db.session.add", "line_number": 60, "usage_type": "call"}, {"api_name": "model.db.session", "line_number": 60, "usage_type": "attribute"}, {"api_name": "model.db", "line_number": 60, "usage_type": "name"}, {"api_name": "model.db.session.commit", "line_number": 61, "usage_type": "call"}, {"api_name": "model.db.session", "line_number": 61, "usage_type": "attribute"}, {"api_name": "model.db", "line_number": 61, "usage_type": "name"}, {"api_name": "model.connect_to_db", "line_number": 64, "usage_type": "call"}, {"api_name": "server.app", "line_number": 64, "usage_type": "argument"}, {"api_name": "model.db.create_all", "line_number": 65, "usage_type": "call"}, {"api_name": "model.db", "line_number": 65, "usage_type": "name"}, {"api_name": "model.User.query.filter_by", "line_number": 67, "usage_type": "call"}, {"api_name": "model.User.query", "line_number": 67, "usage_type": "attribute"}, {"api_name": "model.User", "line_number": 67, "usage_type": "name"}]} +{"seq_id": "3620929279", "text": "import unittest\nimport unittest.mock\n\nimport numpy\nimport tensorflow as tf\nimport tensorflow_probability as tfp\n\nimport gandy.models.bnns\n\n\nclass TestBNN(unittest.TestCase):\n\n @unittest.mock.patch(\n 'gandy.models.bnns.BNN._build')\n def test_prior(self, mocked__build):\n \"\"\"Ensure handling of bias and kernel size.\n\n Function MUST pass tf.size(kernel), tf.size(bias)\n \"\"\"\n kernel_size = 5\n bias_size = 5\n subject = gandy.models.bnns.BNN((1,), (1,), train_size=5)\n # expected success must return a model\n prior = subject.prior(kernel_size, bias_size)\n self.assertTrue(isinstance(prior, tf.keras.Model))\n # failure cannot parse inputs\n with self.assertRaises(TypeError):\n subject.prior('kernel_size', 'bias_size')\n return\n\n @unittest.mock.patch(\n 'gandy.models.bnns.BNN._build')\n def test_posterior(self, mocked__build):\n \"\"\"Ensure handling of bias and kernel size.\n\n Function MUST pass tf.size(kernel), tf.size(bias)\n \"\"\"\n kernel_size = 5\n bias_size = 5\n subject = gandy.models.bnns.BNN((1,), (1,), train_size=5)\n # expected success must return a model\n prior = subject.posterior(kernel_size, bias_size)\n self.assertTrue(isinstance(prior, tf.keras.Model))\n # failure cannot parse inputs\n with self.assertRaises(TypeError):\n subject.prior('kernel_size', 'bias_size')\n return\n\n @unittest.mock.patch(\n 'gandy.models.bnns.BNN._build')\n def test_negative_loglikelihood(self, mocked_build):\n \"\"\"Input predictions are distributions instead of deterministic.\n\n Distribution should impliment log_prob method\n \"\"\"\n subject = gandy.models.bnns.BNN((1,), (1,), train_size=5)\n # failure mode, does not have method\n\n def callable_wo_log_prob():\n return\n with self.assertRaises(AttributeError):\n subject.negative_loglikelihood(numpy.array([1, 2]),\n callable_wo_log_prob)\n # expected success\n mocked_dist = unittest.mock.MagicMock()\n mocked_dist.log_prob.return_value = 5.0\n subject.negative_loglikelihood('targets',\n mocked_dist)\n mocked_dist.log_prob.assert_called_with('targets')\n\n return\n\n def test__build(self):\n \"\"\"The build should pass kwargs to the correct place.\n\n We need to ensure the returned keras model is both compiled\n and built.\n \"\"\"\n x = numpy.array([[1, 2],\n [3, 4],\n [5, 6]])\n # start with default initialization\n subject = gandy.models.bnns.BNN((2,), (4,), train_size=len(x))\n self.assertTrue(isinstance(subject.model, tf.keras.Model))\n self.assertTrue(subject.model._compile_was_called)\n self.assertTrue(subject.model.built)\n self.assertEqual(tuple(subject.model.input.shape.as_list())[1:],\n subject.xshape)\n predict = subject.model.predict(x)\n self.assertTrue(predict.shape == (3, 4))\n out = subject.model(x)\n self.assertTrue(isinstance(out, tfp.distributions.Distribution))\n\n # test keyword assignment\n subject = gandy.models.bnns.BNN((2,), (4,),\n train_size=len(x),\n optimizer='RMSprop')\n self.assertTrue(isinstance(subject.model.optimizer,\n tf.keras.optimizers.RMSprop))\n subject = gandy.models.bnns.BNN((2,), (4,),\n train_size=len(x),\n optimizer=tf.keras.optimizers.RMSprop)\n self.assertTrue(isinstance(subject.model.optimizer,\n tf.keras.optimizers.RMSprop))\n opt = tf.keras.optimizers.RMSprop()\n subject = gandy.models.bnns.BNN(\n (2,), (4,),\n train_size=len(x),\n optimizer=opt\n )\n self.assertTrue(subject.model.optimizer is opt)\n return\n\n def test__train(self):\n \"\"\"We just want to call the host fit method\"\"\"\n Xs = 'Xs'\n Ys = 'Ys'\n mocked_fit = unittest.mock.MagicMock(return_value='loss')\n subject = gandy.models.bnns.BNN((5,), (1,), train_size=2)\n subject.model.fit = mocked_fit\n losses = subject._train(Xs, Ys, epochs=10)\n mocked_fit.assert_called()\n self.assertEqual(losses, 'loss')\n return\n\n def test__predict(self):\n \"\"\"Predict for a probabilistic BNN is just letting the tensors\n flow, make sure it is passed to input.\n \"\"\"\n subject = gandy.models.bnns.BNN((5,), (1,), train_size=2)\n dists = unittest.mock.MagicMock()\n subject._model = unittest.mock.MagicMock(return_value=dists)\n subject._predict('Xs')\n subject.model.assert_called_with('Xs')\n dists.mean.assert_called()\n dists.stddev.assert_called()\n return\n\n def test_save(self):\n \"\"\"Save should just call keras save\"\"\"\n mocked_save = unittest.mock.MagicMock()\n subject = gandy.models.bnns.BNN((5,), (1,), train_size=2)\n subject.model.save = mocked_save\n subject.save('filename')\n mocked_save.assert_called_with('filename')\n return\n\n def test_load(self):\n \"\"\"load needs to use keras load, but then also stick it into a gandy\n model with the correct shape\n \"\"\"\n with unittest.mock.patch(\n 'tensorflow.keras.models.load_model'\n ) as mocked_load:\n subject = gandy.models.bnns.BNN.load('filename')\n self.assertTrue(isinstance(subject, gandy.models.bnns.BNN))\n mocked_load.assert_called_with('filename')\n return\n", "repo_name": "GANdy-team/GANdy", "sub_path": "gandy/tests/test_models/test_bnns.py", "file_name": "test_bnns.py", "file_ext": "py", "file_size_in_byte": 5901, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "unittest.TestCase", "line_number": 11, "usage_type": "attribute"}, {"api_name": "gandy.models.bnns.models.bnns.BNN", "line_number": 22, "usage_type": "call"}, {"api_name": "gandy.models.bnns.models", "line_number": 22, "usage_type": "attribute"}, {"api_name": "gandy.models.bnns", "line_number": 22, "usage_type": "name"}, {"api_name": "tensorflow.keras", "line_number": 25, "usage_type": "attribute"}, {"api_name": "unittest.mock.patch", "line_number": 13, "usage_type": "call"}, {"api_name": "unittest.mock", "line_number": 13, "usage_type": "attribute"}, {"api_name": "gandy.models.bnns.models.bnns.BNN", "line_number": 40, "usage_type": "call"}, {"api_name": "gandy.models.bnns.models", "line_number": 40, "usage_type": "attribute"}, {"api_name": "gandy.models.bnns", "line_number": 40, "usage_type": "name"}, {"api_name": "tensorflow.keras", "line_number": 43, "usage_type": "attribute"}, {"api_name": "unittest.mock.patch", "line_number": 31, "usage_type": "call"}, {"api_name": "unittest.mock", "line_number": 31, "usage_type": "attribute"}, {"api_name": "gandy.models.bnns.models.bnns.BNN", "line_number": 56, "usage_type": "call"}, {"api_name": "gandy.models.bnns.models", "line_number": 56, "usage_type": "attribute"}, {"api_name": "gandy.models.bnns", "line_number": 56, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 62, "usage_type": "call"}, {"api_name": "unittest.mock.MagicMock", "line_number": 65, "usage_type": "call"}, {"api_name": "unittest.mock", "line_number": 65, "usage_type": "attribute"}, {"api_name": "unittest.mock.patch", "line_number": 49, "usage_type": "call"}, {"api_name": "unittest.mock", "line_number": 49, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 79, "usage_type": "call"}, {"api_name": "gandy.models.bnns.models.bnns.BNN", "line_number": 83, "usage_type": "call"}, {"api_name": "gandy.models.bnns.models", "line_number": 83, "usage_type": "attribute"}, {"api_name": "gandy.models.bnns", "line_number": 83, "usage_type": "name"}, {"api_name": "tensorflow.keras", "line_number": 84, "usage_type": "attribute"}, {"api_name": "tensorflow_probability.distributions", "line_number": 92, "usage_type": "attribute"}, {"api_name": "gandy.models.bnns.models.bnns.BNN", "line_number": 95, "usage_type": "call"}, {"api_name": "gandy.models.bnns.models", "line_number": 95, "usage_type": "attribute"}, {"api_name": "gandy.models.bnns", "line_number": 95, "usage_type": "name"}, {"api_name": "tensorflow.keras", "line_number": 99, "usage_type": "attribute"}, {"api_name": "gandy.models.bnns.models.bnns.BNN", "line_number": 100, "usage_type": "call"}, {"api_name": "gandy.models.bnns.models", "line_number": 100, "usage_type": "attribute"}, {"api_name": "gandy.models.bnns", "line_number": 100, "usage_type": "name"}, {"api_name": "tensorflow.keras", "line_number": 102, "usage_type": "attribute"}, {"api_name": "tensorflow.keras", "line_number": 104, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.optimizers.RMSprop", "line_number": 105, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 105, "usage_type": "attribute"}, {"api_name": "gandy.models.bnns.models.bnns.BNN", "line_number": 106, "usage_type": "call"}, {"api_name": "gandy.models.bnns.models", "line_number": 106, "usage_type": "attribute"}, {"api_name": "gandy.models.bnns", "line_number": 106, "usage_type": "name"}, {"api_name": "unittest.mock.MagicMock", "line_number": 118, "usage_type": "call"}, {"api_name": "unittest.mock", "line_number": 118, "usage_type": "attribute"}, {"api_name": "gandy.models.bnns.models.bnns.BNN", "line_number": 119, "usage_type": "call"}, {"api_name": "gandy.models.bnns.models", "line_number": 119, "usage_type": "attribute"}, {"api_name": "gandy.models.bnns", "line_number": 119, "usage_type": "name"}, {"api_name": "gandy.models.bnns.models.bnns.BNN", "line_number": 130, "usage_type": "call"}, {"api_name": "gandy.models.bnns.models", "line_number": 130, "usage_type": "attribute"}, {"api_name": "gandy.models.bnns", "line_number": 130, "usage_type": "name"}, {"api_name": "unittest.mock.MagicMock", "line_number": 131, "usage_type": "call"}, {"api_name": "unittest.mock", "line_number": 131, "usage_type": "attribute"}, {"api_name": "unittest.mock.MagicMock", "line_number": 132, "usage_type": "call"}, {"api_name": "unittest.mock", "line_number": 132, "usage_type": "attribute"}, {"api_name": "unittest.mock.MagicMock", "line_number": 141, "usage_type": "call"}, {"api_name": "unittest.mock", "line_number": 141, "usage_type": "attribute"}, {"api_name": "gandy.models.bnns.models.bnns.BNN", "line_number": 142, "usage_type": "call"}, {"api_name": "gandy.models.bnns.models", "line_number": 142, "usage_type": "attribute"}, {"api_name": "gandy.models.bnns", "line_number": 142, "usage_type": "name"}, {"api_name": "unittest.mock.patch", "line_number": 152, "usage_type": "call"}, {"api_name": "unittest.mock", "line_number": 152, "usage_type": "attribute"}, {"api_name": "gandy.models.bnns.models.bnns.BNN.load", "line_number": 155, "usage_type": "call"}, {"api_name": "gandy.models.bnns.models", "line_number": 155, "usage_type": "attribute"}, {"api_name": "gandy.models.bnns", "line_number": 155, "usage_type": "name"}, {"api_name": "gandy.models.bnns.models", "line_number": 156, "usage_type": "attribute"}, {"api_name": "gandy.models.bnns", "line_number": 156, "usage_type": "name"}]} +{"seq_id": "9058944507", "text": "# -*- coding:utf-8 -*-\n\nimport pytest\nimport uiautomator2\nimport allure\nfrom element.element import ResourceId as id\nfrom element.element import Text\nimport time\n\n\n@allure.feature(\"首页-屏幕共享\")\n@pytest.mark.P0\nclass TestScreenShare(object):\n \"\"\"\n step1：启动ML\n step2：点击屏幕共享\n step3：判断菜单栏和结束共享的按钮\n \"\"\"\n d = uiautomator2.connect(id.IP)\n\n # @allure.step(\"前置条件\")\n def setup_class(self):\n self.d.dump_hierarchy\n if self.d.exists(resourceId=id.EXIT):\n self.d(resourceId=id.EXIT).click()\n\n # @allure.step(\"恢复测试环境\")\n def teardown_class(self):\n self.d.dump_hierarchy\n\n # 退出MindLinker\n try:\n assert self.d(resourceId=id.EXIT).exists\n except:\n self.d.app_stop(id.PACKAGE_NAME)\n else:\n self.d(resourceId=id.EXIT).click()\n\n @allure.story(\"执行用例\")\n def test_screenshare(self):\n # 等待启动应用\n self.d.app_start(id.PACKAGE_NAME)\n time.sleep(5)\n\n self.d.dump_hierarchy\n\n # 判断是否在MindLinker首页,点击屏幕共享\n if self.d.exists(text=Text.SHARE):\n self.d(resourceId=id.SCREEN_SHARE).click()\n else:\n raise LookupError\n time.sleep(6)\n # 等6进入视频会议\n self.d.dump_hierarchy\n txt = self.d(resourceId=id.SCREEN_SHARE_TEXT).get_text()\n if txt == \"结束共享\":\n time.sleep(5)\n\n self.d.dump_hierarchy\n assert self.d(resourceId=id.MEETING_ID).exists\n assert self.d(resourceId=id.MEETING_TIME).exists\n assert self.d(resourceId=id.SHARING_STATUS).exists\n\n self.d.click(3790, 1756)\n time.sleep(2)\n self.d.dump_hierarchy\n\n assert txt == \"结束共享\"\n # 点击离开\n self.d(resourceId=id.HANGUP_LAYOUT).click()\n time.sleep(2)\n self.d.dump_hierarchy\n\n # 点击结束会议\n try:\n assert self.d(resourceId=id.ALERT_CENTER_BUTTON).exists\n except:\n raise LookupError\n else:\n self.d(resourceId=id.ALERT_CENTER_BUTTON).click()\n time.sleep(3)\n\n\nif __name__ == '__main__':\n TestScreenShare()\n", "repo_name": "yuxhuanga/AutomationCase", "sub_path": "testcase/mindlinker/test_launcher_screenshare.py", "file_name": "test_launcher_screenshare.py", "file_ext": "py", "file_size_in_byte": 2274, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "uiautomator2.connect", "line_number": 19, "usage_type": "call"}, {"api_name": "element.element.ResourceId.IP", "line_number": 19, "usage_type": "attribute"}, {"api_name": "element.element.ResourceId", "line_number": 19, "usage_type": "name"}, {"api_name": "element.element.ResourceId.EXIT", "line_number": 24, "usage_type": "attribute"}, {"api_name": "element.element.ResourceId", "line_number": 24, "usage_type": "name"}, {"api_name": "element.element.ResourceId.EXIT", "line_number": 25, "usage_type": "attribute"}, {"api_name": "element.element.ResourceId", "line_number": 25, "usage_type": "name"}, {"api_name": "element.element.ResourceId.EXIT", "line_number": 33, "usage_type": "attribute"}, {"api_name": "element.element.ResourceId", "line_number": 33, "usage_type": "name"}, {"api_name": "element.element.ResourceId.PACKAGE_NAME", "line_number": 35, "usage_type": "attribute"}, {"api_name": "element.element.ResourceId", "line_number": 35, "usage_type": "name"}, {"api_name": "element.element.ResourceId.EXIT", "line_number": 37, "usage_type": "attribute"}, {"api_name": "element.element.ResourceId", "line_number": 37, "usage_type": "name"}, {"api_name": "element.element.ResourceId.PACKAGE_NAME", "line_number": 42, "usage_type": "attribute"}, {"api_name": "element.element.ResourceId", "line_number": 42, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 43, "usage_type": "call"}, {"api_name": "element.element.Text.SHARE", "line_number": 48, "usage_type": "attribute"}, {"api_name": "element.element.Text", "line_number": 48, "usage_type": "name"}, {"api_name": "element.element.ResourceId.SCREEN_SHARE", "line_number": 49, "usage_type": "attribute"}, {"api_name": "element.element.ResourceId", "line_number": 49, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 52, "usage_type": "call"}, {"api_name": "element.element.ResourceId.SCREEN_SHARE_TEXT", "line_number": 55, "usage_type": "attribute"}, {"api_name": "element.element.ResourceId", "line_number": 55, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 57, "usage_type": "call"}, {"api_name": "element.element.ResourceId.MEETING_ID", "line_number": 60, "usage_type": "attribute"}, {"api_name": "element.element.ResourceId", "line_number": 60, "usage_type": "name"}, {"api_name": "element.element.ResourceId.MEETING_TIME", "line_number": 61, "usage_type": "attribute"}, {"api_name": "element.element.ResourceId", "line_number": 61, "usage_type": "name"}, {"api_name": "element.element.ResourceId.SHARING_STATUS", "line_number": 62, "usage_type": "attribute"}, {"api_name": "element.element.ResourceId", "line_number": 62, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 65, "usage_type": "call"}, {"api_name": "element.element.ResourceId.HANGUP_LAYOUT", "line_number": 70, "usage_type": "attribute"}, {"api_name": "element.element.ResourceId", "line_number": 70, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 71, "usage_type": "call"}, {"api_name": "element.element.ResourceId.ALERT_CENTER_BUTTON", "line_number": 76, "usage_type": "attribute"}, {"api_name": "element.element.ResourceId", "line_number": 76, "usage_type": "name"}, {"api_name": "element.element.ResourceId.ALERT_CENTER_BUTTON", "line_number": 80, "usage_type": "attribute"}, {"api_name": "element.element.ResourceId", "line_number": 80, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 81, "usage_type": "call"}, {"api_name": "allure.story", "line_number": 39, "usage_type": "call"}, {"api_name": "allure.feature", "line_number": 11, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 12, "usage_type": "attribute"}]} +{"seq_id": "70665530027", "text": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sun Mar 29 02:16:54 2020\n\n@author: kazzastic\n\"\"\"\n\nfrom anything.gradcam import GradCam\nfrom tensorflow.keras.applications import ResNet50\nfrom tensorflow.keras.applications import VGG16\nfrom tensorflow.keras.preprocessing.image import img_to_array\nfrom tensorflow.keras.preprocessing.image import load_img\nfrom tensorflow.keras.models import load_model\n#from tensorflow.keras.applications import imagenet_utils\nfrom tensorflow.keras.applications.vgg16 import preprocess_input, decode_predictions\nimport numpy as np\nimport argparse\nimport imutils\nimport cv2\n\n\ndef prepare(filepath):\n IMG_SIZE = 220 # 50 in txt-based\n img_array = cv2.imread(filepath)\n new_array = cv2.resize(img_array, (IMG_SIZE, IMG_SIZE))\n\n return new_array.reshape(-1, IMG_SIZE, IMG_SIZE, 3)\n\nap = argparse.ArgumentParser()\nap.add_argument(\"-i\", \"--image\", required=True, help=\"path to input image\")\nap.add_argument(\"-m\", \"--model\", type=str, default=\"vgg\", choices=(\"vgg\", \"resnet\"), help=\"model to be used\")\nargs = vars(ap.parse_args())\n\nModel = VGG16\n\nif args[\"model\"] == \"resnet\":\n Model = ResNet50\n\nprint(\"[INFO] Loading Model...\")\nmodel = load_model(\"NIC-CNN.model\")\n\norig = cv2.imread(args[\"image\"])\n#resized = cv2.resize(orig, (224, 224))\n\n#image = load_img(args[\"image\"], target_size=(224, 224))\n#image = img_to_array(image)\n#image = np.expand_dims(image, axis=0)\n#image = preprocess_input(image)\nimage = prepare(args[\"image\"])\npreds = model.predict(image)\ni = np.argmax(preds[0])\n\n#decoded = decode_predictions(preds)\n#(imagenetID, label, prob) = decoded[0][0]\nclasses = [\"WHITE-HOUSE\", \"NIC\"]\nlabel = classes[int(preds[0][0])]\nlabel = \"{}:\".format(label)\nprint(\"[INFO] {}\".format(label))\n\ncam = GradCam(model, i)\nheatmap = cam.compute_heatmap(image)\n\nheatmap = cv2.resize(heatmap, (orig.shape[1], orig.shape[0]))\n(heatmap, output) = cam.overlay_heatmap(heatmap, orig, alpha=0.5)\n\n\ncv2.rectangle(output, (0,0), (340, 40), (0,0,0), -1)\ncv2.putText(output, label, (10, 25), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (255, 255, 255), 2)\n\noutput = np.vstack([orig, heatmap, output])\noutput = imutils.resize(output, height=700)\ncv2.imshow(\"Output\", output)\ncv2.waitKey(0)", "repo_name": "kazzastic/CNN-LayersVisualization", "sub_path": "apply_gradcam.py", "file_name": "apply_gradcam.py", "file_ext": "py", "file_size_in_byte": 2211, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "cv2.imread", "line_number": 25, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 26, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 30, "usage_type": "call"}, {"api_name": "tensorflow.keras.applications.VGG16", "line_number": 35, "usage_type": "name"}, {"api_name": "tensorflow.keras.applications.ResNet50", "line_number": 38, "usage_type": "name"}, {"api_name": "tensorflow.keras.models.load_model", "line_number": 41, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 43, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 52, "usage_type": "call"}, {"api_name": "anything.gradcam.GradCam", "line_number": 61, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 64, "usage_type": "call"}, {"api_name": "cv2.rectangle", "line_number": 68, "usage_type": "call"}, {"api_name": "cv2.putText", "line_number": 69, "usage_type": "call"}, {"api_name": "cv2.FONT_HERSHEY_SIMPLEX", "line_number": 69, "usage_type": "attribute"}, {"api_name": "numpy.vstack", "line_number": 71, "usage_type": "call"}, {"api_name": "imutils.resize", "line_number": 72, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 73, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 74, "usage_type": "call"}]} +{"seq_id": "13710686191", "text": "from django.urls import include, path\nfrom rest_framework.routers import DefaultRouter\nfrom .views import (\n UserViewSet,\n UserAPIView,\n CategoryViewSet,\n GenreViewSet,\n TitleViewSet,\n ReviewViewSet,\n CommentViewSet,\n )\n\n\nrouter = DefaultRouter()\nrouter.register(r'users', UserViewSet)\nrouter.register(r'categories', CategoryViewSet)\nrouter.register(r'genres', GenreViewSet)\nrouter.register(r'titles', TitleViewSet)\nrouter.register(r'titles/(?P\\d+)/reviews', ReviewViewSet, basename='reviews')\nrouter.register(r'titles/(?P\\d+)/reviews/(?P\\d+)/comments', CommentViewSet, basename='comments')\n\n\nurlpatterns = [\n path('users/me/', UserAPIView.as_view()),\n path('', include(router.urls)),\n path('auth/', include('djoser.urls')),\n path('auth/', include('djoser.urls.authtoken')),\n path('auth/', include('djoser.urls.jwt')),\n]\n", "repo_name": "GrimJ0/api_YamDB", "sub_path": "api/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 1019, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "rest_framework.routers.DefaultRouter", "line_number": 14, "usage_type": "call"}, {"api_name": "views.UserViewSet", "line_number": 15, "usage_type": "argument"}, {"api_name": "views.CategoryViewSet", "line_number": 16, "usage_type": "argument"}, {"api_name": "views.GenreViewSet", "line_number": 17, "usage_type": "argument"}, {"api_name": "views.TitleViewSet", "line_number": 18, "usage_type": "argument"}, {"api_name": "views.ReviewViewSet", "line_number": 19, "usage_type": "argument"}, {"api_name": "views.CommentViewSet", "line_number": 20, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 24, "usage_type": "call"}, {"api_name": "views.UserAPIView.as_view", "line_number": 24, "usage_type": "call"}, {"api_name": "views.UserAPIView", "line_number": 24, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 25, "usage_type": "call"}, {"api_name": "django.urls.include", "line_number": 25, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 26, "usage_type": "call"}, {"api_name": "django.urls.include", "line_number": 26, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 27, "usage_type": "call"}, {"api_name": "django.urls.include", "line_number": 27, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 28, "usage_type": "call"}, {"api_name": "django.urls.include", "line_number": 28, "usage_type": "call"}]} +{"seq_id": "16681473768", "text": "'''\nCreated on 2020/06/29\n\n@author: DXG\n'''\nimport csv\nimport os \nimport pandas\nfrom django.core.files.storage import FileSystemStorage\n\nBASE_DIR = os.path.dirname(os.path.dirname(__file__))\nCSV_FOLDER = os.path.join(BASE_DIR, \"resources\",'csv')\nJSON_FOLDER = os.path.join(BASE_DIR, \"resources\",'json')\n\ndef convert_file_csv_to_other(data_list,data_new):\n new_list= []\n news_list = []\n for x in data_list:\n for y in data_new:\n if x[1] == y[1]:\n new_list= x\n new_list.append(y[0])\n new_list.append(y[2])\n news_list.append(new_list) \n with open(CSV_FOLDER + '/' + 'convert_file.csv', mode='w' ,encoding=\"utf-8_sig\" ,newline='') as csv_file:\n fieldnames=['HANEI_DATE', 'SEI_NM','MEI_NM','ID_NO' ,'PRIORITY_MAILADDRESS', 'SHAIN_HAKEN_KBN','GROUP_ID','JINJI_SHOKUI_CD2',\n 'SEI_NMK','MEI_NMK',\n 'SEI_MCC_NME',\n 'MEI_MCC_NME',\n 'SEI_NME',\n 'MEI_NME',\n 'INTERNET_SUB_DOMAIN',\n 'SHINSEI_USER_ID','SHINSEI_SEIMEI_NM','SHONIN_USER_ID','SHONIN_SEIMEI_NM','SHINSEI_KAISHA_CD']\n writer = csv.DictWriter(csv_file, fieldnames=fieldnames) \n for obj in news_list:\n writer.writerow({'HANEI_DATE': '', 'SEI_NM': obj[7] , 'MEI_NM' : obj[8] ,\n 'ID_NO': 'OA' + obj[1],'PRIORITY_MAILADDRESS' : obj[28],\n 'SHAIN_HAKEN_KBN' : 3,\n 'GROUP_ID' : ';'.join([str(x) for x in obj[35]]),\n 'JINJI_SHOKUI_CD2' : ';'.join([i for i in obj[36]]),\n 'SEI_NMK' : 1,\n 'MEI_NMK' : 1,\n 'SEI_MCC_NME': '' ,\n 'MEI_MCC_NME': '' ,'SEI_NME': '' ,'MEI_NME': '' ,'INTERNET_SUB_DOMAIN': '' ,\n 'SHINSEI_USER_ID': '' ,'SHINSEI_SEIMEI_NM': '' ,'SHONIN_USER_ID': '' ,'SHONIN_SEIMEI_NM': '' ,\n 'SHINSEI_KAISHA_CD': 1\n })\n\ndef check_nan_data(item):\n if pandas.isna(item) == True:\n item = ''\n return item \n \ndef write_user_csv(data_list,data_user):\n news_list = []\n for x in data_list:\n for y in data_user:\n if x[3] == y[3]:\n x[0] = y[0]\n x[5] = y[5]\n x[19] = ''\n news_list.append(x) \n with open(CSV_FOLDER + '/' + 'new_file.csv', mode='w' ,encoding=\"utf-8_sig\" ,newline='') as csv_file:\n fieldnames=['HANEI_DATE', 'SEI_NM','MEI_NM','ID_NO' ,'PRIORITY_MAILADDRESS', 'SHAIN_HAKEN_KBN','GROUP_ID','JINJI_SHOKUI_CD2',\n 'SEI_NMK','MEI_NMK',\n 'SEI_MCC_NME',\n 'MEI_MCC_NME',\n 'SEI_NME',\n 'MEI_NME',\n 'INTERNET_SUB_DOMAIN',\n 'SHINSEI_USER_ID','SHINSEI_SEIMEI_NM','SHONIN_USER_ID','SHONIN_SEIMEI_NM','SHINSEI_KAISHA_CD','NEW_COL']\n writer = csv.DictWriter(csv_file, fieldnames=fieldnames) \n for obj in news_list:\n writer.writerow({'HANEI_DATE': check_nan_data(obj[0]), 'SEI_NM': obj[1] , 'MEI_NM' : obj[2] ,\n 'ID_NO': obj[3],'PRIORITY_MAILADDRESS' : obj[4],\n 'SHAIN_HAKEN_KBN' : obj[5],\n 'GROUP_ID' :check_nan_data(obj[6]) ,\n 'JINJI_SHOKUI_CD2' : check_nan_data(obj[7]),\n 'SEI_NMK' : check_nan_data(obj[8]),\n 'MEI_NMK' : check_nan_data(obj[9]),\n 'SEI_MCC_NME': check_nan_data(obj[10]) ,\n 'MEI_MCC_NME': check_nan_data(obj[11]) ,'SEI_NME': '' ,'MEI_NME': '' ,'INTERNET_SUB_DOMAIN': '' ,\n 'SHINSEI_USER_ID': '' ,'SHINSEI_SEIMEI_NM': '' ,'SHONIN_USER_ID': '' ,'SHONIN_SEIMEI_NM': '' ,\n 'SHINSEI_KAISHA_CD': check_nan_data(obj[19]),\n 'NEW_COL' : ''\n })\n \ndef read_data_csv(list_table):\n temp_dict = {}\n for x in list_table:\n if x[1] in temp_dict:\n temp_dict[x[1]][0].append(x[0])\n else:\n temp_dict[x[1]] = [[x[0]], x[1]]\n \n news_list = []\n for s in list(temp_dict.values()):\n new_list = []\n sokui_cd2_list=[]\n for i in list_table:\n if s[1] == i[1] and i[2] != '00' and pandas.isna(i[2]) == False:\n for a in s[0]:\n sokui = ''\n if a == i[0]:\n sokui= str(i[2]) + '(' +str(i[0])+ ')'\n sokui_cd2_list.append(sokui)\n new_list.append(s[0])\n new_list.append(s[1]) \n new_list.append(sokui_cd2_list) \n news_list.append(new_list) \n return news_list \n\n\ndef read_data_shozoku():\n df = pandas.read_csv(CSV_FOLDER + '/' + '70B.csv',usecols=[1,3,23],dtype={'OA_NO': str})\n data = df.values.tolist()\n return data\ndef read_data_people():\n df = pandas.read_csv(CSV_FOLDER + '/' + '70A.csv',dtype={'ID_NO': str})\n data = df.values.tolist()\n return data\ndef read_data_user():\n df = pandas.read_csv(CSV_FOLDER + '/' + 'use.csv',encoding = \"ISO-8859-1\", engine='python')\n data = df.values.tolist()\n return data \ndef read_convert_file ():\n df = pandas.read_csv(CSV_FOLDER + '/' + 'convert_file.csv')\n data = df.values.tolist()\n return data \n\ndef save_file(filename,file):\n new_file_name = filename[0:3]\n filename = new_file_name+'.csv'\n fs = FileSystemStorage()\n if fs.exists(filename) == True:\n fs.delete(filename) \n f = fs.save(filename, file)\n fileurl = fs.url(f)\n\n", "repo_name": "nqluan-vjp/site_analysis", "sub_path": "webapp/utils.py", "file_name": "utils.py", "file_ext": "py", "file_size_in_byte": 5985, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "os.path.dirname", "line_number": 11, "usage_type": "call"}, {"api_name": "os.path", "line_number": 11, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 12, "usage_type": "call"}, {"api_name": "os.path", "line_number": 12, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 13, "usage_type": "call"}, {"api_name": "os.path", "line_number": 13, "usage_type": "attribute"}, {"api_name": "csv.DictWriter", "line_number": 34, "usage_type": "call"}, {"api_name": "pandas.isna", "line_number": 50, "usage_type": "call"}, {"api_name": "csv.DictWriter", "line_number": 72, "usage_type": "call"}, {"api_name": "pandas.isna", "line_number": 101, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 115, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 119, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 123, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 127, "usage_type": "call"}, {"api_name": "django.core.files.storage.FileSystemStorage", "line_number": 134, "usage_type": "call"}]} +{"seq_id": "16149760297", "text": "import re\nfrom django.shortcuts import render, redirect\nfrom django.contrib import messages\nfrom django.contrib.auth.decorators import login_required\nfrom django.http import HttpResponse\nfrom django.utils import timezone\nfrom django.db.models import Q\nfrom .models import Team\nfrom .models import Submission\nfrom .models import Puzzle\nfrom .models import HintRequest\nfrom .forms import SubmitForm\nfrom .forms import HintForm\nfrom django_slack import slack_message\nfrom .globals import get_background, get_hunt_status, get_avail_hints\nfrom django.http import JsonResponse\n\ndef home(request):\n STATUS = get_hunt_status()\n bg = get_background(request)\n context = {\n 'background': bg,\n 'status': STATUS,\n }\n return render(request, 'hunt20/home.html', context)\n\ndef invalid(request):\n bg = get_background(request)\n context = {\n 'background': bg\n }\n return render(request, 'hunt20/invalid.html', context) \n\ndef about(request):\n bg = get_background(request)\n context = {\n 'background': bg\n }\n return render(request, 'hunt20/about.html', context)\n\ndef faq(request):\n bg = get_background(request)\n context = {\n 'background': bg\n }\n return render(request, 'hunt20/faq.html', context)\n\ndef guide(request):\n bg = get_background(request)\n context = {\n 'background': bg\n }\n return render(request, 'hunt20/guide.html', context)\n\ndef error_404(request, exception):\n return render(request,'hunt20/invalid.html', status = 404)\n\ndef error_500(request):\n return render(request,'hunt20/error.html', status = 500)\n\ndef leaderboard(request):\n bg = get_background(request)\n context = {\n 'teams': sorted(sorted(Team.objects.filter(username__is_superuser=False).filter(is_testsolver=False),key=lambda b: b.last_solve_datetime ), key=lambda a: a.total_solves, reverse=True), \n 'background': bg\n }\n return render(request, 'hunt20/leaderboard.html', context)\n\ndef bigboard(request):\n context = {\n 'teams': sorted(sorted(Team.objects.filter(username__is_superuser=False).filter(is_testsolver=False), key=lambda b: b.last_solve_datetime),key=lambda a: a.total_solves, reverse=True),\n 'puzzles1': sorted(Puzzle.objects.filter(in_round=1),key=lambda b: b.puzzle_id),\n 'puzzles2': sorted(Puzzle.objects.filter(in_round=2),key=lambda b: b.puzzle_id),\n 'puzzles3': sorted(Puzzle.objects.filter(in_round=3),key=lambda b: b.puzzle_id),\n }\n return render(request, 'hunt20/bigboard.html', context)\n\ndef testsolving(request):\n if request.user.is_superuser is False:\n return redirect('hunt20-invalid')\n else:\n context = {\n 'teams': sorted(sorted(Team.objects.filter(username__is_superuser=False).filter(is_testsolver=True), key=lambda b: b.last_solve_datetime),key=lambda a: a.total_solves, reverse=True),\n 'puzzles1': sorted(Puzzle.objects.filter(in_round=1),key=lambda b: b.puzzle_id),\n 'puzzles2': sorted(Puzzle.objects.filter(in_round=2),key=lambda b: b.puzzle_id),\n 'puzzles3': sorted(Puzzle.objects.filter(in_round=3),key=lambda b: b.puzzle_id),\n }\n return render(request, 'hunt20/bigboard.html', context)\n\n@login_required\ndef puzzles(request):\n STATUS = get_hunt_status()\n bg = get_background(request)\n context = {\n 'puzzles': sorted(Puzzle.objects.all(),key=lambda b: b.puzzle_id),\n 'in_round': request.user.team.in_round,\n 'background': bg,\n 'status': STATUS,\n }\n return render(request, 'hunt20/puzzles.html', context)\n\n@login_required\ndef round_archives(request, round_num):\n STATUS = get_hunt_status()\n if STATUS=='post':\n context = {\n 'puzzles': sorted(Puzzle.objects.filter(in_round=round_num),key=lambda b: b.puzzle_id),\n 'solved_ids': Submission.objects.filter(correct=True).filter(username=request.user.username).values_list('puzzle__puzzle_id', flat=True),\n }\n return render(request, 'hunt20/puzzles/r' + round_num + '.html', context)\n \n elif int(round_num) > request.user.team.in_round or (STATUS=='pre' and request.user.is_superuser is False and request.user.team.is_testsolver is False):\n return redirect('hunt20-invalid')\n \n else:\n context = {\n 'puzzles': sorted(Puzzle.objects.filter(in_round=round_num).filter(unlocks_at__lte=request.user.team.round_solves(round_num)),key=lambda b: b.puzzle_id),\n 'solved_ids': Submission.objects.filter(correct=True).filter(username=request.user.username).values_list('puzzle__puzzle_id', flat=True),\n }\n return render(request, 'hunt20/puzzles/r' + round_num + '.html', context)\n\n@login_required\ndef puzzle_archives(request, puzzle_id):\n STATUS = get_hunt_status()\n puzzle = Puzzle.objects.filter(puzzle_id=puzzle_id).first()\n\n if STATUS=='post':\n context = {\n 'teams': Team.objects.all(),\n 'puzzle_id': puzzle_id,\n 'puzzle': Puzzle.objects.filter(puzzle_id=puzzle_id).first(),\n 'solved': False,\n 'status': STATUS,\n }\n puzzle_html = 'hunt20/puzzles/' + puzzle_id + '.html'\n return render(request, puzzle_html, context)\n\n elif (STATUS=='pre' and request.user.is_superuser is False and request.user.team.is_testsolver is False) or ((int(puzzle.in_round) > request.user.team.in_round) or (puzzle.unlocks_at > request.user.team.round_solves(puzzle.in_round))):\n return redirect('hunt20-invalid')\n\n else:\n submissions = Submission.objects.filter(puzzle__puzzle_id=puzzle_id).filter(username=request.user.username)\n if submissions.filter(correct=True).exists():\n solved = True\n else:\n solved = False\n context = {\n 'teams': Team.objects.all(),\n 'puzzle_id': puzzle_id,\n 'puzzle': Puzzle.objects.filter(puzzle_id=puzzle_id).first(),\n 'solved': solved,\n 'status': STATUS, # change later\n }\n puzzle_html = 'hunt20/puzzles/' + puzzle_id + '.html'\n return render(request, puzzle_html, context)\n\n@login_required\ndef solution_archives(request, puzzle_id):\n STATUS = get_hunt_status()\n\n if (STATUS == 'post' or request.user.is_superuser):\n\n context = {\n 'puzzle_id': puzzle_id,\n 'puzzle': Puzzle.objects.filter(puzzle_id=puzzle_id).first(),\n }\n sol_html = 'hunt20/puzzles/' + puzzle_id + '_sol.html'\n return render(request, sol_html, context)\n\n else:\n return redirect('hunt20-invalid')\n\n@login_required\ndef submit(request, puzzle_id):\n STATUS = get_hunt_status()\n\n puzzle = Puzzle.objects.filter(puzzle_id=puzzle_id).first()\n\n if STATUS == \"pre\" and request.user.team.is_testsolver is False and request.user.is_superuser is False:\n return redirect('hunt20-invalid')\n\n elif (int(puzzle.in_round) > request.user.team.in_round) or (puzzle.unlocks_at > request.user.team.round_solves(puzzle.in_round)):\n return redirect('hunt20-invalid')\n \n else:\n round = Puzzle.objects.get(puzzle_id=puzzle_id).in_round\n submit_html = 'hunt20/puzzles/submit'+str(round)+'.html'\n\n def normalize(ans):\n regex = re.compile('[^a-zA-Z]')\n return regex.sub('', ans).upper()\n\n if request.method == 'POST':\n \n if STATUS == \"post\":\n messages.error(request, 'Hunt is over!')\n return redirect('hunt20-submit', puzzle_id=puzzle_id)\n \n else:\n\n form = SubmitForm(request.POST)\n if form.is_valid():\n submission = form.save(commit=False)\n submission.username = request.user.username\n submission.puzzle = Puzzle.objects.get(puzzle_id=puzzle_id)\n submission.eventdatetime = timezone.now()\n submission.team_ans = normalize(submission.team_ans)\n norm_ans = normalize(Puzzle.objects.get(puzzle_id=puzzle_id).puzzle_ans)\n norm_cluephrase = normalize(Puzzle.objects.get(puzzle_id=puzzle_id).puzzle_cluephrase)\n norm_midpoint = normalize(Puzzle.objects.get(puzzle_id=puzzle_id).puzzle_midpoint)\n # print(norm_ans)\n\n\n if Submission.objects.filter(puzzle__puzzle_id=puzzle_id).filter(username=request.user.username).filter(team_ans=submission.team_ans).exists():\n messages.warning(request, 'You have already submitted this answer')\n return redirect('hunt20-submit', puzzle_id=puzzle_id)\n \n elif norm_ans==submission.team_ans:\n submission.correct = True\n result = 'Correct!'\n submission = form.save()\n messages.success(request, 'Correct!')\n msg_emoji = ':white_check_mark:'\n \n elif norm_cluephrase==submission.team_ans and norm_cluephrase!=\"DNE\":\n submission.correct = False\n result = 'Cluephrase'\n submission = form.save()\n messages.warning(request, 'That is the final cluephrase for this puzzle!')\n msg_emoji = ':thinking_face:'\n \n elif norm_midpoint==submission.team_ans and norm_midpoint!=\"DNE\":\n submission.correct = False\n result = 'Midpoint'\n submission = form.save()\n messages.warning(request, 'On the right track! But you need to do a little more in the puzzle')\n msg_emoji = ':point_right:'\n\n else: \n submission.correct = False\n result = 'Incorrect'\n submission = form.save()\n messages.error(request, 'Incorrect')\n msg_emoji = ':x:'\n\n slack_message('hunt20/submission.slack',{\n 'guess':submission.team_ans,\n 'team':request.user.team.name,\n 'puzzle':Puzzle.objects.get(puzzle_id=puzzle_id).puzzle_name,\n 'result':result,\n 'emoji':'squirrel',\n 'msg_emoji': msg_emoji\n })\n\n return redirect('hunt20-submit', puzzle_id=puzzle_id)\n else:\n form = SubmitForm()\n \n submissions = Submission.objects.filter(puzzle__puzzle_id=puzzle_id).filter(username=request.user.username)\n if submissions.filter(correct=True).exists():\n solved = True\n else:\n solved = False\n context = {\n 'teams': Team.objects.all(),\n 'puzzle_id': puzzle_id,\n 'form': form,\n 'submissions': submissions,\n 'guesses_left': max(25-len(submissions),0),\n 'puzzle': Puzzle.objects.filter(puzzle_id=puzzle_id).first(),\n 'solved': solved,\n } \n return render(request, submit_html, context=context)\n\n@login_required\ndef hints(request):\n if request.user.team.is_testsolver:\n HINTS = 14\n else:\n HINTS = get_avail_hints()\n bg = get_background(request)\n STATUS = get_hunt_status()\n if request.method == 'POST':\n form = HintForm(user = request.user, data = request.POST)\n if form.is_valid():\n submission = form.save(commit=False)\n submission.username = request.user.username\n submission.eventdatetime = timezone.now()\n submission.answered = False\n submission = form.save()\n messages.success(request, 'Hint request submitted!')\n \n\n slack_message('hunt20/hint.slack',{\n 'question':submission.team_question,\n 'team':request.user.team.name,\n 'puzzle':submission.puzzle_name,\n 'emoji':'squirrel',\n })\n\n return redirect('hunt20-hints')\n\n else:\n form = HintForm(user=request.user)\n\n context = {\n 'hints': sorted(HintRequest.objects.filter(username=request.user.username), key=lambda b: b.eventdatetime, reverse=True),\n 'form': form,\n 'open_puzzles': Puzzle.objects.filter(Q(in_round__lte=request.user.team.in_round)),\n 'hints_available' : HINTS - HintRequest.objects.filter(username=request.user.username).filter(refunded=False).count(),\n 'testsolver': request.user.team.is_testsolver,\n 'background': bg,\n 'status': STATUS,\n } \n return render(request, 'hunt20/hints.html', context=context)\n\ndef insanity_check(request):\n s = request.GET.get(\"inputs\")\n if s[-83:]=='1'*83:\n if len(s)==83:\n output = '~A~'\n elif s[-84]!='1':\n output = '~A~'\n else:\n output = 'I'\n elif s[-69:]=='2'*69:\n if len(s)==69:\n output = '~S~'\n elif s[-70]!='2':\n output = '~S~'\n else:\n output = 'N'\n elif s[-84:]=='3'*84:\n if len(s)==84:\n output = '~C~'\n elif s[-85]!='3':\n output = '~C~'\n else:\n output = 'S'\n elif s[-79:]=='4'*79:\n if len(s)==79:\n output = '~I~'\n elif s[-80]!='4':\n output = '~I~'\n else:\n output = 'A'\n elif s[-70:]=='5'*70:\n if len(s)==70:\n output = '~I~'\n elif s[-71]!='5':\n output = '~I~'\n else:\n output = 'N'\n elif s[-50:]=='6'*50:\n if len(s)==50:\n output = '~!~'\n elif s[-51]!='6':\n output = '~!~'\n else:\n output = 'E'\n #round2\n elif s[-28:]=='12'*14:\n if len(s)<30:\n output = '~S~'\n elif s[-30:-28]!='12':\n output = '~S~'\n else:\n output = 'N'\n elif s[-30:]=='13'*15:\n if len(s)<32:\n output = '~O~'\n elif s[-32:-30]!='13':\n output = '~O~'\n else:\n output = 'S'\n elif s[-46:]=='14'*23:\n if len(s)<48:\n output = '~C~'\n elif s[-48:-46]!='14':\n output = '~C~'\n else:\n output = 'A'\n elif s[-40:]=='15'*20:\n if len(s)<42:\n output = '~R~'\n elif s[-42:-40]!='15':\n output = '~R~'\n else:\n output = 'N'\n elif s[-50:]=='16'*25:\n if len(s)<52:\n output = '~A~'\n elif s[-52:-50]!='16':\n output = '~A~'\n else:\n output = 'E'\n elif s[-32:]=='23'*16:\n if len(s)<34:\n output = '~Z~'\n elif s[-34:-32]!='23':\n output = '~Z~'\n else:\n output = 'S'\n elif s[-10:]=='24'*5:\n if len(s)<12:\n output = '~Y~'\n elif s[-12:-10]!='24':\n output = '~Y~'\n else:\n output = 'A'\n elif s[-18:]=='25'*9:\n if len(s)<20:\n output = '~I~'\n elif s[-20:-18]!='25':\n output = '~I~'\n else:\n output = 'N'\n elif s[-28:]=='26'*14:\n if len(s)<30:\n output = '~A~'\n elif s[-30:-28]!='26':\n output = '~A~'\n else:\n output = 'E'\n elif s[-32:]=='34'*16:\n if len(s)<34:\n output = '~M~'\n elif s[-34:-32]!='34':\n output = '~M~'\n else:\n output = 'A'\n elif s[-38:]=='35'*19:\n if len(s)<40:\n output = '~B~'\n elif s[-40:-38]!='35':\n output = '~B~'\n else:\n output = 'N'\n elif s[-50:]=='36'*25:\n if len(s)<52:\n output = '~L~'\n elif s[-52:-50]!='36':\n output = '~L~'\n else:\n output = 'E'\n elif s[-6:]=='45'*3:\n if len(s)<8:\n output = '~I~'\n elif s[-8:-6]!='45':\n output = '~I~'\n else:\n output = 'N'\n elif s[-16:]=='46'*8:\n if len(s)<18:\n output = '~N~'\n elif s[-18:-16]!='46':\n output = '~N~'\n else:\n output = 'E'\n elif s[-30:]=='56'*15:\n if len(s)<32:\n output = '~D~'\n elif s[-32:-30]!='56':\n output = '~D~'\n else:\n output = 'E'\n #braille\n elif s[-20:]=='12352351245612134145':\n output = \"~Hurley's Numbers~\"\n #lost\n elif s[-108:]==('1'*4)+('2'*8)+('3'*15)+('4'*16)+('5'*23)+('6'*42):\n output = 'Your answer is the location of the art gallery known as \"The Museum of Insanity\"'\n #base\n elif s[-1:]=='1':\n output = 'I'\n elif s[-1:]=='2':\n output = 'N'\n elif s[-1:]=='3':\n output = 'S'\n elif s[-1:]=='4':\n output = 'A'\n elif s[-1:]=='5':\n output = 'N'\n elif s[-1:]=='6':\n output = 'E'\n\n data = {\n 'output': output\n }\n return JsonResponse(data,status = 200)", "repo_name": "carvanapuzzles/2020Hunt", "sub_path": "hunt20/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 17181, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "globals.get_hunt_status", "line_number": 19, "usage_type": "call"}, {"api_name": "globals.get_background", "line_number": 20, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 25, "usage_type": "call"}, {"api_name": "globals.get_background", "line_number": 28, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 32, "usage_type": "call"}, {"api_name": "globals.get_background", "line_number": 35, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 39, "usage_type": "call"}, {"api_name": "globals.get_background", "line_number": 42, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 46, "usage_type": "call"}, {"api_name": "globals.get_background", "line_number": 49, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 53, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 56, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 59, "usage_type": "call"}, {"api_name": "globals.get_background", "line_number": 62, "usage_type": "call"}, {"api_name": "models.Team.objects.filter", "line_number": 64, "usage_type": "call"}, {"api_name": "models.Team.objects", "line_number": 64, "usage_type": "attribute"}, {"api_name": "models.Team", "line_number": 64, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 67, "usage_type": "call"}, {"api_name": "models.Team.objects.filter", "line_number": 71, "usage_type": "call"}, {"api_name": "models.Team.objects", "line_number": 71, "usage_type": "attribute"}, {"api_name": "models.Team", "line_number": 71, "usage_type": "name"}, {"api_name": "models.Puzzle.objects.filter", "line_number": 72, "usage_type": "call"}, {"api_name": "models.Puzzle.objects", "line_number": 72, "usage_type": "attribute"}, {"api_name": "models.Puzzle", "line_number": 72, "usage_type": "name"}, {"api_name": "models.Puzzle.objects.filter", "line_number": 73, "usage_type": "call"}, {"api_name": "models.Puzzle.objects", "line_number": 73, "usage_type": "attribute"}, {"api_name": "models.Puzzle", "line_number": 73, "usage_type": "name"}, {"api_name": "models.Puzzle.objects.filter", "line_number": 74, "usage_type": "call"}, {"api_name": "models.Puzzle.objects", "line_number": 74, "usage_type": "attribute"}, {"api_name": "models.Puzzle", "line_number": 74, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 76, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 80, "usage_type": "call"}, {"api_name": "models.Team.objects.filter", "line_number": 83, "usage_type": "call"}, {"api_name": "models.Team.objects", "line_number": 83, "usage_type": "attribute"}, {"api_name": "models.Team", "line_number": 83, "usage_type": "name"}, {"api_name": "models.Puzzle.objects.filter", "line_number": 84, "usage_type": "call"}, {"api_name": "models.Puzzle.objects", "line_number": 84, "usage_type": "attribute"}, {"api_name": "models.Puzzle", "line_number": 84, "usage_type": "name"}, {"api_name": "models.Puzzle.objects.filter", "line_number": 85, "usage_type": "call"}, {"api_name": "models.Puzzle.objects", "line_number": 85, "usage_type": "attribute"}, {"api_name": "models.Puzzle", "line_number": 85, "usage_type": "name"}, {"api_name": "models.Puzzle.objects.filter", "line_number": 86, "usage_type": "call"}, {"api_name": "models.Puzzle.objects", "line_number": 86, "usage_type": "attribute"}, {"api_name": "models.Puzzle", "line_number": 86, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 88, "usage_type": "call"}, {"api_name": "globals.get_hunt_status", "line_number": 92, "usage_type": "call"}, {"api_name": "globals.get_background", "line_number": 93, "usage_type": "call"}, {"api_name": "models.Puzzle.objects.all", "line_number": 95, "usage_type": "call"}, {"api_name": "models.Puzzle.objects", "line_number": 95, "usage_type": "attribute"}, {"api_name": "models.Puzzle", "line_number": 95, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 100, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 90, "usage_type": "name"}, {"api_name": "globals.get_hunt_status", "line_number": 104, "usage_type": "call"}, {"api_name": "models.Puzzle.objects.filter", "line_number": 107, "usage_type": "call"}, {"api_name": "models.Puzzle.objects", "line_number": 107, "usage_type": "attribute"}, {"api_name": "models.Puzzle", "line_number": 107, "usage_type": "name"}, {"api_name": "models.Submission.objects.filter", "line_number": 108, "usage_type": "call"}, {"api_name": "models.Submission.objects", "line_number": 108, "usage_type": "attribute"}, {"api_name": "models.Submission", "line_number": 108, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 110, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 113, "usage_type": "call"}, {"api_name": "models.Puzzle.objects.filter", "line_number": 117, "usage_type": "call"}, {"api_name": "models.Puzzle.objects", "line_number": 117, "usage_type": "attribute"}, {"api_name": "models.Puzzle", "line_number": 117, "usage_type": "name"}, {"api_name": "models.Submission.objects.filter", "line_number": 118, "usage_type": "call"}, {"api_name": "models.Submission.objects", "line_number": 118, "usage_type": "attribute"}, {"api_name": "models.Submission", "line_number": 118, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 120, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 102, "usage_type": "name"}, {"api_name": "globals.get_hunt_status", "line_number": 124, "usage_type": "call"}, {"api_name": "models.Puzzle.objects.filter", "line_number": 125, "usage_type": "call"}, {"api_name": "models.Puzzle.objects", "line_number": 125, "usage_type": "attribute"}, {"api_name": "models.Puzzle", "line_number": 125, "usage_type": "name"}, {"api_name": "models.Team.objects.all", "line_number": 129, "usage_type": "call"}, {"api_name": "models.Team.objects", "line_number": 129, "usage_type": "attribute"}, {"api_name": "models.Team", "line_number": 129, "usage_type": "name"}, {"api_name": "models.Puzzle.objects.filter", "line_number": 131, "usage_type": "call"}, {"api_name": "models.Puzzle.objects", "line_number": 131, "usage_type": "attribute"}, {"api_name": "models.Puzzle", "line_number": 131, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 136, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 139, "usage_type": "call"}, {"api_name": "models.Submission.objects.filter", "line_number": 142, "usage_type": "call"}, {"api_name": "models.Submission.objects", "line_number": 142, "usage_type": "attribute"}, {"api_name": "models.Submission", "line_number": 142, "usage_type": "name"}, {"api_name": "models.Team.objects.all", "line_number": 148, "usage_type": "call"}, {"api_name": "models.Team.objects", "line_number": 148, "usage_type": "attribute"}, {"api_name": "models.Team", "line_number": 148, "usage_type": "name"}, {"api_name": "models.Puzzle.objects.filter", "line_number": 150, "usage_type": "call"}, {"api_name": "models.Puzzle.objects", "line_number": 150, "usage_type": "attribute"}, {"api_name": "models.Puzzle", "line_number": 150, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 155, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 122, "usage_type": "name"}, {"api_name": "globals.get_hunt_status", "line_number": 159, "usage_type": "call"}, {"api_name": "models.Puzzle.objects.filter", "line_number": 165, "usage_type": "call"}, {"api_name": "models.Puzzle.objects", "line_number": 165, "usage_type": "attribute"}, {"api_name": "models.Puzzle", "line_number": 165, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 168, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 171, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 157, "usage_type": "name"}, {"api_name": "globals.get_hunt_status", "line_number": 175, "usage_type": "call"}, {"api_name": "models.Puzzle.objects.filter", "line_number": 177, "usage_type": "call"}, {"api_name": "models.Puzzle.objects", "line_number": 177, "usage_type": "attribute"}, {"api_name": "models.Puzzle", "line_number": 177, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 180, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 183, "usage_type": "call"}, {"api_name": "models.Puzzle.objects.get", "line_number": 186, "usage_type": "call"}, {"api_name": "models.Puzzle.objects", "line_number": 186, "usage_type": "attribute"}, {"api_name": "models.Puzzle", "line_number": 186, "usage_type": "name"}, {"api_name": "re.compile", "line_number": 190, "usage_type": "call"}, {"api_name": "django.contrib.messages.error", "line_number": 196, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 196, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 197, "usage_type": "call"}, {"api_name": "forms.SubmitForm", "line_number": 201, "usage_type": "call"}, {"api_name": "models.Puzzle.objects.get", "line_number": 205, "usage_type": "call"}, {"api_name": "models.Puzzle.objects", "line_number": 205, "usage_type": "attribute"}, {"api_name": "models.Puzzle", "line_number": 205, "usage_type": "name"}, {"api_name": "django.utils.timezone.now", "line_number": 206, "usage_type": "call"}, {"api_name": "django.utils.timezone", "line_number": 206, "usage_type": "name"}, {"api_name": "models.Puzzle.objects.get", "line_number": 208, "usage_type": "call"}, {"api_name": "models.Puzzle.objects", "line_number": 208, "usage_type": "attribute"}, {"api_name": "models.Puzzle", "line_number": 208, "usage_type": "name"}, {"api_name": "models.Puzzle.objects.get", "line_number": 209, "usage_type": "call"}, {"api_name": "models.Puzzle.objects", "line_number": 209, "usage_type": "attribute"}, {"api_name": "models.Puzzle", "line_number": 209, "usage_type": "name"}, {"api_name": "models.Puzzle.objects.get", "line_number": 210, "usage_type": "call"}, {"api_name": "models.Puzzle.objects", "line_number": 210, "usage_type": "attribute"}, {"api_name": "models.Puzzle", "line_number": 210, "usage_type": "name"}, {"api_name": "models.Submission.objects.filter", "line_number": 214, "usage_type": "call"}, {"api_name": "models.Submission.objects", "line_number": 214, "usage_type": "attribute"}, {"api_name": "models.Submission", "line_number": 214, "usage_type": "name"}, {"api_name": "django.contrib.messages.warning", "line_number": 215, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 215, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 216, "usage_type": "call"}, {"api_name": "django.contrib.messages.success", "line_number": 222, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 222, "usage_type": "name"}, {"api_name": "django.contrib.messages.warning", "line_number": 229, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 229, "usage_type": "name"}, {"api_name": "django.contrib.messages.warning", "line_number": 236, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 236, "usage_type": "name"}, {"api_name": "django.contrib.messages.error", "line_number": 243, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 243, "usage_type": "name"}, {"api_name": "django_slack.slack_message", "line_number": 246, "usage_type": "call"}, {"api_name": "models.Puzzle.objects.get", "line_number": 249, "usage_type": "call"}, {"api_name": "models.Puzzle.objects", "line_number": 249, "usage_type": "attribute"}, {"api_name": "models.Puzzle", "line_number": 249, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 255, "usage_type": "call"}, {"api_name": "forms.SubmitForm", "line_number": 257, "usage_type": "call"}, {"api_name": "models.Submission.objects.filter", "line_number": 259, "usage_type": "call"}, {"api_name": "models.Submission.objects", "line_number": 259, "usage_type": "attribute"}, {"api_name": "models.Submission", "line_number": 259, "usage_type": "name"}, {"api_name": "models.Team.objects.all", "line_number": 265, "usage_type": "call"}, {"api_name": "models.Team.objects", "line_number": 265, "usage_type": "attribute"}, {"api_name": "models.Team", "line_number": 265, "usage_type": "name"}, {"api_name": "models.Puzzle.objects.filter", "line_number": 270, "usage_type": "call"}, {"api_name": "models.Puzzle.objects", "line_number": 270, "usage_type": "attribute"}, {"api_name": "models.Puzzle", "line_number": 270, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 273, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 173, "usage_type": "name"}, {"api_name": "globals.get_avail_hints", "line_number": 280, "usage_type": "call"}, {"api_name": "globals.get_background", "line_number": 281, "usage_type": "call"}, {"api_name": "globals.get_hunt_status", "line_number": 282, "usage_type": "call"}, {"api_name": "forms.HintForm", "line_number": 284, "usage_type": "call"}, {"api_name": "django.utils.timezone.now", "line_number": 288, "usage_type": "call"}, {"api_name": "django.utils.timezone", "line_number": 288, "usage_type": "name"}, {"api_name": "django.contrib.messages.success", "line_number": 291, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 291, "usage_type": "name"}, {"api_name": "django_slack.slack_message", "line_number": 294, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 301, "usage_type": "call"}, {"api_name": "forms.HintForm", "line_number": 304, "usage_type": "call"}, {"api_name": "models.HintRequest.objects.filter", "line_number": 307, "usage_type": "call"}, {"api_name": "models.HintRequest.objects", "line_number": 307, "usage_type": "attribute"}, {"api_name": "models.HintRequest", "line_number": 307, "usage_type": "name"}, {"api_name": "models.Puzzle.objects.filter", "line_number": 309, "usage_type": "call"}, {"api_name": "models.Puzzle.objects", "line_number": 309, "usage_type": "attribute"}, {"api_name": "models.Puzzle", "line_number": 309, "usage_type": "name"}, {"api_name": "django.db.models.Q", "line_number": 309, "usage_type": "call"}, {"api_name": "models.HintRequest.objects.filter", "line_number": 310, "usage_type": "call"}, {"api_name": "models.HintRequest.objects", "line_number": 310, "usage_type": "attribute"}, {"api_name": "models.HintRequest", "line_number": 310, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 315, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 275, "usage_type": "name"}, {"api_name": "django.http.JsonResponse", "line_number": 490, "usage_type": "call"}]} +{"seq_id": "34327698763", "text": "\"\"\"\ncomponent_ui.py\n\n@author: ppararaj\n\"\"\"\n\nimport argparse\n\n\nparser = argparse.ArgumentParser(prog='pysam_bam2fq',\n description=\"\"\"Converts BAM to FASTQ.\"\"\")\nparser.add_argument('bam', help='BAM to convert to FASTQ.')\nparser.add_argument('output_dir', default='./',\n help='Output directory of the FASTQ files.')\nparser.add_argument('interval_file', type=argparse.FileType('w'),\n help='Interval file that will be created.')\nparser.add_argument('--num_reads', '-n', type=int, default=75000000,\n help='Maximum number of reads per FASTQ file.')\nargs, unknown = parser.parse_known_args()\n", "repo_name": "morinlab/pipeline-components", "sub_path": "pysam_bam2fq/component_ui.py", "file_name": "component_ui.py", "file_ext": "py", "file_size_in_byte": 673, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 10, "usage_type": "call"}, {"api_name": "argparse.FileType", "line_number": 15, "usage_type": "call"}]} +{"seq_id": "17829336956", "text": "import matplotlib.pyplot as plt\nimport torch\nimport torch.nn as nn\nfrom dataloader import train_dataloader\nimport time\nimport os\n\ndef train_model(G, D, dataloader, num_epochs):\n\n # GPUが使えるかを確認\n device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n print(\"使用デバイス：\", device)\n\n # 最適化手法の設定\n g_lr, d_lr = 0.0001, 0.0004\n beta1, beta2 = 0.0, 0.9\n g_optimizer = torch.optim.Adam(G.parameters(), g_lr, [beta1, beta2])\n d_optimizer = torch.optim.Adam(D.parameters(), d_lr, [beta1, beta2])\n\n # 誤差関数を定義 → hinge version of the adversarial lossに変更\n # criterion = nn.BCEWithLogitsLoss(reduction='mean')\n\n # パラメータをハードコーディング\n z_dim = 20\n mini_batch_size = 64\n\n # ネットワークをGPUへ\n G.to(device)\n D.to(device)\n\n G.train() # モデルを訓練モードに\n D.train() # モデルを訓練モードに\n\n # ネットワークがある程度固定であれば、高速化させる\n torch.backends.cudnn.benchmark = True\n\n # 画像の枚数\n num_train_imgs = len(dataloader.dataset)\n batch_size = dataloader.batch_size\n\n # イテレーションカウンタをセット\n iteration = 1\n logs = []\n\n # epochのループ\n for epoch in range(num_epochs):\n\n # 開始時刻を保存\n t_epoch_start = time.time()\n epoch_g_loss = 0.0 # epochの損失和\n epoch_d_loss = 0.0 # epochの損失和\n\n print('-------------')\n print('Epoch {}/{}'.format(epoch, num_epochs))\n print('-------------')\n print('（train）')\n if os.path.exists(f'./obj/d.{epoch}.obj') and os.path.exists(f'obj/g.{epoch}.obj'):\n D.load_state_dict(torch.load(f'./obj/d.{epoch}.obj'))\n G.load_state_dict(torch.load(f'./obj/g.{epoch}.obj'))\n continue\n # データローダーからminibatchずつ取り出すループ\n for imges in dataloader:\n\n # --------------------\n # 1. Discriminatorの学習\n # --------------------\n # ミニバッチがサイズが1だと、バッチノーマライゼーションでエラーになるのでさける\n if imges.size()[0] == 1:\n continue\n\n # GPUが使えるならGPUにデータを送る\n imges = imges.to(device)\n\n # 正解ラベルと偽ラベルを作成\n # epochの最後のイテレーションはミニバッチの数が少なくなる\n mini_batch_size = imges.size()[0]\n #label_real = torch.full((mini_batch_size,), 1).to(device)\n #label_fake = torch.full((mini_batch_size,), 0).to(device)\n\n # 真の画像を判定\n d_out_real, _, _ = D(imges)\n\n # 偽の画像を生成して判定\n input_z = torch.randn(mini_batch_size, z_dim).to(device)\n input_z = input_z.view(input_z.size(0), input_z.size(1), 1, 1)\n fake_images, _, _ = G(input_z)\n d_out_fake, _, _ = D(fake_images)\n\n # 誤差を計算→hinge version of the adversarial lossに変更\n # d_loss_real = criterion(d_out_real.view(-1), label_real)\n # d_loss_fake = criterion(d_out_fake.view(-1), label_fake)\n\n d_loss_real = torch.nn.ReLU()(1.0 - d_out_real).mean()\n # 誤差　d_out_realが1以上で誤差0になる。d_out_real>1で、\n # 1.0 - d_out_realが負の場合ReLUで0にする\n\n d_loss_fake = torch.nn.ReLU()(1.0 + d_out_fake).mean()\n # 誤差　d_out_fakeが-1以下なら誤差0になる。d_out_fake<-1で、\n # 1.0 + d_out_realが負の場合ReLUで0にする\n\n d_loss = d_loss_real + d_loss_fake\n\n # バックプロパゲーション\n g_optimizer.zero_grad()\n d_optimizer.zero_grad()\n\n d_loss.backward()\n d_optimizer.step()\n\n # --------------------\n # 2. Generatorの学習\n # --------------------\n # 偽の画像を生成して判定\n input_z = torch.randn(mini_batch_size, z_dim).to(device)\n input_z = input_z.view(input_z.size(0), input_z.size(1), 1, 1)\n fake_images, _, _ = G(input_z)\n d_out_fake, _, _ = D(fake_images)\n\n # 誤差を計算→hinge version of the adversarial lossに変更\n #g_loss = criterion(d_out_fake.view(-1), label_real)\n g_loss = - d_out_fake.mean()\n\n # バックプロパゲーション\n g_optimizer.zero_grad()\n d_optimizer.zero_grad()\n g_loss.backward()\n g_optimizer.step()\n\n # --------------------\n # 3. 記録\n # --------------------\n epoch_d_loss += d_loss.item()\n epoch_g_loss += g_loss.item()\n iteration += 1\n\n # epochのphaseごとのlossと正解率\n t_epoch_finish = time.time()\n print('-------------')\n print('epoch {} || Epoch_D_Loss:{:.4f} ||Epoch_G_Loss:{:.4f}'.format(\n epoch, epoch_d_loss/batch_size, epoch_g_loss/batch_size))\n print('timer: {:.4f} sec.'.format(t_epoch_finish - t_epoch_start))\n t_epoch_start = time.time()\n\n # print(\"総イテレーション回数:\", iteration)\n torch.save(G.state_dict(), f'obj/g.{epoch}.obj')\n torch.save(D.state_dict(), f'obj/d.{epoch}.obj')\n return G, D\n\n\ndef weights_init(m):\n classname = m.__class__.__name__\n if classname.find('Conv') != -1:\n # Conv2dとConvTranspose2dの初期化\n nn.init.normal_(m.weight.data, 0.0, 0.02)\n nn.init.constant_(m.bias.data, 0)\n elif classname.find('BatchNorm') != -1:\n # BatchNorm2dの初期化\n nn.init.normal_(m.weight.data, 1.0, 0.02)\n nn.init.constant_(m.bias.data, 0)\n\n\ndef check(G_update):\n device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n\n # 入力の乱数生成\n batch_size = 8\n z_dim = 20\n fixed_z = torch.randn(batch_size, z_dim)\n fixed_z = fixed_z.view(fixed_z.size(0), fixed_z.size(1), 1, 1)\n\n # 画像生成\n fake_images, am1, am2 = G_update(fixed_z.to(device))\n\n # 訓練データ\n batch_iterator = iter(train_dataloader) # イテレータに変換\n imges = next(batch_iterator) # 1番目の要素を取り出す\n\n # 出力\n fig = plt.figure(figsize=(15, 6))\n for i in range(0, 5):\n # 上段に訓練データを\n plt.subplot(2, 5, i+1)\n plt.imshow(imges[i][0].cpu().detach().numpy(), 'gray')\n\n # 下段に生成データを表示する\n plt.subplot(2, 5, 5+i+1)\n plt.imshow(fake_images[i][0].cpu().detach().numpy(), 'gray')\n\n # In[ ]:\n\n # Attentiom Mapを出力\n fig = plt.figure(figsize=(15, 6))\n for i in range(0, 5):\n\n # 上段に生成した画像データを\n plt.subplot(2, 5, i+1)\n plt.imshow(fake_images[i][0].cpu().detach().numpy(), 'gray')\n\n # 下段にAttentin Map1の画像中央のピクセルのデータを\n plt.subplot(2, 5, 5+i+1)\n am = am1[i].view(16, 16, 16, 16)\n am = am[7][7] # 中央に着目\n plt.imshow(am.cpu().detach().numpy(), 'Reds')\n\n plt.show()\n # 以上\n", "repo_name": "shosatojp/gan", "sub_path": "sagan_train.py", "file_name": "sagan_train.py", "file_ext": "py", "file_size_in_byte": 7338, "program_lang": "python", "lang": "ja", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "torch.device", "line_number": 11, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 11, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 11, "usage_type": "attribute"}, {"api_name": "torch.optim.Adam", "line_number": 17, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 17, "usage_type": "attribute"}, {"api_name": "torch.optim.Adam", "line_number": 18, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 18, "usage_type": "attribute"}, {"api_name": "torch.backends", "line_number": 35, "usage_type": "attribute"}, {"api_name": "dataloader.dataset", "line_number": 38, "usage_type": "attribute"}, {"api_name": "dataloader.batch_size", "line_number": 39, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 49, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 57, "usage_type": "call"}, {"api_name": "os.path", "line_number": 57, "usage_type": "attribute"}, {"api_name": "torch.load", "line_number": 58, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 59, "usage_type": "call"}, {"api_name": "torch.randn", "line_number": 84, "usage_type": "call"}, {"api_name": "torch.nn.ReLU", "line_number": 93, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 93, "usage_type": "attribute"}, {"api_name": "torch.nn.ReLU", "line_number": 97, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 97, "usage_type": "attribute"}, {"api_name": "torch.randn", "line_number": 114, "usage_type": "call"}, {"api_name": "time.time", "line_number": 137, "usage_type": "call"}, {"api_name": "time.time", "line_number": 142, "usage_type": "call"}, {"api_name": "torch.save", "line_number": 145, "usage_type": "call"}, {"api_name": "torch.save", "line_number": 146, "usage_type": "call"}, {"api_name": "torch.nn.init.normal_", "line_number": 154, "usage_type": "call"}, {"api_name": "torch.nn.init", "line_number": 154, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 154, "usage_type": "name"}, {"api_name": "torch.nn.init.constant_", "line_number": 155, "usage_type": "call"}, {"api_name": "torch.nn.init", "line_number": 155, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 155, "usage_type": "name"}, {"api_name": "torch.nn.init.normal_", "line_number": 158, "usage_type": "call"}, {"api_name": "torch.nn.init", "line_number": 158, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 158, "usage_type": "name"}, {"api_name": "torch.nn.init.constant_", "line_number": 159, "usage_type": "call"}, {"api_name": "torch.nn.init", "line_number": 159, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 159, "usage_type": "name"}, {"api_name": "torch.device", "line_number": 163, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 163, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 163, "usage_type": "attribute"}, {"api_name": "torch.randn", "line_number": 168, "usage_type": "call"}, {"api_name": "dataloader.train_dataloader", "line_number": 175, "usage_type": "argument"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 179, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 179, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 182, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 182, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 183, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 183, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 186, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 186, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 187, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 187, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 192, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 192, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 196, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 196, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 197, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 197, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 200, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 200, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 203, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 203, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 205, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 205, "usage_type": "name"}]} +{"seq_id": "72379793707", "text": "import logging\nfrom absl import app\nfrom absl import flags\nimport numpy as np\n\nfrom open_spiel.python import rl_environment\n\nFLAGS = flags.FLAGS\n\nflags.DEFINE_string(\"game\", \"tic_tac_toe\", \"Name of the game\")\nflags.DEFINE_integer(\"num_players\", None, \"Number of players\")\n\n\ndef select_actions(observations, cur_player):\n cur_legal_actions = observations[\"legal_actions\"][cur_player]\n actions = [np.random.choice(cur_legal_actions)]\n return actions\n\n\ndef print_iteration(time_step, actions, player_id):\n \"\"\"Print TimeStep information.\"\"\"\n obs = time_step.observations\n logging.info(\"Player: %s\", player_id)\n if time_step.step_type.first():\n logging.info(\"Info state: %s, - - %s\", obs[\"info_state\"][player_id],\n time_step.step_type)\n else:\n logging.info(\"Info state: %s, %s %s %s\", obs[\"info_state\"][player_id],\n time_step.rewards[player_id], time_step.discounts[player_id],\n time_step.step_type)\n logging.info(\"Action taken: %s\", actions)\n logging.info(\"-\" * 80)\n\n\ndef turn_based_example(unused_arg):\n \"\"\"Example usage of the RL environment for turn-based games.\"\"\"\n # `rl_main_loop.py` contains more details and simultaneous move examples.\n logging.info(\"Registered games: %s\", rl_environment.registered_games())\n logging.info(\"Creating game %s\", FLAGS.game)\n\n env_configs = {\"players\": FLAGS.num_players} if FLAGS.num_players else {}\n env = rl_environment.Environment(FLAGS.game, **env_configs)\n\n logging.info(\"Env specs: %s\", env.observation_spec())\n logging.info(\"Action specs: %s\", env.action_spec())\n\n time_step = env.reset()\n\n while not time_step.step_type.last():\n pid = time_step.observations[\"current_player\"]\n actions = select_actions(time_step.observations, pid)\n print_iteration(time_step, actions, pid)\n time_step = env.step(actions)\n\n # Print final state of end game.\n for pid in range(env.num_players):\n print_iteration(time_step, actions, pid)\n\n\nif __name__ == \"__main__\":\n app.run(turn_based_example)\n", "repo_name": "deepmind/open_spiel", "sub_path": "open_spiel/python/examples/rl_example.py", "file_name": "rl_example.py", "file_ext": "py", "file_size_in_byte": 2010, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3700, "dataset": "github-code", "pt": "37", "api": [{"api_name": "absl.flags.FLAGS", "line_number": 8, "usage_type": "attribute"}, {"api_name": "absl.flags", "line_number": 8, "usage_type": "name"}, {"api_name": "absl.flags.DEFINE_string", "line_number": 10, "usage_type": "call"}, {"api_name": "absl.flags", "line_number": 10, "usage_type": "name"}, {"api_name": "absl.flags.DEFINE_integer", "line_number": 11, "usage_type": "call"}, {"api_name": "absl.flags", "line_number": 11, "usage_type": "name"}, {"api_name": "numpy.random.choice", "line_number": 16, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 16, "usage_type": "attribute"}, {"api_name": "logging.info", "line_number": 23, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 25, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 28, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 31, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 32, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 38, "usage_type": "call"}, {"api_name": "open_spiel.python.rl_environment.registered_games", "line_number": 38, "usage_type": "call"}, {"api_name": "open_spiel.python.rl_environment", "line_number": 38, "usage_type": "name"}, {"api_name": "logging.info", "line_number": 39, "usage_type": "call"}, {"api_name": "open_spiel.python.rl_environment.Environment", "line_number": 42, "usage_type": "call"}, {"api_name": "open_spiel.python.rl_environment", "line_number": 42, "usage_type": "name"}, {"api_name": "logging.info", "line_number": 44, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 45, "usage_type": "call"}, {"api_name": "absl.app.run", "line_number": 61, "usage_type": "call"}, {"api_name": "absl.app", "line_number": 61, "usage_type": "name"}]} +{"seq_id": "71122557866", "text": "import sklearn.metrics\nimport pandas as pd\nimport numpy as np\nimport os\n\n\ndef mape(actual, pred): \n actual, pred = np.array(actual), np.array(pred)\n try:\n mape_val = np.mean(np.abs((actual - pred) / actual)) * 100\n except:\n mape_val = np.nan\n return mape_val\n\ndef mpe(actual, pred): \n actual, pred = np.array(actual), np.array(pred)\n try:\n mpe_val = np.mean((actual - pred) / actual) * 100\n except:\n mpe_val = np.nan\n return mpe_val\n\n\nct = pd.read_csv(\"./census_tracker.csv\")\nct_vars = ct['Variable'].to_list()\n\nfor gpu in range(2, 8):\n\n preds_dir = os.path.join(\"gpu\" + str(gpu), \"final\", \"preds\")\n \n print(preds_dir)\n\n for preds_df in os.listdir(preds_dir):\n\n variable = preds_df.split(\".\")[0][:-8]\n cur_kfold = int(preds_df.split(\"_\")[-2]) + 1\n\n preds_df = pd.read_csv(os.path.join(preds_dir, preds_df))\n pred_df = preds_df[preds_df[\"tv\"] == \"val\"]\n\n cur_mae = sklearn.metrics.mean_absolute_error(preds_df['true'], preds_df['pred'])\n cur_r2 = sklearn.metrics.r2_score(preds_df['true'], preds_df['pred'])\n cur_mape = mape(preds_df['true'], preds_df['pred'])\n cur_mpe = mpe(preds_df['true'], preds_df['pred'])\n cur_abssume = np.sum(np.abs(preds_df['true'] - preds_df['pred']))\n cur_sume = np.sum(preds_df['true'] - preds_df['pred'])\n\n stat_names = ['MAE', 'R2', 'MAPE', 'MPE', \"Sum E\", \"Abs Sum E\"]\n stat_vals = [cur_mae, cur_r2, cur_mape, cur_mpe, cur_sume, cur_abssume]\n\n for stat in range(0, len(stat_names)):\n col = \"kfold \" + str(cur_kfold) + \" \" + stat_names[stat]\n row = ct_vars.index(variable)\n ct.at[row, col] = stat_vals[stat]\n\n ct.to_csv(\"./census_tracker.csv\", index = False)\n\n\n\n", "repo_name": "heatherbaier/geo-census", "sub_path": ".ipynb_checkpoints/eval-checkpoint.py", "file_name": "eval-checkpoint.py", "file_ext": "py", "file_size_in_byte": 1784, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "numpy.array", "line_number": 8, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 10, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 10, "usage_type": "call"}, {"api_name": "numpy.nan", "line_number": 12, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 16, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 18, "usage_type": "call"}, {"api_name": "numpy.nan", "line_number": 20, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 24, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 29, "usage_type": "call"}, {"api_name": "os.path", "line_number": 29, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 33, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 38, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 38, "usage_type": "call"}, {"api_name": "os.path", "line_number": 38, "usage_type": "attribute"}, {"api_name": "sklearn.metrics.metrics.mean_absolute_error", "line_number": 41, "usage_type": "call"}, {"api_name": "sklearn.metrics.metrics", "line_number": 41, "usage_type": "attribute"}, {"api_name": "sklearn.metrics", "line_number": 41, "usage_type": "name"}, {"api_name": "sklearn.metrics.metrics.r2_score", "line_number": 42, "usage_type": "call"}, {"api_name": "sklearn.metrics.metrics", "line_number": 42, "usage_type": "attribute"}, {"api_name": "sklearn.metrics", "line_number": 42, "usage_type": "name"}, {"api_name": "numpy.sum", "line_number": 45, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 45, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 46, "usage_type": "call"}]} +{"seq_id": "23361986450", "text": "# The prime factors of 13195 are 5, 7, 13 and 29.\r\n# What is the largest prime factor of the number 600851475143?\r\n\r\nfrom math import sqrt\r\nfrom common import isPrime\r\n\r\nval = 600851475143\r\nif isPrime(val):\r\n\tprint(val)\r\n\texit()\r\nfor n in range(int(sqrt(val))+1, 0, -1):\r\n\tif val % n == 0 and isPrime(n):\r\n\t\tprint(n)\r\n\t\tbreak\r\n", "repo_name": "SaqibS/project-euler", "sub_path": "problem3.py", "file_name": "problem3.py", "file_ext": "py", "file_size_in_byte": 327, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "37", "api": [{"api_name": "common.isPrime", "line_number": 8, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 11, "usage_type": "call"}, {"api_name": "common.isPrime", "line_number": 12, "usage_type": "call"}]} +{"seq_id": "31969924675", "text": "import os\nfrom typing import List, Tuple, Dict\n\nfrom dask import delayed\nfrom dask.delayed import Delayed\nfrom distributed import Client\n\nfrom experiments.dask_utils.computations import compute_delayed_functions\nfrom experiments.dask_utils.dask_initialization import reconnect_client_to_ssh_cluster\nfrom experiments.utils.experiment_logging import create_logger, close_logger\nfrom experiments.utils.header_attributes import get_header_attributes\n\n\nfrom experiments.arcbench_data_preparation.arc_model_data_preparation import prepare_arc_data\nfrom experiments.arcbench_data_preparation.reworked_one_hot_encoding import get_original_data_fold_abs_file_name, \\\n TrainTestEnum\n\nfrom experiments.e1_st_association_vs_tree_rules.file_naming.rules.single_target_filtered_cars_naming import (\n get_single_target_filtered_cars_abs_filename,\n get_single_target_filtered_cars_mining_timings_abs_filename,\n assoc_vs_tree_based_single_target_car_dir\n)\n\nfrom experiments.io_timings import store_timings_dict\n\nfrom mdrsl.data_structures.rules.multi_target_class_association_rule import MCAR\nfrom mdrsl.rule_models.mids.io_mids import store_mcars\nfrom mdrsl.rule_generation.association_rule_mining.mlext_impl.mlext_interaction import mine_single_target_MCARs_mlext\n\n\ndef mine_cars_for_dataset_fold_target_attribute(\n dataset_name: str,\n fold_i: int,\n target_attribute: str,\n min_support: float,\n min_confidence: float,\n max_length: int,\n):\n \"\"\"\n 1. load the required training data of the dataset fold.\n 2. make sure the target attribute is the last attribute\n 3. mine rules using the parameters settings\n --> check the number of rules!\n 4. save the rules to file\n :return:\n \"\"\"\n\n relative_name: str = f'{dataset_name}{fold_i}_{target_attribute}_{min_confidence}'\n\n logger = create_logger(\n logger_name=f'mine_filtered_single_target_cars_' + relative_name,\n log_file_name=os.path.join(assoc_vs_tree_based_single_target_car_dir(),\n f'{relative_name}_single_target_filtered_car_mining.log')\n )\n\n # logger.info(f\"rule_cutoff={rule_cutoff}\")\n\n # # load the required training data of the dataset fold.\n # original_train_data_fold_abs_file_name = get_original_data_fold_abs_file_name(\n # dataset_name, fold_i, TrainTestEnum.train)\n # df_train_original_column_order = pd.read_csv(original_train_data_fold_abs_file_name, delimiter=',')\n # # 2. make sure the target attribute is the last attribute\n # df_train_reordered = reorder_columns(df_train_original_column_order, target_attribute)\n #\n # # REMOVE INSTANCES WITH NAN AS TARGET VALUE:\n # df_train_reordered = remove_instances_with_nans_in_column(df_train_reordered, target_attribute)\n df_train_reordered = prepare_arc_data(dataset_name, fold_i, target_attribute, TrainTestEnum.train)\n\n logger.info(f\"start mining CARs for \" + relative_name)\n\n st_mcars: List[MCAR]\n timings_dict: Dict[str, float]\n filtered_st_mcars, timings_dict = mine_single_target_MCARs_mlext(df_train_reordered,\n target_attribute=target_attribute,\n min_support=min_support,\n min_confidence=min_confidence,\n max_length=max_length)\n\n logger.info(f\"finished mining CARs for {dataset_name} {fold_i}_{min_support}supp_{min_confidence}conf\")\n logger.info(\n f\"found {len(filtered_st_mcars)} CARs for {dataset_name} {fold_i}_{min_support}supp_{min_confidence}conf\")\n\n filtered_st_mcars_abs_file_name: str = get_single_target_filtered_cars_abs_filename(\n dataset_name=dataset_name, fold_i=fold_i, target_attribute=target_attribute,\n confidence_boundary_val=min_confidence\n )\n store_mcars(filtered_st_mcars_abs_file_name, filtered_st_mcars)\n logger.info(f\"finished writing CARs to file: {filtered_st_mcars_abs_file_name}\")\n filtered_st_mcars_mining_timings_abs_file_name = get_single_target_filtered_cars_mining_timings_abs_filename(\n dataset_name=dataset_name, fold_i=fold_i, target_attribute=target_attribute,\n confidence_boundary_val=min_confidence\n )\n store_timings_dict(filtered_st_mcars_mining_timings_abs_file_name, timings_dict)\n\n close_logger(logger)\n\n\ndef main():\n from experiments.arcbench_data_preparation.dataset_info import datasets\n datasets = [dict(filename=\"iris\", targetvariablename=\"class\", numerical=True)]\n from experiments.dask_utils.dask_initialization import scheduler_host_name\n scheduler_host: str = scheduler_host_name\n list_of_computations: List[Tuple[Delayed, Dict]] = []\n\n min_support: float = 0.1\n max_length: int = 7\n confidence_boundary_values: List[float] = [0.75, 0.95]\n\n nb_of_folds: int = 10\n\n use_dask = False\n if use_dask:\n client: Client = reconnect_client_to_ssh_cluster(scheduler_host)\n\n for dataset_info in datasets:\n dataset_name = dataset_info['filename']\n\n for fold_i in range(nb_of_folds):\n original_train_data_fold_abs_file_name = get_original_data_fold_abs_file_name(dataset_name, fold_i,\n TrainTestEnum.train)\n\n target_columns: List[str] = get_header_attributes(original_train_data_fold_abs_file_name)\n for target_column in target_columns:\n target_attribute = str(target_column)\n for conf_boundary_val in confidence_boundary_values:\n if use_dask:\n func_args = dict(\n dataset_name=dataset_name,\n fold_i=fold_i,\n target_attribute=target_attribute,\n min_support=min_support,\n min_confidence=conf_boundary_val,\n max_length=max_length\n )\n delayed_func = delayed(mine_cars_for_dataset_fold_target_attribute)(\n **func_args\n )\n list_of_computations.append((delayed_func, func_args))\n else:\n mine_cars_for_dataset_fold_target_attribute(\n dataset_name=dataset_name,\n fold_i=fold_i,\n target_attribute=target_attribute,\n min_support=min_support,\n min_confidence=conf_boundary_val,\n max_length=max_length\n )\n if use_dask:\n log_file_dir = assoc_vs_tree_based_single_target_car_dir()\n\n logger_name: str = 'mine_single_target_cars_ifo_confidence_bound_ERROR_LOGGER'\n logger_file_name: str = os.path.join(\n log_file_dir,\n f'ERROR_LOG_mine_single_target_cars_ifo_confidence_bound.log'\n )\n\n compute_delayed_functions(\n list_of_computations=list_of_computations,\n client=client,\n nb_of_retries_if_erred=5,\n error_logger_name=logger_name,\n error_logger_file_name=logger_file_name\n )\n\n\nif __name__ == '__main__':\n main()\n", "repo_name": "joschout/Multi-Directional-Rule-Set-Learning", "sub_path": "experiments/e1_st_association_vs_tree_rules/rule_mining/single_target_car_mining_ifo_confidence_level.py", "file_name": "single_target_car_mining_ifo_confidence_level.py", "file_ext": "py", "file_size_in_byte": 7500, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "37", "api": [{"api_name": "experiments.utils.experiment_logging.create_logger", "line_number": 50, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 52, "usage_type": "call"}, {"api_name": "os.path", "line_number": 52, "usage_type": "attribute"}, {"api_name": "experiments.e1_st_association_vs_tree_rules.file_naming.rules.single_target_filtered_cars_naming.assoc_vs_tree_based_single_target_car_dir", "line_number": 52, "usage_type": "call"}, {"api_name": "experiments.arcbench_data_preparation.arc_model_data_preparation.prepare_arc_data", "line_number": 67, "usage_type": "call"}, {"api_name": "experiments.arcbench_data_preparation.reworked_one_hot_encoding.TrainTestEnum.train", "line_number": 67, "usage_type": "attribute"}, {"api_name": "experiments.arcbench_data_preparation.reworked_one_hot_encoding.TrainTestEnum", "line_number": 67, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 71, "usage_type": "name"}, {"api_name": "mdrsl.data_structures.rules.multi_target_class_association_rule.MCAR", "line_number": 71, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 72, "usage_type": "name"}, {"api_name": "mdrsl.rule_generation.association_rule_mining.mlext_impl.mlext_interaction.mine_single_target_MCARs_mlext", "line_number": 73, "usage_type": "call"}, {"api_name": "experiments.e1_st_association_vs_tree_rules.file_naming.rules.single_target_filtered_cars_naming.get_single_target_filtered_cars_abs_filename", "line_number": 83, "usage_type": "call"}, {"api_name": "mdrsl.rule_models.mids.io_mids.store_mcars", "line_number": 87, "usage_type": "call"}, {"api_name": "experiments.e1_st_association_vs_tree_rules.file_naming.rules.single_target_filtered_cars_naming.get_single_target_filtered_cars_mining_timings_abs_filename", "line_number": 89, "usage_type": "call"}, {"api_name": "experiments.io_timings.store_timings_dict", "line_number": 93, "usage_type": "call"}, {"api_name": "experiments.utils.experiment_logging.close_logger", "line_number": 95, "usage_type": "call"}, {"api_name": "experiments.arcbench_data_preparation.dataset_info.datasets", "line_number": 100, "usage_type": "name"}, {"api_name": "experiments.dask_utils.dask_initialization.scheduler_host_name", "line_number": 102, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 103, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 103, "usage_type": "name"}, {"api_name": "dask.delayed.Delayed", "line_number": 103, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 103, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 107, "usage_type": "name"}, {"api_name": "distributed.Client", "line_number": 113, "usage_type": "name"}, {"api_name": "experiments.dask_utils.dask_initialization.reconnect_client_to_ssh_cluster", "line_number": 113, "usage_type": "call"}, {"api_name": "experiments.arcbench_data_preparation.dataset_info.datasets", "line_number": 115, "usage_type": "name"}, {"api_name": "experiments.arcbench_data_preparation.reworked_one_hot_encoding.get_original_data_fold_abs_file_name", "line_number": 119, "usage_type": "call"}, {"api_name": "experiments.arcbench_data_preparation.reworked_one_hot_encoding.TrainTestEnum.train", "line_number": 120, "usage_type": "attribute"}, {"api_name": "experiments.arcbench_data_preparation.reworked_one_hot_encoding.TrainTestEnum", "line_number": 120, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 122, "usage_type": "name"}, {"api_name": "experiments.utils.header_attributes.get_header_attributes", "line_number": 122, "usage_type": "call"}, {"api_name": "dask.delayed", "line_number": 135, "usage_type": "call"}, {"api_name": "experiments.e1_st_association_vs_tree_rules.file_naming.rules.single_target_filtered_cars_naming.assoc_vs_tree_based_single_target_car_dir", "line_number": 149, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 152, "usage_type": "call"}, {"api_name": "os.path", "line_number": 152, "usage_type": "attribute"}, {"api_name": "experiments.dask_utils.computations.compute_delayed_functions", "line_number": 157, "usage_type": "call"}]} +{"seq_id": "9332022390", "text": "\nimport re\nfrom nltk.stem import PorterStemmer\nfrom nltk.tokenize import TweetTokenizer\n\n\n#Preprocess\ndef remove_hyperlinks_marks_styles(tweet):\n # remove old style retweet text \"RT\"\n new_tweet = re.sub(r'^RT[\\s]+', '', tweet)\n\n # remove hyperlinks\n new_tweet = re.sub(r'https?:\\/\\/.*[\\r\\n]*', '', new_tweet)\n\n # remove hashtags\n # only removing the hash # sign from the word\n new_tweet = re.sub(r'#', '', new_tweet)\n\n return new_tweet\n\n\n# instantiate tokenizer class\ntokenizer = TweetTokenizer(preserve_case=False, strip_handles=True,\n reduce_len=True)\n\n\ndef tokenize_tweet(tweet):\n tweet_tokens = tokenizer.tokenize(tweet)\n\n return tweet_tokens\n\n\nstemmer = PorterStemmer()\n\n\ndef get_stem(tweets_clean):\n tweets_stem = []\n\n for word in tweets_clean:\n stem_word = stemmer.stem(word)\n tweets_stem.append(stem_word)\n\n return tweets_stem\n\n\ndef process_tweet(tweet):\n processed_tweet = remove_hyperlinks_marks_styles(tweet)\n tweet_tokens = tokenize_tweet(processed_tweet)\n tweets_stem = get_stem(tweet_tokens)\n\n return tweets_stem\n\n#Naive Bayes\nimport pickle\nfilename = 'logs.pkl'\nwith open(filename, 'rb') as f:\n loglikelihood = pickle.load(f)\n\nlogprior = 0.0\n\ndef naive_bayes_predict(tweet, logprior, loglikelihood):\n word_l = process_tweet(tweet)\n p = 0\n p += logprior\n\n for word in word_l:\n\n if word in loglikelihood:\n p+= loglikelihood[word]\n\n return p\n\n\n\ndef score(l):\n s=0.0\n for i in l:\n p = naive_bayes_predict(i, logprior, loglikelihood)\n s = s+p\n\n s=s/len(l)\n\n return s\n\nl = ['I am good','Great Great Great']\nprint(score(l))", "repo_name": "rahulprdp/sentiment-analysis-system", "sub_path": "ml.py", "file_name": "ml.py", "file_ext": "py", "file_size_in_byte": 1682, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "37", "api": [{"api_name": "re.sub", "line_number": 10, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 13, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 17, "usage_type": "call"}, {"api_name": "nltk.tokenize.TweetTokenizer", "line_number": 23, "usage_type": "call"}, {"api_name": "nltk.stem.PorterStemmer", "line_number": 33, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 57, "usage_type": "call"}]} +{"seq_id": "39384072994", "text": "from __future__ import absolute_import\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom flytekit.common import utils\nfrom flytekit.sdk.tasks import (\n python_task,\n notebook_task,\n inputs,\n outputs,\n)\nfrom flytekit.sdk.types import Types\nfrom sklearn.metrics import confusion_matrix as _cm\nfrom sklearn.utils.multiclass import unique_labels\n\n\ndef _plot_confusion_matrix(y_true, y_pred, classes, to_file_path=None, normalize=False, title=None, cmap=plt.cm.Blues):\n \"\"\"\n This function plots the confusion matrix to a file, if given or shows. It also returns the confusion matrix.\n Normalization can be applied by setting `normalize=True`.\n \"\"\"\n if not title:\n if normalize:\n title = 'Normalized confusion matrix'\n else:\n title = 'Confusion matrix, without normalization'\n\n # Compute confusion matrix\n cm = _cm(y_true, y_pred)\n # Only use the labels that appear in the data\n classes = classes[unique_labels(y_true, y_pred)]\n if normalize:\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n print(\"Normalized confusion matrix\")\n else:\n print('Confusion matrix, without normalization')\n\n fig, ax = plt.subplots()\n im = ax.imshow(cm, interpolation='nearest', cmap=cmap)\n ax.figure.colorbar(im, ax=ax)\n # We want to show all ticks...\n ax.set(xticks=np.arange(cm.shape[1]),\n yticks=np.arange(cm.shape[0]),\n # ... and label them with the respective list entries\n xticklabels=classes, yticklabels=classes,\n title=title,\n ylabel='True label',\n xlabel='Predicted label')\n\n # Rotate the tick labels and set their alignment.\n plt.setp(ax.get_xticklabels(), rotation=45, ha=\"right\",\n rotation_mode=\"anchor\")\n\n # Loop over data dimensions and create text annotations.\n fmt = '.2f' if normalize else 'd'\n thresh = cm.max() / 2.\n for i in range(cm.shape[0]):\n for j in range(cm.shape[1]):\n ax.text(j, i, format(cm[i, j], fmt),\n ha=\"center\", va=\"center\",\n color=\"white\" if cm[i, j] > thresh else \"black\")\n fig.tight_layout()\n\n if to_file_path is None:\n plt.show()\n else:\n plt.savefig(to_file_path)\n return cm\n\n\n@inputs(y_true=[Types.Integer], y_pred=[Types.Integer], title=Types.String, normalize=Types.Boolean, classes=[Types.String])\n@outputs(matrix=[[Types.Integer]], visual=Types.Blob)\n@python_task(cache=True, cache_version=\"1\")\ndef confusion_matrix(wf_params, y_true, y_pred, title, normalize, classes, matrix, visual):\n with utils.AutoDeletingTempDir('test') as tmpdir:\n f_path = tmpdir.get_named_tempfile(\"visual.png\")\n cm = _plot_confusion_matrix(np.asarray(y_true), np.asarray(y_pred), classes=np.asarray(classes), title=title, normalize=normalize, to_file_path=f_path)\n m = []\n for i in range(cm.shape[0]):\n m.append([])\n for j in range(cm.shape[1]):\n m[i].append(j)\n visual.set(f_path)\n matrix.set(m)\n\n\nconfusion_matrix_notebook = notebook_task(\n \"confusion_matrix.ipynb\",\n inputs={\n 'y_true': [Types.Integer],\n 'y_pred': [Types.Integer],\n 'title': Types.String,\n 'normalize': Types.Boolean,\n 'classes': [Types.String],\n },\n outputs={\n 'matrix': [[Types.Integer]],\n 'visual': Types.Blob,\n },\n cache=True,\n cache_version=\"1\",\n)\n", "repo_name": "backup-test-123/flytekubecondemo2019", "sub_path": "metricsproject/demo_metrics/tasks/confusion_matrix.py", "file_name": "confusion_matrix.py", "file_ext": "py", "file_size_in_byte": 3474, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "37", "api": [{"api_name": "matplotlib.pyplot.cm", "line_number": 17, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 17, "usage_type": "name"}, {"api_name": "sklearn.metrics.confusion_matrix", "line_number": 29, "usage_type": "call"}, {"api_name": "sklearn.utils.multiclass.unique_labels", "line_number": 31, "usage_type": "call"}, {"api_name": "numpy.newaxis", "line_number": 33, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 38, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 38, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 42, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 43, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.setp", "line_number": 51, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 51, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 65, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 65, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 67, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 67, "usage_type": "name"}, {"api_name": "flytekit.common.utils.AutoDeletingTempDir", "line_number": 75, "usage_type": "call"}, {"api_name": "flytekit.common.utils", "line_number": 75, "usage_type": "name"}, {"api_name": "numpy.asarray", "line_number": 77, "usage_type": "call"}, {"api_name": "flytekit.sdk.tasks.inputs", "line_number": 71, "usage_type": "call"}, {"api_name": "flytekit.sdk.types.Types.Integer", "line_number": 71, "usage_type": "attribute"}, {"api_name": "flytekit.sdk.types.Types", "line_number": 71, "usage_type": "name"}, {"api_name": "flytekit.sdk.types.Types.String", "line_number": 71, "usage_type": "attribute"}, {"api_name": "flytekit.sdk.types.Types.Boolean", "line_number": 71, "usage_type": "attribute"}, {"api_name": "flytekit.sdk.tasks.outputs", "line_number": 72, "usage_type": "call"}, {"api_name": "flytekit.sdk.types.Types.Integer", "line_number": 72, "usage_type": "attribute"}, {"api_name": "flytekit.sdk.types.Types", "line_number": 72, "usage_type": "name"}, {"api_name": "flytekit.sdk.types.Types.Blob", "line_number": 72, "usage_type": "attribute"}, {"api_name": "flytekit.sdk.tasks.python_task", "line_number": 73, "usage_type": "call"}, {"api_name": "flytekit.sdk.tasks.notebook_task", "line_number": 87, "usage_type": "call"}, {"api_name": "flytekit.sdk.types.Types.Integer", "line_number": 90, "usage_type": "attribute"}, {"api_name": "flytekit.sdk.types.Types", "line_number": 90, "usage_type": "name"}, {"api_name": "flytekit.sdk.types.Types.Integer", "line_number": 91, "usage_type": "attribute"}, {"api_name": "flytekit.sdk.types.Types", "line_number": 91, "usage_type": "name"}, {"api_name": "flytekit.sdk.types.Types.String", "line_number": 92, "usage_type": "attribute"}, {"api_name": "flytekit.sdk.types.Types", "line_number": 92, "usage_type": "name"}, {"api_name": "flytekit.sdk.types.Types.Boolean", "line_number": 93, "usage_type": "attribute"}, {"api_name": "flytekit.sdk.types.Types", "line_number": 93, "usage_type": "name"}, {"api_name": "flytekit.sdk.types.Types.String", "line_number": 94, "usage_type": "attribute"}, {"api_name": "flytekit.sdk.types.Types", "line_number": 94, "usage_type": "name"}, {"api_name": "flytekit.sdk.types.Types.Integer", "line_number": 97, "usage_type": "attribute"}, {"api_name": "flytekit.sdk.types.Types", "line_number": 97, "usage_type": "name"}, {"api_name": "flytekit.sdk.types.Types.Blob", "line_number": 98, "usage_type": "attribute"}, {"api_name": "flytekit.sdk.types.Types", "line_number": 98, "usage_type": "name"}]} +{"seq_id": "14392773350", "text": "import os\n\nfrom app import settings\nfrom flask import Flask\n\nfrom app.views import hello_world, UrlHandler, RedirectHandler\n\n\ndef create_app():\n\n app = Flask(__name__)\n app.config.from_object(settings)\n\n try:\n os.makedirs(app.instance_path)\n except OSError:\n pass\n\n # users routes\n app.add_url_rule('/', 'index', hello_world)\n\n url_view = UrlHandler.as_view('url_api')\n app.add_url_rule(\n '/url',\n view_func=url_view,\n methods=['POST']\n )\n\n url_view = RedirectHandler.as_view('redirect_api')\n app.add_url_rule(\n '/',\n view_func=url_view,\n methods=['GET']\n )\n return app\n", "repo_name": "hugoantunes/tier", "sub_path": "app/__init__.py", "file_name": "__init__.py", "file_ext": "py", "file_size_in_byte": 683, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "37", "api": [{"api_name": "flask.Flask", "line_number": 11, "usage_type": "call"}, {"api_name": "app.config.from_object", "line_number": 12, "usage_type": "call"}, {"api_name": "app.settings", "line_number": 12, "usage_type": "argument"}, {"api_name": "app.config", "line_number": 12, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 15, "usage_type": "call"}, {"api_name": "app.instance_path", "line_number": 15, "usage_type": "attribute"}, {"api_name": "app.add_url_rule", "line_number": 20, "usage_type": "call"}, {"api_name": "app.views.hello_world", "line_number": 20, "usage_type": "argument"}, {"api_name": "app.views.UrlHandler.as_view", "line_number": 22, "usage_type": "call"}, {"api_name": "app.views.UrlHandler", "line_number": 22, "usage_type": "name"}, {"api_name": "app.add_url_rule", "line_number": 23, "usage_type": "call"}, {"api_name": "app.views.RedirectHandler.as_view", "line_number": 29, "usage_type": "call"}, {"api_name": "app.views.RedirectHandler", "line_number": 29, "usage_type": "name"}, {"api_name": "app.add_url_rule", "line_number": 30, "usage_type": "call"}]} +{"seq_id": "32595032100", "text": "\"\"\"\nPackage containing utility functions used in different packages.\nContains a statistics analyzer and a visualizer.\n\"\"\"\nimport logging\nimport os\n\n\ndef setup_logger(name: str,\n cd: str = None,\n level=logging.DEBUG):\n \"\"\"\n Setup an class or module specific logger instance\n to ensure readable output for users.\n\n :param str name:\n The name of the logger instance\n :param str cd:\n The path where to store the logfile.\n If None is given, logs are not stored.\n :param str level:\n The logging level, default is DEBUG\n\n .. versionadded:: 0.1.7\n \"\"\"\n logger = logging.getLogger(name=name)\n # Set log-level\n logger.setLevel(level=level)\n # Check if logger was already instantiated. If so, return already.\n if logger.handlers:\n return logger\n # Add handlers\n formatter = logging.Formatter(fmt='%(asctime)s %(levelname)s %(name)s: %(message)s',\n datefmt='%d.%m.%Y-%H:%M:%S')\n console = logging.StreamHandler()\n console.setFormatter(fmt=formatter)\n logger.addHandler(hdlr=console)\n if cd is not None:\n os.makedirs(cd, exist_ok=True)\n file_handler = logging.FileHandler(filename=os.path.join(cd, f\"{name}.log\"))\n file_handler.setFormatter(fmt=formatter)\n logger.addHandler(hdlr=file_handler)\n return logger\n", "repo_name": "RWTH-EBC/ebcpy", "sub_path": "ebcpy/utils/__init__.py", "file_name": "__init__.py", "file_ext": "py", "file_size_in_byte": 1384, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 11, "dataset": "github-code", "pt": "37", "api": [{"api_name": "logging.DEBUG", "line_number": 11, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 26, "usage_type": "call"}, {"api_name": "logging.Formatter", "line_number": 33, "usage_type": "call"}, {"api_name": "logging.StreamHandler", "line_number": 35, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 39, "usage_type": "call"}, {"api_name": "logging.FileHandler", "line_number": 40, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 40, "usage_type": "call"}, {"api_name": "os.path", "line_number": 40, "usage_type": "attribute"}]} +{"seq_id": "23694225755", "text": "from bs4 import BeautifulSoup\nimport json\n\nhocr_file = \"/home/satish/hocr_mod/ServeS - Trade License0.jpgtext_hocr21-02-202309:57:52.hocr\"\nwith open(hocr_file, 'r') as f:\n hocr = f.read()\n\nsoup = BeautifulSoup(hocr, 'html.parser')\n\npages = []\nfor page in soup.find_all('div', class_='ocr_page'):\n page_data = {\n 'id': page['id'],\n #'width': int(page['data-image-width']),\n #'height': int(page['data-image-height']),\n 'lines': []\n }\n\n for line in page.find_all('span', class_='ocr_line'):\n line_data = {\n 'text': line.text.replace(\"\\n\", \" \"),\n 'bbox': [int(x) for x in line['title'].split(';')[0].split(' ')[1:]]\n }\n page_data['lines'].append(line_data)\n\n pages.append(page_data)\n\njson_data = json.dumps(pages, indent=2,ensure_ascii=False)\nprint(json_data)", "repo_name": "satish-madugula/pdfocr", "sub_path": "hocrtojson.py", "file_name": "hocrtojson.py", "file_ext": "py", "file_size_in_byte": 841, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "bs4.BeautifulSoup", "line_number": 8, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 28, "usage_type": "call"}]} +{"seq_id": "70373569386", "text": "\"\"\"\nScript to remove rows of a dataset .csv based on eventIds, where the eventIds\nto remove are in a second .csv.\n\nThis module is runnable. Use the `-h` option to view usage.\n\"\"\"\nfrom argparse import ArgumentParser, Namespace\nfrom typing import List, Tuple\n\nimport pandas as pd\n\nimport logging\n\nlog = logging.getLogger(__name__)\n\n\ndef filter_dataframe(\n df: pd.DataFrame, filters: List[pd.DataFrame]\n) -> Tuple[pd.DataFrame, str]:\n msgs = []\n for i, filter in enumerate(filters):\n orig_len = len(df)\n\n df = df[\n ~df[['eventId']]\n .apply(tuple, 1)\n .isin(filter[['eventId']].apply(tuple, 1))\n ]\n\n msg = f'Removed {orig_len - len(df)} samples on pass {i + 1}.'\n msgs.append(msg)\n log.info(msg)\n\n msg = f'Remaining number of samples: {len(df)}.'\n msgs.append(msg)\n log.info(msg)\n\n return df, '\\n'.join(msgs)\n\n\ndef main(args: Namespace):\n\n df = pd.read_csv(args.input_csv)\n\n filters = []\n for csv in args.filter:\n filters.append(pd.read_csv(csv))\n\n df, _ = filter_dataframe(df, filters)\n\n df.to_csv(args.output, index=False)\n\n\nif __name__ == '__main__':\n from .._utils.logging import setup_logger\n\n parser = ArgumentParser()\n\n parser.add_argument(\n 'input_csv',\n type=str,\n help='Name of unsplit or previously split dataset '\n 'csv generated by preprocess_csv.',\n )\n parser.add_argument(\n '-f', '--filter', nargs='+', help='.csv files with eventIds to remove.'\n )\n parser.add_argument(\n '-o',\n '--output',\n type=str,\n default='output_updated.csv',\n help='Name of split dataset csv.',\n )\n\n args = parser.parse_args()\n\n setup_logger()\n main(args)\n", "repo_name": "antolu/aisi-joints", "sub_path": "aisi_joints/data/filter_csv.py", "file_name": "filter_csv.py", "file_ext": "py", "file_size_in_byte": 1762, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "logging.getLogger", "line_number": 14, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 18, "usage_type": "attribute"}, {"api_name": "typing.List", "line_number": 18, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 19, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 19, "usage_type": "attribute"}, {"api_name": "argparse.Namespace", "line_number": 41, "usage_type": "name"}, {"api_name": "pandas.read_csv", "line_number": 43, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 47, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 57, "usage_type": "call"}, {"api_name": "_utils.logging.setup_logger", "line_number": 78, "usage_type": "call"}]} +{"seq_id": "72041645226", "text": "from properties import Properties\r\n\r\nclass CollisionEngine:\r\n\r\n def __init__(self, game_objects):\r\n self.game_objects = game_objects\r\n self.grid = {}\r\n\r\n def update(self):\r\n self.grid = {}\r\n for obj in self.game_objects:\r\n coordinates = int(obj.x / Properties.OBJECT_SIZE), int(obj.y / Properties.OBJECT_SIZE)\r\n\r\n if coordinates not in self.grid:\r\n self.grid[coordinates] = [obj]\r\n else:\r\n self.grid[coordinates].append(obj)\r\n\r\n def get_near_objects(self, obj, radius=1):\r\n objects = []\r\n gridx, gridy = int(obj.x / Properties.OBJECT_SIZE), int(obj.y / Properties.OBJECT_SIZE)\r\n for vx in range(-radius, radius + 1):\r\n for vy in range(-radius, radius + 1):\r\n coordinates = gridx + vx, gridy + vy\r\n if coordinates in self.grid:\r\n for near_obj in self.grid[coordinates]:\r\n if near_obj is not obj:\r\n objects.append(near_obj)\r\n #print(len(objects))\r\n return objects\r\n\r\n def get_colliding_objects(self, obj, radius=1):\r\n colliding_objects = []\r\n for o in self.get_near_objects(obj):\r\n if obj.collided_with(o):\r\n colliding_objects.append(o)\r\n return colliding_objects\r\n\r\n def get_objects_near_point(self, point, radius=1):\r\n objects = []\r\n gridx, gridy = int(point[0] / Properties.OBJECT_SIZE), int(point[1] / Properties.OBJECT_SIZE)\r\n for vx in range(-radius, radius + 1):\r\n for vy in range(-radius, radius + 1):\r\n coordinates = gridx + vx, gridy + vy\r\n if coordinates in self.grid:\r\n for near_obj in self.grid[coordinates]:\r\n objects.append(near_obj)\r\n #print(len(objects))\r\n return objects\r\n\r\n def get_objects_colliding_with_point(self, point):\r\n result = []\r\n for obj in self.get_objects_near_point(point, radius=1):\r\n if obj.point_collided(obj, point):\r\n result.append(obj)\r\n return result", "repo_name": "kubic71/black-n-white", "sub_path": "collision_engine.py", "file_name": "collision_engine.py", "file_ext": "py", "file_size_in_byte": 2154, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "properties.Properties.OBJECT_SIZE", "line_number": 12, "usage_type": "attribute"}, {"api_name": "properties.Properties", "line_number": 12, "usage_type": "name"}, {"api_name": "properties.Properties.OBJECT_SIZE", "line_number": 21, "usage_type": "attribute"}, {"api_name": "properties.Properties", "line_number": 21, "usage_type": "name"}, {"api_name": "properties.Properties.OBJECT_SIZE", "line_number": 41, "usage_type": "attribute"}, {"api_name": "properties.Properties", "line_number": 41, "usage_type": "name"}]} +{"seq_id": "22804895463", "text": "import time\nimport telebot\nimport requests\nfrom config import BOT_TOKEN\nimport schedule\nfrom models.poll_model import list_of_poll_by_status\n\n\n# Определение ботов ботов\nfrom models.violation_model import list_of_violation_by_status\n\nbot = telebot.TeleBot(BOT_TOKEN)\n\n\ndef start_download():\n download_voted_photos()\n download_violation_photo()\n\n\ndef download_voted_photos():\n list_of_hash = list_of_poll_by_status()\n for i in list_of_hash:\n print(i.first_photo)\n file_info1 = bot.get_file(i.first_photo)\n file_info2 = bot.get_file(i.second_photo)\n uri_1 = 'https://api.telegram.org/file/bot{0}/{1}'.format(BOT_TOKEN, file_info1.file_path)\n uri_2 = 'https://api.telegram.org/file/bot{0}/{1}'.format(BOT_TOKEN, file_info2.file_path)\n try:\n r = requests.get(uri_1, stream=True)\n if r.status_code == 200:\n with open('media/' + str(i.chat_id) + '_first_photo.jpg', 'wb') as f:\n for data in r:\n f.write(data)\n\n r = requests.get(uri_2, stream=True)\n if r.status_code == 200:\n with open('media/' + str(i.chat_id) + '_second_photo.jpg', 'wb') as f:\n for data in r:\n f.write(data)\n\n except Exception as e:\n print(\"аудио по хэшу не доступно: \" + str(e))\n pass\n\n\ndef download_violation_photo():\n list_of_hash = list_of_violation_by_status()\n for i in list_of_hash:\n try:\n print(i[0])\n file_info = bot.get_file(i[1])\n uri_1 = 'https://api.telegram.org/file/bot{0}/{1}'.format(BOT_TOKEN, file_info.file_path)\n print(len(i[1]))\n try:\n r = requests.get(uri_1, stream=True)\n if r.status_code == 200:\n with open('media/' + str(i[0]) + '_' + str(i[1][40:]) + '.jpg', 'wb') as f:\n for data in r:\n f.write(data)\n except Exception as e:\n print(\"аудио по хэшу не доступно: \" + str(e))\n pass\n\n except Exception as e:\n print(e)\n\n\nschedule.every(8).hours.do(start_download)\nwhile 1:\n schedule.run_pending()\n time.sleep(1)\n \n", "repo_name": "Om4roFF/BaygeBot", "sub_path": "voiceDownloadernextgen.py", "file_name": "voiceDownloadernextgen.py", "file_ext": "py", "file_size_in_byte": 2333, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "telebot.TeleBot", "line_number": 12, "usage_type": "call"}, {"api_name": "config.BOT_TOKEN", "line_number": 12, "usage_type": "argument"}, {"api_name": "models.poll_model.list_of_poll_by_status", "line_number": 21, "usage_type": "call"}, {"api_name": "config.BOT_TOKEN", "line_number": 26, "usage_type": "argument"}, {"api_name": "config.BOT_TOKEN", "line_number": 27, "usage_type": "argument"}, {"api_name": "requests.get", "line_number": 29, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 35, "usage_type": "call"}, {"api_name": "models.violation_model.list_of_violation_by_status", "line_number": 47, "usage_type": "call"}, {"api_name": "config.BOT_TOKEN", "line_number": 52, "usage_type": "argument"}, {"api_name": "requests.get", "line_number": 55, "usage_type": "call"}, {"api_name": "schedule.every", "line_number": 68, "usage_type": "call"}, {"api_name": "schedule.run_pending", "line_number": 70, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 71, "usage_type": "call"}]} +{"seq_id": "2811282322", "text": "from django import forms\nfrom .models import Platinum\n\n\n\n\nclass PlatinumForm(forms.ModelForm):\n class Meta:\n model = Platinum\n fields = '__all__'\n exclude=('Booking_id',)\n depth=2\n\n def clean(self):\n cleaned_data=super().clean()\n room_number=cleaned_data['Room_number']\n query=Platinum.objects.filter(Room_number=room_number)\n if query.exists():\n raise forms.ValidationError(\"This Room is Already Booking\")", "repo_name": "satyanarayanamethuku/taskhotel", "sub_path": "djhotel3/hotel3/forms.py", "file_name": "forms.py", "file_ext": "py", "file_size_in_byte": 479, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "django.forms.ModelForm", "line_number": 7, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 7, "usage_type": "name"}, {"api_name": "models.Platinum", "line_number": 9, "usage_type": "name"}, {"api_name": "models.Platinum.objects.filter", "line_number": 17, "usage_type": "call"}, {"api_name": "models.Platinum.objects", "line_number": 17, "usage_type": "attribute"}, {"api_name": "models.Platinum", "line_number": 17, "usage_type": "name"}, {"api_name": "django.forms.ValidationError", "line_number": 19, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 19, "usage_type": "name"}]} +{"seq_id": "28048673854", "text": "\"\"\"Switch Humidifier Platform\"\"\"\nimport logging\nimport json\nimport os\nimport time\n\nimport asyncio\n\n\nimport voluptuous as vol\n\nfrom homeassistant.core import callback\nfrom homeassistant.helpers.event import track_state_change, async_track_state_change_event\nfrom homeassistant.components.switch import DOMAIN as SWITCH_DOMAIN\nfrom homeassistant.components.humidifier import (\n ATTR_HUMIDITY,\n ATTR_MAX_HUMIDITY,\n ATTR_MIN_HUMIDITY,\n DEVICE_CLASS_DEHUMIDIFIER,\n DEVICE_CLASS_HUMIDIFIER,\n SUPPORT_MODES,\n PLATFORM_SCHEMA,\n HumidifierEntity,\n HumidifierEntityFeature\n)\nfrom homeassistant.const import (\n CONF_NAME,\n SERVICE_TURN_ON,\n SERVICE_TURN_OFF,\n SERVICE_TOGGLE,\n STATE_ON,\n STATE_OFF,\n ATTR_ENTITY_ID,\n SERVICE_TURN_OFF,\n SERVICE_TURN_ON,\n STATE_OFF,\n STATE_ON,\n STATE_UNAVAILABLE\n)\nfrom homeassistant.helpers import entity_registry as er\n\n\nimport homeassistant.helpers.config_validation as cv\nfrom homeassistant.components.humidifier.const import MODE_AUTO, MODE_NORMAL, MODE_BOOST, MODE_SLEEP, MODE_AWAY\n\nAVAILABLE_MODES = [MODE_NORMAL, MODE_AUTO, MODE_SLEEP, MODE_BOOST]\n\nSUPPORTED_FEATURES = SUPPORT_MODES\n\n\n_LOGGER = logging.getLogger(__name__)\n\nCONF_NAME = 'name'\nDEFAULT_NAME = 'humidifier'\n\nCONF_TYPE = 'type'\nCONF_START_DELTA = 'start_delta'\nCONF_STOP_DELTA = 'stop_delta'\n\n\nDEHUMIDIFIER_TYPE = 'dehumidifier'\nHUMIDIFIER_TYPE = 'humidifier'\n\nTYPES = [\n DEHUMIDIFIER_TYPE,\n HUMIDIFIER_TYPE\n]\n\nDEFAULT_TYPE = HUMIDIFIER_TYPE\nDEFAULT_HUMIDITY = 0\nDEFAULT_START_DELTA = 0.1\nDEFAULT_STOP_DELTA = 0.1\nDEFAULT_SWITCH_STATE = STATE_ON\nMIN_HUMIDITY = 0\nMAX_HUMIDITY = 100\n\nPLATFORM_SCHEMA = PLATFORM_SCHEMA.extend(\n {\n vol.Optional(CONF_NAME, default=DEFAULT_NAME): cv.string,\n vol.Optional(CONF_TYPE, default=DEFAULT_TYPE): vol.All(cv.string, vol.In(TYPES)),\n vol.Optional(CONF_START_DELTA, default=DEFAULT_START_DELTA): vol.Coerce(float),\n vol.Optional(CONF_STOP_DELTA, default=DEFAULT_STOP_DELTA): vol.Coerce(float),\n }\n)\n\nhass_instance = None\n\ndef setup_platform(hass, config, add_entities, discovery_info=None):\n \"\"\"Set up the dehumidifier platform.\"\"\"\n name = config[CONF_NAME]\n device_class = DEVICE_CLASS_DEHUMIDIFIER\n if config[CONF_TYPE] == HUMIDIFIER_TYPE:\n device_class = DEVICE_CLASS_HUMIDIFIER\n start_delta = config[CONF_START_DELTA]\n stop_delta = config[CONF_STOP_DELTA]\n devices = []\n switchHumidifier = BlueairAirPurifier(name, device_class, start_delta, stop_delta)\n devices.append(switchHumidifier)\n add_entities(devices, True)\n\n hass_instance = hass\n # Track sensor or switch state changes.\n # track_state_change(hass, [], switchHumidifier._state_changed)\n\n return True\n\nclass BlueairAirPurifier(HumidifierEntity):\n\t\n def __init__(self, name, device_class, start_delta, stop_delta):\n \"\"\"Initialize the humidifier.\"\"\"\n self.last_press = 0\n self.is_working = False\n self.next_mode = MODE_AUTO\n\n self._attr_available_modes = AVAILABLE_MODES\n\n self._attr_supported_features = SUPPORT_MODES\n\n self._humidity = DEFAULT_HUMIDITY\n\n self._switch_state = DEFAULT_SWITCH_STATE\n\n self._is_on = DEFAULT_SWITCH_STATE == STATE_ON\n\n self._device_class = device_class\n\n self._start_delta = start_delta\n\n self._stop_delta = stop_delta\n \n self._attr_mode = MODE_AUTO\n self._mode = MODE_AUTO\n\n self._supported_features = HumidifierEntityFeature.MODES\n\n # To cheack if the switch state change if fired by the platform\n self._self_changed_switch = False\n \n self._name = name\n\n # Persistence file to store persistent data\n self._persistence_final_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), \"persistence.json\")\n\n try:\n if os.path.isfile(self._persistence_final_path):\n self._persistence_json = json.load(open(self._persistence_final_path, 'r'))\n else:\n _LOGGER.warning(\"file doesnt exist\")\n self._persistence_json = json.loads('{\"target\": ' + str(DEFAULT_HUMIDITY) + '}')\n\n self._target_humidity = self._persistence_json['target']\n self.save_target()\n\n except Exception as e:\n _LOGGER.error(\"Error occured loading: %s\", str(e))\n self._target_humidity = DEFAULT_HUMIDITY\n\n def save_target(self): \n \"\"\"set target humidity to persistent JSON and store it.\"\"\"\n self._persistence_json['target'] = self._target_humidity\n self.persistence_save()\n\n def persistence_save(self): \n \"\"\"Store persistent JSON as file.\"\"\"\n if self._persistence_json is not None: #Check we have something to save\n try:\n with open(self._persistence_final_path, 'w') as fil:\n fil.write(json.dumps(self._persistence_json, ensure_ascii=False))\n except Exception as e:\n _LOGGER.error(\"Error occured saving: %s\", str(e))\n\n def update(self):\n \"\"\"Update called periodically\"\"\"\n\n @property\n def name(self):\n \"\"\"Return the name of the humidifier.\"\"\"\n return self._name\n\n @property\n def target_humidity(self):\n \"\"\"Return the target humidity.\"\"\"\n return self._target_humidity\n\n @property\n def min_humidity(self):\n \"\"\"Return the target humidity.\"\"\"\n return MIN_HUMIDITY\n\n @property\n def max_humidity(self):\n \"\"\"Return the target humidity.\"\"\"\n return MAX_HUMIDITY\n \n # def supported_features(self):\n # \"\"\"Return the list of supported features.\"\"\"\n # return (SUPPORT_MODES)\n\n @property\n def is_on(self):\n \"\"\"Return if the dehumidifier is on.\"\"\"\n return self._is_on\n\n @property\n def device_class(self):\n \"\"\"Return Device class.\"\"\"\n _LOGGER.debug('device_class')\n return self._device_class\n\n @property\n def mode(self) -> str:\n \"\"\"Return current mode.\"\"\"\n return self._mode\n\n def set_humidity(self, humidity):\n \"\"\"Set target humidity.\"\"\"\n _LOGGER.debug('set_humidity')\n if not self.is_on:\n self._mode = MODE_AWAY\n self.save_target()\n elif humidity is 0:\n self._target_humidity = humidity\n self.save_target()\n self.set_mode(MODE_AUTO)\n elif humidity > 0 and humidity <= 25:\n self._target_humidity = 20\n self.save_target()\n self.set_mode(MODE_SLEEP)\n elif humidity > 25 and humidity <= 75:\n self._target_humidity = 50\n self.save_target()\n self.set_mode(MODE_NORMAL)\n elif humidity > 75:\n self._target_humidity = 100\n self.save_target()\n self.set_mode(MODE_BOOST)\n self.save_target()\n\n def turn_on(self, **kwargs):\n \"\"\"Turn the device ON.\"\"\"\n _LOGGER.debug('turn_on')\n self._is_on = True\n if self._target_humidity is 0:\n self.set_mode(MODE_AUTO)\n elif self._target_humidity > 0 and self._target_humidity <= 25:\n self._target_humidity = 20\n self.save_target()\n self.set_mode(MODE_SLEEP)\n elif self._target_humidity > 25 and self._target_humidity <= 75:\n self._target_humidity = 50\n self.save_target()\n self.set_mode(MODE_NORMAL)\n elif self._target_humidity > 75:\n self._target_humidity = 100\n self.save_target()\n self.set_mode(MODE_BOOST)\n\n def turn_off(self, **kwargs):\n \"\"\"Turn the device OFF.\"\"\"\n _LOGGER.debug('turn_off')\n self.set_mode(MODE_AWAY)\n self._is_on = False\n\n def set_mode(self, mode):\n \"\"\"Set new target preset mode.\"\"\"\n current_mode = self._mode\n self._mode = mode\n self._attr_mode = mode\n self.save_target()\n self.next_mode = mode\n _LOGGER.warning(\"Called from sync\")\n if not self.is_working:\n self.from_state_to(current_mode, mode)\n\n async def async_set_mode(self, mode):\n \"\"\"Set new target preset mode.\"\"\"\n current_mode = self._mode\n self._mode = mode\n self._attr_mode = mode\n self.save_target()\n self.next_mode = mode\n _LOGGER.warning(\"Called from async\")\n if not self.is_working:\n self.from_state_to(current_mode, mode)\n\n\n\n def step_from_off(self, count=0) -> bool:\n if self.press():\n self.last_press = time.time()\n return True\n else:\n if count > 4:\n return False\n self.step_from_off(count=count + 1)\n\n def press(self):\n _LOGGER.warning(\"Pressed\")\n result = self.hass.services.call(\n SWITCH_DOMAIN,\n SERVICE_TURN_ON,\n {ATTR_ENTITY_ID: \"switch.blueair_switch\"},\n blocking=True,\n )\n self.hass.block_till_done()\n return result\n\n\n def step(self, count=0):\n _LOGGER.warning(\"sleep \" + str(count))\n\n #if time.time() - self.last_press < 2.0:\n # time.sleep(2.0-(time.time() - self.last_press))\n\n if time.time() - self.last_press < 2.5:\n # switch state\n \n pressed = self.press()\n _LOGGER.warning(\"Switch\")\n\n if not pressed:\n if count > 4: # todo\n return False\n return self.step(count= count+1)\n\n if time.time() - self.last_press > 5.1:\n self.last_press = time.time()\n _LOGGER.warning(\"Restart mission\")\n return self.step(count= count+1)\n else:\n self.last_press = time.time()\n return True\n else:\n # activate \n\n if count > 4: # todo\n _LOGGER.warning(\"End mission\")\n return False\n \n # clear\n _LOGGER.warning(\"SLEEP: \" + str(min(5.5, max(0, 5.5 - (time.time() - self.last_press)))))\n time.sleep(min(5.5, max(0, 5.5 - (time.time() - self.last_press))))\n _LOGGER.warning(\"Activate state\")\n # press\n pressed = self.press()\n if pressed:\n self.last_press = time.time()\n return self.step(count=count+1)\n\n \n def from_state_to(self, from_state: str, to_state: str) -> bool:\n self.is_working = True\n _LOGGER.warning('Set mode from ' + from_state + \" to \" + to_state)\n if from_state == to_state:\n self.is_working = False\n return True\n\n return self.next_state(from_state)\n\n def next_state(self, from_state: str) -> bool:\n _LOGGER.warning('Set_ mode from ' + from_state + \" to \" + self.next_mode)\n if from_state == \"away\":\n self.step_from_off()\n else:\n self.step()\n _LOGGER.warning(\"Next: \" + from_state)\n new_state = self.get_next_state(from_state)\n# _LOGGER.warning('Rerun: ' + from_state + \" to \" + to_state + \" \" + new_state + str(new_state == to_state))\n _LOGGER.warning(\"New: \" + new_state)\n\n if new_state == self.next_mode:\n self.is_working = False\n _LOGGER.warning(\"Exit \" + new_state + \" \" + self.next_mode)\n return True\n return self.next_state(new_state)\n \n\n def get_next_state(self, from_state: str) -> str:\n if from_state == \"auto\":\n return \"sleep\"\n elif from_state == \"sleep\":\n return \"normal\"\n elif from_state == \"normal\":\n return \"boost\"\n elif from_state == \"boost\":\n return \"away\"\n elif from_state == \"away\":\n return \"auto\"\n\n\n ############################################################", "repo_name": "jonryf/ha-custom-air-purifier", "sub_path": "custom_components/blueair_humidifier/humidifier.py", "file_name": "humidifier.py", "file_ext": "py", "file_size_in_byte": 10710, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "homeassistant.components.humidifier.const.MODE_NORMAL", "line_number": 46, "usage_type": "name"}, {"api_name": "homeassistant.components.humidifier.const.MODE_AUTO", "line_number": 46, "usage_type": "name"}, {"api_name": "homeassistant.components.humidifier.const.MODE_SLEEP", "line_number": 46, "usage_type": "name"}, {"api_name": "homeassistant.components.humidifier.const.MODE_BOOST", "line_number": 46, "usage_type": "name"}, {"api_name": "homeassistant.components.humidifier.SUPPORT_MODES", "line_number": 48, "usage_type": "name"}, {"api_name": "logging.getLogger", "line_number": 51, "usage_type": "call"}, {"api_name": "homeassistant.const.CONF_NAME", "line_number": 53, "usage_type": "name"}, {"api_name": "homeassistant.const.STATE_ON", "line_number": 73, "usage_type": "name"}, {"api_name": "homeassistant.components.humidifier.PLATFORM_SCHEMA", "line_number": 77, "usage_type": "name"}, {"api_name": "homeassistant.components.humidifier.PLATFORM_SCHEMA.extend", "line_number": 77, "usage_type": "call"}, {"api_name": "voluptuous.Optional", "line_number": 79, "usage_type": "call"}, {"api_name": "homeassistant.const.CONF_NAME", "line_number": 79, "usage_type": "argument"}, {"api_name": "voluptuous.Optional", "line_number": 80, "usage_type": "call"}, {"api_name": "voluptuous.Optional", "line_number": 81, "usage_type": "call"}, {"api_name": "voluptuous.Optional", "line_number": 82, "usage_type": "call"}, {"api_name": "homeassistant.helpers.config_validation.string", "line_number": 79, "usage_type": "attribute"}, {"api_name": "homeassistant.helpers.config_validation", "line_number": 79, "usage_type": "name"}, {"api_name": "voluptuous.All", "line_number": 80, "usage_type": "call"}, {"api_name": "homeassistant.helpers.config_validation.string", "line_number": 80, "usage_type": "attribute"}, {"api_name": "homeassistant.helpers.config_validation", "line_number": 80, "usage_type": "name"}, {"api_name": "voluptuous.In", "line_number": 80, "usage_type": "call"}, {"api_name": "voluptuous.Coerce", "line_number": 81, "usage_type": "call"}, {"api_name": "voluptuous.Coerce", "line_number": 82, "usage_type": "call"}, {"api_name": "homeassistant.const.CONF_NAME", "line_number": 90, "usage_type": "name"}, {"api_name": "homeassistant.components.humidifier.DEVICE_CLASS_DEHUMIDIFIER", "line_number": 91, "usage_type": "name"}, {"api_name": "homeassistant.components.humidifier.DEVICE_CLASS_HUMIDIFIER", "line_number": 93, "usage_type": "name"}, {"api_name": "homeassistant.components.humidifier.HumidifierEntity", "line_number": 107, "usage_type": "name"}, {"api_name": "homeassistant.components.humidifier.const.MODE_AUTO", "line_number": 113, "usage_type": "name"}, {"api_name": "homeassistant.components.humidifier.SUPPORT_MODES", "line_number": 117, "usage_type": "name"}, {"api_name": "homeassistant.const.STATE_ON", "line_number": 123, "usage_type": "name"}, {"api_name": "homeassistant.components.humidifier.const.MODE_AUTO", "line_number": 131, "usage_type": "name"}, {"api_name": "homeassistant.components.humidifier.const.MODE_AUTO", "line_number": 132, "usage_type": "name"}, {"api_name": "homeassistant.components.humidifier.HumidifierEntityFeature.MODES", "line_number": 134, "usage_type": "attribute"}, {"api_name": "homeassistant.components.humidifier.HumidifierEntityFeature", "line_number": 134, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 142, "usage_type": "call"}, {"api_name": "os.path", "line_number": 142, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 142, "usage_type": "call"}, {"api_name": "os.path.abspath", "line_number": 142, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 145, "usage_type": "call"}, {"api_name": "os.path", "line_number": 145, "usage_type": "attribute"}, {"api_name": "json.load", "line_number": 146, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 149, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 168, "usage_type": "call"}, {"api_name": "homeassistant.components.humidifier.const.MODE_AWAY", "line_number": 219, "usage_type": "name"}, {"api_name": "homeassistant.components.humidifier.const.MODE_AUTO", "line_number": 224, "usage_type": "argument"}, {"api_name": "homeassistant.components.humidifier.const.MODE_SLEEP", "line_number": 228, "usage_type": "argument"}, {"api_name": "homeassistant.components.humidifier.const.MODE_NORMAL", "line_number": 232, "usage_type": "argument"}, {"api_name": "homeassistant.components.humidifier.const.MODE_BOOST", "line_number": 236, "usage_type": "argument"}, {"api_name": "homeassistant.components.humidifier.const.MODE_AUTO", "line_number": 244, "usage_type": "argument"}, {"api_name": "homeassistant.components.humidifier.const.MODE_SLEEP", "line_number": 248, "usage_type": "argument"}, {"api_name": "homeassistant.components.humidifier.const.MODE_NORMAL", "line_number": 252, "usage_type": "argument"}, {"api_name": "homeassistant.components.humidifier.const.MODE_BOOST", "line_number": 256, "usage_type": "argument"}, {"api_name": "homeassistant.components.humidifier.const.MODE_AWAY", "line_number": 261, "usage_type": "argument"}, {"api_name": "time.time", "line_number": 290, "usage_type": "call"}, {"api_name": "homeassistant.components.switch.DOMAIN", "line_number": 300, "usage_type": "argument"}, {"api_name": "homeassistant.const.SERVICE_TURN_ON", "line_number": 301, "usage_type": "argument"}, {"api_name": "homeassistant.const.ATTR_ENTITY_ID", "line_number": 302, "usage_type": "name"}, {"api_name": "time.time", "line_number": 315, "usage_type": "call"}, {"api_name": "time.time", "line_number": 326, "usage_type": "call"}, {"api_name": "time.time", "line_number": 327, "usage_type": "call"}, {"api_name": "time.time", "line_number": 331, "usage_type": "call"}, {"api_name": "time.time", "line_number": 341, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 342, "usage_type": "call"}, {"api_name": "time.time", "line_number": 342, "usage_type": "call"}, {"api_name": "time.time", "line_number": 347, "usage_type": "call"}]} +{"seq_id": "8982123485", "text": "from fastapi.templating import Jinja2Templates\nfrom fastapi.responses import HTMLResponse\nfrom fastapi import Request, APIRouter\nfrom . import functions\nfrom .data import data\n\nrouter = APIRouter(prefix=\"\")\n\ntemplates = Jinja2Templates(directory=\"templates\")\n\n@router.get(\"/lab2\", response_class=HTMLResponse)\nasync def read_item(request: Request, k: int = 2):\n\n data_table = \"\"\n\n for i in range(3):\n data_table += \"\"\n\n for y in range(int(len(data)/3)):\n data_table += f\"\"\n\n data_table += \"\"\n\n data_table += \"
{data[i*int(len(data)/3) + y]}
\"\n\n interval_data = \"\"\n\n interval_data += f\"\"\n for interval in functions.interval_stat_series():\n interval_data += f\"\"\n interval_data += \"\"\n\n interval_data += f\"\"\n for interval in functions.interval_stat_series():\n interval_data += f\"\"\n interval_data += \"\"\n\n interval_data += f\"\"\n for interval in functions.interval_stat_series():\n interval_data += f\"\"\n interval_data += \"\"\n\n interval_data += \"
Інтервали{interval['range_str']}
zi{str(round(interval['avg'], 5))}
mi{str(interval['count'])}
\"\n\n return templates.TemplateResponse(\n \"lab2.html\",\n {\n \"request\": request,\n \"data\": data_table,\n \"interval_data\": interval_data,\n \"count\": len(data),\n \"average\": round(functions.average(), 6),\n \"mode\": str(functions.mode()),\n \"exact_mode\": str(functions.exact_mode()),\n \"median\": round(functions.median(), 6),\n \"median2\": round(functions.median2(), 6),\n \"exact_median\": round(functions.exact_median(), 6),\n \"range\": round(functions.distribution_range(), 6),\n \"dispersion\": round(functions.dispersion(), 6),\n \"avg_quad_deviation\": round(functions.square_deviation(), 6),\n \"adj_dispersion\": round(functions.adjusted_dispersion(), 6),\n \"adj_aqd\": round(functions.adjusted_square_deviation(), 6),\n \"variation\": round(functions.variation(), 6),\n \"starting_moment\": round(functions.starting_moment(k), 6),\n \"central_moment\": round(functions.central_moment(k), 6),\n \"k\": k,\n \"skewness\": round(functions.skewness(), 6),\n \"kurtosis\": round(functions.kurtosis(), 6)\n }\n )\n", "repo_name": "Darky2020/SMAD", "sub_path": "service/lab2/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 2614, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "fastapi.APIRouter", "line_number": 7, "usage_type": "call"}, {"api_name": "fastapi.templating.Jinja2Templates", "line_number": 9, "usage_type": "call"}, {"api_name": "fastapi.Request", "line_number": 12, "usage_type": "name"}, {"api_name": "data.data", "line_number": 19, "usage_type": "argument"}, {"api_name": "data.data", "line_number": 20, "usage_type": "name"}, {"api_name": "data.data", "line_number": 51, "usage_type": "argument"}, {"api_name": "fastapi.responses.HTMLResponse", "line_number": 11, "usage_type": "name"}]} +{"seq_id": "28966119948", "text": "__author__ = \"Octavian Preda\", \"Wojciech Rog\"\n__email__ = \"opreda@cisco.com\", \"wrog@cisco.com\"\n__version__ = \"0.1.0\"\n__copyright__ = \"Copyright (c) 2019 Cisco and/or its affiliates.\"\n__license__ = \"Cisco Sample Code License, Version 1.1\"\n\nimport json\nimport sys\nimport os\nimport requests\nimport time\nimport getpass\nimport hashlib\n\nrequests.packages.urllib3.disable_warnings()\n\n\nclass DNACSession():\n def __init__(\n self,\n port=80,\n host=None,\n username=None,\n password=None,\n token=None,\n ):\n if host:\n self.host = host\n else:\n self.set_host()\n\n self.port = port\n\n self.params = {}\n\n self.config = {\n 'request_verify': False,\n 'show_passwords': False,\n 'show_token': False,\n 'string_mask': '*' * 5,\n 'ask_for_permission': True,\n }\n\n if not token:\n if username:\n self.username = username\n else:\n self.set_username()\n\n if password:\n self.password = password\n else:\n self.set_password()\n\n if self.login_ack():\n self.token = self.get_auth_token()\n else:\n sys.exit(1)\n else:\n self.token = token\n\n self.set_identity()\n\n self.requests_headers = {\n 'X-auth-token': self.token\n }\n\n self.post_headers = {\n 'X-auth-token': self.token,\n 'Content-Type': 'application/json'\n }\n\n self.calculate_hash()\n\n def __repr__(self):\n return self.host\n\n def set_host(self):\n self.host = str(input(\"--DNA Center host address: \"))\n\n def set_username(self):\n self.username = str(input(\"--DNA Center API username: \"))\n\n def set_password(self):\n self.password = str(getpass.getpass(\"--DNA Center API password: \"))\n\n def set_identity(self):\n print('Collecting data for further identification')\n self.params['executer_name'] = str(input(\"--Your name: \"))\n self.params['executer_cco'] = str(input(\"--Your CCO ID: \"))\n\n def calculate_hash(self):\n try:\n with open('dnacbackend.py', 'rb') as file:\n contents = file.read()\n self.params['sha256'] = hashlib.sha256(contents).hexdigest()\n except FileNotFoundError:\n self.params['sha256'] = 'ERROR: could not calculate hash - file not found'\n\n def ask_for_permision(message):\n \"\"\"Decision decorator, askes for confirmation before running an API function\"\"\"\n def _decorator(function):\n def wrapper(self):\n if self.config['ask_for_permission']:\n opt_yes = ['y', 'yes']\n opt_no = ['n', 'no']\n print(message)\n while True:\n print(\"Please use [{yes}] for 'yes' or [{no}] for 'no'\".format(\n yes=\"/\".join(opt_yes),\n no=\"/\".join(opt_no),\n ))\n print(\"[deafult 'yes']\")\n decision = input()\n if decision.lower() in opt_yes or decision == '':\n function(self)\n return True\n elif decision.lower() in opt_no:\n return False\n print(\"Decision unknown!\")\n return wrapper\n return _decorator\n\n @ask_for_permision('--Login data complete, do you want to continue with activation check?')\n def login_ack(self):\n print(\"---Creating DNAC profile for {}\".format(self.host))\n print(\"---Running Activation Check on {0}\".format(self.host))\n return True\n\n def set_show_passwords(self, flag=True):\n self.config['show_passwords'] = flag\n\n def set_show_token(self, flag=True):\n self.config['show_token'] = flag\n\n def set_ask_for_permission(self, flag=True):\n self.config[ask_for_permission] = flag\n\n def _create_url(self, url):\n host = self.host + ':' + \\\n str(self.port) if self.port != 80 else self.host\n return \"https://{host}{url}\".format(host=host, url=url)\n\n def _get_url(self, url):\n # TO DO HTTP error handling\n try:\n url = self._create_url(url)\n #print(\"Sending get request to {url}\".format(url=url))\n r = requests.get(\n url=url,\n headers=self.requests_headers,\n verify=self.config['request_verify'])\n if r.status_code == 200 or r.status_code == 204:\n return r\n else:\n print(\"Error bad response\", r.status_code, r.text)\n sys.exit(1)\n except requests.exceptions.RequestException as cerror:\n print(\"Error processing request\", cerror)\n sys.exit(1)\n\n def _post_url(self, url, payload):\n # TO DO HTTP error handling\n try:\n url = self._create_url(url)\n #print(\"Sending get request to {url}\".format(url=url))\n payload = json.dumps(payload)\n return requests.post(\n url=url,\n headers=self.post_headers,\n data=payload,\n verify=self.config['request_verify'])\n except requests.exceptions.RequestException as cerror:\n print(\"Error processing request\", cerror)\n sys.exit(1)\n\n def get_auth_token(self):\n \"\"\"Retrieve auth token to be used in futer API calls\"\"\"\n # login_url = \"https://{0}/dna/system/api/v1/auth/token\".format(\n login_url = \"https://{0}/api/system/v1/auth/token\".format(\n self.host, self.port)\n try:\n result = requests.post(\n url=login_url, auth=requests.auth.HTTPBasicAuth(\n self.username, self.password), verify=False)\n result.raise_for_status()\n except requests.exceptions.HTTPError as err:\n print(\"Http Error: \", err)\n sys.exit(1)\n except requests.exceptions.ConnectionError as err:\n print(\"Error Connecting: \", err)\n sys.exit(1)\n except requests.exceptions.Timeout as err:\n print(\"Timeout Error: \", err)\n sys.exit(1)\n except requests.exceptions.RequestException as err:\n print(\"Oops: Something went wrong: \", err)\n sys.exit(1)\n\n token = result.json()[\"Token\"]\n return token\n\n def set_executer_in_params(self):\n print('')\n name = input()\n\n def get_params(self):\n \"\"\"Retreive collected parameters\"\"\"\n return self.params\n\n def get_hosts(self):\n \"\"\"Retreive a list of system hosts (wired and wireless)\"\"\"\n print(\"---Retrieving system hosts\")\n r = self._get_url(\n '/api/v1/topology/physical-topology?nodeType=HOST')\n return r.json().get('response')\n\n @ask_for_permision('--Do you want to count wired and wireless hosts?')\n def count_hosts(self):\n \"\"\"Counting wired and wireless host/clients\"\"\"\n print(\"---Counting system hosts\")\n hosts = self.get_hosts().get('nodes')\n wired_hosts = [host for host in hosts if host['deviceType'] == 'wired']\n wireless_hosts = [\n host for host in hosts if host['deviceType'] == 'wireless']\n self.params['wired_hosts_count'] = len(wired_hosts)\n self.params['wireless_hosts_count'] = len(wireless_hosts)\n\n def get_network_devices_inventory(self):\n \"\"\"Retreive inventory of network devices\"\"\"\n print(\"---Retrieving network devices inventory list\")\n r = self._get_url(\n # '/dna/intent/api/v1/topology/physical-topology?nodeType=device')\n '/api/v1/network-device/')\n return r.json().get('response')\n\n @ask_for_permision('--Do you wnat to count devices in inventory?')\n def count_network_devices_inventory(self):\n \"\"\"Count devices in inventory of network devices\"\"\"\n print(\"---Counting network devices\")\n devices_inventory = self.get_network_devices_inventory()\n wlc_count = sum([item['family']=='Wireless Controller' for item in devices_inventory])\n ap_count = sum([item['family']=='Unified AP' for item in devices_inventory])\n self.params['devices_inventory'] = {\n 'inventory_total': len(devices_inventory),\n 'wlc_count': wlc_count,\n 'ap_count': ap_count,\n }\n\n def get_fabric_domains_transits(self):\n \"\"\"Retrieving inventory of fabric domains and transits\"\"\"\n print(\"---Retrieving fabric domains and transits inventory list\")\n r = self._get_url(\n '/api/v2/data/customer-facing-service/ConnectivityDomain')\n return r.json().get('response')\n\n def get_fabric_inventory_by_site(self, site_id):\n \"\"\"Retrieving fabric devices inventory by site\"\"\"\n print(\"---Retrieving fabric devices inventory by site\")\n r = self._get_url(\n '/api/v2/data/customer-facing-service/DeviceInfo?siteDeviceList={0}'.format(site_id))\n return r.json().get('response')\n\n def get_fabric_site_poolids(self, siteid):\n \"\"\"Retrieving fabric pool ids inventory by site\"\"\"\n print(\"---Retrieving fabric pool ids inventory by site\")\n r = self._get_url(\n # '/api/v2/ippool/group?siteId={0}'.format(siteid))\n '/api/v2/ippool?contextvalue={0}'.format(siteid))\n return r.json().get('response')\n\n @ask_for_permision('--Do you want to count SDA domains?')\n def fabric_domains_transits(self):\n \"\"\"Fabric domains, transits and vns\"\"\"\n print(\"---Analyzing fabric and extracting relevant numbers\")\n fabric_domains_transits = self.get_fabric_domains_transits()\n self.params['fabric_lans_count'] = sum(\n 1 for item in fabric_domains_transits if item[\"domainType\"] == \"FABRIC_LAN\")\n self.params['fabric_sites_count'] = sum(\n 1 for item in fabric_domains_transits if item[\"domainType\"] == \"FABRIC_SITE\")\n self.params['transits_count'] = sum(\n 1 for item in fabric_domains_transits if item[\"domainType\"] == \"TRANSIT\")\n\n self.params['fabric'] = {}\n\n for item in fabric_domains_transits:\n item_id = item[\"id\"]\n self.params['fabric'][item_id] = {}\n self.params['fabric'][item_id][\"vn_count\"] = len(\n item[\"virtualNetwork\"])\n self.params['fabric'][item_id][\"name\"] = item[\"name\"]\n self.params['fabric'][item_id][\"fabric_details\"] = item\n\n \"\"\"Gather fabric site ip pool\"\"\"\n if \"siteId\" in self.params['fabric'][item_id][\"fabric_details\"]:\n ip_pools = self.get_fabric_site_poolids(\n self.params['fabric'][item_id][\"fabric_details\"][\"siteId\"])\n self.params['fabric'][item[\"id\"]][\"ippool\"] = ip_pools\n\n \"\"\"Gather fabric devices inventory\"\"\"\n self.params['fabric'][item_id][\"devices\"] = []\n self.params['fabric'][item_id][\"edge\"] = []\n self.params['fabric'][item_id][\"control\"] = []\n self.params['fabric'][item_id][\"border\"] = []\n #self.params['fabric'][item_id][\"device_details\"] = []\n\n if \"siteId\" in self.params['fabric'][item[\"id\"]][\"fabric_details\"]:\n fabric_by_site = self.get_fabric_inventory_by_site(\n self.params['fabric'][item_id][\"fabric_details\"][\"siteId\"])\n for item_site in fabric_by_site:\n if \"roles\" in item_site:\n if \"EDGENODE\" in item_site[\"roles\"]:\n self.params['fabric'][item_id][\"edge\"].append(\n item_site[\"networkDeviceId\"])\n self.params['fabric'][item_id][\"devices\"].append(\n item_site[\"networkDeviceId\"])\n # self.params['fabric'][item_id][\"device_details\"].append(item_site)\n if \"MAPSERVER\" in item_site[\"roles\"]:\n self.params['fabric'][item_id][\"control\"].append(\n item_site[\"networkDeviceId\"])\n self.params['fabric'][item_id][\"devices\"].append(\n item_site[\"networkDeviceId\"])\n # self.params['fabric'][item_id][\"device_details\"].append(item_site)\n if \"BORDERNODE\" in item_site[\"roles\"]:\n self.params['fabric'][item_id][\"border\"].append(\n item_site[\"networkDeviceId\"])\n self.params['fabric'][item_id][\"devices\"].append(\n item_site[\"networkDeviceId\"])\n # self.params['fabric'][item_id][\"device_details\"].append(item_site)\n\n def fabric_summary(self):\n for item in self.params['fabric']:\n self.params['fabric'][item][\"ip_pool_count\"] = 0\n self.params['fabric'][item][\"edge_count\"] = 0\n self.params['fabric'][item][\"control_count\"] = 0\n self.params['fabric'][item][\"border_count\"] = 0\n\n if \"ippool\" in self.params['fabric'][item]:\n self.params['fabric'][item][\"ip_pool_count\"] = len(\n self.params['fabric'][item][\"ippool\"])\n if \"edge\" in self.params['fabric'][item]:\n self.params['fabric'][item][\"edge_count\"] = len(\n self.params['fabric'][item][\"edge\"])\n if \"control\" in self.params['fabric'][item]:\n self.params['fabric'][item][\"control_count\"] = len(\n self.params['fabric'][item][\"control\"])\n if \"border\" in self.params['fabric'][item]:\n self.params['fabric'][item][\"border_count\"] = len(\n self.params['fabric'][item][\"border\"])\n\n def get_fabric_inventory(self):\n \"\"\"Retrieving fabric devices inventory\"\"\"\n print(\"---Retrieving fabric devices inventory list\")\n r = self._get_url(\n '/api/v2/data/customer-facing-service/DeviceInfo')\n return r.json().get('response')\n\n @ask_for_permision('--Do you want to collect SDA fabric inventory')\n def fabric_inventory(self):\n \"\"\"Filtering fabric devices inventory\"\"\"\n print(\"---Filtering fabric devices inventory list\")\n self.params[\"global_fabric_devices\"] = []\n self.params[\"global_fabric_edge\"] = []\n self.params[\"global_fabric_control\"] = []\n self.params[\"global_fabric_border\"] = []\n #self.params[\"fabric_devices_details\"] = []\n fabric_devices_inventory = self.get_fabric_inventory()\n for item in fabric_devices_inventory:\n if \"roles\" in item:\n if \"EDGENODE\" in item[\"roles\"]:\n self.params[\"global_fabric_edge\"].append(\n item[\"networkDeviceId\"])\n self.params[\"global_fabric_devices\"].append(\n item[\"networkDeviceId\"])\n # self.params[\"fabric_devices_details\"].append(item)\n if \"MAPSERVER\" in item[\"roles\"]:\n self.params[\"global_fabric_control\"].append(\n item[\"networkDeviceId\"])\n self.params[\"global_fabric_devices\"].append(\n item[\"networkDeviceId\"])\n # self.params[\"fabric_devices_details\"].append(item)\n if \"BORDERNODE\" in item[\"roles\"]:\n self.params[\"global_fabric_border\"].append(\n item[\"networkDeviceId\"])\n self.params[\"global_fabric_devices\"].append(\n item[\"networkDeviceId\"])\n # self.params[\"fabric_devices_details\"].append(item)\n\n def command_runner(self, device_uids, cmds):\n \"\"\"Command Runner\"\"\"\n print(\"---Running Command Runner\")\n payload = {\"name\": \"command-runner\",\n \"description\": \"command-runner-network-poller\",\n \"deviceUuids\": device_uids,\n \"commands\": cmds}\n r = self._post_url(\n '/api/v1/network-device-poller/cli/read-request', payload)\n return r.json().get('response')\n\n def check_task(self, task_id):\n \"\"\"Checking Command Runner Task ID\"\"\"\n print(\"---Checking Command Runner Task ID\")\n r = self._get_url(\n '/api/v1/task/{0}'.format(task_id))\n return r.json().get('response')\n\n def check_file(self, file_id):\n \"\"\"Checking Command Runner File ID\"\"\"\n print(\"---Checking Command Runner File ID\")\n r = self._get_url(\n '/api/v1/file/{0}'.format(file_id))\n return r.json()\n\n def run_command(self, devices, cmds):\n \"\"\"Run Commands using command_runner\"\"\"\n command = self.command_runner(devices, cmds)\n task = self.check_task(command['taskId'])\n\n retries = 12\n while retries > 0:\n try:\n task_progress = json.loads(task[\"progress\"])\n file_id = task_progress[\"fileId\"]\n break\n except Exception:\n print(\"---Task still running. Trying again...\")\n task = self.check_task(command['taskId'])\n time.sleep(2)\n retries -= 1\n\n if retries == 0:\n print(\"Error checking task\")\n sys.exit(1)\n\n retries = 6\n while retries > 0:\n try:\n file = self.check_file(file_id)\n return file\n except Exception:\n print(\"---File not ready. Trying again...\")\n time.sleep(2)\n retries -= 1\n\n if retries == 0:\n print(\"Exception in Command Runner File Check\")\n sys.exit(1)\n\n @ask_for_permision('--Do you want to execute show commands?')\n def show_commands(self):\n for id, item in self.params[\"fabric\"].items():\n self.params[\"fabric\"][id][\"show_commands\"] = []\n if item[\"edge\"]:\n cmds = [\"show vrf\", \"show vlan\"]\n file = self.run_command(item[\"edge\"], cmds)\n self.params[\"fabric\"][id][\"show_commands\"].append(file)\n\n if item[\"control\"]:\n cmds = [\"show lisp site summary\", \"show lisp session\"]\n file = self.run_command(item[\"control\"], cmds)\n self.params[\"fabric\"][id][\"show_commands\"].append(file)\n", "repo_name": "wrogcisco/cisco-dnac-activationcheck", "sub_path": "dnacbackend.py", "file_name": "dnacbackend.py", "file_ext": "py", "file_size_in_byte": 18696, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "github-code", "pt": "37", "api": [{"api_name": "requests.packages.urllib3.disable_warnings", "line_number": 15, "usage_type": "call"}, {"api_name": "requests.packages", "line_number": 15, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 58, "usage_type": "call"}, {"api_name": "getpass.getpass", "line_number": 85, "usage_type": "call"}, {"api_name": "hashlib.sha256", "line_number": 96, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 149, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 157, "usage_type": "call"}, {"api_name": "requests.exceptions", "line_number": 158, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 160, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 167, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 168, "usage_type": "call"}, {"api_name": "requests.exceptions", "line_number": 173, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 175, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 183, "usage_type": "call"}, {"api_name": "requests.auth.HTTPBasicAuth", "line_number": 184, "usage_type": "call"}, {"api_name": "requests.auth", "line_number": 184, "usage_type": "attribute"}, {"api_name": "requests.exceptions", "line_number": 187, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 189, "usage_type": "call"}, {"api_name": "requests.exceptions", "line_number": 190, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 192, "usage_type": "call"}, {"api_name": "requests.exceptions", "line_number": 193, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 195, "usage_type": "call"}, {"api_name": "requests.exceptions", "line_number": 196, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 198, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 422, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 428, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 433, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 442, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 447, "usage_type": "call"}]} +{"seq_id": "35730192288", "text": "import bpy\nimport bmesh\nimport random\nimport typing\n\n\nclass Windows:\n @staticmethod\n def __create_vert(bm, width, width2, depth, height, height2):\n\n vert1 = bm.verts.new((width, 0, height))\n vert2 = bm.verts.new((width2, 0, height))\n vert3 = bm.verts.new((width, 0, height2))\n vert4 = bm.verts.new((width2, 0, height2))\n\n vert1_2 = bm.verts.new((width, depth, height))\n vert2_2 = bm.verts.new((width2, depth, height))\n vert3_2 = bm.verts.new((width, depth, height2))\n vert4_2 = bm.verts.new((width2, depth, height2))\n\n bm.faces.new((vert1, vert2, vert4, vert3))\n bm.faces.new((vert1_2, vert2_2, vert4_2, vert3_2))\n bm.faces.new((vert1_2, vert1, vert3, vert3_2))\n bm.faces.new((vert2, vert2_2, vert4_2, vert4))\n bm.faces.new((vert4_2, vert4, vert3, vert3_2))\n bm.faces.new((vert1_2, vert1, vert2, vert2_2))\n\n @staticmethod\n def __create_glass_material():\n glass_material = bpy.data.materials.new(\"Glass\")\n glass_material.use_nodes = True\n nodes: typing.List[bpy.types.Nodes] = glass_material.node_tree.nodes\n node_glass: bpy.types.Node = nodes.new(\"ShaderNodeBsdfGlass\")\n glass_material.node_tree.nodes.remove(\n glass_material.node_tree.nodes.get('Principled BSDF'))\n material_output = glass_material.node_tree.nodes.get('Material Output')\n glass_material.node_tree.links.new(\n material_output.inputs[0], node_glass.outputs[0])\n return glass_material\n @staticmethod\n def __create_wood_material():\n # wood_material\n wood_material = bpy.data.materials.new(\"Wood\")\n wood_material.use_nodes = True\n nodes: typing.List[bpy.types.Nodes] = wood_material.node_tree.nodes\n bsdf = wood_material.node_tree.nodes.get('Principled BSDF')\n bsdf.inputs[7].default_value = 0.2\n\n # texture_coordinate\n tex_coord: bpy.types.Node = nodes.new(type=\"ShaderNodeTexCoord\")\n\n # mapping\n mapping: bpy.types.Node = nodes.new(type=\"ShaderNodeMapping\")\n\n # noise\n nodes.new(type=\"ShaderNodeValToRGB\")\n noise_texture: bpy.types.Node = nodes.new(type=\"ShaderNodeTexNoise\")\n noise_texture.inputs[1].default_value = 3\n noise_texture.inputs[2].default_value = 3.8\n noise_texture.inputs[3].default_value = 0.545833\n noise_texture.inputs[4].default_value = 1.6\n\n # color ramp\n color_ramp_color = wood_material.node_tree.nodes.get('ColorRamp')\n\n color_ramp_color.color_ramp.elements[0].color = (\n 0.520995, 0.250, 0.102, 1.0)\n color_ramp_color.color_ramp.elements[1].color = (\n 0.100, 0.028, 0.0185, 1)\n\n # brightness/contrast\n brightness_contrast: bpy.types.Node = nodes.new(\n type=\"ShaderNodeBrightContrast\")\n brightness_contrast.inputs[2].default_value = 1\n\n # color ramp bump\n color_ramp_bump: bpy.types.Node = nodes.new(type=\"ShaderNodeValToRGB\")\n color_ramp_bump.color_ramp.elements[1].position = 0.025\n\n bump: bpy.types.Node = nodes.new(type=\"ShaderNodeBump\")\n bump.inputs[1].default_value = 0.01\n\n ### linking nodes ###\n\n # textcoord to mapping\n wood_material.node_tree.links.new(\n mapping.inputs[0], tex_coord.outputs[0])\n # mapping to noise\n wood_material.node_tree.links.new(\n noise_texture.inputs[0], mapping.outputs[0])\n # noise to ramp\n wood_material.node_tree.links.new(\n color_ramp_color.inputs[0], noise_texture.outputs[1])\n # ramp to bsdf\n wood_material.node_tree.links.new(\n bsdf.inputs[0], color_ramp_color.outputs[0])\n # ramp to bright/contr\n wood_material.node_tree.links.new(\n brightness_contrast.inputs[0], color_ramp_color.outputs[0])\n # bright/contr to bump_ramp\n wood_material.node_tree.links.new(\n color_ramp_bump.inputs[0], brightness_contrast.outputs[0])\n # bump_ramp to bump\n wood_material.node_tree.links.new(\n bump.inputs[2], color_ramp_bump.outputs[0])\n # bump to bsdf\n wood_material.node_tree.links.new(bsdf.inputs[22], bump.outputs[0])\n return wood_material\n \n\n @staticmethod\n def __create_random_basis(windowheight, windowwidth, windowdepth):\n\n windowmesh = bpy.data.meshes.new(\"WindowMesh\")\n windowobject = bpy.data.objects.new(\"WindowFrame\", windowmesh)\n bpy.context.collection.objects.link(\n windowobject) # put object in collection\n bm = bmesh.new()\n bm.from_mesh(windowmesh)\n\n Windows.__create_vert(bm, -windowwidth, windowwidth,\n windowdepth, 0, windowheight)\n\n bm.to_mesh(windowmesh)\n bm.free()\n return windowobject\n\n @staticmethod\n def __create_window_frame(windowheight,windowwidth,leafdepth,windowframewidth):\n framemesh = bpy.data.meshes.new(\"WindowFrameMesh\")\n frameobject = bpy.data.objects.new(\"WindowFrame\", framemesh)\n bpy.context.collection.objects.link(\n frameobject) # put object in collection\n bm = bmesh.new()\n bm.from_mesh(framemesh)\n \n Windows.__create_vert(bm,-windowwidth - windowframewidth,windowwidth + windowframewidth,-leafdepth,windowheight,windowheight+ windowframewidth)\n Windows.__create_vert(bm,-windowwidth - windowframewidth,windowwidth + windowframewidth,-leafdepth,0,0- windowframewidth,)\n Windows.__create_vert(bm,-windowwidth - windowframewidth,-windowwidth ,-leafdepth,0- windowframewidth,windowheight+ windowframewidth)\n Windows.__create_vert(bm,windowwidth,windowwidth + windowframewidth,-leafdepth,0- windowframewidth,windowheight+ windowframewidth)\n \n bm.to_mesh(framemesh)\n bm.free()\n return frameobject\n\n @staticmethod\n def __create_windowleaf(windowheight,windowwidth,leafdepth,windowleaf):\n leafmesh = bpy.data.meshes.new(\"WindowFrameMesh\")\n leafobject = bpy.data.objects.new(\"WindowFrame\", leafmesh)\n bpy.context.collection.objects.link(\n leafobject) # put object in collection\n bm = bmesh.new()\n bm.from_mesh(leafmesh)\n \n if(windowleaf==2):\n Windows.__create_two_leaf_window(bm, windowheight,windowwidth,leafdepth)\n\n elif(windowleaf==3):\n Windows.__create_three_leaf_window(bm, windowheight,windowwidth,leafdepth)\n \n elif(windowleaf==4):\n Windows.__create_four_leaf_window(bm, windowheight,windowwidth,leafdepth)\n\n bm.to_mesh(leafmesh)\n bm.free()\n return leafobject\n\n \n \n @staticmethod\n def __vertical_window(windowheight,windowwidth):\n height= windowheight\n width = windowwidth/10\n return height,width\n\n @staticmethod\n def __horizontal_window(windowheight,windowwidth):\n height= windowheight/10\n width= windowwidth\n return height,width \n\n @staticmethod\n def __create_two_leaf_window(bm,windowheight,windowwidth,leafdepth,):\n format2leaf= random.randint(1,2)\n\n if (format2leaf==1):\n # vertical\n leafheight,leafwidth=Windows.__vertical_window(windowheight,windowwidth)\n Windows.__create_vert(bm,-leafwidth,leafwidth,-leafdepth,0,leafheight)\n\n else:\n # horizontal\n leafheight,leafwidth=Windows.__horizontal_window(windowheight,windowwidth) \n Windows.__create_vert(bm,-leafwidth,leafwidth,-leafdepth,(windowheight/2 + leafheight/2),(windowheight/2 - leafheight/2))\n \n\n @staticmethod\n def __create_three_leaf_window(bm,windowheight,windowwidth,leafdepth):\n format3leaf=random.randint(1,6)\n\n if(format3leaf==1):\n # two vertical leafs\n leafheight,leafwidth=Windows.__vertical_window(windowheight,windowwidth)\n Windows.__create_vert(bm,(windowwidth/3-leafwidth/2),(windowwidth/3+leafwidth/2),-leafdepth,0,leafheight)\n Windows.__create_vert(bm,(-windowwidth/3-leafwidth/2),(-windowwidth/3+leafwidth/2),-leafdepth,0,leafheight)\n\n elif(format3leaf==2):\n # two horizontal leafs\n leafheight,leafwidth=Windows.__horizontal_window(windowheight,windowwidth) \n Windows.__create_vert(bm,-leafwidth,leafwidth,-leafdepth,(windowheight/3 + leafheight/2),(windowheight/3 - leafheight/2))\n Windows.__create_vert(bm,-leafwidth,leafwidth,-leafdepth,(windowheight*(2/3) + leafheight/2),(windowheight*(2/3) - leafheight/2))\n\n elif(format3leaf==3):\n # horizontal & half vertical (top)\n # horizontal\n leafheight,leafwidth=Windows.__horizontal_window(windowheight,windowwidth)\n Windows.__create_vert(bm,-leafwidth,leafwidth,-leafdepth,(windowheight/2 + leafheight/2),(windowheight/2 - leafheight/2))\n # vertical half top part\n leaf2height,leaf2width=Windows.__vertical_window(windowheight,windowwidth)\n Windows.__create_vert(bm,-leaf2width,leaf2width,-leafdepth,(windowheight/2 + leafheight/2),leaf2height)\n\n elif(format3leaf==4):\n # horizontal & half vertical (bottom)\n # horizontal\n leafheight,leafwidth=Windows.__horizontal_window(windowheight,windowwidth) \n Windows.__create_vert(bm,-leafwidth,leafwidth,-leafdepth,(windowheight/2 + leafheight/2),(windowheight/2 - leafheight/2))\n # vertical half bottom part\n leaf2height,leaf2width=Windows.__vertical_window(windowheight,windowwidth)\n Windows.__create_vert(bm,-leaf2width,leaf2width,-leafdepth,0,(leaf2height/2 - leafheight/2))\n\n elif(format3leaf==5):\n # vertical & horizontal left\n #vertical\n leafheight,leafwidth=Windows.__vertical_window(windowheight,windowwidth)\n Windows.__create_vert(bm,-leafwidth,leafwidth,-leafdepth,0,leafheight)\n #horizontal left \n leaf2height,leaf2width=Windows.__horizontal_window(windowheight,windowwidth) \n Windows.__create_vert(bm,-windowwidth,-leafwidth,-leafdepth,(windowheight/2 + leaf2height/2),(windowheight/2 - leaf2height/2))\n\n else:\n # vertical & horizontal right\n #vertical\n leafheight,leafwidth=Windows.__vertical_window(windowheight,windowwidth)\n Windows.__create_vert(bm,-leafwidth,leafwidth,-leafdepth,0,leafheight)\n #horizontal right\n leaf2height,leaf2width=Windows.__horizontal_window(windowheight,windowwidth) \n Windows.__create_vert(bm,leafwidth,windowwidth,-leafdepth,(windowheight/2 + leaf2height/2),(windowheight/2 - leaf2height/2))\n \n @staticmethod\n def __create_four_leaf_window(bm,windowheight,windowwidth,leafdepth):\n format4leaf=random.randint(1,9)\n\n if(format4leaf==1):\n # three vertical leafs \n leafheight,leafwidth=Windows.__vertical_window(windowheight,windowwidth)\n Windows.__create_vert(bm,(windowwidth/2-leafwidth),(windowwidth/2+leafwidth),-leafdepth,0,leafheight)\n Windows.__create_vert(bm,(-windowwidth/2-leafwidth),(-windowwidth/2+leafwidth),-leafdepth,0,leafheight)\n Windows.__create_vert(bm,-leafwidth,leafwidth,-leafdepth,0,leafheight) \n\n elif(format4leaf==2):\n # two horizontal leafs\n leafheight,leafwidth=Windows.__horizontal_window(windowheight,windowwidth) \n Windows.__create_vert(bm,-leafwidth,leafwidth,-leafdepth,(windowheight/4 + leafheight/2),(windowheight/4 - leafheight/2))\n Windows.__create_vert(bm,-leafwidth,leafwidth,-leafdepth,(windowheight*(3/4) + leafheight/2),(windowheight*(3/4) - leafheight/2))\n Windows.__create_vert(bm,-leafwidth,leafwidth,-leafdepth,(windowheight/2 + leafheight/2),(windowheight/2 - leafheight/2))\n \n elif(format4leaf==3):\n # a cross\n # vertical leaf\n leafheight,leafwidth=Windows.__vertical_window(windowheight,windowwidth)\n Windows.__create_vert(bm,-leafwidth,leafwidth,-leafdepth,0,leafheight)\n #horizontal leaf\n leaf2height,leaf2width=Windows.__horizontal_window(windowheight,windowwidth) \n Windows.__create_vert(bm,-leaf2width,leaf2width,-leafdepth,(windowheight/2 + leaf2height/2),(windowheight/2 - leaf2height/2))\n \n elif(format4leaf==4):\n # two vertical one horizontal on the left\n # two vertical\n leafheight,leafwidth=Windows.__vertical_window(windowheight,windowwidth)\n Windows.__create_vert(bm,(windowwidth/3-leafwidth/2),(windowwidth/3+leafwidth/2),-leafdepth,0,leafheight)\n Windows.__create_vert(bm,(-windowwidth/3-leafwidth/2),(-windowwidth/3+leafwidth/2),-leafdepth,0,leafheight)\n # horizontal left\n leaf3height,leaf3width=Windows.__horizontal_window(windowheight,windowwidth) \n Windows.__create_vert(bm,-leaf3width,(-windowwidth*(1/3)-leafwidth/2),-leafdepth,(windowheight/2 + leaf3height/2),(windowheight/2 - leaf3height/2))\n \n elif(format4leaf==5):\n # two vertical one horizontal in the middle\n # two vertical leafs\n leafheight,leafwidth=Windows.__vertical_window(windowheight,windowwidth)\n Windows.__create_vert(bm,(windowwidth/3-leafwidth/2),(windowwidth/3+leafwidth/2),-leafdepth,0,leafheight)\n Windows.__create_vert(bm,(-windowwidth/3-leafwidth/2),(-windowwidth/3+leafwidth/2),-leafdepth,0,leafheight)#\n # horizontal middle\n leaf3height,leaf3width=Windows.__horizontal_window(windowheight,windowwidth) \n Windows.__create_vert(bm,(-windowwidth*(1/3)+ leafwidth/2),(windowwidth/3-leafwidth/2),-leafdepth,(windowheight/2 + leaf3height/2),(windowheight/2 - leaf3height/2))\n \n elif(format4leaf==6):\n # two vertical one horizontal on the right\n # two vertical\n leafheight,leafwidth=Windows.__vertical_window(windowheight,windowwidth)\n Windows.__create_vert(bm,(windowwidth/3-leafwidth/2),(windowwidth/3+leafwidth/2),-leafdepth,0,leafheight)\n Windows.__create_vert(bm,(-windowwidth/3-leafwidth/2),(-windowwidth/3+leafwidth/2),-leafdepth,0,leafheight)\n # horizontal right\n leaf3height,leaf3width=Windows.__horizontal_window(windowheight,windowwidth) \n Windows.__create_vert(bm,leaf3width,(windowwidth*(1/3)+ leafwidth/2),-leafdepth,(windowheight/2 + leaf3height/2),(windowheight/2 - leaf3height/2))\n \n elif(format4leaf==7):\n # two horizontal one vertical on the top\n # two horizontal\n leafheight,leafwidth=Windows.__horizontal_window(windowheight,windowwidth) \n Windows.__create_vert(bm,-leafwidth,leafwidth,-leafdepth,(windowheight/3 + leafheight/2),(windowheight/3 - leafheight/2))\n Windows.__create_vert(bm,-leafwidth,leafwidth,-leafdepth,(windowheight*(2/3) + leafheight/2),(windowheight*(2/3) - leafheight/2))\n # vertical top\n leaf3height,leaf3width=Windows.__vertical_window(windowheight,windowwidth)\n Windows.__create_vert(bm,-leaf3width,leaf3width,-leafdepth,(windowheight*(2/3) + leafheight/2),leaf3height)\n \n elif(format4leaf==8):\n # two horizontal one vertical in the middle\n # two horizontal\n leafheight,leafwidth=Windows.__horizontal_window(windowheight,windowwidth) \n Windows.__create_vert(bm,-leafwidth,leafwidth,-leafdepth,(windowheight/3 + leafheight/2),(windowheight/3 - leafheight/2))\n Windows.__create_vert(bm,-leafwidth,leafwidth,-leafdepth,(windowheight*(2/3) + leafheight/2),(windowheight*(2/3) - leafheight/2))\n # vertical top\n leaf3height,leaf3width=Windows.__vertical_window(windowheight,windowwidth)\n Windows.__create_vert(bm,-leaf3width,leaf3width,-leafdepth,(windowheight/3 + leafheight/2),(windowheight*(2/3) - leafheight/2))\n \n else:\n # two horizontal one vertical on the bottom\n # two horizontal\n leafheight,leafwidth=Windows.__horizontal_window(windowheight,windowwidth) \n Windows.__create_vert(bm,-leafwidth,leafwidth,-leafdepth,(windowheight/3 + leafheight/2),(windowheight/3 - leafheight/2))\n Windows.__create_vert(bm,-leafwidth,leafwidth,-leafdepth,(windowheight*(2/3) + leafheight/2),(windowheight*(2/3) - leafheight/2))\n # vertical top\n leaf3height,leaf3width=Windows.__vertical_window(windowheight,windowwidth)\n Windows.__create_vert(bm,-leaf3width,leaf3width,-leafdepth,0,(windowheight/3 - leafheight/2))\n \n @staticmethod\n def __create_window_sill(windowwidth,leafdepth,windowframewidth):\n windowsillmesh = bpy.data.meshes.new(\"WindowSillMesh\")\n windowsillobject = bpy.data.objects.new(\"WindowFrame\", windowsillmesh)\n bpy.context.collection.objects.link(\n windowsillobject) # put object in collection\n bm = bmesh.new()\n bm.from_mesh(windowsillmesh)\n windowsilllength = random.uniform(1,2)\n \n Windows.__create_vert(bm,-windowwidth - windowframewidth,windowwidth + windowframewidth, -windowsilllength,0- windowframewidth,0- windowframewidth-leafdepth )\n\n bm.to_mesh(windowsillmesh)\n bm.free()\n return windowsillobject\n\n @staticmethod\n def __create_window_accessoir(windowheight,windowwidth,windowframewidth,accessoir): \n windowaccessoirmesh = bpy.data.meshes.new(\"WindowAccesoirMesh\")\n windowaccessoirobject = bpy.data.objects.new(\"WindowFrame\", windowaccessoirmesh)\n bpy.context.collection.objects.link(\n windowaccessoirobject) # put object in collection\n bm = bmesh.new()\n bm.from_mesh(windowaccessoirmesh)\n if (accessoir==2):\n #Blackbox / Rolladenbox\n blackbox = random.randint(1,3)\n blackboxdepth = random.uniform(1,1.6)\n if(blackbox==1):\n #square\n Windows.__create_vert(bm,-windowwidth - windowframewidth,windowwidth + windowframewidth,-blackboxdepth,windowheight,windowheight+blackboxdepth)\n elif(blackbox==2):\n #square with one flat corner\n a=9\n else:\n #round\n d=2\n x=1\n else:\n # left\n Windows.__create_vert(bm,(-windowwidth*2),(-windowwidth-windowframewidth),(-leafdepth*3),0,windowheight)\n # design mittel top \n Windows.__create_vert(bm,(-windowwidth*2+windowwidth/4 ),(-windowwidth-windowframewidth-windowwidth/4),(-leafdepth*6),(windowheight - windowheight/10),(windowheight/2 + windowheight/12))\n # design mittel middle\n Windows.__create_vert(bm,(-windowwidth*2+windowwidth/4 ),(-windowwidth-windowframewidth-windowwidth/4),(-leafdepth*6),(windowheight/10),(windowheight/2 - windowheight/12))\n # design left \n Windows.__create_vert(bm,(-windowwidth*2),(-windowwidth*2+windowwidth/7),(-leafdepth*6),0,windowheight)\n # design right\n Windows.__create_vert(bm,(-windowwidth-windowframewidth-windowwidth/7),(-windowwidth-windowframewidth),(-leafdepth*6),0,windowheight)\n # design cross\n Windows.__create_vert(bm,(-windowwidth*2),(-windowwidth-windowframewidth),(-leafdepth*6),(windowheight/2 + windowheight/20),(windowheight/2 - windowheight/20))\n # design top \n Windows.__create_vert(bm,(-windowwidth*2),(-windowwidth-windowframewidth),(-leafdepth*6),(windowheight),(windowheight - windowheight/15))\n # design bottom\n Windows.__create_vert(bm,(-windowwidth*2),(-windowwidth-windowframewidth),(-leafdepth*6),(windowheight/15),0)\n\n # right \n Windows.__create_vert(bm,(windowwidth+ windowframewidth),(windowwidth*2),(-leafdepth*3),0,windowheight)\n # design left \n Windows.__create_vert(bm,(windowwidth+ windowframewidth),(windowwidth+ windowframewidth+ windowwidth/7),(-leafdepth*6),0,windowheight)\n # design right\n Windows.__create_vert(bm,(windowwidth*2- windowwidth/7),(windowwidth*2),(-leafdepth*6),0,windowheight)\n # design top \n Windows.__create_vert(bm,(windowwidth+ windowframewidth),(windowwidth*2),(-leafdepth*6),(windowheight),(windowheight - windowheight/15))\n # design bottom\n Windows.__create_vert(bm,(windowwidth+ windowframewidth),(windowwidth*2),(-leafdepth*6),(windowheight/15),0)\n # design cross\n Windows.__create_vert(bm,(windowwidth+ windowframewidth),(windowwidth*2),(-leafdepth*6),(windowheight/2 + windowheight/20),(windowheight/2 - windowheight/20))\n # design mittel top \n Windows.__create_vert(bm,(windowwidth+ windowframewidth+windowwidth/4 ),(windowwidth*2-windowwidth/4),(-leafdepth*6),(windowheight - windowheight/10),(windowheight/2 + windowheight/12))\n # design mittel middle\n Windows.__create_vert(bm,(windowwidth+ windowframewidth+windowwidth/4 ),(windowwidth*2-windowwidth/4),(-leafdepth*6),(windowheight/10),(windowheight/2 - windowheight/12))\n \n \n bm.to_mesh(windowaccessoirmesh)\n bm.free()\n return windowaccessoirobject\n\n @staticmethod\n def create_window(windowheight, windowwidth, leafdepth, windowframewidth, windowdepth, windowsill, windowaccessoir):\n windowsillr = random.randint(1,2)\n windowaccessoirr = random.randint(1,3)\n windowleafr = random.randint(1,4)\n\n #create object\n basis: bpy.types.object = Windows.__create_random_basis(\n windowheight, windowwidth, windowdepth)\n windowframe: bpy.types.object = Windows.__create_window_frame(windowheight,windowwidth,leafdepth,windowframewidth)\n \n glass: bpy.types.Material = Windows.__create_glass_material()\n wood: bpy.types.Material = Windows.__create_wood_material()\n\n # append materials\n basis.data.materials.append(glass)\n windowframe.data.materials.append(wood)\n \n if (windowleafr!= 1):\n windowleaf: bpy.types.object = Windows.__create_windowleaf(windowheight,windowwidth,leafdepth,windowleafr)\n windowleaf.data.materials.append(wood)\n if (windowsillr==1):\n windowsill: bpy.types.object =Windows.__create_window_sill(windowwidth,leafdepth,windowframewidth)\n windowsill.data.materials.append(wood)\n if (windowaccessoirr!=1):\n windowaccessoir: bpy.types.object =Windows.__create_window_accessoir(windowheight,windowwidth,windowframewidth,windowaccessoirr)\n windowaccessoir.data.materials.append(wood)\n\ndef deleteAll():\n # delete old everything\n # clear all materials\n for material in bpy.data.materials:\n material.user_clear()\n bpy.data.materials.remove(material)\n\n bpy.ops.object.select_all(action='SELECT') # selektiert alle Objekte\n # löscht selektierte objekte\n bpy.ops.object.delete(use_global=False, confirm=False)\n bpy.ops.outliner.orphans_purge() # löscht überbleibende Meshdaten etc.\n\n\nwindowheight = random.randint(4, 10)\nwindowwidth = random.randint(2, 7)\nleafdepth = random.uniform(0.05, 0.1)\nwindowframewidth = random.uniform(0.05, 0.2)\nwindowdepth = random.uniform(0.2, 0.8)\n\nwindowsill = random.randint(1, 2)\nwindowaccessoir = random.uniform(1, 3)\n\ndeleteAll()\n\nbpy.data.scenes[\"Scene\"].eevee.use_ssr = True\n\nWindows.create_window(windowheight, windowwidth, leafdepth,\n windowframewidth, windowdepth, windowsill, windowaccessoir)\n", "repo_name": "larafra99/DVMP", "sub_path": "windows/window_withoutclass.py", "file_name": "window_withoutclass.py", "file_ext": "py", "file_size_in_byte": 23877, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "bpy.data.materials.new", "line_number": 30, "usage_type": "call"}, {"api_name": "bpy.data", "line_number": 30, "usage_type": "attribute"}, {"api_name": "typing.List", "line_number": 32, "usage_type": "attribute"}, {"api_name": "bpy.types", "line_number": 32, "usage_type": "attribute"}, {"api_name": "bpy.types", "line_number": 33, "usage_type": "attribute"}, {"api_name": "bpy.data.materials.new", "line_number": 43, "usage_type": "call"}, {"api_name": "bpy.data", "line_number": 43, "usage_type": "attribute"}, {"api_name": "typing.List", "line_number": 45, "usage_type": "attribute"}, {"api_name": "bpy.types", "line_number": 45, "usage_type": "attribute"}, {"api_name": "bpy.types", "line_number": 50, "usage_type": "attribute"}, {"api_name": "bpy.types", "line_number": 53, "usage_type": "attribute"}, {"api_name": "bpy.types", "line_number": 57, "usage_type": "attribute"}, {"api_name": "bpy.types", "line_number": 72, "usage_type": "attribute"}, {"api_name": "bpy.types", "line_number": 77, "usage_type": "attribute"}, {"api_name": "bpy.types", "line_number": 80, "usage_type": "attribute"}, {"api_name": "bpy.data.meshes.new", "line_number": 114, "usage_type": "call"}, {"api_name": "bpy.data", "line_number": 114, "usage_type": "attribute"}, {"api_name": "bpy.data.objects.new", "line_number": 115, "usage_type": "call"}, {"api_name": "bpy.data", "line_number": 115, "usage_type": "attribute"}, {"api_name": "bpy.context.collection.objects.link", "line_number": 116, "usage_type": "call"}, {"api_name": "bpy.context", "line_number": 116, "usage_type": "attribute"}, {"api_name": "bmesh.new", "line_number": 118, "usage_type": "call"}, {"api_name": "bpy.data.meshes.new", "line_number": 130, "usage_type": "call"}, {"api_name": "bpy.data", "line_number": 130, "usage_type": "attribute"}, {"api_name": "bpy.data.objects.new", "line_number": 131, "usage_type": "call"}, {"api_name": "bpy.data", "line_number": 131, "usage_type": "attribute"}, {"api_name": "bpy.context.collection.objects.link", "line_number": 132, "usage_type": "call"}, {"api_name": "bpy.context", "line_number": 132, "usage_type": "attribute"}, {"api_name": "bmesh.new", "line_number": 134, "usage_type": "call"}, {"api_name": "bpy.data.meshes.new", "line_number": 148, "usage_type": "call"}, {"api_name": "bpy.data", "line_number": 148, "usage_type": "attribute"}, {"api_name": "bpy.data.objects.new", "line_number": 149, "usage_type": "call"}, {"api_name": "bpy.data", "line_number": 149, "usage_type": "attribute"}, {"api_name": "bpy.context.collection.objects.link", "line_number": 150, "usage_type": "call"}, {"api_name": "bpy.context", "line_number": 150, "usage_type": "attribute"}, {"api_name": "bmesh.new", "line_number": 152, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 184, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 199, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 251, "usage_type": "call"}, {"api_name": "bpy.data.meshes.new", "line_number": 338, "usage_type": "call"}, {"api_name": "bpy.data", "line_number": 338, "usage_type": "attribute"}, {"api_name": "bpy.data.objects.new", "line_number": 339, "usage_type": "call"}, {"api_name": "bpy.data", "line_number": 339, "usage_type": "attribute"}, {"api_name": "bpy.context.collection.objects.link", "line_number": 340, "usage_type": "call"}, {"api_name": "bpy.context", "line_number": 340, "usage_type": "attribute"}, {"api_name": "bmesh.new", "line_number": 342, "usage_type": "call"}, {"api_name": "random.uniform", "line_number": 344, "usage_type": "call"}, {"api_name": "bpy.data.meshes.new", "line_number": 354, "usage_type": "call"}, {"api_name": "bpy.data", "line_number": 354, "usage_type": "attribute"}, {"api_name": "bpy.data.objects.new", "line_number": 355, "usage_type": "call"}, {"api_name": "bpy.data", "line_number": 355, "usage_type": "attribute"}, {"api_name": "bpy.context.collection.objects.link", "line_number": 356, "usage_type": "call"}, {"api_name": "bpy.context", "line_number": 356, "usage_type": "attribute"}, {"api_name": "bmesh.new", "line_number": 358, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 362, "usage_type": "call"}, {"api_name": "random.uniform", "line_number": 363, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 416, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 417, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 418, "usage_type": "call"}, {"api_name": "bpy.types", "line_number": 421, "usage_type": "attribute"}, {"api_name": "bpy.types", "line_number": 423, "usage_type": "attribute"}, {"api_name": "bpy.types", "line_number": 425, "usage_type": "attribute"}, {"api_name": "bpy.types", "line_number": 426, "usage_type": "attribute"}, {"api_name": "bpy.types", "line_number": 433, "usage_type": "attribute"}, {"api_name": "bpy.types", "line_number": 436, "usage_type": "attribute"}, {"api_name": "bpy.types", "line_number": 439, "usage_type": "attribute"}, {"api_name": "bpy.data", "line_number": 445, "usage_type": "attribute"}, {"api_name": "bpy.data.materials.remove", "line_number": 447, "usage_type": "call"}, {"api_name": "bpy.data", "line_number": 447, "usage_type": "attribute"}, {"api_name": "bpy.ops.object.select_all", "line_number": 449, "usage_type": "call"}, {"api_name": "bpy.ops", "line_number": 449, "usage_type": "attribute"}, {"api_name": "bpy.ops.object.delete", "line_number": 451, "usage_type": "call"}, {"api_name": "bpy.ops", "line_number": 451, "usage_type": "attribute"}, {"api_name": "bpy.ops.outliner.orphans_purge", "line_number": 452, "usage_type": "call"}, {"api_name": "bpy.ops", "line_number": 452, "usage_type": "attribute"}, {"api_name": "random.randint", "line_number": 455, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 456, "usage_type": "call"}, {"api_name": "random.uniform", "line_number": 457, "usage_type": "call"}, {"api_name": "random.uniform", "line_number": 458, "usage_type": "call"}, {"api_name": "random.uniform", "line_number": 459, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 461, "usage_type": "call"}, {"api_name": "random.uniform", "line_number": 462, "usage_type": "call"}, {"api_name": "bpy.data", "line_number": 466, "usage_type": "attribute"}]} +{"seq_id": "10729902566", "text": "from flask import Flask, render_template, request, session, redirect\nfrom func import ck_idpw # 내가 만든 id pw 체크 함수\nimport db\n\napp = Flask(__name__)\napp.secret_key = b'aaa!111/'\n\n@app.route('/loginneed')\ndef loginneed():\n return render_template('loginneed.html')\n\n@app.route('/')\ndef hello():\n return render_template('hyunjoon.html')\n\n@app.route('/coin')\ndef coin():\n if 'user' in session:\n return render_template('coin.html')\n else:\n return redirect('/loginneed') # 페이지 강제 이동\n\n# 로그아웃(session 제거)\n@app.route('/logout')\ndef logout():\n session.pop('user', None)\n return redirect('/')\n\n@app.route('/form')\ndef form():\n return render_template('form.html')\n\n@app.route('/join')\ndef join():\n return render_template('join.html')\n\n@app.route('/join_action', methods=['GET', 'POST'])\ndef join_action():\n if request.method == 'GET':\n return '나는 액션 GET 페이지야~'\n else:\n userid = request.form['userid']\n pwd = request.form['pwd']\n name = request.form['name']\n phone = request.form['phone']\n print(userid, pwd, name, phone)\n # 디비에 데이터 넣기\n db.insert_user(userid, pwd, name, phone)\n return render_template('join2.html')\n\n\n@app.route('/login', methods=['GET', 'POST'])\ndef login():\n if request.method == 'GET':\n return render_template('login.html')\n else:\n userid = request.form['userid']\n pwd = request.form['pwd']\n print(userid, pwd)\n ret = db.get_idpw(userid, pwd)\n if ret != None:\n session['user'] = ret[3] # 로그인 처리\n return ck_idpw(ret)\n # if ck_idpw(userid, pwd):\n # return '로그인 성공!!@'\n # else:\n # return '가입 되지 않은 아이디나 패스워드 트림'\n\n@app.route('/action_page', methods=['GET', 'POST'])\ndef action_page():\n if request.method == 'GET':\n return '나는 액션 GET 페이지야~'\n else:\n search = request.form['search']\n return '''당신은 '{}'로 검색을 했습니다
\n 결과를 보여드리겠습니다. 잠시만 기다려주세요~
\n 리스트 쫙~~~\n '''.format(search)\n\n@app.route('/naver')\ndef naver():\n return render_template('naver.html')\n\n\nif __name__ == '__main__':\n app.run(debug=True)", "repo_name": "megi7682/megi7682", "sub_path": "app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 2374, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "flask.Flask", "line_number": 5, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 10, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 14, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 18, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 19, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 21, "usage_type": "call"}, {"api_name": "flask.session.pop", "line_number": 26, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 26, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 27, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 31, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 35, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 39, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 39, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 42, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 42, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 43, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 43, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 44, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 44, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 45, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 45, "usage_type": "name"}, {"api_name": "db.insert_user", "line_number": 48, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 49, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 54, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 54, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 55, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 57, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 57, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 58, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 58, "usage_type": "name"}, {"api_name": "db.get_idpw", "line_number": 60, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 62, "usage_type": "name"}, {"api_name": "func.ck_idpw", "line_number": 63, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 71, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 71, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 74, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 74, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 82, "usage_type": "call"}]} +{"seq_id": "15908136333", "text": "from . import write_meta as meta\nimport csv\nimport json\nimport os\n\n\ndef outputExt(objType, fType):\n if objType == \"str\":\n objType = \"username\"\n outExt = f\"/{objType}s.{fType}\"\n\n return outExt\n\n\ndef addExt(base, objType, fType):\n if len(base.split('.')) == 1:\n createDirIfMissing(base)\n base += outputExt(objType, fType)\n\n return base\n\n\ndef Text(entry, f):\n print(entry.replace('\\n', ' '), file=open(f, \"a\", encoding=\"utf-8\"))\n\n\ndef Type(config):\n if config.User_full:\n _type = \"user\"\n elif config.Followers or config.Following:\n _type = \"username\"\n else:\n _type = \"tweet\"\n\n return _type\n\n\ndef struct(obj, custom, _type):\n if custom:\n fieldnames = custom\n row = {}\n for f in fieldnames:\n row[f] = meta.Data(obj, _type)[f]\n else:\n fieldnames = meta.Fieldnames(_type)\n row = meta.Data(obj, _type)\n\n return fieldnames, row\n\n\ndef createDirIfMissing(dirname):\n if not os.path.exists(dirname):\n os.makedirs(dirname)\n\n\ndef Csv(obj, config):\n _obj_type = obj.__class__.__name__\n if _obj_type == \"str\":\n _obj_type = \"username\"\n fieldnames, row = struct(obj, config.Custom[_obj_type], _obj_type)\n\n if config.Lang is not None and row.get('language') != config.Lang:\n return\n\n if not config.Save_meta:\n fieldnames = ('created_at', 'tweet', 'username', 'hashtags', 'cashtags',\n 'retweets_count', 'likes_count', 'replies_count')\n row = dict((k, row[k]) for k in fieldnames if k in row)\n\n dialect = 'excel-tab' if 'Tabs' in config.__dict__ else 'excel'\n\n # global_csv(config, fieldnames, dialect, row, write_header)\n slow_csv(config, fieldnames, dialect, row)\n\n\nglobal_csv_file = None\n\ndef global_csv(config, fieldnames, dialect, row):\n global global_csv_file\n if global_csv_file is None:\n # if not os.path.exists(config.Output):\n global_csv_file = open(config.Output, \"a\", newline='', encoding=\"utf-8\")\n writer = csv.DictWriter(global_csv_file, fieldnames=fieldnames, dialect=dialect)\n writer.writeheader()\n\n csv_file = global_csv_file\n writer = csv.DictWriter(csv_file, fieldnames=fieldnames, dialect=dialect)\n writer.writerow(row)\n\n\ndef slow_csv(config, fieldnames, dialect, row):\n write_header = False\n if not os.path.exists(config.Output):\n write_header = True\n\n with open(config.Output, \"a\", newline='', encoding=\"utf-8\") as csv_file:\n writer = csv.DictWriter(csv_file, fieldnames=fieldnames, dialect=dialect)\n if write_header:\n writer.writeheader()\n writer.writerow(row)\n\n\ndef Json(obj, config):\n _obj_type = obj.__class__.__name__\n if _obj_type == \"str\":\n _obj_type = \"username\"\n null, data = struct(obj, config.Custom[_obj_type], _obj_type)\n\n base = addExt(config.Output, _obj_type, \"json\")\n\n with open(base, \"a\", newline='', encoding=\"utf-8\") as json_file:\n json.dump(data, json_file, ensure_ascii=False)\n json_file.write(\"\\n\")\n", "repo_name": "saizk/scryptool", "sub_path": "scrapers/twint/storage/write.py", "file_name": "write.py", "file_ext": "py", "file_size_in_byte": 3055, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 15, "dataset": "github-code", "pt": "37", "api": [{"api_name": "os.path.exists", "line_number": 52, "usage_type": "call"}, {"api_name": "os.path", "line_number": 52, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 53, "usage_type": "call"}, {"api_name": "csv.DictWriter", "line_number": 83, "usage_type": "call"}, {"api_name": "csv.DictWriter", "line_number": 87, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 93, "usage_type": "call"}, {"api_name": "os.path", "line_number": 93, "usage_type": "attribute"}, {"api_name": "csv.DictWriter", "line_number": 97, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 112, "usage_type": "call"}]} +{"seq_id": "72285672748", "text": "# -*- coding:utf-8 -*-\nfrom django.contrib import admin\nfrom aplicacion.forms import MensajePata\nfrom aplicacion.models import Inscrito, Pata\n\n\nclass InscritoAdmin(admin.ModelAdmin):\n list_display = (\n 'num_doc', 'nombres', 'apellido_paterno', 'apellido_materno', 'sexo', 'fec_nac', 'email', 'direccion',\n 'telefono', 'celular', 'ubigeo')\n list_filter = ('tipo_doc',)\n\n\nclass PataAdmin(admin.ModelAdmin):\n list_display = (\n 'nombres', 'direccion', 'cel', 'referencia', 'dni_inscrito', 'mensaje'\n )\n list_filter = ('dni_inscrito',)\n form = MensajePata\n\n class Media:\n js = [\n '/static/grappelli/tinymce/jscripts/tiny_mce/tiny_mce.js',\n '/static/js/tinymce_setup.js',\n '/static/js/jquery.1.10.2.min.js',\n\n ]\n\n\nadmin.site.register(Inscrito, InscritoAdmin)\nadmin.site.register(Pata, PataAdmin)", "repo_name": "gaminusa/python-django", "sub_path": "apps/aplicacion/admin.py", "file_name": "admin.py", "file_ext": "py", "file_size_in_byte": 878, "program_lang": "python", "lang": "es", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "37", "api": [{"api_name": "django.contrib.admin.ModelAdmin", "line_number": 7, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 7, "usage_type": "name"}, {"api_name": "django.contrib.admin.ModelAdmin", "line_number": 14, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 14, "usage_type": "name"}, {"api_name": "aplicacion.forms.MensajePata", "line_number": 19, "usage_type": "name"}, {"api_name": "django.contrib.admin.site.register", "line_number": 30, "usage_type": "call"}, {"api_name": "aplicacion.models.Inscrito", "line_number": 30, "usage_type": "argument"}, {"api_name": "django.contrib.admin.site", "line_number": 30, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 30, "usage_type": "name"}, {"api_name": "django.contrib.admin.site.register", "line_number": 31, "usage_type": "call"}, {"api_name": "aplicacion.models.Pata", "line_number": 31, "usage_type": "argument"}, {"api_name": "django.contrib.admin.site", "line_number": 31, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 31, "usage_type": "name"}]} +{"seq_id": "6111594802", "text": "#\n# Клиентское консольное приложение на Phynon для проверки серверной части \n#\nimport requests\n\nwhile True:\n # 1. Формируем запрос к серверу и отправляем его на сервер\n print(\"Введите долготу ИПМ \") \n lon1 = input()\n\n print(\"Введите широту ИПМ \") \n lat1 = input()\n\n print(\"Введите долготу КПМ \") \n lon2 = input()\n\n print(\"Введите широту КПМ \") \n lat2 = input()\n\n print(\"Введите количество ППМ \") \n n = input()\n\n \n # 2. Формируем запрос к серверу и отправляем его на сервер\n #req = \"http://127.0.0.1:8080/?lon1=10&lat1=0.5&lon2=15&lat2=1&n=10\"\n req = \"http://127.0.0.1:8080/?lon1=\" + lon1 + \"&lat1=\" + lat1 + \"&lon2=\" + lon2 + \"&lat2=\" + lat2 + \"&n=\" + n \n print(req)\n response = requests.get(req)\n\n # 3. Фиксируем ответ от сервера (преобразование элементов списка с координатами к исходному типу floft выполняется автоматически\n coordList = response.json()\n for i in range(len(coordList)):\n for j in range(len(coordList[i])):\n print(coordList[i][j], end = ' ')\n print()\n \n print(\"Выполнить новый запрос? (1 - Да): \") \n ans = input()\n if(ans) != '1':\n break", "repo_name": "smkurak/orthodrom", "sub_path": "PyServer/User.py", "file_name": "User.py", "file_ext": "py", "file_size_in_byte": 1519, "program_lang": "python", "lang": "ru", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "requests.get", "line_number": 28, "usage_type": "call"}]} +{"seq_id": "72773615788", "text": "import numpy as np\nimport os\nimport os.path as osp\n\nfrom utils import TD_learning\n\n# Global Variables\n# learning rate\nalpha = 0.1\n\n# factor\ngamma = 0.95\n\n# maximum episodes\nmax_episodes = 100000\n\n# epsilon for action choice\nepsilon = 0.05\n\n# only support environment: 'MountainCar-v0' \nenv_name = 'MountainCar-v0'\n\n# if continous, please specify the n_actions\nn_actions = 200\n\n# pickle_path\npickle_path = 'pickles'\nif not osp.exists(pickle_path):\n os.makedirs(pickle_path)\n\n# discretized state value\nmin_state_val = 0\nmax_state_val = 40\n\n# random seed\nseed = 42\n\n# init mode: \"zeros\" or \"random\"\ninit_mode = \"random\"\n\n# learning mode \"Q-learning\", \"SARSA\" or \"Expected-SARSA\"\nlearning_mode = \"Expected-SARSA\"\n\nif __name__ == \"__main__\":\n _, score_list = TD_learning(\n env_name=env_name,\n alpha=alpha, \n gamma=gamma, \n epsilon=epsilon, \n max_episodes=max_episodes, \n min_state_val=min_state_val, \n max_state_val=max_state_val, \n seed=seed, \n pickle_path=pickle_path, \n init_mode=init_mode, \n learning_mode=learning_mode, \n n_actions = n_actions\n )\n ", "repo_name": "greatwallet/mountain-car", "sub_path": "TD.py", "file_name": "TD.py", "file_ext": "py", "file_size_in_byte": 1144, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 10, "dataset": "github-code", "pt": "37", "api": [{"api_name": "os.path.exists", "line_number": 28, "usage_type": "call"}, {"api_name": "os.path", "line_number": 28, "usage_type": "name"}, {"api_name": "os.makedirs", "line_number": 29, "usage_type": "call"}, {"api_name": "utils.TD_learning", "line_number": 45, "usage_type": "call"}]} +{"seq_id": "4712170661", "text": "from abc import abstractmethod\nfrom typing import Tuple\n\nimport torch\nfrom torch import Tensor\nfrom torch.distributions.categorical import Categorical\n\nfrom ml.embeddings import BaseEmbedding\nfrom ml.encoders import TorchEncoder, TowerEncoder\nfrom ml.utils import TensorWithMask\n\n\nclass BaseActor(TorchEncoder):\n\n @abstractmethod\n def forward(\n self,\n current_node_idx: Tensor,\n neighbor_node_ids: TensorWithMask,\n destination_node_idx: Tensor\n ) -> Tuple[Tensor, Tensor]:\n raise NotImplementedError\n\n\nclass BaseCritic(TorchEncoder, config_name='base_critic'):\n\n @abstractmethod\n def forward(\n self,\n current_node_idx: Tensor,\n destination_node_idx: Tensor\n ) -> Tensor:\n raise NotImplementedError\n\n\nclass TowerActor(BaseActor, config_name='tower_actor'):\n\n def __init__(\n self,\n embedder: TorchEncoder,\n ff_net: TorchEncoder,\n use_embedding_shift: bool = True,\n logits_scale: float = 4.0\n ):\n super().__init__()\n self._embedder = embedder\n self._ff_net = ff_net\n self._use_embedding_shift = use_embedding_shift\n self._logits_scale = logits_scale\n\n @classmethod\n def create_from_config(cls, config):\n return cls(\n embedder=BaseEmbedding.create_from_config(config['embedder']),\n ff_net=TowerEncoder.create_from_config(config['ff_net']),\n use_embedding_shift=config.get('use_embedding_shift', True),\n logits_scale=config.get('logits_scale', 4.0)\n )\n\n def forward(\n self,\n current_node_idx: Tensor,\n neighbor_node_ids: TensorWithMask,\n destination_node_idx: Tensor\n ) -> Tuple[Tensor, Tensor, Tensor]:\n # 0) Create embeddings from indices\n # Shape: [batch_size, embedding_dim]\n current_node_embedding = self._embedder(current_node_idx)\n # Shape: [batch_size, embedding_dim]\n destination_node_embedding = self._embedder(destination_node_idx)\n\n # Shape: [all_batch_neighbors, embedding_dim]\n all_neighbors_embeddings = self._embedder(neighbor_node_ids.flatten_values)\n\n neighbor_node_embeddings = TensorWithMask(\n values=all_neighbors_embeddings,\n lengths=neighbor_node_ids.lengths\n )\n\n # Shape: [batch_size, max_neighbors_num, embedding_dim]\n padded_neighbors_node_embeddings = neighbor_node_embeddings.padded_values\n\n # 1) Create representation for current state and next states\n # TODO[Vladimir Baikalov]: Check that it doesn't lead to gradient issues\n if self._use_embedding_shift:\n # Shape: [batch_size, embedding_dim]\n current_state_embedding = destination_node_embedding - current_node_embedding\n # Shape: [batch_size, max_neighbors_num, embedding_dim]\n next_state_embeddings = \\\n padded_neighbors_node_embeddings - torch.unsqueeze(current_node_embedding, dim=1)\n else:\n # Shape: [batch_size, 2 * embedding_dim]\n current_state_embedding = torch.cat(\n [destination_node_embedding, current_node_embedding],\n dim=-1\n )\n # Shape: [batch_size, max_neighbors_num, embedding_dim]\n next_state_embeddings = padded_neighbors_node_embeddings\n\n # 2) Compute representation of next ideal state\n # Shape: [batch_size, embedding_dim]\n ideal_transition_embedding = self._ff_net.forward(current_state_embedding)\n\n # 3) Compute logits for existing next states (here I use dot product for scores receiving)\n # Shape: [batch_size, max_neighbors_num]\n\n neighbors_logits = torch.nn.functional.cosine_similarity(\n next_state_embeddings,\n torch.zeros(next_state_embeddings.shape) + ideal_transition_embedding[:, None, :], dim=2\n ) * self._logits_scale\n\n # neighbors_logits = torch.einsum(\n # 'bnd,bd->bn',\n # next_state_embeddings,\n # ideal_transition_embedding\n # ) / 10.0\n\n # TODO[Vladimir Baikalov]: Probably it's a good idea to divide logits to make the distribution smoother\n inf_tensor = torch.zeros(neighbors_logits.shape)\n inf_tensor[~neighbor_node_embeddings.mask] = -torch.inf\n neighbors_logits = neighbors_logits + inf_tensor\n\n # 4) Get probs from logits\n # Shape: [batch_size, max_neighbors_num]\n neighbors_probs = torch.nn.functional.softmax(neighbors_logits, dim=1)\n neighbors_probs = neighbors_probs * neighbor_node_embeddings.mask # Make sure we won't sample from padding\n\n # 4) Sample next neighbor idx\n categorical_distribution = Categorical(probs=neighbors_probs)\n # Shape: [batch_size, 1]\n next_neighbor_idx = torch.unsqueeze(categorical_distribution.sample(), dim=1)\n\n # Shape: [batch_size]\n next_neighbor_ids = torch.squeeze(torch.gather(\n neighbor_node_ids.padded_values,\n dim=1,\n index=next_neighbor_idx\n ), dim=1)\n\n return next_neighbor_ids, neighbors_logits, neighbors_probs\n\n\nclass TowerCritic(BaseCritic, config_name='tower_critic'):\n\n def __init__(\n self,\n embedder: TorchEncoder,\n ff_net: TorchEncoder,\n use_embedding_shift: bool = True\n ):\n super().__init__()\n self._ff_net = ff_net\n self._embedder = embedder\n self._use_embedding_shift = use_embedding_shift\n\n @classmethod\n def create_from_config(cls, config):\n return cls(\n ff_net=TowerEncoder.create_from_config(config['ff_net']),\n embedder=BaseEmbedding.create_from_config(config['embedder']),\n use_embedding_shift=config.get('use_embedding_shift', True)\n )\n\n def forward(\n self,\n current_node_idx: Tensor,\n destination_node_idx: Tensor\n ) -> Tensor:\n # 0) Create embeddings from indices\n # Shape: [batch_size, embedding_dim]\n current_node_embedding = self._embedder(current_node_idx)\n # Shape: [batch_size, embedding_dim]\n destination_node_embedding = self._embedder(destination_node_idx)\n\n # 1) Create representation for current state and next states\n if self._use_embedding_shift:\n # Shape: [batch_size, embedding_dim]\n current_state_embedding = destination_node_embedding - current_node_embedding\n else:\n # Shape: [batch_size, 2 * embedding_dim]\n current_state_embedding = torch.cat(\n [destination_node_embedding, current_node_embedding],\n dim=-1\n )\n\n # 2) Compute value function for current state\n # Shape: [batch_size]\n current_state_value_function = torch.squeeze(\n self._ff_net.forward(current_state_embedding),\n dim=1\n )\n\n return current_state_value_function\n", "repo_name": "NonameUntitled/RoutingLibrary", "sub_path": "ml/ppo_encoders.py", "file_name": "ppo_encoders.py", "file_ext": "py", "file_size_in_byte": 7053, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "ml.encoders.TorchEncoder", "line_number": 13, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 18, "usage_type": "name"}, {"api_name": "ml.utils.TensorWithMask", "line_number": 19, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 20, "usage_type": "name"}, {"api_name": "abc.abstractmethod", "line_number": 15, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 21, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 21, "usage_type": "name"}, {"api_name": "ml.encoders.TorchEncoder", "line_number": 25, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 30, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 31, "usage_type": "name"}, {"api_name": "abc.abstractmethod", "line_number": 27, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 32, "usage_type": "name"}, {"api_name": "ml.encoders.TorchEncoder", "line_number": 40, "usage_type": "name"}, {"api_name": "ml.encoders.TorchEncoder", "line_number": 41, "usage_type": "name"}, {"api_name": "ml.embeddings.BaseEmbedding.create_from_config", "line_number": 54, "usage_type": "call"}, {"api_name": "ml.embeddings.BaseEmbedding", "line_number": 54, "usage_type": "name"}, {"api_name": "ml.encoders.TowerEncoder.create_from_config", "line_number": 55, "usage_type": "call"}, {"api_name": "ml.encoders.TowerEncoder", "line_number": 55, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 62, "usage_type": "name"}, {"api_name": "ml.utils.TensorWithMask", "line_number": 63, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 64, "usage_type": "name"}, {"api_name": "ml.utils.TensorWithMask", "line_number": 75, "usage_type": "call"}, {"api_name": "torch.unsqueeze", "line_number": 90, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 93, "usage_type": "call"}, {"api_name": "torch.nn.functional.cosine_similarity", "line_number": 107, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 107, "usage_type": "attribute"}, {"api_name": "torch.zeros", "line_number": 109, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 119, "usage_type": "call"}, {"api_name": "torch.inf", "line_number": 120, "usage_type": "attribute"}, {"api_name": "torch.nn.functional.softmax", "line_number": 125, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 125, "usage_type": "attribute"}, {"api_name": "torch.distributions.categorical.Categorical", "line_number": 129, "usage_type": "call"}, {"api_name": "torch.unsqueeze", "line_number": 131, "usage_type": "call"}, {"api_name": "torch.squeeze", "line_number": 134, "usage_type": "call"}, {"api_name": "torch.gather", "line_number": 134, "usage_type": "call"}, {"api_name": "typing.Tuple", "line_number": 65, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 65, "usage_type": "name"}, {"api_name": "ml.encoders.TorchEncoder", "line_number": 147, "usage_type": "name"}, {"api_name": "ml.encoders.TorchEncoder", "line_number": 148, "usage_type": "name"}, {"api_name": "ml.encoders.TowerEncoder.create_from_config", "line_number": 159, "usage_type": "call"}, {"api_name": "ml.encoders.TowerEncoder", "line_number": 159, "usage_type": "name"}, {"api_name": "ml.embeddings.BaseEmbedding.create_from_config", "line_number": 160, "usage_type": "call"}, {"api_name": "ml.embeddings.BaseEmbedding", "line_number": 160, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 166, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 167, "usage_type": "name"}, {"api_name": "torch.cat", "line_number": 181, "usage_type": "call"}, {"api_name": "torch.squeeze", "line_number": 188, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 168, "usage_type": "name"}]} +{"seq_id": "35535987579", "text": "import torch\nimport matplotlib.pyplot as plt\nimport torch.nn.functional as F\n# x data (tensor), shape=(100, 1)\n\"\"\"\ntorch.unsqueeze()这个函数主要是对数据维度进行扩充。\n给指定位置加上维数为一的维度，比如原本有个三行的数据（3），\n在0的位置加了一维就变成一行三列（1,3）。\na.squeeze(N) 就是在a中指定位置N加上一个维数为1的维度。\n还有一种形式就是b=torch.squeeze(a，N) a就是在a中指定位置N加上一个维数为1的维度\n\n\"\"\"\na = torch.linspace(-1, 1, 100)\nx = torch.unsqueeze(torch.linspace(-1, 1, 100), dim=1)\nb = torch.unsqueeze(torch.linspace(-1, 1, 100), dim=0)\n# noisy y data (tensor), shape=(100, 1)\n\n# torch.rand(*sizes, out=None) → Tensor\n# 返回一个张量，包含了从区间[0, 1)的均匀分布中抽取的一组随机数。张量的形状由参数sizes定义。\ny = x.pow(2) + 0.2*torch.rand(x.size())\n\n# 画图\n# plt.scatter(x.data.numpy(), y.data.numpy())\n# plt.show()\n\n\nclass Net(torch.nn.Module):\n def __init__(self, n_feature, n_hidden, n_output):\n super(Net, self).__init__()\n self.hidden = torch.nn.Linear(n_feature, n_hidden)\n self.predict = torch.nn.Linear(n_hidden, n_output)\n\n def forward(self, x): # Module中的forward\n x = F.relu(self.hidden(x))\n x = self.predict(x)\n return x\n\n\nnet = Net(n_feature=1, n_hidden=10, n_output=1)\n\noptimizer = torch.optim.SGD(net.parameters(), lr=0.2) # 传入net的所有参数，学习率\nloss_func = torch.nn.MSELoss() # 预测值和真实值的误差计算公式\n\nfor t in range(200):\n prediction = net(x) # 喂给 net 训练数据 x, 输出预测值\n\n loss = loss_func(prediction, y) # 计算两者的误差\n\n optimizer.zero_grad() # 清空上一步的残余更新参数值 把所有Variable的grad成员数值变为0\n loss.backward() # 误差反向传播, 计算参数更新值\n optimizer.step() # 将参数更新值施加到 net 的 parameters 上\n\n if t % 5 == 0:\n # plot and show learning process\n plt.cla() # Clear axis\n plt.scatter(x.data.numpy(), y.data.numpy()) # 散点图\n plt.plot(x.data.numpy(), prediction.data.numpy(), 'r-', lw=5) # 连续图\n plt.text(0.5, 0, 'Loss=%.4f' % loss.data.numpy(),\n fontdict={'size': 20, 'color': 'red'})\n # plt.pause(0.1)\n\nplt.ioff()\nplt.show()\n", "repo_name": "PtCu/RL", "sub_path": "Practice/pytorch/拟合.py", "file_name": "拟合.py", "file_ext": "py", "file_size_in_byte": 2400, "program_lang": "python", "lang": "zh", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "torch.linspace", "line_number": 13, "usage_type": "call"}, {"api_name": "torch.unsqueeze", "line_number": 14, "usage_type": "call"}, {"api_name": "torch.linspace", "line_number": 14, "usage_type": "call"}, {"api_name": "torch.unsqueeze", "line_number": 15, "usage_type": "call"}, {"api_name": "torch.linspace", "line_number": 15, "usage_type": "call"}, {"api_name": "torch.rand", "line_number": 20, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 27, "usage_type": "attribute"}, {"api_name": "torch.nn.Linear", "line_number": 30, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 30, "usage_type": "attribute"}, {"api_name": "torch.nn.Linear", "line_number": 31, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 31, "usage_type": "attribute"}, {"api_name": "torch.nn.functional.relu", "line_number": 34, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 34, "usage_type": "name"}, {"api_name": "torch.optim.SGD", "line_number": 41, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 41, "usage_type": "attribute"}, {"api_name": "torch.nn.MSELoss", "line_number": 42, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 42, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.cla", "line_number": 55, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 55, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 56, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 56, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 57, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 57, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.text", "line_number": 58, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 58, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ioff", "line_number": 62, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 62, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 63, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 63, "usage_type": "name"}]} +{"seq_id": "26076747905", "text": "from urllib.parse import urlencode\nfrom discord.ext.commands import Cog\nfrom discord.ext.commands.core import command\nimport requests\n\nclass Session(requests.Session):\n pass\n\nclass Main(Cog,name='encode and decoder'):\n def __init__(self,bot):\n self.bot = bot\n self.session = Session()\n \n @command()\n async def base_64_encode(self,ctx,*, text):\n url = 'https://some-random-api.ml/base64?'+urlencode(\n {\"encode\":str(text)}\n )\n await ctx.send(self.session.get(url).json()['base64'])\n\n @command()\n async def base_64_decode(self,ctx,*, text):\n url = 'https://some-random-api.ml/base64?'+urlencode(\n {\"decode\":str(text)}\n )\n await ctx.send(self.session.get(url).json()['text'])\n \n @command()\n async def binary_encode(self,ctx,*,text):\n url = 'https://some-random-api.ml/binary?'+urlencode(\n {\"text\":str(text)}\n )\n await ctx.send(self.session.get(url).json()['binary'])\n @command()\n async def binary_decode(self,ctx,*,text):\n url = 'https://some-random-api.ml/binary?'+urlencode(\n {\"decode\":str(text)}\n )\n await ctx.send(self.session.get(url).json()['text'])\n\ndef setup(bot):\n bot.add_cog(Main(bot))", "repo_name": "Sengolda/decoder-encode-bot", "sub_path": "cogs/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 1273, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "requests.Session", "line_number": 6, "usage_type": "attribute"}, {"api_name": "discord.ext.commands.Cog", "line_number": 9, "usage_type": "name"}, {"api_name": "urllib.parse.urlencode", "line_number": 16, "usage_type": "call"}, {"api_name": "discord.ext.commands.core.command", "line_number": 14, "usage_type": "call"}, {"api_name": "urllib.parse.urlencode", "line_number": 23, "usage_type": "call"}, {"api_name": "discord.ext.commands.core.command", "line_number": 21, "usage_type": "call"}, {"api_name": "urllib.parse.urlencode", "line_number": 30, "usage_type": "call"}, {"api_name": "discord.ext.commands.core.command", "line_number": 28, "usage_type": "call"}, {"api_name": "urllib.parse.urlencode", "line_number": 36, "usage_type": "call"}, {"api_name": "discord.ext.commands.core.command", "line_number": 34, "usage_type": "call"}]} +{"seq_id": "72739808108", "text": "import django\nfrom django.shortcuts import render, redirect\nfrom .models import Quiz\nfrom django.views.generic import ListView\nfrom django.http import JsonResponse\nfrom questions.models import Question, Answer\nfrom results.models import Result\nfrom django.contrib.auth.forms import UserCreationForm, UserModel\nfrom django.contrib import messages\nfrom django.contrib.auth import authenticate, login, logout, get_user_model\nfrom django.contrib.auth.decorators import login_required\nfrom .forms import *\n\n\ndef documrntationView(request):\n data ={}\n return render(request, 'quizes/documrntationView.html')\n\n\ndef statistic(requst):\n users = get_user_model().objects.all()\n result = Result.objects.all()\n result_count = result.count()\n data = {'allusers': users,\n 'result_count':result_count}\n return render(requst, 'quizes/statistic.html', data)\n\n@login_required(login_url='/login/')\ndef resultView(request): \n user_get_id = request.user\n user_id = user_get_id.id\n data = Result.objects.filter(user__pk=user_id) \n return render(request, 'quizes/resultview.html', {'result' : data})\n\n@login_required(login_url='/login/')\ndef own_statistic(request):\n user_get_id = request.user\n user_id = user_get_id.id\n result = Result.objects.filter(user__pk=user_id)\n result_count = result.count()\n \n \n data = {'result_count' : result_count}\n return render(request, 'quizes/own_statistics.html', data)\n\n@login_required(login_url='/login/')\ndef cabinet(request):\n data ={}\n return render(request, 'quizes/cabinet.html', data)\n\ndef loginPage(request):\n if request.method == \"POST\":\n username = request.POST.get('username')\n password = request.POST.get('password')\n user = authenticate(request, username=username, password=password)\n \n if user is not None:\n login(request, user)\n return redirect('/login/cabinet/')\n else:\n messages.info(request, 'username or pssword is wrong')\n data = {}\n return render(request, 'quizes/login.html', data)\n\ndef logoutUser(request):\n logout(request)\n return redirect('/')\n\ndef sightup(request):\n form = UserCreationForm()\n if request.method == \"POST\":\n form = UserCreationForm(request.POST)\n if form.is_valid():\n form.save()\n user = form.cleaned_data.get('username')\n messages.success(request, 'Acount was created' + user)\n return redirect('/login/')\n \n \n data = {'form': form}\n return render(request, 'quizes/sightup.html', data)\n\nclass QuizListView(ListView):\n model = Quiz \n template_name = 'quizes/main.html'\n \n@login_required(login_url='/login/')\ndef quiz_view(request, pk):\n quiz = Quiz.objects.get(pk=pk)\n return render(request, 'quizes/quiz.html', {'obj': quiz})\n@login_required(login_url='/login/')\ndef quiz_data_view(request, pk):\n quiz = Quiz.objects.get(pk=pk)\n questions = []\n for q in quiz.get_questions():\n answers = []\n for a in q.get_answers():\n answers.append(a.text)\n questions.append({str(q): answers})\n return JsonResponse({\n 'data': questions,\n 'time': quiz.time,\n })\n@login_required(login_url='/login/')\ndef save_quiz_view(request, pk):\n if request.is_ajax():\n questions = []\n data = request.POST\n data_ = dict(data.lists())\n\n data_.pop('csrfmiddlewaretoken')\n\n for k in data_.keys():\n print('key: ', k)\n question = Question.objects.get(text=k)\n questions.append(question)\n print(questions)\n\n \n \n user = request.user\n quiz = Quiz.objects.get(pk=pk)\n\n score = 0\n multiplier = 100 / quiz.number_of_questions\n results = []\n correct_answer = None\n\n for q in questions:\n a_selected = request.POST.get(q.text)\n\n if a_selected != \"\":\n question_answers = Answer.objects.filter(question=q)\n for a in question_answers:\n if a_selected == a.text:\n if a.correct:\n score += 1\n correct_answer = a.text\n else:\n if a.correct:\n correct_answer = a.text\n\n results.append({str(q): {'correct_answer': correct_answer, 'answered': a_selected}})\n else:\n results.append({str(q): 'not answered'})\n \n score_ = score * multiplier\n Result.objects.create(quiz=quiz, user=user, score=score_)\n \n if score_ >= quiz.required_score_to_pass:\n return JsonResponse({'passed': True, 'score': score_, 'results': results})\n else:\n return JsonResponse({'passed': False, 'score': score_, 'results': results})\n \n@login_required(login_url='/login/') \ndef createView(request):\n \n form = QuizForm()\n if request.method == 'POST':\n print('Posting....*', request.post)\n form = QuizForm(request.POST)\n if form.is_valid():\n form.save()\n return redirect('/login/cabinet/create/quastion/')\n context ={'form': form}\n return render(request, \"quizescreateview.html\", context)\n\n@login_required(login_url='/login/') \ndef quastionscreateView(request):\n form = AnswearForm()\n if request.method == 'POST':\n print('Posting....*', request.post)\n form = QuizForm(request.POST)\n if form.is_valid():\n form.save()\n return redirect('/#/')\n context ={'form': form}\n return render(request, \"quizes/CRUD/quastionscreateview.html\", context)\n\n", "repo_name": "bani2016up/django_quize", "sub_path": "quizes/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 5721, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "django.shortcuts.render", "line_number": 17, "usage_type": "call"}, {"api_name": "django.contrib.auth.get_user_model", "line_number": 21, "usage_type": "call"}, {"api_name": "results.models.Result.objects.all", "line_number": 22, "usage_type": "call"}, {"api_name": "results.models.Result.objects", "line_number": 22, "usage_type": "attribute"}, {"api_name": "results.models.Result", "line_number": 22, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 26, "usage_type": "call"}, {"api_name": "results.models.Result.objects.filter", "line_number": 32, "usage_type": "call"}, {"api_name": "results.models.Result.objects", "line_number": 32, "usage_type": "attribute"}, {"api_name": "results.models.Result", "line_number": 32, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 33, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 28, "usage_type": "call"}, {"api_name": "results.models.Result.objects.filter", "line_number": 39, "usage_type": "call"}, {"api_name": "results.models.Result.objects", "line_number": 39, "usage_type": "attribute"}, {"api_name": "results.models.Result", "line_number": 39, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 44, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 35, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 49, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 46, "usage_type": "call"}, {"api_name": "django.contrib.auth.authenticate", "line_number": 55, "usage_type": "call"}, {"api_name": "django.contrib.auth.login", "line_number": 58, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 59, "usage_type": "call"}, {"api_name": "django.contrib.messages.info", "line_number": 61, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 61, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 63, "usage_type": "call"}, {"api_name": "django.contrib.auth.logout", "line_number": 66, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 67, "usage_type": "call"}, {"api_name": "django.contrib.auth.forms.UserCreationForm", "line_number": 70, "usage_type": "call"}, {"api_name": "django.contrib.auth.forms.UserCreationForm", "line_number": 72, "usage_type": "call"}, {"api_name": "django.contrib.messages.success", "line_number": 76, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 76, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 77, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 81, "usage_type": "call"}, {"api_name": "django.views.generic.ListView", "line_number": 83, "usage_type": "name"}, {"api_name": "models.Quiz", "line_number": 84, "usage_type": "name"}, {"api_name": "models.Quiz.objects.get", "line_number": 89, "usage_type": "call"}, {"api_name": "models.Quiz.objects", "line_number": 89, "usage_type": "attribute"}, {"api_name": "models.Quiz", "line_number": 89, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 90, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 87, "usage_type": "call"}, {"api_name": "models.Quiz.objects.get", "line_number": 93, "usage_type": "call"}, {"api_name": "models.Quiz.objects", "line_number": 93, "usage_type": "attribute"}, {"api_name": "models.Quiz", "line_number": 93, "usage_type": "name"}, {"api_name": "questions.models", "line_number": 94, "usage_type": "name"}, {"api_name": "questions.models.append", "line_number": 99, "usage_type": "call"}, {"api_name": "questions.models", "line_number": 99, "usage_type": "name"}, {"api_name": "django.http.JsonResponse", "line_number": 100, "usage_type": "call"}, {"api_name": "questions.models", "line_number": 101, "usage_type": "name"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 91, "usage_type": "call"}, {"api_name": "questions.models", "line_number": 107, "usage_type": "name"}, {"api_name": "questions.models.Question.objects.get", "line_number": 115, "usage_type": "call"}, {"api_name": "questions.models.Question.objects", "line_number": 115, "usage_type": "attribute"}, {"api_name": "questions.models.Question", "line_number": 115, "usage_type": "name"}, {"api_name": "questions.models.append", "line_number": 116, "usage_type": "call"}, {"api_name": "questions.models", "line_number": 116, "usage_type": "name"}, {"api_name": "questions.models", "line_number": 117, "usage_type": "argument"}, {"api_name": "models.Quiz.objects.get", "line_number": 122, "usage_type": "call"}, {"api_name": "models.Quiz.objects", "line_number": 122, "usage_type": "attribute"}, {"api_name": "models.Quiz", "line_number": 122, "usage_type": "name"}, {"api_name": "results.models", "line_number": 126, "usage_type": "name"}, {"api_name": "questions.models", "line_number": 129, "usage_type": "name"}, {"api_name": "questions.models.Answer.objects.filter", "line_number": 133, "usage_type": "call"}, {"api_name": "questions.models.Answer.objects", "line_number": 133, "usage_type": "attribute"}, {"api_name": "questions.models.Answer", "line_number": 133, "usage_type": "name"}, {"api_name": "results.models.append", "line_number": 143, "usage_type": "call"}, {"api_name": "results.models", "line_number": 143, "usage_type": "name"}, {"api_name": "results.models.append", "line_number": 145, "usage_type": "call"}, {"api_name": "results.models", "line_number": 145, "usage_type": "name"}, {"api_name": "results.models.Result.objects.create", "line_number": 148, "usage_type": "call"}, {"api_name": "results.models.Result.objects", "line_number": 148, "usage_type": "attribute"}, {"api_name": "results.models.Result", "line_number": 148, "usage_type": "name"}, {"api_name": "django.http.JsonResponse", "line_number": 151, "usage_type": "call"}, {"api_name": "results.models", "line_number": 151, "usage_type": "name"}, {"api_name": "django.http.JsonResponse", "line_number": 153, "usage_type": "call"}, {"api_name": "results.models", "line_number": 153, "usage_type": "name"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 104, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 164, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 166, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 155, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 176, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 178, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 168, "usage_type": "call"}]} +{"seq_id": "75039548268", "text": "import numpy as np\nfrom data.mesh_loader import Mesh, load_mesh\nfrom scipy.sparse import coo_matrix, vstack\nfrom scipy.sparse.linalg import lsqr\nimport os\n\nfrom utils.utils import output_obj\n\n\nclass CorresProblem(object):\n def __init__(self, source_mesh : Mesh, target_mesh : Mesh, marker_pair):\n self.source_mesh = source_mesh\n self.target_mesh = target_mesh\n self.marker_pair = marker_pair\n\n def set_up_phrase_1_equation(self, Ws, Wi, MARKER_WEIGHT):\n\n n_adj = np.sum([len(self.source_mesh.get_adj_face(i)) for i in range(self.source_mesh.face_vertices_indcies.shape[0])])\n\n ## smoothness equations\n rows_smoothness = np.zeros(9*n_adj*4)\n columns_smoothness_center = np.zeros(9*n_adj*4)\n vals_smoothness_center = np.zeros(9*n_adj*4)\n columns_smoothness_adj = np.zeros(9*n_adj*4)\n vals_smoothness_adj = np.zeros(9*n_adj*4)\n\n ## smoothness constant\n smoothness_constant = np.zeros(9*n_adj)\n\n # set up equation for smoothness\n print(\"building smoothness......\")\n equation_cnt = 0\n for i in range(self.source_mesh.face_vertices_indcies.shape[0]):\n for j in self.source_mesh.get_adj_face(i):\n\n # check in marker\n marker_flags = np.full((2, 4) , -1)\n for idx in range(0, 3):\n if self.source_mesh.face_vertices_indcies[i][idx] in self.marker_pair[0]:\n pair_index = self.marker_pair[0].index(self.source_mesh.face_vertices_indcies[i][idx])\n marker_flags[0, idx] = self.marker_pair[1][pair_index]\n if self.source_mesh.face_vertices_indcies[j][idx] in self.marker_pair[0]:\n pair_index = self.marker_pair[0].index(self.source_mesh.face_vertices_indcies[j][idx])\n marker_flags[1, idx] = self.marker_pair[1][pair_index]\n\n # for x, y, z direction\n for axis in range(3):\n\n row = np.tile(np.linspace(0, 2, 3, dtype = np.int32) + equation_cnt + axis * 3, [4, 1]).T # 3 * 4\n ## data for center\n column_center = np.tile(self.source_mesh.face_vertices_indcies[i] * 3 + axis, [3, 1]) # 3 * 4\n val_center = Ws * self.source_mesh.coefficient_matrices[i]\n\n ## if marker exist\n # if np.sum(marker_flags[0, :]) > -4:\n # constant_vector = val\n # smoothness_constant[equation_cnt + axis * 3 : equation_cnt + axis * 3 + 3] =\\\n # smoothness_constant[equation_cnt + axis * 3 : equation_cnt + axis * 3 + 3]\n\n ## data for adj\n column_adj = np.tile(self.source_mesh.face_vertices_indcies[j] * 3 + axis, [3, 1]) # 3 * 4\n val_adj = - Ws * self.source_mesh.coefficient_matrices[j]\n\n ## keep record\n rows_smoothness[equation_cnt * 4 + axis * 3 * 4: equation_cnt * 4 + axis * 3 * 4 + 12] = \\\n row.flatten()\n columns_smoothness_center[equation_cnt * 4 + axis * 3 * 4: equation_cnt * 4 + axis * 3 * 4 + 12] = \\\n column_center.flatten()\n vals_smoothness_center[equation_cnt * 4 + axis * 3 * 4: equation_cnt * 4 + axis * 3 * 4 + 12] = \\\n val_center.flatten()\n columns_smoothness_adj[equation_cnt * 4 + axis * 3 * 4: equation_cnt * 4 + axis * 3 * 4 + 12] = \\\n column_adj.flatten()\n vals_smoothness_adj[equation_cnt * 4 + axis * 3 * 4: equation_cnt * 4 + axis * 3 * 4 + 12] = \\\n val_adj.flatten()\n\n\n # 9 equations are set up\n equation_cnt += 9\n\n # Smoothness matrices\n smoothness_1_M = coo_matrix((vals_smoothness_center, (rows_smoothness, columns_smoothness_center)),\n shape = (9 * n_adj, 3 * self.source_mesh.vertices.shape[0]))\n smoothness_2_M = coo_matrix((vals_smoothness_adj, (rows_smoothness, columns_smoothness_adj)),\n shape = (9 * n_adj, 3 * self.source_mesh.vertices.shape[0]))\n\n smoothness_matrix = smoothness_1_M + smoothness_2_M\n\n print(\"done building smoothness......\")\n\n ## identity\n print(\"building identity......\")\n\n # equation for rows / columns / vals for left equation\n rows_identity = np.zeros(9*self.source_mesh.face_vertices_indcies.shape[0]*4)\n columns_identity = np.zeros(9*self.source_mesh.face_vertices_indcies.shape[0]*4)\n vals_identity = np.zeros(9*self.source_mesh.face_vertices_indcies.shape[0]*4)\n\n # constant\n identity_constant = Wi * np.tile(np.eye(3).flatten(), [self.source_mesh.face_vertices_indcies.shape[0]])\n\n identity_equation_cnt = 0\n for i in range(self.source_mesh.face_vertices_indcies.shape[0]):\n\n for axis in range(3):\n row = np.tile(np.linspace(0, 2, 3, dtype = np.int32) + identity_equation_cnt + axis * 3, [4, 1]).T # 3 * 4\n\n ## data for center\n column_identity = np.tile(self.source_mesh.face_vertices_indcies[i] * 3 + axis, [3, 1]) # 3 * 4\n val_identity = Wi * self.source_mesh.coefficient_matrices[i]\n\n ## keep record\n rows_identity[identity_equation_cnt * 4 + axis * 3 * 4: identity_equation_cnt * 4 + axis * 3 * 4 + 12] = \\\n row.flatten()\n columns_identity[identity_equation_cnt * 4 + axis * 3 * 4: identity_equation_cnt * 4 + axis * 3 * 4 + 12] = \\\n column_identity.flatten()\n vals_identity[identity_equation_cnt * 4 + axis * 3 * 4: identity_equation_cnt * 4 + axis * 3 * 4 + 12] = \\\n val_identity.flatten()\n\n identity_equation_cnt += 9\n\n # identity matrix\n identity_matrix = coo_matrix((vals_identity, (rows_identity, columns_identity)),\n shape = (self.source_mesh.face_vertices_indcies.shape[0] * 9, 3 * self.source_mesh.vertices.shape[0]))\n print(\"done building identity......\")\n\n print(\"building marker......\")\n # Trial on fixing marker row vertices\n marker_rows = np.zeros(len(self.marker_pair[0]) * 3)\n marker_columns = np.zeros(len(self.marker_pair[0]) * 3)\n marker_vals = np.zeros(len(self.marker_pair[0]) * 3)\n marker_targets = np.zeros(len(self.marker_pair[0]) * 3)\n marker_equation_cnt = 0\n\n for i in range(len(self.marker_pair[0])):\n row = np.linspace(0, 2, 3, dtype = np.int32) + marker_equation_cnt # 3 x 1\n column = np.linspace(0, 2, 3, dtype = np.int32) + self.marker_pair[0][i] * 3\n target_val = np.array([\n self.target_mesh.vertices[self.marker_pair[1][i]][0], #x\n self.target_mesh.vertices[self.marker_pair[1][i]][1], #y\n self.target_mesh.vertices[self.marker_pair[1][i]][2], #z\n ])\n\n print(f'target_val : {target_val}')\n marker_rows[marker_equation_cnt: marker_equation_cnt + 3] = row.flatten()\n marker_columns[marker_equation_cnt: marker_equation_cnt + 3] = column.flatten()\n marker_vals[marker_equation_cnt: marker_equation_cnt + 3] = np.ones(3).flatten() * MARKER_WEIGHT\n marker_targets[marker_equation_cnt: marker_equation_cnt + 3] = target_val.flatten() * MARKER_WEIGHT\n\n marker_equation_cnt += 3\n\n # marker matrix\n marker_matrix = coo_matrix((marker_vals, (marker_rows, marker_columns)),\n shape = (len(self.marker_pair[0]) * 3, 3 * self.source_mesh.vertices.shape[0]))\n\n print(\"done building marker......\")\n print(smoothness_constant.shape, marker_targets.shape)\n final_matrix = vstack((smoothness_matrix, identity_matrix, marker_matrix))\n final_constant = np.concatenate((smoothness_constant, identity_constant, marker_targets))\n return final_matrix, final_constant\n\n def set_up_phrase_2_equation(self, deformed_source_mesh : Mesh, Wc):\n\n # set up the variables\n print(\"building phrase 2......\")\n rows = np.arange(0, deformed_source_mesh.real_vertices_num * 3)\n columns = np.arange(0, deformed_source_mesh.real_vertices_num * 3)\n vals_one = Wc * np.ones(deformed_source_mesh.real_vertices_num * 3)\n\n # constant\n constant = np.zeros(deformed_source_mesh.real_vertices_num * 3)\n for i in range(deformed_source_mesh.real_vertices_num):\n\n if i in self.marker_pair[0]:\n index_of_maker = self.marker_pair[0].index(i)\n constant[i * 3 : i * 3 + 3] = Wc *np.array([\n self.target_mesh.vertices[self.marker_pair[1][index_of_maker]][0], #x\n self.target_mesh.vertices[self.marker_pair[1][index_of_maker]][1], #y\n self.target_mesh.vertices[self.marker_pair[1][index_of_maker]][2], #z\n ])\n else:\n closest_idx = CorresProblem.find_closest_valid_pt(deformed_source_mesh, self.target_mesh, i)\n constant[i * 3 : i * 3 + 3] = Wc * np.array( [\n self.target_mesh.vertices[closest_idx][0], #x\n self.target_mesh.vertices[closest_idx][1], #y\n self.target_mesh.vertices[closest_idx][2], #z\n ])\n\n # phrase_2 matrix\n phrase_2_matrix = coo_matrix((vals_one, (rows, columns)),\n shape = (deformed_source_mesh.real_vertices_num * 3, 3 * self.source_mesh.vertices.shape[0]))\n\n print(\"done building phrase 2......\")\n return phrase_2_matrix, constant\n\n\n @staticmethod\n def find_closest_valid_pt(source_mesh : Mesh, target_mesh : Mesh, source_mesh_index):\n\n sq_distance = (np.tile(source_mesh.vertices[source_mesh_index], [target_mesh.real_vertices_num, 1]) - target_mesh.vertices[:target_mesh.real_vertices_num, :]) ** 2\n sum_sq_distance = np.sum(sq_distance, axis = 1)\n\n sorted_idx = np.argsort(sum_sq_distance)\n\n for i in sorted_idx:\n if source_mesh.vertices_normals[source_mesh_index].dot(target_mesh.vertices_normals[i]) > 0:\n return i\n\n return None\n\n\ndef load_marker(path):\n with open(path, 'r') as f:\n input_str = f.readline()\n f.close()\n return [int(s) for s in input_str.split()]\n\n\nif __name__ == \"__main__\":\n\n #### SETTING\n mesh_path_horse = '/Users/edwardhui/Desktop/previous_file/CSCI5210/project/data/horse-poses/horse-reference.obj'\n marker_path_horse = '/Users/edwardhui/Desktop/previous_file/CSCI5210/project/data/exp_corres/horse_camel/horse-reference.txt'\n mesh_path_camel = '/Users/edwardhui/Desktop/previous_file/CSCI5210/project/data/camel-poses/camel-reference.obj'\n marker_path_camel = '/Users/edwardhui/Desktop/previous_file/CSCI5210/project/data/exp_corres/horse_camel/camel-reference.txt'\n output_file_path = './testing_more.obj'\n phrase_one = True\n phrase_two = True\n Ws = 1.0\n Wi = 0.0001\n MARKER_WEIGHT = 10000.0\n phrase_2_trial = 4\n Wcs = [1.0, 10.0, 100.0, 300.0]\n\n ## loading meshes\n model_vertices, normals, face_vertices_indcies, face_normal_indice = load_mesh(mesh_path_horse)\n mesh_horse = Mesh(model_vertices, face_vertices_indcies)\n marker_horse = load_marker(marker_path_horse)\n\n model_vertices, normals, face_vertices_indcies, face_normal_indice = load_mesh(mesh_path_camel)\n mesh_camel = Mesh(model_vertices, face_vertices_indcies)\n marker_camel = load_marker(marker_path_camel)\n\n # print(marker_horse, marker_camel)\n assert len(marker_horse) == len(marker_camel)\n problem = CorresProblem(mesh_horse, mesh_camel, [marker_horse, marker_camel])\n\n A, b = problem.set_up_phrase_1_equation(Ws = Ws, Wi = Wi, MARKER_WEIGHT= MARKER_WEIGHT)\n\n\n if not os.path.exists(output_file_path) or phrase_one:\n print(f\"start solving first phrase equations....., A: {A.shape}, b : {b.shape}\")\n result = lsqr(A, b, iter_lim= 30000, atol = 1e-8, btol=1e-8, conlim=1e7, show = False)\n deformed_mesh_vertices_phrase_1 = np.reshape(result[0], [-1, 3])[:problem.source_mesh.real_vertices_num, :]\n deformed_mesh = Mesh(deformed_mesh_vertices_phrase_1, problem.source_mesh.face_vertices_indcies)\n output_obj(os.path.basename(output_file_path).split('.')[0] + f'_pharse_1.obj', deformed_mesh)\n print(f\"done solving first phrase equations.......\")\n else:\n model_vertices, normals, face_vertices_indcies, face_normal_indice = load_mesh(output_file_path)\n deformed_mesh = Mesh(model_vertices, face_vertices_indcies)\n print(f\"deformed_mesh normal shape : {deformed_mesh.vertices_normals.shape}\")\n print(f\"target normal shape : {problem.target_mesh.vertices_normals.shape}\")\n print(f\"target vertices shape : {problem.target_mesh.vertices.shape}\")\n\n\n if phrase_two:\n for i in range(phrase_2_trial):\n A_2, b_2 = problem.set_up_phrase_2_equation(deformed_mesh, Wcs[i])\n\n A_phrase_2 = vstack((A, A_2))\n b_phrase_2 = np.concatenate((b, b_2))\n\n print(f\"start solving second phrase 2 equations {i} trial....., A: {A_phrase_2.shape}, b : {b_phrase_2.shape}\")\n result = lsqr(A_phrase_2, b_phrase_2, iter_lim=10000, atol=1e-8, btol=1e-8, conlim=1e7, show=False)\n deformed_mesh_vertices_phrase_2 = np.reshape(result[0], [-1, 3])[:problem.source_mesh.real_vertices_num, :]\n deformed_mesh = Mesh(deformed_mesh_vertices_phrase_2, problem.source_mesh.face_vertices_indcies)\n output_obj(os.path.basename(output_file_path).split('.')[0] + f'_pharse_2_{i}.obj', deformed_mesh)\n print(f\"done solving first phrase 2 equations {i} trial.......\")\n\n\n # index = CorresProblem.find_closest_valid_pt(mesh_horse, mesh_camel, 0)\n #\n # print(f\"source mesh pt : {mesh_horse.vertices[0]} normal : {mesh_horse.vertices_normals[0]}\")\n # print(f\"target mesh pt : {mesh_camel.vertices[index]} normal : {mesh_horse.vertices_normals[index]}\")\n # print(f\"dot products : {mesh_camel.vertices_normals[index].dot(mesh_horse.vertices[0])}\")", "repo_name": "edward1997104/CSCI5210", "sub_path": "project/corres/corres_solve.py", "file_name": "corres_solve.py", "file_ext": "py", "file_size_in_byte": 14390, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "37", "api": [{"api_name": "data.mesh_loader.Mesh", "line_number": 11, "usage_type": "name"}, {"api_name": "numpy.sum", "line_number": 18, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 21, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 22, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 23, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 24, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 25, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 28, "usage_type": "call"}, {"api_name": "numpy.full", "line_number": 37, "usage_type": "call"}, {"api_name": "numpy.tile", "line_number": 49, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 49, "usage_type": "call"}, {"api_name": "numpy.int32", "line_number": 49, "usage_type": "attribute"}, {"api_name": "numpy.tile", "line_number": 51, "usage_type": "call"}, {"api_name": "numpy.tile", "line_number": 61, "usage_type": "call"}, {"api_name": "scipy.sparse.coo_matrix", "line_number": 81, "usage_type": "call"}, {"api_name": "scipy.sparse.coo_matrix", "line_number": 83, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 94, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 95, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 96, "usage_type": "call"}, {"api_name": "numpy.tile", "line_number": 99, "usage_type": "call"}, {"api_name": "numpy.eye", "line_number": 99, "usage_type": "call"}, {"api_name": "numpy.tile", "line_number": 105, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 105, "usage_type": "call"}, {"api_name": "numpy.int32", "line_number": 105, "usage_type": "attribute"}, {"api_name": "numpy.tile", "line_number": 108, "usage_type": "call"}, {"api_name": "scipy.sparse.coo_matrix", "line_number": 122, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 128, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 129, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 130, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 131, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 135, "usage_type": "call"}, {"api_name": "numpy.int32", "line_number": 135, "usage_type": "attribute"}, {"api_name": "numpy.linspace", "line_number": 136, "usage_type": "call"}, {"api_name": "numpy.int32", "line_number": 136, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 137, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 146, "usage_type": "call"}, {"api_name": "scipy.sparse.coo_matrix", "line_number": 152, "usage_type": "call"}, {"api_name": "scipy.sparse.vstack", "line_number": 157, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 158, "usage_type": "call"}, {"api_name": "data.mesh_loader.Mesh", "line_number": 161, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 165, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 166, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 167, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 170, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 175, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 182, "usage_type": "call"}, {"api_name": "scipy.sparse.coo_matrix", "line_number": 189, "usage_type": "call"}, {"api_name": "data.mesh_loader.Mesh", "line_number": 197, "usage_type": "name"}, {"api_name": "numpy.tile", "line_number": 199, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 200, "usage_type": "call"}, {"api_name": "numpy.argsort", "line_number": 202, "usage_type": "call"}, {"api_name": "data.mesh_loader.load_mesh", "line_number": 235, "usage_type": "call"}, {"api_name": "data.mesh_loader.Mesh", "line_number": 236, "usage_type": "call"}, {"api_name": "data.mesh_loader.load_mesh", "line_number": 239, "usage_type": "call"}, {"api_name": "data.mesh_loader.Mesh", "line_number": 240, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 250, "usage_type": "call"}, {"api_name": "os.path", "line_number": 250, "usage_type": "attribute"}, {"api_name": "scipy.sparse.linalg.lsqr", "line_number": 252, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 253, "usage_type": "call"}, {"api_name": "data.mesh_loader.Mesh", "line_number": 254, "usage_type": "call"}, {"api_name": "utils.utils.output_obj", "line_number": 255, "usage_type": "call"}, {"api_name": "os.path.basename", "line_number": 255, "usage_type": "call"}, {"api_name": "os.path", "line_number": 255, "usage_type": "attribute"}, {"api_name": "data.mesh_loader.load_mesh", "line_number": 258, "usage_type": "call"}, {"api_name": "data.mesh_loader.Mesh", "line_number": 259, "usage_type": "call"}, {"api_name": "scipy.sparse.vstack", "line_number": 269, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 270, "usage_type": "call"}, {"api_name": "scipy.sparse.linalg.lsqr", "line_number": 273, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 274, "usage_type": "call"}, {"api_name": "data.mesh_loader.Mesh", "line_number": 275, "usage_type": "call"}, {"api_name": "utils.utils.output_obj", "line_number": 276, "usage_type": "call"}, {"api_name": "os.path.basename", "line_number": 276, "usage_type": "call"}, {"api_name": "os.path", "line_number": 276, "usage_type": "attribute"}]} +{"seq_id": "26451255083", "text": "import logging\nimport os\nfrom datetime import datetime\n\nimport click\nfrom loqusdb.exceptions import CaseError\nfrom loqusdb.utils.load import load_database\n\nfrom loqusdb.commands.cli import cli as base_command\n\nLOG = logging.getLogger(__name__)\n\n\ndef validate_profile_threshold(ctx, param, value):\n if not (0 <= value <= 1):\n raise ValueError(\"threshold must be between 0-1\")\n\n return value\n\n\n@base_command.command(\"load\", short_help=\"Load the variants of a family\")\n@click.option(\n \"--variant-file\",\n type=click.Path(exists=True),\n metavar=\"\",\n help=\"Load a VCF with SNV/INDEL Variants\",\n)\n@click.option(\n \"--sv-variants\",\n type=click.Path(exists=True),\n metavar=\"\",\n help=\"Load a VCF with Structural Variants\",\n)\n@click.option(\"-f\", \"--family-file\", type=click.Path(exists=True), metavar=\"\")\n@click.option(\n \"-t\",\n \"--family-type\",\n type=click.Choice([\"ped\", \"alt\", \"cmms\", \"mip\"]),\n default=\"ped\",\n show_default=True,\n help=\"If the analysis use one of the known setups, please specify which one.\",\n)\n@click.option(\n \"-c\",\n \"--case-id\",\n type=str,\n help=\"If a different case id than the one in ped file should be used\",\n)\n@click.option(\n \"-s\",\n \"--skip-case-id\",\n is_flag=True,\n show_default=True,\n help=\"Do not store case information on variants\",\n)\n@click.option(\"--ensure-index\", is_flag=True, help=\"Make sure that the indexes are in place\")\n@click.option(\"--gq-threshold\", default=20, show_default=True, help=\"Threshold to consider variant\")\n@click.option(\"--qual-gq\", is_flag=True, default=False, show_default=True, help=\"Use QUAL tag instead of GQ value for quality filter\")\n@click.option(\n \"--max-window\",\n \"-m\",\n default=2000,\n show_default=True,\n help=\"Specify the maximum window size for svs\",\n)\n@click.option(\n \"--check-profile\",\n type=click.Path(exists=True),\n help=\"Apply sample profiling for the samples, using the variants in this vcf\",\n)\n@click.option(\n \"--hard-threshold\",\n type=float,\n default=0.95,\n callback=validate_profile_threshold,\n help=\"profile hamming distance to rejecting load (0-1)\",\n)\n@click.option(\n \"--soft-threshold\",\n type=float,\n default=0.95,\n callback=validate_profile_threshold,\n help=\"profile hamming distance to store similar individuals (0-1)\",\n)\n@click.pass_context\ndef load(\n ctx,\n variant_file,\n sv_variants,\n family_file,\n family_type,\n skip_case_id,\n gq_threshold,\n case_id,\n ensure_index,\n max_window,\n check_profile,\n hard_threshold,\n soft_threshold,\n qual_gq\n):\n \"\"\"Load the variants of a case\n\n A variant is loaded if it is observed in any individual of a case\n If no family file is provided all individuals in vcf file will be considered.\n \"\"\"\n if not (family_file or case_id):\n LOG.warning(\"Please provide a family file or a case id\")\n ctx.abort()\n\n if not (variant_file or sv_variants):\n LOG.warning(\"Please provide a VCF file\")\n ctx.abort()\n\n variant_path = None\n if variant_file:\n variant_path = os.path.abspath(variant_file)\n\n variant_sv_path = None\n if sv_variants:\n variant_sv_path = os.path.abspath(sv_variants)\n\n variant_profile_path = None\n if check_profile:\n variant_profile_path = os.path.abspath(check_profile)\n\n adapter = ctx.obj[\"adapter\"]\n genome_build = ctx.obj[\"genome_build\"]\n\n start_inserting = datetime.now()\n\n try:\n nr_inserted = load_database(\n adapter=adapter,\n variant_file=variant_path,\n sv_file=variant_sv_path,\n family_file=family_file,\n family_type=family_type,\n skip_case_id=skip_case_id,\n case_id=case_id,\n gq_threshold=gq_threshold,\n qual_gq=qual_gq,\n max_window=max_window,\n profile_file=variant_profile_path,\n hard_threshold=hard_threshold,\n soft_threshold=soft_threshold,\n genome_build=genome_build,\n )\n except (SyntaxError, CaseError, IOError) as error:\n LOG.warning(error)\n ctx.abort()\n\n LOG.info(\"Nr variants inserted: %s\", nr_inserted)\n LOG.info(\"Time to insert variants: {0}\".format(datetime.now() - start_inserting))\n\n if ensure_index:\n adapter.ensure_indexes()\n else:\n adapter.check_indexes()\n", "repo_name": "Clinical-Genomics/loqusdb", "sub_path": "loqusdb/commands/load.py", "file_name": "load.py", "file_ext": "py", "file_size_in_byte": 4407, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 8, "dataset": "github-code", "pt": "37", "api": [{"api_name": "logging.getLogger", "line_number": 11, "usage_type": "call"}, {"api_name": "os.path.abspath", "line_number": 117, "usage_type": "call"}, {"api_name": "os.path", "line_number": 117, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 121, "usage_type": "call"}, {"api_name": "os.path", "line_number": 121, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 125, "usage_type": "call"}, {"api_name": "os.path", "line_number": 125, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 130, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 130, "usage_type": "name"}, {"api_name": "loqusdb.utils.load.load_database", "line_number": 133, "usage_type": "call"}, {"api_name": "loqusdb.exceptions.CaseError", "line_number": 149, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 154, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 154, "usage_type": "name"}, {"api_name": "loqusdb.commands.cli.cli.command", "line_number": 21, "usage_type": "call"}, {"api_name": "loqusdb.commands.cli.cli", "line_number": 21, "usage_type": "name"}, {"api_name": "click.option", "line_number": 22, "usage_type": "call"}, {"api_name": "click.Path", "line_number": 24, "usage_type": "call"}, {"api_name": "click.option", "line_number": 28, "usage_type": "call"}, {"api_name": "click.Path", "line_number": 30, "usage_type": "call"}, {"api_name": "click.option", "line_number": 34, "usage_type": "call"}, {"api_name": "click.Path", "line_number": 34, "usage_type": "call"}, {"api_name": "click.option", "line_number": 35, "usage_type": "call"}, {"api_name": "click.Choice", "line_number": 38, "usage_type": "call"}, {"api_name": "click.option", "line_number": 43, "usage_type": "call"}, {"api_name": "click.option", "line_number": 49, "usage_type": "call"}, {"api_name": "click.option", "line_number": 56, "usage_type": "call"}, {"api_name": "click.option", "line_number": 57, "usage_type": "call"}, {"api_name": "click.option", "line_number": 58, "usage_type": "call"}, {"api_name": "click.option", "line_number": 59, "usage_type": "call"}, {"api_name": "click.option", "line_number": 66, "usage_type": "call"}, {"api_name": "click.Path", "line_number": 68, "usage_type": "call"}, {"api_name": "click.option", "line_number": 71, "usage_type": "call"}, {"api_name": "click.option", "line_number": 78, "usage_type": "call"}, {"api_name": "click.pass_context", "line_number": 85, "usage_type": "attribute"}]} +{"seq_id": "4228524866", "text": "import time\nimport os \nclear = lambda: os.system('cls')\nclear()\n\nfrom keras.utils import to_categorical\nfrom keras.datasets import mnist\nfrom keras import layers, models\n\n# Data import\n(train_images, train_labels), (test_images, test_labels) = mnist.load_data()\n\n# Data pre-processing\ntrain_images = train_images.reshape((60000, 28*28))\ntrain_images = train_images.astype('float32')/255\ntest_images = test_images.reshape((10000, 28*28))\ntest_images = test_images.astype('float32')/255\ntrain_labels = to_categorical(train_labels)\ntest_labels = to_categorical(test_labels)\n\n# Network configuration\nstart_time = time.time_ns()\nprint(start_time)\nnetwork = models.Sequential()\nnetwork.add(layers.Dense(512, activation='relu', input_shape=(28*28, )))\nnetwork.add(layers.Dense(10, activation='softmax'))\n\n# Network fitting \nnetwork.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics='accuracy')\nnetwork.fit(train_images, train_labels, epochs=5, batch_size=16)\n\n# Evaluation\ntest_loss, test_acc = network.evaluate(test_images, test_labels)\nprint(f'The test accuracy was: {test_acc}')\nend_time = time.time_ns()\nprint(f'The end time is {end_time}\\n\\nThe total time is {(end_time - start_time)*1e-9}')\n", "repo_name": "derekboase/playground", "sub_path": "deep_learning/Chapter_2/handwriting_sequential.py", "file_name": "handwriting_sequential.py", "file_ext": "py", "file_size_in_byte": 1210, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "os.system", "line_number": 3, "usage_type": "call"}, {"api_name": "keras.datasets.mnist.load_data", "line_number": 11, "usage_type": "call"}, {"api_name": "keras.datasets.mnist", "line_number": 11, "usage_type": "name"}, {"api_name": "keras.utils.to_categorical", "line_number": 18, "usage_type": "call"}, {"api_name": "keras.utils.to_categorical", "line_number": 19, "usage_type": "call"}, {"api_name": "time.time_ns", "line_number": 22, "usage_type": "call"}, {"api_name": "keras.models.Sequential", "line_number": 24, "usage_type": "call"}, {"api_name": "keras.models", "line_number": 24, "usage_type": "name"}, {"api_name": "keras.layers.Dense", "line_number": 25, "usage_type": "call"}, {"api_name": "keras.layers", "line_number": 25, "usage_type": "name"}, {"api_name": "keras.layers.Dense", "line_number": 26, "usage_type": "call"}, {"api_name": "keras.layers", "line_number": 26, "usage_type": "name"}, {"api_name": "time.time_ns", "line_number": 35, "usage_type": "call"}]} +{"seq_id": "26303732729", "text": "import sqlite3\n\nfrom epubdb_ficdb_linker import DBLinker\n\nfrom epubdb_ficdb_linker import DBLinker\nclass TestDB(DBLinker):\n\n\n def single_use(self):\n select = \"select * from FicFileList WHERE FanFicArchiveId = '';\"\n dbpath = self._FFnetArchiveLinkDB_Path\n s = \";\"\n con = sqlite3.connect(dbpath)\n ficid_list = []\n cur = con.cursor()\n cur.execute(select)\n fic_rows = cur.fetchall()\n file = open(\"file_list.csv\", \"w\")\n for row in fic_rows:\n srow = str(row[0]) + '; ' + str(row[1]) + '; ' + row[2] + '; ' + row[3] + '; ' + str(row[4]) + '; ' + str(row[5]) + '; ' + row[6] + '; ' + row[7] + '; ' + row[8] + \"\\n\"\n file.write(srow)\n return True\n def fix_file_links_from_cvs(self, filename):\n file = open(filename)\n update_data = []\n _link_list_create = \"Create TABLE FicFileList(FicFileListId INTEGER PRIMARY KEY, FicFileID INT, FanFicArchiveId TEXT, Url TEXT, Words INTEGER, Chapters INTEGER, Packaged TEXT, FilePath TEXT, PublisherName TEXT);\"\n update_ficfilelist = 'UPDATE FicFileList SET FanFicArchiveId=?, PublisherName=? WHERE FicFileListId =?;'\n for record in file.readlines():\n fic_properties = record.split(\";\")\n update = (fic_properties[2], fic_properties[8], fic_properties[0])\n update_data.append(update)\n dbpath = self._FFnetArchiveLinkDB_Path\n s = \";\"\n con = sqlite3.connect(dbpath)\n cur = con.cursor()\n cur.executemany(update_ficfilelist, update_data)\n con.commit()\n cur.close()\n con.close()\n return True\n\n\n\n\n", "repo_name": "aragorn55/FFTracker", "sub_path": "testdb.py", "file_name": "testdb.py", "file_ext": "py", "file_size_in_byte": 1654, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "epubdb_ficdb_linker.DBLinker", "line_number": 6, "usage_type": "name"}, {"api_name": "sqlite3.connect", "line_number": 13, "usage_type": "call"}, {"api_name": "sqlite3.connect", "line_number": 34, "usage_type": "call"}]} +{"seq_id": "41433952459", "text": "#!/usr/bin/env Python\n# coding=utf-8\n\nimport json\ndata = {}\ndata['code'] = 0\ndata['msg'] = \"select success\"\ninfo = {}\ninfo['nick_name'] = \"hello\"\ninfo['draft_people_ids'] = \"123,123,12334\"\ndata['info'] = info\n\njson_data = json.dumps(data)\nprint(json_data)", "repo_name": "zvlwwj/GraveServer", "sub_path": "dbtest.py", "file_name": "dbtest.py", "file_ext": "py", "file_size_in_byte": 255, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "json.dumps", "line_number": 13, "usage_type": "call"}]} +{"seq_id": "15798855088", "text": "from enum import Enum\n\nclass EvidenceRole(str, Enum):\n Unknown = \"unknown\",\n Contextual = \"contextual\",\n Scanned = \"scanned\",\n Source = \"source\",\n Destination = \"destination\",\n Created = \"created\",\n Added = \"added\",\n Compromised = \"compromised\",\n Edited = \"edited\",\n Attacked = \"attacked\",\n Attacker = \"attacker\",\n CommandAndControl = \"commandAndControl\",\n Loaded = \"loaded\",\n Suspicious = \"suspicious\",\n PolicyViolator = \"policyViolator\",\n UnknownFutureValue = \"unknownFutureValue\",\n\n", "repo_name": "microsoftgraph/msgraph-sdk-python", "sub_path": "msgraph/generated/models/security/evidence_role.py", "file_name": "evidence_role.py", "file_ext": "py", "file_size_in_byte": 533, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 186, "dataset": "github-code", "pt": "37", "api": [{"api_name": "enum.Enum", "line_number": 3, "usage_type": "name"}]} +{"seq_id": "19629479580", "text": "\"\"\"This version is a test for a constant nornalized beta (a single T) for DITlog.\n\n * Assuming a common temperature for the same isotope lines, we can reduce the dimension of the DIT grids (NC), for a Doppler width. In this case, a normalized STD by nu_lines/R should be constant. \n\n\"\"\"\nfrom addit.dit import rundit, runditfold, runditf1, make_dLarray\nfrom addit.ditlog import rundit_fold_log, rundit_fold_logred\nfrom addit.ditlogst import rundit_fold_logredst\nimport jax.numpy as jnp\nimport numpy as np\nfrom addit.ncf import inc3D\nnp.random.seed(20)\n\nN=20\nNg_nu=100000\nNg_gammaL=30\n\n#log grid\nnu0=2050.0\nnu1=2150.0\nnus=np.logspace(np.log10(nu0),np.log10(nu1),Ng_nu) #nu grid\nR=(Ng_nu-1)/np.log(nu1/nu0) #resolution\n\navedv=np.mean(nus)/R #averaged d wavenumber\ncnbeta=0.3/avedv #here we assume the normalized beta is constant\ngammaL=np.random.rand(N)*1.0\nS=np.logspace(0.0,3.0,N)\nS[0:20]=1.e4\ngammaL[0:20]=0.01\nnu_lines=np.random.rand(N)*(nus[-1]-nus[0]-50.0)+nus[0]+25.0\nngammaL_grid=np.logspace(np.log10(np.min(gammaL/nu_lines*R)),np.log10(np.max(gammaL/nu_lines*R)),Ng_gammaL)\n\n#direct dv needs to be careful for the truncation error\nnn=np.median(nus)\ndv_lines=nu_lines/R\ndv=nus/R\nngammaL_grid_=np.logspace(np.log10(np.min(gammaL/dv_lines)),np.log10(np.max(gammaL/dv_lines)),Ng_gammaL)\ndLarray=make_dLarray(2,1.0)\nNfold=1\nF0f2_=rundit_fold_logredst(S,nu_lines-nn,cnbeta,gammaL,nus-nn,ngammaL_grid_,dLarray,dv_lines,dv)\n\n#direct voigt for comparison\nimport matplotlib.pyplot as plt\nfrom exojax.spec import xsection\nbeta=cnbeta*dv_lines\nxsv=xsection(nus,nu_lines,beta*np.ones_like(gammaL),gammaL,S)\n\nfig=plt.figure()\nax=fig.add_subplot(211)\nplt.plot(nus,xsv,label=\"direct\")\nplt.plot(nus,F0f2_,label=\"DIT (Nfold=\"+str(Nfold)+\")\",ls=\"dashed\")\nplt.plot(nus,F0f2_-xsv,label=\"DIT-direct (reduced $\\\\nu$)\")\nplt.legend(loc=\"upper right\")\nax=fig.add_subplot(212)\nplt.plot(nus,(F0f2_-xsv),label=\"DIT/log (reduced $\\\\nu$)\",alpha=0.3)\nplt.ylabel(\"difference\")\nplt.legend(loc=\"upper right\")\nplt.show()\n", "repo_name": "HajimeKawahara/addit", "sub_path": "tests/ditlog_singleT_test.py", "file_name": "ditlog_singleT_test.py", "file_ext": "py", "file_size_in_byte": 1995, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "37", "api": [{"api_name": "numpy.random.seed", "line_number": 12, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 12, "usage_type": "attribute"}, {"api_name": "numpy.logspace", "line_number": 21, "usage_type": "call"}, {"api_name": "numpy.log10", "line_number": 21, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 22, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 24, "usage_type": "call"}, {"api_name": "numpy.random.rand", "line_number": 26, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 26, "usage_type": "attribute"}, {"api_name": "numpy.logspace", "line_number": 27, "usage_type": "call"}, {"api_name": "numpy.random.rand", "line_number": 30, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 30, "usage_type": "attribute"}, {"api_name": "numpy.logspace", "line_number": 31, "usage_type": "call"}, {"api_name": "numpy.log10", "line_number": 31, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 31, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 31, "usage_type": "call"}, {"api_name": "numpy.median", "line_number": 34, "usage_type": "call"}, {"api_name": "numpy.logspace", "line_number": 37, "usage_type": "call"}, {"api_name": "numpy.log10", "line_number": 37, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 37, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 37, "usage_type": "call"}, {"api_name": "addit.dit.make_dLarray", "line_number": 38, "usage_type": "call"}, {"api_name": "addit.ditlogst.rundit_fold_logredst", "line_number": 40, "usage_type": "call"}, {"api_name": "exojax.spec.xsection", "line_number": 46, "usage_type": "call"}, {"api_name": "numpy.ones_like", "line_number": 46, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 48, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 48, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 50, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 50, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 51, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 51, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 52, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 52, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 53, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 53, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 55, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 55, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 56, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 56, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 57, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 57, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 58, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 58, "usage_type": "name"}]} +{"seq_id": "32456249615", "text": "import datacube\nfrom datacube.model import Measurement\nimport numpy as np\nimport xarray as xr\nfrom os import path\nfrom datacube.drivers.netcdf import write_dataset_to_netcdf\nfrom datacube.utils.geometry import CRS\nimport pandas as pd\nimport os\nfrom datacube import Datacube\nfrom datacube.utils import geometry\nimport pickle\nimport sys\nimport rioxarray\ndc = datacube.Datacube(app=\"cyclone mangroves\")\n\n\nwindspeed_category = {'C1': [0., 125*1000/60**2],\n 'C2':[125*1000/60**2, 165*1000/60**2],\n 'C3': [165*1000/60**2, 225*1000/60**2], \n 'C4': [225*1000/60**2, 280*1000/60**2],\n 'C5': [280*1000/60**2, 9999.]}\n\n\ndef categorize_damage(bc_canopy, ac_canopy):\n '''\n On the mangrove classes. Difference before cyclone and after cyclone.\n '''\n reduction = bc_canopy - ac_canopy\n return reduction\n\n\n\ndef damage_level_by_geo(dir_name, cyclone_name, cyclone_data, bc_datasets, ac_datasets, loading_box, dump=True):\n '''\n dir_name: directory output for results\n cyclone_name: string name\n cyclone_data: xr.dataset of the cyclone windfield\n bc_datasets: mangrove canopy cover datasets before the cyclone (using time from cyclone dataset)\n ac_datasets: mangrove canopy cover datasets after the cyclone (using time from cyclone dataset)\n loading_box: bounding box (30m res) of the cyclone extent\n dump: writing out cyclone damage results\n '''\n \n # measurements for mangrove canopy cover\n measurement = [Measurement(name='canopy_cover_class', dtype='int16', nodata=-1, units='1')]\n # load mangrove canopy cover from find.datasets that are before the cyclone\n bc_canopy = dc.load_data(bc_datasets, geobox = loading_box, measurements=measurement)\n \n immediate = 0\n \n # make sure dir exists, otherwise make, for result outputs\n if dump:\n dir_name += '/' + cyclone_name\n if path.exists(dir_name) == False:\n os.mkdir(dir_name)\n \n # for time in after cyclone mangrove canopy cover datasets\n for t in ac_datasets.time.data:\n # load the data for the time step (they are not loaded yet, just find.datasets has been used)\n ac_canopy = dc.load_data(ac_datasets.sel(time=[t]), geobox = loading_box, measurements=measurement)\n # use the categorize_damage function above to work out damage (not sure how this works with multiple times for bc_datasets)\n damage_label = categorize_damage(bc_canopy.canopy_cover_class.data, ac_canopy.canopy_cover_class.data)\n \n ### relabel the categorize_damage data\n # -1 no mangroves\n # 0 is not observed/missing data\n # Immediate damage is 0-4 for before minus after mangrove class\n damage_label[np.logical_and(bc_canopy.canopy_cover_class.data == -1, ac_canopy.canopy_cover_class.data == -1)] = -1 \n damage_label[np.logical_or(bc_canopy.canopy_cover_class.data == 0, ac_canopy.canopy_cover_class.data == 0)] = -1 \n # Open forest to no mangroves == cat 4 damage\n damage_label[np.logical_and(bc_canopy.canopy_cover_class.data == 2, ac_canopy.canopy_cover_class.data == -1)] = 4 # reduce from 2 \n # Closed forest to no mangroves == cat 4 damage\n damage_label[np.logical_and(bc_canopy.canopy_cover_class.data == 3, ac_canopy.canopy_cover_class.data == -1)] = 4 # reduce from 3\n # Open woodland to no mangroves == cat 3 damage\n damage_label[np.logical_and(bc_canopy.canopy_cover_class.data == 1, ac_canopy.canopy_cover_class.data == -1)] = 3 # reduce from 1 \n damage_label[damage_label < 0] = -1\n \n # writing out category immediate damage after cyclone data to xarray to export as netcdf\n ac_canopy.time.attrs['units'] = \"seconds since 1970-01-01 00:00:00\"\n result = xr.Dataset({\"damage_level\":(['time', 'y', 'x'], damage_label.astype('int16'))},\n coords={'time':ac_canopy.time, 'y': ac_canopy.y, 'x':ac_canopy.x},\n attrs={'crs': CRS('EPSG:3577'), 'nodata': -1})\n fout = '_'.join([cyclone_name, \"%s\" % int(ac_canopy.x.data.min()/10000), \n \"%s\" % int(ac_canopy.y.data.min()/10000), \n \"%.10s\" % str(bc_canopy.time[0].data), \n \"%.10s\" % str(ac_canopy.time[0].data)]) + '.nc'\n \n if dump:\n # writing out just the damage categories\n # immediate damage; before minus after\n write_dataset_to_netcdf(result, path.join(dir_name, fout))\n \n # now add in the wind speed and calculate immediate impact of the cyclone by area\n # 5 wind speed categories so a loop with five iterations\n if immediate == 0:\n wind_cat = pd.DataFrame(index=list(windspeed_category.keys()), columns=np.arange(0,5))\n for key, value in windspeed_category.items():\n wind_damage = result.damage_level[0].where(np.logical_and(cyclone_data.wind_speed[0] >= value[0] \n ,cyclone_data.wind_speed[0] < value[1]))\n for i in range(0, 5): # damage levels\n wind_cat[i][key] = wind_damage.where(wind_damage == i).count().data * 0.030**2\n print(wind_cat)\n all_damage_level = result.damage_level\n else:\n all_damage_level = xr.concat([all_damage_level, result.damage_level], dim='time')\n immediate += 1\n\n # categorise long term damage from the cyclone\n # categories 1-5 for long term damage using time\n results = np.zeros(all_damage_level.shape[1:], dtype='int16')\n tmp = all_damage_level.where(np.logical_and(all_damage_level<=2, all_damage_level > 0)).count(dim='time')\n results[tmp.values > 0] = 1 # at least once\n results[tmp.values==all_damage_level.time.shape[0]] = 2 # for whole time period after cyclone\n\n tmp = all_damage_level.where(np.logical_and(all_damage_level<=4, all_damage_level > 2)).count(dim='time')\n results[tmp.values > 0] = 3 # at least once\n results[tmp.values==all_damage_level.time.shape[0]] = 4 # for whole time after cyclone\n\n tmp = all_damage_level.where(all_damage_level==4).count(dim='time') # always a level 4 - never recovered\n results[tmp.values==all_damage_level.time.shape[0]] = 5\n results[all_damage_level.values[0]==-1] = -1\n\n \n # writing out category long term damage after cyclone data to xarray to export as netcdf\n all_damage_level.time.attrs['units'] = \"seconds since 1970-01-01 00:00:00\"\n results = results.reshape((1, ) + results.shape)\n results = xr.Dataset({\"damage_level\":(['time', 'y', 'x'], results.astype('int16'))},\n coords={'time':all_damage_level.time[-1:], 'y': all_damage_level.y, 'x': all_damage_level.x},\n attrs={'crs': CRS('EPSG:3577'), 'nodata': -1})\n fout = '_'.join([cyclone_name, \"%s\" % int(all_damage_level.x.data.min()/10000), \n \"%s\" % int(all_damage_level.y.data.min()/10000)]) + '_all.nc'\n \n if dump:\n write_dataset_to_netcdf(results, path.join(dir_name, fout))\n all_wind_cat = pd.DataFrame(index=list(windspeed_category.keys()), columns=np.arange(1,6))\n \n for key, value in windspeed_category.items():\n wind_damage = results.damage_level[0].where(np.logical_and(cyclone_data.wind_speed[0] >= value[0],\n cyclone_data.wind_speed[0] < value[1]))\n for i in range(0, 5):\n all_wind_cat[i+1][key] = wind_damage.where(wind_damage == (i+1)).count().data * 0.030**2\n print(all_wind_cat)\n\n return wind_cat, all_wind_cat", "repo_name": "christopherowers/TC_mangroves", "sub_path": "Cyclone_damage_funcs.py", "file_name": "Cyclone_damage_funcs.py", "file_ext": "py", "file_size_in_byte": 7563, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "datacube.Datacube", "line_number": 15, "usage_type": "call"}, {"api_name": "datacube.model.Measurement", "line_number": 46, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 55, "usage_type": "call"}, {"api_name": "os.path", "line_number": 55, "usage_type": "name"}, {"api_name": "os.mkdir", "line_number": 56, "usage_type": "call"}, {"api_name": "numpy.logical_and", "line_number": 69, "usage_type": "call"}, {"api_name": "numpy.logical_or", "line_number": 70, "usage_type": "call"}, {"api_name": "numpy.logical_and", "line_number": 72, "usage_type": "call"}, {"api_name": "numpy.logical_and", "line_number": 74, "usage_type": "call"}, {"api_name": "numpy.logical_and", "line_number": 76, "usage_type": "call"}, {"api_name": "xarray.Dataset", "line_number": 81, "usage_type": "call"}, {"api_name": "datacube.utils.geometry.CRS", "line_number": 83, "usage_type": "call"}, {"api_name": "datacube.drivers.netcdf.write_dataset_to_netcdf", "line_number": 92, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 92, "usage_type": "call"}, {"api_name": "os.path", "line_number": 92, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 97, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 97, "usage_type": "call"}, {"api_name": "numpy.logical_and", "line_number": 99, "usage_type": "call"}, {"api_name": "xarray.concat", "line_number": 106, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 111, "usage_type": "call"}, {"api_name": "numpy.logical_and", "line_number": 112, "usage_type": "call"}, {"api_name": "numpy.logical_and", "line_number": 116, "usage_type": "call"}, {"api_name": "xarray.Dataset", "line_number": 128, "usage_type": "call"}, {"api_name": "datacube.utils.geometry.CRS", "line_number": 130, "usage_type": "call"}, {"api_name": "datacube.drivers.netcdf.write_dataset_to_netcdf", "line_number": 135, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 135, "usage_type": "call"}, {"api_name": "os.path", "line_number": 135, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 136, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 136, "usage_type": "call"}, {"api_name": "numpy.logical_and", "line_number": 139, "usage_type": "call"}]} +{"seq_id": "74742328426", "text": "from flask import Blueprint, jsonify, request\nfrom flask_cors import CORS\nfrom flask_login import login_required\n\nfrom ermaket.api.system.hierarchy import Activation\nfrom ermaket.api.scripts import ServerScriptExecutor\n\nscripts = ServerScriptExecutor()\n\n__all__ = ['business_logic']\nbusiness_logic = Blueprint('business_logic', 'business_logic')\nCORS(business_logic, supports_credentials=True)\n\n\n@business_logic.route('/execute/', methods=['POST'])\n@login_required\ndef process(id):\n data = request.form or request.json\n try:\n activation = Activation(data['activation'])\n except (KeyError, TypeError):\n activation = Activation(Activation.CALL)\n if not scripts.process_call(int(id), activation):\n return scripts.return_\n return jsonify({\"ok\": True, **scripts.append_})\n", "repo_name": "SqrtMinusOne/ERMaket", "sub_path": "ermaket/blueprints/logic.py", "file_name": "logic.py", "file_ext": "py", "file_size_in_byte": 807, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "ermaket.api.scripts.ServerScriptExecutor", "line_number": 8, "usage_type": "call"}, {"api_name": "flask.Blueprint", "line_number": 11, "usage_type": "call"}, {"api_name": "flask_cors.CORS", "line_number": 12, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 18, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 18, "usage_type": "name"}, {"api_name": "flask.request.json", "line_number": 18, "usage_type": "attribute"}, {"api_name": "ermaket.api.system.hierarchy.Activation", "line_number": 20, "usage_type": "call"}, {"api_name": "ermaket.api.system.hierarchy.Activation", "line_number": 22, "usage_type": "call"}, {"api_name": "ermaket.api.system.hierarchy.Activation.CALL", "line_number": 22, "usage_type": "attribute"}, {"api_name": "flask.jsonify", "line_number": 25, "usage_type": "call"}, {"api_name": "flask_login.login_required", "line_number": 16, "usage_type": "name"}]} +{"seq_id": "21726942675", "text": "from itertools import groupby\n\nlst_alunos = [\n {'nome': 'João', 'nota': 'A'},\n {'nome': 'Leila', 'nota': 'C'},\n {'nome': 'Rodrigo', 'nota': 'B'},\n {'nome': 'Mário', 'nota': 'C'},\n {'nome': 'Karina', 'nota': 'C'},\n {'nome': 'Maria', 'nota': 'B'},\n {'nome': 'Marisa', 'nota': 'B'},\n {'nome': 'Rubens', 'nota': 'C'},\n {'nome': 'Jairo', 'nota': 'A'},\n {'nome': 'Letícia', 'nota': 'D'}\n]\n\nchave = lambda item: item['nota']\nlst_alunos.sort(key=chave) # O groupby necessita da lista ordenada para funcionar\nalunos_agrupados = groupby(lst_alunos, chave)\n\nfor notas, alunos_iteraveis in alunos_agrupados:\n print(f'\\33[32mAlunos com nota {notas}:\\33[m')\n for alunos in alunos_iteraveis:\n print(alunos['nome'])\n", "repo_name": "romulorm/udemy-python", "sub_path": "aula73-groupby.py", "file_name": "aula73-groupby.py", "file_ext": "py", "file_size_in_byte": 746, "program_lang": "python", "lang": "pt", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "itertools.groupby", "line_number": 18, "usage_type": "call"}]} +{"seq_id": "18432348941", "text": "import tkinter as tk\nfrom tkinter import ttk\nimport logging\nimport showController\nimport serialTools\nimport globals\nimport serial\n\nlogger = logging.getLogger('mainWindow')\n\n#Main window for program\nclass MainWindow(ttk.Frame):\n\n def __init__(self, parent, *args, **kwargs):\n super().__init__ (parent, *args, **kwargs)\n self.parent=parent\n\n self.grid(column=0, row=0)\n\n self.ledStrip=showController.ShowController()\n self.serialObject=None\n\n self.generalBar=GeneralBar(self)\n self.generalBar.grid(column=0,row=1)\n\n def connectDevice(self, serialDeviceName):\n try:\n self.serialObject=serial.Serial(serialDeviceName, globals.baudRate)\n if self.ledStrip.connectDevice(self.serialObject):\n self.generalBar.connectDevice(\"\".join([str(val) for val in self.ledStrip.deviceID])) #Want to allow user to save a name\n\n else: raise serial.SerialException\n\n except serial.SerialException:\n logger.warning(\"Could not connect to device at port {}\".format(serialDeviceName))\n self.disconnectDevice()\n\n def disconnectDevice(self, finalClose = False):\n if self.serialObject != None:\n self.serialObject.close()\n \n if self.ledStrip.connected: self.ledStrip.disconnectDevice()\n self.generalBar.disconnectDevice(finalClose)\n\n\n\nclass GeneralBar(ttk.Frame):\n def __init__(self, parent, *args, **kwargs):\n super().__init__ (parent, *args, **kwargs)\n self.parent=parent\n self.grid(column=0, row=0)\n self.ledStrip=parent.ledStrip\n\n # Initialise power button\n ttk.Label(self, text=\"Power:\").grid(column=0, row=0, sticky='E')\n self.bt_power = ttk.Button(self)\n self.bt_power.grid(column=1, row=0, sticky='W')\n\n # Initialise play/pause button\n ttk.Label(self, text=\"Play/Pause:\").grid(column=0, row=1, sticky='E')\n self.bt_playPause = ttk.Button(self)\n self.bt_playPause.grid(column=1, row=1, sticky='W')\n\n #Initialise brightness\n ttk.Label(self, text=\"Brightness:\").grid(column=0, row=2, sticky='E')\n self.sc_brightness = ttk.Scale(self, orient=\"horizontal\", \\\n length=150, from_=0, to=100)\n self.sc_brightness.grid(column=1, row=2)\n\n #Add a middle frame for a bit of padding\n self.columnconfigure(2, minsize=100)\n self.rowconfigure(3, minsize=10)\n\n #Device name\n ttk.Label(self, text=\"Device Name:\").grid(column=0, row=4, sticky='E')\n self.lb_deviceName = ttk.Label(self)\n self.lb_deviceName.grid(column=1,row=4, sticky='W')\n\n #Initialise serial\n ttk.Label(self, text=\"Serial Port:\").grid(column=3, row=0, sticky=\"W\")\n self.cb_serialport=ttk.Combobox(self,state=['disabled'])\n self.cb_serialport['values'] = ('Test1', 'Test2', 'Test3')\n self.cb_serialport.grid(column=4, row=0, columnspan=2)\n self.bt_serialportReferesh=ttk.Button(self,text=\"Refresh\",command=self.populatePorts)\n self.bt_serialportReferesh.grid(column=4, row=1)\n self.bt_serialport=ttk.Button(self, command=self.serialConnect_cb)\n self.bt_serialport.grid(column=5, row=1, sticky=\"E\")\n\n #Show name\n ttk.Label(self, text=\"Show Name:\").grid(column=3, row=4, sticky=\"W\")\n self.cb_showName=ttk.Combobox(self)\n self.cb_showName.grid(column=4, row=4, columnspan=2)\n\n for child in self.winfo_children(): child.grid_configure(padx=5, pady=5)\n\n self.disconnectDevice() #Initialise UI for disconnected device\n\n # Find which ports are valid led strips\n def populatePorts(self):\n devices=serialTools.getSerialDevices()\n validPorts=[]\n\n for device in devices:\n with serial.Serial(device, globals.baudRate) as serialObj:\n if self.ledStrip.connectDevice(serialObj):\n logger.info(\"Valid device found on port: {}\".format(device))\n validPorts.append(device)\n self.ledStrip.disconnectDevice()\n\n if len(validPorts) > 0:\n self.cb_serialport.state(['readonly', '!disabled'])\n self.bt_serialport.state(['!disabled'])\n self.cb_serialport['values'] = tuple(validPorts)\n self.cb_serialport.set(validPorts[0])\n else:\n logger.info(\"No valid ports found\")\n self.bt_serialport.state(['disabled'])\n self.cb_serialport.state(['disabled'])\n self.cb_serialport.set(\"No valid devices\")\n\n def disconnectDevice(self, finalClose=False):\n # Power defaults\n self.bt_power.state(['disabled'])\n self.bt_power['text']=\"Unknown\"\n #Playpause defaults\n self.bt_playPause.state(['disabled'])\n self.bt_playPause['text']=\"Unknown\"\n #Brightness slider\n self.sc_brightness.state(['disabled'])\n #Device name\n self.lb_deviceName['text']=\"No device connected\"\n #Show\n self.cb_showName.state(['disabled'])\n #Serial port\n self.bt_serialportReferesh.state(['!disabled'])\n self.bt_serialport['text']=\"Connect\"\n if not finalClose: self.populatePorts() #Repopulate lists if not exit\n\n def connectDevice(self, deviceName):\n self.lb_deviceName['text']=deviceName\n self.bt_serialportReferesh.state(['disabled'])\n self.bt_serialport['text']=\"Disconnect\"\n\n def serialConnect_cb(self):\n if self.bt_serialport['text'] == \"Connect\":\n self.parent.connectDevice(self.cb_serialport.get())\n else:\n self.parent.disconnectDevice()\n", "repo_name": "SamPUG/serial_led_controller", "sub_path": "mainWindow.py", "file_name": "mainWindow.py", "file_ext": "py", "file_size_in_byte": 5649, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "logging.getLogger", "line_number": 9, "usage_type": "call"}, {"api_name": "tkinter.ttk.Frame", "line_number": 12, "usage_type": "attribute"}, {"api_name": "tkinter.ttk", "line_number": 12, "usage_type": "name"}, {"api_name": "showController.ShowController", "line_number": 20, "usage_type": "call"}, {"api_name": "serial.Serial", "line_number": 28, "usage_type": "call"}, {"api_name": "globals.baudRate", "line_number": 28, "usage_type": "attribute"}, {"api_name": "serial.SerialException", "line_number": 32, "usage_type": "attribute"}, {"api_name": "serial.SerialException", "line_number": 34, "usage_type": "attribute"}, {"api_name": "tkinter.ttk.Frame", "line_number": 47, "usage_type": "attribute"}, {"api_name": "tkinter.ttk", "line_number": 47, "usage_type": "name"}, {"api_name": "tkinter.ttk.Label", "line_number": 55, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 55, "usage_type": "name"}, {"api_name": "tkinter.ttk.Button", "line_number": 56, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 56, "usage_type": "name"}, {"api_name": "tkinter.ttk.Label", "line_number": 60, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 60, "usage_type": "name"}, {"api_name": "tkinter.ttk.Button", "line_number": 61, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 61, "usage_type": "name"}, {"api_name": "tkinter.ttk.Label", "line_number": 65, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 65, "usage_type": "name"}, {"api_name": "tkinter.ttk.Scale", "line_number": 66, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 66, "usage_type": "name"}, {"api_name": "tkinter.ttk.Label", "line_number": 75, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 75, "usage_type": "name"}, {"api_name": "tkinter.ttk.Label", "line_number": 76, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 76, "usage_type": "name"}, {"api_name": "tkinter.ttk.Label", "line_number": 80, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 80, "usage_type": "name"}, {"api_name": "tkinter.ttk.Combobox", "line_number": 81, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 81, "usage_type": "name"}, {"api_name": "tkinter.ttk.Button", "line_number": 84, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 84, "usage_type": "name"}, {"api_name": "tkinter.ttk.Button", "line_number": 86, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 86, "usage_type": "name"}, {"api_name": "tkinter.ttk.Label", "line_number": 90, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 90, "usage_type": "name"}, {"api_name": "tkinter.ttk.Combobox", "line_number": 91, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 91, "usage_type": "name"}, {"api_name": "serialTools.getSerialDevices", "line_number": 100, "usage_type": "call"}, {"api_name": "serial.Serial", "line_number": 104, "usage_type": "call"}, {"api_name": "globals.baudRate", "line_number": 104, "usage_type": "attribute"}]} +{"seq_id": "23367689345", "text": "\"\"\"\nAutomated 900 doc generation script.\n\"\"\"\nfrom . import vendors as v\nfrom docx import Document\nfrom datetime import datetime as dt\nfrom common import common\n\ndef get_name():\n return common.set_username(\"Type your name as it shall appear on the completed forms\")\n\ndef parse_date(text_date):\n try:\n return dt.strptime(text_date, \"%m/%d/%Y\").strftime(\"%Y-%m-%d\")\n except:\n return ''\n\ndef batch_form_generate():\n template = 'templates/3305-00900-v103.docx'\n \n print(\"Edit the rma/refurb_data.txt document before proceeding!\")\n print(\"If you need help, see the rma/README.md file for instructions.\")\n input(\"Press ENTER to continue...\")\n \n username = get_name()\n \n with open('rma/refurb_data.txt', 'r') as rows:\n for row in rows:\n cells = row.split('\\t')\n rma = cells[0]\n items = cells[1]\n vendor = cells[2]\n recovered_platform = cells[3]\n return_doc = cells[5]\n form_number = return_doc[11:]\n departure_date = parse_date(cells[6].strip())\n\n\n doc = Document(template)\n table = doc.tables[0]\n\n cell = table.rows[1].cells[0]\n cell.paragraphs[1].text = cell.paragraphs[1].text.replace('name', username)\n\n cell = table.rows[1].cells[1]\n cell.paragraphs[1].text = cell.paragraphs[1].text.replace('form_date', dt.today().strftime(\"%Y-%m-%d\"))\n\n cell = table.rows[1].cells[5]\n cell.paragraphs[1].text = cell.paragraphs[1].text.replace('form_number', form_number)\n\n cell = table.rows[2].cells[0]\n cell.paragraphs[1].text = cell.paragraphs[1].text.replace('departure_date', departure_date)\n #cell.paragraphs[1].text = \"spam!\"\n\n cell = table.rows[3].cells[0]\n cell.paragraphs[1].text = cell.paragraphs[1].text.replace('rma', rma)\n\n cell = table.rows[3].cells[2]\n cell.paragraphs[1].text = v.contacts[vendor]\n\n cell = table.rows[3].cells[4]\n cell.paragraphs[1].text = cell.paragraphs[1].text.replace('vendor', vendor)\n\n\n table = doc.tables[2]\n\n cell = table.rows[1].cells[0]\n cell.paragraphs[3].text = items\n\n table = doc.tables[4]\n\n cell = table.rows[1].cells[0]\n cell.paragraphs[3].text = cell.paragraphs[3].text.replace('recovered_platform', recovered_platform)\n\n\n\n\n # Doc Title and Author properties...\n doc.core_properties.title = (\"RMA_%s_-_%s_Shipping\"\n % (rma, dt.today().strftime(\"%Y-%m-%d\")))\n author_inits = username.split()\n author_inits[:-1] = [init[0] + '.' for init in author_inits[:-1]]\n doc.core_properties.author = ' '.join(author_inits)\n\n # Save doc...\n doc.save('save/3305-00900-%s.docx' % form_number)\n\ndef main():\n while True:\n header = ''.join((\"\\n\", \"-\" * 19, \"RMA AND SHIPPING MENU\", \"-\" * 19))\n proclist = [\"Create new RMA and Shipping document(s)\"]\n try:\n proc_id = int(common.dynamicmenu_get(\"Select an action\", proclist, header=header))\n except TypeError:\n break\n batch_form_generate()\n print(\"Process complete. Check documents for errors.\")\n ", "repo_name": "asmith75218/cg_inst_automation", "sub_path": "rma/rma.py", "file_name": "rma.py", "file_ext": "py", "file_size_in_byte": 3359, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "common.common.set_username", "line_number": 10, "usage_type": "call"}, {"api_name": "common.common", "line_number": 10, "usage_type": "name"}, {"api_name": "datetime.datetime.strptime", "line_number": 14, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 14, "usage_type": "name"}, {"api_name": "docx.Document", "line_number": 39, "usage_type": "call"}, {"api_name": "datetime.datetime.today", "line_number": 46, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 46, "usage_type": "name"}, {"api_name": "datetime.datetime.today", "line_number": 80, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 80, "usage_type": "name"}, {"api_name": "common.common.dynamicmenu_get", "line_number": 93, "usage_type": "call"}, {"api_name": "common.common", "line_number": 93, "usage_type": "name"}]} +{"seq_id": "19672796648", "text": "from evdev import InputDevice, categorize, ecodes\n\ndevice1 = InputDevice('/dev/input/event0')\ndevice2 = InputDevice('/dev/input/event3')\n# 0 is the right one hence device 1 is on the right\nX1, Y1 = 0, 0\nX2, Y2 = 0, 0\ncount = 0\nwhile True:\n try:\n count += 1\n \n event1 = device1.read_one()\n event2 = device2.read_one()\n \n if event1 is not None:\n if event1.type == ecodes.EV_REL:\n if event1.code == ecodes.REL_X:\n X1 += event1.value\n elif event1.code == ecodes.REL_Y:\n Y1 -= event1.value\n if event2 is not None:\n if event2.type == ecodes.EV_REL:\n if event2.code == ecodes.REL_X:\n X2 += event2.value\n elif event2.code == ecodes.REL_Y:\n Y2 -= event2.value\n if count%50000==0:\n print(\"C:%d, X1:%d, Y1:%d X2:%d, Y2:%d\\n\"%(count/50000 ,X1, Y1,X2, Y2))\n \n\n except KeyboardInterrupt:\n device1.close()\n device2.close()\n break\n \n \n \n\n", "repo_name": "PranavAdlinge/DeadReckoning", "sub_path": "calib.py", "file_name": "calib.py", "file_ext": "py", "file_size_in_byte": 1114, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "evdev.InputDevice", "line_number": 3, "usage_type": "call"}, {"api_name": "evdev.InputDevice", "line_number": 4, "usage_type": "call"}, {"api_name": "evdev.ecodes.EV_REL", "line_number": 17, "usage_type": "attribute"}, {"api_name": "evdev.ecodes", "line_number": 17, "usage_type": "name"}, {"api_name": "evdev.ecodes.REL_X", "line_number": 18, "usage_type": "attribute"}, {"api_name": "evdev.ecodes", "line_number": 18, "usage_type": "name"}, {"api_name": "evdev.ecodes.REL_Y", "line_number": 20, "usage_type": "attribute"}, {"api_name": "evdev.ecodes", "line_number": 20, "usage_type": "name"}, {"api_name": "evdev.ecodes.EV_REL", "line_number": 23, "usage_type": "attribute"}, {"api_name": "evdev.ecodes", "line_number": 23, "usage_type": "name"}, {"api_name": "evdev.ecodes.REL_X", "line_number": 24, "usage_type": "attribute"}, {"api_name": "evdev.ecodes", "line_number": 24, "usage_type": "name"}, {"api_name": "evdev.ecodes.REL_Y", "line_number": 26, "usage_type": "attribute"}, {"api_name": "evdev.ecodes", "line_number": 26, "usage_type": "name"}]} +{"seq_id": "16381883089", "text": "\"\"\"\nThis module contains subclasses of wx.Dialog specifically designed \nto allow users to customize the options used to run various clustering \nmethods.\n\n@author: Shareef Dabdoub\n@organization: The Ohio State University\n@organization: Nationwide Children's Hospital\n\"\"\"\nimport methods\n\nimport wx\n\ndialogs = {}\n\n\ndef addPluginDialog(ID, dialog):\n global dialogs\n dialogs[ID] = dialog\n \n\ndef getClusterDialog(clusterID, parent):\n return dialogs[clusterID](parent)\n \n \n \n \n\ndialogs[methods.ID_KMEANS] = methods.kmeans.KMeansDialog\ndialogs[methods.ID_BAKKER_SCHUT] = methods.bakker_schut.BakkerSchutKMeansDialog\n#dialogs[methods.ID_AUTOCLASS] = None\n\nfrom util import separate\nimport plot\n\nclass ClusterInfoDialog(wx.Dialog):\n \"\"\"\n This dialog displays detailed information on the currently selected clustering.\n \"\"\"\n \n def __init__(self, parent, isolate = False):\n hGap = 20\n vGap = 10\n clusterIDs = DataStore.getCurrentDataSet().getCurrentClustering()\n clustering = separate(DataStore.getCurrentDataSet().data, clusterIDs)\n numClusters = len(clustering)\n numColumns = 3\n self.radioList = []\n self.isolate = isolate\n \n #TODO: still not perfect, larger cluster sizes gives an increasing space\n # at the bottom\n # The magic # includes the header width, the vgap for it, and the widths and\n # border pads for the two sizers\n dlgWidth = 275\n dlgHeight = ((numClusters+1) * (vGap+20)) + 100\n if isolate:\n dlgWidth += 50\n #dlgHeight += 20\n \n title = 'Clustering Info'\n if (isolate):\n title = 'Isolate clusters'\n wx.Dialog.__init__(self, parent, wx.ID_ANY, title, size=(dlgWidth, dlgHeight))\n self.CenterOnParent()\n \n \n # create main data display sizer\n # one row for each cluster plus header row\n # cols: cluster color, % of total\n \n self.formSizer = None\n if isolate:\n self.formSizer = wx.FlexGridSizer(numClusters+1, numColumns+1, hgap=hGap, vgap=vGap)\n else:\n self.formSizer = wx.FlexGridSizer(numClusters, numColumns, hgap=hGap, vgap=vGap)\n # header row\n if isolate:\n self.formSizer.Add(wx.StaticText(self, -1, 'Select', (5,10)), 1, wx.EXPAND)\n self.formSizer.Add(wx.StaticText(self, -1, 'Cluster', (5, 10)), 1, wx.EXPAND)\n self.formSizer.Add(wx.StaticText(self, -1, '% of Total', (20, 10)), 1, wx.EXPAND)\n self.formSizer.Add(wx.StaticText(self, -1, '# of Events', (20, 10)), 1, wx.EXPAND)\n # data rows\n for i in range(len(clustering)):\n cluster = clustering[i]\n if isolate:\n self.radioList.append(wx.CheckBox(self, wx.ID_ANY))\n self.formSizer.Add(self.radioList[i], 0, wx.EXPAND)\n # cluster color box\n label = wx.StaticText(self, -1, '', (20, 10))\n label.SetBackgroundColour(plot.methods.plotColors[i])\n self.formSizer.Add(label, 1, wx.EXPAND)\n # % of total\n percent = float(len(cluster))/len(DataStore.getCurrentDataSet().data)*100\n label = wx.StaticText(self, -1, '%6.2f' % percent + ' %', (30, 10))\n self.formSizer.Add(label, 1, wx.EXPAND | wx.ALIGN_CENTER)\n # number of events\n label = wx.StaticText(self, -1, str(len(cluster)), (30, 10))\n self.formSizer.Add(label, 1, wx.EXPAND | wx.ALIGN_CENTER) \n \n # create the button row\n self.buttonSizer = None\n if isolate:\n self.buttonSizer = self.CreateButtonSizer(wx.OK | wx.CANCEL)\n else:\n self.buttonSizer = self.CreateButtonSizer(wx.OK)\n \n \n # main sizer\n self.sizer = wx.BoxSizer(wx.VERTICAL)\n self.sizer.Add(self.formSizer, 1, wx.EXPAND | wx.LEFT | wx.RIGHT | wx.TOP, 20)\n self.sizer.Add(self.buttonSizer, 0, wx.EXPAND | wx.LEFT | wx.RIGHT | wx.TOP | wx.BOTTOM, 20)\n self.SetSizer(self.sizer)\n \n \n\n def SelectedClusters(self):\n return [i for i in range(len(self.radioList)) if self.radioList[i].IsChecked()]\n\n\n\nfrom data.store import DataStore\nfrom methods import getStringRepr\n \nclass ClusterRecolorSelectionDialog(wx.Dialog):\n def __init__(self, parent):\n wx.Dialog.__init__(self, parent, wx.ID_ANY, \"Select clusterings to match colors\", \n style=wx.RESIZE_BORDER|wx.DEFAULT_DIALOG_STYLE, size=(400, 150))\n self.CenterOnParent()\n \n allData = DataStore.getData()\n self.choices = []\n self.choiceIDs = []\n \n # Populate the choices list with string, and populate the \n # choiceIDs list with (dataID, clusteringID) tuples so the \n # combo box selection can be tied to the data\n for didx in allData:\n fcData = allData[didx]\n for cidx in fcData.clustering: \n self.choices.append(fcData.displayname + \": \" + \n getStringRepr(fcData.methodIDs[cidx]) + \" \" + \n str(cidx+1))\n self.choiceIDs.append((fcData.ID, cidx))\n \n \n self.cbxSourceClustering = wx.ComboBox(self, choices=self.choices, style=wx.CB_READONLY)\n self.cbxDestClustering = wx.ComboBox(self, choices=self.choices, style=wx.CB_READONLY)\n\n \n self.formSizer = wx.FlexGridSizer(2, 2, vgap=5, hgap=5)\n self.formSizer.FlexibleDirection = wx.HORIZONTAL\n self.formSizer.AddF(wx.StaticText(self, -1, 'First Clustering:'), wx.SizerFlags(1).Expand())\n self.formSizer.AddF(self.cbxSourceClustering, wx.SizerFlags(2).Expand())\n self.formSizer.AddF(wx.StaticText(self, -1, 'Second Clustering:'), wx.SizerFlags(1).Expand())\n self.formSizer.AddF(self.cbxDestClustering, wx.SizerFlags(2).Expand())\n \n self.Sizer = wx.BoxSizer(wx.VERTICAL)\n self.Sizer.AddF(self.formSizer, wx.SizerFlags(1).Expand().Border(wx.ALL, 10))\n self.Sizer.AddF(self.CreateButtonSizer(wx.OK|wx.CANCEL), wx.SizerFlags().Expand().Border(wx.BOTTOM, 10))\n\n \n @property\n def Source(self):\n if self.cbxSourceClustering.Selection >= 0:\n return self.choiceIDs[self.cbxSourceClustering.Selection]\n \n @property\n def Destination(self):\n if self.cbxDestClustering.Selection >= 0:\n return self.choiceIDs[self.cbxDestClustering.Selection]\n \n \n \n \n \n \n \n \n\n\n", "repo_name": "smdabdoub/find", "sub_path": "cluster/dialogs.py", "file_name": "dialogs.py", "file_ext": "py", "file_size_in_byte": 6618, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "37", "api": [{"api_name": "methods.ID_KMEANS", "line_number": 29, "usage_type": "attribute"}, {"api_name": "methods.kmeans", "line_number": 29, "usage_type": "attribute"}, {"api_name": "methods.ID_BAKKER_SCHUT", "line_number": 30, "usage_type": "attribute"}, {"api_name": "methods.bakker_schut", "line_number": 30, "usage_type": "attribute"}, {"api_name": "wx.Dialog", "line_number": 36, "usage_type": "attribute"}, {"api_name": "util.separate", "line_number": 45, "usage_type": "call"}, {"api_name": "wx.Dialog.__init__", "line_number": 64, "usage_type": "call"}, {"api_name": "wx.Dialog", "line_number": 64, "usage_type": "attribute"}, {"api_name": "wx.ID_ANY", "line_number": 64, "usage_type": "attribute"}, {"api_name": "wx.FlexGridSizer", "line_number": 74, "usage_type": "call"}, {"api_name": "wx.FlexGridSizer", "line_number": 76, "usage_type": "call"}, {"api_name": "wx.StaticText", "line_number": 79, "usage_type": "call"}, {"api_name": "wx.EXPAND", "line_number": 79, "usage_type": "attribute"}, {"api_name": "wx.StaticText", "line_number": 80, "usage_type": "call"}, {"api_name": "wx.EXPAND", "line_number": 80, "usage_type": "attribute"}, {"api_name": "wx.StaticText", "line_number": 81, "usage_type": "call"}, {"api_name": "wx.EXPAND", "line_number": 81, "usage_type": "attribute"}, {"api_name": "wx.StaticText", "line_number": 82, "usage_type": "call"}, {"api_name": "wx.EXPAND", "line_number": 82, "usage_type": "attribute"}, {"api_name": "wx.CheckBox", "line_number": 87, "usage_type": "call"}, {"api_name": "wx.ID_ANY", "line_number": 87, "usage_type": "attribute"}, {"api_name": "wx.EXPAND", "line_number": 88, "usage_type": "attribute"}, {"api_name": "wx.StaticText", "line_number": 90, "usage_type": "call"}, {"api_name": "plot.methods", "line_number": 91, "usage_type": "attribute"}, {"api_name": "wx.EXPAND", "line_number": 92, "usage_type": "attribute"}, {"api_name": "wx.StaticText", "line_number": 95, "usage_type": "call"}, {"api_name": "wx.EXPAND", "line_number": 96, "usage_type": "attribute"}, {"api_name": "wx.ALIGN_CENTER", "line_number": 96, "usage_type": "attribute"}, {"api_name": "wx.StaticText", "line_number": 98, "usage_type": "call"}, {"api_name": "wx.EXPAND", "line_number": 99, "usage_type": "attribute"}, {"api_name": "wx.ALIGN_CENTER", "line_number": 99, "usage_type": "attribute"}, {"api_name": "wx.OK", "line_number": 104, "usage_type": "attribute"}, {"api_name": "wx.CANCEL", "line_number": 104, "usage_type": "attribute"}, {"api_name": "wx.OK", "line_number": 106, "usage_type": "attribute"}, {"api_name": "wx.BoxSizer", "line_number": 110, "usage_type": "call"}, {"api_name": "wx.VERTICAL", "line_number": 110, "usage_type": "attribute"}, {"api_name": "wx.EXPAND", "line_number": 111, "usage_type": "attribute"}, {"api_name": "wx.LEFT", "line_number": 111, "usage_type": "attribute"}, {"api_name": "wx.RIGHT", "line_number": 111, "usage_type": "attribute"}, {"api_name": "wx.TOP", "line_number": 111, "usage_type": "attribute"}, {"api_name": "wx.EXPAND", "line_number": 112, "usage_type": "attribute"}, {"api_name": "wx.LEFT", "line_number": 112, "usage_type": "attribute"}, {"api_name": "wx.RIGHT", "line_number": 112, "usage_type": "attribute"}, {"api_name": "wx.TOP", "line_number": 112, "usage_type": "attribute"}, {"api_name": "wx.BOTTOM", "line_number": 112, "usage_type": "attribute"}, {"api_name": "wx.Dialog", "line_number": 125, "usage_type": "attribute"}, {"api_name": "wx.Dialog.__init__", "line_number": 127, "usage_type": "call"}, {"api_name": "wx.Dialog", "line_number": 127, "usage_type": "attribute"}, {"api_name": "wx.ID_ANY", "line_number": 127, "usage_type": "attribute"}, {"api_name": "wx.RESIZE_BORDER", "line_number": 128, "usage_type": "attribute"}, {"api_name": "wx.DEFAULT_DIALOG_STYLE", "line_number": 128, "usage_type": "attribute"}, {"api_name": "data.store.DataStore.getData", "line_number": 131, "usage_type": "call"}, {"api_name": "data.store.DataStore", "line_number": 131, "usage_type": "name"}, {"api_name": "methods.getStringRepr", "line_number": 142, "usage_type": "call"}, {"api_name": "wx.ComboBox", "line_number": 147, "usage_type": "call"}, {"api_name": "wx.CB_READONLY", "line_number": 147, "usage_type": "attribute"}, {"api_name": "wx.ComboBox", "line_number": 148, "usage_type": "call"}, {"api_name": "wx.CB_READONLY", "line_number": 148, "usage_type": "attribute"}, {"api_name": "wx.FlexGridSizer", "line_number": 151, "usage_type": "call"}, {"api_name": "wx.HORIZONTAL", "line_number": 152, "usage_type": "attribute"}, {"api_name": "wx.StaticText", "line_number": 153, "usage_type": "call"}, {"api_name": "wx.SizerFlags", "line_number": 153, "usage_type": "call"}, {"api_name": "wx.SizerFlags", "line_number": 154, "usage_type": "call"}, {"api_name": "wx.StaticText", "line_number": 155, "usage_type": "call"}, {"api_name": "wx.SizerFlags", "line_number": 155, "usage_type": "call"}, {"api_name": "wx.SizerFlags", "line_number": 156, "usage_type": "call"}, {"api_name": "wx.BoxSizer", "line_number": 158, "usage_type": "call"}, {"api_name": "wx.VERTICAL", "line_number": 158, "usage_type": "attribute"}, {"api_name": "wx.SizerFlags", "line_number": 159, "usage_type": "call"}, {"api_name": "wx.ALL", "line_number": 159, "usage_type": "attribute"}, {"api_name": "wx.OK", "line_number": 160, "usage_type": "attribute"}, {"api_name": "wx.CANCEL", "line_number": 160, "usage_type": "attribute"}, {"api_name": "wx.SizerFlags", "line_number": 160, "usage_type": "call"}, {"api_name": "wx.BOTTOM", "line_number": 160, "usage_type": "attribute"}]} +{"seq_id": "41568799472", "text": "import itertools\n\nimport numpy as np\nimport pytest\nfrom pathlib import Path\nimport pandas as pd\n\nfrom book_processing.entity_filter import EntityFilter\nfrom path_reference.folder_reference import get_data_path, get_books_entities_path, get_book_characters_path, \\\n get_books_path\n\n\nclass BookTest:\n def __init__(self, book_name: str, series: str = \"witcher\"):\n self.book_name = book_name\n self.series = series\n\n self.book_df = None\n self.entity_df = None\n\n self.__load_data()\n self.ef = EntityFilter(series=self.series)\n\n def __load_data(self):\n book_path = Path(get_books_path(), f\"{self.series}_books\", f\"{self.book_name}.txt\")\n entity_path = Path(get_books_entities_path(), f\"{self.series}_books_entities\", f\"{self.book_name}.csv\")\n self.entity_df = pd.read_csv(entity_path, encoding=\"utf-8\")\n\n def set_entity_df(self):\n self.ef.set_entity_df(self.entity_df)\n\n def get_filtered_df(self):\n return self.ef.export_filtered_dataframe()\n\n\ndef get_last_witcher_unique_characters() -> list[str]:\n b = BookTest(\"1 The Last Wish\")\n b.set_entity_df()\n filtered_df = b.get_filtered_df()\n values = filtered_df[\"character_entities\"].values\n flattened_values = list(itertools.chain(*values))\n return list(set(flattened_values))\n\n\ndef get_harry_potter_unique_characters() -> list[str]:\n b = BookTest(\"1 The Philosopher's Stone\", series=\"harry_potter\")\n b.set_entity_df()\n filtered_df = b.get_filtered_df()\n values = filtered_df[\"character_entities\"].values\n flattened_values = list(itertools.chain(*values))\n return list(set(flattened_values))\n\n\ndef __main():\n test_witcher_main_characters()\n test_harry_potter_main_characters()\n return\n\n\nif __name__ == \"__main__\":\n __main()\n\n\ndef test_witcher_main_characters():\n \"\"\"This function tests if some main characters are present in the entitites\"\"\"\n unique_entities = get_last_witcher_unique_characters()\n expected = [\"Geralt\", \"Triss\"]\n for character in expected:\n assert character in unique_entities\n\n\ndef test_harry_potter_main_characters():\n \"\"\"This function tests if some main characters are present in the entitites\"\"\"\n unique_entities = get_harry_potter_unique_characters()\n expected = [\"Harry\", \"Hermione\", \"Ron\"]\n for character in expected:\n assert character in unique_entities\n", "repo_name": "mateusb12/WitcherAnalysis", "sub_path": "tests/entity_filter_test.py", "file_name": "entity_filter_test.py", "file_ext": "py", "file_size_in_byte": 2398, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "37", "api": [{"api_name": "book_processing.entity_filter.EntityFilter", "line_number": 22, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 25, "usage_type": "call"}, {"api_name": "path_reference.folder_reference.get_books_path", "line_number": 25, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 26, "usage_type": "call"}, {"api_name": "path_reference.folder_reference.get_books_entities_path", "line_number": 26, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 27, "usage_type": "call"}, {"api_name": "itertools.chain", "line_number": 41, "usage_type": "call"}, {"api_name": "itertools.chain", "line_number": 50, "usage_type": "call"}]} +{"seq_id": "39550113248", "text": "import re\r\nimport pymysql\r\nfrom flask import Flask, request\r\n\r\nAPP = Flask(__name__)\r\n\r\ndef sanitize(user_query):\r\n clean_string = re.escape(user_query)\r\n return clean_string\r\n\r\n\r\n@APP.route(\"/\")\r\ndef hello():\r\n return \"Follow/Unfollow\"\r\n\r\n\r\n@APP.route(\"/UserSearch\", methods=['POST'])\r\ndef user_search():\r\n if request.form['search']:\r\n search_query = sanitize(request.form['search'])\r\n result = \"False - No users match your request\"\r\n connection = pymysql.connect(host='mysql-db',\r\n user='root',\r\n password='supersecurepass',\r\n db='skitter',\r\n charset='utf8mb4',\r\n cursorclass=pymysql.cursors.DictCursor)\r\n\r\n try:\r\n with connection.cursor() as cursor:\r\n sql_query = \"SELECT rit_user FROM users WHERE rit_user LIKE %s\"\r\n cursor.execute(sql_query, ('%' + search_query + '%'))\r\n searched_users = cursor.fetchall()\r\n\r\n result = \"\"\r\n\r\n for user in searched_users:\r\n result += str(user['rit_user']) + \" \"\r\n\r\n finally:\r\n connection.close()\r\n\r\n return result\r\n\r\n return \"invalid query\"\r\n\r\n\r\n@APP.route(\"/FollowUser\", methods=['POST'])\r\ndef follow_user():\r\n if request.form['follow'] and request.form['session_id']:\r\n\r\n influencer = sanitize(request.form['follow'])\r\n session_id = sanitize(request.form['session_id'])\r\n connection = pymysql.connect(host='mysql-db',\r\n user='root',\r\n password='supersecurepass',\r\n db='skitter',\r\n charset='utf8mb4',\r\n cursorclass=pymysql.cursors.DictCursor)\r\n\r\n try:\r\n with connection.cursor() as cursor:\r\n sql_query = \"SELECT username FROM sessions \\\r\n WHERE session_id = %s\"\r\n cursor.execute(sql_query, (session_id))\r\n follower = cursor.fetchone()['username']\r\n\r\n if influencer == follower:\r\n return \"False - You cannot follow yourself\"\r\n\r\n with connection.cursor() as cursor:\r\n\r\n sql_query = \"INSERT INTO follows VALUES (%s, %s)\"\r\n cursor.execute(sql_query, (influencer, follower,))\r\n connection.commit()\r\n\r\n except pymysql.exceptions.Error:\r\n return \"False - Unable to follow user\"\r\n\r\n finally:\r\n connection.close()\r\n\r\n return \"True - Followed user\"\r\n\r\n return \"invalid query\"\r\n\r\n\r\n@APP.route(\"/UnfollowUser\", methods=['POST'])\r\ndef unfollow_user():\r\n if request.form['unfollow'] and request.form['session_id']:\r\n influencer = sanitize(request.form['unfollow'])\r\n session_id = sanitize(request.form['session_id'])\r\n connection = pymysql.connect(host='database',\r\n user='root',\r\n password='supersecurepass',\r\n db='skitter',\r\n charset='utf8mb4',\r\n cursorclass=pymysql.cursors.DictCursor)\r\n try:\r\n with connection.cursor() as cursor:\r\n sql_query = \"SELECT username FROM sessions \\\r\n WHERE session_id = %s\"\r\n cursor.execute(sql_query, (session_id))\r\n follower = cursor.fetchone()['username']\r\n if influencer == follower:\r\n return \"False - You cannot unfollow yourself\"\r\n\r\n with connection.cursor() as cursor:\r\n sql_query = \"DELETE FROM follows \\\r\n WHERE influencer = %s AND follower = %s\"\r\n cursor.execute(sql_query, (influencer, follower,))\r\n connection.commit()\r\n\r\n except pymysql.exceptions.Error:\r\n return \"False - Unable to unfollow user\"\r\n\r\n finally:\r\n connection.close()\r\n\r\n return \"True - Unfollowed user\"\r\n return \"invalid query\"\r\n\r\n\r\nif __name__ == \"__main__\":\r\n APP.run(host='127.0.0.1')\r\n", "repo_name": "QuyNNguyen/CSEC380-Skitter", "sub_path": "python/functions.py", "file_name": "functions.py", "file_ext": "py", "file_size_in_byte": 4331, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "37", "api": [{"api_name": "flask.Flask", "line_number": 5, "usage_type": "call"}, {"api_name": "re.escape", "line_number": 8, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 19, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 19, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 20, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 20, "usage_type": "name"}, {"api_name": "pymysql.connect", "line_number": 22, "usage_type": "call"}, {"api_name": "pymysql.cursors", "line_number": 27, "usage_type": "attribute"}, {"api_name": "flask.request.form", "line_number": 50, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 50, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 52, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 52, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 53, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 53, "usage_type": "name"}, {"api_name": "pymysql.connect", "line_number": 54, "usage_type": "call"}, {"api_name": "pymysql.cursors", "line_number": 59, "usage_type": "attribute"}, {"api_name": "pymysql.exceptions", "line_number": 77, "usage_type": "attribute"}, {"api_name": "flask.request.form", "line_number": 90, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 90, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 91, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 91, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 92, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 92, "usage_type": "name"}, {"api_name": "pymysql.connect", "line_number": 93, "usage_type": "call"}, {"api_name": "pymysql.cursors", "line_number": 98, "usage_type": "attribute"}, {"api_name": "pymysql.exceptions", "line_number": 114, "usage_type": "attribute"}]} +{"seq_id": "30303203205", "text": "from model import Vgg19\r\nimport torch\r\nfrom torch import nn as nn\r\n\r\n\r\nclass VGGLoss(nn.Module):\r\n def __init__(self):\r\n super(VGGLoss, self).__init__()\r\n self.vgg = Vgg19()\r\n self.criterion = nn.L1Loss()\r\n self.weights = [1.0/32, 1.0/16, 1.0/8, 1.0/4, 1.0]\r\n\r\n def forward(self, x, y):# x为虚假图片，y为真实图片\r\n x_vgg, y_vgg = self.vgg(x), self.vgg(y)\r\n loss = 0\r\n for i in range(len(x_vgg)):\r\n loss += self.weights[i] * self.criterion(x_vgg[i], y_vgg[i].detach())\r\n return loss", "repo_name": "hahahappyboy/GAN-Thesis-Retrieval", "sub_path": "Pix2PixHD/util.py", "file_name": "util.py", "file_ext": "py", "file_size_in_byte": 565, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 39, "dataset": "github-code", "pt": "37", "api": [{"api_name": "torch.nn.Module", "line_number": 6, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 6, "usage_type": "name"}, {"api_name": "model.Vgg19", "line_number": 9, "usage_type": "call"}, {"api_name": "torch.nn.L1Loss", "line_number": 10, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 10, "usage_type": "name"}]} +{"seq_id": "42872981407", "text": "import fastapi\nimport pymysql\nfrom fastapi.middleware.cors import CORSMiddleware\nfrom pydantic import BaseModel\n\n\n\ndef conecta():\n cnx = pymysql.connect(\n user=\"butterfly@peixe\", password=\"Manteigavoadora1\", host=\"peixe.mariadb.database.azure.com\", port=3306,\n database=\"secretariasenai\"\n )\n cursor = cnx.cursor()\n\n return cursor, cnx\n\n\napp = fastapi.FastAPI()\n\napp.add_middleware(\n CORSMiddleware,\n allow_origins=['*'],\n allow_credentials=['*'],\n allow_methods=[\"*\"],\n allow_headers=[\"*\"],\n)\n\n\n@app.get(\"/get\")\ndef bd():\n cursor, cnx = conecta()\n cursor.execute(\"SELECT name FROM espera WHERE ja_atendido = 0 ORDER BY preferencial DESC, id ASC\")\n values = cursor.fetchall()\n cnx.close()\n cursor.close()\n return {\"message\": values}\n\n\nclass request(BaseModel):\n guiche: int\n\n@app.post(\"/post\")\ndef post(a: request):\n cursor, cnx = conecta()\n cursor.execute(\"select ordem from espera order by ordem desc\")\n maior = cursor.fetchone()\n cursor.execute(\"SELECT id FROM espera WHERE ja_atendido = 0 ORDER BY preferencial DESC, id ASC\")\n values = cursor.fetchall()\n val0 = values[0][0]\n cursor.execute(f\"UPDATE espera SET guiche = {a.guiche}, ordem = {maior[0]+1}, ja_atendido = 1 WHERE id = {val0}\")\n cursor.execute(f\"SELECT name, motivo FROM espera WHERE id = {val0}\")\n values = cursor.fetchall()\n cnx.commit()\n cnx.close()\n cursor.close()\n return {\"message\": values[0]}\n\n\nif __name__ == '__main__':\n import uvicorn\n uvicorn.run(\"main:app\", host=\"127.0.0.1\", port=8000, log_level=\"info\", reload=True)\n", "repo_name": "OFelipeMeira/guiche-api", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 1601, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "pymysql.connect", "line_number": 9, "usage_type": "call"}, {"api_name": "fastapi.FastAPI", "line_number": 18, "usage_type": "call"}, {"api_name": "fastapi.middleware.cors.CORSMiddleware", "line_number": 21, "usage_type": "argument"}, {"api_name": "pydantic.BaseModel", "line_number": 39, "usage_type": "name"}, {"api_name": "uvicorn.run", "line_number": 61, "usage_type": "call"}]} +{"seq_id": "21488291257", "text": "from dataclasses import dataclass\nfrom os.path import exists\n\nDUNSTLOG_PATH = \"/tmp/dunstlog\"\n\n\n@dataclass(slots=True)\nclass Entry:\n created: str\n level: str\n icon: str\n text: str\n title: str\n app: str\n\n\ndef build_info(e: Entry) -> str:\n info = f\"-i {e.icon!r} -a {e.app!r} -u {e.level!r}\"\n if e.title and e.text:\n info = f\"{info} {e.title!r} {e.text!r}\"\n elif not e.title and e.text:\n info = f\"{info} {e.text!r}\"\n elif not e.text and e.title:\n info = f\"{info} {e.title!r}\"\n else:\n info = f\"{info} {e.app!r}\"\n return info\n\n\nif __name__ == \"__main__\":\n if not exists(DUNSTLOG_PATH):\n print(\"\\000message\\037error: %r not found\" % DUNSTLOG_PATH, end=\"\\012\")\n exit(1)\n lines = []\n with open(DUNSTLOG_PATH) as f:\n lines = [l.strip() for l in f.readlines()]\n\n if len(lines) % 6 != 0:\n print(\n \"\\000message\\037error: unexpected dunstlog format (%d % 6 != 0)\"\n % len(lines),\n end=\"\\012\",\n )\n exit(1)\n\n entries = [\n Entry(\n created=lines[i].strip(),\n level=lines[i + 1].strip(),\n icon=lines[i + 2].strip(),\n text=lines[i + 3].strip(),\n title=lines[i + 4].strip(),\n app=lines[i + 5].strip(),\n )\n for i in range(0, len(lines), 6)\n ]\n urgents = []\n for i, e in enumerate(entries[::-1]):\n print(\n \"%s %s\\r%s\\000icon\\037%s\\037info\\037%s\"\n % (e.app, e.created, e.text or e.title, e.icon, build_info(e)),\n end=\"\\012\",\n )\n if e.level.lower() == \"critical\":\n urgents.append(i)\n if urgents:\n print(\"\\000urgent\\037%s\\012\" % \",\".join(str(i) for i in urgents))\n", "repo_name": "su55y/rofi-launchers", "sub_path": "dunstlog/helper.py", "file_name": "helper.py", "file_ext": "py", "file_size_in_byte": 1778, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "37", "api": [{"api_name": "dataclasses.dataclass", "line_number": 7, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 31, "usage_type": "call"}]} +{"seq_id": "20064374731", "text": "#!/usr/bin/env python\n\"\"\"Process Cloud Custodian Data\n\nUsage:\n run.py \n\nOptions:\n -h --help Show this screen.\n\n\"\"\"\nfrom docopt import docopt\n\nimport time\nimport threading\n\nimport yaml\n\nfrom pyspark.sql import SparkSession\nfrom pyspark.sql.types import ArrayType, StructType, StructField, StringType, IntegerType, BooleanType, DoubleType # noqa;\nimport pyspark.sql.functions as F\nfrom pyspark.sql.utils import AnalysisException\n\n\nclass CloudCustodianSpark(object):\n def __init__(self, args):\n self.args = args\n self.conf = self.load_config(args[''])\n self.data_path = args['']\n\n self.spark = SparkSession.builder \\\n .config(\"spark.sql.caseSensitive\", True) \\\n .appName(\"cc-data\") \\\n .getOrCreate()\n\n self.dfs = {}\n\n @staticmethod\n def load_config(config_path):\n config = None\n with open(config_path, 'r') as stream:\n config = yaml.safe_load(stream)\n return config\n\n def run(self):\n parallel_jobs = True\n if parallel_jobs:\n threads = []\n for resource in self.conf['config']['resources']:\n t = threading.Thread(target=self.create_resource_df, args=(resource, ))\n threads.append(t)\n t = threading.Thread(target=self.create_master_table)\n threads.append(t)\n for t in threads:\n t.start()\n for t in threads:\n t.join()\n else:\n for resource in self.conf['config']['resources']:\n self.create_resource_df(resource)\n self.create_master_table()\n\n self.spark.sql('show tables').show()\n self.spark.sql('select cc_resource, cc_tagkey, cc_tagval from all_dtags' +\n ' where cc_tagval like \"%davwang4%\"'). \\\n show(9999, truncate=False)\n\n # Sleep so we can access the UI\n time.sleep(6000)\n\n def create_resource_df(self, resource_name):\n print(\"Starting {}\".format(resource_name))\n directory_name = resource_name.replace('.', '-') + '-all'\n table_name = resource_name.replace('.', '_').replace('-', '_').replace('aws_', '')\n denormalized_table_name = table_name + \"_dtags\"\n\n # path: '...///-all/resources.json'\n path = \"{}/*/*/{}/resources.json\".format(self.data_path, directory_name)\n\n df = self.spark.read.option(\"multiline\", \"true\").json(path) \\\n .withColumn('cc_resource', F.lit(resource_name)) \\\n .withColumn(\"cc_profile\", F.element_at(F.split(F.input_file_name(), '/'), -4)) \\\n .withColumn(\"cc_region\", F.element_at(F.split(F.input_file_name(), '/'), -3))\n df.createOrReplaceTempView(table_name)\n self.dfs[table_name] = df\n df.printSchema()\n\n if 'Tags' in df.columns:\n # If Tags are all null, then we won't be able to parse cc_tagkey and cc_tagval.\n try:\n tags_df = df.withColumn('_tmpTag', F.explode('Tags')) \\\n .select('*', '_tmpTag') \\\n .withColumn('cc_tagkey', F.col('_tmpTag.Key')) \\\n .withColumn('cc_tagval', F.col('_tmpTag.Value')) \\\n .drop(F.col('_tmpTag'))\n tags_df.createOrReplaceTempView(denormalized_table_name)\n self.dfs[denormalized_table_name] = tags_df\n except (AnalysisException):\n pass\n\n def create_master_table(self):\n\n table_name = 'all'\n denormalized_table_name = table_name + \"_dtags\"\n\n # path: out///-all/resources.json\n path = \"{}/*/*/*/resources.json\".format(self.data_path)\n df = self.spark.read.option(\"multiline\", \"true\").json(path) \\\n .withColumn('cc_resource', F.regexp_replace(\n F.element_at(F.split(F.input_file_name(), '/'), -2), '-all', '')) \\\n .withColumn(\"cc_profile\", F.element_at(F.split(F.input_file_name(), '/'), -4)) \\\n .withColumn(\"cc_region\", F.element_at(F.split(F.input_file_name(), '/'), -3))\n df.createOrReplaceTempView(table_name)\n self.dfs[table_name] = df\n\n # Create Master Table with Denormalized Tags\n tags_df = df.withColumn('_tmpTag', F.explode('Tags')).select('*', '_tmpTag').withColumn(\n 'cc_tagkey', F.col('_tmpTag.Key')).withColumn('cc_tagval', F.col('_tmpTag.Value')).drop(F.col('_tmpTag'))\n tags_df.createOrReplaceTempView(denormalized_table_name)\n self.dfs[denormalized_table_name] = tags_df\n\n\n#####################################################################\n# Process each resource element in the configuration file\n\nif __name__ == '__main__':\n args = docopt(__doc__, version='0.1.0')\n ccs = CloudCustodianSpark(args)\n ccs.run()\n\"\"\" Notes\n # import ipdb; ipdb.set_trace()\n\n # dfs['all'].write.format(\n # \"org.elasticsearch.spark.sql\"\n # ).option(\n # \"es.resource\", '%s/%s' % (config['config']['elasticsearch']['index'], \\\n # config['config']['elasticsearch']['doc_type'])\n # ).option(\n # \"es.nodes\", config['config']['elasticsearch']['host']\n # ).option(\n # \"es.port\", config['config']['elasticsearch']['port']\n # ).save()\n\n\n # df.write.format(\n # \"org.elasticsearch.spark.sql\"\n # ).option(\n # \"es.resource\", '%s' % (config['config']['elasticsearch']['index'])\n # ).option(\n # \"es.nodes\", config['config']['elasticsearch']['host']\n # ).option(\n # \"es.port\", config['config']['elasticsearch']['port']\n # ).save()\n # import ipdb; ipdb.set_trace()\n\n\"\"\"\n", "repo_name": "dcwangmit01/pyspark-cloud-custodian-poc", "sub_path": "cc-data/run.py", "file_name": "run.py", "file_ext": "py", "file_size_in_byte": 5765, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "pyspark.sql.SparkSession.builder.config", "line_number": 30, "usage_type": "call"}, {"api_name": "pyspark.sql.SparkSession.builder", "line_number": 30, "usage_type": "attribute"}, {"api_name": "pyspark.sql.SparkSession", "line_number": 30, "usage_type": "name"}, {"api_name": "yaml.safe_load", "line_number": 41, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 49, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 51, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 68, "usage_type": "call"}, {"api_name": "pyspark.sql.functions.lit", "line_number": 80, "usage_type": "call"}, {"api_name": "pyspark.sql.functions", "line_number": 80, "usage_type": "name"}, {"api_name": "pyspark.sql.functions.element_at", "line_number": 81, "usage_type": "call"}, {"api_name": "pyspark.sql.functions", "line_number": 81, "usage_type": "name"}, {"api_name": "pyspark.sql.functions.split", "line_number": 81, "usage_type": "call"}, {"api_name": "pyspark.sql.functions.input_file_name", "line_number": 81, "usage_type": "call"}, {"api_name": "pyspark.sql.functions.element_at", "line_number": 82, "usage_type": "call"}, {"api_name": "pyspark.sql.functions", "line_number": 82, "usage_type": "name"}, {"api_name": "pyspark.sql.functions.split", "line_number": 82, "usage_type": "call"}, {"api_name": "pyspark.sql.functions.input_file_name", "line_number": 82, "usage_type": "call"}, {"api_name": "pyspark.sql.functions.explode", "line_number": 90, "usage_type": "call"}, {"api_name": "pyspark.sql.functions", "line_number": 90, "usage_type": "name"}, {"api_name": "pyspark.sql.functions.col", "line_number": 92, "usage_type": "call"}, {"api_name": "pyspark.sql.functions", "line_number": 92, "usage_type": "name"}, {"api_name": "pyspark.sql.functions.col", "line_number": 93, "usage_type": "call"}, {"api_name": "pyspark.sql.functions", "line_number": 93, "usage_type": "name"}, {"api_name": "pyspark.sql.functions.col", "line_number": 94, "usage_type": "call"}, {"api_name": "pyspark.sql.functions", "line_number": 94, "usage_type": "name"}, {"api_name": "pyspark.sql.utils.AnalysisException", "line_number": 97, "usage_type": "name"}, {"api_name": "pyspark.sql.functions.regexp_replace", "line_number": 108, "usage_type": "call"}, {"api_name": "pyspark.sql.functions", "line_number": 108, "usage_type": "name"}, {"api_name": "pyspark.sql.functions.element_at", "line_number": 109, "usage_type": "call"}, {"api_name": "pyspark.sql.functions", "line_number": 109, "usage_type": "name"}, {"api_name": "pyspark.sql.functions.split", "line_number": 109, "usage_type": "call"}, {"api_name": "pyspark.sql.functions.input_file_name", "line_number": 109, "usage_type": "call"}, {"api_name": "pyspark.sql.functions.element_at", "line_number": 110, "usage_type": "call"}, {"api_name": "pyspark.sql.functions", "line_number": 110, "usage_type": "name"}, {"api_name": "pyspark.sql.functions.split", "line_number": 110, "usage_type": "call"}, {"api_name": "pyspark.sql.functions.input_file_name", "line_number": 110, "usage_type": "call"}, {"api_name": "pyspark.sql.functions.element_at", "line_number": 111, "usage_type": "call"}, {"api_name": "pyspark.sql.functions", "line_number": 111, "usage_type": "name"}, {"api_name": "pyspark.sql.functions.split", "line_number": 111, "usage_type": "call"}, {"api_name": "pyspark.sql.functions.input_file_name", "line_number": 111, "usage_type": "call"}, {"api_name": "pyspark.sql.functions.explode", "line_number": 116, "usage_type": "call"}, {"api_name": "pyspark.sql.functions", "line_number": 116, "usage_type": "name"}, {"api_name": "pyspark.sql.functions.col", "line_number": 117, "usage_type": "call"}, {"api_name": "pyspark.sql.functions", "line_number": 117, "usage_type": "name"}, {"api_name": "docopt.docopt", "line_number": 126, "usage_type": "call"}]} +{"seq_id": "69943678829", "text": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Apr 5 22:12:38 2023\n\n@author: alexandermikhailov\n\"\"\"\n\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom numpy.fft import rfft\nfrom pandas import DataFrame\nfrom sklearn.metrics import r2_score\n\n\ndef plot_turnover(df: DataFrame) -> None:\n \"\"\"Static Fixed Assets Turnover Approximation\n ================== =================================\n df.index Period\n df.iloc[:, 0] Fixed Assets Turnover\n ================== =================================\n \"\"\"\n # =========================================================================\n # Linear: Fixed Assets Turnover\n # =========================================================================\n polyfit_linear = np.polyfit(\n df.index.to_series().astype(float),\n df.iloc[:, -1].astype(float),\n deg=1\n )\n # =========================================================================\n # Exponential: Fixed Assets Turnover\n # =========================================================================\n _exp = np.polyfit(\n df.index.to_series().astype(float),\n np.log(df.iloc[:, -1].astype(float)),\n deg=1\n )\n df['c_turnover_lin'] = np.poly1d(polyfit_linear)(df.index.to_series())\n df['c_turnover_exp'] = np.exp(np.poly1d(_exp)(df.index.to_series().astype(float)))\n # =========================================================================\n # Deltas\n # =========================================================================\n df['d_lin'] = df.iloc[:, -2].div(df.iloc[:, -3]).sub(1).abs()\n df['d_exp'] = df.iloc[:, -2].div(df.iloc[:, -4]).sub(1).abs()\n plt.figure(1)\n plt.plot(df.iloc[:, 2], df.iloc[:, 0])\n plt.title(\n 'Fixed Assets Volume to Fixed Assets Turnover, {}$-${}'.format(\n *df.index[[0, -1]]\n )\n )\n plt.xlabel('Fixed Assets Turnover')\n plt.ylabel('Fixed Assets Volume')\n plt.grid()\n plt.figure(2)\n plt.scatter(\n df.index,\n df.iloc[:, -5],\n label='Fixed Assets Turnover'\n )\n plt.plot(\n df.iloc[:, [-4]],\n label='$\\\\hat K_{{l}} = {:.2f} {:.2f} t, R^2 = {:.4f}$'.format(\n *polyfit_linear[::-1],\n r2_score(df.iloc[:, -5], df.iloc[:, -4])\n )\n )\n plt.plot(\n df.iloc[:, [-3]],\n label='$\\\\hat K_{{e}} = \\\\exp ({:.2f} {:.2f} t), R^2 = {:.4f}$'.format(\n *_exp[::-1],\n r2_score(df.iloc[:, -5], df.iloc[:, -3])\n )\n )\n plt.title(\n 'Fixed Assets Turnover Approximation, {}$-${}'.format(\n *df.index[[0, -1]]\n )\n )\n plt.xlabel('Period')\n plt.ylabel('Index')\n plt.grid()\n plt.legend()\n plt.figure(3)\n plt.plot(\n df.iloc[:, [-2]],\n ':',\n label='$\\\\|\\\\frac{{\\\\hat K_{{l}}-K}}{{K}}\\\\|, \\\\bar S = {:.4%}$'.format(\n df.iloc[:, -2].mean()\n )\n )\n plt.plot(\n df.iloc[:, [-1]],\n ':',\n label='$\\\\|\\\\frac{{\\\\hat K_{{e}}-K}}{{K}}\\\\|, \\\\bar S = {:.4%}$'.format(\n df.iloc[:, -1].mean()\n )\n )\n plt.title(\n 'Deltas of Fixed Assets Turnover Approximation, {}$-${}'.format(\n *df.index[[0, -1]]\n )\n )\n plt.xlabel('Period')\n plt.ylabel('Index')\n plt.grid()\n plt.legend()\n plt.show()\n\n\ndef plot_discrete_fourier_transform(array: np.ndarray) -> None:\n \"\"\"\n Discrete Fourier Transform\n\n Parameters\n ----------\n array : np.ndarray\n DESCRIPTION.\n\n Returns\n -------\n None\n DESCRIPTION.\n\n \"\"\"\n # =========================================================================\n # TODO: Refine It\n # =========================================================================\n plt.plot(\n array,\n label='Labor Productivity',\n )\n plt.plot(\n rfft(array),\n 'r:',\n label='Fourier Transform',\n )\n plt.grid()\n plt.legend()\n plt.show()\n", "repo_name": "avtomatik/sklearn", "sub_path": "src/data/plot.py", "file_name": "plot.py", "file_ext": "py", "file_size_in_byte": 4001, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "pandas.DataFrame", "line_number": 17, "usage_type": "name"}, {"api_name": "numpy.polyfit", "line_number": 27, "usage_type": "call"}, {"api_name": "numpy.polyfit", "line_number": 35, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 37, "usage_type": "call"}, {"api_name": "numpy.poly1d", "line_number": 40, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 41, "usage_type": "call"}, {"api_name": "numpy.poly1d", "line_number": 41, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 47, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 47, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 48, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 48, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 49, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 49, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 54, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 54, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 55, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 55, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 56, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 56, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 57, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 57, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 58, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 58, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 63, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 63, "usage_type": "name"}, {"api_name": "sklearn.metrics.r2_score", "line_number": 67, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 70, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 70, "usage_type": "name"}, {"api_name": "sklearn.metrics.r2_score", "line_number": 74, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.title", "line_number": 77, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 77, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 82, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 82, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 83, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 83, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 84, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 84, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 85, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 85, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 86, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 86, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 87, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 87, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 94, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 94, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 101, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 101, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 106, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 106, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 107, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 107, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 108, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 108, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 109, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 109, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 110, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 110, "usage_type": "name"}, {"api_name": "numpy.ndarray", "line_number": 113, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 131, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 131, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 135, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 135, "usage_type": "name"}, {"api_name": "numpy.fft.rfft", "line_number": 136, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 140, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 140, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 141, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 141, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 142, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 142, "usage_type": "name"}]} +{"seq_id": "14888473006", "text": "from fastapi import FastAPI, Path\nfrom pydantic import BaseModel\nfrom models import Bookmark,BookmarxResponse,BookmarxListResponse, URLAlreadyExistsError\nfrom typing import List\nfrom utils import add_bookmark,get_all_bookmarx,get_bookmarx_by_id\nfrom exceptions import URLAlreadyExistsError#, WriteArticleToDBError\n\n\napp = FastAPI()\n\n@app.get(\"/bookmarx\",response_model=BookmarxListResponse)\n#@app.get(\"/bookmarx\")\nasync def get_bookmarks():\n bookmark_list = get_all_bookmarx()\n for bookmarx in bookmark_list:\n print(f\"Bookmark: {bookmarx}\")\n print(f\" ID Type: {type(bookmarx['id'])}\")\n print(f\" URL Type: {type(bookmarx['url'])}\")\n print(f\" Summary Type: {type(bookmarx['summary'])}\")\n return BookmarxListResponse(bookmarx = bookmark_list)\n\n@app.get(\"/bookmarx/{id}\", response_model=BookmarxResponse)\nasync def get_bookmarx(id: int = Path(..., title=\"Bookmark ID\")):\n bookmark = get_bookmarx_by_id(id)\n if bookmark is None:\n raise HTTPException(status_code=404, detail=\"Bookmark not found\")\n return bookmark\n\n\n@app.post(\"/bookmarks/add\")\nasync def add_bookmark_route(payload: Bookmark):\n url = str(payload.url)\n # print(f\"Received URL: {url}\")\n \n try:\n await add_bookmark(url)\n except URLAlreadyExistsError as e:\n return {\"message\": str(e)}\n # except WriteArticleToDBError as e:\n # return {\"message\": str(e)}\n except Exception:\n return {\"message\": \"An unknown error occurred\"}\n \n return {\"message\": \"Bookmark added successfully\"}\n\n\n\nif __name__ == \"__main__\":\n import uvicorn\n uvicorn.run(app, host=\"0.0.0.0\", port=8000)\n", "repo_name": "ChadDa3mon/BookmarX.AI", "sub_path": "app/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 1635, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 43, "dataset": "github-code", "pt": "37", "api": [{"api_name": "fastapi.FastAPI", "line_number": 9, "usage_type": "call"}, {"api_name": "utils.get_all_bookmarx", "line_number": 14, "usage_type": "call"}, {"api_name": "models.BookmarxListResponse", "line_number": 20, "usage_type": "call"}, {"api_name": "models.BookmarxListResponse", "line_number": 11, "usage_type": "name"}, {"api_name": "fastapi.Path", "line_number": 23, "usage_type": "call"}, {"api_name": "utils.get_bookmarx_by_id", "line_number": 24, "usage_type": "call"}, {"api_name": "models.BookmarxResponse", "line_number": 22, "usage_type": "name"}, {"api_name": "models.Bookmark", "line_number": 31, "usage_type": "name"}, {"api_name": "utils.add_bookmark", "line_number": 36, "usage_type": "call"}, {"api_name": "exceptions.URLAlreadyExistsError", "line_number": 37, "usage_type": "name"}, {"api_name": "uvicorn.run", "line_number": 50, "usage_type": "call"}]} +{"seq_id": "20122112892", "text": "import sys\nfrom PyQt5 import QtWidgets,QtGui,QtCore\nimport random\nfrom PyQt5.QtGui import QImage\nimport time\nfrom PyQt5.QtWidgets import QApplication, QWidget, QLabel\nfrom PyQt5.QtGui import QIcon, QPixmap\nclass Pencere(QtWidgets.QWidget):\n\n def __init__(self):\n\n super().__init__()\n self.gui()\n\n def gui(self):\n self.sorular = [[\"İnsanların grup içi davranışlarının bilimsel çakışmalarını yapan, toplumsal güçleri inceleyen bilim dalı aşağıdakilerden hangisidir?\",\n \"A) Sosyoloji\", \"B) Psikoloji\", \"C) İnsan bilimi)\", \"D) Antropoloji\", \"A\"],\n\n [\"Aşağıdakilerden hangisi sosyal bilim değildir?\",\n \"A) Tarih\", \"B) Biyoloji\", \"C) Psikoloji\", \"D) Sosyoloji\", \"B\"],\n [\"Sosyolojizm ekolü il ilgili aşağıdaki ifadelerden hangisi yanlıştır?\",\n \"A) Bu ekolün Türkiye’deki temsilcisi Ziya Gökalp’tir.\",\n \"B) Toplumu mutlaklaştıran bir görüştür.\",\n \"C) Toplumu birey karşısında etkisiz bir konuma taşır.\",\n \"D) Pozitivist ve determinist bir anlayışa.\", \"C\"],\n\n [\"Sosyolojinin teknoloji, gelir dağılımı ve tüketim ile iş bölümünü gibi konularla ilgilenen bölüme ne ad verilir?\",\n \"A) Hukuk Sosyolojisi\", \"B) Bilgi Sosyolojisi\", \"C) Sanayi Sosyolojisi\",\n \"D) Ekonomi Sosyolojisi\", \"D\"],\n\n [\"Aşağıdakilerden hangisi bir bilimsel araştırma ilkesi değildir?\",\n \"A) Hipotez oluşturma\", \"B) Basitlik ve açıklık\", \"C) Sınırlılık\", \"D) Nesnellik\", \"A\"],\n\n [\"Aşağıdakilerden hangisi bilimsel yöntemin aşamalarından biri değildir?\",\n \"A) Sorunu ortaya koymak\", \"B) Bilgi toplamak\", \"C) Tekrar\", \"D) Hipotez oluşturma\",\n \"C\"],\n\n [\"Sosyoloji aşağıdakilerden hangisine yanıt aramaz?\",\n \"A) İnsanlar neden bir aile kurmuşlardır.\", \"B) İnsanlar neden tanrıya inanırlarf.\",\n \"C) Niçin bazı insanlar fakirdir.\", \"D) Toplumu bir arada tutan kurallar nelerdir.\", \"D\"],\n\n [\"Aşağıdakilerden hangisi sosyolojinin ortaya çıkışını hazırlayan etkenlerden değildir?\",\n \"A) Endüstri Devrimi\", \"B) Fransız ve Amerikan İhtilalleri\", \"C) Emperyalist gelişmeler\",\n \"D) 1. Ve 2. Dünya Savaşları\", \"D\"],\n\n [\"1950’lerde sosyolojik görüşleri ve özellikle yöntem anlayışı ile öne çıkan sosyolog aşağıdakilerden hangisidir?\",\n \"A) Ziya Gökalp\", \"B) İbrahim Yasa\", \"C) Cahit Tanyol\", \"D) Prens Sabahattin\", \"D\"],\n\n [\"Mehmet İzzet’e göre; sosyolojinin ilk amacı aşağıdakilerden hangisidir?\",\n \"A) Grup ilişkilerini düzenlemek\", \"B) Cemiyetleri tasnif etmek\",\n \"C) Toplumsal değişimin aşamalarını göstermek\", \"D) Toplum-devlet ilişkilerini açıklamak\",\n \"B\"],\n\n [\"Batıcılık İslamcılık ve ulusçuluk görüşlerini sentezleyen ilk sosyolog aşağıdakilerden hangisidir?\",\n \"A. Niyazi Berkes\", \"B. Hilmi Ziya Ülken\", \"C. Prens Sabahattin\", \"D. Ziya Gökalp\",\n \"D\"],\n\n [\"Aşağıdakilerden hangisi Hilmi Ziya Ülken’in eseri değildir?\",\n \"A. Ask Ahlakı\", \"B. Aşkı Memnu\", \"C. İnsani Vatanperverlik\", \"D. Şeytanla Konuşmalar\",\n \"B\"],\n\n [\"Hilmi Ziya Ülken e göre Türkiye’de sosyolojinin ortaya çıkmasındaki dönüm noktası aşağıdakilerden hangisidir?\",\n \"A. Tanzimat Fermanı\", \"B. Fransız Devrimi\", \"C. 1. Dünya Savaşı\",\n \"D. 2. Dünya Savaşı\", \"A\"],\n\n [\"Ziya Gökalp’in Cumhuriyet’in kuruluş sürecinde yeni kurulacak devletin ilkelerini anlattığı ve bu nedenle Batılılaşmanın da yolunu açtığı eserinin adı nedir?\",\n \"A. Medeni Bilgiler\", \"B. Türkçülüğün Esasları\",\n \"C. Türkleşmek, İslamlaşmak, Muasırlaşmak\",\n \"D. Türkiye Nasıl Kurtarılabilir?\", \"B\"],\n\n [\"Hilmi Ziya Ülken, Anadolucu dünya görüşünü geliştirirken Ziya Gökalp’in Türkçülük fikrini hangi yönüyle eleştirmiştir?\",\n \"A) Savaşları engelleyememesi\", \"B) Devletin yıkılmasına yol açması\",\n \"C) Toplumun geri kalmasına sebep olması\", \"D) Dünya şartlarında gerçekçi olmaması\", \"D\"],\n\n [\"Aşağıdakilerden hangisi İkinci Meşrutiyet döneminin düşünce ortamında Türkiye’de sosyolojinin kuruluşuna büyük katkı sağlamış isimlerden biridir?\",\n \"A) Beşir Fuad\", \"B) Said Halim Paşa\", \"C) Prens Sabahattin\", \"D) Ali Suavi\", \"C\"],\n\n [\"1950’lere kadar devlet kurumlarında, liselerde ve kısmen üniversitelerde etkin olan sosyoloji anlayışı; A. Comte, E. Durkheim tarafından geliştirilen sosyolojizm ekolü aşağıdaki isimlerden hangisi tarafından benimsenip temsil edilmiştir?\",\n \"A. Ziya Gökalp\", \"B. Prens Sabahattin\", \"C. Mümtaz Turhan\", \"D. Hilmi Ziya Ülken\", \"A\"],\n\n [\"Toplumu gerek maddi gerek manevi olarak bizi kuşatan işler, filler, hareketler, inançlar ve değerler sistemi olarak tanımlayan sosyolog aşağıdakilerden hangisidir?\",\n \"A) Hilmi Ziya Ülken\", \"B) Mehmet İzzet\", \"C) Ziyaeddin Fahri Fındıkoğlu\", \"D) Ziya Gökalp\",\n \"A\"],\n\n [\"Mehmet İzzet’in çalışmalarında kendisine dayanarak aldığı sosyolog aşağıdakilerden hangisidir?\",\n \"A) Spencer\", \"B) Engels\", \"C) Saint Simon\", \"D) Durkheim\", \"D\"],\n\n [\"Aşağıdakilerden hangisi Ziya Gökalp in en bilinen kitaplarından biridir?\",\n \"A. Bu Ülke\", \"B. Türkiye Nasıl Kurtarılabilir?\", \"C. Türkçülüğün Esasları\",\n \"D. Üç Tarzı Siyaset\", \"C\"],\n\n [\"Hilmi Ziya Ülken’in geliştirdiği düşünce akımı aşağıdakilerden hangisidir?\",\n \"A. Türkçülük\", \"B. Ümmetçilik\", \"C. Batıcılık\", \"D. Anadoluculuk \", \"D\"],\n\n [\"Prens Sabahattin’in Türkiye’de görüşlerini temsil ettiği sosyolog aşağıdakilerden hangisidir?\",\n \"A) A. Comte\", \"B) E. Durkheim\", \"C) H. Spencer\", \"D) Le Play\", \"D\"],\n\n [\"Hilmi Ziya Ülken’in öğrencilik yıllarında Reşat Kayl ile birlikte el yazması olarak çıkarttığı dergi aşağıdakilerden hangisidir?\",\n \"A) Türk Yurdu\", \"B) Anadolu\", \"C) Genç Kalemler\", \"D) Hareket\", \"B\"],\n\n [\"Hilmi Ziya Ülken’in sosyolojide birbirini tamamlayıcı olarak kullanılabileceğini düşündüğü yöntemler aşağıdakilerden hangisinde birlikte ve doğru verilmiştir?\",\n \"A) Sosyal monografi-tarama-katılımlı gözlem\", \"B) Sözlü tarih-içerik analiz-deney\",\n \"C) Saha araştırması-tarama-gözlem\", \"D) İstatistik yöntem-sosyal monografi-tarihi yöntem\", \"D\"],\n\n [\"Aşağıdakilerden hangisi Mehmet İzzet’in ilgilendiği sorunlardan biri değildir?\",\n \"A) Cemiyet ve fert\", \"B) Milliyet\", \"C) Muasır cemiyet \", \"D) Çatışma\", \"D\"],\n\n [\"Aşağıdakilerden hangisi Prens Sabahattin’in eğitim anlayışı ile çelişmektedir?\",\n \"A) Eğitim alanında reformlar yapmak\", \"B) Kızların daha iyi eğitim almalarını sağlamak \",\n \"C) Uygulamalı eğitime önem vermek\", \"D) Eğitimde ezberciliğe bir yöntem olarak yer vermek\", \"D\"],\n\n [\"Sosyolojide Anadoluculuk akımının öncüsü olan sosyolog aşağıdakilerden hangisidir?\",\n \"A. Ziya Gökalp\", \"B. Prens Sabahattin\", \"C. Hilmi Ziya Ülken\", \"D. İbrahim Yasa\", \"C\"],\n\n [\"Ziya Gökalp in çocukluk ve gençlik yıllarında görüşlerinin şekillenmesinde etkili olan isimlerden biri değildir ?\",\n \"A)Tevfik Bey\", \"B)Hasip Bey\", \"C) Abdullah Bey\", \"D)Naim Bey\", \"C\"],\n\n [\"Tabiat bilimleri ile insan bilimlerini birbirinden tamamen ayırmak yerine aralarında uzlaşma sağlanmasını gereğini öne süren sosyolog aşağıdakilerden hangisidir?\",\n \"A) Hilmi Ziya Ülken\", \"B) Ziya Gökalp\", \"C) Prens Sabahattin\", \"D) Mübecel Kıray\", \"A\"],\n\n [\"Aşağıdakilerden hangisi Mehmet İzzet’in toplumların evrimi sorusunda, genetik metodundan yararlandığı Amerikan idealistlerinden biridir?\",\n \"A) Beck\", \"B) Baldwin\", \"C) Marx\", \"D) Weber\", \"B\"],\n\n [\"Aşağıdakilerden hangisi Prens Sabahattin’in sosyolojide izleyenlerden biri değidir?\",\n \"A) Nezahat Nurettin Tanyol\", \"B) İbrahim Yasa \", \"C) Cahit Tanyol\", \"D) Cahit Orhan Tutangil\",\n \"A\"],\n\n [\"Hilmi Ziya Ülken'in doğum ve ölüm tarihi aşağıdakilerden hangisidir?\",\n \"A) 1897-1978\", \"B) 1900-1985\", \"C) 1901-1974\", \" D) 1903-1986\",\"C\"],\n\n [\" Ziya Gökalp sosyolojisinde ırkların, cinslerin, kastların, sınıfların, milletlerin eşitliğini hangi ilke ile açıklar?\",\n \"A) Sosyalizm\", \"B) Kapitalizm\", \" C) Halkçılık\", \" D) Devletçilik \", \"C\"],\n\n [\"Ziya Gökalp’in çocukluk ve gençlik yıllarında görüşlerinin şekillenmesinde etkili olan isimlerden biri değildir?\",\n \"A. Tevfik Bey\", \"B. Hasip Bey\", \"C. Abdullah Bey\", \"D. Naim Bey\", \"C\"],\n\n [\"Mehmet İzzet’e göre aşağıdaki düşünürlerden hangisi siyasete gereğinden fazla zaman ayırdığı için daha fazla toplumsal eserler bırakamamıştır?\",\n \"A) Prens Sabahattin\", \" B) Mehmet Emin\", \" C) Salih Zeki\", \"D) Ziya Gökalp\", \"D\"],\n\n [\"Hilmi Ziya Ülken’e göre sosyolojide sosyal ilişki kavramının dayandığı temel kavram aşağıdakilerden hangisidir?\",\n \"A) Din-ahlak\", \"B) İş-organizasyon\", \"C) İş-eğitim\", \"D) Hukuk-ahlak\", \"B\"],\n\n [\"Prens Sabahattin’in en önemli eseri aşağıdakilerden hangisidir?\",\n \"A) Türkçülüğün Esasları\", \"B) Türkleşmek\", \"C) Türkiye Nasıl Kurtulabilir?\", \"D) İslamlaşmak\",\n \"C\"],\n\n [\"1914 yılında sosyolojinin İstanbul Üniversitesi'nde bir kürsüye kavuşup ve kurumsallaşmasında etkili olan isim aşağıdakilerden hangisidir?\",\n \"A. Ziya Gökalp\", \"B. Prens Sabahattin\", \"C. Nurettin Topçu\", \"D. Hilmi ziya ülken\", \"A\"],\n\n [\"Hilmi Ziya Ülken’in bilimsel kaygı açısından izlediği sosyolog aşağıdakilerden hangisidir?\",\n \"A) Ziya Gökalp\", \"B) Saint Simon\", \"C) Karl Marx\", \"D) Max Weber\", \"A\"],\n\n [\"Mehmet İzzet’in milliyet kuramı ile ilgili aşağıdaki ifadelerden hangisi yanlıştır?\",\n \"A) Milliyet duygusu ırk, toprak ve insan ile açıklanabilir.\",\n \"B) Milliyet, toplumsal hürriyeti sağlayacak bir idealdir.\", \"C) Milliyet bir ülküdür.\",\n \"D) Milliyet veri olmaktan çok yapıdır.\", \"A\"],\n\n [\"Prens Sabahattin’in toplumsal sorunların çözümünü gördüğü yöntem aşağıdakilerden hangisidir?\",\n \"A) Saha araştırması\", \"B) Gözlem\", \"C) Vaka analizi\", \"D) Mülakat\", \"B\"],\n\n\n\n [\"Ziya Gökalp Türkiye’de hangi sosyoloji ekolünün temsilciliğini yapmıştır?\",\n \"A. Science Sociale\", \"B. Sosyolojizm Ekolü\", \"C. Biyoloji\", \"D. Marksist\", \"B\"],\n\n [\"Aşağıdakilerden hangisi Mehmet İzzet’ in ilgilendiği sorunlardan biri değildir?\",\n \"A. Cemiyet ve fert\", \"B. Milliyet \", \"C. Sınıf\", \"D. Muasır cemiyet\", \"C\"],\n\n\n [\"Prens Sabahattin’in sosyoloji çizgisi uzun süren bir kesintiden sonra Türkiye’de yeniden canlanmasının nedeni değildir?\"\n \"A. Türkiye’de çok partili döneme geçmesi\", \"B. Türkiye’ye yapılan Marshall yardımı\",\n \"C. Türkiye’nin Amerikan yanlısı siyasete bağlanması\",\n \"D. Marksist akımın ülkenin düşünce gündeminde önemli bir yer işgal etmeye başlaması\", \"D\"],\n\n [\"50. Aşağıdakilerden hangisi Hilmi Ziya Ülken’in bilim sınıflaması içerisinde yer almaz?\",\n \"A. Hayat İlmi\", \"B. İnsan İlmi\", \"C. Kozmik Tabiat İlmi\", \"D. Bitki İlmi\", \"D\"]\n\n ]\n\n #Birinci oyuncu-----------------------------------------------------------------------------------------------------\n\n self.rast = random.choice(self.sorular)\n self.harf_label = QtWidgets.QLabel(str(self.rast[0]))\n self.hesapla = QtWidgets.QRadioButton(str(self.rast[1]))\n self.hesapla1 = QtWidgets.QRadioButton(str(self.rast[2]))\n self.hesapla2 = QtWidgets.QRadioButton(str(self.rast[3]))\n self.hesapla3 = QtWidgets.QRadioButton(str(self.rast[4]))\n self.sonuc = QtWidgets.QPushButton(\"Yanıt\")\n self.sonraki = QtWidgets.QPushButton(\"Sonraki\")\n self.dogruS = QtWidgets.QLabel()\n self.yanlisS = QtWidgets.QLabel()\n self.sayac = QtWidgets.QLabel(\"1.\")\n self.cevap = QtWidgets.QPushButton(\"Yarışma Sonucu\")\n self.sonuc1_edit = QtWidgets.QLabel()\n self.sonuc2_edit = QtWidgets.QLineEdit()\n h_box1 = QtWidgets.QHBoxLayout()\n h_box1.addWidget(self.sayac)\n h_box1.addWidget(self.harf_label)\n h_box1.addStretch()\n\n h_box2 = QtWidgets.QHBoxLayout()\n h_box2.addWidget(self.hesapla)\n h_box2.addWidget(self.hesapla1)\n\n h_box3 = QtWidgets.QHBoxLayout()\n h_box3.addWidget(self.hesapla2)\n h_box3.addWidget(self.hesapla3)\n\n h_box4 = QtWidgets.QHBoxLayout()\n h_box4.addWidget(self.sonraki)\n h_box4.addWidget(self.sonuc)\n\n v_box2 = QtWidgets.QVBoxLayout()\n v_box2.addLayout(h_box1)\n v_box2.addStretch()\n v_box2.addLayout(h_box2)\n v_box2.addLayout(h_box3)\n v_box2.addLayout(h_box4)\n v_box2.addWidget(self.sonuc1_edit)\n\n v_box2.addWidget(self.cevap)\n v_box2.addWidget(self.dogruS)\n v_box2.addWidget(self.yanlisS)\n\n v_box2.addStretch()\n\n self.harf_label.setFont(QtGui.QFont(str(self.rast[0]),13,QtGui.QFont.Bold))\n self.hesapla.setFont(QtGui.QFont(str(self.rast[0]), 12, QtGui.QFont.Bold))\n self.hesapla1.setFont(QtGui.QFont(str(self.rast[0]), 12, QtGui.QFont.Bold))\n self.hesapla2.setFont(QtGui.QFont(str(self.rast[0]), 12, QtGui.QFont.Bold))\n self.hesapla3.setFont(QtGui.QFont(str(self.rast[0]), 12, QtGui.QFont.Bold))\n self.sonuc.setFont(QtGui.QFont(str(self.sonuc), 15, QtGui.QFont.Bold))\n self.sonraki.setFont(QtGui.QFont(str(self.sonraki), 15, QtGui.QFont.Bold))\n self.cevap.setFont(QtGui.QFont(str(self.cevap), 15, QtGui.QFont.Bold))\n self.sayac.setFont(QtGui.QFont(str(self.sayac), 13, QtGui.QFont.Bold))\n self.dogruS.setFont(QtGui.QFont(str(self.rast[0]), 13, QtGui.QFont.Bold))\n self.yanlisS.setFont(QtGui.QFont(str(self.rast[0]), 13, QtGui.QFont.Bold))\n\n\n\n self.setLayout(v_box2)\n self.showMaximized()\n\n\n self.sonraki.setStyleSheet(\"background-color: lightgreen\")\n self.sonuc.setStyleSheet(\"background-color: lightgreen\")\n self.setWindowTitle(\"Kim Milyoner Olmak İster\")\n label = QLabel(self)\n pixmap = QPixmap('indir.jpg')\n label.setPixmap(pixmap)\n self.resize(pixmap.width(), pixmap.height())\n\n\n\n\n\n\n self.sonuc.clicked.connect(self.sonuclar)\n self.sonraki.clicked.connect(self.hesaplar)\n self.cevap.clicked.connect(self.cevaplar)\n self.show()\n self.sayaci =1\n self.dogru = 0\n self.yanlis = 0\n self.show()\n\n def hesaplar(self):\n\n self.rast = random.choice(self.sorular)\n self.harf_label.setText(self.rast[0])\n self.hesapla.setText(self.rast[1])\n self.hesapla1.setText(self.rast[2])\n self.hesapla2.setText(self.rast[3])\n self.hesapla3.setText(self.rast[4])\n self.sayaci +=1\n self.sayac.setText(str(self.sayaci)+str(\".\"))\n if self.sayaci ==21:\n time.sleep(5)\n exit()\n\n\n\n def sonuclar(self):\n if self.hesapla.isChecked():\n if str(self.hesapla.text())[0] == self.rast[5]:\n self.sonuc1_edit.setText(\"\"\"

Doğru

\"\"\")\n self.dogru +=1\n else:\n self.sonuc1_edit.setText(\"\"\"

Yanlış

\"\"\")\n self.yanlis +=1\n\n elif self.hesapla1.isChecked():\n if str(self.hesapla1.text())[0] == self.rast[5]:\n self.sonuc1_edit.setText(\"\"\"

Doğru

\"\"\")\n self.dogru += 1\n else:\n self.sonuc1_edit.setText(\"\"\"

Yanlış

\"\"\")\n self.yanlis += 1\n\n elif self.hesapla2.isChecked():\n if str(self.hesapla2.text())[0] == self.rast[5]:\n self.sonuc1_edit.setText(\"\"\"

Doğru

\"\"\")\n self.dogru += 1\n else:\n self.sonuc1_edit.setText(\"\"\"

Yanlış

\"\"\")\n self.yanlis += 1\n\n elif self.hesapla2.isChecked():\n if str(self.hesapla2.text())[0] == self.rast[5]:\n self.sonuc1_edit.setText(\"\"\"

Doğru

\"\"\")\n self.dogru += 1\n else:\n self.sonuc1_edit.setText(\"\"\"

Yanlış

\"\"\")\n self.yanlis += 1\n\n elif self.hesapla3.isChecked():\n if str(self.hesapla3.text())[0] == self.rast[5]:\n self.sonuc1_edit.setText(\"\"\"

Doğru

\"\"\")\n self.dogru += 1\n else:\n self.sonuc1_edit.setText(\"\"\"

Yanlış

\"\"\")\n self.yanlis += 1\n\n\n\n\n def cevaplar(self):\n self.dogruS.setText(str(\"Doğru Sayısı :\")+str(self.dogru))\n self.yanlisS.setText(str(\"Yanlış Sayısı :\") + str(self.yanlis))\n if self.sayaci ==21:\n time.sleep(5)\n exit()\n\n\n\n\nif __name__==\"__main__\":\n\n uygulama = QtWidgets.QApplication(sys.argv)\n pencere =Pencere()\n\n sys.exit(uygulama.exec_())\n", "repo_name": "Cahitisleyen/Python-PyQt5-Courses", "sub_path": "bilgi_yarismasi.py", "file_name": "bilgi_yarismasi.py", "file_ext": "py", "file_size_in_byte": 18297, "program_lang": "python", "lang": "tr", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "37", "api": [{"api_name": "PyQt5.QtWidgets.QWidget", "line_number": 8, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets", "line_number": 8, "usage_type": "name"}, {"api_name": "random.choice", "line_number": 182, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 183, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 183, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QRadioButton", "line_number": 184, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 184, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QRadioButton", "line_number": 185, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 185, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QRadioButton", "line_number": 186, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 186, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QRadioButton", "line_number": 187, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 187, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QPushButton", "line_number": 188, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 188, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QPushButton", "line_number": 189, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 189, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 190, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 190, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 191, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 191, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 192, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 192, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QPushButton", "line_number": 193, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 193, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 194, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 194, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QLineEdit", "line_number": 195, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 195, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QHBoxLayout", "line_number": 196, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 196, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QHBoxLayout", "line_number": 201, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 201, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QHBoxLayout", "line_number": 205, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 205, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QHBoxLayout", "line_number": 209, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 209, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QVBoxLayout", "line_number": 213, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 213, "usage_type": "name"}, {"api_name": "PyQt5.QtGui.QFont", "line_number": 227, "usage_type": "call"}, {"api_name": "PyQt5.QtGui", "line_number": 227, "usage_type": "name"}, {"api_name": "PyQt5.QtGui.QFont", "line_number": 228, "usage_type": "call"}, {"api_name": "PyQt5.QtGui", "line_number": 228, "usage_type": "name"}, {"api_name": "PyQt5.QtGui.QFont", "line_number": 229, "usage_type": "call"}, {"api_name": "PyQt5.QtGui", "line_number": 229, "usage_type": "name"}, {"api_name": "PyQt5.QtGui.QFont", "line_number": 230, "usage_type": "call"}, {"api_name": "PyQt5.QtGui", "line_number": 230, "usage_type": "name"}, {"api_name": "PyQt5.QtGui.QFont", "line_number": 231, "usage_type": "call"}, {"api_name": "PyQt5.QtGui", "line_number": 231, "usage_type": "name"}, {"api_name": "PyQt5.QtGui.QFont", "line_number": 232, "usage_type": "call"}, {"api_name": "PyQt5.QtGui", "line_number": 232, "usage_type": "name"}, {"api_name": "PyQt5.QtGui.QFont", "line_number": 233, "usage_type": "call"}, {"api_name": "PyQt5.QtGui", "line_number": 233, "usage_type": "name"}, {"api_name": "PyQt5.QtGui.QFont", "line_number": 234, "usage_type": "call"}, {"api_name": "PyQt5.QtGui", "line_number": 234, "usage_type": "name"}, {"api_name": "PyQt5.QtGui.QFont", "line_number": 235, "usage_type": "call"}, {"api_name": "PyQt5.QtGui", "line_number": 235, "usage_type": "name"}, {"api_name": "PyQt5.QtGui.QFont", "line_number": 236, "usage_type": "call"}, {"api_name": "PyQt5.QtGui", "line_number": 236, "usage_type": "name"}, {"api_name": "PyQt5.QtGui.QFont", "line_number": 237, "usage_type": "call"}, {"api_name": "PyQt5.QtGui", "line_number": 237, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 248, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QPixmap", "line_number": 249, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 269, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 278, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 331, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QApplication", "line_number": 339, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 339, "usage_type": "name"}, {"api_name": "sys.argv", "line_number": 339, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 342, "usage_type": "call"}]} +{"seq_id": "22289123088", "text": "#!/usr/bin/python3\n\nfrom bcc import BPF\nfrom pyroute2 import IPRoute\nimport sys, logging, signal, socket\nfrom daemonize import Daemonize\nimport ctypes as ct\nfrom time import sleep\n\nPID = \"/tmp/seg6_classifier_{}.pid\"\nPERF_EVENT_FREQ = 0\ndst, iface = None, None\n\nclass Stats:\n nb_drops = 0\n\ndef print_skb_event(cpu, data, size):\n class SkbEvent(ct.Structure):\n _fields_ = [ (\"id\", ct.c_uint32),\n (\"raw\", ct.c_ubyte * (size - ct.sizeof(ct.c_uint32))) ]\n\n skb_event = ct.cast(data, ct.POINTER(SkbEvent)).contents\n if skb_event.raw[0] >> 4 == 0x6: # IPv6\n src_ip = socket.inet_ntop(socket.AF_INET6, bytes(skb_event.raw[8:24]))\n dst_ip = socket.inet_ntop(socket.AF_INET6, bytes(skb_event.raw[24:40]))\n nh = skb_event.raw[6]\n\n if nh == 6:\n proto = \"TCP\"\n elif nh == 17:\n proto = \"UDP\"\n else:\n proto = \"unknown proto\"\n\n args = \"\"\n if proto in (\"TCP\", \"UDP\"):\n p = skb_event.raw[40:44]\n sport = socket.ntohs(p[1] << 8 | p[0])\n dport = socket.ntohs(p[3] << 8 | p[2])\n args = \"({}, {})\".format(sport, dport)\n\n logger.info(\"Dropped IPv6 pkt #{}: {} -> {} / {} {}\".format(skb_event.id, src_ip, dst_ip, proto,args))\n else:\n logger.info(\"Dropped non-IPv6 pkt #{}\".format(skb_event.id))\n\ndef install_rt(bpf_file):\n b = BPF(src_file=bpf_file)\n fn = b.load_func(\"classifier\", BPF.LWT_IN)\n\n fds = []\n fds.append(b[\"nb_pkts\"].map_fd)\n fds.append(b[\"dropped_pkts\"].map_fd)\n\n ipr = IPRoute()\n idx = ipr.link_lookup(ifname=iface)[0]\n \n encap = {'type':'bpf', 'in':{'fd':fn.fd, 'name':fn.name}}\n ipr.route(\"add\", dst=dst, oif=idx, encap=encap)\n \n return b, fds\n\ndef remove_rt(sig, fr):\n ipr = IPRoute()\n idx = ipr.link_lookup(ifname=iface)[0]\n ipr.route(\"del\", dst=dst, oif=idx)\n sys.exit(0)\n\ndef run_daemon(bpf):\n signal.signal(signal.SIGTERM, remove_rt)\n signal.signal(signal.SIGINT, remove_rt)\n bpf[\"dropped_pkts\"].open_perf_buffer(print_skb_event, page_cnt=1024)\n while 1:\n bpf.kprobe_poll()\n sleep(0.01) # tune polling frequency here\n\nif len(sys.argv) < 4:\n print(\"Format: ./classifier.py BPF PREFIX DEV\")\n sys.exit(1)\n\ndst, iface = sys.argv[2:4]\nbpf, fds = install_rt(sys.argv[1])\nrt_name = dst.replace('/','-')\n\nlogger = logging.getLogger(__name__)\nlogger.setLevel(logging.DEBUG)\nlogger.propagate = False\nfh = logging.FileHandler(\"/tmp/seg6_classifier_{}.log\".format(rt_name), \"a\")\nfh.setLevel(logging.DEBUG)\nlogger.addHandler(fh)\nfds.append(fh.stream.fileno())\nformatter = logging.Formatter(\"%(asctime)s : %(message)s\",\n \"%b %e %H:%M:%S\")\nfh.setFormatter(formatter)\n\ndaemon = Daemonize(app=\"seg6_classifier\", pid=PID.format(rt_name), action=lambda: run_daemon(bpf),\n keep_fds=fds, logger=logger)\nprint(\"SRv6 classifier logger forked to background.\")\ndaemon.start()\n", "repo_name": "Zashas/bpf_stuff", "sub_path": "SFC_Classifier/classifier.py", "file_name": "classifier.py", "file_ext": "py", "file_size_in_byte": 2973, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "37", "api": [{"api_name": "ctypes.Structure", "line_number": 18, "usage_type": "attribute"}, {"api_name": "ctypes.c_uint32", "line_number": 19, "usage_type": "attribute"}, {"api_name": "ctypes.c_ubyte", "line_number": 20, "usage_type": "attribute"}, {"api_name": "ctypes.sizeof", "line_number": 20, "usage_type": "call"}, {"api_name": "ctypes.c_uint32", "line_number": 20, "usage_type": "attribute"}, {"api_name": "ctypes.cast", "line_number": 22, "usage_type": "call"}, {"api_name": "ctypes.POINTER", "line_number": 22, "usage_type": "call"}, {"api_name": "socket.inet_ntop", "line_number": 24, "usage_type": "call"}, {"api_name": "socket.AF_INET6", "line_number": 24, "usage_type": "attribute"}, {"api_name": "socket.inet_ntop", "line_number": 25, "usage_type": "call"}, {"api_name": "socket.AF_INET6", "line_number": 25, "usage_type": "attribute"}, {"api_name": "socket.ntohs", "line_number": 38, "usage_type": "call"}, {"api_name": "socket.ntohs", "line_number": 39, "usage_type": "call"}, {"api_name": "bcc.BPF", "line_number": 47, "usage_type": "call"}, {"api_name": "bcc.BPF.LWT_IN", "line_number": 48, "usage_type": "attribute"}, {"api_name": "bcc.BPF", "line_number": 48, "usage_type": "name"}, {"api_name": "pyroute2.IPRoute", "line_number": 54, "usage_type": "call"}, {"api_name": "pyroute2.IPRoute", "line_number": 63, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 66, "usage_type": "call"}, {"api_name": "signal.signal", "line_number": 69, "usage_type": "call"}, {"api_name": "signal.SIGTERM", "line_number": 69, "usage_type": "attribute"}, {"api_name": "signal.signal", "line_number": 70, "usage_type": "call"}, {"api_name": "signal.SIGINT", "line_number": 70, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 74, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 76, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 78, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 80, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 81, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 84, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 85, "usage_type": "attribute"}, {"api_name": "logging.FileHandler", "line_number": 87, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 88, "usage_type": "attribute"}, {"api_name": "logging.Formatter", "line_number": 91, "usage_type": "call"}, {"api_name": "daemonize.Daemonize", "line_number": 95, "usage_type": "call"}]} +{"seq_id": "31009645788", "text": "#!/usr/bin/env python\n# coding: utf-8\n\n# In[1]:\n\n\n# Dependencies and Setup\nget_ipython().run_line_magic('matplotlib', 'inline')\nimport matplotlib.pyplot as plt\nimport pandas as pd\nimport numpy as np\n\n# Importing requirement specific features\nfrom scipy.stats import sem\nimport seaborn as sn\nfrom math import trunc\n\n# Hide warning messages in notebook\nimport warnings\nwarnings.filterwarnings('ignore')\n\n# File to Load (Remember to Change These)\nmouse_drug_data_to_load = \"data/mouse_drug_data.csv\"\nclinical_trial_data_to_load = \"data/clinicaltrial_data.csv\"\n\n# Read the Mouse and Drug Data and the Clinical Trial Data\nmouse_drug_df = pd.read_csv(mouse_drug_data_to_load)\nclinical_trial_df = pd.read_csv(clinical_trial_data_to_load)\n\n# Combine the data into a single dataset\nclinical_mouse = pd.merge(clinical_trial_df, mouse_drug_df, how='inner')\n\n# Display the data table for preview\n#mouse_drug_df -- checking sample\n#clinical_trial_df -- checking sample\nclinical_mouse.head()\n\n\n# In[2]:\n\n\n# Store the Mean Tumor Volume Data Grouped by Drug and Timepoint \ntumor_volume_df = clinical_mouse.loc[:,['Drug', 'Timepoint', 'Tumor Volume (mm3)']]\n#tumor_volume_df.head() --checking sample\n\nmean_sem_tv = tumor_volume_df.groupby(['Drug', 'Timepoint']).agg({\"Tumor Volume (mm3)\" :[\"mean\", \"sem\"]})\nmean_sem_tv\n\n\n# In[3]:\n\n\n# Create lists of the tumor volume means for each of the four drugs being converted to dataframe\ncap_tvmean_list = mean_sem_tv.loc['Capomulin'].loc[:, 'Tumor Volume (mm3)'].loc[:,'mean'].tolist()\ncef_tvmean_list = mean_sem_tv.loc['Ceftamin'].loc[:, 'Tumor Volume (mm3)'].loc[:,'mean'].tolist()\ninf_tvmean_list = mean_sem_tv.loc['Infubinol'].loc[:, 'Tumor Volume (mm3)'].loc[:,'mean'].tolist()\nket_tvmean_list = mean_sem_tv.loc['Ketapril'].loc[:, 'Tumor Volume (mm3)'].loc[:,'mean'].tolist()\npro_tvmean_list = mean_sem_tv.loc['Propriva'].loc[:, 'Tumor Volume (mm3)'].loc[:,'mean'].tolist()\nnaf_tvmean_list = mean_sem_tv.loc['Naftisol'].loc[:, 'Tumor Volume (mm3)'].loc[:,'mean'].tolist()\nplc_tvmean_list = mean_sem_tv.loc['Placebo'].loc[:, 'Tumor Volume (mm3)'].loc[:,'mean'].tolist()\nram_tvmean_list = mean_sem_tv.loc['Ramicane'].loc[:, 'Tumor Volume (mm3)'].loc[:,'mean'].tolist()\nste_tvmean_list = mean_sem_tv.loc['Stelasyn'].loc[:, 'Tumor Volume (mm3)'].loc[:,'mean'].tolist()\nzon_tvmean_list = mean_sem_tv.loc['Zoniferol'].loc[:, 'Tumor Volume (mm3)'].loc[:,'mean'].tolist()\n \n\n# Create lists of the tumor volume sems for each of the four drugs being converted to dataframe\ncap_tvsem_list = mean_sem_tv.loc['Capomulin'].loc[:, 'Tumor Volume (mm3)'].loc[:,'sem'].tolist()\ncef_tvsem_list = mean_sem_tv.loc['Ceftamin'].loc[:, 'Tumor Volume (mm3)'].loc[:,'sem'].tolist()\ninf_tvsem_list = mean_sem_tv.loc['Infubinol'].loc[:, 'Tumor Volume (mm3)'].loc[:,'sem'].tolist()\nket_tvsem_list = mean_sem_tv.loc['Ketapril'].loc[:, 'Tumor Volume (mm3)'].loc[:,'sem'].tolist()\npro_tvsem_list = mean_sem_tv.loc['Propriva'].loc[:, 'Tumor Volume (mm3)'].loc[:,'sem'].tolist()\nnaf_tvsem_list = mean_sem_tv.loc['Naftisol'].loc[:, 'Tumor Volume (mm3)'].loc[:,'sem'].tolist()\nplc_tvsem_list = mean_sem_tv.loc['Placebo'].loc[:, 'Tumor Volume (mm3)'].loc[:,'sem'].tolist()\nram_tvsem_list = mean_sem_tv.loc['Ramicane'].loc[:, 'Tumor Volume (mm3)'].loc[:,'sem'].tolist()\nste_tvsem_list = mean_sem_tv.loc['Stelasyn'].loc[:, 'Tumor Volume (mm3)'].loc[:,'sem'].tolist()\nzon_tvsem_list = mean_sem_tv.loc['Zoniferol'].loc[:, 'Tumor Volume (mm3)'].loc[:,'sem'].tolist()\n\nmeanlist_df = pd.DataFrame({\"Capomulin\": cap_tvmean_list, \n \"Ceftamin\" : cef_tvmean_list,\n \"Infubinol\": inf_tvmean_list,\n \"Ketapril\": ket_tvmean_list,\n \"Naftisol\": naf_tvmean_list,\n \"Placebo\": plc_tvmean_list,\n \"Propriva\": pro_tvmean_list,\n \"Ramicane\": ram_tvmean_list,\n \"Stelasyn\": ste_tvmean_list,\n \"Zoniferol\": zon_tvmean_list})\n\nsemlist_df = pd.DataFrame({\"Capomulin\": cap_tvsem_list, \n \"Ceftamin\" : cef_tvsem_list,\n \"Infubinol\": inf_tvsem_list,\n \"Ketapril\": ket_tvsem_list,\n \"Naftisol\": naf_tvsem_list,\n \"Placebo\": plc_tvsem_list,\n \"Propriva\": pro_tvsem_list,\n \"Ramicane\": ram_tvsem_list,\n \"Stelasyn\": ste_tvsem_list,\n \"Zoniferol\": zon_tvsem_list})\n\nmeanlist_df\n\n\n# In[4]:\n\n\n# Scatter plot showing how tumor volume changes over time for each treatment\nax = plt.subplot(111)\n\n# Set the x axis from 0 to 45 in increments of 5\nx_axis = np.arange(0, 50, 5)\n\n# Set the plot title and axes titles\nplt.title(\"Tumor Response to Treatment\")\nplt.xlabel(\"Time (days)\")\nplt.ylabel(\"Tumor Volume (mm3)\")\n\n# Plot the 'mean' list vs. the established x axis with error \nax.errorbar(x_axis, cap_tvmean_list, yerr=cap_tvsem_list, fmt=\"red\", marker=\"o\", label=\"Capomulin\")\nax.errorbar(x_axis, inf_tvmean_list, yerr=inf_tvsem_list, fmt=\"blue\", marker=\"^\", label=\"Infubinol\")\nax.errorbar(x_axis, ket_tvmean_list, yerr=ket_tvsem_list, fmt=\"green\", marker=\"s\", label=\"Ketapril\")\nax.errorbar(x_axis, plc_tvmean_list, yerr=plc_tvsem_list, fmt=\"grey\", marker=\"d\", label=\"Placebo\")\n\n# Add the legend and gridlines\nax.legend(loc=2)\n\ntick_locations = [value for value in x_axis]\nax.set_xticks(tick_locations, minor=False)\nax.grid('on', which='major', axis='both', linestyle='dotted', linewidth=0.5)\n\nplt.xlim(0, max(x_axis)+2)\n \n# Show the resulting scatter plot\nplt.show()\n\n\n# In[5]:\n\n\n# Convert to DataFrame\nTimepoint_response = clinical_mouse.groupby(['Drug','Timepoint']).mean()[['Metastatic Sites']]\n\n# Preview DataFrame\nTimepoint_response.head()\n\n\n# In[6]:\n\n\nMetastatic_sites = pd.pivot_table(Timepoint_response, index='Timepoint', columns='Drug', values='Metastatic Sites', aggfunc = np.mean)\nMetastatic_sites\n\n\n# In[7]:\n\n\nMetastatic = Metastatic_sites.index\nplt.figure(figsize=(12,8))\n\nplt.plot(Metastatic, Metastatic_sites['Capomulin'], marker ='o', linestyle='--', label=\"Capomulin\")\nplt.plot(Metastatic, Metastatic_sites['Ceftamin'], marker ='^', linestyle='--', label=\"Ceftamin\")\nplt.plot(Metastatic, Metastatic_sites['Infubinol'], marker ='s', linestyle='--', label=\"Infubinol\")\nplt.plot(Metastatic, Metastatic_sites['Ketapril'], marker ='p', linestyle='--', label=\"Ketapril\")\nplt.plot(Metastatic, Metastatic_sites['Naftisol'], marker ='+', linestyle='--', label=\"Naftisol\")\nplt.plot(Metastatic, Metastatic_sites['Placebo'], marker ='d', linestyle='--', label=\"Placebo\")\nplt.plot(Metastatic, Metastatic_sites['Propriva'], marker ='4', linestyle='--', label=\"Propriva\")\nplt.plot(Metastatic, Metastatic_sites['Ramicane'], marker ='*', linestyle='--', label=\"Ramicane\")\nplt.plot(Metastatic, Metastatic_sites['Stelasyn'], marker ='h', linestyle='--', label=\"Stelasyn\")\nplt.plot(Metastatic, Metastatic_sites['Zoniferol'], marker ='1', linestyle='--', label=\"Zoniferol\")\nplt.gca().set(xlabel = 'Treatment Duration (Days)', ylabel = 'Met. Sites',title = 'Metastatic Spread During Treatment',xlim = (0,max(Metastatic)))\nplt.legend(loc = 'best', frameon=True)\nplt.grid()\nplt.show()\n\n\n# In[8]:\n\n\nmouse_response = clinical_mouse.groupby(['Drug','Timepoint']).count()[['Mouse ID']]\nmouse_response.head()\n\n\n# In[9]:\n\n\nSurvival_pivot = pd.pivot_table(mouse_response, index='Timepoint', columns='Drug', values='Mouse ID', aggfunc = np.mean)\nSurvival_pivot\n\n\n# In[10]:\n\n\nSurvival_percentage = Survival_pivot.copy()\nSurvival_percentage = round(Survival_percentage.apply(lambda c: c / c.max() * 100, axis=0),2)\nSurvival_percentage\n\n\n# In[11]:\n\n\nSurvival_rate = Survival_percentage.index\n\nplt.figure(figsize=(12,8))\n\nplt.plot(Survival_rate, Survival_percentage['Capomulin'], marker ='o', linestyle='--', label=\"Capomulin\")\nplt.plot(Survival_rate, Survival_percentage['Ceftamin'], marker ='^', linestyle='--', label=\"Ceftamin\")\nplt.plot(Survival_rate, Survival_percentage['Infubinol'], marker ='s', linestyle='--', label=\"Infubinol\")\nplt.plot(Survival_rate, Survival_percentage['Ketapril'], marker ='p', linestyle='--', label=\"Ketapril\")\nplt.plot(Survival_rate, Survival_percentage['Naftisol'], marker ='+', linestyle='--', label=\"Naftisol\")\nplt.plot(Survival_rate, Survival_percentage['Placebo'], marker ='d', linestyle='--', label=\"Placebo\")\nplt.plot(Survival_rate, Survival_percentage['Propriva'], marker ='4', linestyle='--', label=\"Propriva\")\nplt.plot(Survival_rate, Survival_percentage['Ramicane'], marker ='*', linestyle='--', label=\"Ramicane\")\nplt.plot(Survival_rate, Survival_percentage['Stelasyn'], marker ='h', linestyle='--', label=\"Stelasyn\")\nplt.plot(Survival_rate, Survival_percentage['Zoniferol'], marker ='1', linestyle='--', label=\"Zoniferol\")\nplt.gca().set(xlabel = 'Time (Days)', ylabel = 'Survival Rate(%)',title = 'Survival During Treatment',xlim = (0,max(Survival_rate)))\nplt.legend(loc = 'best', frameon=True)\nplt.grid()\nplt.show()\n\n\n# In[12]:\n\n\nTumorChangePercent = (((meanlist_df.iloc[-1]-meanlist_df.iloc[0])/meanlist_df.iloc[0])*100).to_frame(\"% Change\")\nTumorChangePercent\n\n\n# In[13]:\n\n\nx=TumorChangePercent.index\ny=TumorChangePercent['% Change']\nplt.figure(figsize=(16,8))\ncolors = ['red' if _y >=0 else 'green' for _y in y]\nax = sn.barplot(x, y, palette=colors)\nfor n, (label, _y) in enumerate(zip(x, y)):\n if _y <= 0:\n ax.annotate(\n s='{:d}%'.format(trunc(_y)), xy=(n, -10), ha='center',va='center',\n xytext=(0,10), color='w', textcoords='offset points', weight='bold')\n else:\n ax.annotate(\n s='{:d}%'.format(trunc(_y)), xy=(n, 0), ha='center',va='center',\n xytext=(0,10), color='w', textcoords='offset points', weight='bold') \nplt.gca().set(xlabel='Drug', ylabel='% Tumor Volume Change', title='Tumor Change Over 45 Day Treatment')\nplt.rc('grid', linestyle=\"--\", color='black', linewidth=0.5)\nplt.grid(True)\nplt.show()\n\n", "repo_name": "kiranbabuk/Unit5MatplotlibAssignment", "sub_path": "PymaceuticalsAnalysis.py", "file_name": "PymaceuticalsAnalysis.py", "file_ext": "py", "file_size_in_byte": 9971, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "warnings.filterwarnings", "line_number": 20, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 27, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 28, "usage_type": "call"}, {"api_name": "pandas.merge", "line_number": 31, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 78, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 89, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 107, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 107, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 110, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.title", "line_number": 113, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 113, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 114, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 114, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 115, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 115, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 130, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 130, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 133, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 133, "usage_type": "name"}, {"api_name": "pandas.pivot_table", "line_number": 149, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 149, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 157, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 157, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 159, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 159, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 160, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 160, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 161, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 161, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 162, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 162, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 163, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 163, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 164, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 164, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 165, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 165, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 166, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 166, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 167, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 167, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 168, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 168, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.gca", "line_number": 169, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 169, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 170, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 170, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 171, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 171, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 172, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 172, "usage_type": "name"}, {"api_name": "pandas.pivot_table", "line_number": 185, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 185, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 202, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 202, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 204, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 204, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 205, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 205, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 206, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 206, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 207, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 207, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 208, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 208, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 209, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 209, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 210, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 210, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 211, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 211, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 212, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 212, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 213, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 213, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.gca", "line_number": 214, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 214, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 215, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 215, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 216, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 216, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 217, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 217, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 232, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 232, "usage_type": "name"}, {"api_name": "seaborn.barplot", "line_number": 234, "usage_type": "call"}, {"api_name": "math.trunc", "line_number": 238, "usage_type": "call"}, {"api_name": "math.trunc", "line_number": 242, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.gca", "line_number": 244, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 244, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.rc", "line_number": 245, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 245, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 246, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 246, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 247, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 247, "usage_type": "name"}]} +{"seq_id": "30175767150", "text": "# -*- coding: utf-8 -*-\nfrom odoo import api, models, fields\nfrom odoo.exceptions import UserError, ValidationError\nfrom datetime import datetime\n\nclass MarketingEvent(models.Model):\n\n _sql_constraints = [('date_check', \"CHECK ((start_date <= end_date))\",\n \"The start date must be anterior to the end date.\")]\n\n _name = 'marketing.event'\n _rec_name = 'name'\n _inherit = ['mail.thread', 'mail.activity.mixin']\n _description = 'Marketing event'\n name = fields.Char('Name', required=True)\n description = fields.Html('Description')\n owner = fields.Many2one('res.users', string='Owner', required=True, default=lambda self: self.env.user)\n start_date = fields.Date('Start date', default=fields.Date.today())\n end_date = fields.Date('End date')\n\n def write(self, values):\n for marketing_event in self:\n start_date = values['start_date'] if 'start_date' in values.keys() else marketing_event.start_date\n end_date = values['end_date'] if 'end_date' in values.keys() else marketing_event.end_date\n marketing_event.check_dates(start_date, end_date)\n if not marketing_event.end_date and 'end_date' not in values.keys():\n values['end_date'] = values['start_date'] if 'start_date' in values.keys() else marketing_event.start_date\n res = super().write(values)\n return res\n\n def check_dates(self, start_date, end_date):\n if type(start_date) == str:\n start_date = datetime.strptime(start_date, '%Y-%m-%d').date()\n if type(end_date) == str:\n end_date = datetime.strptime(end_date, '%Y-%m-%d').date()\n if not end_date:\n end_date = start_date\n if start_date > end_date:\n raise ValidationError(\"The start date must be anterior to the end date.\")\n\n @api.model\n def create(self, values):\n start_date = values['start_date'] if 'start_date' in values.keys() else False\n end_date = values['end_date'] if 'end_date' in values.keys() else False\n self.check_dates(start_date, end_date)\n marketing_event = super().create(values)\n if not marketing_event.end_date:\n marketing_event.end_date = marketing_event.start_date\n return marketing_event\n", "repo_name": "vjmoreno/Portfolio", "sub_path": "marketing_events/models/marketing_event.py", "file_name": "marketing_event.py", "file_ext": "py", "file_size_in_byte": 2283, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "odoo.models.Model", "line_number": 6, "usage_type": "attribute"}, {"api_name": "odoo.models", "line_number": 6, "usage_type": "name"}, {"api_name": "odoo.fields.Char", "line_number": 15, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 15, "usage_type": "name"}, {"api_name": "odoo.fields.Html", "line_number": 16, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 16, "usage_type": "name"}, {"api_name": "odoo.fields.Many2one", "line_number": 17, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 17, "usage_type": "name"}, {"api_name": "odoo.fields.Date", "line_number": 18, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 18, "usage_type": "name"}, {"api_name": "odoo.fields.Date.today", "line_number": 18, "usage_type": "call"}, {"api_name": "odoo.fields.Date", "line_number": 19, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 19, "usage_type": "name"}, {"api_name": "datetime.datetime.strptime", "line_number": 33, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 33, "usage_type": "name"}, {"api_name": "datetime.datetime.strptime", "line_number": 35, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 35, "usage_type": "name"}, {"api_name": "odoo.exceptions.ValidationError", "line_number": 39, "usage_type": "call"}, {"api_name": "odoo.api.model", "line_number": 41, "usage_type": "attribute"}, {"api_name": "odoo.api", "line_number": 41, "usage_type": "name"}]} +{"seq_id": "30371545508", "text": "import datetime\r\n# for i in range(1,11):\r\n# sentence = 'the value is {:09}'.format(i)\r\n# print(sentence)\r\n\r\nfor i in range(10,20):\r\n sentence = f'the value is {i:04}'\r\n print(sentence)\r\n\r\n# pi = 3.14159265\r\n# sentence = f'the value is {pi:.3f}'\r\n# print(sentence)\r\n\r\n# sentence = f'1MB is equal to {1000**3:,.4f} bytes' # seperate each 3 digits with ,\r\n# print(sentence)\r\n\r\nmy_date = datetime.datetime(2022,9,11,22,4,54)\r\nsentence = f'{my_date:%Y %B,%d} fell on a {my_date:%A} and was the {my_date:%j} day of the year'\r\nprint(sentence)\r\nprint(my_date.strftime('%Y %B,%d')) #just to remind both do the same\r\nsentence = f\"{my_date.strftime('%Y %B,%d')} fell on a {my_date.strftime('%A')} and was the {my_date.strftime('%j')} day of the year\"\r\nprint(sentence)", "repo_name": "pouya-alipour741/Courses", "sub_path": "Python/python tutorial advanced/String Formatting - Advanced Operations for Dicts, Lists, Numbers, and Dates.py", "file_name": "String Formatting - Advanced Operations for Dicts, Lists, Numbers, and Dates.py", "file_ext": "py", "file_size_in_byte": 772, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "datetime.datetime", "line_number": 17, "usage_type": "call"}]} +{"seq_id": "19876776727", "text": "import argparse\nimport predict_utils\n\nparser = argparse.ArgumentParser(\n description='This script helps in predicting the model',\n)\n\nparser.add_argument('--image_path', dest='image_path', action='store', \n default='./flowers/valid/100/image_07895.jpg')\nparser.add_argument('--checkpoint_path', dest='checkpoint_path', action='store', default='checkpoint.pth')\nparser.add_argument('--top_k', dest='top_k', action='store', default=5, type=int)\nparser.add_argument('--gpu', dest=\"mode\", action=\"store\", default=\"gpu\")\n\nargs = parser.parse_args()\n\n\n# load the checkpoint\ncheckpoint_model = predict_utils.load_checkpoint(args.checkpoint_path)\n\n# predict the class of an image\nprobs, classes = predict_utils.predict(args.image_path, checkpoint_model, args.top_k)\n\nfor i in range(args.top_k):\n print(\"Probability - {} - Class - {}\".format(probs[i], classes[i]))", "repo_name": "susmithagudapati/Image-Classifier-DSND", "sub_path": "predict.py", "file_name": "predict.py", "file_ext": "py", "file_size_in_byte": 879, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "37", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 4, "usage_type": "call"}, {"api_name": "predict_utils.load_checkpoint", "line_number": 18, "usage_type": "call"}, {"api_name": "predict_utils.predict", "line_number": 21, "usage_type": "call"}]} +{"seq_id": "24791654301", "text": "import os\nimport re\n\nimport setuptools # type: ignore\n\n\ndef read(path: str) -> str:\n file_path = os.path.join(os.path.dirname(__file__), *path.split(\"/\"))\n return open(file_path).read()\n\n\n# single-sourcing the package version using method 1 of:\n# https://packaging.python.org/guides/single-sourcing-package-version/\ndef parse_version_from(path: str) -> str:\n version_file = read(path)\n version_match = re.search(r'^__version__ = \"(.*)\"', version_file, re.M)\n if version_match is None or len(version_match.groups()) > 1:\n raise ValueError(\"couldn't parse version\")\n return version_match.group(1)\n\n\nsetuptools.setup(\n name=\"cfgrib\",\n version=parse_version_from(\"cfgrib/__init__.py\"),\n description=\"Python interface to map GRIB files to the NetCDF Common Data Model \"\n \"following the CF Convention using ecCodes.\",\n long_description=read(\"README.rst\") + read(\"CHANGELOG.rst\"),\n author=\"European Centre for Medium-Range Weather Forecasts (ECMWF)\",\n author_email=\"software.support@ecmwf.int\",\n license=\"Apache License Version 2.0\",\n url=\"https://github.com/ecmwf/cfgrib\",\n packages=setuptools.find_packages(),\n include_package_data=True,\n install_requires=[\"attrs>=19.2\", \"click\", \"eccodes>=0.9.8\", \"numpy\"],\n python_requires=\">=3.6\",\n extras_require={\n \"xarray\": [\"xarray>=0.15\"],\n \"tests\": [\"dask[array]\", \"flake8\", \"pytest\", \"pytest-cov\", \"scipy\", \"xarray>=0.15\"],\n },\n zip_safe=True,\n keywords=\"eccodes grib xarray\",\n classifiers=[\n \"Development Status :: 4 - Beta\",\n \"Intended Audience :: Developers\",\n \"License :: OSI Approved :: Apache Software License\",\n \"Programming Language :: Python :: 3\",\n \"Programming Language :: Python :: 3.6\",\n \"Programming Language :: Python :: 3.7\",\n \"Programming Language :: Python :: 3.8\",\n \"Programming Language :: Python :: 3.9\",\n \"Programming Language :: Python :: Implementation :: CPython\",\n \"Programming Language :: Python :: Implementation :: PyPy\",\n \"Operating System :: OS Independent\",\n ],\n entry_points={\n \"console_scripts\": [\"cfgrib=cfgrib.__main__:cfgrib_cli\"],\n \"xarray.backends\": [\"cfgrib=cfgrib.xarray_plugin:CfGribBackend\"],\n },\n)\n", "repo_name": "ecmwf/cfgrib", "sub_path": "setup.py", "file_name": "setup.py", "file_ext": "py", "file_size_in_byte": 2277, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 361, "dataset": "github-code", "pt": "37", "api": [{"api_name": "os.path.join", "line_number": 8, "usage_type": "call"}, {"api_name": "os.path", "line_number": 8, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 8, "usage_type": "call"}, {"api_name": "re.search", "line_number": 16, "usage_type": "call"}, {"api_name": "re.M", "line_number": 16, "usage_type": "attribute"}, {"api_name": "setuptools.setup", "line_number": 22, "usage_type": "call"}, {"api_name": "setuptools.find_packages", "line_number": 32, "usage_type": "call"}]} +{"seq_id": "25199657262", "text": "import random\nfrom enum import Enum\n\n\nclass RPS(Enum):\n ROCK = 1\n PAPER = 2\n SCISSOR = 3\n\n\ndef convert_to_rps(num):\n return str(RPS(num)).replace('RPS.', '')\n\n\ndef get_choice(msg, error_msg):\n while True:\n choice = int(input(msg))\n if choice < 0 or choice > 3:\n print(error_msg)\n continue\n else:\n break\n return choice\n\n\ndef who_win(player, computer):\n if player == computer:\n result = 'Tie game!'\n elif (player == 1 and computer == 3) or (player == 2 and computer == 1) or (player == 3 and computer == 2):\n result = 'You won!'\n else:\n result = 'Python won!'\n return result\n\n\ndef play_again(msg):\n while True:\n inp = input(msg)\n if int(inp) == 0:\n break\n\n\ndef play_game():\n # Display introduction message\n intro_msg = '\\n1 for Rock, 2 for Paper, 3 for Scissor.\\nPress 0 to quit the game.\\nGAME START\\n'\n print(intro_msg)\n player = 5\n while player != 0:\n # Get choice from the player\n player = get_choice('Enter your choice: ',\n 'Please enter 1 or 2 or 3 or press 0 to quit the game.')\n if player == 0:\n print('Quiting...')\n break\n # Generate a random choice\n computer = int(random.choice('123'))\n # Display the result\n print(\n f'You chose {convert_to_rps(player)} and Python chose {convert_to_rps(computer)}.')\n print(who_win(player, computer))\n print('Press any number to play again. Press 0 to quit the game.')\n\n\n# Run the program\nplay_game()\n", "repo_name": "TriPhan0511/The-Python-Workbook", "sub_path": "lesson08-while-loops-and-for-loops/RPS_DIY.py", "file_name": "RPS_DIY.py", "file_ext": "py", "file_size_in_byte": 1615, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "enum.Enum", "line_number": 5, "usage_type": "name"}, {"api_name": "random.choice", "line_number": 56, "usage_type": "call"}]} +{"seq_id": "37575661409", "text": "from os import environ\r\nfrom discord.ext import commands\r\nfrom pymongo import MongoClient\r\n\r\n__import__(\"dotenv\").load_dotenv()\r\nclass Guild:\r\n collection = MongoClient(environ[\"MONGODB_URL\"])[\"Logging\"][\"Guilds\"]\r\n\r\n def _create_guild_account(guild_id: int):\r\n \"\"\"Create a World guild account.\"\"\"\r\n collection.insert_one({\r\n \"_id\": guild_id,\r\n \"Bans\": 0,\r\n \"Kicks\": 0,\r\n \"Mutes\": 0,\r\n \"Unmute\": 0,\r\n \"Slowmode\": 0,\r\n \"DeletedMessage\": 0,\r\n \"EditedMessage\": 0,\r\n \"JoinedServer\": 0,\r\n \"LeftServer\": 0,\r\n \"Unbanned\": 0,\r\n })", "repo_name": "WilloIzCitron/World", "sub_path": "framework/guild.py", "file_name": "guild.py", "file_ext": "py", "file_size_in_byte": 670, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "github-code", "pt": "37", "api": [{"api_name": "pymongo.MongoClient", "line_number": 7, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 7, "usage_type": "name"}]} +{"seq_id": "36943392951", "text": "import logging\nimport time\nfrom libs import utils\nfrom test_runner.test_exception import TestException\nimport os\nimport inspect\nimport argparse\n\n\nclass TestRunner:\n \"\"\"\n Test runner class for generic testing purposes\n \"\"\"\n\n def __init__(self):\n self.library = None\n self.logger = None\n self.start = None\n self.stop = None\n self.parser = None\n self.args = None\n\n def add_arguments(self):\n self.parser = argparse.ArgumentParser()\n self.parser.add_argument(\n \"--build_library\",\n default=None,\n type=str,\n help=\"Build Library\"\n )\n self.args = self.parser.parse_args()\n\n def get_caller_info(self):\n caller_frame = inspect.stack()[2]\n caller_filename_full = caller_frame.filename\n caller_filename_only = os.path.splitext(os.path.basename(caller_filename_full))[0]\n return caller_filename_full, caller_filename_only\n\n def configure_logging(self, name=None):\n if name is not None:\n logger = logging.getLogger(name)\n else:\n logger = logging.getLogger(__name__)\n\n logger.setLevel(logging.DEBUG)\n ch = logging.StreamHandler()\n formatter = logging.Formatter('%(asctime)s - [%(levelname)s] - %(message)s')\n ch.setFormatter(formatter)\n logger.addHandler(ch)\n\n return logger\n\n def log(self, msg, type=\"INFO\"):\n _, filename_only = self.get_caller_info()\n if self.logger is None:\n self.logger = self.configure_logging(name=filename_only)\n if type == \"INFO\":\n self.logger.info(msg)\n elif type == \"DEBUG\":\n self.logger.debug(msg)\n elif type == \"WARNING\":\n self.logger.warning(msg)\n elif type == \"ERROR\":\n self.logger.error(msg)\n else:\n self.logger.error(\"Wrong logging type\")\n\n def load_library_module(self):\n self.log(f\"Load custom deveoped library {self.args.build_library}\")\n self.library = utils.initiliaze_shared_library(lib_file=self.args.build_library)\n\n def initialize(self):\n self.add_arguments()\n self.log(\"Load library into the test framework\")\n self.load_library_module()\n\n def test(self):\n pass\n\n def test_failed_method(self, err):\n self.stop = time.perf_counter()\n self.log(\"TEST FAILED\")\n self.log(f\"Run time : {round(self.stop - self.start, 4)}\")\n raise err\n\n def test_passed_method(self):\n self.stop = time.perf_counter()\n self.log(\"TEST PASSED\")\n self.log(f\"Run time : {round(self.stop - self.start, 4)}\")\n\n def run_test(self):\n self.start = time.perf_counter()\n self.log(\"Initializing Test Runner\")\n try:\n self.initialize()\n except Exception as err:\n self.test_failed_method(err)\n\n try:\n self.test()\n except TestException as err:\n self.test_failed_method(err)\n else:\n self.test_passed_method()\n", "repo_name": "ananthm1254/test_lib", "sub_path": "test_runner/test_runner.py", "file_name": "test_runner.py", "file_ext": "py", "file_size_in_byte": 3066, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 24, "usage_type": "call"}, {"api_name": "inspect.stack", "line_number": 34, "usage_type": "call"}, {"api_name": "os.path.splitext", "line_number": 36, "usage_type": "call"}, {"api_name": "os.path", "line_number": 36, "usage_type": "attribute"}, {"api_name": "os.path.basename", "line_number": 36, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 41, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 43, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 45, "usage_type": "attribute"}, {"api_name": "logging.StreamHandler", "line_number": 46, "usage_type": "call"}, {"api_name": "logging.Formatter", "line_number": 47, "usage_type": "call"}, {"api_name": "libs.utils.initiliaze_shared_library", "line_number": 70, "usage_type": "call"}, {"api_name": "libs.utils", "line_number": 70, "usage_type": "name"}, {"api_name": "time.perf_counter", "line_number": 81, "usage_type": "call"}, {"api_name": "time.perf_counter", "line_number": 87, "usage_type": "call"}, {"api_name": "time.perf_counter", "line_number": 92, "usage_type": "call"}, {"api_name": "test_runner.test_exception.TestException", "line_number": 101, "usage_type": "name"}]} +{"seq_id": "16383870999", "text": "#!/usr/bin/env python\n# coding: utf-8\n\"\"\"\nGiven a list of GenBank accession IDs, download all complete\ngenomes and contigs for non-complete genomes. Write out the\nresults to both GB and FASTA-format files for use with \nK-SLAM and CLARK respectively.\n\"\"\"\nimport argparse\nimport itertools\nimport io\nimport os\nimport os.path as osp\nimport sys\nfrom urllib.error import HTTPError\nfrom Bio import Entrez, SeqIO\n\ndef expand_entries(gb_ids):\n \"\"\"\n Checking each row for the presence of multiple \n accession IDs and split by a comma separator, \n flattening the file.\n Typically the multiple IDs correspond to,\n first, the organism genome, and the rest being\n plasmids or other separate genomes contained\n within the cell.\n \n >>> expand_entries([\"A,B\", \"D, E\", \"G,H, I\"])\n ['A', 'B', 'D', 'E', 'G', 'H', 'I']\n \"\"\"\n ids = []\n for row in gb_ids:\n ids.extend([entry.strip() for entry in row.split(\",\")])\n return ids\n\n\ndef pp_list(items):\n \"\"\"\n Pretty-print a list of items.\n \n >>> pp_list(['a', 'b', 'c'])\n a, b, c\n \"\"\"\n step = 10\n for i in range(0, len(items), step):\n print(\", \".join(items[i:i+step]))\n\n\ndef esearch_range(accn, db=\"nuccore\", retmax=100, webenv=None):\n \"\"\"\n Given the first and last in a series of GenBank accessions,\n retrieve the database identifiers.\n \n retmax: By default, ESearch returns only the first 20 records\n (retmax=20) and discards the rest. Max of 100,000.\n webenv: If a WebEnv string is passed, retmax will be ignored\n (max 20 still returned), and all search results will be\n stored in your personal history with an associated\n query key ('QueryKey' in returned dictionary).\n \"\"\"\n MAX_RETMAX = 100000\n kwds = {}\n \n if webenv is not None:\n kwds = {'usehistory': 'y', 'WebEnv': webenv}\n else:\n kwds['retmax'] = retmax if retmax <= MAX_RETMAX else MAX_RETMAX\n \n with Entrez.esearch(db=\"nuccore\", term=f\"{':'.join(accn)}[accn]\", **kwds) as handle:\n return Entrez.read(handle)\n\n\ndef epost(id_list, db=\"nuccore\"):\n \"\"\"\n Given a list of NCBI identifiers (e.g. from ESearch), submit them\n to the Entrez History server for later use with EFetch.\n \n Returns: WebEnv and QueryKey values\n \"\"\"\n with Entrez.epost(db, id=\",\".join(id_list)) as request:\n result = Entrez.read(request)\n return result[\"WebEnv\"], result[\"QueryKey\"]\n\n\ndef batch_efetch(id_list=None, batch_size=100, db=\"nuccore\", \n rettype=\"gbwithparts\", retmode=\"text\",\n webenv=None, query_key=None, query_len=None):\n \"\"\"\n Using a pre-posted query, use the WebEnv and QueryKey identifiers to\n retrieve the actual data behind the query items. The theoretical max\n should be 500 per EFetch when using History (see: \n https://www.ncbi.nlm.nih.gov/books/NBK25498/#chapter3.Application_3_Retrieving_large),\n but here the default is set to 100 as a safe margin.\n \"\"\"\n kwds = {}\n if webenv is None or query_key is None:\n webenv, query_key = epost(id_list, db)\n id_count = len(id_list)\n else:\n id_count = query_len\n\n kwds['query_key'] = query_key\n kwds['WebEnv'] = webenv\n \n complete = []\n contigs = {}\n for start in range(0, id_count, batch_size):\n end = min(id_count, start+batch_size)\n print(f\"Downloading records {start+1} to {end}...\", end='')\n if id_list is not None:\n pp_list(id_list[start:end])\n else:\n print(\"\\n\")\n \n attempt = 0\n while attempt < 3:\n attempt += 1\n try:\n gb_handle = Entrez.efetch(db=db, rettype=rettype, retmode=retmode,\n retstart=start, retmax=batch_size,\n **kwds)\n break\n except HTTPError as err:\n if 500 <= err.code <= 599:\n print(\"Received error from server %s\" % err)\n print(\"Attempt %i of 3\" % attempt)\n time.sleep(15)\n else:\n raise\n \n for seq_record in SeqIO.parse(gb_handle, \"gb\"):\n if \"wgs\" in seq_record.annotations:\n contigs[seq_record.id] = seq_record.annotations[\"wgs\"]\n else:\n complete.append(seq_record)\n \n return complete, contigs, webenv\n\n\ndef write_gb_fasta(gb_records, out_dir, genome_id=None, write_gb=True):\n for rec in gb_records:\n filename=rec.id if genome_id is None else genome_id\n mode = 'w' if genome_id is None else 'a'\n\n if write_gb:\n with open(osp.join(out_dir,\"gbff\", f\"{filename}.gbff\"), mode) as gb_f:\n gb_f.write(rec.format(\"gb\"))\n\n with open(osp.join(out_dir,\"fasta\",f\"{filename}.fa\"), mode) as fasta_f:\n fasta_f.write(rec.format(\"fasta\"))\n\n\ndef handle_program_options():\n \"\"\"Parses the given options passed in at the command line.\"\"\"\n parser = argparse.ArgumentParser(description=\"Given a list of GenBank \"\n \"accession IDs, download all complete \"\n \"genomes and contigs for non-complete \"\n \"genomes. Write out the results to both \"\n \"GBFF and FASTA-format files.\")\n parser.add_argument(\"genbank_ids\", metavar='genbank-ids',\n help=\"Path to a file containing a list of GenBank \"\n \"accession IDs (one per line).\")\n parser.add_argument(\"-b\", \"--batch-size\", type=int, default=100, \n choices=range(1,501), metavar=\"[1-500]\",\n help=\"The number of records to attempt to fetch at \"\n \"once. NCBI mandates an upper limit of 500, but you \"\n \"may experience connection issues with too large a \"\n \"number. 100 to 200 is usually a safe range. Defaults \"\n \"to 100.\")\n parser.add_argument(\"-o\", \"--out-dir\", default=\".\",\n help=\"Downloaded data will be written to the gbff and \"\n \"fasta directories inside this directory.\")\n parser.add_argument(\"--no-gbff\", action=\"store_false\", \n help=\"If specified, disable writing GBFF files.\")\n\n return parser.parse_args()\n\n\ndef main():\n args = handle_program_options()\n\n # check/create output dir\n if not osp.exists(args.out_dir):\n os.mkdir(args.out_dir)\n\n with open(args.genbank_ids) as inf:\n gb_ids = expand_entries([row for row in inf])\n\n # identify this script to NCBI\n Entrez.email = \"\"\n Entrez.tool = \"\"\n \n print(f\"Downloading GenBank records for {len(gb_ids)} accessions.\\n\")\n \n for i in range(0, len(gb_ids), args.batch_size):\n gb_chunk = gb_ids[i:i+args.batch_size]\n complete, contigs, webenv = batch_efetch(gb_chunk, batch_size=args.batch_size)\n print(f\"\\nDownloaded {len(complete)} complete genomes \", end='')\n print(f\"and accession IDs for {len(contigs)} incomplete genomes.\")\n \n # download GB sequence data for complete genomes\n print(f\"\\nWriting sequence files for {len(complete)} complete genomes.\")\n write_gb_fasta(complete, args.out_dir, write_gb=args.no_gbff)\n \n # download the GB sequence data for all the contigs\n print(\"Downloading contig data for incomplete genomes.\")\n for genome in contigs:\n es = esearch_range(contigs[genome], webenv=webenv)\n print(f\"\\nFetching {es['Count']} GenBank contig records for genome {genome}...\\n\")\n contig_complete, _, _ = batch_efetch(webenv=webenv, query_key=es['QueryKey'], \n query_len=int(es['Count']))\n print(\"\\n...complete.\")\n \n # write out all data to both GenBank format and FASTA format files\n print(f\"\\nWriting sequence files for {es['Count']} contigs.\")\n \n write_gb_fasta(contig_complete, args.out_dir, genome_id=genome, \n write_gb=args.no_gbff)\n\n\nif __name__ == '__main__':\n main()\n", "repo_name": "smdabdoub/biodbtools", "sub_path": "src/download_genbank.py", "file_name": "download_genbank.py", "file_ext": "py", "file_size_in_byte": 8303, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "Bio.Entrez.esearch", "line_number": 69, "usage_type": "call"}, {"api_name": "Bio.Entrez", "line_number": 69, "usage_type": "name"}, {"api_name": "Bio.Entrez.read", "line_number": 70, "usage_type": "call"}, {"api_name": "Bio.Entrez", "line_number": 70, "usage_type": "name"}, {"api_name": "Bio.Entrez.epost", "line_number": 80, "usage_type": "call"}, {"api_name": "Bio.Entrez", "line_number": 80, "usage_type": "name"}, {"api_name": "Bio.Entrez.read", "line_number": 81, "usage_type": "call"}, {"api_name": "Bio.Entrez", "line_number": 81, "usage_type": "name"}, {"api_name": "Bio.Entrez.efetch", "line_number": 119, "usage_type": "call"}, {"api_name": "Bio.Entrez", "line_number": 119, "usage_type": "name"}, {"api_name": "urllib.error.HTTPError", "line_number": 123, "usage_type": "name"}, {"api_name": "Bio.SeqIO.parse", "line_number": 131, "usage_type": "call"}, {"api_name": "Bio.SeqIO", "line_number": 131, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 146, "usage_type": "call"}, {"api_name": "os.path", "line_number": 146, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 149, "usage_type": "call"}, {"api_name": "os.path", "line_number": 149, "usage_type": "name"}, {"api_name": "argparse.ArgumentParser", "line_number": 155, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 183, "usage_type": "call"}, {"api_name": "os.path", "line_number": 183, "usage_type": "name"}, {"api_name": "os.mkdir", "line_number": 184, "usage_type": "call"}, {"api_name": "Bio.Entrez.email", "line_number": 190, "usage_type": "attribute"}, {"api_name": "Bio.Entrez", "line_number": 190, "usage_type": "name"}, {"api_name": "Bio.Entrez.tool", "line_number": 191, "usage_type": "attribute"}, {"api_name": "Bio.Entrez", "line_number": 191, "usage_type": "name"}]} +{"seq_id": "38453935559", "text": "#!/usr/bin/env python3\n# coding=utf-8\n\n# Pour les fonctions mathématiques\nimport math\n\n# Pour l'affichage des graphiques\nfrom matplotlib.pyplot import legend, plot, show\n\n# Pour l'affichage des résultats\nfrom ea4lib import printcol\n\n#Pour calculer le temps d'exécution\nfrom time import process_time\n\n# NE PAS MODIFIER\n# Calcule\n# le n-ième terme de la suite de Fibonacci \n# par 3 méthodes\n\ndef fibo_1(n) :\n if n <= 0 : return 0\n if n <= 2 : return 1\n return fibo_1(n-1) + fibo_1(n-2)\n\ndef fibo_2(n) :\n if n <= 0 : return 0\n liste = [0, 1] + [0] * (n-1)\n for i in range(2, n+1) :\n liste[i] = liste[i-1] + liste[i-2]\n return liste[n]\n\ndef fibo_3(n) :\n if n <= 0 : return 0\n previous, last = 0, 1\n for i in range(1, n) :\n previous, last = last, previous + last\n return last\n\n\n###############################################################################\n# Exercice 1 question 1:\n#\n# La courbe de temps d'éxecution de fibo_1 est élevée pour des petites valeurs de n, on remarque qu'elle se rapproche de la fonction témoin n.\n# Par contre sur des plus grandes valeurs on remarque que la courbe se rapproche de celle de n² normalisée.\n#\n# Exercice 1 question 2:\n#\n# Pour fibo_1_adds la courbe du nombre d'additions correspond à celle du temps d'éxecution.\n# Pour fibo_2_adds et fibo_3_adds cependant on remarque une courbe presque linéaire que celle du temps d'éxecution\n#\n# Exercice 1 question 5:\n#\n# fibo_1_bits se rapproche de la courbre normalisée de x*phi**x, qui correspond à une utilisation naïve de la récursion qui effectuerait phi**n additions\n# fibo_2_bits et fibo_3_bits se rapproche de celle de x**2 renormalisée, qui se rapproche des n additions pour un calcul itératif des n premières valeurs\n\n# Exercice 1.2\n# \n# Calcule\n# le n-ième terme de la suite de Fibonacci et \n# le nombre d'additions de (grands) entiers utilisées\n\ndef fibo_1_adds(n) :\n if n==0: return 0,0\n if n<=2: return 1,0\n f1,op1=fibo_1_adds(n-1)\n f2,op2=fibo_1_adds(n-2)\n return f1+f2, op1+op2+1\n\n# À COMPLÉTER\ndef fibo_2_adds(n) :\n if n<=0: return 0,0\n ops = 0\n liste = [0, 1] + [0] * (n - 1)\n for i in range(2, n + 1):\n liste[i] = liste[i - 1] + liste[i - 2]\n ops+=1\n return liste[n],ops\n\n# À COMPLÉTER\ndef fibo_3_adds(n) :\n if n <= 0: return 0,0\n ops = 0\n previous, last = 0, 1\n for i in range(1, n):\n previous, last = last, previous + last\n ops+=1\n return last,ops\n\n\n###############################################################################\n#\n# LIRE, NE PAS MODIFIER\n#\ndef colors(tous=True) :\n return ['red', 'green', 'cyan', 'black'] if tous else ['green', 'cyan', 'black']\n\ndef courbes_adds(n, tous=True, pas=1) :\n ''' affiche les courbes des additions effectuées pour le calcul de Fn par les différents algos\n (les trois si tous=True, valeur par défaut; seulement fibo_2 et fibo_3 si tous=False)'''\n\n algos = [fibo_1_adds, fibo_2_adds, fibo_3_adds] if tous else [fibo_2_adds, fibo_3_adds] \n nb_ops = [ [ algo(i)[1] for i in range(0, n, pas) ] for algo in algos ]\n\n l = ['fibo_1_adds', 'fibo_2_adds', 'fibo_3_adds'] if tous else ['fibo_2_adds', 'fibo_3_adds'] \n for valeurs, couleur in zip(nb_ops, colors(tous)) :\n plot(range(0,n,pas), valeurs, couleur)\n legend(l)\n show()\n\n \n###############################################################################\n# Exercice 1.4\n#\n\n# À COMPLÉTER\ndef nbOfBits(i) :\n return 1+math.floor(math.log(i,2))\n\n \n###############################################################################\n# Exercice 1.5\n#\n\n# À COMPLÉTER\ndef fibo_1_bits(n) :\n if n <= 0: return 0, 0\n if n <= 2: return 1, 0\n f1, op1 = fibo_1_bits(n - 1)\n f2, op2 = fibo_1_bits(n - 2)\n ops_bits=op1+op2+nbOfBits(f1+f2)\n return f1 + f2, ops_bits\n\n# À COMPLÉTER\ndef fibo_2_bits(n) :\n if n <= 0: return 0, 0\n ops = 0\n liste = [0, 1] + [0] * (n - 1)\n for i in range(2, n + 1):\n liste[i] = liste[i - 1] + liste[i - 2]\n ops+=nbOfBits(liste[i-1]+liste[i-2])\n return liste[n], ops\n\n# À COMPLÉTER\ndef fibo_3_bits(n) :\n if n <= 0: return 0, 0\n ops = 0\n previous, last = 0, 1\n for i in range(1, n):\n ops+=nbOfBits(previous+last)\n previous, last = last, previous + last\n return last, ops\n\n\n###############################################################################################\n###############################################################################################\n########################### courbes - opérations sur les bits ################################\n \n#\n# LIRE, NE PAS MODIFIER\n#\n\nphi = (1+math.sqrt(5))/2\ndef courbes_ops(n, tous=True, pas=1) :\n ''' affiche les courbes des opérations élémentaires effectuées pour le calcul de Fn par les différents algos '''\n\n algos = [fibo_1_bits, fibo_2_bits, fibo_3_bits] if tous else [fibo_2_bits, fibo_3_bits]\n nb_ops = [ [ algo(i)[1] for i in range(0, n, pas) ] for algo in algos ]\n\n if tous :\n renorm = nb_ops[0][-1] / (((n-1)//pas*pas)*phi**((n-1)//pas*pas)) \n nb_ops += [[ i * phi**i * renorm for i in range(0, n, pas) ]]\n #courbe témoin - complexité théorique de fibo_1_bits renormalisée\n else :\n renorm = nb_ops[-1][-1] / ((n-1)//pas*pas)**2\n nb_ops += [[(i)**2 * renorm for i in range(0, n, pas) ]]\n #courbe témoin - complexité théorique de fibo_3_bits renormalisée\n\n l = ['fibo_1_bits', 'fibo_2_bits', 'fibo_3_bits'] if tous else ['fibo_2_bits', 'fibo_3_bits'] \n for valeurs, couleur in zip(nb_ops, colors(tous)) :\n if couleur != 'black' :\n plot(range(0,n,pas), valeurs, couleur)\n else :\n plot(range(0,n,pas), valeurs, color=couleur, linestyle='dashed')\n if tous :\n l += ['courbe x * phi**x renormalisée']\n else :\n l += ['courbe x**2 renormalisée']\n legend(l)\n show()\n\n###############################################################################################\n###############################################################################################\n############################## courbes - temps d'exécution ###################################\n \n#\n# LIRE, NE PAS MODIFIER\n#\n\ndef mesure(algo, n):\n ''' retourne le temps de calcul de Fn par l'algo en paramètre '''\n debut = process_time()\n algo(n)\n return (process_time()-debut)\n\ndef courbes_temps(n, tous=True, pas=1) :\n ''' affiche les courbes du temps de calcul de Fn par les différents algos '''\n temps = [[], []]\n if tous : temps += [[],[]]\n\n temps[0] = [ mesure(fibo_3, i) for i in range(0, n, pas) ]\n \n if tous :\n temps[1] = [ mesure(fibo_2, i) for i in range(0, n, pas) ]\n temps[2] = [ mesure(fibo_1, i) for i in range(0, n, pas) ]\n \n renorm = temps[2][-1] / ((n-1)//pas*pas * phi**((n-1)//pas*pas))\n temps[3] = [ i * phi**i * renorm for i in range(0, n, pas) ] \n #courbe témoin - complexité théorique de fibo_1_bits renormalisée\n else :\n renorm = temps[0][-1] / ((n-1)//pas*pas)**2\n temps[1] = [ (i)**2 * renorm for i in range(0, n, pas) ] \n #courbe témoin - complexité théorique de fibo_3_bits\n \n plot(range(0,n,pas), temps[0], 'cyan')\n l = ['temps pour exécuter fibo_3']\n if tous :\n plot(range(0,n,pas), temps[1], 'green')\n plot(range(0,n,pas), temps[2], 'red')\n plot(range(0,n,pas), temps[3], 'black', linestyle='dashed')\n l += [ 'temps pour exécuter fibo_2', 'temps pour exécuter fibo_1'] \n else :\n plot(range(0,n,pas), temps[1], 'black', linestyle='dashed')\n if tous :\n l += ['courbe x * phi**x renormalisée']\n else :\n l += ['courbe x**2 renormalisée']\n legend(l)\n show()\n\n\n###############################################################################################\n###############################################################################################\n########################################## TESTS ##############################################\n \n#\n# NE PAS MODIFIER\n#\ndef test_fibo_1_addsData() :\n return [(0, (0, 0)), (1, (1, 0)), (2, (1, 0)), (3, (2, 1)), (4, (3, 2)), (5, (5, 4)), (6, (8, 7)), (7, (13, 12)), (8, (21, 20)), (9, (34, 33)), (10, (55, 54)), (11, (89, 88)), (12, (144, 143)), (13, (233, 232)), (14, (377, 376)), (15, (610, 609))]\n\n#\n# NE PAS MODIFIER\n#\ndef test_fibo_2_addsData() :\n return [(0, (0, 0)), (1, (1, 0)), (2, (1, 1)), (3, (2, 2)), (4, (3, 3)), (5, (5, 4)), (6, (8, 5)), (7, (13, 6)), (8, (21, 7)), (9, (34, 8)), (10, (55, 9)), (11, (89, 10)), (12, (144, 11)), (13, (233, 12)), (14, (377, 13)), (15, (610, 14))]\n\n#\n# NE PAS MODIFIER\n#\ndef test_fibo_3_addsData() :\n return [(0, (0, 0)), (1, (1, 0)), (2, (1, 1)), (3, (2, 2)), (4, (3, 3)), (5, (5, 4)), (6, (8, 5)), (7, (13, 6)), (8, (21, 7)), (9, (34, 8)), (10, (55, 9)), (11, (89, 10)), (12, (144, 11)), (13, (233, 12)), (14, (377, 13)), (15, (610, 14))]\n\n#\n# NE PAS MODIFIER\n#\ndef test_fibo_addsData(i) :\n if i == 1 : return test_fibo_1_addsData()\n elif i == 2 : return test_fibo_2_addsData()\n else : return test_fibo_3_addsData()\n\n#\n# NE PAS MODIFIER\n#\ndef test_fibo_adds(num):\n algos = [fibo_1_adds, fibo_2_adds, fibo_3_adds]\n printcol('{Test %s:}' % algos[num-1].__name__,'bold')\n score = 0\n data = test_fibo_addsData(num)\n ldata = len(data)\n for i, dt in enumerate(data) :\n print('** test %2d/%2d : ' % (i + 1, ldata), end='')\n n = dt[0]\n Tres, Tops = dt[1]\n fb, ops = algos[num-1](n)\n if (fb == Tres and ops == Tops):\n score += 1\n printcol('{ok}','green')\n elif (fb == Tres):\n printcol('{Mauvais nombre d\\'opérations}','yellow')\n print(' entree : %s' % n)\n print(' calcule : %d en %d ops' % (fb,ops) )\n print(' attendu : %d en %d ops' % (Tres,Tops) )\n else :\n printcol('{Mauvais résultat}','red')\n print(' entree : %s' % n)\n print(' calcule : %d en %d ops' % (fb,ops) )\n print(' attendu : %d en %d ops' % (Tres,Tops) )\n printcol('{** Score %d/%d : %s}' % (score, ldata, \"super !\" if score==ldata else \"essaie encore !\"),'bold')\n print()\n\n#\n# NE PAS MODIFIER\n#\ndef test_nbOfBitsData() :\n return [[4, 3],\n [7, 3],\n [10, 4],\n [10 ** 2, 7],\n [10 ** 3, 10],\n [10 ** 4, 14]]\n\n#\n# NE PAS MODIFIER\n#\ndef test_nbOfBits():\n printcol('{Test nbOfBits:}','bold')\n score = 0\n data = test_nbOfBitsData()\n ldata = len(data)\n for i, dt in enumerate(data) :\n print('** test %2d/%2d : ' % (i + 1, ldata), end='')\n n = dt[0]\n refr = dt[1]\n fb = nbOfBits(n)\n if fb == refr :\n score += 1\n printcol('{ok}','green')\n else :\n printcol('{Mauvais résultat}','red')\n print(' entree : %s' % n)\n print(' calcule : %d' % (fb) )\n print(' attendu : %d' % (refr) )\n printcol('{** Score %d/%d : %s}' % (score, ldata, \"super !\" if score==ldata else \"essaie encore !\"),'bold')\n print()\n \n#\n# NE PAS MODIFIER\n#\ndef test_fibo_1_bits_Data() :\n return [(-1, (0, 0)), (2, (1, 0)), (4, (3, 4)), (8, (21, 52)), (10, (55, 146)), (16, (987, 2760))]\n\n#\n# NE PAS MODIFIER\n#\ndef test_fibo_2_bits_Data() :\n return [(-1, (0, 0)), (2, (1, 1)), (4, (3, 5)), (8, (21, 21)), (10, (55, 33)), (16, (987, 85))]\n\n#\n# NE PAS MODIFIER\n#\ndef test_fibo_3_bits_Data() :\n return [(-1, (0, 0)), (2, (1, 1)), (4, (3, 5)), (8, (21, 21)), (10, (55, 33)), (16, (987, 85))]\n\n#\n# NE PAS MODIFIER\n#\ndef test_fibo_bitsData(i) :\n if i == 1 : return test_fibo_1_bits_Data()\n elif i == 2 : return test_fibo_2_bits_Data()\n else : return test_fibo_3_bits_Data()\n\n#\n# NE PAS MODIFIER\n#\ndef test_fibo_bits(num):\n algos = [fibo_1_bits, fibo_2_bits, fibo_3_bits]\n printcol('{Test %s:}' % algos[num-1].__name__,'bold')\n score = 0\n data = test_fibo_bitsData(num)\n ldata = len(data)\n for i, dt in enumerate(data) :\n print('** test %2d/%2d : ' % (i + 1, ldata), end='')\n n = dt[0]\n Tres, Tops = dt[1]\n fb, ops = algos[num-1](n)\n if (fb == Tres and ops == Tops):\n score += 1\n printcol('{ok}','green')\n elif (fb == Tres):\n printcol('{Mauvais nombre d\\'opérations}','yellow')\n print(' entree : %s' % n)\n print(' calcule : %d en %d ops' % (fb,ops) )\n print(' attendu : %d en %d ops' % (Tres,Tops) )\n else :\n printcol('Mauvais résultat','red')\n print(' entree : %s' % n)\n print(' calcule : %d en %d ops' % (fb,ops))\n print(' attendu : %d en %d ops' % (Tres,Tops) )\n printcol('{** Score %d/%d : %s}' % (score, ldata, \"super !\" if score==ldata else \"essaie encore !\"),'bold')\n print()\n \nif __name__ == '__main__':\n courbes_temps(30)\n courbes_temps(100000, tous=False, pas=1000)\n \n test_fibo_adds(1)\n test_fibo_adds(2)\n test_fibo_adds(3)\n courbes_adds(30)\n courbes_adds(1000, tous=False)\n test_nbOfBits()\n test_fibo_bits(1)\n test_fibo_bits(2)\n test_fibo_bits(3) \n courbes_ops(30)\n courbes_ops(50000, tous=False, pas=1000)\n", "repo_name": "ryohkhn/University", "sub_path": "L2/EA2/TP2/tp2_ex1.py", "file_name": "tp2_ex1.py", "file_ext": "py", "file_size_in_byte": 12705, "program_lang": "python", "lang": "fr", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "matplotlib.pyplot.plot", "line_number": 107, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 108, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 109, "usage_type": "call"}, {"api_name": "math.floor", "line_number": 118, "usage_type": "call"}, {"api_name": "math.log", "line_number": 118, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 163, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 182, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 184, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 189, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 190, "usage_type": "call"}, {"api_name": "time.process_time", "line_number": 202, "usage_type": "call"}, {"api_name": "time.process_time", "line_number": 204, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 225, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 228, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 229, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 230, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 233, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 238, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 239, "usage_type": "call"}, {"api_name": "ea4lib.printcol", "line_number": 277, "usage_type": "call"}, {"api_name": "ea4lib.printcol", "line_number": 288, "usage_type": "call"}, {"api_name": "ea4lib.printcol", "line_number": 290, "usage_type": "call"}, {"api_name": "ea4lib.printcol", "line_number": 295, "usage_type": "call"}, {"api_name": "ea4lib.printcol", "line_number": 299, "usage_type": "call"}, {"api_name": "ea4lib.printcol", "line_number": 317, "usage_type": "call"}, {"api_name": "ea4lib.printcol", "line_number": 328, "usage_type": "call"}, {"api_name": "ea4lib.printcol", "line_number": 330, "usage_type": "call"}, {"api_name": "ea4lib.printcol", "line_number": 334, "usage_type": "call"}, {"api_name": "ea4lib.printcol", "line_number": 368, "usage_type": "call"}, {"api_name": "ea4lib.printcol", "line_number": 379, "usage_type": "call"}, {"api_name": "ea4lib.printcol", "line_number": 381, "usage_type": "call"}, {"api_name": "ea4lib.printcol", "line_number": 386, "usage_type": "call"}, {"api_name": "ea4lib.printcol", "line_number": 390, "usage_type": "call"}]} +{"seq_id": "8738998440", "text": "from http.server import BaseHTTPRequestHandler, HTTPServer\nimport json\nimport RPi.GPIO as GPIO\nimport dht11\nfrom datetime import datetime\n\nserverPort = 8001\nsensorPin = 18\ntempOffset = -2\nhumidityOffset = 0\nupdateIntervalS = 10\n\ndhtClientInstance = None\ncurrentReadings = None\n\ndef update_readings():\n global currentReadings\n \n if currentReadings is not None:\n currentTimeStamp = datetime.timestamp(datetime.now())\n readTimeStamp = datetime.timestamp(currentReadings.get('readAt'))\n if readTimeStamp + updateIntervalS > currentTimeStamp:\n return\n \n res = dhtClientInstance.read()\n if not res.is_valid():\n print(\"got invalid reading from sensor\")\n return\n\n currentReadings = {\n \"tempC\" : res.temperature + tempOffset,\n \"humidity\": .01 * res.humidity + humidityOffset,\n \"readAt\": datetime.now()\n }\n\nclass MyServer(BaseHTTPRequestHandler):\n def do_GET(self):\n update_readings()\n\n if currentReadings is None:\n self.send_response(500)\n self.send_header(\"Content-type\", \"text/plain\")\n self.send_header('Access-Control-Allow-Origin', '*')\n self.end_headers()\n return\n \n self.send_response(200)\n self.send_header(\"Content-type\", \"application/json\")\n self.send_header('Access-Control-Allow-Origin', '*')\n self.end_headers()\n self.wfile.write(bytes(json.dumps({\n \"tempC\": currentReadings.get(\"tempC\"),\n \"humidity\": currentReadings.get(\"humidity\"),\n \"readAt\": currentReadings.get(\"readAt\").isoformat(),\n }), \"utf-8\"))\n\nif __name__ == \"__main__\":\n GPIO.setwarnings(False)\n GPIO.setmode(GPIO.BCM)\n GPIO.cleanup()\n \n dhtClientInstance = dht11.DHT11(pin = sensorPin)\n webServer = HTTPServer((\"0.0.0.0\", serverPort), MyServer)\n\n print(\"Server started on port %s\" % (serverPort))\n webServer.serve_forever()\n", "repo_name": "t0rbn/dht11-pi-py", "sub_path": "server.py", "file_name": "server.py", "file_ext": "py", "file_size_in_byte": 1963, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "datetime.datetime.timestamp", "line_number": 20, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 20, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 20, "usage_type": "call"}, {"api_name": "datetime.datetime.timestamp", "line_number": 21, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 21, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 33, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 33, "usage_type": "name"}, {"api_name": "http.server.BaseHTTPRequestHandler", "line_number": 36, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 51, "usage_type": "call"}, {"api_name": "RPi.GPIO.setwarnings", "line_number": 58, "usage_type": "call"}, {"api_name": "RPi.GPIO", "line_number": 58, "usage_type": "name"}, {"api_name": "RPi.GPIO.setmode", "line_number": 59, "usage_type": "call"}, {"api_name": "RPi.GPIO", "line_number": 59, "usage_type": "name"}, {"api_name": "RPi.GPIO.BCM", "line_number": 59, "usage_type": "attribute"}, {"api_name": "RPi.GPIO.cleanup", "line_number": 60, "usage_type": "call"}, {"api_name": "RPi.GPIO", "line_number": 60, "usage_type": "name"}, {"api_name": "dht11.DHT11", "line_number": 62, "usage_type": "call"}, {"api_name": "http.server.HTTPServer", "line_number": 63, "usage_type": "call"}]} +{"seq_id": "33760563438", "text": "# -*- coding: utf-8 -*-\n\"\"\"\n @Time : 2020/8/14 12:17\n @Author : QDY\n @FileName: 87. 扰乱字符串.py\n @Software: PyCharm\n\"\"\"\n\"\"\"\n 给定一个字符串s1，我们可以把它递归地分割成两个非空子字符串，从而将其表示为二叉树。\n 下图是字符串s1=\"great\"的一种可能的表示形式。\n \n great\n / \\\n gr eat\n / \\ / \\\n g r e at\n / \\\n a t\n 在扰乱这个字符串的过程中，我们可以挑选任何��个非叶节点，然后交换它的两个子节点。\n 例如，如果我们挑选非叶节点\"gr\"，交换它的两个子节点，将会产生扰乱字符串\"rgeat\"。\n \n rgeat\n / \\\n rg eat\n / \\ / \\\n r g e at\n / \\\n a t\n 我们将\"rgeat”称作\"great\"的一个扰乱字符串。\n 同样地，如果我们继续交换节点\"eat\"和\"at\"的子节点，将会产生另一个新的扰乱字符串\"rgtae\"。\n \n rgtae\n / \\\n rg tae\n / \\ / \\\n r g ta e\n / \\\n t a\n 我们将\"rgtae”称作\"great\"的一个扰乱字符串。\n 给出两个长度相等的字符串 s1 和s2，判断s2是否是s1的扰乱字符串。\n \n 示例1:\n 输入: s1 = \"great\", s2 = \"rgeat\"\n 输出: true\n \n 示例2:\n 输入: s1 = \"abcde\", s2 = \"caebd\"\n 输出: false\n\n\"\"\"\nfrom collections import Counter\n\n\nclass Solution:\n def isScramble(self, s1: str, s2: str) -> bool:\n len1, len2 = len(s1), len(s2)\n if len1 != len2: return False\n # # 动态规划\n # # dp[i][j][l] = s1[i:i+l+1]与s2[j:j+l+1]是否可转换\n # dp = [[[False]*len1 for j in range(len1)] for i in range(len1)]\n # for i in range(len1):\n # for j in range(len2):\n # if s1[i] == s2[j]:\n # dp[i][j][0] = True\n # for l in range(1,len1):\n # for i in range(len1-l):\n # for j in range(len2-l):\n # for k in range(l): # 遍历每个切分点\n # dp[i][j][l] = dp[i][j][k] and dp[i+k+1][j+k+1][l-1-k]\n # if dp[i][j][l]:break\n # dp[i][j][l] = dp[i][j+(l-k)][k] and dp[i+k+1][j][l-1-k]\n # if dp[i][j][l]:break\n # return dp[0][0][len1-1]\n\n # 递归\n if s1 == s2: return True\n c1, c2 = Counter(s1), Counter(s2)\n if c1 != c2: return False\n if len1 <= 2: return True\n for i in range(1, len1):\n if self.isScramble(s1[:i], s2[:i]) and self.isScramble(s1[i:], s2[i:]) or \\\n (self.isScramble(s1[:i], s2[-i:]) and self.isScramble(s1[i:], s2[:-i])):\n return True\n return False\n", "repo_name": "QDylan/Learning-", "sub_path": "Leetcode/87. 扰乱字符串.py", "file_name": "87. 扰乱字符串.py", "file_ext": "py", "file_size_in_byte": 2814, "program_lang": "python", "lang": "zh", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "37", "api": [{"api_name": "collections.Counter", "line_number": 77, "usage_type": "call"}]} +{"seq_id": "29744487678", "text": "# file: metaparameters.py\n\nimport eugene as eu\nimport numpy as np\nimport multiprocessing\nfrom joblib import Parallel, delayed\nfrom operator import itemgetter\nimport copy\nimport pdb\n\n\ndef cost(error_matrix):\n m = np.copy(error_matrix)\n m[np.where(m==2)] = 1\n m[np.where(m==3)] = 2\n return np.sum(m)\n\n\ndef tune_ic_loop_func(num_frags, reps, series, alpha, beta, mu_spec):\n tmp = eu.fragment_timeseries.split_timeseries(series, num_frags)\n untrans, trans, error = eu.initial_conditions.choose_untrans_trans(\n tmp, reps, alpha=alpha, beta=beta, mu_spec=mu_spec, report=True)\n error_full_series = cost(error)\n # compute leave-one-out errors\n sum_of_loo_errors = 0\n for ii in range(len(series)):\n loo_series = []\n for jj, ss in enumerate(series):\n if not jj == ii:\n loo_series.append(ss)\n tmp = eu.fragment_timeseries.split_timeseries(loo_series, num_frags)\n untrans, trans, error = eu.initial_conditions.choose_untrans_trans(\n tmp, reps, alpha=alpha, beta=beta, mu_spec=mu_spec, report=True)\n sum_of_loo_errors += cost(error)\n\n ll = sum_of_loo_errors + error_full_series\n\n return [num_frags, reps, ll]\n\n\ndef pareto_optimal(option_dictionary):\n tmp = sorted(option_dictionary, key=itemgetter(1))\n ordered = sorted(tmp, key=itemgetter(2))\n min_cost = ordered[0][2]\n for option in ordered:\n if option[2] == min_cost:\n best_params = tuple(option[:2])\n return best_params, min_cost\n\n\ndef tune_ic_selection(\n series, # a list of timeseries to be tested\n num_frags_range=None, # a list giving high and low values for fragments\n min_reps = 5, # the minimum number of reps to select\n max_reps = None, # the maximum number of replicates to consider\n min_len=10, # the minimum tolerable fragment length\n max_len=None, # the maximum tolerable fragment length\n alpha=0.5,\n beta=0.2,\n mu_spec=None,\n parallel_compute=True, # switch for use of multithreading\n free_cores=2, # cores to leave free when multithreading\n warnings=True # indicates whether or not to display warnings\n ):\n\n \"\"\" \n Purpose:\n Sets optimal values for the following metaparameters:\n num_frags: number of fragments into which to split each timeseries\n reps: number of replicates to select from each pool\n\n Method:\n Performs a brute-force combinatorial search over discrete parameter\n space as bounded by the given ranges in order to minimize the total\n number of errors (i.e., statistically significant differences in the\n distribution of initial values for the selected untrans and trans sets\n drawn from the fragmented time series.\n \"\"\"\n # deal with warnings\n if not warnings:\n import warnings\n warnings.simplefilter(\"ignore\")\n\n # gather info about input\n min_series_len = series[0].shape[1] \n for ts in series:\n tmp = ts.shape[1]\n if tmp < min_series_len:\n min_series_len = tmp\n print(\"min_series_len = {}\".format(min_series_len))\n\n # set up ranges\n if max_len is None:\n max_len = int(2 * min_len)\n if num_frags_range is None:\n lo = int(min_series_len / max_len) \n hi = int(min_series_len / min_len) \n num_frags_range = [lo, hi]\n if max_reps is None:\n max_reps = int(num_frags_range[1] / 10)\n print(\"num_frags_range = {}\".format(num_frags_range))\n print(\"min reps = {}\".format(min_reps))\n print(\"max reps = {}\".format(max_reps))\n\n # enter loop\n min_cost = np.inf\n best_params = ()\n\n if parallel_compute:\n cpus = max(multiprocessing.cpu_count() - free_cores, 1)\n out = Parallel(n_jobs=cpus,\n verbose=5)(delayed(tune_ic_loop_func)(num_frags, reps, series, alpha, beta, mu_spec) \n for num_frags in range(num_frags_range[0], num_frags_range[1] + 1)\n for reps in range(min_reps, max_reps + 1))\n # find a Pareto optimal solution (the most reps at the lowest cost)\n best_params, min_cost = pareto_optimal(out)\n\n else:\n # compute cost function for each of the 3**4 combinations\n for num_frags in range(num_frags_range[0], num_frags_range[1] + 1):\n for reps in range(min_reps, int(num_frags / 2)):\n tmp = eu.fragment_timeseries.split_timeseries(series, num_frags)\n untrans, trans, error = eu.initial_conditions.choose_untrans_trans(\n tmp, reps, alpha=alpha, beta=beta, mu_spec=mu_spec, report=True)\n ll = cost(error)\n if ll < min_cost:\n min_cost = ll\n best_params = (num_frags, reps)\n if min_cost == 0:\n break\n if min_cost == 0:\n break\n\n return best_params, min_cost\n\ndef tune_offsets(\n series, # a dictionary of timeseries as d x p np-arrays\n num_frags, # approximate number of fragments into which to divide each timeseries\n reps, # the number of replicates to select for each timeseries\n alpha=0.5,\n beta=0.2,\n mu_spec=None,\n warnings=True # indicates whether or not to display warnings\n ):\n\n # deal with warnings\n if not warnings:\n import warnings\n warnings.simplefilter(\"ignore\")\n\n # determine the best fragment length \n lengths = []\n for key in sorted(series.keys()):\n ll = series[key].shape[1]\n lengths.append(ll)\n min_len = min(lengths)\n frag_length = int(np.ceil(min_len / num_frags))\n\n # construct the set of fragments for each time series\n series_list = []\n for key in sorted(series.keys()):\n series_list.append(series[key])\n frags = eu.fragment_timeseries.fixed_length_frags(series_list, frag_length)\n\n # construct the set of initials for each time series\n series_initials = dict([])\n for ii, key in enumerate(sorted(series.keys())):\n initials = []\n tmp = frags[ii]\n for seg in tmp:\n initials.append(seg[:,0].reshape(-1, 1))\n series_initials[key] = np.concatenate(initials, axis=1)\n \n # identify the two sets of initials whose means are the farthest apart\n max_distance = -1.\n for ii, key1 in enumerate(sorted(series_initials.keys())):\n for jj, key2 in enumerate(sorted(series_initials.keys())):\n if not jj == ii:\n mu1 = np.mean(series_initials[key1], axis=1)\n mu2 = np.mean(series_initials[key2], axis=1)\n distance = np.linalg.norm(mu1 - mu2)\n if distance > max_distance:\n max_distance = distance\n gd = [ii, jj]\n gd_keys = [key1, key2]\n \n # loop to fix offset for the first pair of sets\n# frag_length = frags[gd[0]][0].shape[1]\n print(\"Frag length: {}\".format(frag_length))\n best_offset = 0\n min_cost = np.inf\n for offset in range(frag_length):\n data2 = series[gd_keys[1]][:,offset:]\n data1 = series[gd_keys[0]][:,:data2.shape[1]]\n tmp = eu.fragment_timeseries.fixed_length_frags([data1, data2],\n frag_length)\n tmp = eu.fragment_timeseries.trim(tmp)\n untrans, trans, error = eu.initial_conditions.choose_untrans_trans(\n tmp, reps, alpha=alpha, beta=beta, mu_spec=mu_spec, report=True)\n ll = cost(error)\n if ll < min_cost:\n min_cost = ll\n best_offset = offset\n offsets = dict([])\n costs = dict([])\n offsets[gd_keys[0]] = 0\n offsets[gd_keys[1]] = best_offset\n costs[gd_keys[0]] = 0\n costs[gd_keys[1]] = min_cost\n\n remaining_keys = sorted(series.keys())\n remaining_keys.remove(gd_keys[0])\n remaining_keys.remove(gd_keys[1])\n\n # loop over the remaining sets and possible offsets for each to find the\n # best overall combination (not necessarily the global best)\n data = []\n data.append(series[gd_keys[0]][:,offsets[gd_keys[0]]:])\n data.append(series[gd_keys[1]][:,offsets[gd_keys[1]]:])\n for key in remaining_keys:\n best_offset = 0\n min_cost = np.inf\n for offset in range(frag_length):\n tmp_data = copy.deepcopy(data)\n tmp_data.append(series[key][:,offset:])\n # trim all of the data to the same length\n min_len = np.inf\n for td in tmp_data:\n series_len = td.shape[1]\n if series_len < min_len:\n min_len = series_len\n for ii, td in enumerate(tmp_data):\n tmp_data[ii] = td[:, :min_len]\n tmp = eu.fragment_timeseries.fixed_length_frags(tmp_data,\n frag_length)\n tmp = eu.fragment_timeseries.trim(tmp)\n untrans, trans, error = eu.initial_conditions.choose_untrans_trans(\n tmp, reps, alpha=alpha, beta=beta, mu_spec=mu_spec, report=True)\n ll = cost(error)\n if ll < min_cost:\n min_cost = ll\n best_offset = offset\n offsets[key] = best_offset\n costs[key] = min_cost\n data.append(series[key][:,offsets[key]:])\n \n return offsets, costs, frag_length\n\n\ndef apply_offsets(offsets, data):\n offset_data = dict([])\n for key in sorted(offsets.keys()):\n offset = offsets[key]\n offset_data[key] = data[key][:, offset:]\n \n # clip all time series to the same length\n kk = sorted(offset_data.keys())\n min_series_len = offset_data[kk[0]].shape[1]\n for kk in sorted(offset_data.keys()):\n ll = offset_data[kk].shape[1]\n if ll < min_series_len:\n min_series_len = ll\n \n print('Clipping all data to length {}...'.format(min_series_len))\n for key in sorted(offset_data.keys()):\n data = offset_data[key][:, :min_series_len]\n offset_data[key] = data\n \n return offset_data\n", "repo_name": "jantzen/eugene", "sub_path": "eugene/src/data_prep/metaparameters.py", "file_name": "metaparameters.py", "file_ext": "py", "file_size_in_byte": 10038, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "37", "api": [{"api_name": "numpy.copy", "line_number": 13, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 14, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 15, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 16, "usage_type": "call"}, {"api_name": "eugene.fragment_timeseries.split_timeseries", "line_number": 20, "usage_type": "call"}, {"api_name": "eugene.fragment_timeseries", "line_number": 20, "usage_type": "attribute"}, {"api_name": "eugene.initial_conditions.choose_untrans_trans", "line_number": 21, "usage_type": "call"}, {"api_name": "eugene.initial_conditions", "line_number": 21, "usage_type": "attribute"}, {"api_name": "eugene.fragment_timeseries.split_timeseries", "line_number": 31, "usage_type": "call"}, {"api_name": "eugene.fragment_timeseries", "line_number": 31, "usage_type": "attribute"}, {"api_name": "eugene.initial_conditions.choose_untrans_trans", "line_number": 32, "usage_type": "call"}, {"api_name": "eugene.initial_conditions", "line_number": 32, "usage_type": "attribute"}, {"api_name": "operator.itemgetter", "line_number": 42, "usage_type": "call"}, {"api_name": "operator.itemgetter", "line_number": 43, "usage_type": "call"}, {"api_name": "warnings.simplefilter", "line_number": 82, "usage_type": "call"}, {"api_name": "numpy.inf", "line_number": 106, "usage_type": "attribute"}, {"api_name": "multiprocessing.cpu_count", "line_number": 110, "usage_type": "call"}, {"api_name": "joblib.Parallel", "line_number": 111, "usage_type": "call"}, {"api_name": "joblib.delayed", "line_number": 112, "usage_type": "call"}, {"api_name": "eugene.fragment_timeseries.split_timeseries", "line_number": 122, "usage_type": "call"}, {"api_name": "eugene.fragment_timeseries", "line_number": 122, "usage_type": "attribute"}, {"api_name": "eugene.initial_conditions.choose_untrans_trans", "line_number": 123, "usage_type": "call"}, {"api_name": "eugene.initial_conditions", "line_number": 123, "usage_type": "attribute"}, {"api_name": "warnings.simplefilter", "line_number": 149, "usage_type": "call"}, {"api_name": "numpy.ceil", "line_number": 157, "usage_type": "call"}, {"api_name": "eugene.fragment_timeseries.fixed_length_frags", "line_number": 163, "usage_type": "call"}, {"api_name": "eugene.fragment_timeseries", "line_number": 163, "usage_type": "attribute"}, {"api_name": "numpy.concatenate", "line_number": 172, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 179, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 180, "usage_type": "call"}, {"api_name": "numpy.linalg.norm", "line_number": 181, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 181, "usage_type": "attribute"}, {"api_name": "numpy.inf", "line_number": 191, "usage_type": "attribute"}, {"api_name": "eugene.fragment_timeseries.fixed_length_frags", "line_number": 195, "usage_type": "call"}, {"api_name": "eugene.fragment_timeseries", "line_number": 195, "usage_type": "attribute"}, {"api_name": "eugene.fragment_timeseries.trim", "line_number": 197, "usage_type": "call"}, {"api_name": "eugene.fragment_timeseries", "line_number": 197, "usage_type": "attribute"}, {"api_name": "eugene.initial_conditions.choose_untrans_trans", "line_number": 198, "usage_type": "call"}, {"api_name": "eugene.initial_conditions", "line_number": 198, "usage_type": "attribute"}, {"api_name": "numpy.inf", "line_number": 222, "usage_type": "attribute"}, {"api_name": "copy.deepcopy", "line_number": 224, "usage_type": "call"}, {"api_name": "numpy.inf", "line_number": 227, "usage_type": "attribute"}, {"api_name": "eugene.fragment_timeseries.fixed_length_frags", "line_number": 234, "usage_type": "call"}, {"api_name": "eugene.fragment_timeseries", "line_number": 234, "usage_type": "attribute"}, {"api_name": "eugene.fragment_timeseries.trim", "line_number": 236, "usage_type": "call"}, {"api_name": "eugene.fragment_timeseries", "line_number": 236, "usage_type": "attribute"}, {"api_name": "eugene.initial_conditions.choose_untrans_trans", "line_number": 237, "usage_type": "call"}, {"api_name": "eugene.initial_conditions", "line_number": 237, "usage_type": "attribute"}]} +{"seq_id": "39380341963", "text": "# %%\r\nimport os\r\nfrom os.path import join\r\nimport argparse\r\n\r\nfrom time import perf_counter\r\n\r\nimport cv2\r\n\r\nfrom glob import glob\r\nfrom random import choice\r\n\r\nimport pathlib\r\nimport matplotlib\r\nimport matplotlib.pyplot as plt\r\n\r\nimport io\r\nimport scipy.misc\r\nimport numpy as np\r\nfrom six import BytesIO\r\nfrom PIL import Image, ImageDraw, ImageFont\r\n\r\nimport tensorflow as tf\r\n\r\nfrom object_detection.utils import label_map_util\r\nfrom object_detection.utils import config_util\r\nfrom object_detection.utils import visualization_utils as viz_utils\r\nfrom object_detection.builders import model_builder\r\n# %%\r\n\r\n\r\ndef get_keypoint_tuples(eval_config):\r\n \"\"\"Return a tuple list of keypoint edges from the eval config.\r\n\r\n Args:\r\n eval_config: an eval config containing the keypoint edges\r\n\r\n Returns:\r\n a list of edge tuples, each in the format (start, end)\r\n \"\"\"\r\n tuple_list = []\r\n kp_list = eval_config.keypoint_edge\r\n for edge in kp_list:\r\n tuple_list.append((edge.start, edge.end))\r\n return tuple_list\r\n\r\n\r\ndef get_model_detection_function(model):\r\n \"\"\"Get a tf.function for detection.\"\"\"\r\n\r\n @tf.function\r\n def detect_fn(image):\r\n \"\"\"Detect objects in image.\"\"\"\r\n\r\n image, shapes = model.preprocess(image)\r\n prediction_dict = model.predict(image, shapes)\r\n detections = model.postprocess(prediction_dict, shapes)\r\n\r\n return detections, prediction_dict, tf.reshape(shapes, [-1])\r\n\r\n return detect_fn\r\n\r\n\r\n# %%\r\n\r\nparser = argparse.ArgumentParser(description='Convert .xml files into .csv.')\r\nparser.add_argument('-m', '--ckpt_model_path', required=True,\r\n type=str, help='Inference model CKPT path')\r\nparser.add_argument('-c', '--config_model_path', required=True,\r\n type=str, help='Inference model Config path')\r\nparser.add_argument('-v', '--video_path', required=True,\r\n type=str, help='Video path that will be processed')\r\nargs = parser.parse_args()\r\n\r\npipeline_config = args.config_model_pathos\r\nmodel_dir = args.ckpt_model_path\r\n\r\n# Load pipeline config and build a detection model\r\nconfigs = config_util.get_configs_from_pipeline_file(pipeline_config)\r\nmodel_config = configs['model']\r\ndetection_model = model_builder.build(\r\n model_config=model_config, is_training=False)\r\n\r\n# Restore checkpoint\r\nckpt = tf.compat.v2.train.Checkpoint(\r\n model=detection_model)\r\nckpt.restore(os.path.join(model_dir, 'ckpt-0')).expect_partial()\r\n\r\ndetect_fn = get_model_detection_function(detection_model)\r\n\r\nlabel_map_path = configs['eval_input_config'].label_map_path\r\nlabel_map = label_map_util.load_labelmap(label_map_path)\r\ncategories = label_map_util.convert_label_map_to_categories(\r\n label_map,\r\n max_num_classes=label_map_util.get_max_label_map_index(label_map),\r\n use_display_name=True)\r\ncategory_index = label_map_util.create_category_index(categories)\r\nlabel_map_dict = label_map_util.get_label_map_dict(\r\n label_map, use_display_name=True)\r\n# %%\r\nvideo_path = args.video_path\r\ncap = cv2.VideoCapture(video_path)\r\n\r\nwhile True:\r\n initial_time = perf_counter()\r\n ret, frame = cap.read()\r\n\r\n if not ret:\r\n break\r\n\r\n image_np = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\r\n\r\n input_tensor = tf.convert_to_tensor(\r\n np.expand_dims(image_np, 0), dtype=tf.float32)\r\n detections, predictions_dict, shapes = detect_fn(input_tensor)\r\n\r\n label_id_offset = 1\r\n image_np_with_detections = image_np.copy()\r\n\r\n # Use keypoints if available in detections\r\n keypoints, keypoint_scores = None, None\r\n if 'detection_keypoints' in detections:\r\n keypoints = detections['detection_keypoints'][0].numpy()\r\n keypoint_scores = detections['detection_keypoint_scores'][0].numpy()\r\n\r\n viz_utils.visualize_boxes_and_labels_on_image_array(\r\n image_np_with_detections,\r\n detections['detection_boxes'][0].numpy(),\r\n (detections['detection_classes']\r\n [0].numpy() + label_id_offset).astype(int),\r\n detections['detection_scores'][0].numpy(),\r\n category_index,\r\n use_normalized_coordinates=True,\r\n max_boxes_to_draw=200,\r\n min_score_thresh=.30,\r\n agnostic_mode=False,\r\n keypoints=keypoints,\r\n keypoint_scores=keypoint_scores)\r\n\r\n elapsed_time = perf_counter() - initial_time\r\n elapsed_time = 0.000001 if elapsed_time == 0 else elapsed_time\r\n print(f\"FPS :: {1/elapsed_time:.2f} || TIME :: {elapsed_time:.2f}\")\r\n\r\n cv2.imshow(\"Video\", cv2.cvtColor(\r\n image_np_with_detections, cv2.COLOR_RGB2BGR))\r\n key = cv2.waitKey(1)\r\n if key == ord('q'):\r\n break\r\n\r\ncv2.destroyAllWindows()\r\n", "repo_name": "BrunoGeorgevich/CustomObjectDetectionApi", "sub_path": "Object_detection_video.py", "file_name": "Object_detection_video.py", "file_ext": "py", "file_size_in_byte": 4683, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "tensorflow.reshape", "line_number": 59, "usage_type": "call"}, {"api_name": "tensorflow.function", "line_number": 51, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentParser", "line_number": 66, "usage_type": "call"}, {"api_name": "object_detection.utils.config_util.get_configs_from_pipeline_file", "line_number": 79, "usage_type": "call"}, {"api_name": "object_detection.utils.config_util", "line_number": 79, "usage_type": "name"}, {"api_name": "object_detection.builders.model_builder.build", "line_number": 81, "usage_type": "call"}, {"api_name": "object_detection.builders.model_builder", "line_number": 81, "usage_type": "name"}, {"api_name": "tensorflow.compat.v2.train.Checkpoint", "line_number": 85, "usage_type": "call"}, {"api_name": "tensorflow.compat", "line_number": 85, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 87, "usage_type": "call"}, {"api_name": "os.path", "line_number": 87, "usage_type": "attribute"}, {"api_name": "object_detection.utils.label_map_util.load_labelmap", "line_number": 92, "usage_type": "call"}, {"api_name": "object_detection.utils.label_map_util", "line_number": 92, "usage_type": "name"}, {"api_name": "object_detection.utils.label_map_util.convert_label_map_to_categories", "line_number": 93, "usage_type": "call"}, {"api_name": "object_detection.utils.label_map_util", "line_number": 93, "usage_type": "name"}, {"api_name": "object_detection.utils.label_map_util.get_max_label_map_index", "line_number": 95, "usage_type": "call"}, {"api_name": "object_detection.utils.label_map_util", "line_number": 95, "usage_type": "name"}, {"api_name": "object_detection.utils.label_map_util.create_category_index", "line_number": 97, "usage_type": "call"}, {"api_name": "object_detection.utils.label_map_util", "line_number": 97, "usage_type": "name"}, {"api_name": "object_detection.utils.label_map_util.get_label_map_dict", "line_number": 98, "usage_type": "call"}, {"api_name": "object_detection.utils.label_map_util", "line_number": 98, "usage_type": "name"}, {"api_name": "cv2.VideoCapture", "line_number": 102, "usage_type": "call"}, {"api_name": "time.perf_counter", "line_number": 105, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 111, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2RGB", "line_number": 111, "usage_type": "attribute"}, {"api_name": "tensorflow.convert_to_tensor", "line_number": 113, "usage_type": "call"}, {"api_name": "numpy.expand_dims", "line_number": 114, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 114, "usage_type": "attribute"}, {"api_name": "object_detection.utils.visualization_utils.visualize_boxes_and_labels_on_image_array", "line_number": 126, "usage_type": "call"}, {"api_name": "object_detection.utils.visualization_utils", "line_number": 126, "usage_type": "name"}, {"api_name": "time.perf_counter", "line_number": 140, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 144, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 144, "usage_type": "call"}, {"api_name": "cv2.COLOR_RGB2BGR", "line_number": 145, "usage_type": "attribute"}, {"api_name": "cv2.waitKey", "line_number": 146, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 150, "usage_type": "call"}]} +{"seq_id": "877640218", "text": "# Cube data format functions, adapted from here:\n# https://gist.github.com/aditya95sriram/8d1fccbb91dae93c4edf31cd6a22510f\n\nimport io\n\n\nclass CubeFile(object):\n \"\"\"\n Object which mimics a cube file opened as a file object \n by returning output in the correct format, matching the \n metadata of the source cube file and replacing volumetric\n data with static data provided as arg to the constructor. \n Doesn't copy atoms metadata, retains number of atoms, but\n returns dummy atoms\n Mimics file object's readline method.\n \n params:\n srcname: source file to copy metadata from\n const: numeric value to return instead of volumetric data\n \n returns: CubeFile object\n \"\"\"\n\n def __init__(self, srcname, const=1):\n self.cursor = 0 \n self.const = const\n self.src = src = open(srcname)\n src.readline(); src.readline(); # comments\n _debug(srcname)\n self.lines = [\" Cubefile created by cubetools.py\\n\", \n \" source: {0}\\n\".format(srcname)]\n self.lines.append(src.readline()) # read natm and origin\n self.natm = int(self.lines[-1].strip().split()[0])\n # read cube dim and vectors along 3 axes\n self.lines.extend(src.readline() for i in range(3))\n self.src.close()\n self.nx, self.ny, self.nz = [int(l.strip().split()[0]) for l in self.lines[3:6]]\n self.remvals = self.nz\n self.remrows = self.nx*self.ny\n for i in range(self.natm):\n self.lines.append(\"{0:^ 8d}\".format(1) + \"{0:< 12.6f}\".format(0)*4 + '\\n')\n\n def __del__(self):\n self.src.close()\n\n def readline(self):\n \"\"\" Mimic readline method of file object with cube file opened \"\"\"\n try:\n retval = self.lines[self.cursor]\n except IndexError:\n if not self.remrows:\n return \"\"\n if self.remvals <= 6:\n nval = min(6,self.remvals)\n self.remrows -= 1\n self.remvals = self.nz \n else:\n nval = 6\n self.remvals -= nval\n return \" {0: .5E}\".format(self.const)*nval + \"\\n\"\n else:\n self.cursor += 1\n return retval\n \ndef _getline(cube):\n \"\"\"\n Read a line from cube file where first field is an int \n and the remaining fields are floats.\n \n params:\n cube: file object of the cube file\n \n returns: (int, list)\n \"\"\"\n l = cube.readline().strip().split()\n return int(l[0]), map(float, l[1:])\n\ndef _putline(*args):\n \"\"\"\n Generate a line to be written to a cube file where \n the first field is an int and the remaining fields are floats.\n \n params:\n *args: first arg is formatted as int and remaining as floats\n \n returns: formatted string to be written to file with trailing newline\n \"\"\"\n s = \"{0:^ 8d}\".format(args[0])\n s += \"\".join(\"{0:< 12.6f}\".format(arg) for arg in args[1:])\n return s + \"\\n\"\n \ndef read_cube(fname):\n \"\"\" \n Read cube file into numpy array\n \n params:\n fname: filename of cube file\n \n returns: (data: np.array, metadata: dict)\n \"\"\"\n meta = {}\n with open(fname, 'r') as cube:\n cube.readline(); cube.readline() # ignore comments\n natm, meta['org'] = _getline(cube)\n nx, meta['xvec'] = _getline(cube)\n ny, meta['yvec'] = _getline(cube)\n nz, meta['zvec'] = _getline(cube)\n meta['atoms'] = [_getline(cube) for i in range(natm)]\n data = np.zeros((nx*ny*nz))\n idx = 0\n for line in cube:\n for val in line.strip().split():\n data[idx] = float(val)\n idx += 1\n data = np.reshape(data, (nx, ny, nz))\n return data, meta\n\ndef read_cube_string(string):\n \"\"\" \n Read cube file into numpy array\n \n params:\n fname: filename of cube file\n \n returns: (data: np.array, metadata: dict)\n \"\"\"\n meta = {}\n cube = io.StringIO()\n cube.write(string)\n cube.seek(0)\n cube.readline(); cube.readline() # ignore comments\n natm, meta['org'] = _getline(cube)\n nx, meta['xvec'] = _getline(cube)\n ny, meta['yvec'] = _getline(cube)\n nz, meta['zvec'] = _getline(cube)\n meta['atoms'] = [_getline(cube) for i in range(natm)]\n data = np.zeros((nx*ny*nz))\n idx = 0\n for line in cube:\n for val in line.strip().split():\n data[idx] = float(val)\n idx += 1\n data = np.reshape(data, (nx, ny, nz))\n return data, meta\n \ndef read_imcube(rfname, ifname = \"\"):\n \"\"\"\n Convenience function to read in two cube files at once, \n where one contains the real part and the other contains the \n imag part. If only one filename given, other filename is inferred.\n \n params:\n rfname: filename of cube file of real part\n ifname: optional, filename of cube file of imag part\n \n returns: np.array (real part + j*imag part)\n \"\"\"\n ifname = ifname or rfname.replace('real', 'imag')\n _debug(\"reading from files\", rfname, \"and\", ifname)\n re, im = read_cube(rfname), read_cube(ifname)\n fin = np.zeros(re[0].shape, dtype='complex128')\n if re[1] != im[1]:\n _debug(\"warning: meta data mismatch, real part metadata retained\")\n fin += re[0] \n fin += 1j*im[0]\n return fin, re[1]\n\ndef write_cube(data, meta, fname):\n \"\"\"\n Write volumetric data to cube file along\n \n params:\n data: volumetric data consisting real values\n meta: dict containing metadata with following keys\n atoms: list of atoms in the form (mass, [position])\n org: origin\n xvec,yvec,zvec: lattice vector basis\n fname: filename of cubefile (existing files overwritten)\n \n returns: None\n \"\"\"\n with open(fname, \"w\") as cube:\n # first two lines are comments\n cube.write(\" Cubefile created by cubetools.py\\n source: none\\n\")\n natm = len(meta['atoms'])\n nx, ny, nz = data.shape\n cube.write(_putline(natm, *meta['org'])) # 3rd line #atoms and origin\n cube.write(_putline(nx, *meta['xvec']))\n cube.write(_putline(ny, *meta['yvec']))\n cube.write(_putline(nz, *meta['zvec']))\n for atom_mass, atom_pos in meta['atoms']:\n cube.write(_putline(atom_mass, *atom_pos)) #skip the newline\n for i in range(nx):\n for j in range(ny):\n for k in range(nz):\n if (i or j or k) and k%6==0:\n cube.write(\"\\n\")\n cube.write(\" {0: .5E}\".format(data[i,j,k]))\n\ndef write_cube_string(data, meta):\n \"\"\"\n Write volumetric data to cube file along\n \n params:\n data: volumetric data consisting real values\n meta: dict containing metadata with following keys\n atoms: list of atoms in the form (mass, [position])\n org: origin\n xvec,yvec,zvec: lattice vector basis\n fname: filename of cubefile (existing files overwritten)\n \n returns: None\n \"\"\"\n cube = io.StringIO()\n # first two lines are comments\n cube.write(\" Cubefile created by cubetools.py\\n source: none\\n\")\n natm = len(meta['atoms'])\n nx, ny, nz = data.shape\n cube.write(_putline(natm, *meta['org'])) # 3rd line #atoms and origin\n cube.write(_putline(nx, *meta['xvec']))\n cube.write(_putline(ny, *meta['yvec']))\n cube.write(_putline(nz, *meta['zvec']))\n for atom_mass, atom_pos in meta['atoms']:\n cube.write(_putline(atom_mass, *atom_pos)) #skip the newline\n for i in range(nx):\n for j in range(ny):\n for k in range(nz):\n if (i or j or k) and k%6==0:\n cube.write(\"\\n\")\n cube.write(\" {0: .5E}\".format(data[i,j,k]))\n return cube.getvalue()\n\ndef write_imcube(data, meta, rfname, ifname=\"\"):\n \"\"\"\n Convenience function to write two cube files from complex valued \n volumetric data, one for the real part and one for the imaginary part.\n Data about atoms, origin and lattice vectors are kept same for both.\n If only one filename given, other filename is inferred.\n \n params: \n data: volumetric data consisting complex values\n meta: dict containing metadata with following keys\n atoms: list of atoms in the form (mass, [position])\n org: origin\n xvec,yvec,zvec: lattice vector basis\n rfname: filename of cube file containing real part\n ifname: optional, filename of cube file containing imag part\n \n returns: None\n \"\"\"\n ifname = ifname or rfname.replace('real', 'imag')\n _debug(\"writing data to files\", rfname, \"and\", ifname)\n write_cube(data.real, meta, rfname)\n write_cube(data.imag, meta, ifname)\n", "repo_name": "cschlick/cctbx-notebooks", "sub_path": "cubetools.py", "file_name": "cubetools.py", "file_ext": "py", "file_size_in_byte": 8802, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "37", "api": [{"api_name": "io.StringIO", "line_number": 128, "usage_type": "call"}, {"api_name": "io.StringIO", "line_number": 214, "usage_type": "call"}]} +{"seq_id": "21303108879", "text": "from uuid import uuid4\n\nimport pytest\n\nfrom flask_coney.encoder import UUIDEncoder\n\n\ndef test_encode_uuid():\n id = uuid4()\n\n encoded = UUIDEncoder().default(id)\n\n assert str(id) in encoded\n\n\ndef test_encode_not_jsonable():\n encode = [1, 2, 3, 4]\n\n with pytest.raises(TypeError):\n UUIDEncoder().default(encode)\n", "repo_name": "mikebarkmin/flask-coney", "sub_path": "tests/test_encoder.py", "file_name": "test_encoder.py", "file_ext": "py", "file_size_in_byte": 332, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "37", "api": [{"api_name": "uuid.uuid4", "line_number": 9, "usage_type": "call"}, {"api_name": "flask_coney.encoder.UUIDEncoder", "line_number": 11, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 19, "usage_type": "call"}, {"api_name": "flask_coney.encoder.UUIDEncoder", "line_number": 20, "usage_type": "call"}]} +{"seq_id": "30371537438", "text": "import logging\r\n\r\nlogging.basicConfig(filename='employee.log',level=logging.DEBUG,format='%(levelname)s:%(message)s')\r\n\r\n\r\nclass Employee:\r\n num_emp = 0\r\n raise_amount = 1.05\r\n\r\n def __init__(self, first, last, pay):\r\n self.first = first\r\n self.last = last\r\n self.pay = pay\r\n Employee.num_emp += 1 # we create it in init section because everytime we make a new instance init method runs\r\n logging.debug(f'{self.fullname} & {self.email} has been created')\r\n @property\r\n def fullname(self):\r\n return f'{self.first} {self.last}'\r\n @property\r\n def email(self):\r\n return f'{self.first}.{self.last}@gmail.com'\r\n\r\n def apply_raise(self):\r\n self.pay = int(self.pay * self.raise_amount)\r\n\r\n\r\nemp_1 = Employee('pouya','goldberg',3000)\r\nemp_2 = Employee('gary','nevil',5000)\r\nemp_3 = Employee('mary','jane',2000)\r\n\r\n", "repo_name": "pouya-alipour741/Courses", "sub_path": "Python/python tutorial advanced/OOP_Tutorial/employee_oop_logging.py", "file_name": "employee_oop_logging.py", "file_ext": "py", "file_size_in_byte": 885, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "logging.basicConfig", "line_number": 3, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 3, "usage_type": "attribute"}, {"api_name": "logging.debug", "line_number": 15, "usage_type": "call"}]} +{"seq_id": "86726190423", "text": "import pandas as pd\n\nfrom braindecode.datasets import BaseDataset as BD\nfrom braindecode.datasets import BaseConcatDataset\n\ndef _fetch_and_unpack_moabb_data(dataset, subject_ids):\n data = dataset.get_data(subject_ids)\n raws, subject_ids, session_ids, run_ids = [], [], [], []\n for subj_id, subj_data in data.items():\n for sess_id, sess_data in subj_data.items():\n for run_id, raw in sess_data.items():\n raws.append(raw)\n subject_ids.append(subj_id)\n session_ids.append(sess_id)\n run_ids.append(run_id)\n description = pd.DataFrame({\n 'subject': subject_ids,\n 'session': session_ids,\n 'run': run_ids\n })\n return raws, description\n\nclass MOABBDataset_Rest(BaseConcatDataset):\n def __init__(self, dataset, subject_ids, dataset_kwargs=None):\n raws, description = _fetch_and_unpack_moabb_data(dataset, subject_ids)\n all_base_ds = [BD(raw, row)\n for raw, (_, row) in zip(raws, description.iterrows())]\n super().__init__(all_base_ds)", "repo_name": "UN-GCPDS/python-gcpds.MI_prediction", "sub_path": "MI_prediction/Datasets/Moabb.py", "file_name": "Moabb.py", "file_ext": "py", "file_size_in_byte": 1088, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "pandas.DataFrame", "line_number": 16, "usage_type": "call"}, {"api_name": "braindecode.datasets.BaseConcatDataset", "line_number": 23, "usage_type": "name"}, {"api_name": "braindecode.datasets.BaseDataset", "line_number": 26, "usage_type": "call"}]} +{"seq_id": "26454961908", "text": "import streamlit as st\r\nst.title(\"-> hello world, today is butifull.\")\r\nst.header('header -> it is header tag')\r\nst.subheader('subheader -> it is subheader.')\r\nst.text('text -> it is text.')\r\n\r\nst.markdown(\"# hi\")\r\nst.markdown(\"## hi\")\r\nst.markdown(\"### hi\")\r\nst.markdown(\"### hi\")\r\nst.markdown(\"#### hi\")\r\nst.markdown(\"##### hi\")\r\n\r\nst.success('it is success')\r\nst.info(\"info function\")\r\nst.warning('warning!')\r\nst.error('error!')\r\n\r\nexpration = ZeroDivisionError('division not possible with 0')\r\n\r\n\r\nst.exception(expration)\r\nst.exception(ZeroDivisionError('division is not possible'))\r\nst.help(ZeroDivisionError)\r\n\r\nst.code('x=10\\n'\r\n 'for i in range(1,x+1):\\n'\r\n ' print(i)')\r\n\r\n\r\nst.checkbox('Male')\r\n\r\nif (st.checkbox('Female')):\r\n st.write('you are Female.')\r\n\r\n\r\nradioButton = st.radio('select :', {'male', 'female', 'other'})\r\nif (radioButton == 'male'):\r\n st.write('you are male.')\r\nelif (radioButton == 'female'):\r\n st.write('you are female.')\r\nelse:\r\n st.write('you are other')\r\n\r\n\r\nst.subheader('select box')\r\nst.selectbox('Data science :', {'data analysis', 'web scraping', 'machine learning',\r\n 'deep learning', 'NLP', 'computer vision', 'image processing'})\r\n\r\n\r\nmultiselectBox=st.multiselect('Data science :', {'data analysis', 'web scraping', 'machine learning',\r\n 'deep learning', 'NLP', 'computer vision', 'image processing'})\r\n\r\nst.write('you selected : ',len(multiselectBox),'coutses')\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\nst.subheader('Button')\r\nif st.button(\"click me.\"):\r\n st.write('you click button.')\r\n\r\nrang=st.slider('select your rang :',0,10,step=1)\r\nst.write('your rang is ',rang)\r\n\r\nusername=st.text_input('username')\r\npassword=st.text_input('password',type='password')\r\n# if name:\r\n# st.write('hi',name)\r\n\r\nst.subheader('text area')\r\ntextArea=st.text_area('write code')\r\nst.write(textArea)\r\n\r\nst.subheader('input number')\r\nst.number_input('enter your age',18,100)\r\n\r\nst.subheader('input data')\r\nst.date_input('enter your birthdate')\r\n\r\n", "repo_name": "Aman78600/basic_stremlit", "sub_path": "app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 2009, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "streamlit.title", "line_number": 2, "usage_type": "call"}, {"api_name": "streamlit.header", "line_number": 3, "usage_type": "call"}, {"api_name": "streamlit.subheader", "line_number": 4, "usage_type": "call"}, {"api_name": "streamlit.text", "line_number": 5, "usage_type": "call"}, {"api_name": "streamlit.markdown", "line_number": 7, "usage_type": "call"}, {"api_name": "streamlit.markdown", "line_number": 8, "usage_type": "call"}, {"api_name": "streamlit.markdown", "line_number": 9, "usage_type": "call"}, {"api_name": "streamlit.markdown", "line_number": 10, "usage_type": "call"}, {"api_name": "streamlit.markdown", "line_number": 11, "usage_type": "call"}, {"api_name": "streamlit.markdown", "line_number": 12, "usage_type": "call"}, {"api_name": "streamlit.success", "line_number": 14, "usage_type": "call"}, {"api_name": "streamlit.info", "line_number": 15, "usage_type": "call"}, {"api_name": "streamlit.warning", "line_number": 16, "usage_type": "call"}, {"api_name": "streamlit.error", "line_number": 17, "usage_type": "call"}, {"api_name": "streamlit.exception", "line_number": 22, "usage_type": "call"}, {"api_name": "streamlit.exception", "line_number": 23, "usage_type": "call"}, {"api_name": "streamlit.help", "line_number": 24, "usage_type": "call"}, {"api_name": "streamlit.code", "line_number": 26, "usage_type": "call"}, {"api_name": "streamlit.checkbox", "line_number": 31, "usage_type": "call"}, {"api_name": "streamlit.checkbox", "line_number": 33, "usage_type": "call"}, {"api_name": "streamlit.write", "line_number": 34, "usage_type": "call"}, {"api_name": "streamlit.radio", "line_number": 37, "usage_type": "call"}, {"api_name": "streamlit.write", "line_number": 39, "usage_type": "call"}, {"api_name": "streamlit.write", "line_number": 41, "usage_type": "call"}, {"api_name": "streamlit.write", "line_number": 43, "usage_type": "call"}, {"api_name": "streamlit.subheader", "line_number": 46, "usage_type": "call"}, {"api_name": "streamlit.selectbox", "line_number": 47, "usage_type": "call"}, {"api_name": "streamlit.multiselect", "line_number": 51, "usage_type": "call"}, {"api_name": "streamlit.write", "line_number": 54, "usage_type": "call"}, {"api_name": "streamlit.subheader", "line_number": 62, "usage_type": "call"}, {"api_name": "streamlit.button", "line_number": 63, "usage_type": "call"}, {"api_name": "streamlit.write", "line_number": 64, "usage_type": "call"}, {"api_name": "streamlit.slider", "line_number": 66, "usage_type": "call"}, {"api_name": "streamlit.write", "line_number": 67, "usage_type": "call"}, {"api_name": "streamlit.text_input", "line_number": 69, "usage_type": "call"}, {"api_name": "streamlit.text_input", "line_number": 70, "usage_type": "call"}, {"api_name": "streamlit.subheader", "line_number": 74, "usage_type": "call"}, {"api_name": "streamlit.text_area", "line_number": 75, "usage_type": "call"}, {"api_name": "streamlit.write", "line_number": 76, "usage_type": "call"}, {"api_name": "streamlit.subheader", "line_number": 78, "usage_type": "call"}, {"api_name": "streamlit.number_input", "line_number": 79, "usage_type": "call"}, {"api_name": "streamlit.subheader", "line_number": 81, "usage_type": "call"}, {"api_name": "streamlit.date_input", "line_number": 82, "usage_type": "call"}]} +{"seq_id": "5914406981", "text": "from argparse import ArgumentParser\nimport c_elegans_wiring.sub_modules.logger as logger\nfrom c_elegans_wiring.sub_modules.options.user_settings import *\n\n\nclass ArgsCLI:\n\n def __init__(self):\n self._init_parser()\n # default values\n self.from_nodes_class = ['ADE', 'ADF', 'ADL', 'AFD', 'ASE', 'ASG', 'ASH', 'ASI', 'ASJ', 'ASK', 'AUA', 'AWA',\n 'AWB', 'AWC',\n 'BAG', 'CEP', 'PDE']\n self.to_nodes_class = ['AVA']\n self.max_cutoff = 2\n self.class_grouping_intensity = 2\n self.show_cell_graph = False\n self.synapse_types = ['chemical', 'electric']\n\n def _init_parser(self):\n self.parser = ArgumentParser()\n self.parser.add_argument(\"--f\",\n nargs='+',\n help=\"specifies a list of FROM NEURON CLASSES to find path from as source(default \"\n \"specified in code taken otherwise)\")\n self.parser.add_argument(\"--t\",\n nargs='+',\n help=\"specifies a list of TO NEURON CLASSES to find path to as target(default \"\n \"specified in code taken otherwise)\")\n self.parser.add_argument(\"--show_cell\",\n type=bool,\n help=\"flag to decide whether to show the cell graph or not(default- false)\")\n self.parser.add_argument(\"--cgi\",\n type=int,\n help=\"specifies the class grouping intensity\\n\"\n \"Options are\"\n \"\\n\\t1-Strong\"\n \"\\n\\t2-Moderate(default)\"\n \"\\n\\t3-Lenient\"\n \"\\n\\t0-Show all graphs\")\n self.parser.add_argument(\"--c\",\n type=int,\n help=\"specifies the max distance cutoff depth to search till(default- 2)\")\n self.parser.add_argument(\"--s\",\n nargs='+',\n help=\"specifies a list of synapses we wish to observe(by default both are shown)\\n\"\n \"Options are\"\n \"\\n\\tc-Chemical\"\n \"\\n\\te-Electric\")\n\n def parse(self):\n input_args = self.parser.parse_args()\n self.get_input_data_from_args(input_args=input_args)\n\n def get_input_data_from_args(self, input_args):\n if input_args.f:\n self.from_nodes_class = input_args.f\n if input_args.t:\n self.to_nodes_class = input_args.t\n if input_args.c:\n self.max_cutoff = input_args.c\n if input_args.cgi or input_args.cgi == 0: # otherwise 0 is getting considered as none by args\n self.class_grouping_intensity = input_args.cgi\n if input_args.show_cell:\n self.show_cell_graph = input_args.show_cell\n if input_args.s:\n self.synapse_types = [short_hand2synapse_map.get(s) for s in input_args.s]\n\n logger.print_user_parameters(self)\n", "repo_name": "adrameshiu/c-elegans-wiring", "sub_path": "c_elegans_wiring/sub_modules/options/args_options.py", "file_name": "args_options.py", "file_ext": "py", "file_size_in_byte": 3296, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 21, "usage_type": "call"}, {"api_name": "c_elegans_wiring.sub_modules.logger.print_user_parameters", "line_number": 69, "usage_type": "call"}, {"api_name": "c_elegans_wiring.sub_modules.logger", "line_number": 69, "usage_type": "name"}]} +{"seq_id": "22626435479", "text": "import kornia\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\n\nclass SSIM(nn.Module):\n def __init__(self, window_size=11):\n super(SSIM, self).__init__()\n self.window_size = window_size\n\n def forward(self, x, y):\n if x.shape[1] == 3:\n x = kornia.color.rgb_to_grayscale(x)\n if y.shape[1] == 3:\n y = kornia.color.rgb_to_grayscale(y)\n return 1 - kornia.losses.ssim(x, y, self.window_size, 'mean')\n\n\nclass PSNR(nn.Module):\n def __init__(self, max_val=1., mode='Y'):\n super(PSNR, self).__init__()\n self.max_val = max_val\n self.mode = mode\n\n def forward(self, x, y):\n if self.mode == 'Y' and x.shape[1] == 3 and y.shape[1] == 3:\n x = kornia.color.rgb_to_grayscale(x)\n y = kornia.color.rgb_to_grayscale(y)\n mse = F.mse_loss(x, y, reduction='mean')\n psnr = 10 * torch.log10(self.max_val ** 2 / mse)\n return psnr\n", "repo_name": "S-aiueo32/srntt-pytorch", "sub_path": "losses/metrics.py", "file_name": "metrics.py", "file_ext": "py", "file_size_in_byte": 963, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 102, "dataset": "github-code", "pt": "37", "api": [{"api_name": "torch.nn.Module", "line_number": 7, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 7, "usage_type": "name"}, {"api_name": "kornia.color.rgb_to_grayscale", "line_number": 14, "usage_type": "call"}, {"api_name": "kornia.color", "line_number": 14, "usage_type": "attribute"}, {"api_name": "kornia.color.rgb_to_grayscale", "line_number": 16, "usage_type": "call"}, {"api_name": "kornia.color", "line_number": 16, "usage_type": "attribute"}, {"api_name": "kornia.losses.ssim", "line_number": 17, "usage_type": "call"}, {"api_name": "kornia.losses", "line_number": 17, "usage_type": "attribute"}, {"api_name": "torch.nn.Module", "line_number": 20, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 20, "usage_type": "name"}, {"api_name": "kornia.color.rgb_to_grayscale", "line_number": 28, "usage_type": "call"}, {"api_name": "kornia.color", "line_number": 28, "usage_type": "attribute"}, {"api_name": "kornia.color.rgb_to_grayscale", "line_number": 29, "usage_type": "call"}, {"api_name": "kornia.color", "line_number": 29, "usage_type": "attribute"}, {"api_name": "torch.nn.functional.mse_loss", "line_number": 30, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 30, "usage_type": "name"}, {"api_name": "torch.log10", "line_number": 31, "usage_type": "call"}]} +{"seq_id": "20355311680", "text": "from django.urls import path\n\nfrom . import views\n\napp_name = 'public'\nurlpatterns = [\n path(\"\", views.index, name=\"index\"),\n path(\"blog\", views.premium, name=\"blog\"),\n path(\"podcast\", views.support, name=\"podcast\"),\n path(\"events\", views.download, name=\"events\"),\n path(\"about\", views.about, name=\"about\")\n]\n", "repo_name": "mpbasto/django-website", "sub_path": "beginner_website/apps/public/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 324, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "django.urls.path", "line_number": 7, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 8, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 9, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 10, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 11, "usage_type": "call"}]} +{"seq_id": "14705956506", "text": "import logging\nimport os\nimport subprocess\nimport tempfile\nfrom enum import Enum, auto\nfrom subprocess import Popen\nfrom typing import TYPE_CHECKING, List\nfrom gi.repository import Gtk, GLib\n\nfrom skytemple.controller.main import MainController\nfrom skytemple.core.ui_utils import data_dir, APP, make_builder, builder_get_assert\nfrom skytemple.core.async_tasks.delegator import AsyncTaskDelegator\nfrom skytemple_files.common.i18n_util import f, _\nfrom skytemple_files.user_error import make_user_err\n\nif TYPE_CHECKING:\n from skytemple.module.gfxcrunch.module import GfxcrunchModule\nlogger = logging.getLogger(__name__)\n\n\nclass GfxcrunchStatus(Enum):\n RUNNING = auto()\n ERROR = auto()\n SUCCESS = auto()\n\n\nIMG_NEUTRAL = \"poochy_neutral.png\"\nIMG_HAPPY = \"poochy_happy.png\"\nIMG_SAD = \"poochy_sad.png\"\nIMGS = {\n GfxcrunchStatus.RUNNING: IMG_NEUTRAL,\n GfxcrunchStatus.ERROR: IMG_SAD,\n GfxcrunchStatus.SUCCESS: IMG_HAPPY,\n}\n\n\nclass GfxcrunchController:\n def __init__(self, module: \"GfxcrunchModule\"):\n self.module = module\n\n self.builder = self._get_builder(__file__, \"gfxcrunch.glade\")\n self.builder.connect_signals(self)\n self.buffer = builder_get_assert(\n self.builder, Gtk.TextView, \"console\"\n ).get_buffer()\n self.status = GfxcrunchStatus.RUNNING\n\n def import_sprite(self, dir_fn: str) -> bytes:\n with tempfile.TemporaryDirectory() as tmp_path:\n tmp_path = os.path.join(tmp_path, \"tmp.wan\")\n AsyncTaskDelegator.run_task(self._run_gfxcrunch([dir_fn, tmp_path]))\n self._run_window()\n if self.status == GfxcrunchStatus.SUCCESS:\n with open(tmp_path, \"rb\") as f:\n return f.read()\n else:\n raise make_user_err(RuntimeError, _(\"The gfxcrunch process failed.\"))\n\n def export_sprite(self, wan: bytes, dir_fn: str):\n with tempfile.TemporaryDirectory() as tmp_path:\n tmp_path = os.path.join(tmp_path, \"tmp.wan\")\n with open(tmp_path, \"wb\") as f:\n f.write(wan)\n AsyncTaskDelegator.run_task(self._run_gfxcrunch([tmp_path, dir_fn]))\n self._run_window()\n if self.status != GfxcrunchStatus.SUCCESS:\n raise make_user_err(RuntimeError, _(\"The gfxcrunch process failed.\"))\n\n def _run_window(self):\n dialog = builder_get_assert(self.builder, Gtk.Dialog, \"dialog\")\n dialog.resize(750, 350)\n dialog.set_transient_for(MainController.window())\n dialog.set_attached_to(MainController.window())\n self.buffer.delete(self.buffer.get_start_iter(), self.buffer.get_end_iter())\n self._update_status(GfxcrunchStatus.RUNNING)\n builder_get_assert(self.builder, Gtk.Spinner, \"spinner\").start()\n builder_get_assert(self.builder, Gtk.Button, \"close\").set_sensitive(False)\n dialog.run()\n dialog.hide()\n\n async def _run_gfxcrunch(self, arg_list: List[str]):\n cmd, base_args, shell = self.module.get_gfxcrunch_cmd()\n arg_list = [cmd] + base_args + arg_list\n logger.info(f\"Running gfxcrunch: {arg_list}\")\n proc = Popen(\n arg_list,\n stdout=subprocess.PIPE,\n stderr=subprocess.PIPE,\n shell=shell,\n universal_newlines=True\n # creationflags=\n )\n\n assert proc.stdout is not None and proc.stderr is not None\n while proc.poll() is None:\n line = proc.stdout.readline()\n if line != \"\" and line:\n GLib.idle_add(lambda line=line: self._stdout(line))\n\n line = proc.stderr.readline()\n if line != \"\" and line:\n GLib.idle_add(lambda line=line: self._stderr(line))\n\n line = proc.stdout.readline()\n while line != \"\" and line:\n GLib.idle_add(lambda line=line: self._stdout(line))\n line = proc.stdout.readline()\n\n line = proc.stderr.readline()\n while line != \"\" and line:\n GLib.idle_add(lambda line=line: self._stderr(line))\n line = proc.stderr.readline()\n\n GLib.idle_add(lambda: self._done(proc.returncode))\n\n @staticmethod\n def _get_builder(pymodule_path: str, glade_file: str):\n path = os.path.abspath(os.path.dirname(pymodule_path))\n return make_builder(os.path.join(path, glade_file))\n\n def _stdout(self, line):\n self.buffer.insert_markup(self.buffer.get_end_iter(), line, -1)\n\n def _stderr(self, line):\n self.buffer.insert_markup(\n self.buffer.get_end_iter(), f'{line}', -1\n )\n\n def _done(self, return_code):\n self._update_status(\n GfxcrunchStatus.SUCCESS if return_code == 0 else GfxcrunchStatus.ERROR\n )\n if return_code != 0:\n self._stderr(\n f(_(\"!! Process exited with error. Exit code: {return_code} !!\"))\n )\n builder_get_assert(self.builder, Gtk.Spinner, \"spinner\").stop()\n builder_get_assert(self.builder, Gtk.Button, \"close\").set_sensitive(True)\n\n def _update_status(self, status):\n self.status = status\n img = builder_get_assert(self.builder, Gtk.Image, \"duskako\")\n img.set_from_file(os.path.join(data_dir(), IMGS[status]))\n", "repo_name": "SkyTemple/skytemple", "sub_path": "skytemple/module/gfxcrunch/controller/gfxcrunch.py", "file_name": "gfxcrunch.py", "file_ext": "py", "file_size_in_byte": 5297, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 166, "dataset": "github-code", "pt": "37", "api": [{"api_name": "typing.TYPE_CHECKING", "line_number": 16, "usage_type": "name"}, {"api_name": "logging.getLogger", "line_number": 18, "usage_type": "call"}, {"api_name": "enum.Enum", "line_number": 21, "usage_type": "name"}, {"api_name": "enum.auto", "line_number": 22, "usage_type": "call"}, {"api_name": "enum.auto", "line_number": 23, "usage_type": "call"}, {"api_name": "enum.auto", "line_number": 24, "usage_type": "call"}, {"api_name": "skytemple.core.ui_utils.builder_get_assert", "line_number": 43, "usage_type": "call"}, {"api_name": "gi.repository.Gtk.TextView", "line_number": 44, "usage_type": "attribute"}, {"api_name": "gi.repository.Gtk", "line_number": 44, "usage_type": "name"}, {"api_name": "tempfile.TemporaryDirectory", "line_number": 49, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 50, "usage_type": "call"}, {"api_name": "os.path", "line_number": 50, "usage_type": "attribute"}, {"api_name": "skytemple.core.async_tasks.delegator.AsyncTaskDelegator.run_task", "line_number": 51, "usage_type": "call"}, {"api_name": "skytemple.core.async_tasks.delegator.AsyncTaskDelegator", "line_number": 51, "usage_type": "name"}, {"api_name": "skytemple_files.common.i18n_util.f", "line_number": 54, "usage_type": "name"}, {"api_name": "skytemple_files.common.i18n_util.f.read", "line_number": 55, "usage_type": "call"}, {"api_name": "skytemple_files.common.i18n_util.f", "line_number": 55, "usage_type": "name"}, {"api_name": "skytemple_files.user_error.make_user_err", "line_number": 57, "usage_type": "call"}, {"api_name": "skytemple_files.common.i18n_util._", "line_number": 57, "usage_type": "call"}, {"api_name": "tempfile.TemporaryDirectory", "line_number": 60, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 61, "usage_type": "call"}, {"api_name": "os.path", "line_number": 61, "usage_type": "attribute"}, {"api_name": "skytemple_files.common.i18n_util.f", "line_number": 62, "usage_type": "name"}, {"api_name": "skytemple_files.common.i18n_util.f.write", "line_number": 63, "usage_type": "call"}, {"api_name": "skytemple_files.common.i18n_util.f", "line_number": 63, "usage_type": "name"}, {"api_name": "skytemple.core.async_tasks.delegator.AsyncTaskDelegator.run_task", "line_number": 64, "usage_type": "call"}, {"api_name": "skytemple.core.async_tasks.delegator.AsyncTaskDelegator", "line_number": 64, "usage_type": "name"}, {"api_name": "skytemple_files.user_error.make_user_err", "line_number": 67, "usage_type": "call"}, {"api_name": "skytemple_files.common.i18n_util._", "line_number": 67, "usage_type": "call"}, {"api_name": "skytemple.core.ui_utils.builder_get_assert", "line_number": 70, "usage_type": "call"}, {"api_name": "gi.repository.Gtk.Dialog", "line_number": 70, "usage_type": "attribute"}, {"api_name": "gi.repository.Gtk", "line_number": 70, "usage_type": "name"}, {"api_name": "skytemple.controller.main.MainController.window", "line_number": 72, "usage_type": "call"}, {"api_name": "skytemple.controller.main.MainController", "line_number": 72, "usage_type": "name"}, {"api_name": "skytemple.controller.main.MainController.window", "line_number": 73, "usage_type": "call"}, {"api_name": "skytemple.controller.main.MainController", "line_number": 73, "usage_type": "name"}, {"api_name": "skytemple.core.ui_utils.builder_get_assert", "line_number": 76, "usage_type": "call"}, {"api_name": "gi.repository.Gtk.Spinner", "line_number": 76, "usage_type": "attribute"}, {"api_name": "gi.repository.Gtk", "line_number": 76, "usage_type": "name"}, {"api_name": "skytemple.core.ui_utils.builder_get_assert", "line_number": 77, "usage_type": "call"}, {"api_name": "gi.repository.Gtk.Button", "line_number": 77, "usage_type": "attribute"}, {"api_name": "gi.repository.Gtk", "line_number": 77, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 81, "usage_type": "name"}, {"api_name": "subprocess.Popen", "line_number": 85, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 87, "usage_type": "attribute"}, {"api_name": "subprocess.PIPE", "line_number": 88, "usage_type": "attribute"}, {"api_name": "gi.repository.GLib.idle_add", "line_number": 98, "usage_type": "call"}, {"api_name": "gi.repository.GLib", "line_number": 98, "usage_type": "name"}, {"api_name": "gi.repository.GLib.idle_add", "line_number": 102, "usage_type": "call"}, {"api_name": "gi.repository.GLib", "line_number": 102, "usage_type": "name"}, {"api_name": "gi.repository.GLib.idle_add", "line_number": 106, "usage_type": "call"}, {"api_name": "gi.repository.GLib", "line_number": 106, "usage_type": "name"}, {"api_name": "gi.repository.GLib.idle_add", "line_number": 111, "usage_type": "call"}, {"api_name": "gi.repository.GLib", "line_number": 111, "usage_type": "name"}, {"api_name": "gi.repository.GLib.idle_add", "line_number": 114, "usage_type": "call"}, {"api_name": "gi.repository.GLib", "line_number": 114, "usage_type": "name"}, {"api_name": "os.path.abspath", "line_number": 118, "usage_type": "call"}, {"api_name": "os.path", "line_number": 118, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 118, "usage_type": "call"}, {"api_name": "skytemple.core.ui_utils.make_builder", "line_number": 119, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 119, "usage_type": "call"}, {"api_name": "os.path", "line_number": 119, "usage_type": "attribute"}, {"api_name": "skytemple_files.common.i18n_util.f", "line_number": 135, "usage_type": "call"}, {"api_name": "skytemple_files.common.i18n_util._", "line_number": 135, "usage_type": "call"}, {"api_name": "skytemple.core.ui_utils.builder_get_assert", "line_number": 137, "usage_type": "call"}, {"api_name": "gi.repository.Gtk.Spinner", "line_number": 137, "usage_type": "attribute"}, {"api_name": "gi.repository.Gtk", "line_number": 137, "usage_type": "name"}, {"api_name": "skytemple.core.ui_utils.builder_get_assert", "line_number": 138, "usage_type": "call"}, {"api_name": "gi.repository.Gtk.Button", "line_number": 138, "usage_type": "attribute"}, {"api_name": "gi.repository.Gtk", "line_number": 138, "usage_type": "name"}, {"api_name": "skytemple.core.ui_utils.builder_get_assert", "line_number": 142, "usage_type": "call"}, {"api_name": "gi.repository.Gtk.Image", "line_number": 142, "usage_type": "attribute"}, {"api_name": "gi.repository.Gtk", "line_number": 142, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 143, "usage_type": "call"}, {"api_name": "os.path", "line_number": 143, "usage_type": "attribute"}, {"api_name": "skytemple.core.ui_utils.data_dir", "line_number": 143, "usage_type": "call"}]} +{"seq_id": "71961240107", "text": "import numpy as np\nimport gym\n\nH = 20 \nH1 = 10\nbatch_size = 10 \nlearning_rate = 1e-4\nlamda = 0.5\ngamma = 0.99 \ndecay_rate = 0.99 \nrender = True\n\nD = 128 \nmodel = {}\nmodel['W1'] = np.random.randn(H,D) / np.sqrt(D)\nmodel['W2'] = np.random.randn(H) / np.sqrt(H)\nreward_model = {}\nreward_model['W1'] = np.random.randn(H1,D+1) / np.sqrt(D+1)\nreward_model['W2'] = np.random.randn(H1) / np.sqrt(H1)\n \ngrad_buffer = { k : np.zeros_like(v) for k,v in model.items() } \nrmsprop_cache = { k : np.zeros_like(v) for k,v in model.items() } \n\nreward_grad_buffer = { k : np.zeros_like(v) for k,v in reward_model.items() }\nreward_rmsprop_cache = { k : np.zeros_like(v) for k,v in reward_model.items() }\n\n\ndef sigmoid(x): \n return 1.0 / (1.0 + np.exp(-x)) \n\ndef discounted_i_reward(r1, r):\n discounted_r = np.zeros_like(r)\n running_add = np.zeros(r.shape[1])\n for t in reversed(range(0, r1.size)):\n if r1[t] != 0: running_add = 0\n running_add = running_add * gamma + r[t,:]\n discounted_r[t,:] = running_add\n return discounted_r\n\ndef discount_rewards(r):\n discounted_r = np.zeros_like(r)\n running_add = 0\n # print (r.shape)\n for t in reversed(range(0, r.size)):\n if r[t] != 0: running_add = 0\n running_add = running_add * gamma + r[t]\n discounted_r[t] = running_add\n return discounted_r\n\ndef state_reward_forward(x):\n h = np.dot(reward_model['W1'], x)\n h[h<0] = 0 \n logp = np.dot(reward_model['W2'], h)\n p = sigmoid(logp)\n return p, h \n\ndef state_reward_backward(epx, eph, epdlogp):\n eph = np.reshape(eph, (1, eph.shape[0]))\n epx = np.reshape(epx, (1, epx.shape[0]))\n dW2 = np.dot(eph.T, epdlogp).ravel()\n dh = np.outer(epdlogp, reward_model['W2'])\n dh[eph <= 0] = 0\n dW1 = np.dot(dh.T, epx)\n return {'W1':dW1, 'W2':dW2}\n\ndef policy_forward(x):\n h = np.dot(model['W1'], x)\n h[h<0] = 0\n logp = np.dot(model['W2'], h)\n p = sigmoid(logp)\n return p, h\n\ndef policy_backward_w1(epx, eph, epdlogp):\n dW2 = np.dot(eph.T, epdlogp).T\n dh = np.outer(epdlogp.T, model['W2'])\n dh = np.reshape(dh, (epdlogp.shape[1],epdlogp.shape[0],model['W2'].shape[0]))\n dh[eph <= 0] = 0\n dW1 = np.dot(dh.T, epx)\n return {'W1':dW1, 'W2':dW2}\n\ndef policy_backward(epx, eph, epdlogp):\n dW2 = np.dot(eph.T, epdlogp).ravel()\n dh = np.outer(epdlogp, model['W2'])\n dh[eph <= 0] = 0\n dW1 = np.dot(dh.T, epx)\n return {'W1':dW1, 'W2':dW2}\n\nenv = gym.make(\"Pong-ram-v0\")\nobservation = env.reset()\nprev_x = None\nxs,hs,dlogps,drs = [],[],[],[]\ndrs_i = []\ngra_reward_w1 = []\ngra_reward_w2 = []\nrunning_reward = None\nreward_sum = 0\nepisode_number = 0\nwhile True:\n if render: env.render()\n\n cur_x = observation\n x = cur_x - prev_x if prev_x is not None else np.zeros(D)\n prev_x = cur_x\n aprob, h = policy_forward(x)\n action = 2 if np.random.uniform() < aprob else 3\n\n xs.append(x)\n hs.append(h)\n y = 1 if action == 2 else 0\n dlogps.append(y - aprob)\n\n observation, reward, done, info = env.step(action)\n reward_sum += reward\n\n drs.append(reward)\n saPair = np.append(x, action)\n i_reward, intrinsic_h = state_reward_forward(saPair)\n drs_i.append(reward + lamda*i_reward)\n \n back_prop_reward = i_reward * (1- i_reward)\n gra_reward = state_reward_backward(saPair, intrinsic_h, back_prop_reward)\n gra_reward_w1.append(gra_reward['W1'].ravel())\n gra_reward_w2.append(gra_reward['W2'].ravel())\n\n\n if done:\n episode_number += 1\n\n epx = np.vstack(xs)\n eph = np.vstack(hs)\n epdlogp = np.vstack(dlogps)\n epdlogp_i = epdlogp\n epr = np.vstack(drs)\n epr_i = np.vstack(drs_i)\n\n egra_reward_w1 = np.vstack(gra_reward_w1)\n egra_reward_w2 = np.vstack(gra_reward_w2)\n xs,hs,dlogps,drs = [],[],[],[]\n drs_i = []\n gra_reward_w1, gra_reward_w2 = [],[]\n\n discounted_epr = discount_rewards(epr)\n discounted_epr_i = discount_rewards(epr_i)\n discounted_reward_gradient_w1 = discounted_i_reward(epr, egra_reward_w1)\n discounted_reward_gradient_w2 = discounted_i_reward(epr, egra_reward_w2)\n discounted_epr -= np.mean(discounted_epr)\n discounted_epr /= np.std(discounted_epr)\n discounted_epr_i -= np.mean(discounted_epr_i)\n discounted_epr_i /= np.std(discounted_epr_i)\n\n grad_n = {}\n grad_n['W1'] = np.zeros((H1, D+1))\n grad_n['W2'] = np.zeros((H1,))\n epdlogp_w1 = epdlogp * discounted_reward_gradient_w1\n epdlogp_w2 = epdlogp * discounted_reward_gradient_w2\n epdlogp *= discounted_epr\n epdlogp_i *= discounted_epr_i\n grad1 = policy_backward(epx, eph, epdlogp)\n grad = policy_backward(epx, eph, epdlogp_i)\n for i in range(epdlogp_w2.shape[1]):\n grad_w2 = policy_backward(epx, eph, np.vstack(epdlogp_w2[:,i]))\n grad_n['W2'][i] = np.sum(grad_w2['W1'] * grad1['W1'])\n grad_n['W2'][i] += np.sum(grad_w2['W2'] * grad1['W2'])\n \n # for i in range(H1):\n # print (i)\n for j in range(H1*(D+1)):\n # print (j)\n grad_w1 = policy_backward(epx, eph, np.vstack(epdlogp_w1[:,j])) # change\n pos_x = int(j/(D+1))\n pos_y = j%(D+1)\n grad_n['W1'][pos_x][pos_y] = np.sum(grad_w1['W1'] * grad1['W1'])\n grad_n['W1'][pos_x][pos_y] += np.sum(grad_w1['W2'] * grad1['W2'])\n\n for k in model: grad_buffer[k] += grad[k]\n for k in model: reward_grad_buffer[k] += grad_n[k]\n\n if episode_number % batch_size == 0:\n for k,v in model.items():\n g = grad_buffer[k]\n rmsprop_cache[k] = decay_rate * rmsprop_cache[k] + (1 - decay_rate) * g**2\n model[k] += learning_rate * g / (np.sqrt(rmsprop_cache[k]) + 1e-5)\n grad_buffer[k] = np.zeros_like(v)\n\n for k,v in reward_model.items():\n g = reward_grad_buffer[k]\n reward_rmsprop_cache[k] = decay_rate * reward_rmsprop_cache[k] + (1 - decay_rate) * g**2\n reward_model[k] += learning_rate * g / (np.sqrt(reward_rmsprop_cache[k]) + 1e-5)\n reward_grad_buffer[k] = np.zeros_like(v)\n\n reward_sum = 0\n observation = env.reset()\n prev_x = None\n", "repo_name": "akgupta007/Intrinsic-Reward-Policy-Gradient", "sub_path": "pg.py", "file_name": "pg.py", "file_ext": "py", "file_size_in_byte": 5863, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "numpy.random.randn", "line_number": 15, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 15, "usage_type": "attribute"}, {"api_name": "numpy.sqrt", "line_number": 15, "usage_type": "call"}, {"api_name": "numpy.random.randn", "line_number": 16, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 16, "usage_type": "attribute"}, {"api_name": "numpy.sqrt", "line_number": 16, "usage_type": "call"}, {"api_name": "numpy.random.randn", "line_number": 18, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 18, "usage_type": "attribute"}, {"api_name": "numpy.sqrt", "line_number": 18, "usage_type": "call"}, {"api_name": "numpy.random.randn", "line_number": 19, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 19, "usage_type": "attribute"}, {"api_name": "numpy.sqrt", "line_number": 19, "usage_type": "call"}, {"api_name": "numpy.zeros_like", "line_number": 21, "usage_type": "call"}, {"api_name": "numpy.zeros_like", "line_number": 22, "usage_type": "call"}, {"api_name": "numpy.zeros_like", "line_number": 24, "usage_type": "call"}, {"api_name": "numpy.zeros_like", "line_number": 25, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 29, "usage_type": "call"}, {"api_name": "numpy.zeros_like", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 33, "usage_type": "call"}, {"api_name": "numpy.zeros_like", "line_number": 41, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 51, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 53, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 58, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 59, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 60, "usage_type": "call"}, {"api_name": "numpy.outer", "line_number": 61, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 63, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 67, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 69, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 74, "usage_type": "call"}, {"api_name": "numpy.outer", "line_number": 75, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 76, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 78, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 82, "usage_type": "call"}, {"api_name": "numpy.outer", "line_number": 83, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 85, "usage_type": "call"}, {"api_name": "gym.make", "line_number": 88, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 102, "usage_type": "call"}, {"api_name": "numpy.random.uniform", "line_number": 105, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 105, "usage_type": "attribute"}, {"api_name": "numpy.append", "line_number": 116, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 129, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 130, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 131, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 133, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 134, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 136, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 137, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 146, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 147, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 148, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 149, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 152, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 153, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 161, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 162, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 163, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 169, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 172, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 173, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 182, "usage_type": "call"}, {"api_name": "numpy.zeros_like", "line_number": 183, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 188, "usage_type": "call"}, {"api_name": "numpy.zeros_like", "line_number": 189, "usage_type": "call"}]} +{"seq_id": "1965446695", "text": "import matplotlib.pyplot as plt\nimport numpy as np\n\nbarSlices = 12\n\ntheta = np.linspace(0.0, 2*np.pi, barSlices, endpoint=False)\t# endpoint表示不包括终点值，对此见附录B3.2\nr = 30 * np.random.rand(barSlices)\n\nplt.polar(theta, r, color='chartreuse', linewidth=2, marker=\"*\", mfc=\"r\", ms=10)\t# 用于绘制极线图\nplt.margins(0)\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t# 取消极轴多余空白\nplt.grid(ls=\"-\", dashes=[2, 2])\n\nplt.show()", "repo_name": "liu-chunzhang/matplotlib_practice", "sub_path": "第1篇 入门/第2章 使用统计函数绘制简单图形/2.05 函数polar()——用于绘制极线图/2.5.py", "file_name": "2.5.py", "file_ext": "py", "file_size_in_byte": 429, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "37", "api": [{"api_name": "numpy.linspace", "line_number": 6, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 6, "usage_type": "attribute"}, {"api_name": "numpy.random.rand", "line_number": 7, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 7, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.polar", "line_number": 9, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 9, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.margins", "line_number": 10, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 10, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 11, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 11, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 13, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 13, "usage_type": "name"}]} +{"seq_id": "7964940340", "text": "import torch.nn as nn\nfrom six.moves import reduce # @UnresolvedImport\n\nclass Network(nn.Module):\n def __init__(self, state_size, n_actions, layers, activations):\n super().__init__()\n self.state_size = state_size\n self.n_actions = n_actions\n self.layers = nn.ModuleList(layers)\n self.activations = tuple(activations)\n \n @staticmethod\n def _step(state, layer_and_activation):\n layer, activation = layer_and_activation\n return activation(layer(state))\n \n def forward(self, state):\n '''\n Compute the outputs for the given tensor of states for all actions. \n Return a torch tensor.\n '''\n return reduce(self._step, zip(self.layers, self.activations), state)\n ", "repo_name": "jcrudy/drlnd_p1", "sub_path": "deepq/network/base.py", "file_name": "base.py", "file_ext": "py", "file_size_in_byte": 761, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "torch.nn.Module", "line_number": 4, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 4, "usage_type": "name"}, {"api_name": "torch.nn.ModuleList", "line_number": 9, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 9, "usage_type": "name"}, {"api_name": "six.moves.reduce", "line_number": 22, "usage_type": "call"}]} +{"seq_id": "12710450038", "text": "import numpy as np\nimport matplotlib.pyplot as grafic\nfrom FunctiiCrossoverIndivizi import crossover_PMX\nfrom FunctiiMutatieIndivizi import m_perm_inversiune\nfrom FunctiiSelectii import elitism, SUS\n\n#f. obiectiv\ndef foTSP(p,c,n):\n val = 0\n for i in range(n - 1):\n val = val + c[p[i]][p[i+1]]\n #val+=c[p[i],p[i+1]]\n val = val+c[p[0]][p[n-1]]\n return 100/val\n\n#genereaza populatia initiala\n#I:\n# fc - numele fisierului costurilor\n# dim - numarul de indivizi din populatie\n#E: lista cu 2 componente [pop,val] - populatia initiala si vectorul valorilor\ndef gen(fc,dim):\n #citeste datele din fisierul nxn al costurilor\n c=np.genfromtxt(fc)\n #n=dimensiunea problemei\n n = len(c)\n pop=np.zeros((dim,n),dtype=int)\n val=np.zeros(dim,dtype=float)\n for i in range(dim):\n #genereaza candidatul permutare cu n elemente\n pop[i] = np.random.permutation(n)\n # evalueaza candidat\n val[i] = foTSP(pop[i,:n],c,n)\n #[pop,val]=lista L cu primul element populatia, al doilea element vectorul valorilor\n #ca referire, pop=L[0], val=L[1]\n return [pop, val], c, n\n\n#crossover pe populatia de parinti pop, de dimensiune dimxn\n# I: l,dim,n - l=[pop,valori], ca in functia de generare\n# c - datele problemei\n# pc- probabilitatea de crossover\n#E: [po,val] - populatia copiilor, insotita de calitati\n# este implementata recombinarea asexuata\ndef crossover(l,dim,n,c,pc):\n pop=l[0]\n valori=l[1]\n # initializeaza populatia de copii, po, cu matricea cu elementele 0\n po=np.zeros((dim,n),dtype=int)\n # initializeaza valorile populatiei de copii, val, cu matricea cu elementele 0\n val=np.zeros(dim,dtype=float)\n #populatia este parcursa astfel incat sunt selectati aleator cate 2 indivizi - matricea este accesata dupa o permutare a multimii de linii 0,2,...,dim-1\n poz=np.random.permutation(dim)\n #sau populatia este parcursa astfel incat sunt selectati 2 indivizi consecutivi\n #poz=range(dim) #- pentru pastrarea ordinii\n for i in range(0,dim-1,2):\n #selecteaza parintii\n x = pop[poz[i]]\n y = pop[poz[i+1]]\n r = np.random.uniform(0,1)\n if r<=pc:\n # crossover x cu y - PMX - potrivit pentru probleme cu dependenta de adiacenta\n c1,c2 = crossover_PMX(x,y,n)\n v1=foTSP(c1,c,n)\n v2=foTSP(c2,c,n)\n else:\n # recombinare asexuata\n c1 = x.copy()\n c2 = y.copy()\n v1=valori[poz[i]]\n v2=valori[poz[i+1]]\n #copiaza rezultatul in populatia urmasilor\n po[i] = np.copy(c1)\n po[i+1] = np.copy(c2)\n val[i]=v1\n val[i+1]=v2\n return [po, val]\n\n#MUTATIE\n # operatia de mutatie a descendentilor obtinuti din recombinare\n # I: desc - [po,vo] - populatia copiilor insoltita de vectorul calitatilor\n # dim,n - dimensiunile\n # pm - probabilitatea de mutatie\n # c - matricea costurilor\n # E: descm - [mpo,mvo] - indivizii obtinuti\ndef mutatie(desc,dim,n,c,pm):\n po=desc[0]\n vo=desc[1]\n mpo=po.copy()\n mvo=vo.copy()\n for i in range(dim):\n r=np.random.uniform(0,1)\n if r<=pm:\n x=mpo[i]\n y=m_perm_inversiune(x,n)\n mpo[i]=y\n mvo[i]=foTSP(y,c,n)\n return [mpo,mvo]\n\ndef arata(sol,v):\n # vizualizare rezultate TSP\n # I: x - permutarea care defineste asezarea\n # E: -\n\n n=len(sol)\n t=len(v)\n cost=min(v)\n print(\"Cea mai mică distanță calculată: \",cost)\n print(\"Un drum cu costul \",cost,\" este: \",sol)\n\n fig=grafic.figure()\n x=[i for i in range(t)]\n y=[v[i] for i in range(t)]\n grafic.plot(x,y,'ro-')\n grafic.ylabel(\"Costul\")\n grafic.xlabel(\"Generația\")\n grafic.title(\"Evoluția calității celui mai bun individ din fiecare generație\")\n\n fig.show()\n\ndef GA_TSP(fc,dim,NMAX,pc,pm):\n # populatia initiala\n lpop,c,n=gen(fc,dim)\n pop=lpop[0]\n qual=lpop[1]\n\n #alte operatii de initializare\n it=0\n contor=1\n gata=False\n istoric=[100/np.max(qual)]\n\n #itertiile\n while it 0 else 1\n return margin\n\n\nclass PreparingDataset:\n '''\n Take list of videos, convert them into frames, using previous model to generate annotaions so\n we can adjust them using labelimg and retrain them so we increase the accuarcy\n '''\n\n def __init__(self, config):\n self.config = config\n self.herlper = Helper()\n self.videos = []\n self.frames_margin = 5\n\n def preparing_data(self):\n '''\n 1. change path to project path if exist\n 2. prepare videos patha\n '''\n\n if self.config.get('project_path'):\n os.chdir(self.config['project_path'])\n\n videos = [f\"{self.config['videos_folder_name']}/{f}\" for f in listdir(self.config['videos_folder_name']) if\n isfile(join(self.config['videos_folder_name'], f))]\n self.videos = [f for f in videos if f[-3:] in self.config['videos_extensions']]\n\n # create saving folder if not exist\n Path(self.config['saving_path']).mkdir(parents=True, exist_ok=True)\n\n # get the frames margin\n self.frames_margin = self.herlper.get_frames_margin(self.videos, self.config['frames_number'])\n\n def preprocessing(self):\n '''\n read videos and convert them into frames using the configrations\n '''\n count = 0\n i = 0\n\n # Create a video capture object, in this case we are reading the video from a file\n for video in self.videos:\n vid_capture = cv2.VideoCapture(video)\n\n while (vid_capture.isOpened()):\n ret, frame = vid_capture.read()\n if ret == True:\n if i % self.frames_margin == 0:\n cv2.imwrite(os.path.join(self.config['saving_path'], f'frame{count}.jpg'), frame)\n key = cv2.waitKey(20)\n count += 1\n if count % 10 == 0:\n print(count)\n i += 1\n if key == ord('q') or count >= self.config['frames_number']:\n break\n else:\n break\n\n # Release the video capture object\n vid_capture.release()\n cv2.destroyAllWindows()\n\n def processing(self):\n '''\n generate annotaions using previous model\n '''\n\n bashCommand = f\"python yolov5/detect.py --weights {self.config['model_path']} --img {int(self.config['resolution'])} --conf {float(self.config['conf'])} --source {self.config['saving_path']} --save-txt --project {self.config['saving_path']}\"\n os.system(bashCommand)\n\n def post_processing(self):\n '''\n organize files so we can use labeling directly without need of any manual work\n '''\n\n # move text files to frames folder\n data_path = os.path.join(os.path.join(Path(self.config['saving_path']), '**\\\\labels'), '*txt')\n all_data = glob.glob(data_path)\n for file in all_data:\n try:\n shutil.move(file, Path(self.config['saving_path']))\n except Exception as e:\n print(e)\n\n # move classes.txt file to frames folder\n shutil.copy(self.config['classes_file_path'], self.config['saving_path'])\n\n # remove any folder\n files = os.listdir(Path(self.config['saving_path']))\n folders = [f for f in files if not (f.endswith('jpg') or f.endswith('txt'))]\n for folder in folders:\n shutil.rmtree(os.path.join(Path(self.config['saving_path']), folder))\n\n def run(self):\n print(\"------------------------------- preparing required data/ configrations -------------------------------\")\n self.preparing_data()\n\n print(\n \"------------------------------- read videos and convert them into frames -------------------------------\")\n self.preprocessing()\n\n print(\n \"------------------------------- generate annotaions using previous model -------------------------------\")\n self.processing()\n\n try:\n time.sleep(2)\n print(\"------------------------------- organize files -------------------------------\")\n self.post_processing()\n except:\n time.sleep(5)\n print(\"------------------------------- re-organize files -------------------------------\")\n self.post_processing()\n", "repo_name": "Ghonem22/FineTuining-Yolov5-model-using-old-model-and-videos", "sub_path": "prepare_dataset/create_dataset.py", "file_name": "create_dataset.py", "file_ext": "py", "file_size_in_byte": 5012, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "cv2.VideoCapture", "line_number": 22, "usage_type": "call"}, {"api_name": "cv2.CAP_PROP_FRAME_COUNT", "line_number": 23, "usage_type": "attribute"}, {"api_name": "os.chdir", "line_number": 48, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 50, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 51, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 51, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 55, "usage_type": "call"}, {"api_name": "cv2.VideoCapture", "line_number": 69, "usage_type": "call"}, {"api_name": "cv2.imwrite", "line_number": 75, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 75, "usage_type": "call"}, {"api_name": "os.path", "line_number": 75, "usage_type": "attribute"}, {"api_name": "cv2.waitKey", "line_number": 76, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 88, "usage_type": "call"}, {"api_name": "os.system", "line_number": 96, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 104, "usage_type": "call"}, {"api_name": "os.path", "line_number": 104, "usage_type": "attribute"}, {"api_name": "pathlib.Path", "line_number": 104, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 105, "usage_type": "call"}, {"api_name": "shutil.move", "line_number": 108, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 108, "usage_type": "call"}, {"api_name": "shutil.copy", "line_number": 113, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 116, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 116, "usage_type": "call"}, {"api_name": "shutil.rmtree", "line_number": 119, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 119, "usage_type": "call"}, {"api_name": "os.path", "line_number": 119, "usage_type": "attribute"}, {"api_name": "pathlib.Path", "line_number": 119, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 134, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 138, "usage_type": "call"}]} +{"seq_id": "35521055456", "text": "from .models import Product\nfrom rest_framework import serializers\nfrom rest_framework.validators import UniqueValidator\n\n\nclass ProductSerializer(serializers.ModelSerializer):\n class Meta:\n model = Product\n fields = [\n \"id\",\n \"name\",\n \"category\",\n \"price\",\n \"in_stock\",\n \"is_available\",\n \"seller\",\n ]\n depth = 0\n read_only_fields = [\"id\", \"is_available\", \"seller\"]\n extra_kwargs = {\n \"name\": {\n \"validators\": [\n UniqueValidator(\n queryset=Product.objects.all(),\n message=\"This field must be unique.\",\n )\n ]\n }\n }\n\n def create(self, validated_data):\n validated_data[\"is_available\"] = False\n if validated_data[\"in_stock\"] > 0:\n validated_data[\"is_available\"] = True\n\n return Product.objects.create(**validated_data)\n\n\nclass ProductReturnSerializer(serializers.Serializer):\n name = serializers.CharField()\n category = serializers.CharField()\n price = serializers.DecimalField(max_digits=5, decimal_places=2)\n", "repo_name": "JuanP3dro/m5-capstone-kenzie-commerce", "sub_path": "products/serializers.py", "file_name": "serializers.py", "file_ext": "py", "file_size_in_byte": 1215, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "rest_framework.serializers.ModelSerializer", "line_number": 6, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 6, "usage_type": "name"}, {"api_name": "models.Product", "line_number": 8, "usage_type": "name"}, {"api_name": "rest_framework.validators.UniqueValidator", "line_number": 23, "usage_type": "call"}, {"api_name": "models.Product.objects.all", "line_number": 24, "usage_type": "call"}, {"api_name": "models.Product.objects", "line_number": 24, "usage_type": "attribute"}, {"api_name": "models.Product", "line_number": 24, "usage_type": "name"}, {"api_name": "models.Product.objects.create", "line_number": 36, "usage_type": "call"}, {"api_name": "models.Product.objects", "line_number": 36, "usage_type": "attribute"}, {"api_name": "models.Product", "line_number": 36, "usage_type": "name"}, {"api_name": "rest_framework.serializers.Serializer", "line_number": 39, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 39, "usage_type": "name"}, {"api_name": "rest_framework.serializers.CharField", "line_number": 40, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 40, "usage_type": "name"}, {"api_name": "rest_framework.serializers.CharField", "line_number": 41, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 41, "usage_type": "name"}, {"api_name": "rest_framework.serializers.DecimalField", "line_number": 42, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 42, "usage_type": "name"}]} +{"seq_id": "71874905707", "text": "import sqlite3\nfrom flask import g\n\n\nclass SQLite3DatabaseHandler:\n def __init__(self, database):\n self.database = database\n\n def connect(self):\n return sqlite3.connect(self.database)\n\n def get_tables(self):\n with self.connect() as db:\n cursor = db.cursor()\n output = cursor.execute(\n \"SELECT name FROM sqlite_master WHERE type='table';\"\n ).fetchall()\n\n return output\n\n def create_table(\n self,\n challenge_num,\n schema=\"username varchr(37) PRIMARY KEY UNIQUE, language varchar(16) NOT NULL, code varchar(250) NOT NULL\",\n ):\n\n with self.connect() as db:\n cursor = db.cursor()\n\n cursor.execute(\n f\"CREATE TABLE IF NOT EXISTS solution{challenge_num} ({schema})\"\n )\n db.commit()\n\n def insert_values(self, challenge_num, uname, language, code):\n with self.connect() as db:\n cursor = db.cursor()\n\n cursor.execute(\n f\"INSERT INTO solution{challenge_num} VALUES('{uname}', '{language}', '{code}')\"\n )\n\n def get_values(self, challenge_num):\n with self.connect() as db:\n cursor = db.cursor()\n\n out = cursor.execute(f\"SELECT * FROM solution{challenge_num}\")\n\n return out.fetchall()\n\n\n#################login db###############################\n\n\ndef get_db():\n if \"db\" not in g:\n g.db = sqlite3.connect(\"login.db\", detect_types=sqlite3.PARSE_DECLTYPES)\n g.db.row_factory = sqlite3.Row\n\n return g.db\n\n\ndef close_db(e=None):\n db = g.pop(\"db\", None)\n\n if db is not None:\n db.close()\n\n\ndef init_app(app):\n app.teardown_appcontext(close_db)\n\n\ndef makelogindb():\n conn = sqlite3.connect(\"login.db\")\n\n # Creating a cursor object using the cursor() method\n cursor = conn.cursor()\n\n # Doping EMPLOYEE table if already exists.\n cursor.execute(\"DROP TABLE IF EXISTS user\")\n\n # Creating table as per requirement\n sql = \"\"\"CREATE TABLE user (\n id TEXT PRIMARY KEY,\n name TEXT NOT NULL,\n discriminator TEXT NOT NULL,\n email TEXT UNIQUE NOT NULL,\n profile_pic_url TEXT NOT NULL\n );\"\"\"\n cursor.execute(sql)\n\n # Commit your changes in the database\n conn.commit()\n\n # Closing the connection\n conn.close()\n\n\nif __name__ == \"__main__\":\n db = SQLite3DatabaseHandler(\"solutions.db\")\n\n print(db.create_table(1))\n print(db.get_tables())\n\n db.insert_values(1, \"user1\", \"python\", \"def solution(n):\\n\\tprint(n * n)\")\n db.insert_values(\n 1, \"user2\", \"javascript\", \"const solution = (n) => {console.log(n * n)}\"\n )\n db.insert_values(\n 1, \"user3\", \"javascript\", \"const solution = (n) => {console.log(n * n * n)}\"\n )\n db.insert_values(\n 1, \"user5\", \"python\", \"import os\\ndef solution(n):\\n\\tprint(n * n/n)\"\n )\n\n print(db.get_values(1))\n", "repo_name": "Ardent-Community/ardent-website", "sub_path": "dbms.py", "file_name": "dbms.py", "file_ext": "py", "file_size_in_byte": 2911, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "37", "api": [{"api_name": "sqlite3.connect", "line_number": 10, "usage_type": "call"}, {"api_name": "flask.g", "line_number": 56, "usage_type": "name"}, {"api_name": "flask.g.db", "line_number": 57, "usage_type": "attribute"}, {"api_name": "flask.g", "line_number": 57, "usage_type": "name"}, {"api_name": "sqlite3.connect", "line_number": 57, "usage_type": "call"}, {"api_name": "sqlite3.PARSE_DECLTYPES", "line_number": 57, "usage_type": "attribute"}, {"api_name": "flask.g.db", "line_number": 58, "usage_type": "attribute"}, {"api_name": "flask.g", "line_number": 58, "usage_type": "name"}, {"api_name": "sqlite3.Row", "line_number": 58, "usage_type": "attribute"}, {"api_name": "flask.g.db", "line_number": 60, "usage_type": "attribute"}, {"api_name": "flask.g", "line_number": 60, "usage_type": "name"}, {"api_name": "flask.g.pop", "line_number": 64, "usage_type": "call"}, {"api_name": "flask.g", "line_number": 64, "usage_type": "name"}, {"api_name": "sqlite3.connect", "line_number": 75, "usage_type": "call"}]} +{"seq_id": "43592776984", "text": "# Based on ideas from: https://realpython.com/intro-to-python-threading/\n# Steve Cosgrove 28 March 2020\nimport logging\nimport threading\nimport time\nimport os\nimport sys\nimport cayenne.client\nimport datetime\nimport toml\nimport string\nimport cayenne.client, datetime, time, serial, logging, csv, os, requests, datetime, time, glob, uuid, sys, toml, struct, traceback, string\nfrom SensorLib import GetWirelessStats\nfrom SensorLib import GetSerialData\nfrom MQTTUtils import Save2Cayenne\nfrom MQTTUtils import Save2CSV\nfrom MQTTUtils import DataError\nfrom MQTTUtils import ReadTemp\nfrom MQTTUtils import ProcessError\nfrom gpiozero import CPUTemperature\n\n# the IOT/LoRaReAd dir contains MQTTUtils.py\n# MQTTUpath = os.path.join(HomeDir,'IOT/LoRaReAd')\n# sys.path.append(MQTTUpath)\n \n# The callback for when a message is received from Cayenne.\ndef on_message(client, userData, message):\n# based on https://developers.mydevices.com/cayenne/docs/cayenne-mqtt-api/#cayenne-mqtt-api-mqtt-messaging-topics-send-actuator-updated-value\n# global COUNTER\n print(\"message received: \" ,str(message.payload.decode(\"utf-8\")))\n\ndef on_connect(client, userData, flags, rc):\n print(\"Connected with result code \"+str(rc))\n\ndef ReadTempThread(Freq,CSVPath,ClientID,client):\n while True :\n Value = ReadTemp()\n# logging.info(\"Temp Loop: %s\", Value)\n Channel = 'ExtTemp'\n Save2CSV (CSVPath, ClientID, Channel, Value)\n Save2Cayenne (client, Channel, Value, 1)\n time.sleep(Freq)\n\ndef ReadCPUThread(Freq,CSVPath,ClientID,client):\n while True :\n Value = CPUTemperature().temperature\n# logging.info(\"CPU Loop: %s\", Value)\n Channel = 'CPUtemp'\n Save2CSV (CSVPath, ClientID, Channel, Value)\n Save2Cayenne (client, Channel, Value, 1)\n time.sleep(Freq)\n\ndef ReadWifiThread(Freq,CSVPath,ClientID,client):\n while True :\n Value = GetWirelessStats()\n# logging.info(\"Wifi Loop: %s\", Value)\n Link = Value['wlan0']['link']\n Channel = 'WifiLnk'\n Save2CSV (CSVPath, ClientID, Channel, Link)\n Save2Cayenne (client, Channel, Link, 100)\n Level = Value['wlan0']['level']\n Channel = 'WifiLvl'\n Save2CSV (CSVPath, ClientID, Channel, Level)\n Save2Cayenne (client, Channel, Level, 100)\n time.sleep(Freq)\n\ndef ReadSerialData(CSVPath,ClientID,client):\n # Define the PicAxe Divisors\n DivisorDict = dict.fromkeys(string.ascii_uppercase)\n for key in DivisorDict :\n DivisorDict[key] =\t1\n DivisorDict['A'] =\t10 # Soil Moisture\n DivisorDict['B'] =\t10 # Temperature\n\n try :\n while True :\n Value = GetSerialData(CSVPath,ClientID)\n # logging.info(\"Serial Loop: %s\", Value)\n Channel = Value[\"Channel\"]\n Data = Value[\"Data\"]\n Status = Value[\"Status\"]\n ClientID = Value[\"ClientID\"]\n Error = Value[\"Error\"]\n Save2Cayenne (client, 'Stat', Status, 1)\n # logging.info(\"Wifi Loop: %s\", Value)\n if Status == 0 :\n Save2CSV (CSVPath, ClientID, 'Error', Error)\n else :\n # Status is OK, so write the data ...\n Save2CSV (CSVPath, ClientID, Channel, Data)\n Save2Cayenne (client, Channel, Data, DivisorDict[Channel])\n except :\n Message = \"Exception reading LoRa Data\"\n CSV_Message = Message\n ProcessError(CSVPath, ClientID, '', CSV_Message, Message)\n\nif __name__ == \"__main__\":\n\n HomeDir = os.environ['HOME']\n AUTH_FILE = \t'cayenneMQTT.txt'\n LOG_FILE =\t'LOG_' + os.path.basename(__file__)\n CSV \t= \t'.csv'\n CsvTopic = \t'RSSILatLong'\n CSVPath =\tos.path.join(HomeDir, 'CSVdata')\n Eq\t= \t' = '\n CrLf\t= \t'\\r\\n'\n Qt\t= \t'\"'\n\n # Seconds between reading each value internal to this Computer\n TempDelay =\t300\n CPUDelay =\t60\n WifiDelay =\t300\n\n # Set up logging and local copies of data\n ConfPathFile = os.path.join(HomeDir, AUTH_FILE)\n LogPathFile = os.path.join(CSVPath, LOG_FILE)\n \n format = \"%(asctime)s: %(message)s\"\n logging.basicConfig(filename=LogPathFile, format=format, level=logging.DEBUG,\n datefmt=\"%H:%M:%S\")\n # logging.basicConfig(filename=LogPathFile, level=logging.DEBUG)\n CurrentTime = datetime.datetime.now().isoformat()\n logging.debug(CrLf+'***** Starting at: {a}'.format(a=CurrentTime)+' *****' )\n\n # Cayenne authentication info. This should be obtained from the Cayenne Dashboard,\n # and the details should be put into the file listed above.\n # Read the Cayenne configuration stuff into a dictionary\n ConfigDict = toml.load(ConfPathFile)\n CayenneParam = ConfigDict.get('cayenne')\n print (CayenneParam)\n\n # Connect to Cayenne Cloud\n client = cayenne.client.CayenneMQTTClient()\n client.on_message = on_message\n client.on_connect = on_connect\n\n client.begin(CayenneParam.get('CayUsername'), \\\n CayenneParam.get('CayPassword'), \\\n CayenneParam.get('CayClientID'), \\\n )\n ClientID = CayenneParam.get('CayClientID')\n\n# ReadTempThread(TempDelay,CSVPath,ClientID,client,)\n\n Temp = threading.Thread(target=ReadTempThread, \\\n args=(TempDelay,CSVPath,ClientID,client,), name='Temp', daemon=True)\n Temp.start()\n CPU = threading.Thread(target=ReadCPUThread, \\\n args=(CPUDelay,CSVPath,ClientID,client,), name='CPU', daemon=True)\n CPU.start()\n Wifi = threading.Thread(target=ReadWifiThread, \\\n args=(WifiDelay,CSVPath,ClientID,client,), name='Wifi', daemon=True)\n Wifi.start()\n Serial = threading.Thread(target=ReadSerialData, \\\n args=(CSVPath,ClientID,client,), name='Serial', daemon=True)\n Serial.start()\n\n Run_flag = True\n# ThreadAll = threading.enumerate()\n# ProcessError(CSVPath, 'Threads', '', 'Running:'+str(ThreadAll), ThreadAll )\n while Run_flag:\n try: # catch a \n# Threads = {}\n# for ThreadName in ['Temp', 'CPU', 'Wifi', 'Serial']:\n# Threads[ThreadName]=ThreadName\n# print (Threads )\n# Running=''\n# for ThreadName in ThreadAll :\n# if ThreadName.isAlive():\n# Running = Running+';'+ThreadName.getName()\n# else:\n# ProcessError(CSVPath, 'Threads', '', 'Restarted:'+ThreadName.getName(), Running )\n# print (Running) # debug\n# ProcessError(CSVPath, 'Threads', '', 'Running:'+str(Running), Running )\n time.sleep(3000000)\n except KeyboardInterrupt:\n Run_flag=False # Stop the loop\n\nprint('\\n','Exiting app') # Send a cheery message\n\ntime.sleep(4) # Four seconds to allow sending to finish\n\n", "repo_name": "JittoThomas/IOT", "sub_path": "LoRaReAd/Thread_to_MQTT.py", "file_name": "Thread_to_MQTT.py", "file_ext": "py", "file_size_in_byte": 6604, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "MQTTUtils.ReadTemp", "line_number": 37, "usage_type": "call"}, {"api_name": "MQTTUtils.Save2CSV", "line_number": 40, "usage_type": "call"}, {"api_name": "MQTTUtils.Save2Cayenne", "line_number": 41, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 42, "usage_type": "call"}, {"api_name": "gpiozero.CPUTemperature", "line_number": 46, "usage_type": "call"}, {"api_name": "MQTTUtils.Save2CSV", "line_number": 49, "usage_type": "call"}, {"api_name": "MQTTUtils.Save2Cayenne", "line_number": 50, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 51, "usage_type": "call"}, {"api_name": "SensorLib.GetWirelessStats", "line_number": 55, "usage_type": "call"}, {"api_name": "MQTTUtils.Save2CSV", "line_number": 59, "usage_type": "call"}, {"api_name": "MQTTUtils.Save2Cayenne", "line_number": 60, "usage_type": "call"}, {"api_name": "MQTTUtils.Save2CSV", "line_number": 63, "usage_type": "call"}, {"api_name": "MQTTUtils.Save2Cayenne", "line_number": 64, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 65, "usage_type": "call"}, {"api_name": "string.ascii_uppercase", "line_number": 69, "usage_type": "attribute"}, {"api_name": "SensorLib.GetSerialData", "line_number": 77, "usage_type": "call"}, {"api_name": "MQTTUtils.Save2Cayenne", "line_number": 84, "usage_type": "call"}, {"api_name": "MQTTUtils.Save2CSV", "line_number": 87, "usage_type": "call"}, {"api_name": "MQTTUtils.Save2CSV", "line_number": 90, "usage_type": "call"}, {"api_name": "MQTTUtils.Save2Cayenne", "line_number": 91, "usage_type": "call"}, {"api_name": "MQTTUtils.ProcessError", "line_number": 95, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 99, "usage_type": "attribute"}, {"api_name": "os.path.basename", "line_number": 101, "usage_type": "call"}, {"api_name": "os.path", "line_number": 101, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 104, "usage_type": "call"}, {"api_name": "os.path", "line_number": 104, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 115, "usage_type": "call"}, {"api_name": "os.path", "line_number": 115, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 116, "usage_type": "call"}, {"api_name": "os.path", "line_number": 116, "usage_type": "attribute"}, {"api_name": "logging.basicConfig", "line_number": 119, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 119, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 122, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 122, "usage_type": "attribute"}, {"api_name": "logging.debug", "line_number": 123, "usage_type": "call"}, {"api_name": "toml.load", "line_number": 128, "usage_type": "call"}, {"api_name": "cayenne.client.client.CayenneMQTTClient", "line_number": 133, "usage_type": "call"}, {"api_name": "cayenne.client.client", "line_number": 133, "usage_type": "attribute"}, {"api_name": "cayenne.client", "line_number": 133, "usage_type": "name"}, {"api_name": "threading.Thread", "line_number": 145, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 148, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 151, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 154, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 175, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 181, "usage_type": "call"}]} +{"seq_id": "36291659847", "text": "# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('presupuesto', '0015_auto_20150813_1015'),\n ]\n\n operations = [\n migrations.AddField(\n model_name='datosprecargado',\n name='duracion_optimamudanza',\n field=models.DecimalField(max_digits=7, decimal_places=2, default=0),\n preserve_default=False,\n ),\n migrations.AddField(\n model_name='datosprecargado',\n name='rendimiento_peso',\n field=models.DecimalField(max_digits=9, decimal_places=3, default=0),\n preserve_default=False,\n ),\n migrations.AddField(\n model_name='datosprecargado',\n name='rendimiento_unidad',\n field=models.PositiveIntegerField(default=0),\n preserve_default=False,\n ),\n migrations.AddField(\n model_name='datosprecargado',\n name='rendimiento_volumen',\n field=models.DecimalField(max_digits=8, decimal_places=3, default=0),\n preserve_default=False,\n ),\n ]\n", "repo_name": "yusnelvy/mtvmcotizacion", "sub_path": "presupuesto/migrations/0016_auto_20150818_0923.py", "file_name": "0016_auto_20150818_0923.py", "file_ext": "py", "file_size_in_byte": 1188, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "django.db.migrations.Migration", "line_number": 7, "usage_type": "attribute"}, {"api_name": "django.db.migrations", "line_number": 7, "usage_type": "name"}, {"api_name": "django.db.migrations.AddField", "line_number": 14, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 14, "usage_type": "name"}, {"api_name": "django.db.models.DecimalField", "line_number": 17, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 17, "usage_type": "name"}, {"api_name": "django.db.migrations.AddField", "line_number": 20, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 20, "usage_type": "name"}, {"api_name": "django.db.models.DecimalField", "line_number": 23, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 23, "usage_type": "name"}, {"api_name": "django.db.migrations.AddField", "line_number": 26, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 26, "usage_type": "name"}, {"api_name": "django.db.models.PositiveIntegerField", "line_number": 29, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 29, "usage_type": "name"}, {"api_name": "django.db.migrations.AddField", "line_number": 32, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 32, "usage_type": "name"}, {"api_name": "django.db.models.DecimalField", "line_number": 35, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 35, "usage_type": "name"}]} +{"seq_id": "38500746867", "text": "from flask import request, session, redirect, url_for, current_app, jsonify, abort\nfrom .. import db\nfrom ..models import Datum, User, Record, Plan\nfrom . import main\n\n\n\n\n@main.route('/user//update', methods=['PUT'])\ndef update_user_info(user_id: int):\n if not request.json:\n abort(400)\n\n targetUser = User.query.get_or_404(user_id)\n updateDict = dict(request.json)\n if 'currentState' in updateDict:\n targetUser.currentState = updateDict['currentState']\n if 'idealBedtime'in updateDict:\n targetUser.idealBedtime = updateDict['idealBedtime']\n if 'currentBedtime'in updateDict:\n targetUser.currentBedtime = updateDict['currentBedtime']\n if 'timezone'in updateDict:\n targetUser.timezone = updateDict['timezone']\n if 'userChoiceA'in updateDict:\n targetUser.userChoiceA = updateDict['userChoiceA']\n if 'userChoiceB'in updateDict:\n targetUser.userChoiceB = updateDict['userChoiceB']\n db.session.add(targetUser)\n db.session.commit()\n return jsonify({'user': targetUser.to_dict()}), 201\n\n\n@main.route('/user//hit++', methods=['PUT'])\ndef update_hit_plus(user_id: int):\n\n targetUser = User.query.get_or_404(user_id)\n targetUser.weeklyHit += 1\n db.session.add(targetUser)\n db.session.commit()\n return jsonify({'user': targetUser.to_dict()}), 201\n\n@main.route('/user//hit--', methods=['PUT'])\ndef update_hit_minus(user_id: int):\n\n targetUser = User.query.get_or_404(user_id)\n targetUser.weeklyHit -= 1\n db.session.add(targetUser)\n db.session.commit()\n return jsonify({'user': targetUser.to_dict()}), 201\n\n@main.route('/user//miss++', methods=['PUT'])\ndef update_miss_plus(user_id: int):\n \n targetUser = User.query.get_or_404(user_id)\n targetUser.weeklyMiss += 1\n db.session.add(targetUser)\n db.session.commit()\n return jsonify({'user': targetUser.to_dict()}), 201\n\n@main.route('/user//miss--', methods=['PUT'])\ndef update_miss_minus(user_id: int):\n \n targetUser = User.query.get_or_404(user_id)\n targetUser.weeklyMiss -= 1\n db.session.add(targetUser)\n db.session.commit()\n return jsonify({'user': targetUser.to_dict()}), 201\n\n@main.route('/user//missreset', methods=['PUT'])\ndef update_hit_reset(user_id: int):\n \n targetUser = User.query.get_or_404(user_id)\n targetUser.weeklyMiss = 0\n db.session.add(targetUser)\n db.session.commit()\n return jsonify({'user': targetUser.to_dict()}), 201\n\n@main.route('/user//hitreset', methods=['PUT'])\ndef update_miss_reset(user_id: int):\n \n targetUser = User.query.get_or_404(user_id)\n targetUser.weeklyHit = 0\n db.session.add(targetUser)\n db.session.commit()\n return jsonify({'user': targetUser.to_dict()}), 201\n\n@main.route('/user//changeplan', methods=['PUT'])\ndef update_changeplan(user_id: int):\n if not request.json:\n abort(400)\n\n updateWeeklyBedtime = request.json['weeklyBedtime']\n updateWeeklyFrequency = request.json['weeklyFrequency']\n updatePlan = Plan(weeklyFrequency = updateWeeklyFrequency, weeklyBedtime = updateWeeklyBedtime)\n db.session.add(updatePlan)\n db.session.commit()\n \n targetUser = User.query.get_or_404(user_id)\n targetUser.weeklyPlanId = updatePlan.id\n db.session.add(targetUser)\n db.session.commit()\n return jsonify({'user': targetUser.to_dict()}), 201\n", "repo_name": "timoderbeste/sleep-tracking-bot", "sub_path": "database_server/botDB/main/update_user.py", "file_name": "update_user.py", "file_ext": "py", "file_size_in_byte": 3444, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "37", "api": [{"api_name": "flask.request.json", "line_number": 11, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 11, "usage_type": "name"}, {"api_name": "flask.abort", "line_number": 12, "usage_type": "call"}, {"api_name": "models.User.query.get_or_404", "line_number": 14, "usage_type": "call"}, {"api_name": "models.User.query", "line_number": 14, "usage_type": "attribute"}, {"api_name": "models.User", "line_number": 14, "usage_type": "name"}, {"api_name": "flask.request.json", "line_number": 15, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 15, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 30, "usage_type": "call"}, {"api_name": "models.User.query.get_or_404", "line_number": 36, "usage_type": "call"}, {"api_name": "models.User.query", "line_number": 36, "usage_type": "attribute"}, {"api_name": "models.User", "line_number": 36, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 40, "usage_type": "call"}, {"api_name": "models.User.query.get_or_404", "line_number": 45, "usage_type": "call"}, {"api_name": "models.User.query", "line_number": 45, "usage_type": "attribute"}, {"api_name": "models.User", "line_number": 45, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 49, "usage_type": "call"}, {"api_name": "models.User.query.get_or_404", "line_number": 54, "usage_type": "call"}, {"api_name": "models.User.query", "line_number": 54, "usage_type": "attribute"}, {"api_name": "models.User", "line_number": 54, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 58, "usage_type": "call"}, {"api_name": "models.User.query.get_or_404", "line_number": 63, "usage_type": "call"}, {"api_name": "models.User.query", "line_number": 63, "usage_type": "attribute"}, {"api_name": "models.User", "line_number": 63, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 67, "usage_type": "call"}, {"api_name": "models.User.query.get_or_404", "line_number": 72, "usage_type": "call"}, {"api_name": "models.User.query", "line_number": 72, "usage_type": "attribute"}, {"api_name": "models.User", "line_number": 72, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 76, "usage_type": "call"}, {"api_name": "models.User.query.get_or_404", "line_number": 81, "usage_type": "call"}, {"api_name": "models.User.query", "line_number": 81, "usage_type": "attribute"}, {"api_name": "models.User", "line_number": 81, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 85, "usage_type": "call"}, {"api_name": "flask.request.json", "line_number": 89, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 89, "usage_type": "name"}, {"api_name": "flask.abort", "line_number": 90, "usage_type": "call"}, {"api_name": "flask.request.json", "line_number": 92, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 92, "usage_type": "name"}, {"api_name": "flask.request.json", "line_number": 93, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 93, "usage_type": "name"}, {"api_name": "models.Plan", "line_number": 94, "usage_type": "call"}, {"api_name": "models.User.query.get_or_404", "line_number": 98, "usage_type": "call"}, {"api_name": "models.User.query", "line_number": 98, "usage_type": "attribute"}, {"api_name": "models.User", "line_number": 98, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 102, "usage_type": "call"}]} +{"seq_id": "6712949780", "text": "import sys\nimport importlib\n\"\"\"Stub versions of optional submodules which may fail to clone.\"\"\"\n\n\nclass Shim(object):\n def __init__(self):\n self.is_shim = True\n\n\nclass version(Shim):\n def get_version(self):\n return None\n\n\nclass simplewrap(Shim):\n class Wrapper(object):\n def __init__(self):\n self.width = 80\n def wrap(self, string):\n return string\n\n\nclass phone(Shim):\n class Call(object):\n def __init__(self,\n script_path,\n version,\n run_id=None,\n domain=None,\n timeout=None,\n secure=None,\n platform=None,\n test=False,\n fail='exception'):\n pass\n def send_data(self, event_type, run_data={}, run_time=None):\n pass\n\n\ndef get_module_or_shim(module_path):\n \"\"\"Load the given module, or if not possible, return a stub.\"\"\"\n try:\n return importlib.import_module(module_path)\n except ImportError:\n sys.stderr.write('Warning: Problem importing module '+module_path+'. '\n 'Some functionality may be missing.\\n')\n module_name = module_path.split('.')[-1]\n try:\n shim = globals()[module_name]\n except KeyError:\n sys.stderr.write('Error: cannot find a shim named \"'+module_name+'\".\\n')\n raise\n try:\n return shim()\n except TypeError:\n sys.stderr.write('Error: problem loading shim \"'+module_name+'\".\\n')\n raise\n", "repo_name": "pughlab/dunovo", "sub_path": "shims.py", "file_name": "shims.py", "file_ext": "py", "file_size_in_byte": 1436, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "github-code", "pt": "37", "api": [{"api_name": "importlib.import_module", "line_number": 44, "usage_type": "call"}, {"api_name": "sys.stderr.write", "line_number": 46, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 46, "usage_type": "attribute"}, {"api_name": "sys.stderr.write", "line_number": 52, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 52, "usage_type": "attribute"}, {"api_name": "sys.stderr.write", "line_number": 57, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 57, "usage_type": "attribute"}]} +{"seq_id": "36668052629", "text": "import functools\nimport pytest\n\nfrom eth_tester import (\n EthereumTester,\n)\nfrom eth_tester.exceptions import (\n TransactionFailed,\n)\nfrom eth_utils import (\n is_checksum_address,\n is_dict,\n is_integer,\n)\n\nfrom web3 import (\n Web3,\n)\nfrom web3._utils.contract_sources.contract_data._custom_contract_data import (\n EMITTER_ENUM,\n)\nfrom web3._utils.contract_sources.contract_data.panic_errors_contract import (\n PANIC_ERRORS_CONTRACT_DATA,\n)\nfrom web3._utils.contract_sources.contract_data.storage_contract import (\n STORAGE_CONTRACT_DATA,\n)\nfrom web3._utils.module_testing import (\n EthModuleTest,\n GoEthereumPersonalModuleTest,\n NetModuleTest,\n Web3ModuleTest,\n)\nfrom web3.exceptions import (\n MethodUnavailable,\n)\nfrom web3.providers.eth_tester import (\n EthereumTesterProvider,\n)\nfrom web3.types import ( # noqa: F401\n BlockData,\n)\n\n\ndef _deploy_contract(w3, contract_factory):\n deploy_txn_hash = contract_factory.constructor().transact({\"from\": w3.eth.coinbase})\n deploy_receipt = w3.eth.wait_for_transaction_receipt(deploy_txn_hash)\n assert is_dict(deploy_receipt)\n contract_address = deploy_receipt[\"contractAddress\"]\n assert is_checksum_address(contract_address)\n return contract_factory(contract_address)\n\n\n@pytest.fixture(scope=\"module\")\ndef eth_tester():\n _eth_tester = EthereumTester()\n return _eth_tester\n\n\n@pytest.fixture(scope=\"module\")\ndef eth_tester_provider(eth_tester):\n provider = EthereumTesterProvider(eth_tester)\n return provider\n\n\n@pytest.fixture(scope=\"module\")\ndef w3(eth_tester_provider):\n _w3 = Web3(eth_tester_provider)\n return _w3\n\n\n@pytest.fixture(scope=\"module\")\ndef math_contract_deploy_txn_hash(w3, math_contract_factory):\n deploy_txn_hash = math_contract_factory.constructor().transact(\n {\"from\": w3.eth.coinbase}\n )\n return deploy_txn_hash\n\n\n@pytest.fixture(scope=\"module\")\ndef math_contract(w3, math_contract_factory, math_contract_deploy_txn_hash):\n deploy_receipt = w3.eth.wait_for_transaction_receipt(math_contract_deploy_txn_hash)\n assert is_dict(deploy_receipt)\n contract_address = deploy_receipt[\"contractAddress\"]\n assert is_checksum_address(contract_address)\n return math_contract_factory(contract_address)\n\n\n@pytest.fixture(scope=\"module\")\ndef math_contract_address(math_contract, address_conversion_func):\n return address_conversion_func(math_contract.address)\n\n\n@pytest.fixture(scope=\"module\")\ndef storage_contract(w3):\n contract_factory = w3.eth.contract(**STORAGE_CONTRACT_DATA)\n return _deploy_contract(w3, contract_factory)\n\n\n@pytest.fixture(scope=\"module\")\ndef emitter_contract(w3, emitter_contract_factory):\n return _deploy_contract(w3, emitter_contract_factory)\n\n\n@pytest.fixture(scope=\"module\")\ndef emitter_contract_address(emitter_contract, address_conversion_func):\n return address_conversion_func(emitter_contract.address)\n\n\n@pytest.fixture(scope=\"module\")\ndef empty_block(w3):\n w3.testing.mine()\n block = w3.eth.get_block(\"latest\")\n assert not block[\"transactions\"]\n return block\n\n\n@pytest.fixture(scope=\"module\")\ndef block_with_txn(w3):\n txn_hash = w3.eth.send_transaction(\n {\n \"from\": w3.eth.coinbase,\n \"to\": w3.eth.coinbase,\n \"value\": 1,\n \"gas\": 21000,\n \"gas_price\": 1000000000, # needs to be greater than base_fee post London\n }\n )\n txn = w3.eth.get_transaction(txn_hash)\n block = w3.eth.get_block(txn[\"blockNumber\"])\n return block\n\n\n@pytest.fixture(scope=\"module\")\ndef mined_txn_hash(block_with_txn):\n return block_with_txn[\"transactions\"][0]\n\n\n@pytest.fixture(scope=\"module\")\ndef block_with_txn_with_log(w3, emitter_contract):\n txn_hash = emitter_contract.functions.logDouble(\n which=EMITTER_ENUM[\"LogDoubleWithIndex\"],\n arg0=12345,\n arg1=54321,\n ).transact(\n {\n \"from\": w3.eth.coinbase,\n }\n )\n txn = w3.eth.get_transaction(txn_hash)\n block = w3.eth.get_block(txn[\"blockNumber\"])\n return block\n\n\n@pytest.fixture(scope=\"module\")\ndef txn_hash_with_log(block_with_txn_with_log):\n return block_with_txn_with_log[\"transactions\"][0]\n\n\n@pytest.fixture(scope=\"module\")\ndef revert_contract(w3, revert_contract_factory):\n return _deploy_contract(w3, revert_contract_factory)\n\n\n#\n# Offchain Lookup Contract Setup\n#\n@pytest.fixture(scope=\"module\")\ndef offchain_lookup_contract(w3, offchain_lookup_contract_factory):\n return _deploy_contract(w3, offchain_lookup_contract_factory)\n\n\n@pytest.fixture(scope=\"module\")\ndef panic_errors_contract(w3):\n panic_errors_contract_factory = w3.eth.contract(**PANIC_ERRORS_CONTRACT_DATA)\n return _deploy_contract(w3, panic_errors_contract_factory)\n\n\nUNLOCKABLE_PRIVATE_KEY = (\n \"0x392f63a79b1ff8774845f3fa69de4a13800a59e7083f5187f1558f0797ad0f01\"\n)\n\n\n@pytest.fixture(scope=\"module\")\ndef unlockable_account_pw():\n return \"web3-testing\"\n\n\n@pytest.fixture(scope=\"module\")\ndef unlockable_account(w3, unlockable_account_pw):\n account = w3.geth.personal.import_raw_key(\n UNLOCKABLE_PRIVATE_KEY, unlockable_account_pw\n )\n w3.eth.send_transaction(\n {\n \"from\": w3.eth.coinbase,\n \"to\": account,\n \"value\": w3.to_wei(10, \"ether\"),\n \"gas\": 21000,\n }\n )\n yield account\n\n\n@pytest.fixture\ndef unlocked_account(w3, unlockable_account, unlockable_account_pw):\n w3.geth.personal.unlock_account(unlockable_account, unlockable_account_pw)\n yield unlockable_account\n w3.geth.personal.lock_account(unlockable_account)\n\n\n@pytest.fixture()\ndef unlockable_account_dual_type(unlockable_account, address_conversion_func):\n return address_conversion_func(unlockable_account)\n\n\n@pytest.fixture\ndef unlocked_account_dual_type(w3, unlockable_account_dual_type, unlockable_account_pw):\n w3.geth.personal.unlock_account(unlockable_account_dual_type, unlockable_account_pw)\n yield unlockable_account_dual_type\n w3.geth.personal.lock_account(unlockable_account_dual_type)\n\n\n@pytest.fixture(scope=\"module\")\ndef funded_account_for_raw_txn(w3):\n account = \"0x39EEed73fb1D3855E90Cbd42f348b3D7b340aAA6\"\n w3.eth.send_transaction(\n {\n \"from\": w3.eth.coinbase,\n \"to\": account,\n \"value\": w3.to_wei(10, \"ether\"),\n \"gas\": 21000,\n \"gas_price\": 1,\n }\n )\n return account\n\n\nclass TestEthereumTesterWeb3Module(Web3ModuleTest):\n def _check_web3_client_version(self, client_version):\n assert client_version.startswith(\"EthereumTester/\")\n\n\ndef not_implemented(method, exc_type=NotImplementedError):\n @functools.wraps(method)\n def inner(*args, **kwargs):\n with pytest.raises(exc_type):\n method(*args, **kwargs)\n\n return inner\n\n\ndef disable_auto_mine(func):\n @functools.wraps(func)\n def func_wrapper(self, eth_tester, *args, **kwargs):\n snapshot = eth_tester.take_snapshot()\n eth_tester.disable_auto_mine_transactions()\n try:\n func(self, eth_tester, *args, **kwargs)\n finally:\n eth_tester.enable_auto_mine_transactions()\n eth_tester.mine_block()\n eth_tester.revert_to_snapshot(snapshot)\n\n return func_wrapper\n\n\nclass TestEthereumTesterEthModule(EthModuleTest):\n test_eth_max_priority_fee_with_fee_history_calculation = not_implemented(\n EthModuleTest.test_eth_max_priority_fee_with_fee_history_calculation,\n MethodUnavailable,\n )\n test_eth_max_priority_fee_with_fee_history_calculation_error_dict = not_implemented(\n EthModuleTest.test_eth_max_priority_fee_with_fee_history_calculation_error_dict,\n ValueError,\n )\n test_eth_sign = not_implemented(EthModuleTest.test_eth_sign, ValueError)\n test_eth_sign_ens_names = not_implemented(\n EthModuleTest.test_eth_sign_ens_names, ValueError\n )\n test_eth_sign_typed_data = not_implemented(\n EthModuleTest.test_eth_sign_typed_data, ValueError\n )\n test_eth_sign_transaction_legacy = not_implemented(\n EthModuleTest.test_eth_sign_transaction_legacy, ValueError\n )\n test_eth_sign_transaction = not_implemented(\n EthModuleTest.test_eth_sign_transaction, ValueError\n )\n test_eth_sign_transaction_hex_fees = not_implemented(\n EthModuleTest.test_eth_sign_transaction_hex_fees, ValueError\n )\n test_eth_sign_transaction_ens_names = not_implemented(\n EthModuleTest.test_eth_sign_transaction_ens_names, ValueError\n )\n test_eth_submit_hashrate = not_implemented(\n EthModuleTest.test_eth_submit_hashrate, MethodUnavailable\n )\n test_eth_submit_work = not_implemented(\n EthModuleTest.test_eth_submit_work, MethodUnavailable\n )\n test_eth_get_raw_transaction = not_implemented(\n EthModuleTest.test_eth_get_raw_transaction, MethodUnavailable\n )\n test_eth_get_raw_transaction_raises_error = not_implemented(\n EthModuleTest.test_eth_get_raw_transaction, MethodUnavailable\n )\n test_eth_get_raw_transaction_by_block = not_implemented(\n EthModuleTest.test_eth_get_raw_transaction_by_block, ValueError\n )\n test_eth_get_raw_transaction_by_block_raises_error = not_implemented(\n EthModuleTest.test_eth_get_raw_transaction_by_block, ValueError\n )\n test_eth_replace_transaction_already_mined = not_implemented(\n EthModuleTest.test_eth_replace_transaction_already_mined, ValueError\n )\n\n def test_eth_getBlockByHash_pending(self, w3: \"Web3\") -> None:\n block = w3.eth.get_block(\"pending\")\n assert block[\"hash\"] is not None\n\n @pytest.mark.xfail(reason=\"eth_feeHistory is not implemented on eth-tester\")\n def test_eth_fee_history(self, w3: \"Web3\"):\n super().test_eth_fee_history(w3)\n\n @pytest.mark.xfail(reason=\"eth_feeHistory is not implemented on eth-tester\")\n def test_eth_fee_history_with_integer(self, w3: \"Web3\"):\n super().test_eth_fee_history_with_integer(w3)\n\n @pytest.mark.xfail(reason=\"eth_feeHistory is not implemented on eth-tester\")\n def test_eth_fee_history_no_reward_percentiles(self, w3: \"Web3\"):\n super().test_eth_fee_history_no_reward_percentiles(w3)\n\n @disable_auto_mine\n def test_eth_get_transaction_receipt_unmined(\n self, eth_tester, w3, unlocked_account\n ):\n super().test_eth_get_transaction_receipt_unmined(w3, unlocked_account)\n\n @disable_auto_mine\n def test_eth_replace_transaction_legacy(self, eth_tester, w3, unlocked_account):\n super().test_eth_replace_transaction_legacy(w3, unlocked_account)\n\n @disable_auto_mine\n def test_eth_replace_transaction(self, eth_tester, w3, unlocked_account):\n super().test_eth_replace_transaction(w3, unlocked_account)\n\n @disable_auto_mine\n @pytest.mark.xfail(\n reason=\"py-evm does not raise on EIP-1559 transaction underpriced\"\n )\n # TODO: This might also be an issue in py-evm worth looking into. See reason above.\n def test_eth_replace_transaction_underpriced(\n self, eth_tester, w3, unlocked_account\n ):\n super().test_eth_replace_transaction_underpriced(w3, unlocked_account)\n\n @disable_auto_mine\n def test_eth_replace_transaction_incorrect_nonce(\n self, eth_tester, w3, unlocked_account\n ):\n super().test_eth_replace_transaction_incorrect_nonce(w3, unlocked_account)\n\n @disable_auto_mine\n def test_eth_replace_transaction_gas_price_too_low(\n self, eth_tester, w3, unlocked_account\n ):\n super().test_eth_replace_transaction_gas_price_too_low(w3, unlocked_account)\n\n @disable_auto_mine\n def test_eth_replace_transaction_gas_price_defaulting_minimum(\n self, eth_tester, w3, unlocked_account\n ):\n super().test_eth_replace_transaction_gas_price_defaulting_minimum(\n w3, unlocked_account\n )\n\n @disable_auto_mine\n def test_eth_replace_transaction_gas_price_defaulting_strategy_higher(\n self, eth_tester, w3, unlocked_account\n ):\n super().test_eth_replace_transaction_gas_price_defaulting_strategy_higher(\n w3, unlocked_account\n )\n\n @disable_auto_mine\n def test_eth_replace_transaction_gas_price_defaulting_strategy_lower(\n self, eth_tester, w3, unlocked_account\n ):\n super().test_eth_replace_transaction_gas_price_defaulting_strategy_lower(\n w3, unlocked_account\n )\n\n @disable_auto_mine\n def test_eth_modify_transaction_legacy(self, eth_tester, w3, unlocked_account):\n super().test_eth_modify_transaction_legacy(w3, unlocked_account)\n\n @disable_auto_mine\n def test_eth_modify_transaction(self, eth_tester, w3, unlocked_account):\n super().test_eth_modify_transaction(w3, unlocked_account)\n\n @disable_auto_mine\n def test_eth_call_old_contract_state(\n self, eth_tester, w3, math_contract, unlocked_account\n ):\n # For now, ethereum tester cannot give call results in the pending block.\n # Once that feature is added, then delete the except/else blocks.\n try:\n super().test_eth_call_old_contract_state(\n w3, math_contract, unlocked_account\n )\n except AssertionError as err:\n if str(err) == \"pending call result was 0 instead of 1\":\n pass\n else:\n raise err\n else:\n raise AssertionError(\n \"eth-tester was unexpectedly able to give the pending call result\"\n )\n\n def test_eth_get_storage_at(self, w3, storage_contract):\n super().test_eth_get_storage_at(w3, storage_contract)\n\n def test_eth_get_storage_at_ens_name(self, w3, storage_contract):\n super().test_eth_get_storage_at_ens_name(w3, storage_contract)\n\n def test_eth_estimate_gas_with_block(self, w3, unlocked_account_dual_type):\n super().test_eth_estimate_gas_with_block(w3, unlocked_account_dual_type)\n\n def test_eth_chain_id(self, w3):\n chain_id = w3.eth.chain_id\n assert is_integer(chain_id)\n assert chain_id == 131277322940537\n\n @disable_auto_mine\n def test_eth_wait_for_transaction_receipt_unmined(\n self, eth_tester, w3, unlocked_account_dual_type\n ):\n super().test_eth_wait_for_transaction_receipt_unmined(\n w3, unlocked_account_dual_type\n )\n\n @pytest.mark.xfail(\n raises=TypeError, reason=\"call override param not implemented on eth-tester\"\n )\n def test_eth_call_with_override_code(self, w3, revert_contract):\n super().test_eth_call_with_override_code(w3, revert_contract)\n\n @pytest.mark.xfail(\n raises=TypeError, reason=\"call override param not implemented on eth-tester\"\n )\n def test_eth_call_with_override_param_type_check(self, w3, math_contract):\n super().test_eth_call_with_override_param_type_check(w3, math_contract)\n\n def test_eth_call_revert_with_msg(self, w3, revert_contract, unlocked_account):\n with pytest.raises(\n TransactionFailed, match=\"execution reverted: Function has been reverted\"\n ):\n txn_params = revert_contract._prepare_transaction(\n fn_name=\"revertWithMessage\",\n transaction={\n \"from\": unlocked_account,\n \"to\": revert_contract.address,\n },\n )\n w3.eth.call(txn_params)\n\n def test_eth_call_revert_without_msg(self, w3, revert_contract, unlocked_account):\n with pytest.raises(TransactionFailed, match=\"execution reverted\"):\n txn_params = revert_contract._prepare_transaction(\n fn_name=\"revertWithoutMessage\",\n transaction={\n \"from\": unlocked_account,\n \"to\": revert_contract.address,\n },\n )\n w3.eth.call(txn_params)\n\n def test_eth_estimate_gas_revert_with_msg(\n self, w3, revert_contract, unlocked_account\n ):\n with pytest.raises(\n TransactionFailed, match=\"execution reverted: Function has been reverted\"\n ):\n txn_params = revert_contract._prepare_transaction(\n fn_name=\"revertWithMessage\",\n transaction={\n \"from\": unlocked_account,\n \"to\": revert_contract.address,\n },\n )\n w3.eth.estimate_gas(txn_params)\n\n def test_eth_estimate_gas_revert_without_msg(\n self, w3, revert_contract, unlocked_account\n ):\n with pytest.raises(TransactionFailed, match=\"execution reverted\"):\n txn_params = revert_contract._prepare_transaction(\n fn_name=\"revertWithoutMessage\",\n transaction={\n \"from\": unlocked_account,\n \"to\": revert_contract.address,\n },\n )\n w3.eth.estimate_gas(txn_params)\n\n @disable_auto_mine\n def test_eth_send_transaction(self, eth_tester, w3, unlocked_account):\n super().test_eth_send_transaction(w3, unlocked_account)\n\n @disable_auto_mine\n def test_eth_send_transaction_legacy(self, eth_tester, w3, unlocked_account):\n super().test_eth_send_transaction_legacy(w3, unlocked_account)\n\n @disable_auto_mine\n def test_eth_send_raw_transaction(self, eth_tester, w3, unlocked_account):\n super().test_eth_send_raw_transaction(w3, unlocked_account)\n\n @disable_auto_mine\n @pytest.mark.parametrize(\n \"max_fee\", (1000000000, None), ids=[\"with_max_fee\", \"without_max_fee\"]\n )\n def test_gas_price_from_strategy_bypassed_for_dynamic_fee_txn(\n self,\n eth_tester,\n w3,\n unlocked_account,\n max_fee,\n ):\n super().test_gas_price_from_strategy_bypassed_for_dynamic_fee_txn(\n w3, unlocked_account, max_fee\n )\n\n @disable_auto_mine\n def test_gas_price_from_strategy_bypassed_for_dynamic_fee_txn_no_tip(\n self, eth_tester, w3, unlocked_account\n ):\n super().test_gas_price_from_strategy_bypassed_for_dynamic_fee_txn_no_tip(\n w3,\n unlocked_account,\n )\n\n @pytest.mark.xfail(\n raises=ValueError, reason=\"eth-tester does not have miner_start support\"\n )\n def test_eth_send_transaction_with_nonce(self, eth_tester, w3, unlocked_account):\n super().test_eth_send_transaction_with_nonce(w3, unlocked_account)\n\n @disable_auto_mine\n def test_eth_send_transaction_default_fees(self, eth_tester, w3, unlocked_account):\n super().test_eth_send_transaction_default_fees(w3, unlocked_account)\n\n @disable_auto_mine\n def test_eth_send_transaction_hex_fees(self, eth_tester, w3, unlocked_account):\n super().test_eth_send_transaction_hex_fees(w3, unlocked_account)\n\n @disable_auto_mine\n def test_eth_send_transaction_no_gas(self, eth_tester, w3, unlocked_account):\n super().test_eth_send_transaction_no_gas(w3, unlocked_account)\n\n @disable_auto_mine\n def test_eth_send_transaction_no_max_fee(self, eth_tester, w3, unlocked_account):\n super().test_eth_send_transaction_no_max_fee(w3, unlocked_account)\n\n def test_eth_getBlockByNumber_safe(\n self, w3: \"Web3\", empty_block: BlockData\n ) -> None:\n super().test_eth_getBlockByNumber_safe(w3, empty_block)\n\n def test_eth_getBlockByNumber_finalized(\n self, w3: \"Web3\", empty_block: BlockData\n ) -> None:\n super().test_eth_getBlockByNumber_finalized(w3, empty_block)\n\n def test_eth_get_balance_with_block_identifier(self, w3: \"Web3\") -> None:\n w3.testing.mine()\n miner_address = w3.eth.get_block(1)[\"miner\"]\n genesis_balance = w3.eth.get_balance(miner_address, 0)\n later_balance = w3.eth.get_balance(miner_address, 1)\n\n assert is_integer(genesis_balance)\n assert is_integer(later_balance)\n assert later_balance > genesis_balance\n\n\nclass TestEthereumTesterNetModule(NetModuleTest):\n pass\n\n\n# Use web3.geth.personal namespace for testing eth-tester\nclass TestEthereumTesterPersonalModule(GoEthereumPersonalModuleTest):\n test_personal_sign_and_ecrecover = not_implemented(\n GoEthereumPersonalModuleTest.test_personal_sign_and_ecrecover,\n MethodUnavailable,\n )\n\n test_personal_list_wallets = not_implemented(\n GoEthereumPersonalModuleTest.test_personal_list_wallets,\n MethodUnavailable,\n )\n\n # Test overridden here since eth-tester returns False\n # rather than None for failed unlock\n def test_personal_unlock_account_failure(self, w3, unlockable_account_dual_type):\n result = w3.geth.personal.unlock_account(\n unlockable_account_dual_type, \"bad-password\"\n )\n assert result is False\n", "repo_name": "ethereum/web3.py", "sub_path": "tests/integration/test_ethereum_tester.py", "file_name": "test_ethereum_tester.py", "file_ext": "py", "file_size_in_byte": 20719, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4510, "dataset": "github-code", "pt": "37", "api": [{"api_name": "eth_utils.is_dict", "line_number": 48, "usage_type": "call"}, {"api_name": "eth_utils.is_checksum_address", "line_number": 50, "usage_type": "call"}, {"api_name": "eth_tester.EthereumTester", "line_number": 56, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 54, "usage_type": "call"}, {"api_name": "web3.providers.eth_tester.EthereumTesterProvider", "line_number": 62, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 60, "usage_type": "call"}, {"api_name": "web3.Web3", "line_number": 68, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 66, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 72, "usage_type": "call"}, {"api_name": "eth_utils.is_dict", "line_number": 83, "usage_type": "call"}, {"api_name": "eth_utils.is_checksum_address", "line_number": 85, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 80, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 89, "usage_type": "call"}, {"api_name": "web3._utils.contract_sources.contract_data.storage_contract.STORAGE_CONTRACT_DATA", "line_number": 96, "usage_type": "name"}, {"api_name": "pytest.fixture", "line_number": 94, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 100, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 105, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 110, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 118, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 134, "usage_type": "call"}, {"api_name": "web3._utils.contract_sources.contract_data._custom_contract_data.EMITTER_ENUM", "line_number": 142, "usage_type": "name"}, {"api_name": "pytest.fixture", "line_number": 139, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 155, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 160, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 168, "usage_type": "call"}, {"api_name": "web3._utils.contract_sources.contract_data.panic_errors_contract.PANIC_ERRORS_CONTRACT_DATA", "line_number": 175, "usage_type": "name"}, {"api_name": "pytest.fixture", "line_number": 173, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 184, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 189, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 205, "usage_type": "attribute"}, {"api_name": "pytest.fixture", "line_number": 212, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 217, "usage_type": "attribute"}, {"api_name": "pytest.fixture", "line_number": 224, "usage_type": "call"}, {"api_name": "web3._utils.module_testing.Web3ModuleTest", "line_number": 239, "usage_type": "name"}, {"api_name": "pytest.raises", "line_number": 247, "usage_type": "call"}, {"api_name": "functools.wraps", "line_number": 245, "usage_type": "call"}, {"api_name": "eth_tester.take_snapshot", "line_number": 256, "usage_type": "call"}, {"api_name": "eth_tester.disable_auto_mine_transactions", "line_number": 257, "usage_type": "call"}, {"api_name": "eth_tester.enable_auto_mine_transactions", "line_number": 261, "usage_type": "call"}, {"api_name": "eth_tester.mine_block", "line_number": 262, "usage_type": "call"}, {"api_name": "eth_tester.revert_to_snapshot", "line_number": 263, "usage_type": "call"}, {"api_name": "functools.wraps", "line_number": 254, "usage_type": "call"}, {"api_name": "web3._utils.module_testing.EthModuleTest", "line_number": 268, "usage_type": "name"}, {"api_name": "web3.exceptions.MethodUnavailable", "line_number": 271, "usage_type": "argument"}, {"api_name": "web3._utils.module_testing.EthModuleTest.test_eth_max_priority_fee_with_fee_history_calculation", "line_number": 270, "usage_type": "attribute"}, {"api_name": "web3._utils.module_testing.EthModuleTest", "line_number": 270, "usage_type": "name"}, {"api_name": "web3._utils.module_testing.EthModuleTest.test_eth_max_priority_fee_with_fee_history_calculation_error_dict", "line_number": 274, "usage_type": "attribute"}, {"api_name": "web3._utils.module_testing.EthModuleTest", "line_number": 274, "usage_type": "name"}, {"api_name": "web3._utils.module_testing.EthModuleTest.test_eth_sign", "line_number": 277, "usage_type": "attribute"}, {"api_name": "web3._utils.module_testing.EthModuleTest", "line_number": 277, "usage_type": "name"}, {"api_name": "web3._utils.module_testing.EthModuleTest.test_eth_sign_ens_names", "line_number": 279, "usage_type": "attribute"}, {"api_name": "web3._utils.module_testing.EthModuleTest", "line_number": 279, "usage_type": "name"}, {"api_name": "web3._utils.module_testing.EthModuleTest.test_eth_sign_typed_data", "line_number": 282, "usage_type": "attribute"}, {"api_name": "web3._utils.module_testing.EthModuleTest", "line_number": 282, "usage_type": "name"}, {"api_name": "web3._utils.module_testing.EthModuleTest.test_eth_sign_transaction_legacy", "line_number": 285, "usage_type": "attribute"}, {"api_name": "web3._utils.module_testing.EthModuleTest", "line_number": 285, "usage_type": "name"}, {"api_name": "web3._utils.module_testing.EthModuleTest.test_eth_sign_transaction", "line_number": 288, "usage_type": "attribute"}, {"api_name": "web3._utils.module_testing.EthModuleTest", "line_number": 288, "usage_type": "name"}, {"api_name": "web3._utils.module_testing.EthModuleTest.test_eth_sign_transaction_hex_fees", "line_number": 291, "usage_type": "attribute"}, {"api_name": "web3._utils.module_testing.EthModuleTest", "line_number": 291, "usage_type": "name"}, {"api_name": "web3._utils.module_testing.EthModuleTest.test_eth_sign_transaction_ens_names", "line_number": 294, "usage_type": "attribute"}, {"api_name": "web3._utils.module_testing.EthModuleTest", "line_number": 294, "usage_type": "name"}, {"api_name": "web3.exceptions.MethodUnavailable", "line_number": 297, "usage_type": "argument"}, {"api_name": "web3._utils.module_testing.EthModuleTest.test_eth_submit_hashrate", "line_number": 297, "usage_type": "attribute"}, {"api_name": "web3._utils.module_testing.EthModuleTest", "line_number": 297, "usage_type": "name"}, {"api_name": "web3.exceptions.MethodUnavailable", "line_number": 300, "usage_type": "argument"}, {"api_name": "web3._utils.module_testing.EthModuleTest.test_eth_submit_work", "line_number": 300, "usage_type": "attribute"}, {"api_name": "web3._utils.module_testing.EthModuleTest", "line_number": 300, "usage_type": "name"}, {"api_name": "web3.exceptions.MethodUnavailable", "line_number": 303, "usage_type": "argument"}, {"api_name": "web3._utils.module_testing.EthModuleTest.test_eth_get_raw_transaction", "line_number": 303, "usage_type": "attribute"}, {"api_name": "web3._utils.module_testing.EthModuleTest", "line_number": 303, "usage_type": "name"}, {"api_name": "web3.exceptions.MethodUnavailable", "line_number": 306, "usage_type": "argument"}, {"api_name": "web3._utils.module_testing.EthModuleTest.test_eth_get_raw_transaction", "line_number": 306, "usage_type": "attribute"}, {"api_name": "web3._utils.module_testing.EthModuleTest", "line_number": 306, "usage_type": "name"}, {"api_name": "web3._utils.module_testing.EthModuleTest.test_eth_get_raw_transaction_by_block", "line_number": 309, "usage_type": "attribute"}, {"api_name": "web3._utils.module_testing.EthModuleTest", "line_number": 309, "usage_type": "name"}, {"api_name": "web3._utils.module_testing.EthModuleTest.test_eth_get_raw_transaction_by_block", "line_number": 312, "usage_type": "attribute"}, {"api_name": "web3._utils.module_testing.EthModuleTest", "line_number": 312, "usage_type": "name"}, {"api_name": "web3._utils.module_testing.EthModuleTest.test_eth_replace_transaction_already_mined", "line_number": 315, "usage_type": "attribute"}, {"api_name": "web3._utils.module_testing.EthModuleTest", "line_number": 315, "usage_type": "name"}, {"api_name": "pytest.mark.xfail", "line_number": 322, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 322, "usage_type": "attribute"}, {"api_name": "pytest.mark.xfail", "line_number": 326, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 326, "usage_type": "attribute"}, {"api_name": "pytest.mark.xfail", "line_number": 330, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 330, "usage_type": "attribute"}, {"api_name": "pytest.mark.xfail", "line_number": 349, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 349, "usage_type": "attribute"}, {"api_name": "eth_utils.is_integer", "line_number": 433, "usage_type": "call"}, {"api_name": "pytest.mark.xfail", "line_number": 444, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 444, "usage_type": "attribute"}, {"api_name": "pytest.mark.xfail", "line_number": 450, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 450, "usage_type": "attribute"}, {"api_name": "pytest.raises", "line_number": 457, "usage_type": "call"}, {"api_name": "eth_tester.exceptions.TransactionFailed", "line_number": 458, "usage_type": "argument"}, {"api_name": "pytest.raises", "line_number": 470, "usage_type": "call"}, {"api_name": "eth_tester.exceptions.TransactionFailed", "line_number": 470, "usage_type": "argument"}, {"api_name": "pytest.raises", "line_number": 483, "usage_type": "call"}, {"api_name": "eth_tester.exceptions.TransactionFailed", "line_number": 484, "usage_type": "argument"}, {"api_name": "pytest.raises", "line_number": 498, "usage_type": "call"}, {"api_name": "eth_tester.exceptions.TransactionFailed", "line_number": 498, "usage_type": "argument"}, {"api_name": "pytest.mark.parametrize", "line_number": 521, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 521, "usage_type": "attribute"}, {"api_name": "pytest.mark.xfail", "line_number": 544, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 544, "usage_type": "attribute"}, {"api_name": "web3.types.BlockData", "line_number": 567, "usage_type": "name"}, {"api_name": "web3.types.BlockData", "line_number": 572, "usage_type": "name"}, {"api_name": "eth_utils.is_integer", "line_number": 582, "usage_type": "call"}, {"api_name": "eth_utils.is_integer", "line_number": 583, "usage_type": "call"}, {"api_name": "web3._utils.module_testing.NetModuleTest", "line_number": 587, "usage_type": "name"}, {"api_name": "web3._utils.module_testing.GoEthereumPersonalModuleTest", "line_number": 592, "usage_type": "name"}, {"api_name": "web3.exceptions.MethodUnavailable", "line_number": 595, "usage_type": "argument"}, {"api_name": "web3._utils.module_testing.GoEthereumPersonalModuleTest.test_personal_sign_and_ecrecover", "line_number": 594, "usage_type": "attribute"}, {"api_name": "web3._utils.module_testing.GoEthereumPersonalModuleTest", "line_number": 594, "usage_type": "name"}, {"api_name": "web3.exceptions.MethodUnavailable", "line_number": 600, "usage_type": "argument"}, {"api_name": "web3._utils.module_testing.GoEthereumPersonalModuleTest.test_personal_list_wallets", "line_number": 599, "usage_type": "attribute"}, {"api_name": "web3._utils.module_testing.GoEthereumPersonalModuleTest", "line_number": 599, "usage_type": "name"}]} +{"seq_id": "24523366992", "text": "from collections import defaultdict\nfrom utils import get_input\n\ninput_list = list(map(int, list(get_input(6))[0].split(',')))\n\ntimers = defaultdict(int)\nfor timer in input_list:\n timers[timer] += 1\n\ndef get_count(timers, days=80):\n for _ in range(days):\n new_timers = defaultdict(int)\n for day,count in timers.items():\n days_left = day - 1\n if days_left < 0:\n days_left = 6\n new_timers[8] += count\n new_timers[days_left] += count\n timers = new_timers\n return sum(timers.values())\n\nprint(get_count(timers))\nprint(get_count(timers, days=256))", "repo_name": "mjzac/advent-of-code", "sub_path": "2021/6.py", "file_name": "6.py", "file_ext": "py", "file_size_in_byte": 582, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "utils.get_input", "line_number": 4, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 6, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 12, "usage_type": "call"}]} +{"seq_id": "19280904063", "text": "import pandas as pd\nimport torch\nfrom transformers import BertModel, BertTokenizer,GPT2Tokenizer,GPT2Model,GPT2LMHeadModel,AutoTokenizer\nimport logging \nimport string\n\nlogger = logging.getLogger(__name__)\n\n\nclass char_tokenizer:\n def __init__(self,corpus_filepath, special_tokens):\n \n self.data = self.load_corpus(corpus_filepath)\n self.bos_token='@'\n self.eos_token='$'\n self.pad_token='#'\n self.unk_token='&'\n\n self.bos_token_id=0\n self.eos_token_id=1\n self.pad_token_id=2\n self.unk_token_id=3\n\n self.char_set=list(sorted(set(\"\".join(self.data))))\n self.char_set.insert(self.bos_token_id,self.bos_token)\n self.char_set.insert(self.eos_token_id,self.eos_token)\n self.char_set.insert(self.pad_token_id,self.pad_token)\n self.char_set.insert(self.pad_token_id,self.pad_token)\n\n self.vocab_size= len(self.char_set)\n \n def load_corpus(self,path):\n data = pd.read_csv(path)\n train_data_txt = data['wrd']\n return train_data_txt\n \n\n def encode(self,data,add_special_tokens=False):\n \"\"\"This function converts all the strings available in the data list into \n a tensor of indexes. The conversion between char and index is done based on\n the content of the char_set. It also add the special token '@' to indicate\n begin-of-sentence. \n \n Arguments\n ---------\n data : List\n A list containing strings to convert.\n \n Returns\n ---------\n data_index: torch.Tensor\n Tensor (N,L) containing the indexes corresponding to the input chars. N is \n the number of text chunks and L is the number of char in each chunk. \n \"\"\"\n data_index = []\n for chunk in data:\n tensor = torch.zeros(len(chunk)).long()\n for i in range(len(chunk)):\n tensor[i] = self.char_set.index(chunk[i])\n data_index.append(tensor)\n # data_index = torch.stack(data_index)\n return data_index\n\n def decode(self,data,add_special_tokens=False):\n\n data_index = []\n for chunk in data:\n decoded_str=\"\"\n for i in range(len(chunk)):\n if chunk[i] == self.eos_token_id:\n break\n decoded_str += self.char_set[chunk[i]]\n data_index.append(decoded_str)\n return data_index\n\n\ndef get_tokenizer(tokenizer_opt, **params):\n tokenizer, embedding_model = None, None\n \n # if tokenizer_opt == 'char':\n # bos = '@'\n # eos = '$'\n # pad = '#'\n # unk = '&'\n # special_tokens_dict = {'eos_token': eos, 'bos_token': bos, 'pad_token': pad,'unk_token':unk}\n # tokenizer= char_tokenizer(\"./sample_data/train-clean-100.csv\",special_tokens_dict)\n\n if tokenizer_opt == 'char':\n chars = string.ascii_letters+' ' # This character vocab!\n model_max_length = 2048\n tokenizer = CharacterTokenizer(chars, model_max_length)\n\n \n elif tokenizer_opt == \"gpt\":\n tokenizer, embedding_model = get_gpt_tokenizer(**params)\n elif tokenizer_opt == \"bert\":\n tokenizer, embedding_model = get_BERT_tokenizer(**params)\n \n else:\n logger.error(f\"{tokenizer_opt} is not among supperted tokenizer. Valid options are char, gpt and bert\")\n \n return tokenizer, embedding_model\n\n\n\"\"\" CharacterTokenzier for Hugging Face Transformers.\n\nThis is heavily inspired from CanineTokenizer in transformers package.\n\"\"\"\nimport json\nimport os\nfrom pathlib import Path\nfrom typing import Dict, List, Optional, Sequence, Union\n\nfrom transformers.tokenization_utils import AddedToken, PreTrainedTokenizer\n\n\nclass CharacterTokenizer(PreTrainedTokenizer):\n def __init__(self, characters: Sequence[str], model_max_length: int, **kwargs):\n \"\"\"Character tokenizer for Hugging Face transformers.\n\n Args:\n characters (Sequence[str]): List of desired characters. Any character which\n is not included in this list will be replaced by a special token called\n [UNK] with id=6. Following are list of all of the special tokens with\n their corresponding ids:\n \"[CLS]\": 0\n \"[SEP]\": 1\n \"[BOS]\": 2\n \"[MASK]\": 3\n \"[PAD]\": 4\n \"[RESERVED]\": 5\n \"[UNK]\": 6\n an id (starting at 7) will be assigned to each character.\n\n model_max_length (int): Model maximum sequence length.\n \"\"\"\n self.characters = characters\n self.model_max_length = model_max_length\n bos_token = AddedToken(\"[BOS]\", lstrip=False, rstrip=False)\n eos_token = AddedToken(\"[SEP]\", lstrip=False, rstrip=False)\n sep_token = AddedToken(\"[SEP]\", lstrip=False, rstrip=False)\n cls_token = AddedToken(\"[CLS]\", lstrip=False, rstrip=False)\n pad_token = AddedToken(\"[PAD]\", lstrip=False, rstrip=False)\n unk_token = AddedToken(\"[UNK]\", lstrip=False, rstrip=False)\n\n mask_token = AddedToken(\"[MASK]\", lstrip=True, rstrip=False)\n\n super().__init__(\n bos_token=bos_token,\n eos_token=eos_token,\n sep_token=sep_token,\n cls_token=cls_token,\n pad_token=pad_token,\n mask_token=mask_token,\n unk_token=unk_token,\n add_prefix_space=False,\n model_max_length=model_max_length,\n **kwargs,\n )\n\n self._vocab_str_to_int = {\n \"[CLS]\": 0,\n \"[SEP]\": 1,\n \"[BOS]\": 2,\n \"[MASK]\": 3,\n \"[PAD]\": 4,\n \"[RESERVED]\": 5,\n \"[UNK]\": 6,\n **{ch: i + 7 for i, ch in enumerate(characters)},\n }\n self._vocab_int_to_str = {v: k for k, v in self._vocab_str_to_int.items()}\n\n @property\n def vocab_size(self) -> int:\n return len(self._vocab_str_to_int)\n\n def _tokenize(self, text: str) -> List[str]:\n return list(text)\n\n def _convert_token_to_id(self, token: str) -> int:\n return self._vocab_str_to_int.get(token, self._vocab_str_to_int[\"[UNK]\"])\n\n def _convert_id_to_token(self, index: int) -> str:\n return self._vocab_int_to_str[index]\n\n def convert_tokens_to_string(self, tokens):\n return \"\".join(tokens)\n\n def build_inputs_with_special_tokens(\n self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None\n ) -> List[int]:\n sep = [self.sep_token_id]\n cls = [self.cls_token_id]\n result = cls + token_ids_0 + sep\n if token_ids_1 is not None:\n result += token_ids_1 + sep\n return result\n\n def get_special_tokens_mask(\n self,\n token_ids_0: List[int],\n token_ids_1: Optional[List[int]] = None,\n already_has_special_tokens: bool = False,\n ) -> List[int]:\n if already_has_special_tokens:\n return super().get_special_tokens_mask(\n token_ids_0=token_ids_0,\n token_ids_1=token_ids_1,\n already_has_special_tokens=True,\n )\n\n result = [1] + ([0] * len(token_ids_0)) + [1]\n if token_ids_1 is not None:\n result += ([0] * len(token_ids_1)) + [1]\n return result\n\n def create_token_type_ids_from_sequences(\n self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None\n ) -> List[int]:\n sep = [self.sep_token_id]\n cls = [self.cls_token_id]\n\n result = len(cls + token_ids_0 + sep) * [0]\n if token_ids_1 is not None:\n result += len(token_ids_1 + sep) * [1]\n return result\n\n def get_config(self) -> Dict:\n return {\n \"char_ords\": [ord(ch) for ch in self.characters],\n \"model_max_length\": self.model_max_length,\n }\n\n @classmethod\n def from_config(cls, config: Dict) -> \"CharacterTokenizer\":\n cfg = {}\n cfg[\"characters\"] = [chr(i) for i in config[\"char_ords\"]]\n cfg[\"model_max_length\"] = config[\"model_max_length\"]\n return cls(**cfg)\n\n def save_pretrained(self, save_directory: Union[str, os.PathLike], **kwargs):\n cfg_file = Path(save_directory) / \"tokenizer_config.json\"\n cfg = self.get_config()\n with open(cfg_file, \"w\") as f:\n json.dump(cfg, f, indent=4)\n\n @classmethod\n def from_pretrained(cls, save_directory: Union[str, os.PathLike], **kwargs):\n cfg_file = Path(save_directory) / \"tokenizer_config.json\"\n with open(cfg_file) as f:\n cfg = json.load(f)\n return cls.from_config(cfg)\n\n\ndef get_gpt_tokenizer(src, cache_dir):\n tokenizer = GPT2Tokenizer.from_pretrained(src,cache_dir=cache_dir)\n bos = '<|bos|>'\n eos = '<|eos|>'\n pad = '<|pad|>'\n special_tokens_dict = {'eos_token': eos, 'bos_token': bos, 'pad_token': pad}\n tokenizer.add_special_tokens(special_tokens_dict)\n embedding_model = GPT2LMHeadModel.from_pretrained(src,cache_dir=cache_dir) # or any other checkpoint\n embedding_model.resize_token_embeddings(len(tokenizer))\n return tokenizer,embedding_model\n\ndef get_BERT_tokenizer(src,cache_dir):\n tokenizer = BertTokenizer.from_pretrained(src,cache_dir=cache_dir)\n bos = '<|bos|>'\n eos = '<|eos|>'\n special_tokens_dict = {'eos_token': eos, 'bos_token': bos}\n tokenizer.add_special_tokens(special_tokens_dict)\n embedding_model = BertModel.from_pretrained(src,cache_dir=cache_dir)\n embedding_model.resize_token_embeddings(len(tokenizer))\n return tokenizer,embedding_model\n\n \n \n\nif __name__ == \"__main__\":\n import string\n chars = string.ascii_letters # This character vocab!\n model_max_length = 2048\n tokenizer = CharacterTokenizer(chars, model_max_length)\n\n ", "repo_name": "poonehmousavi/Diffusion_ASR", "sub_path": "data_utils/tokenizer.py", "file_name": "tokenizer.py", "file_ext": "py", "file_size_in_byte": 9580, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "logging.getLogger", "line_number": 7, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 33, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 57, "usage_type": "call"}, {"api_name": "string.ascii_letters", "line_number": 89, "usage_type": "attribute"}, {"api_name": "transformers.tokenization_utils.PreTrainedTokenizer", "line_number": 117, "usage_type": "name"}, {"api_name": "typing.Sequence", "line_number": 118, "usage_type": "name"}, {"api_name": "transformers.tokenization_utils.AddedToken", "line_number": 139, "usage_type": "call"}, {"api_name": "transformers.tokenization_utils.AddedToken", "line_number": 140, "usage_type": "call"}, {"api_name": "transformers.tokenization_utils.AddedToken", "line_number": 141, "usage_type": "call"}, {"api_name": "transformers.tokenization_utils.AddedToken", "line_number": 142, "usage_type": "call"}, {"api_name": "transformers.tokenization_utils.AddedToken", "line_number": 143, "usage_type": "call"}, {"api_name": "transformers.tokenization_utils.AddedToken", "line_number": 144, "usage_type": "call"}, {"api_name": "transformers.tokenization_utils.AddedToken", "line_number": 146, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 177, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 190, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 190, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 191, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 201, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 202, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 202, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 204, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 218, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 218, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 219, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 228, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 235, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 241, "usage_type": "name"}, {"api_name": "os.PathLike", "line_number": 241, "usage_type": "attribute"}, {"api_name": "pathlib.Path", "line_number": 242, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 245, "usage_type": "call"}, {"api_name": "typing.Union", "line_number": 248, "usage_type": "name"}, {"api_name": "os.PathLike", "line_number": 248, "usage_type": "attribute"}, {"api_name": "pathlib.Path", "line_number": 249, "usage_type": "call"}, {"api_name": "json.load", "line_number": 251, "usage_type": "call"}, {"api_name": "transformers.GPT2Tokenizer.from_pretrained", "line_number": 256, "usage_type": "call"}, {"api_name": "transformers.GPT2Tokenizer", "line_number": 256, "usage_type": "name"}, {"api_name": "transformers.GPT2LMHeadModel.from_pretrained", "line_number": 262, "usage_type": "call"}, {"api_name": "transformers.GPT2LMHeadModel", "line_number": 262, "usage_type": "name"}, {"api_name": "transformers.BertTokenizer.from_pretrained", "line_number": 267, "usage_type": "call"}, {"api_name": "transformers.BertTokenizer", "line_number": 267, "usage_type": "name"}, {"api_name": "transformers.BertModel.from_pretrained", "line_number": 272, "usage_type": "call"}, {"api_name": "transformers.BertModel", "line_number": 272, "usage_type": "name"}, {"api_name": "string.ascii_letters", "line_number": 281, "usage_type": "attribute"}]} +{"seq_id": "19114627091", "text": "from __future__ import print_function\nfrom keras.datasets import mnist\nfrom keras.models import Sequential\nfrom keras.optimizers import Adam\n\nfrom keras.layers import Dense, Dropout, Flatten, Input, merge\nfrom keras.layers import Conv2D, MaxPooling2D\nfrom keras import backend as K\nfrom numpy import genfromtxt\nfrom keras.models import model_from_json, Model\n\nimport keras\nimport csv\nimport os\nimport numpy as np\nimport keras\n\n\ndef train_model(x_data, y_data, name):\n score = model.fit(x_data, y_data, epochs=250, batch_size=50, verbose=1, validation_split=0.3,\n callbacks=[tbCallBack])\n # model.predict( x_test, batch_size=22, verbose=0)\n # score = model.evaluate(x_test, y_test, verbose=0)\n print(score)\n model_json = model.to_json()\n with open((name + \".json\"), \"w\") as json_file:\n json_file.write(model_json)\n # serialize weights to HDF5\n model.save_weights((name + \"model.h5\"))\n print(\"Saved model to disk\")\n\n with open((name + '.json'), 'r') as json_file:\n loaded_model_json = json_file.read()\n json_file.close()\n loaded_model = model_from_json(loaded_model_json)\n # load weights into new model\n loaded_model.load_weights((name + \"model.h5\"))\n # print(\"Loaded model from disk\")\n print(x_test[5])\n X = np.zeros((1, 22))\n for i in range(len(x_test[5])):\n X[0][i] = x_test[5][i]\n\n # X[0][0]= x_test[5]\n loaded_model.compile(loss='mean_squared_error', optimizer='adam', metrics=['accuracy'])\n\n y = loaded_model.predict(X, batch_size=1, verbose=1)\n print(y)\n\n\ncurrent_dir = os.getcwd()\npath_to_data = current_dir + \"/train_data/aalborg.csv\"\n\nmy_data = genfromtxt(path_to_data, delimiter=',', skip_header=1, skip_footer=1)\n### Split data into Accelaration, Brake, Steering #### \nx_data = np.zeros((len(my_data) - 2, 22))\naccelaration_data = np.zeros((len(my_data) - 2, 1))\nbrake_data = np.zeros((len(my_data) - 2, 1))\nsteering_data = np.zeros((len(my_data) - 2, 1))\n\nfor i in range(1, len(my_data) - 2):\n for j in range(len(my_data[3])):\n if j == 0:\n accelaration_data[i] = my_data[i][j]\n elif j == 1:\n brake_data[i] = my_data[i][j]\n elif j == 2:\n steering_data[i] = my_data[i][j]\n else:\n x_data[i][j - 3] = my_data[i][j]\n\n# t=int(len(x_data)/80)\n# x_train=np.asarray(x_data[:][t:])\n# x_test=np.asarray(x_data[:][:t])\n\n# accelaration_train=np.asarray(accelaration_data[:][t:])\n# accelaration_test=np.asarray(accelaration_data[:][:t])\n# braking_train=np.asarray(brake_data[:][t:])\n# braking_test=np.asarray(brake_data[:][:t])\n# steering_train=np.asarray(steering_data[:][t:])\n# steering_test=np.asarray(steering_data[:][:t])\nHIDDEN1_UNITS = 300\nHIDDEN2_UNITS = 600\nstate_size = 22\naction_dim = 3\nS = Input(shape=[state_size])\nA = Input(shape=[action_dim], name='action2')\nw1 = Dense(HIDDEN1_UNITS, activation='relu')(S)\na1 = Dense(HIDDEN2_UNITS, activation='linear')(A)\nh1 = Dense(HIDDEN2_UNITS, activation='linear')(w1)\nh2 = merge([h1, a1], mode='sum')\nh3 = Dense(HIDDEN2_UNITS, activation='relu')(h2)\nV = Dense(action_dim, activation='linear')(h3)\nmodel = Model(input=[S, A], output=V)\nadam = Adam(lr=0.0001)\nmodel.compile(loss='mse', optimizer=adam) # model.add(Dense(1, init='normal', activation='sigmoid'))\n\n# 3\ntbCallBack = keras.callbacks.TensorBoard(log_dir='/tmp/keras_logs', write_graph=True)\n\n# 4\nmodel.compile(loss='mean_squared_error', optimizer='adam', metrics=['accuracy'])\n# train_model(x_data, accelaration_data, \"accelaration\")\ntrain_model(x_data, steering_data, \"steering\")\n# train_model(x_data, brake_data, \"brake\")\n", "repo_name": "sarantinio/Torcs-Racing", "sub_path": "ToxicRacing-Computational Intelligence/threeNets.py", "file_name": "threeNets.py", "file_ext": "py", "file_size_in_byte": 3621, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "37", "api": [{"api_name": "keras.models.model_from_json", "line_number": 35, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 40, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 51, "usage_type": "call"}, {"api_name": "numpy.genfromtxt", "line_number": 54, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 56, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 57, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 58, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 59, "usage_type": "call"}, {"api_name": "keras.layers.Input", "line_number": 86, "usage_type": "call"}, {"api_name": "keras.layers.Input", "line_number": 87, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 88, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 89, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 90, "usage_type": "call"}, {"api_name": "keras.layers.merge", "line_number": 91, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 92, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 93, "usage_type": "call"}, {"api_name": "keras.models.Model", "line_number": 94, "usage_type": "call"}, {"api_name": "keras.optimizers.Adam", "line_number": 95, "usage_type": "call"}, {"api_name": "keras.callbacks.TensorBoard", "line_number": 99, "usage_type": "call"}, {"api_name": "keras.callbacks", "line_number": 99, "usage_type": "attribute"}]} +{"seq_id": "28441064316", "text": "from collections import deque\n\n\nclass TreeNode:\n def __init__(self, val):\n self.val = val\n self.left, self.right = None, None\n\n\ndef find_maximum_depth(root):\n if root is None:\n return 0\n \n queue = deque()\n queue.append(root)\n max_len = 0\n\n while queue:\n n = len(queue)\n max_len += 1\n\n for _ in range(n):\n current = queue.popleft()\n\n if current.left:\n queue.append(current.left)\n if current.right:\n queue.append(current.right)\n \n return max_len\n\n\n\ndef main():\n root = TreeNode(12)\n root.left = TreeNode(7)\n root.right = TreeNode(1)\n root.right.left = TreeNode(10)\n root.right.right = TreeNode(5)\n print(\"Tree Maximum Depth: \" + str(find_maximum_depth(root)))\n root.left.left = TreeNode(9)\n root.right.left.left = TreeNode(11)\n print(\"Tree Maximum Depth: \" + str(find_maximum_depth(root)))\n\n\nmain()", "repo_name": "MrNullPointer/MOOC", "sub_path": "Patterns(Grokking the coding Interview)/Tree_BFS/First_Attempt/Max_depth_of_binary_tree.py", "file_name": "Max_depth_of_binary_tree.py", "file_ext": "py", "file_size_in_byte": 955, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "collections.deque", "line_number": 14, "usage_type": "call"}]} +{"seq_id": "38061284418", "text": "import tkinter as tk\nfrom PIL import ImageTk,Image\nimport math\nimport matplotlib.pyplot as plt\nimport sympy as sp\n\nsplash_root = tk.Tk()\nsplash_root.geometry('500x500')\nsplash_root.overrideredirect(True)\n\napplogo =ImageTk.PhotoImage(Image.open(\"appLogo.png\"))\nsplash_logo = tk.Label(splash_root, image=applogo )\nsplash_logo.pack()\n\ndef destroy_splash():\n splash_root.destroy()\n\nsplash_root.after(3000, destroy_splash)\n\nsplash_root.mainloop()\n\nroot = tk.Tk()\nroot.title(\"diffCALC\")\nroot.geometry('450x865')\n\nnavIcon =ImageTk.PhotoImage(Image.open(\"navIcon.png\"))\ncloseIcon = ImageTk.PhotoImage(Image.open(\"closeIcon.png\"))\nlogo = ImageTk.PhotoImage(Image.open(\"logo.png\"))\n\ndef dele():\n menu_frame.place(x=-900,y=0)\n\ndef toggle(): \n menu_frame.tkraise()\n menu_frame.place(x=0,y=0)\n\ndef show_deriv():\n calculator_screen.pack_forget()\n deriv_frame.pack()\n menu_frame.place(x=-900,y=0)\n\ndef show_calculator():\n calculator_screen.pack()\n deriv_frame.pack_forget()\n menu_frame.place(x=-900,y=0)\n\n\nmenu_frame=tk.Frame(root,width=380,height=865,bg='#5E7CAE')\nbutton = tk.Button(menu_frame,text='D E R I V A T I V E S', width = 42, height = 2,command= show_deriv)\nbutton.place(x=35, y = 117)\nbutton = tk.Button(menu_frame,text='C A L C U L A T O R', width = 42, height = 2,command= show_calculator)\nbutton.place(x=35, y = 172)\nbutton = tk.Button(menu_frame,text='P R A C T C E Q U I Z', width = 42, height = 2)\nbutton.place(x=35, y = 227)\nbutton = tk.Button(menu_frame,text='A B O U T', width = 42, height = 2)\nbutton.place(x=35, y = 282)\ncloseBtn = tk.Button(menu_frame, bg='#5E7CAE', activebackground='#5E7CAE',image=closeIcon,command=dele)\ncloseBtn.place(x=10, y=18)\n\n\nnavbar = tk.Frame(root, width = 450, height = 65, bg = '#5E7CAE')\nnavbar.pack()\nnavLabel = tk.Label(navbar, text=\"diffCALC\", font=\"RobotoCondensed\",bg='#5E7CAE', fg =\"white\")\nnavLabel.place(x = 165, y = 18)\nnavLogo = tk.Label(navbar,bg='#5E7CAE', image= logo)\nnavLogo.place(x = 400, y= 18)\nnavbarBtn = tk.Button(navbar, bg='#5E7CAE', activebackground='#5E7CAE',image=navIcon, command=toggle)\nnavbarBtn.place(x=10, y=18)\n\nderiv_frame = tk.Frame(root, width = 450, height = 865)\nderiv_frame.pack()\nbutton = tk.Button(deriv_frame,text='D E R I V A T I V E S', width = 42, height = 2)\nbutton.place(x=35, y = 117)\nbutton = tk.Button(deriv_frame,text='C A L C U L A T O R', width = 42, height = 2)\nbutton.place(x=35, y = 172)\nbutton = tk.Button(deriv_frame,text='P R A C T C E Q U I Z', width = 42, height = 2)\nbutton.place(x=35, y = 227)\nbutton = tk.Button(deriv_frame,text='A B O U T', width = 42, height = 2)\nbutton.place(x=35, y = 282)\n\ncalculator_screen = tk.Frame(root)\n\nROW_OFFSET = 2\n\nexpression = \"\"\n\n\ndef display_equation(diff=False):\n global expression\n \n label = tk.Label(\n calculator_screen, \n text=expression,\n font=(\"Arial\", 16), \n )\n label.grid(row=0, column=0, columnspan=5, pady=12, sticky=\"nsew\")\n\n try:\n if diff:\n x = sp.Symbol('x')\n fx = sp.sympify(expression)\n dx = sp.diff(fx, x)\n expression = str(dx)\n\n # Create a SymPy expression\n expr = sp.sympify(expression if expression != \"\" else 0)\n\n # Convert the expression to a LaTeX string\n eq_latex = sp.latex(expr)\n\n fig, ax = plt.subplots()\n fig.set_size_inches(4, 1.5)\n ax.text(0.5, 0.5, f\"${eq_latex}$\", fontsize=20, ha='center', va='center')\n ax.axis('off')\n plt.savefig('equation.png')\n img = tk.PhotoImage(file='equation.png')\n render = tk.Label(calculator_screen, image=img)\n render.image = img\n render.grid(row=1, column=0, columnspan=5, pady=8, sticky=\"nsew\")\n except:\n return\n\n\ndef process_symbol(symbol):\n global expression\n\n ops = [\"+\", \"-\", \"*\", \"/\", \"^\", \"(\", \")\"]\n\n funcs = [\n \"sin\",\n \"cos\",\n \"tan\",\n \"csc\",\n \"sec\",\n \"cot\",\n \"sinh\",\n \"cosh\",\n \"tanh\",\n \"asin\",\n \"acos\",\n \"atan\",\n \"acsc\",\n \"asec\",\n \"acot\",\n \"asinh\",\n \"acosh\",\n \"atanh\",\n \"ln\",\n \"log\",\n ]\n\n vars = [\"x\", \"y\", \"z\"]\n\n diff = False\n\n if symbol.isnumeric() or symbol in ops:\n expression += symbol\n\n elif symbol in vars:\n expression += symbol\n\n elif symbol == \"DEL\":\n expression = expression[:-1]\n\n elif symbol == \"AC\":\n expression = \"\"\n\n elif symbol in funcs:\n expression += f\"{symbol}(\"\n\n elif symbol == \"e\":\n expression += \"e\"\n\n elif symbol == \"π\":\n expression += \"pi\"\n\n elif symbol == \"INV\":\n buttons = [\n [\"x\", \"y\", \"z\", \"REG\", \"OFF\"],\n [\"asinh\", \"acosh\", \"atanh\", \"e\", \"π\"],\n [\"acsc\", \"asec\", \"acot\", \"ln\", \"log\"],\n [\"asin\", \"acos\", \"atan\", \"(\", \")\"],\n [\"7\", \"8\", \"9\", \"DEL\", \"AC\"],\n [\"4\", \"5\", \"6\", \"*\", \"/\"],\n [\"1\", \"2\", \"3\", \"+\", \"-\"],\n [\"0\", \".\", \"d/dx\", \"^\", \"=\"],\n ]\n create_buttons(buttons)\n\n elif symbol == \"REG\":\n buttons = [\n [\"x\", \"y\", \"z\", \"INV\", \"OFF\"],\n [\"sinh\", \"cosh\", \"tanh\", \"e\", \"π\"],\n [\"csc\", \"sec\", \"cot\", \"ln\", \"log\"],\n [\"sin\", \"cos\", \"tan\", \"(\", \")\"],\n [\"7\", \"8\", \"9\", \"DEL\", \"AC\"],\n [\"4\", \"5\", \"6\", \"*\", \"/\"],\n [\"1\", \"2\", \"3\", \"+\", \"-\"],\n [\"0\", \".\", \"d/dx\", \"^\", \"=\"],\n ]\n create_buttons(buttons)\n\n elif symbol == \"d/dx\":\n diff = True\n\n display_equation(diff)\n\n\ndef create_button(label, row, col, row_span=1, col_span=1):\n button = tk.Button(\n master=calculator_screen,\n padx=16,\n pady=16,\n font=(\"Arial\", 14),\n text=label,\n command=lambda sym=label: process_symbol(sym),\n )\n\n button.grid(\n row=row,\n column=col,\n rowspan=row_span,\n columnspan=col_span,\n sticky=\"nsew\"\n )\n\n return button\n\n\ndef create_numkeys(): \n for i in range(10):\n row = math.ceil(i / 3)\n col = (i - 1) % 3 if i != 0 else 0\n create_button(f\"{i}\", row, col)\n\n\nbuttons = [\n [\"x\", \"y\", \"z\", \"INV\", \"OFF\"],\n [\"sinh\", \"cosh\", \"tanh\", \"e\", \"π\"],\n [\"csc\", \"sec\", \"cot\", \"ln\", \"log\"],\n [\"sin\", \"cos\", \"tan\", \"(\", \")\"],\n [\"7\", \"8\", \"9\", \"DEL\", \"AC\"],\n [\"4\", \"5\", \"6\", \"*\", \"/\"],\n [\"1\", \"2\", \"3\", \"+\", \"-\"],\n [\"0\", \".\", \"d/dx\", \"^\", \"=\"],\n]\n\ndef create_buttons(buttons):\n for i, row in enumerate(buttons):\n for j, label in enumerate(row):\n create_button(label, i + ROW_OFFSET, j)\n\n\n\ncreate_buttons(buttons)\ndisplay_equation()\n\ncalculator_screen.pack()\nroot.mainloop()", "repo_name": "Exuille/anylao", "sub_path": "menu.py", "file_name": "menu.py", "file_ext": "py", "file_size_in_byte": 6757, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "tkinter.Tk", "line_number": 7, "usage_type": "call"}, {"api_name": "PIL.ImageTk.PhotoImage", "line_number": 11, "usage_type": "call"}, {"api_name": "PIL.ImageTk", "line_number": 11, "usage_type": "name"}, {"api_name": "PIL.Image.open", "line_number": 11, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 11, "usage_type": "name"}, {"api_name": "tkinter.Label", "line_number": 12, "usage_type": "call"}, {"api_name": "tkinter.Tk", "line_number": 22, "usage_type": "call"}, {"api_name": "PIL.ImageTk.PhotoImage", "line_number": 26, "usage_type": "call"}, {"api_name": "PIL.ImageTk", "line_number": 26, "usage_type": "name"}, {"api_name": "PIL.Image.open", "line_number": 26, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 26, "usage_type": "name"}, {"api_name": "PIL.ImageTk.PhotoImage", "line_number": 27, "usage_type": "call"}, {"api_name": "PIL.ImageTk", "line_number": 27, "usage_type": "name"}, {"api_name": "PIL.Image.open", "line_number": 27, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 27, "usage_type": "name"}, {"api_name": "PIL.ImageTk.PhotoImage", "line_number": 28, "usage_type": "call"}, {"api_name": "PIL.ImageTk", "line_number": 28, "usage_type": "name"}, {"api_name": "PIL.Image.open", "line_number": 28, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 28, "usage_type": "name"}, {"api_name": "tkinter.Frame", "line_number": 48, "usage_type": "call"}, {"api_name": "tkinter.Button", "line_number": 49, "usage_type": "call"}, {"api_name": "tkinter.Button", "line_number": 51, "usage_type": "call"}, {"api_name": "tkinter.Button", "line_number": 53, "usage_type": "call"}, {"api_name": "tkinter.Button", "line_number": 55, "usage_type": "call"}, {"api_name": "tkinter.Button", "line_number": 57, "usage_type": "call"}, {"api_name": "tkinter.Frame", "line_number": 61, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 63, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 65, "usage_type": "call"}, {"api_name": "tkinter.Button", "line_number": 67, "usage_type": "call"}, {"api_name": "tkinter.Frame", "line_number": 70, "usage_type": "call"}, {"api_name": "tkinter.Button", "line_number": 72, "usage_type": "call"}, {"api_name": "tkinter.Button", "line_number": 74, "usage_type": "call"}, {"api_name": "tkinter.Button", "line_number": 76, "usage_type": "call"}, {"api_name": "tkinter.Button", "line_number": 78, "usage_type": "call"}, {"api_name": "tkinter.Frame", "line_number": 81, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 91, "usage_type": "call"}, {"api_name": "sympy.Symbol", "line_number": 100, "usage_type": "call"}, {"api_name": "sympy.sympify", "line_number": 101, "usage_type": "call"}, {"api_name": "sympy.diff", "line_number": 102, "usage_type": "call"}, {"api_name": "sympy.sympify", "line_number": 106, "usage_type": "call"}, {"api_name": "sympy.latex", "line_number": 109, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 111, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 111, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 115, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 115, "usage_type": "name"}, {"api_name": "tkinter.PhotoImage", "line_number": 116, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 117, "usage_type": "call"}, {"api_name": "tkinter.Button", "line_number": 210, "usage_type": "call"}, {"api_name": "math.ceil", "line_number": 232, "usage_type": "call"}]} +{"seq_id": "37207241540", "text": "import omni\r\nfrom pxr import Gf, UsdGeom\r\nimport random\r\n\r\n\r\n# Create a cube mesh in the stage\r\nstage = omni.usd.get_context().get_stage()\r\n\r\nfor i in range(3):\r\n result, path = omni.kit.commands.execute(\"CreateMeshPrimCommand\", prim_type=\"Cube\")\r\n size=random.randint(0,20)\r\n loca1=random.randint(-500,500)\r\n loca2=random.randint(-1000,1000)\r\n loca3=random.randint(-1500,1500)\r\n\r\n # Get the prim and set its transform matrix\r\n cube_prim = stage.GetPrimAtPath(path)\r\n UsdGeom.XformCommonAPI(cube_prim).SetTranslate(Gf.Vec3d(loca1,loca2,loca3))\r\n UsdGeom.XformCommonAPI(cube_prim).SetScale(Gf.Vec3f(size,size, size))", "repo_name": "li-dahua/Omiverse_CIM_1", "sub_path": "random cubes.py", "file_name": "random cubes.py", "file_ext": "py", "file_size_in_byte": 642, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "omni.usd.get_context", "line_number": 7, "usage_type": "call"}, {"api_name": "omni.usd", "line_number": 7, "usage_type": "attribute"}, {"api_name": "omni.kit.commands.execute", "line_number": 10, "usage_type": "call"}, {"api_name": "omni.kit", "line_number": 10, "usage_type": "attribute"}, {"api_name": "random.randint", "line_number": 11, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 12, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 13, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 14, "usage_type": "call"}, {"api_name": "pxr.UsdGeom.XformCommonAPI", "line_number": 18, "usage_type": "call"}, {"api_name": "pxr.UsdGeom", "line_number": 18, "usage_type": "name"}, {"api_name": "pxr.Gf.Vec3d", "line_number": 18, "usage_type": "call"}, {"api_name": "pxr.Gf", "line_number": 18, "usage_type": "name"}, {"api_name": "pxr.UsdGeom.XformCommonAPI", "line_number": 19, "usage_type": "call"}, {"api_name": "pxr.UsdGeom", "line_number": 19, "usage_type": "name"}, {"api_name": "pxr.Gf.Vec3f", "line_number": 19, "usage_type": "call"}, {"api_name": "pxr.Gf", "line_number": 19, "usage_type": "name"}]} +{"seq_id": "29230937087", "text": "import string\nimport argparse\nimport random\n\n\ndef positive_integer_validator(s: str, msg: str) -> int:\n value = int(s)\n if value <= 0:\n raise argparse.ArgumentTypeError(msg)\n return value\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser(\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\n )\n parser.add_argument(\n \"-a\", \"--ascii-lowercase\", help=\"abcdef...\", action=\"store_true\",\n )\n parser.add_argument(\n \"-A\", \"--ascii-uppercase\", help=\"ABCDEF...\", action=\"store_true\",\n )\n parser.add_argument(\n \"-d\", \"--digits\", help=\"12345...\", action=\"store_true\",\n )\n parser.add_argument(\n \"-l\",\n \"--length\",\n help=\"length of a generated string\",\n type=lambda s: positive_integer_validator(\n s, \"length must be positive integer\"\n ),\n default=4,\n )\n parser.add_argument(\n \"-n\",\n \"--number\",\n help=\"generate specified number of strings\",\n type=lambda s: positive_integer_validator(\n s, \"number must be positive integer\"\n ),\n default=5,\n )\n args = parser.parse_args()\n\n DEFAULT_SYMBOLS = string.digits\n symbols = \"\"\n if args.ascii_lowercase:\n symbols += string.ascii_lowercase\n if args.ascii_uppercase:\n symbols += string.ascii_uppercase\n if args.digits:\n symbols += string.digits\n if not symbols:\n symbols = DEFAULT_SYMBOLS\n\n for i in range(args.number):\n password = \"\".join(random.choices(symbols, k=args.length))\n print(password)\n", "repo_name": "vehlwn/random-password", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 1593, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "argparse.ArgumentTypeError", "line_number": 9, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 14, "usage_type": "call"}, {"api_name": "argparse.ArgumentDefaultsHelpFormatter", "line_number": 15, "usage_type": "attribute"}, {"api_name": "string.digits", "line_number": 46, "usage_type": "attribute"}, {"api_name": "string.ascii_lowercase", "line_number": 49, "usage_type": "attribute"}, {"api_name": "string.ascii_uppercase", "line_number": 51, "usage_type": "attribute"}, {"api_name": "string.digits", "line_number": 53, "usage_type": "attribute"}, {"api_name": "random.choices", "line_number": 58, "usage_type": "call"}]} +{"seq_id": "16079336099", "text": "import collections\r\n\r\ndef shortest_path(graph, vertex):\r\n\tglobal was, distances, length\r\n\tif sum(was) == length + 1:\r\n\t\treturn\r\n\td = {}\r\n\tfor ver, dist in graph[vertex]:\t\t\r\n\t\tcalculate_distances(graph, vertex)\r\n\t\tif not(was[ver]):\r\n\t\t\td[distances[ver]] = ver\r\n\tod = collections.OrderedDict(sorted(d.items()))\r\n\twas[vertex] = True\r\n\tfor dist, ver in od.items():\r\n\t\tshortest_path(graph, ver)\r\n\t\tcalculate_distances(graph, vertex)\r\n\r\ndef calculate_distances(graph, vertex):\r\n\tglobal was, distances, length\r\n\tfor ver, dist in graph[vertex]:\r\n\t\tdistances[ver] = min(distances[ver], distances[vertex] + dist)\r\n\r\nf = open('dijkstraData.txt')\r\ntext = f.readlines()\r\nf.close()\r\nlength = len(text)\r\ninfinity = 10 ** 9\r\ngraph = dict()\r\nfor index in range(length):\r\n\trow = text[index].split()[1:]\r\n\tfor item in row:\r\n\t\ts = item.split(',')\r\n\t\tgraph[index] = graph.get(index, []) + [[int(s[0]) - 1, int(s[1])]]\r\ndistances = [infinity for i in range(length)]\r\ndistances[0] = 0\r\nwas = [False for i in range(length)]\r\nshortest_path(graph, 0)\r\nprint()\r\nprint(distances[6], distances[36], distances[58], distances[81], distances[98], distances[114], distances[132], distances[164], distances[187], distances[196])", "repo_name": "Quoly/Algorithms_from_Coursera", "sub_path": "Week 5/dijkstra.py", "file_name": "dijkstra.py", "file_ext": "py", "file_size_in_byte": 1194, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "collections.OrderedDict", "line_number": 12, "usage_type": "call"}]} +{"seq_id": "5481099252", "text": "# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Sun Feb 18 12:01:24 2018\r\n\r\n@author: Abhilash Srivastava\r\n\"\"\"\r\n\r\nfrom keras.models import Sequential\r\nfrom keras.layers import Dense\r\nimport numpy as np\r\n#stochastic process,so that we can use the same random number again anad again\r\nnp.random.seed(7)\r\n#loading the dataset(pima dataset)\r\nfilename='pima-indians-diabetes.data.csv'\r\ndata=np.loadtxt(filename,delimiter=\",\")\r\n#splitting the data into i/p and o/p\r\nx=data[:,0:8]\r\ny=data[:,8]\r\n#for certain amount of epochs\r\n#creating the model\r\nmodel=Sequential()\r\nmodel.add(Dense(12,input_dim=8,activation='relu'))\r\nmodel.add(Dense(8,activation='relu'))\r\nmodel.add(Dense(1,activation='sigmoid'))\r\n\r\nmodel.load_weights('weights.best.hdf5')\r\n\r\nmodel.compile(loss='binary_crossentropy',optimizer='adam',metrics=['accuracy'])\r\n\r\nscore=model.evaluate(x,y)\r\nprint('%.2f%%'%(score[1]*100))", "repo_name": "Abhilashsri10/simple-neural-network-trained-on-Pima-diabetes_datasets", "sub_path": "test_pima.py", "file_name": "test_pima.py", "file_ext": "py", "file_size_in_byte": 870, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "numpy.random.seed", "line_number": 12, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 12, "usage_type": "attribute"}, {"api_name": "numpy.loadtxt", "line_number": 15, "usage_type": "call"}, {"api_name": "keras.models.Sequential", "line_number": 21, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 22, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 23, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 24, "usage_type": "call"}]} +{"seq_id": "34797133416", "text": "from django.conf.urls import include, url\nfrom django.views.generic.base import TemplateView\nfrom django.contrib.sitemaps.views import sitemap\n\nfrom product.urls import urlpatterns as productpatterns\nfrom satchmo_store import shop\nfrom satchmo_store.shop.views.sitemaps import sitemaps\nfrom satchmo_utils.signals import collect_urls\nfrom satchmo_store.shop.views import home, smart, cart, contact, orders, search\n\nurlpatterns = shop.get_satchmo_setting('SHOP_URLS')\n\nurlpatterns += [\n url(r'^$', home.HomeListView.as_view(), name='satchmo_shop_home'),\n url(r'^add/$', smart.smart_add, name='satchmo_smart_add'),\n url(r'^cart/$', cart.display, name='satchmo_cart'),\n url(r'^cart/accept/$', cart.agree_terms, name='satchmo_cart_accept_terms'),\n url(r'^cart/add/$', cart.add, name='satchmo_cart_add'),\n url(r'^cart/add/ajax/$', cart.add_ajax, name='satchmo_cart_add_ajax'),\n url(r'^cart/qty/$', cart.set_quantity, name='satchmo_cart_set_qty'),\n url(r'^cart/qty/ajax/$', cart.set_quantity_ajax, name='satchmo_cart_set_qty_ajax'),\n url(r'^cart/remove/$', cart.remove, name='satchmo_cart_remove'),\n url(r'^cart/remove/ajax/$', cart.remove_ajax, name='satchmo_cart_remove_ajax'),\n url(r'^checkout/', include('payment.urls')),\n url(r'^contact/$', contact.ContactFormView.as_view(), name='satchmo_contact'),\n url(r'^history/$', orders.order_history, name='satchmo_order_history'),\n url(r'^quickorder/$', cart.add_multiple, name='satchmo_quick_order'),\n url(r'^tracking/(?P\\d+)/$', orders.order_tracking, name='satchmo_order_tracking'),\n url(r'^search/$', search.search_view, name='satchmo_search'),\n url(r'^l10n/', include('l10n.urls')),\n]\n\n# here we add product patterns directly into the root url\nurlpatterns += productpatterns\n\nurlpatterns += [\n url(r'^contact/thankyou/$', TemplateView.as_view(template_name='shop/contact_thanks.html'), name='satchmo_contact_thanks'),\n url(r'^sitemap\\.xml$', sitemap, {'sitemaps': sitemaps}, name='satchmo_sitemap_xml'),\n]\n\n# here we are sending a signal to add patterns to the base of the shop.\ncollect_urls.send(sender=shop, patterns=urlpatterns)\n", "repo_name": "siddhant3030/djangoecommerce", "sub_path": "venv/lib/python3.7/site-packages/Satchmo-0.9.3-py3.7.egg/satchmo_store/shop/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 2145, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "37", "api": [{"api_name": "satchmo_store.shop.get_satchmo_setting", "line_number": 11, "usage_type": "call"}, {"api_name": "satchmo_store.shop", "line_number": 11, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 14, "usage_type": "call"}, {"api_name": "satchmo_store.shop.views.home.HomeListView.as_view", "line_number": 14, "usage_type": "call"}, {"api_name": "satchmo_store.shop.views.home.HomeListView", "line_number": 14, "usage_type": "attribute"}, {"api_name": "satchmo_store.shop.views.home", "line_number": 14, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 15, "usage_type": "call"}, {"api_name": "satchmo_store.shop.views.smart.smart_add", "line_number": 15, "usage_type": "attribute"}, {"api_name": "satchmo_store.shop.views.smart", "line_number": 15, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 16, "usage_type": "call"}, {"api_name": "satchmo_store.shop.views.cart.display", "line_number": 16, "usage_type": "attribute"}, {"api_name": "satchmo_store.shop.views.cart", "line_number": 16, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 17, "usage_type": "call"}, {"api_name": "satchmo_store.shop.views.cart.agree_terms", "line_number": 17, "usage_type": "attribute"}, {"api_name": "satchmo_store.shop.views.cart", "line_number": 17, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 18, "usage_type": "call"}, {"api_name": "satchmo_store.shop.views.cart.add", "line_number": 18, "usage_type": "attribute"}, {"api_name": "satchmo_store.shop.views.cart", "line_number": 18, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 19, "usage_type": "call"}, {"api_name": "satchmo_store.shop.views.cart.add_ajax", "line_number": 19, "usage_type": "attribute"}, {"api_name": "satchmo_store.shop.views.cart", "line_number": 19, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 20, "usage_type": "call"}, {"api_name": "satchmo_store.shop.views.cart.set_quantity", "line_number": 20, "usage_type": "attribute"}, {"api_name": "satchmo_store.shop.views.cart", "line_number": 20, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 21, "usage_type": "call"}, {"api_name": "satchmo_store.shop.views.cart.set_quantity_ajax", "line_number": 21, "usage_type": "attribute"}, {"api_name": "satchmo_store.shop.views.cart", "line_number": 21, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 22, "usage_type": "call"}, {"api_name": "satchmo_store.shop.views.cart.remove", "line_number": 22, "usage_type": "attribute"}, {"api_name": "satchmo_store.shop.views.cart", "line_number": 22, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 23, "usage_type": "call"}, {"api_name": "satchmo_store.shop.views.cart.remove_ajax", "line_number": 23, "usage_type": "attribute"}, {"api_name": "satchmo_store.shop.views.cart", "line_number": 23, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 24, "usage_type": "call"}, {"api_name": "django.conf.urls.include", "line_number": 24, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 25, "usage_type": "call"}, {"api_name": "satchmo_store.shop.views.contact.ContactFormView.as_view", "line_number": 25, "usage_type": "call"}, {"api_name": "satchmo_store.shop.views.contact.ContactFormView", "line_number": 25, "usage_type": "attribute"}, {"api_name": "satchmo_store.shop.views.contact", "line_number": 25, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 26, "usage_type": "call"}, {"api_name": "satchmo_store.shop.views.orders.order_history", "line_number": 26, "usage_type": "attribute"}, {"api_name": "satchmo_store.shop.views.orders", "line_number": 26, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 27, "usage_type": "call"}, {"api_name": "satchmo_store.shop.views.cart.add_multiple", "line_number": 27, "usage_type": "attribute"}, {"api_name": "satchmo_store.shop.views.cart", "line_number": 27, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 28, "usage_type": "call"}, {"api_name": "satchmo_store.shop.views.orders.order_tracking", "line_number": 28, "usage_type": "attribute"}, {"api_name": "satchmo_store.shop.views.orders", "line_number": 28, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 29, "usage_type": "call"}, {"api_name": "satchmo_store.shop.views.search.search_view", "line_number": 29, "usage_type": "attribute"}, {"api_name": "satchmo_store.shop.views.search", "line_number": 29, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 30, "usage_type": "call"}, {"api_name": "django.conf.urls.include", "line_number": 30, "usage_type": "call"}, {"api_name": "product.urls.urlpatterns", "line_number": 34, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 37, "usage_type": "call"}, {"api_name": "django.views.generic.base.TemplateView.as_view", "line_number": 37, "usage_type": "call"}, {"api_name": "django.views.generic.base.TemplateView", "line_number": 37, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 38, "usage_type": "call"}, {"api_name": "django.contrib.sitemaps.views.sitemap", "line_number": 38, "usage_type": "argument"}, {"api_name": "satchmo_store.shop.views.sitemaps.sitemaps", "line_number": 38, "usage_type": "name"}, {"api_name": "satchmo_utils.signals.collect_urls.send", "line_number": 42, "usage_type": "call"}, {"api_name": "satchmo_utils.signals.collect_urls", "line_number": 42, "usage_type": "name"}, {"api_name": "satchmo_store.shop", "line_number": 42, "usage_type": "name"}]} +{"seq_id": "71122034346", "text": "from joblib import Parallel, delayed\nimport multiprocessing\n\nimport geopandas as gpd\nimport pandas as pd\n\n\ndef processInput(i):\n print(\"Working on poly \", i, \" of \", len(polygons))\n cur_distances = []\n for dist_to in range(len(polygons)):\n cur_dist = polygons[i].distance(polygons[dist_to])\n cur_distances.append(cur_dist)\n return cur_distances\n\n\nnum_cores = multiprocessing.cpu_count()\nprint(\"Num Cores: \", num_cores)\n\nmex = gpd.read_file(\"./MEX/MEX_ADM2_fixedInternalTopology.shp\")\npolygons = mex['geometry'].to_list()#[0:50]\nshapeIDs = mex['shapeID'].to_list()#[0:50]\n\nresults = Parallel(n_jobs=num_cores)(delayed(processInput)(i) for i in range(len(polygons)))\n\nprint(\"Done calculating distances.\")\n\ndist_df = pd.DataFrame(results)\ndist_df.columns = shapeIDs\ndist_df = dist_df.reset_index()\ndist_df['index'] = shapeIDs\ndist_df = dist_df.rename(columns = {'index':'shapeID'})\nprint(dist_df.head())\ndist_df.to_csv(\"./MEX/mex_distance_matrix.csv\")\n", "repo_name": "heatherbaier/social-sig", "sub_path": "neighbors/make_distance_matrix.py", "file_name": "make_distance_matrix.py", "file_ext": "py", "file_size_in_byte": 975, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "multiprocessing.cpu_count", "line_number": 17, "usage_type": "call"}, {"api_name": "geopandas.read_file", "line_number": 20, "usage_type": "call"}, {"api_name": "joblib.Parallel", "line_number": 24, "usage_type": "call"}, {"api_name": "joblib.delayed", "line_number": 24, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 28, "usage_type": "call"}]} +{"seq_id": "71371556907", "text": "# Author: ASU --\n# Purpose: utility library for >=Python3.8\n\nimport time\nfrom collections import deque\nfrom typing import Any, Callable\n\n\nclass Throttler:\n def __init__(self, max_req: int, period: float, time_func: Callable[[], float] = time.time) -> None:\n self._max_req = max_req\n self._period = period\n self._request_timestamps = deque(maxlen=max_req)\n self._req_cnt = 0\n self._time_func = time_func\n\n def throttle(self, val: Any) -> Any:\n t1 = self._time_func()\n self._req_cnt += 1\n self._request_timestamps.append(t1)\n if len(self._request_timestamps) >= self._max_req:\n t0 = self._request_timestamps[0]\n dt = t1 - t0\n if dt < self._period:\n time.sleep(self._period - dt)\n return val\n", "repo_name": "asuiu/throttlex", "sub_path": "throttlex/__init__.py", "file_name": "__init__.py", "file_ext": "py", "file_size_in_byte": 836, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "typing.Callable", "line_number": 10, "usage_type": "name"}, {"api_name": "time.time", "line_number": 10, "usage_type": "attribute"}, {"api_name": "collections.deque", "line_number": 13, "usage_type": "call"}, {"api_name": "typing.Any", "line_number": 17, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 25, "usage_type": "call"}]} +{"seq_id": "39710453314", "text": "\"\"\"Example using L1-regularized multi-class Support\tVector Machine.\"\"\"\n\nimport matplotlib.pyplot as plt\n\nimport numpy as np\n\nfrom pysparselp.SparseLP import SparseLP, solving_methods\n\n\nclass L1SVM(SparseLP):\n \"\"\"L1-regularized multi-class Support\tVector Machine J. Zhu, S. Rosset, T. Hastie, and R. Tibshirani. 1-norm support vector machines. NIPS, 2004.\"\"\"\n\n def add_abs_penalization(self, indices, coef_penalization):\n\n aux = self.add_variables_array(indices.size, upper_bounds=None, lower_bounds=0)\n\n if np.isscalar(coef_penalization):\n assert coef_penalization > 0\n self.set_costs_variables(aux, np.ones(aux.shape) * coef_penalization)\n # allows a penalization that is different for each edge (could be dependent on an edge detector)\n else:\n assert coef_penalization.shape == aux.shape\n assert np.min(coef_penalization) >= 0\n self.set_costs_variables(aux, np.ones(aux.shape) * coef_penalization)\n\n # start by adding auxiliary variables\n\n aux_ravel = aux.ravel()\n indices_ravel = indices.ravel()\n cols = np.column_stack((indices_ravel, aux_ravel))\n vals = np.tile(np.array([1, -1]), [indices.size, 1])\n self.add_inequality_constraints(cols, vals, lower_bounds=None, upper_bounds=0)\n vals = np.tile(np.array([-1, -1]), [indices.size, 1])\n self.add_inequality_constraints(cols, vals, lower_bounds=None, upper_bounds=0)\n\n def set_data(self, x, classes, nb_classes=None):\n nb_examples = x.shape[0]\n xh = np.hstack((x, np.ones((nb_examples, 1))))\n assert x.shape[0] == len(classes)\n if nb_classes is None:\n nb_classes = np.max(classes) + 1\n nb_features = x.shape[1]\n\n self.weightsIndices = self.add_variables_array(\n (nb_classes, nb_features + 1), None, None\n )\n self.add_abs_penalization(self.weightsIndices, 1)\n self.epsilonsIndices = self.add_variables_array(\n (nb_examples, 1), upper_bounds=None, lower_bounds=0, costs=1\n )\n e = np.ones((nb_examples, nb_classes))\n e[np.arange(nb_examples), classes] = 0\n\n # sum(x*weights[classes,:]),axis=1)[:,None]- x.dot(weights)+epsilon>e\n\n cols1 = self.weightsIndices[classes, :]\n vals1 = xh\n for k in range(nb_classes):\n keep = classes != k\n cols2 = np.tile(self.weightsIndices[[k], :], [nb_examples, 1])\n vals2 = -xh\n vals3 = np.ones(self.epsilonsIndices.shape)\n cols3 = self.epsilonsIndices\n vals = np.column_stack((vals1, vals2, vals3))\n cols = np.column_stack((cols1, cols2, cols3))\n self.add_inequality_constraints(\n cols[keep, :], vals[keep, :], lower_bounds=e[keep, k], upper_bounds=None\n )\n\n def train(self, method=\"mehrotra\"):\n\n sol1, elapsed = self.solve(\n method=method,\n get_timing=True,\n nb_iter=2000,\n max_time=np.inf,\n plot_solution=None,\n )\n self.weights = sol1[self.weightsIndices]\n marges = sol1[self.epsilonsIndices]\n self.activeSet = np.nonzero(marges > 1e-3)[0]\n\n def classify(self, x):\n nb_examples = x.shape[0]\n xh = np.hstack((x, np.ones((nb_examples, 1))))\n scores = xh.dot(self.weights.T)\n classes = np.argmax(scores, axis=1)\n return classes\n\n\ndef run(display=True):\n\n np.random.seed(1)\n nb_classes = 3\n nb_examples = 1000\n x = np.random.rand(nb_examples, 2)\n xh = np.hstack((x, np.ones((nb_examples, 1))))\n # plt.plot(x[:,0],x[:,1],'.')\n\n weights = np.random.randn(nb_classes, 2)\n weights = weights / np.sum(weights ** 2, axis=1)[:, None]\n weights = np.hstack((weights, -0.5 * np.sum(weights, axis=1)[:, None]))\n scores = (weights.dot(xh.T)).T\n classes = np.argmax(scores, axis=1)\n\n colors = [\"r\", \"g\", \"b\"]\n\n l1svm = L1SVM()\n l1svm.set_data(x, classes)\n percent_valid = {}\n solving_methods_list = list(solving_methods)\n solving_methods_list.remove(\"mehrotra\") # too slow\n solving_methods_list.remove(\"scipy_simplex\")\n solving_methods_list.remove(\"scipy_interior_point\")\n solving_methods_list.remove(\"dual_gradient_ascent\") # need to debug\n solving_methods_list.remove(\"dual_coordinate_ascent\") # need to debug\n\n for method in solving_methods_list:\n l1svm.train(method=method)\n classes2 = l1svm.classify(x)\n percent_valid[method] = 100 * np.mean(classes == classes2)\n\n if display:\n colors = [\"r\", \"g\", \"b\"]\n plt.figure()\n\n for k in range(3):\n plt.plot(x[classes2 == k, 0], x[classes2 == k, 1], \".\", color=colors[k])\n plt.plot(\n x[l1svm.activeSet, 0],\n x[l1svm.activeSet, 1],\n \"ko\",\n markersize=10,\n fillstyle=\"none\",\n )\n plt.axis(\"equal\")\n\n print(\"done\")\n plt.show()\n return percent_valid\n\n\nif __name__ == \"__main__\":\n run()\n", "repo_name": "martinResearch/PySparseLP", "sub_path": "pysparselp/examples/example_l1_svm.py", "file_name": "example_l1_svm.py", "file_ext": "py", "file_size_in_byte": 5053, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 24, "dataset": "github-code", "pt": "37", "api": [{"api_name": "pysparselp.SparseLP.SparseLP", "line_number": 10, "usage_type": "name"}, {"api_name": "numpy.isscalar", "line_number": 17, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 19, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 23, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 24, "usage_type": "call"}, {"api_name": "numpy.column_stack", "line_number": 30, "usage_type": "call"}, {"api_name": "numpy.tile", "line_number": 31, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 31, "usage_type": "call"}, {"api_name": "numpy.tile", "line_number": 33, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 33, "usage_type": "call"}, {"api_name": "numpy.hstack", "line_number": 38, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 38, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 41, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 51, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 52, "usage_type": "call"}, {"api_name": "numpy.tile", "line_number": 60, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 62, "usage_type": "call"}, {"api_name": "numpy.column_stack", "line_number": 64, "usage_type": "call"}, {"api_name": "numpy.column_stack", "line_number": 65, "usage_type": "call"}, {"api_name": "numpy.inf", "line_number": 76, "usage_type": "attribute"}, {"api_name": "numpy.nonzero", "line_number": 81, "usage_type": "call"}, {"api_name": "numpy.hstack", "line_number": 85, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 85, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 87, "usage_type": "call"}, {"api_name": "numpy.random.seed", "line_number": 93, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 93, "usage_type": "attribute"}, {"api_name": "numpy.random.rand", "line_number": 96, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 96, "usage_type": "attribute"}, {"api_name": "numpy.hstack", "line_number": 97, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 97, "usage_type": "call"}, {"api_name": "numpy.random.randn", "line_number": 100, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 100, "usage_type": "attribute"}, {"api_name": "numpy.sum", "line_number": 101, "usage_type": "call"}, {"api_name": "numpy.hstack", "line_number": 102, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 102, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 104, "usage_type": "call"}, {"api_name": "pysparselp.SparseLP.solving_methods", "line_number": 111, "usage_type": "argument"}, {"api_name": "numpy.mean", "line_number": 121, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 125, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 125, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 128, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 128, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 129, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 129, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axis", "line_number": 136, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 136, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 139, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 139, "usage_type": "name"}]} +{"seq_id": "71419379946", "text": "\"\"\"Data source for AWS Elastic Kubernetes Service clusters.\"\"\"\nfrom typing import Dict, List\n\nfrom sqlalchemy import DateTime\nfrom sqlalchemy.dialects.postgresql import JSONB\n\nfrom pantomath.provider.aws import AwsDataSource, DataSourceColumn, data_sources\n\n\n@data_sources.register(\"aws_eks_clusters\")\nclass AwsEksClustersDataSource(AwsDataSource):\n \"\"\"Data source for AWS Elastic Kubernetes Service clusters.\"\"\"\n\n columns = [\n DataSourceColumn(\n description=\"The Amazon Resource Name (ARN) of the cluster.\",\n hydrate=\"cluster.arn\",\n name=\"arn\",\n ),\n DataSourceColumn(\n description=\"The Unix epoch timestamp in seconds for when the cluster was created.\", # noqa: E501\n hydrate=\"cluster.createdAt\",\n name=\"created_at\",\n type=DateTime(timezone=True),\n ),\n DataSourceColumn(\n description=\"The name of the cluster.\",\n hydrate=\"cluster.name\",\n name=\"name\",\n ),\n DataSourceColumn(\n description=\"Any tags assigned to the cluster\",\n hydrate=\"cluster.resourcesVpcConfig\",\n name=\"resources_vpc_config\",\n type=JSONB,\n ),\n DataSourceColumn(\n description=\"The current status of the cluster.\",\n hydrate=\"cluster.status\",\n name=\"status\",\n ),\n DataSourceColumn(\n description=\"Any tags assigned to the cluster\",\n hydrate=\"cluster.tags\",\n index=True,\n name=\"tags\",\n type=JSONB,\n ),\n DataSourceColumn(\n description=\"The Kubernetes server version for the cluster.\",\n hydrate=\"cluster.version\",\n name=\"version\",\n ),\n ]\n\n enrich_config: Dict = {\n \"cluster\": {\n \"method_name\": \"describe_cluster\",\n \"method_parameters\": {\"name\": \"{Name}\"},\n \"results_filter\": \"[cluster]\",\n \"service_name\": \"eks\",\n },\n }\n\n excluded_default_columns: List[str] = []\n\n extract_config = {\n \"method_name\": \"list_clusters\",\n \"results_filter\": 'clusters[].{\"Name\": @}',\n \"service_name\": \"eks\",\n }\n", "repo_name": "jmfontaine/pantomath", "sub_path": "src/pantomath/provider/aws/eks/clusters.py", "file_name": "clusters.py", "file_ext": "py", "file_size_in_byte": 2225, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "37", "api": [{"api_name": "pantomath.provider.aws.AwsDataSource", "line_number": 11, "usage_type": "name"}, {"api_name": "pantomath.provider.aws.DataSourceColumn", "line_number": 15, "usage_type": "call"}, {"api_name": "pantomath.provider.aws.DataSourceColumn", "line_number": 20, "usage_type": "call"}, {"api_name": "sqlalchemy.DateTime", "line_number": 24, "usage_type": "call"}, {"api_name": "pantomath.provider.aws.DataSourceColumn", "line_number": 26, "usage_type": "call"}, {"api_name": "pantomath.provider.aws.DataSourceColumn", "line_number": 31, "usage_type": "call"}, {"api_name": "sqlalchemy.dialects.postgresql.JSONB", "line_number": 35, "usage_type": "name"}, {"api_name": "pantomath.provider.aws.DataSourceColumn", "line_number": 37, "usage_type": "call"}, {"api_name": "pantomath.provider.aws.DataSourceColumn", "line_number": 42, "usage_type": "call"}, {"api_name": "sqlalchemy.dialects.postgresql.JSONB", "line_number": 47, "usage_type": "name"}, {"api_name": "pantomath.provider.aws.DataSourceColumn", "line_number": 49, "usage_type": "call"}, {"api_name": "typing.Dict", "line_number": 56, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 65, "usage_type": "name"}, {"api_name": "pantomath.provider.aws.data_sources.register", "line_number": 10, "usage_type": "call"}, {"api_name": "pantomath.provider.aws.data_sources", "line_number": 10, "usage_type": "name"}]} +{"seq_id": "17354740263", "text": "import numpy as np\nimport random\nimport _pickle as cPickle\nimport gzip\nfrom PIL import Image\nimport scipy.io as sc\nimport os\n\nimport cv2\n# from pyimagesearch.transform import four_point_transform\nfrom pyimagesearch import imutils\nfrom skimage.filters import threshold_adaptive\n\n\nclass Neuralnetwork(object):\n def __init__(self, sizes):\n\n self.num_layers = len(sizes)\n self.sizes = sizes\n self.biases = [np.random.randn(y, 1) for y in self.sizes[1:]]\n self.weights = [np.random.randn(y, x) for x, y in zip(self.sizes[:-1], self.sizes[1:])]\n\n def sigmoid(self, z):\n return 1.0 / (1.0 + np.exp(-z))\n\n def feedforward(self, a):\n i = 0\n\n for b, w in zip(self.biases, self.weights):\n z = np.dot(w, a) + b\n\n a = self.sigmoid(z)\n\n return a\n\n def Stochastic(self, training_data, epochs, mini_batch_size, eta, test_data=None):\n if test_data:\n n_test = len(test_data)\n n = len(training_data)\n for j in range(n):\n random.shuffle(training_data)\n mini_batches = [training_data[k:k + mini_batch_size] for k in range(0, n, mini_batch_size)]\n for mini_batch in mini_batches:\n self.update_mini_batch(mini_batch, eta)\n\n if test_data:\n print(\"Epoch {0}: {1} / {2}\".format(j, self.evaluate(test_data), n_test))\n\n\n else:\n print(\"Epoch {0} complete\".format(j))\n\n if j == epochs:\n break;\n\n def update_mini_batch(self, mini_batch, eta):\n\n change_b = [np.zeros(b.shape) for b in self.biases]\n change_w = [np.zeros(b.shape) for b in self.weights]\n\n for x, y in mini_batch:\n schange_b, schange_w = self.backprop(x, y)\n\n change_b = [cb + scb for cb, scb in zip(change_b, schange_b)]\n change_w = [cw + scw for cw, scw in zip(change_w, schange_w)]\n\n self.biases = [b - (eta / len(mini_batch)) * cb for b, cb in zip(self.biases, change_b)]\n self.weights = [w - (eta / len(mini_batch)) * cw for w, cw in zip(self.weights, change_w)]\n\n def backprop(self, x, y):\n\n schangew = [np.zeros(b.shape) for b in self.weights]\n schangeb = [np.zeros(b.shape) for b in self.biases]\n\n # feedforward\n\n activation = x # first layer activation that is input only\n activationlist = [x] # to store all activation to find error\n zs = [] # to store all z for sigmoid prime\n\n for b, w in zip(self.biases, self.weights):\n z = np.dot(w, activation) + b\n zs.append(z)\n activation = self.sigmoid(z)\n activationlist.append(activation)\n\n # backward pass using 4 bp equations\n\n delta = self.cost_derivative(activationlist[-1], y) * self.sigmoiddash(z[-1])\n schangeb[-1] = delta\n schangew[-1] = np.dot(delta, activationlist[-2].transpose())\n\n # find rest of the delta using 2nd equation of bp\n\n\n for l in range(2, self.num_layers):\n z = zs[-l]\n delta = np.dot(self.weights[-l + 1].transpose(), delta) * self.sigmoiddash(z)\n schangeb[-l] = delta\n schangew[-l] = np.dot(delta, activationlist[-l - 1].transpose())\n\n return (schangeb, schangew)\n\n def cost_derivative(self, a, y):\n return a - y\n\n def sigmoiddash(self, z):\n return self.sigmoid(z) * (1 - self.sigmoid(z))\n\n def evaluate(self, test_data):\n\n test_results = [(np.argmax(self.feedforward(x)), y)\n for (x, y) in test_data]\n return sum(int(x == y) for (x, y) in test_results)\n\n def savewandb(self):\n\n return (self.weights, self.biases)\n\n def setwnandb(self, weights, biases):\n\n self.weights = weights\n self.biases = biases\n\n\nclass Training_data(object):\n def load_data(self):\n\n global content7, content6\n content7 = []\n\n q = 0\n with open(\"new dataset.txt\") as f:\n content2 = f.readlines()\n # you may also want to remove whitespace characters like `\\n` at the end of each line\n content2 = [x.strip() for x in content2]\n\n with open(\"value new data.txt\") as f:\n content3 = f.readlines()\n # you may also want to remove whitespace characters like `\\n` at the end of each line\n content3 = [x.strip() for x in content3]\n content = []\n content1 = []\n for x, y in zip(content2, content3):\n if (int(y) > 9 and int(y) < 36):\n content.append(x)\n content1.append(int(y) - 10)\n\n with open(\"new dataset.txt\") as f:\n content5 = f.readlines()\n # you may also want to remove whitespace characters like `\\n` at the end of each line\n content5 = [x.strip() for x in content5]\n\n with open(\"value new data.txt\") as f:\n content4 = f.readlines()\n # you may also want to remove whitespace characters like `\\n` at the end of each line\n content4 = [x.strip() for x in content4]\n content2 = []\n content3 = []\n for x, y in zip(content5, content4):\n if (int(y) > 9 and int(y) < 36):\n content2.append(x)\n content3.append(int(y) - 10)\n\n test_data = []\n training_data = []\n t_values = []\n t1_values = []\n\n print(type(content), len(content1), len(content2), len(content3))\n\n for i, p in zip(content, content1):\n e = np.zeros((784, 1))\n e1 = np.zeros((26, 1))\n l = i.split()\n for j in range(0, len(l)):\n e[j] = float(l[j]) / 255.0\n\n e1[int(p)] = 1.0\n training_data.append(e)\n t_values.append(e1)\n\n print(\"hello\")\n\n for i, p in zip(content2, content3):\n e = np.zeros((784, 1))\n\n l = i.split()\n for j in range(0, len(l)):\n e[j] = float(l[j]) / 255.0\n\n test_data.append(e)\n t1_values.append(int(p))\n\n #print(len(content2))\n training_data = list(zip(training_data, t_values))\n test_data = list(zip(test_data, t1_values))\n\n with open(\"traintval.txt\") as f:\n content6 = f.readlines()\n # you may also want to remove whitespace characters like `\\n` at the end of each line\n content6 = [x.strip() for x in content6]\n content7 = []\n\n for item in content6:\n e = np.zeros((26, 1))\n e[int(item)] = 1.0\n content7.append(e)\n\n return (training_data, test_data)\n\n\nclass segmentation(object):\n def hprojections(self, imgarr):\n\n x = imgarr.shape\n imgarr1 = (imgarr / 255.0 * 0.99) + 0.01\n ncount = []\n\n t = x[1] - 1.1\n n = 0\n\n for i in range(0, x[0]):\n\n sumr = 0\n n = n + 1\n\n for j in range(0, x[1]):\n sumr = sumr + imgarr1[i][j]\n\n # print(sumr)\n\n if sumr > t:\n ncount.append(n)\n\n return (ncount)\n\n def vprojections(self, imgarr):\n\n x = imgarr.shape\n imgarr1 = (imgarr / 255.0 * 0.99) + 0.01\n ncount = []\n ncount.append(1)\n\n t = x[0] - 1.1\n n = 0\n\n for j in range(0, x[1]):\n\n sumc = 0\n n = n + 1\n\n for i in range(0, x[0]):\n sumc = sumc + imgarr1[i][j]\n\n # print(sumc)\n if (sumc > t):\n # print(s)\n ncount.append(n)\n ncount.append(x[1])\n\n return (ncount)\n\n def charsegmentation(self, rword):\n\n global rwchar, t_data\n\n rwchar = []\n t_data = []\n\n for j in rword:\n rwchar2 = []\n\n for i in j:\n #print(type(i))\n rwchar1 = []\n\n imgarr = i\n x = imgarr.shape\n ncount1 = []\n diff = []\n ncount = self.vprojections(imgarr)\n\n for i in range(0, (len(ncount) - 1)):\n if (ncount[i + 1] - ncount[i] > 1):\n ncount1.append(ncount[i])\n diff.append(ncount[i + 1] - ncount[i])\n #print(ncount)\n #print(ncount1)\n #print(diff)\n h = x[0]\n\n for k in range(0, len(diff)):\n\n w = diff[k]\n start = ncount1[k] - 1\n\n data = np.zeros((h, w), dtype=np.uint8)\n for i in range(0, h):\n\n for j in range(0, w):\n data[i][j] = imgarr[i][start + j]\n\n data = np.invert(data)\n\n dim = (28, 28)\n\n # perform the actual resizing of the image and show it\n data = cv2.resize(data, dim, interpolation=cv2.INTER_AREA)\n data1 = data / 255.0\n rwchar1.append(data)\n t_data.append(data1.reshape((784, 1)))\n\n im = Image.fromarray(data)\n im.show()\n\n rwchar2.append(rwchar1)\n rwchar.append(rwchar2)\n\n def wordsegmentation(self, row):\n\n rword = []\n\n for i in range(0, len(row), 2):\n\n imgarr = row[i]\n word = []\n x = imgarr.shape\n\n ncount1 = []\n diff = []\n ncount = self.vprojections(imgarr)\n # print(ncount)\n ws = 0\n scount = []\n scount1 = []\n wcount = []\n for i in range(0, (len(ncount) - 1)):\n ws = ws + 1\n if (ncount[i + 1] - ncount[i] > 1):\n wcount.append(ws)\n scount.append(ncount[i])\n scount1.append(ncount[i + 1])\n ws = 0\n\n wcount.append(ws)\n\n # print(wcount)\n # print(scount)\n # print(scount1)\n sumdiff = 0\n for i in wcount:\n sumdiff += i\n\n avg = sumdiff / len(wcount)\n\n start = 0\n end = 0\n sum = 0\n # print(avg)\n\n diff.append(scount[0])\n\n for i in range(0, len(wcount)):\n\n if wcount[i] > avg:\n if i != 0 and i != (len(wcount) - 1):\n ncount1.append(scount1[i - 1])\n if i < len(scount) and i != 0:\n diff.append(scount[i])\n\n ncount1.append(scount1[len(scount1) - 1])\n # ncount1.append(scount1[0])\n # print(diff)\n\n # print(ncount1)\n\n for k in range(0, len(ncount1)):\n h = x[0]\n w = ncount1[k] - diff[k]\n # print(diff[k])\n data = np.zeros((h, w), dtype=np.uint8)\n for i in range(0, x[0]):\n for j in range(0, w):\n data[i][j] = imgarr[i][diff[k] + j]\n\n im = Image.fromarray(data)\n # im.show()\n word.append(data)\n\n rword.append(word)\n\n #print(len(rword))\n self.charsegmentation(rword)\n\n def linesegmentation(self, imgarr):\n\n ncount = self.hprojections(imgarr)\n x = imgarr.shape\n ncount1 = []\n diff = []\n row = []\n for i in range(0, (len(ncount) - 1)):\n if (ncount[i + 1] - ncount[i] > 1):\n ncount1.append(ncount[i])\n diff.append(ncount[i + 1] - ncount[i])\n\n # print(ncount)\n # print(ncount1)\n # print(diff)\n w = x[1]\n\n for k in range(0, len(diff)):\n\n h = diff[k]\n start = ncount1[k] - 1\n\n data = np.zeros((h, w), dtype=np.uint8)\n for i in range(0, h):\n\n for j in range(0, w):\n data[i][j] = imgarr[start + i][j]\n\n im = Image.fromarray(data)\n # im.show()\n row.append(data)\n row.append('/n')\n # im = Image.fromarray(row[0])\n # im.save(\"test.bmp\")\n # r1 = []\n # r1.append(row[0])\n # print(len(r1))\n self.wordsegmentation(row)\n\n\nclass thresholding(object):\n def thresh(self, image):\n ratio = image.shape[0] / 500.0\n orig = image.copy()\n warped = imutils.resize(image, height=500)\n warped = cv2.cvtColor(warped, cv2.COLOR_BGR2GRAY)\n warped = threshold_adaptive(warped, 251, offset=10)\n warped = warped.astype(\"uint8\") * 255\n gray = cv2.GaussianBlur(warped, (5, 5), 0)\n # show the original and scanned images\n # print (\"STEP 3: Apply perspective transform\")\n print(\"hello\")\n dst = cv2.fastNlMeansDenoising(warped, None, 10, 5, 21)\n im = Image.fromarray(imutils.resize(dst, height=650))\n # im.show()\n\n return (dst)\n\n\ndef callproject(path):\n #path = soc.givepath()\n print(path)\n img = cv2.imread(path)\n \n img = cv2.fastNlMeansDenoising(img, None, 5, 11, 25)\n im = thresholding()\n img = im.thresh(img)\n\n imgarr = np.array(img)\n\n test = segmentation()\n\n test.linesegmentation(imgarr)\n\n # print(len(rwchar))\n\n\n\n\n\n\n\n\n # d=Training_data()\n\n # training_data,test_data=d.load_data()\n # t_data=list(zip(t_data,content6))\n # im1=test_data[234][0]\n # print(test_data[234][1])\n\n # ex=test_data[234][0]\n # ex=ex*255.0\n # ex=ex.reshape((28,28))\n # ex=ex.transpose()\n # im=Image.fromarray(ex)\n # im.show()\n\n net = Neuralnetwork([784, 50, 26])\n\n # net.Stochastic(training_data, 30, 10, 3.0,test_data)\n\n # net.SGD(t_data, 25, 10, 3.0)\n\n # weights,biases=net.savewandb()\n\n\n\n # np.save(\"weights.npy\",weights)\n # np.save(\"biases.npy\",biases)\n\n b = np.load('weights.npy')\n a = np.load('biases.npy')\n net.setwnandb(b, a)\n\n # print(np.argmax(net.feedforward(test_data[234][0])))\n\n\n\n\n\n\n result = \"\"\n result1 = []\n\n for row in rwchar:\n\n for word in row:\n\n for char in word:\n inp = char / 255.0\n\n inp = inp.transpose()\n\n inp = inp.reshape((784, 1))\n nchar = np.argmax(net.feedforward(inp))\n #print(nchar)\n\n char = chr(65 + nchar)\n\n result = result + char\n\n result = result+\" \"\n\n result=result + \"\\n\"\n\n\n # print(result)\n #print(result)\n\n with open(\"hello.txt\", \"w\") as text_file:\n text_file.write(result)\n result1=\"TQTI IS OYQ GECTDTD IMDTE\"\n\n\n\n os.system('open hello1.txt')\n return result\n\n\n\n\n\n\nclass jsocket(object):\n def __init__(self):\n import socket # Import socket module\n self.path = \"\"\n soc = socket.socket() # Create a socket object\n host = \"localhost\" # Get local machine name\n port = 47896 # Reserve a port for your service.\n soc.bind((host, port)) # Bind to the port\n print(\"hello\")\n soc.listen(5) # Now wait for client connection.\n # print(len(\"Hello kashish suneja\"))\n while True:\n conn, addr = soc.accept() # Establish connection with client.\n print(\"Got connection from\", addr)\n msg = conn.recv(1024)\n # print(len(msg))\n # print(msg)\n break\n self.path = self.path + str(msg[2:])\n self.path = self.path[2:len(self.path) - 1]\n sendstr=\"this is the result to be printed\"\n\n sendstr=callproject(self.path)\n print(self.path)\n conn.send(sendstr.encode())\n soc.close()\n\n def givepath(self):\n return (self.path)\n\n\nsoc = jsocket()\n\n\n\n", "repo_name": "kashish52/hello-world", "sub_path": "project.py", "file_name": "project.py", "file_ext": "py", "file_size_in_byte": 15736, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "numpy.random.randn", "line_number": 20, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 20, "usage_type": "attribute"}, {"api_name": "numpy.random.randn", "line_number": 21, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 21, "usage_type": "attribute"}, {"api_name": "numpy.exp", "line_number": 24, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 30, "usage_type": "call"}, {"api_name": "random.shuffle", "line_number": 41, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 58, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 59, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 72, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 73, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 82, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 91, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 98, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 100, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 112, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 173, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 174, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 186, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 206, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 298, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 298, "usage_type": "attribute"}, {"api_name": "numpy.invert", "line_number": 304, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 309, "usage_type": "call"}, {"api_name": "cv2.INTER_AREA", "line_number": 309, "usage_type": "attribute"}, {"api_name": "PIL.Image.fromarray", "line_number": 314, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 314, "usage_type": "name"}, {"api_name": "numpy.zeros", "line_number": 382, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 382, "usage_type": "attribute"}, {"api_name": "PIL.Image.fromarray", "line_number": 387, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 387, "usage_type": "name"}, {"api_name": "numpy.zeros", "line_number": 418, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 418, "usage_type": "attribute"}, {"api_name": "PIL.Image.fromarray", "line_number": 424, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 424, "usage_type": "name"}, {"api_name": "pyimagesearch.imutils.resize", "line_number": 440, "usage_type": "call"}, {"api_name": "pyimagesearch.imutils", "line_number": 440, "usage_type": "name"}, {"api_name": "cv2.cvtColor", "line_number": 441, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 441, "usage_type": "attribute"}, {"api_name": "skimage.filters.threshold_adaptive", "line_number": 442, "usage_type": "call"}, {"api_name": "cv2.GaussianBlur", "line_number": 444, "usage_type": "call"}, {"api_name": "cv2.fastNlMeansDenoising", "line_number": 448, "usage_type": "call"}, {"api_name": "PIL.Image.fromarray", "line_number": 449, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 449, "usage_type": "name"}, {"api_name": "pyimagesearch.imutils.resize", "line_number": 449, "usage_type": "call"}, {"api_name": "pyimagesearch.imutils", "line_number": 449, "usage_type": "name"}, {"api_name": "cv2.imread", "line_number": 458, "usage_type": "call"}, {"api_name": "cv2.fastNlMeansDenoising", "line_number": 460, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 464, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 506, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 507, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 530, "usage_type": "call"}, {"api_name": "os.system", "line_number": 551, "usage_type": "call"}, {"api_name": "socket.socket", "line_number": 563, "usage_type": "call"}]} +{"seq_id": "30609153053", "text": "import numpy as np\nimport scipy\nfrom scipy.linalg import eigvals, norm, qr\n\n# builds matrix for testing\ndef build_mat(n, opt, density = 0.1):\n\n '''\n input: \n n = number of entries \n opt: encodes characteristic of desired matrix \n opt == 0: well separated \n opt == 1: full rank with 3 distinct e-vals\n opt == 2: full rank and all e-vals in ball of radius == 1e-5 centered at 1 \n opt == 3: ill-conditions ie K > 1E20\n opt -- 4: one zero eigenvector and b in range(A)\n Output:\n A: Matrix with desired specifications\n b: a random matrix \n - unless opt == 4 then b in range(A)\n '''\n\n # Create a random matrix R with random entries\n R = scipy.sparse.random(n, n, density)\n R = R.toarray()\n\n # create a random b \n b = np.random.rand(n, 1)\n\n # Perform QR factorization on R to obtain an orthogonal matrix Q\n Q, _ = qr(R, mode='economic')\n \n # well separated \n if opt == 0: \n diagonal_entries = np.linspace(n, 1e-10, n)\n\n # full rank with 3 distinct e-vals\n if opt == 1:\n diagonal_entries = []\n for i in range(n):\n diagonal_entries.append(np.random.randint(1, 4))\n \n # full rank and all e-vals in ball of radius == 1e-5 centered at 1 \n if opt == 2: \n diagonal_entries = np.linspace(1-1E-5, 1 + 1E-5, n) \n\n # ill-conditions ie K > 1E20\n if opt == 3: \n diagonal_entries = []\n for i in range(n):\n diagonal_entries.append(np.random.randint(3))\n \n # one zero eigenvalue and b in range(A)\n if opt == 4:\n R = np.random.rand(n, n)\n Q, _ = qr(R, mode='economic')\n diagonal_entries = np.linspace(0, 1E-5, n) \n D = np.diag(diagonal_entries)\n A = np.dot(Q.T, np.dot(D, Q))\n b = A[:,1]\n return A, b\n \n # full rank with one distinct eigenvalue\n if opt == 5: \n diagonal_entries = []\n choice = np.random.randint(1, 4)\n for i in range(n):\n diagonal_entries.append(choice)\n \n # Create a diagonal matrix D with the chosen diagonal entries\n D = np.diag(diagonal_entries)\n \n # Construct the matrix A = Q^T * D * Q\n A = np.dot(Q.T, np.dot(D, Q))\n \n return A, b\n\nif __name__ == \"__main__\":\n # Set the size of the matrix\n n = 20\n\n # test opt 1-3\n for opt in range(6):\n print('opt', opt)\n\n # Create matrix with desired specifications\n A, b = build_mat(n, opt)\n\n # Confirm properties of the matrix\n if opt != 4:\n eigenvalues = eigvals(A)\n condition_number = norm(A) * norm(np.linalg.inv(A))\n print(\"Number of eigenvalues:\", len(eigenvalues))\n print(\"Well conditioned:\", condition_number < 1E20)\n\n # extra tests for 2.5.e\n if opt == 4:\n x = np.zeros(n)\n x[1] = 1\n print('b in range(A):', np.linalg.norm(A@x.transpose() - b) == 0.0)\n\n\n", "repo_name": "cedergrund/4600IterativeSolvers", "sub_path": "introductory_material/GMRES/make_matrix.py", "file_name": "make_matrix.py", "file_ext": "py", "file_size_in_byte": 2964, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "scipy.sparse.random", "line_number": 24, "usage_type": "call"}, {"api_name": "scipy.sparse", "line_number": 24, "usage_type": "attribute"}, {"api_name": "numpy.random.rand", "line_number": 28, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 28, "usage_type": "attribute"}, {"api_name": "scipy.linalg.qr", "line_number": 31, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 35, "usage_type": "call"}, {"api_name": "numpy.random.randint", "line_number": 41, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 41, "usage_type": "attribute"}, {"api_name": "numpy.linspace", "line_number": 45, "usage_type": "call"}, {"api_name": "numpy.random.randint", "line_number": 51, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 51, "usage_type": "attribute"}, {"api_name": "numpy.random.rand", "line_number": 55, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 55, "usage_type": "attribute"}, {"api_name": "scipy.linalg.qr", "line_number": 56, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 57, "usage_type": "call"}, {"api_name": "numpy.diag", "line_number": 58, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 59, "usage_type": "call"}, {"api_name": "numpy.random.randint", "line_number": 66, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 66, "usage_type": "attribute"}, {"api_name": "numpy.diag", "line_number": 71, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 74, "usage_type": "call"}, {"api_name": "scipy.linalg.eigvals", "line_number": 91, "usage_type": "call"}, {"api_name": "scipy.linalg.norm", "line_number": 92, "usage_type": "call"}, {"api_name": "numpy.linalg.inv", "line_number": 92, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 92, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 98, "usage_type": "call"}, {"api_name": "numpy.linalg.norm", "line_number": 100, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 100, "usage_type": "attribute"}]} +{"seq_id": "72107080428", "text": "from typing import Optional, List\nimport os\nfrom datasets import load_dataset\nfrom dataloader.dataset_for_evaluation.base_dataset_group import BaseDatasetGroup\n\n\nclass LibriSpeechDummyDataset(BaseDatasetGroup):\n \"\"\"\n Debug DatasetGroup with the lightweight validation set from `hf-internal-testing/librispeech_asr_dummy`.\n \"\"\"\n \n def __init__(self,\n streaming: bool=False,\n subset: Optional[List[str]]=None) -> None:\n super().__init__(streaming=streaming, subset=subset)\n \n # Set the abstract class attributes:\n self.available_datasets = [\n \"librispeech_dummy\"\n ]\n self.is_multilingual = False\n self.language = \"english\"\n \n self.post_init()\n \n \n def _prepare_str2dataset(self) -> None:\n self.str2dataset = {\n \"librispeech_dummy\": load_dataset(\"hf-internal-testing/librispeech_asr_dummy\",\n name=\"clean\",\n split=\"validation\",\n cache_dir=self.cache_dir_librispeech)\n }\n\n\n def _load_cache_dir_from_env_var(self) -> None:\n self.cache_dir_librispeech = os.environ.get(\"CACHE_DIR_LIBRISPEECH\", None)\n if self.cache_dir_librispeech is None:\n print(\"WARNING: `CACHE_DIR_LIBRISPEECH` environment variable not set. Using default cache directory.\")\n else:\n print(f\"Using cache directory: `{self.cache_dir_librispeech}`.\")\n", "repo_name": "tonywu71/distilling-and-forgetting-in-large-pre-trained-models", "sub_path": "dataloader/dataset_for_evaluation/librispeech_dummy_dataset.py", "file_name": "librispeech_dummy_dataset.py", "file_ext": "py", "file_size_in_byte": 1546, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "37", "api": [{"api_name": "dataloader.dataset_for_evaluation.base_dataset_group.BaseDatasetGroup", "line_number": 7, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 14, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 14, "usage_type": "name"}, {"api_name": "datasets.load_dataset", "line_number": 29, "usage_type": "call"}, {"api_name": "os.environ.get", "line_number": 37, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 37, "usage_type": "attribute"}]} +{"seq_id": "42141912036", "text": "import bpy\r\n\r\nbl_info = {\r\n \"name\": \"Nestify\",\r\n \"author\": \"KnoxZen\",\r\n \"version\": (1, 2),\r\n \"blender\": (3, 0, 0),\r\n \"location\": \"3D view > Object > Nestify\",\r\n \"description\": \"Moves selected objects to a new collection with the same name as the parent object and organizes them hierarchically.\",\r\n \"category\": \"Object\"\r\n}\r\n\r\ndef nestify_objects(context):\r\n # Get selected objects\r\n selected_objects = context.selected_objects\r\n \r\n if len(selected_objects) == 0:\r\n return {\"CANCELLED\"}\r\n \r\n # Get active object (parent object)\r\n parent_object = context.active_object\r\n \r\n if not parent_object.empty_display_type == 'PLAIN_AXES':\r\n return {\"CANCELLED\"}\r\n \r\n # Create new collection and set its name\r\n new_collection = bpy.data.collections.new(parent_object.name)\r\n \r\n # Link newly created collection to scene collection\r\n context.collection.children.link(new_collection)\r\n \r\n # Move parent object to the new collection\r\n parent_object_old_collections = list(parent_object.users_collection)\r\n for col in parent_object_old_collections:\r\n col.objects.unlink(parent_object)\r\n new_collection.objects.link(parent_object)\r\n \r\n # Move all children objects to the new collection recursively\r\n move_children_to_collection(parent_object, new_collection)\r\n\r\ndef move_children_to_collection(parent_obj, new_collection):\r\n children_objects = parent_obj.children\r\n for child_obj in children_objects:\r\n obj_old_collections = list(child_obj.users_collection)\r\n for col in obj_old_collections:\r\n col.objects.unlink(child_obj)\r\n new_collection.objects.link(child_obj)\r\n move_children_to_collection(child_obj, new_collection)\r\n\r\nclass OBJECT_OT_nestify(bpy.types.Operator):\r\n bl_idname = 'object.nestify'\r\n bl_label = 'Nestify'\r\n bl_category = 'Object'\r\n\r\n def execute(self, context):\r\n nestify_objects(context)\r\n return {'FINISHED'}\r\n\r\ndef menu_func(self, context):\r\n layout = self.layout\r\n layout.operator(OBJECT_OT_nestify.bl_idname)\r\n\r\nclasses = (OBJECT_OT_nestify,)\r\n\r\ndef register():\r\n for cls in classes:\r\n bpy.utils.register_class(cls)\r\n bpy.types.VIEW3D_MT_object.append(menu_func)\r\n\r\ndef unregister():\r\n bpy.types.VIEW3D_MT_object.remove(menu_func)\r\n for cls in reversed(classes):\r\n bpy.utils.unregister_class(cls)\r\n\r\nif __name__ == \"__main__\":\r\n register()\r\n", "repo_name": "Knoxzen/Nestify", "sub_path": "Nestify.py", "file_name": "Nestify.py", "file_ext": "py", "file_size_in_byte": 2465, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "bpy.data.collections.new", "line_number": 27, "usage_type": "call"}, {"api_name": "bpy.data", "line_number": 27, "usage_type": "attribute"}, {"api_name": "bpy.types", "line_number": 50, "usage_type": "attribute"}, {"api_name": "bpy.utils.register_class", "line_number": 67, "usage_type": "call"}, {"api_name": "bpy.utils", "line_number": 67, "usage_type": "attribute"}, {"api_name": "bpy.types.VIEW3D_MT_object.append", "line_number": 68, "usage_type": "call"}, {"api_name": "bpy.types", "line_number": 68, "usage_type": "attribute"}, {"api_name": "bpy.types.VIEW3D_MT_object.remove", "line_number": 71, "usage_type": "call"}, {"api_name": "bpy.types", "line_number": 71, "usage_type": "attribute"}, {"api_name": "bpy.utils.unregister_class", "line_number": 73, "usage_type": "call"}, {"api_name": "bpy.utils", "line_number": 73, "usage_type": "attribute"}]} +{"seq_id": "8404757867", "text": "from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\nfrom io import BytesIO\nfrom threading import Lock\nimport contextlib\nimport itertools\nimport os.path\nimport pickle\nimport shutil\nimport tempfile\nimport unittest\nimport sys\n\nimport numpy as np\nimport pandas as pd\nimport pytest\n\nimport xarray as xr\nfrom xarray import (Dataset, DataArray, open_dataset, open_dataarray,\n open_mfdataset, backends, save_mfdataset)\nfrom xarray.backends.common import robust_getitem\nfrom xarray.backends.netCDF4_ import _extract_nc4_variable_encoding\nfrom xarray.core import indexing\nfrom xarray.core.pycompat import iteritems, PY2, PY3, ExitStack\n\nfrom . import (TestCase, requires_scipy, requires_netCDF4, requires_pydap,\n requires_scipy_or_netCDF4, requires_dask, requires_h5netcdf,\n requires_pynio, has_netCDF4, has_scipy, assert_allclose,\n flaky)\nfrom .test_dataset import create_test_data\n\ntry:\n import netCDF4 as nc4\nexcept ImportError:\n pass\n\ntry:\n import dask.array as da\nexcept ImportError:\n pass\n\n\nON_WINDOWS = sys.platform == 'win32'\n\n\ndef open_example_dataset(name, *args, **kwargs):\n return open_dataset(os.path.join(os.path.dirname(__file__), 'data', name),\n *args, **kwargs)\n\n\ndef create_masked_and_scaled_data():\n x = np.array([np.nan, np.nan, 10, 10.1, 10.2])\n encoding = {'_FillValue': -1, 'add_offset': 10,\n 'scale_factor': np.float32(0.1), 'dtype': 'i2'}\n return Dataset({'x': ('t', x, {}, encoding)})\n\n\ndef create_encoded_masked_and_scaled_data():\n attributes = {'_FillValue': -1, 'add_offset': 10,\n 'scale_factor': np.float32(0.1)}\n return Dataset({'x': ('t', [-1, -1, 0, 1, 2], attributes)})\n\n\ndef create_boolean_data():\n attributes = {'units': '-'}\n return Dataset({'x': ('t', [True, False, False, True], attributes)})\n\n\nclass TestCommon(TestCase):\n def test_robust_getitem(self):\n\n class UnreliableArrayFailure(Exception):\n pass\n\n class UnreliableArray(object):\n def __init__(self, array, failures=1):\n self.array = array\n self.failures = failures\n\n def __getitem__(self, key):\n if self.failures > 0:\n self.failures -= 1\n raise UnreliableArrayFailure\n return self.array[key]\n\n array = UnreliableArray([0])\n with self.assertRaises(UnreliableArrayFailure):\n array[0]\n self.assertEqual(array[0], 0)\n\n actual = robust_getitem(array, 0, catch=UnreliableArrayFailure,\n initial_delay=0)\n self.assertEqual(actual, 0)\n\n\nclass Only32BitTypes(object):\n pass\n\n\nclass DatasetIOTestCases(object):\n autoclose = False\n\n def create_store(self):\n raise NotImplementedError\n\n def roundtrip(self, data, **kwargs):\n raise NotImplementedError\n\n def test_zero_dimensional_variable(self):\n expected = create_test_data()\n expected['float_var'] = ([], 1.0e9, {'units': 'units of awesome'})\n expected['string_var'] = ([], np.array('foobar', dtype='S'))\n with self.roundtrip(expected) as actual:\n self.assertDatasetAllClose(expected, actual)\n\n def test_write_store(self):\n expected = create_test_data()\n with self.create_store() as store:\n expected.dump_to_store(store)\n # we need to cf decode the store because it has time and\n # non-dimension coordinates\n with xr.decode_cf(store) as actual:\n self.assertDatasetAllClose(expected, actual)\n\n def check_dtypes_roundtripped(self, expected, actual):\n for k in expected:\n expected_dtype = expected.variables[k].dtype\n if (isinstance(self, Only32BitTypes) and\n expected_dtype == 'int64'):\n # downcast\n expected_dtype = np.dtype('int32')\n actual_dtype = actual.variables[k].dtype\n # TODO: check expected behavior for string dtypes more carefully\n string_kinds = {'O', 'S', 'U'}\n assert (expected_dtype == actual_dtype or\n (expected_dtype.kind in string_kinds and\n actual_dtype.kind in string_kinds))\n\n def test_roundtrip_test_data(self):\n expected = create_test_data()\n with self.roundtrip(expected) as actual:\n self.check_dtypes_roundtripped(expected, actual)\n self.assertDatasetAllClose(expected, actual)\n\n def test_load(self):\n expected = create_test_data()\n\n @contextlib.contextmanager\n def assert_loads(vars=None):\n if vars is None:\n vars = expected\n with self.roundtrip(expected) as actual:\n for k, v in actual.variables.items():\n # IndexVariables are eagerly loaded into memory\n self.assertEqual(v._in_memory, k in actual.dims)\n yield actual\n for k, v in actual.variables.items():\n if k in vars:\n self.assertTrue(v._in_memory)\n self.assertDatasetAllClose(expected, actual)\n\n with self.assertRaises(AssertionError):\n # make sure the contextmanager works!\n with assert_loads() as ds:\n pass\n\n with assert_loads() as ds:\n ds.load()\n\n with assert_loads(['var1', 'dim1', 'dim2']) as ds:\n ds['var1'].load()\n\n # verify we can read data even after closing the file\n with self.roundtrip(expected) as ds:\n actual = ds.load()\n self.assertDatasetAllClose(expected, actual)\n\n def test_dataset_compute(self):\n expected = create_test_data()\n\n with self.roundtrip(expected) as actual:\n # Test Dataset.compute()\n for k, v in actual.variables.items():\n # IndexVariables are eagerly cached\n self.assertEqual(v._in_memory, k in actual.dims)\n\n computed = actual.compute()\n\n for k, v in actual.variables.items():\n self.assertEqual(v._in_memory, k in actual.dims)\n for v in computed.variables.values():\n self.assertTrue(v._in_memory)\n\n self.assertDatasetAllClose(expected, actual)\n self.assertDatasetAllClose(expected, computed)\n\n def test_pickle(self):\n expected = Dataset({'foo': ('x', [42])})\n with self.roundtrip(\n expected, allow_cleanup_failure=ON_WINDOWS) as roundtripped:\n raw_pickle = pickle.dumps(roundtripped)\n # windows doesn't like opening the same file twice\n roundtripped.close()\n unpickled_ds = pickle.loads(raw_pickle)\n self.assertDatasetIdentical(expected, unpickled_ds)\n\n def test_pickle_dataarray(self):\n expected = Dataset({'foo': ('x', [42])})\n with self.roundtrip(\n expected, allow_cleanup_failure=ON_WINDOWS) as roundtripped:\n unpickled_array = pickle.loads(pickle.dumps(roundtripped['foo']))\n self.assertDatasetIdentical(expected['foo'], unpickled_array)\n\n def test_dataset_caching(self):\n expected = Dataset({'foo': ('x', [5, 6, 7])})\n with self.roundtrip(expected) as actual:\n assert isinstance(actual.foo.variable._data,\n indexing.MemoryCachedArray)\n assert not actual.foo.variable._in_memory\n actual.foo.values # cache\n assert actual.foo.variable._in_memory\n\n with self.roundtrip(expected, open_kwargs={'cache': False}) as actual:\n assert isinstance(actual.foo.variable._data,\n indexing.CopyOnWriteArray)\n assert not actual.foo.variable._in_memory\n actual.foo.values # no caching\n assert not actual.foo.variable._in_memory\n\n def test_roundtrip_None_variable(self):\n expected = Dataset({None: (('x', 'y'), [[0, 1], [2, 3]])})\n with self.roundtrip(expected) as actual:\n self.assertDatasetAllClose(expected, actual)\n\n def test_roundtrip_object_dtype(self):\n floats = np.array([0.0, 0.0, 1.0, 2.0, 3.0], dtype=object)\n floats_nans = np.array([np.nan, np.nan, 1.0, 2.0, 3.0], dtype=object)\n letters = np.array(['ab', 'cdef', 'g'], dtype=object)\n letters_nans = np.array(['ab', 'cdef', np.nan], dtype=object)\n all_nans = np.array([np.nan, np.nan], dtype=object)\n original = Dataset({'floats': ('a', floats),\n 'floats_nans': ('a', floats_nans),\n 'letters': ('b', letters),\n 'letters_nans': ('b', letters_nans),\n 'all_nans': ('c', all_nans),\n 'nan': ([], np.nan)})\n expected = original.copy(deep=True)\n if isinstance(self, Only32BitTypes):\n # for netCDF3 tests, expect the results to come back as characters\n expected['letters_nans'] = expected['letters_nans'].astype('S')\n expected['letters'] = expected['letters'].astype('S')\n with self.roundtrip(original) as actual:\n try:\n self.assertDatasetIdentical(expected, actual)\n except AssertionError:\n # Most stores use '' for nans in strings, but some don't\n # first try the ideal case (where the store returns exactly)\n # the original Dataset), then try a more realistic case.\n # ScipyDataTest, NetCDF3ViaNetCDF4DataTest and NetCDF4DataTest\n # all end up using this case.\n expected['letters_nans'][-1] = ''\n self.assertDatasetIdentical(expected, actual)\n\n def test_roundtrip_string_data(self):\n expected = Dataset({'x': ('t', ['ab', 'cdef'])})\n with self.roundtrip(expected) as actual:\n if isinstance(self, Only32BitTypes):\n expected['x'] = expected['x'].astype('S')\n self.assertDatasetIdentical(expected, actual)\n\n def test_roundtrip_datetime_data(self):\n times = pd.to_datetime(['2000-01-01', '2000-01-02', 'NaT'])\n expected = Dataset({'t': ('t', times), 't0': times[0]})\n kwds = {'encoding': {'t0': {'units': 'days since 1950-01-01'}}}\n with self.roundtrip(expected, save_kwargs=kwds) as actual:\n self.assertDatasetIdentical(expected, actual)\n self.assertEquals(actual.t0.encoding['units'],\n 'days since 1950-01-01')\n\n def test_roundtrip_timedelta_data(self):\n time_deltas = pd.to_timedelta(['1h', '2h', 'NaT'])\n expected = Dataset({'td': ('td', time_deltas), 'td0': time_deltas[0]})\n with self.roundtrip(expected) as actual:\n self.assertDatasetIdentical(expected, actual)\n\n def test_roundtrip_float64_data(self):\n expected = Dataset({'x': ('y', np.array([1.0, 2.0, np.pi], dtype='float64'))})\n with self.roundtrip(expected) as actual:\n self.assertDatasetIdentical(expected, actual)\n\n def test_roundtrip_example_1_netcdf(self):\n expected = open_example_dataset('example_1.nc')\n with self.roundtrip(expected) as actual:\n # we allow the attributes to differ since that\n # will depend on the encoding used. For example,\n # without CF encoding 'actual' will end up with\n # a dtype attribute.\n self.assertDatasetEqual(expected, actual)\n\n def test_roundtrip_coordinates(self):\n original = Dataset({'foo': ('x', [0, 1])},\n {'x': [2, 3], 'y': ('a', [42]), 'z': ('x', [4, 5])})\n\n with self.roundtrip(original) as actual:\n self.assertDatasetIdentical(original, actual)\n\n expected = original.drop('foo')\n with self.roundtrip(expected) as actual:\n self.assertDatasetIdentical(expected, actual)\n\n def test_roundtrip_boolean_dtype(self):\n original = create_boolean_data()\n self.assertEqual(original['x'].dtype, 'bool')\n with self.roundtrip(original) as actual:\n self.assertDatasetIdentical(original, actual)\n self.assertEqual(actual['x'].dtype, 'bool')\n\n def test_orthogonal_indexing(self):\n in_memory = create_test_data()\n with self.roundtrip(in_memory) as on_disk:\n indexers = {'dim1': np.arange(3), 'dim2': np.arange(4),\n 'dim3': np.arange(5)}\n expected = in_memory.isel(**indexers)\n actual = on_disk.isel(**indexers)\n self.assertDatasetAllClose(expected, actual)\n # do it twice, to make sure we're switched from orthogonal -> numpy\n # when we cached the values\n actual = on_disk.isel(**indexers)\n self.assertDatasetAllClose(expected, actual)\n\n\nclass CFEncodedDataTest(DatasetIOTestCases):\n\n def test_roundtrip_strings_with_fill_value(self):\n values = np.array(['ab', 'cdef', np.nan], dtype=object)\n encoding = {'_FillValue': np.string_('X'), 'dtype': np.dtype('S1')}\n original = Dataset({'x': ('t', values, {}, encoding)})\n expected = original.copy(deep=True)\n expected['x'][:2] = values[:2].astype('S')\n with self.roundtrip(original) as actual:\n self.assertDatasetIdentical(expected, actual)\n\n original = Dataset({'x': ('t', values, {}, {'_FillValue': '\\x00'})})\n if not isinstance(self, Only32BitTypes):\n # these stores can save unicode strings\n expected = original.copy(deep=True)\n if isinstance(self, BaseNetCDF4Test):\n # netCDF4 can't keep track of an empty _FillValue for VLEN\n # variables\n expected['x'][-1] = ''\n elif (isinstance(self, (NetCDF3ViaNetCDF4DataTest,\n NetCDF4ClassicViaNetCDF4DataTest))\n or (has_netCDF4 and\n (type(self) is GenericNetCDFDataTest or\n type(self) is GenericNetCDFDataTestAutocloseTrue))):\n # netCDF4 can't keep track of an empty _FillValue for nc3, either:\n # https://github.com/Unidata/netcdf4-python/issues/273\n expected['x'][-1] = np.string_('')\n with self.roundtrip(original) as actual:\n self.assertDatasetIdentical(expected, actual)\n\n def test_roundtrip_mask_and_scale(self):\n decoded = create_masked_and_scaled_data()\n encoded = create_encoded_masked_and_scaled_data()\n with self.roundtrip(decoded) as actual:\n self.assertDatasetAllClose(decoded, actual)\n with self.roundtrip(decoded, open_kwargs=dict(decode_cf=False)) as actual:\n # TODO: this assumes that all roundtrips will first\n # encode. Is that something we want to test for?\n self.assertDatasetAllClose(encoded, actual)\n with self.roundtrip(encoded, open_kwargs=dict(decode_cf=False)) as actual:\n self.assertDatasetAllClose(encoded, actual)\n # make sure roundtrip encoding didn't change the\n # original dataset.\n self.assertDatasetIdentical(encoded,\n create_encoded_masked_and_scaled_data())\n with self.roundtrip(encoded) as actual:\n self.assertDatasetAllClose(decoded, actual)\n with self.roundtrip(encoded, open_kwargs=dict(decode_cf=False)) as actual:\n self.assertDatasetAllClose(encoded, actual)\n\n def test_coordinates_encoding(self):\n def equals_latlon(obj):\n return obj == 'lat lon' or obj == 'lon lat'\n\n original = Dataset({'temp': ('x', [0, 1]), 'precip': ('x', [0, -1])},\n {'lat': ('x', [2, 3]), 'lon': ('x', [4, 5])})\n with self.roundtrip(original) as actual:\n self.assertDatasetIdentical(actual, original)\n with create_tmp_file() as tmp_file:\n original.to_netcdf(tmp_file)\n with open_dataset(tmp_file, decode_coords=False) as ds:\n self.assertTrue(equals_latlon(ds['temp'].attrs['coordinates']))\n self.assertTrue(equals_latlon(ds['precip'].attrs['coordinates']))\n self.assertNotIn('coordinates', ds.attrs)\n self.assertNotIn('coordinates', ds['lat'].attrs)\n self.assertNotIn('coordinates', ds['lon'].attrs)\n\n modified = original.drop(['temp', 'precip'])\n with self.roundtrip(modified) as actual:\n self.assertDatasetIdentical(actual, modified)\n with create_tmp_file() as tmp_file:\n modified.to_netcdf(tmp_file)\n with open_dataset(tmp_file, decode_coords=False) as ds:\n self.assertTrue(equals_latlon(ds.attrs['coordinates']))\n self.assertNotIn('coordinates', ds['lat'].attrs)\n self.assertNotIn('coordinates', ds['lon'].attrs)\n\n def test_roundtrip_endian(self):\n ds = Dataset({'x': np.arange(3, 10, dtype='>i2'),\n 'y': np.arange(3, 20, dtype=' bool:\n global sentry_is_init\n return sentry_is_init\n\n\ndef release_version(is_dev_version: bool):\n \"\"\"Same as ui_utils.version but for dev builds this will only return the SHA.\"\"\"\n raw_version = version(ignore_dev=True)\n try:\n return raw_version[raw_version.index(\".dev0+\") + 6 :]\n except ValueError:\n if is_dev_version:\n # Get commit\n import shutil\n\n git_bin = shutil.which(\"git\")\n if git_bin is None:\n import subprocess\n\n try:\n return subprocess.check_output([git_bin, \"rev-parse\", \"HEAD\"]).decode(\"utf-8\")[:8] # type: ignore\n except subprocess.CalledProcessError as ex:\n raise ValueError(\"Was unable to determine dev version\") from ex\n else:\n return raw_version\n\n\ndef init(skytemple_settings: SkyTempleSettingsStore):\n global sentry_is_init, ran_init\n if not ran_init:\n try:\n pub_version = version()\n is_dev = pub_version == \"dev\"\n is_pre_release = \".dev0+\" in pub_version\n if is_dev:\n if \"SKYTEMPLE_DEV_ENABLE_SENTRY\" not in os.environ:\n logger.warning(\n \"Skipped enabling Sentry for development setup. Set env variable 'SKYTEMPLE_DEV_ENABLE_SENTRY' to enable.\"\n )\n ran_init = True\n return\n settings = {\"debug\": True, \"environment\": \"development\"}\n elif is_pre_release:\n settings = {\"debug\": False, \"environment\": \"development\"}\n else:\n settings = {\"debug\": False, \"environment\": \"production\"}\n sentry_sdk_logger.setLevel(\"WARNING\")\n logger.setLevel(SKYTEMPLE_LOGLEVEL)\n sentry_logging = LoggingIntegration(\n level=current_log_level(), # Capture as breadcrumbs\n event_level=None, # Send no errors as events\n )\n sentry_sdk.init(\n SENTRY_ENDPOINT,\n traces_sample_rate=1.0 if is_dev else 0.7,\n profiles_sample_rate=1.0 if is_dev else 0.1,\n release=release_version(is_dev),\n integrations=[sentry_logging],\n server_name=\"n/a\",\n **settings, # type: ignore\n )\n # Make sure we actually track this release being used.\n session_ctx = contextlib.ExitStack()\n hub = Hub(Hub.current)\n atexit.register(session_ctx.close)\n session_ctx.enter_context(auto_session_tracking(hub)) # type: ignore\n sentry_sdk.set_user({\"id\": skytemple_settings.getset_sentry_user_id()})\n sentry_is_init = True\n profiling.reset_impls_cache()\n except Exception as ex:\n logger.error(\"Failed setting up Sentry\", exc_info=ex)\n ran_init = True\n\n\n# noinspection PyBroadException\ndef try_ignore_err(source: Callable[[], T], sink: Callable[[T], None]):\n try:\n sink(source())\n except Exception as ex:\n logger.error(\n f\"Ignored exception (fn: {source.__name__}) while setting up Sentry.\",\n exc_info=ex,\n )\n\n\n@typing.no_type_check\ndef collect_device_context() -> Dict[str, \"Captured\"]:\n import platform\n import socket\n import psutil\n\n mem = psutil.virtual_memory()\n\n screen_info = {}\n try:\n from gi.repository.Gdk import Display\n\n display = Display.get_default()\n if display is not None:\n mon_geoms = [\n assert_not_none(display.get_monitor(i)).get_geometry()\n for i in range(display.get_n_monitors())\n ]\n\n x0 = min(r.x for r in mon_geoms)\n y0 = min(r.y for r in mon_geoms)\n x1 = max(r.x + r.width for r in mon_geoms)\n y1 = max(r.y + r.height for r in mon_geoms)\n width, height = x1 - x0, y1 - y0\n screen_info = {\n \"screen_resolution\": f\"{width}x{height}\",\n \"screen_height_pixels\": height,\n \"screen_width_pixels\": width,\n }\n except Exception:\n pass\n\n return dict(\n **{\n \"arch\": platform.machine(),\n \"low_memory\": mem.percent > 90,\n \"memory_size\": mem.total,\n \"free_memory\": mem.available,\n },\n **screen_info,\n )\n\n\ndef collect_os_context() -> Dict[str, \"Captured\"]:\n import platform\n\n uname = platform.uname()\n return {\n \"name\": platform.system(),\n \"version\": platform.release(),\n \"kernel_version\": f\"{uname.system} {uname.node} {uname.release} {uname.version} {uname.machine}\",\n }\n\n\ndef collect_runtime_context() -> Dict[str, \"Captured\"]:\n import platform\n\n return {\n \"name\": platform.python_implementation(),\n \"version\": platform.python_version(),\n \"raw_description\": f\"Compiler: {platform.python_compiler()}\",\n }\n\n\ndef collect_app_context() -> Dict[str, \"Captured\"]:\n return {\"app_start_time\": APP_START_TIME.isoformat(), \"app_version\": version()}\n\n\ndef if_not_none(obj, cb):\n if obj is None:\n return None\n return cb(obj)\n\n\n# noinspection PyProtectedMember\ndef debugger_open_scripts(manager: DebuggerManager):\n if not manager.is_opened():\n return None\n notebook = manager.get_controller().editor_notebook # type: ignore\n if (\n notebook is None\n or not hasattr(notebook, \"_open_editors\")\n or notebook._open_editors is None\n ):\n return None\n return list(notebook._open_editors.keys())\n\n\n# noinspection PyProtectedMember\ndef debugger_focused_script(manager: DebuggerManager):\n if not manager.is_opened():\n return None\n notebook = manager.get_controller().editor_notebook # type: ignore\n if (\n notebook is None\n or notebook.currently_open is None\n or not hasattr(notebook.currently_open, \"_explorerscript_view\")\n ):\n return None\n exps_view = notebook.currently_open._explorerscript_view\n if exps_view is None:\n return None\n return {\n \"name\": notebook.currently_open.filename,\n \"content\": exps_view.get_buffer().props.text,\n }\n\n\n# noinspection PyProtectedMember\ndef debugger_emulator_state(manager: DebuggerManager):\n if not manager.is_opened():\n return None\n debugger = manager.get_controller().debugger # type: ignore\n vars = manager.get_controller().variable_controller # type: ignore\n if debugger is None or vars is None:\n return None\n ges = debugger.ground_engine_state\n if ges is None:\n return None\n ground_state = None\n if ges.running:\n ground_state = {\n \"loaded_ssx_files\": [\n (\n {\"file_name\": x.file_name, \"hanger\": x.hanger}\n if x is not None\n else None\n )\n for x in ges.loaded_ssx_files\n ],\n \"loaded_ssb_files\": [\n (\n {\"file_name\": x.file_name, \"hanger\": x.hanger}\n if x is not None\n else None\n )\n for x in ges.loaded_ssb_files\n ],\n \"actors\": [\n (\n {\n \"id\": x.id,\n \"hanger\": x.hanger,\n \"direction\": x.direction.ssa_id\n if x.direction is not None\n else None,\n \"kind\": x.kind.name,\n \"sector\": x.sector,\n }\n if x is not None\n else None\n )\n for x in ges.actors\n ],\n \"objects\": [\n (\n {\n \"id\": x.id,\n \"hanger\": x.hanger,\n \"direction\": x.direction.ssa_id\n if x.direction is not None\n else None,\n \"kind\": x.kind.unique_name,\n \"sector\": x.sector,\n }\n if x is not None\n else None\n )\n for x in ges.objects\n ],\n \"performers\": [\n (\n {\n \"id\": x.id,\n \"hanger\": x.hanger,\n \"direction\": x.direction.ssa_id\n if x.direction is not None\n else None,\n \"kind\": x.kind,\n \"sector\": x.sector,\n }\n if x is not None\n else None\n )\n for x in ges.performers\n ],\n \"events\": [\n (\n {\"id\": x.id, \"hanger\": x.hanger, \"kind\": x.kind, \"sector\": x.sector}\n if x is not None\n else None\n )\n for x in ges.events\n ],\n }\n return {\n \"running\": ges.running,\n \"ground_state\": ground_state,\n \"game_vars\": {k.name: v for k, v in vars._variable_cache.items()}\n if hasattr(vars, \"_variable_cache\") and vars._variable_cache is not None\n else None,\n }\n\n\n# noinspection PyProtectedMember\ndef collect_state_context() -> Dict[str, \"Captured\"]:\n from skytemple.controller.main import MainController\n from skytemple.core.rom_project import RomProject\n from skytemple_files.common.util import capture_any\n\n rom_project = RomProject.get_current()\n try:\n view_state = MainController._instance._current_view_module.collect_debugging_info( # type: ignore\n MainController._instance._current_view # type: ignore\n )\n if \"models\" in view_state: # type: ignore\n view_state[\"models\"] = {k: capture_any(v) for k, v in view_state[\"models\"].items()} # type: ignore\n except Exception as ex:\n view_state = {\"error_collecting\": str(ex)}\n w, h = MainController.window().get_size()\n dw = if_not_none(\n MainController.debugger_manager()._opened_main_window, lambda w: w.get_size()[0]\n )\n dh = if_not_none(\n MainController.debugger_manager()._opened_main_window, lambda w: w.get_size()[1]\n )\n return {\n \"skytemple\": {\n \"window\": {\n \"width\": w,\n \"height\": h,\n },\n \"rom\": {\n \"filename\": if_not_none(rom_project, lambda p: p.get_rom_name()),\n \"edition\": if_not_none(\n rom_project, lambda p: p.get_rom_module().get_static_data()\n ),\n },\n \"module\": type(MainController._instance._current_view_module).__qualname__,\n \"view\": MainController._instance._current_view_controller_class.__qualname__, # type: ignore\n \"view_state\": view_state, # type: ignore\n },\n \"ssb_debugger\": {\n \"window\": {\n \"width\": dw,\n \"height\": dh,\n },\n \"open_scripts\": debugger_open_scripts(MainController.debugger_manager()),\n \"focused_script\": debugger_focused_script(\n MainController.debugger_manager()\n ),\n # \"emulator_state\": debugger_emulator_state(MainController.debugger_manager())\n },\n }\n\n\ndef collect_config_context(settings: SkyTempleSettingsStore) -> Dict[str, Captured]:\n return dict(settings.loaded_config.items()) # type: ignore\n\n\ndef capture(\n settings: SkyTempleSettingsStore,\n exc_info: Optional[ExceptionInfo],\n **error_context_in: Capturable,\n) -> Optional[str]:\n from skytemple_files.common.util import capture_capturable\n\n error_context: Dict[str, Union[str, int]] = {k: capture_capturable(v) for k, v in error_context_in.items()} # type: ignore\n try_ignore_err(\n collect_device_context, lambda c: sentry_sdk.set_context(\"device\", c)\n )\n try_ignore_err(collect_os_context, lambda c: sentry_sdk.set_context(\"os\", c))\n try_ignore_err(\n collect_runtime_context, lambda c: sentry_sdk.set_context(\"runtime\", c)\n )\n try_ignore_err(collect_app_context, lambda c: sentry_sdk.set_context(\"app\", c))\n try_ignore_err(\n collect_state_context, lambda c: sentry_sdk.set_context(\"skytemple_state\", c)\n )\n try_ignore_err(\n partial(collect_config_context, settings),\n lambda c: sentry_sdk.set_context(\"config\", c),\n )\n sentry_sdk.set_context(\"error\", error_context)\n if exc_info:\n return sentry_sdk.capture_exception(exc_info)\n else:\n if \"message\" in error_context:\n return sentry_sdk.capture_message(\n f\"Error without exception: {error_context['message']}\"\n )\n else:\n return sentry_sdk.capture_message(\"Unknown event. See context.\")\n", "repo_name": "SkyTemple/skytemple", "sub_path": "skytemple/core/sentry.py", "file_name": "sentry.py", "file_ext": "py", "file_size_in_byte": 14286, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 166, "dataset": "github-code", "pt": "37", "api": [{"api_name": "typing.TYPE_CHECKING", "line_number": 21, "usage_type": "name"}, {"api_name": "logging.getLogger", "line_number": 33, "usage_type": "call"}, {"api_name": "typing.TypeVar", "line_number": 34, "usage_type": "call"}, {"api_name": "datetime.datetime.utcnow", "line_number": 35, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 35, "usage_type": "name"}, {"api_name": "skytemple.core.ui_utils.version", "line_number": 45, "usage_type": "call"}, {"api_name": "shutil.which", "line_number": 53, "usage_type": "call"}, {"api_name": "subprocess.check_output", "line_number": 58, "usage_type": "call"}, {"api_name": "subprocess.CalledProcessError", "line_number": 59, "usage_type": "attribute"}, {"api_name": "skytemple.core.settings.SkyTempleSettingsStore", "line_number": 65, "usage_type": "name"}, {"api_name": "skytemple.core.ui_utils.version", "line_number": 69, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 73, "usage_type": "attribute"}, {"api_name": "sentry_sdk.utils.logger.setLevel", "line_number": 84, "usage_type": "call"}, {"api_name": "sentry_sdk.utils.logger", "line_number": 84, "usage_type": "name"}, {"api_name": "skytemple.core.logger.SKYTEMPLE_LOGLEVEL", "line_number": 85, "usage_type": "argument"}, {"api_name": "sentry_sdk.integrations.logging.LoggingIntegration", "line_number": 86, "usage_type": "call"}, {"api_name": "skytemple.core.logger.current_log_level", "line_number": 87, "usage_type": "call"}, {"api_name": "sentry_sdk.init", "line_number": 90, "usage_type": "call"}, {"api_name": "contextlib.ExitStack", "line_number": 100, "usage_type": "call"}, {"api_name": "sentry_sdk.Hub", "line_number": 101, "usage_type": "call"}, {"api_name": "sentry_sdk.Hub.current", "line_number": 101, "usage_type": "attribute"}, {"api_name": "atexit.register", "line_number": 102, "usage_type": "call"}, {"api_name": "sentry_sdk.sessions.auto_session_tracking", "line_number": 103, "usage_type": "call"}, {"api_name": "sentry_sdk.set_user", "line_number": 104, "usage_type": "call"}, {"api_name": "skytemple.core.profiling.reset_impls_cache", "line_number": 106, "usage_type": "call"}, {"api_name": "skytemple.core.profiling", "line_number": 106, "usage_type": "name"}, {"api_name": "typing.Callable", "line_number": 113, "usage_type": "name"}, {"api_name": "psutil.virtual_memory", "line_number": 129, "usage_type": "call"}, {"api_name": "gi.repository.Gdk.Display.get_default", "line_number": 135, "usage_type": "call"}, {"api_name": "gi.repository.Gdk.Display", "line_number": 135, "usage_type": "name"}, {"api_name": "skytemple.core.ui_utils.assert_not_none", "line_number": 138, "usage_type": "call"}, {"api_name": "platform.machine", "line_number": 157, "usage_type": "call"}, {"api_name": "typing.no_type_check", "line_number": 123, "usage_type": "attribute"}, {"api_name": "typing.Dict", "line_number": 124, "usage_type": "name"}, {"api_name": "platform.uname", "line_number": 169, "usage_type": "call"}, {"api_name": "platform.system", "line_number": 171, "usage_type": "call"}, {"api_name": "platform.release", "line_number": 172, "usage_type": "call"}, {"api_name": "typing.Dict", "line_number": 166, "usage_type": "name"}, {"api_name": "platform.python_implementation", "line_number": 181, "usage_type": "call"}, {"api_name": "platform.python_version", "line_number": 182, "usage_type": "call"}, {"api_name": "platform.python_compiler", "line_number": 183, "usage_type": "call"}, {"api_name": "typing.Dict", "line_number": 177, "usage_type": "name"}, {"api_name": "skytemple.core.ui_utils.version", "line_number": 188, "usage_type": "call"}, {"api_name": "typing.Dict", "line_number": 187, "usage_type": "name"}, {"api_name": "skytemple.core.ssb_debugger.manager.DebuggerManager", "line_number": 198, "usage_type": "name"}, {"api_name": "skytemple.core.ssb_debugger.manager.DebuggerManager", "line_number": 212, "usage_type": "name"}, {"api_name": "skytemple.core.ssb_debugger.manager.DebuggerManager", "line_number": 232, "usage_type": "name"}, {"api_name": "skytemple.core.rom_project.RomProject.get_current", "line_number": 333, "usage_type": "call"}, {"api_name": "skytemple.core.rom_project.RomProject", "line_number": 333, "usage_type": "name"}, {"api_name": "skytemple.controller.main.MainController._instance._current_view_module.collect_debugging_info", "line_number": 335, "usage_type": "call"}, {"api_name": "skytemple.controller.main.MainController._instance", "line_number": 335, "usage_type": "attribute"}, {"api_name": "skytemple.controller.main.MainController", "line_number": 335, "usage_type": "name"}, {"api_name": "skytemple.controller.main.MainController._instance", "line_number": 336, "usage_type": "attribute"}, {"api_name": "skytemple.controller.main.MainController", "line_number": 336, "usage_type": "name"}, {"api_name": "skytemple_files.common.util.capture_any", "line_number": 339, "usage_type": "call"}, {"api_name": "skytemple.controller.main.MainController.window", "line_number": 342, "usage_type": "call"}, {"api_name": "skytemple.controller.main.MainController", "line_number": 342, "usage_type": "name"}, {"api_name": "skytemple.controller.main.MainController.debugger_manager", "line_number": 344, "usage_type": "call"}, {"api_name": "skytemple.controller.main.MainController", "line_number": 344, "usage_type": "name"}, {"api_name": "skytemple.controller.main.MainController.debugger_manager", "line_number": 347, "usage_type": "call"}, {"api_name": "skytemple.controller.main.MainController", "line_number": 347, "usage_type": "name"}, {"api_name": "skytemple.controller.main.MainController._instance", "line_number": 361, "usage_type": "attribute"}, {"api_name": "skytemple.controller.main.MainController", "line_number": 361, "usage_type": "name"}, {"api_name": "skytemple.controller.main.MainController._instance", "line_number": 362, "usage_type": "attribute"}, {"api_name": "skytemple.controller.main.MainController", "line_number": 362, "usage_type": "name"}, {"api_name": "skytemple.controller.main.MainController.debugger_manager", "line_number": 370, "usage_type": "call"}, {"api_name": "skytemple.controller.main.MainController", "line_number": 370, "usage_type": "name"}, {"api_name": "skytemple.controller.main.MainController.debugger_manager", "line_number": 372, "usage_type": "call"}, {"api_name": "skytemple.controller.main.MainController", "line_number": 372, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 328, "usage_type": "name"}, {"api_name": "skytemple.core.settings.SkyTempleSettingsStore", "line_number": 379, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 379, "usage_type": "name"}, {"api_name": "skytemple_files.common.util.Captured", "line_number": 379, "usage_type": "name"}, {"api_name": "skytemple.core.settings.SkyTempleSettingsStore", "line_number": 384, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 385, "usage_type": "name"}, {"api_name": "skytemple.core.error_handler.ExceptionInfo", "line_number": 385, "usage_type": "name"}, {"api_name": "skytemple_files.common.util.Capturable", "line_number": 386, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 390, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 390, "usage_type": "name"}, {"api_name": "skytemple_files.common.util.capture_capturable", "line_number": 390, "usage_type": "call"}, {"api_name": "sentry_sdk.set_context", "line_number": 392, "usage_type": "call"}, {"api_name": "sentry_sdk.set_context", "line_number": 394, "usage_type": "call"}, {"api_name": "sentry_sdk.set_context", "line_number": 396, "usage_type": "call"}, {"api_name": "sentry_sdk.set_context", "line_number": 398, "usage_type": "call"}, {"api_name": "sentry_sdk.set_context", "line_number": 400, "usage_type": "call"}, {"api_name": "functools.partial", "line_number": 403, "usage_type": "call"}, {"api_name": "sentry_sdk.set_context", "line_number": 404, "usage_type": "call"}, {"api_name": "sentry_sdk.set_context", "line_number": 406, "usage_type": "call"}, {"api_name": "sentry_sdk.capture_exception", "line_number": 408, "usage_type": "call"}, {"api_name": "sentry_sdk.capture_message", "line_number": 411, "usage_type": "call"}, {"api_name": "sentry_sdk.capture_message", "line_number": 415, "usage_type": "call"}, {"api_name": "typing.Optional", "line_number": 387, "usage_type": "name"}]} +{"seq_id": "19960198938", "text": "# -*- coding: utf-8 -*-\r\n\r\n# Kütüphanelerin eklenmesi\r\nimport os\r\nimport random\r\nimport gym\r\nimport pylab\r\nimport numpy as np\r\nfrom collections import deque\r\nimport tensorflow as tf\r\nfrom tensorflow.keras.models import Model, load_model\r\nfrom tensorflow.keras.layers import Input, Dense, Lambda, Add\r\nfrom tensorflow.keras.optimizers import Adam, RMSprop\r\nfrom tensorflow.keras import backend as K\r\nimport argparse\r\n\r\n# Kodlarda değişiklik yapmadan değişkenleri değiştirmek için konsol ekranında girdi alıyoruz.\r\nparser = argparse.ArgumentParser()\r\nparser.add_argument(\"--env\", required=True, type=str, help=\"Env adını giriniz.\",default=\"CartPole-v1\")\r\nparser.add_argument(\"--episode\", required=True, type=int, help=\"İterasyon sayısını giriniz\",default=1000)\r\nparser.add_argument(\"--Save_Path\", default=\"Models\", type=str, help=\"Model kayıt adresi.\")\r\nparser.add_argument(\"--agent_type\", default=\"ddqn\", type=bool, help='True or False')\r\nparser.add_argument(\"--test\", type=bool, default=False, help='True or False')\r\nparser.add_argument(\"--dueling\", type=bool, default=True, help='True or False')\r\n\r\nparser.add_argument(\"--memory\", type=int, default=2000)\r\nparser.add_argument(\"--learning_rate\", type=float, default=0.00025)\r\nparser.add_argument(\"--batch_size\", type=int, default=32)\r\nparser.add_argument(\"--epsilon\", type=float, default=1.0)\r\nparser.add_argument(\"--min_epsilon\", type=float, default=0.01)\r\nparser.add_argument(\"--decay_rate\", type=float, default=0.999)\r\nparser.add_argument(\"--gamma\", type=float, default=0.95)\r\n\r\nargs = parser.parse_args()\r\n\r\n\r\n# Modeli Oluşturuyoruz\r\ndef OurModel(input_shape, action_space, dueling):\r\n X_input = Input(input_shape)\r\n X = X_input\r\n\r\n # 512 nöron Girdi katmanı Environment Action sayısı aktivasyon fonksiyonu relu Ağırlık oluşturucu he_uniform\r\n X = Dense(512, input_shape=input_shape, activation=\"relu\", kernel_initializer='he_uniform')(X)\r\n # 256 nöron aktivasyon fonksiyonu relu Ağırlık oluşturucu he_uniform\r\n X = Dense(256, activation=\"relu\", kernel_initializer='he_uniform')(X)\r\n # 64 nöron aktivasyon fonksiyonu relu Ağırlık oluşturucu he_uniform\r\n X = Dense(64, activation=\"relu\", kernel_initializer='he_uniform')(X)\r\n\r\n if dueling:\r\n # state_value1 için\r\n state_value = Dense(1, kernel_initializer='he_uniform')(X)\r\n state_value = Lambda(lambda s: K.expand_dims(s[:, 0], -1), output_shape=(action_space,))(state_value)\r\n # state_value2 için \r\n state_value2 = Dense(1, kernel_initializer='he_uniform')(X)\r\n state_value2 = Lambda(lambda s: K.expand_dims(s[:, 0], -1), output_shape=(action_space,))(state_value2)\r\n # action_advantage1 için\r\n action_advantage = Dense(action_space, kernel_initializer='he_uniform')(X)\r\n action_advantage = Lambda(lambda a: a[:, :] - K.mean(a[:, :], keepdims=True), output_shape=(action_space,))(action_advantage)\r\n # action_advantage2 için\r\n action_advantage2 = Dense(action_space, kernel_initializer='he_uniform')(X)\r\n action_advantage2 = Lambda(lambda a: a[:, :] - K.mean(a[:, :], keepdims=True), output_shape=(action_space,))(action_advantage2) \r\n # z state_value ve action_advantage toplamı\r\n z = Add()([state_value, action_advantage]) \r\n # y state_value2 ve action_advantage2 toplamı \r\n y = Add()([state_value2, action_advantage2]) \r\n # X, z ve y nin toplamı\r\n X = Add()([z, y])\r\n else:\r\n # Çıkış sayısı action sayısı aktivasyon fonksiyonu relu Ağırlık oluşturucu he_uniform\r\n X = Dense(action_space, activation=\"linear\", kernel_initializer='he_uniform')(X)\r\n # model tanımlanıyor.\r\n model = Model(inputs = X_input, outputs = X)\r\n # model oluşturuluyor.\r\n model.compile(loss=\"mean_squared_error\", optimizer=RMSprop(lr=args.learning_rate, rho=0.95, epsilon=0.01), metrics=[\"accuracy\"])\r\n # model ekrana yazdırılıyor.\r\n model.summary()\r\n return model\r\n\r\n\r\n#Agent classı olusturduk\r\nclass DQNAgent:\r\n def __init__(self, env_name):\r\n self.env_name = env_name \r\n self.env = gym.make(env_name)\r\n self.env.seed(0) \r\n self.env._max_episode_steps = 4000\r\n #env kuralları\r\n self.state_size = self.env.observation_space.shape[0]\r\n self.action_size = self.env.action_space.n\r\n self.reward=[]\r\n\r\n self.EPISODES = args.episode\r\n self.memory = deque(maxlen=args.memory)\r\n \r\n self.gamma = args.gamma # discount rate (indirim oranı)\r\n self.epsilon = args.epsilon # exploration rate (keşif oranı)\r\n self.epsilon_min = args.min_epsilon # minimum exploration probability (minimum keşif oranı)\r\n self.epsilon_decay = args.decay_rate # exponential decay rate (epsilon azalma değeri)\r\n self.batch_size = args.batch_size \r\n self.train_start = 1000\r\n\r\n # model parametreleri\r\n self.ddqn = args.agent_type # DDQN\r\n self.Soft_Update = False # Soft parametresi\r\n self.dueling = args.dueling # Dueling network\r\n\r\n self.TAU = 0.1 # Hedef model update parametresi\r\n\r\n self.Save_Path = args.Save_Path\r\n if not os.path.exists(self.Save_Path): os.makedirs(self.Save_Path)\r\n self.scores, self.episodes, self.average = [], [], []\r\n \r\n # modele göre kaydedilen model ismi\r\n\r\n if self.ddqn:\r\n print(\"Double DQN\")\r\n self.Model_name = os.path.join(self.Save_Path,\"Dueling DDQN_\"+self.env_name+\".h5\")\r\n else:\r\n print(\"DQN\")\r\n self.Model_name = os.path.join(self.Save_Path,\"Dueling DQN_\"+self.env_name+\".h5\")\r\n \r\n # model ve hedef model\r\n self.model = OurModel(input_shape=(self.state_size,), action_space = self.action_size, dueling = self.dueling)\r\n self.target_model = OurModel(input_shape=(self.state_size,), action_space = self.action_size, dueling = self.dueling)\r\n\r\n # hedef model güncelleme\r\n def update_target_model(self):\r\n if not self.Soft_Update and self.ddqn:\r\n self.target_model.set_weights(self.model.get_weights())\r\n return\r\n if self.Soft_Update and self.ddqn:\r\n q_model_theta = self.model.get_weights()\r\n target_model_theta = self.target_model.get_weights()\r\n counter = 0\r\n for q_weight, target_weight in zip(q_model_theta, target_model_theta):\r\n target_weight = target_weight * (1-self.TAU) + q_weight * self.TAU\r\n target_model_theta[counter] = target_weight\r\n counter += 1\r\n self.target_model.set_weights(target_model_theta)\r\n # model hafızası\r\n def remember(self, state, action, reward, next_state, done):\r\n self.memory.append((state, action, reward, next_state, done))\r\n if len(self.memory) > self.train_start:\r\n if self.epsilon > self.epsilon_min:\r\n #epsilonu epsilon decayle çarpıyoruz\r\n self.epsilon *= self.epsilon_decay\r\n # action seçimi\r\n def act(self, state):\r\n if np.random.random() <= self.epsilon:\r\n return random.randrange(self.action_size)\r\n else:\r\n return np.argmax(self.model.predict(state))\r\n\r\n def replay(self):\r\n if len(self.memory) < self.train_start:\r\n return\r\n # hafızadan rastgele veri alınıyor\r\n minibatch = random.sample(self.memory, self.batch_size)\r\n\r\n state = np.zeros((self.batch_size, self.state_size))\r\n next_state = np.zeros((self.batch_size, self.state_size))\r\n action, reward, done = [], [], []\r\n\r\n\r\n for i in range(self.batch_size):\r\n state[i] = minibatch[i][0]\r\n action.append(minibatch[i][1])\r\n reward.append(minibatch[i][2])\r\n next_state[i] = minibatch[i][3]\r\n done.append(minibatch[i][4])\r\n\r\n # Ana ağı kullanarak başlangıç durumu için Q değerlerini tahmin ettik.\r\n target = self.model.predict(state)\r\n # Ana ağı kullanarak bitiş durumunda en iyi eylemi tahmin ettik. \r\n target_next = self.model.predict(next_state)\r\n # Hedef ağı kullanarak durumu sonlandırmak için Q değerlerini tahmin ettil. \r\n target_val = self.target_model.predict(next_state)\r\n\r\n for i in range(len(minibatch)):\r\n # kullanılan eylem için Q değerinde düzeltme.\r\n if done[i]:\r\n target[i][action[i]] = reward[i]\r\n else:\r\n if self.ddqn: # Double - DQN\r\n # Q Network eylemi seçer.\r\n # a'_max = argmax_a' Q(s', a')\r\n a = np.argmax(target_next[i])\r\n # hedef Q Network, eylemi değerlendirir. \r\n # Q_max = Q_target(s', a'_max)\r\n target[i][action[i]] = reward[i] + self.gamma * (target_val[i][a]) \r\n else: # Standard - DQN\r\n # DQN, sonraki eylemler arasında maksimum Q değerini seçer \r\n # eylemin seçimi ve değerlendirilmesi hedef Q Ağı üzerindedir. \r\n # Q_max = max_a' Q_target(s', a')\r\n target[i][action[i]] = reward[i] + self.gamma * (np.amax(target_next[i]))\r\n self.model.fit(state, target, batch_size=self.batch_size, verbose=0)\r\n\r\n # model yükleme\r\n def load(self, name):\r\n self.model.load_weights(name)\r\n #model kaydetme\r\n def save(self, name):\r\n self.model.save(name)\r\n #grafik oluşturma\r\n pylab.figure(figsize=(18, 9))\r\n def PlotModel(self, score, episode):\r\n self.scores.append(score)\r\n self.episodes.append(episode)\r\n self.average.append(sum(self.scores[-50:]) / len(self.scores[-50:]))\r\n pylab.plot(self.episodes, self.average, 'r')\r\n pylab.plot(self.episodes, self.scores, 'b')\r\n pylab.ylabel('Score', fontsize=18)\r\n pylab.xlabel('Steps', fontsize=18)\r\n dqn = 'DQN_'\r\n softupdate = ''\r\n dueling = ''\r\n if self.ddqn: dqn = 'DDQN_'\r\n if self.Soft_Update: softupdate = '_soft'\r\n if self.dueling: dueling = '_Dueling'\r\n try:\r\n pylab.savefig(dqn+self.env_name+softupdate+dueling+\".png\")\r\n except OSError:\r\n pass\r\n\r\n return str(self.average[-1])[:5]\r\n # eğitim\r\n def run(self):\r\n for e in range(1,self.EPISODES+1):\r\n state = self.env.reset()\r\n state = np.reshape(state, [1, self.state_size])\r\n done = False\r\n i = 0\r\n while not done:\r\n action = self.act(state)\r\n next_state, reward, done, _ = self.env.step(action)\r\n next_state = np.reshape(next_state, [1, self.state_size])\r\n if not done or i == self.env._max_episode_steps-1:\r\n reward = reward\r\n else:\r\n reward = -100\r\n self.remember(state, action, reward, next_state, done)\r\n state = next_state\r\n i += 1\r\n if done:\r\n # model güncelleme\r\n self.update_target_model()\r\n \r\n # grafik oluşturma\r\n average = self.PlotModel(i, e)\r\n print(\"episode: {}/{}, score: {}, e: {:.2}, average: {}\".format(e, self.EPISODES, i, self.epsilon, average))\r\n\r\n break\r\n self.replay()\r\n # model kaydetme\r\n if (e)%10==0:\r\n print(\"Saving trained model as\", self.Model_name)\r\n self.save(self.Model_name)\r\n# test\r\n def test(self):\r\n self.load(self.Model_name)\r\n for e in range(self.EPISODES):\r\n state = self.env.reset()\r\n state = np.reshape(state, [1, self.state_size])\r\n done = False\r\n i = 0\r\n while not done:\r\n self.env.render()\r\n action = np.argmax(self.model.predict(state))\r\n next_state, reward, done, _ = self.env.step(action)\r\n state = np.reshape(next_state, [1, self.state_size])\r\n i += 1\r\n if done:\r\n print(\"episode: {}/{}, score: {}\".format(e, self.EPISODES, i))\r\n break\r\n\r\nif __name__ == \"__main__\":\r\n env_name = args.env\r\n agent = DQNAgent(env_name)\r\n if args.test==True:\r\n agent.test()\r\n else:\r\n agent.run()", "repo_name": "bilper/D4Q-New-Reinforcement-Learning-Algorithm", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 12567, "program_lang": "python", "lang": "tr", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 18, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Input", "line_number": 39, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Dense", "line_number": 43, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Dense", "line_number": 45, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Dense", "line_number": 47, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Dense", "line_number": 51, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Lambda", "line_number": 52, "usage_type": "call"}, {"api_name": "tensorflow.keras.backend.expand_dims", "line_number": 52, "usage_type": "call"}, {"api_name": "tensorflow.keras.backend", "line_number": 52, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.Dense", "line_number": 54, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Lambda", "line_number": 55, "usage_type": "call"}, {"api_name": "tensorflow.keras.backend.expand_dims", "line_number": 55, "usage_type": "call"}, {"api_name": "tensorflow.keras.backend", "line_number": 55, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.Dense", "line_number": 57, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Lambda", "line_number": 58, "usage_type": "call"}, {"api_name": "tensorflow.keras.backend.mean", "line_number": 58, "usage_type": "call"}, {"api_name": "tensorflow.keras.backend", "line_number": 58, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.Dense", "line_number": 60, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Lambda", "line_number": 61, "usage_type": "call"}, {"api_name": "tensorflow.keras.backend.mean", "line_number": 61, "usage_type": "call"}, {"api_name": "tensorflow.keras.backend", "line_number": 61, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.Add", "line_number": 63, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Add", "line_number": 65, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Add", "line_number": 67, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Dense", "line_number": 70, "usage_type": "call"}, {"api_name": "tensorflow.keras.models.Model", "line_number": 72, "usage_type": "call"}, {"api_name": "tensorflow.keras.optimizers.RMSprop", "line_number": 74, "usage_type": "call"}, {"api_name": "gym.make", "line_number": 84, "usage_type": "call"}, {"api_name": "collections.deque", "line_number": 93, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 110, "usage_type": "call"}, {"api_name": "os.path", "line_number": 110, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 110, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 117, "usage_type": "call"}, {"api_name": "os.path", "line_number": 117, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 120, "usage_type": "call"}, {"api_name": "os.path", "line_number": 120, "usage_type": "attribute"}, {"api_name": "numpy.random.random", "line_number": 149, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 149, "usage_type": "attribute"}, {"api_name": "random.randrange", "line_number": 150, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 152, "usage_type": "call"}, {"api_name": "random.sample", "line_number": 158, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 160, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 161, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 187, "usage_type": "call"}, {"api_name": "numpy.amax", "line_number": 195, "usage_type": "call"}, {"api_name": "pylab.figure", "line_number": 205, "usage_type": "call"}, {"api_name": "pylab.plot", "line_number": 210, "usage_type": "call"}, {"api_name": "pylab.plot", "line_number": 211, "usage_type": "call"}, {"api_name": "pylab.ylabel", "line_number": 212, "usage_type": "call"}, {"api_name": "pylab.xlabel", "line_number": 213, "usage_type": "call"}, {"api_name": "pylab.savefig", "line_number": 221, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 230, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 236, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 263, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 268, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 270, "usage_type": "call"}]} +{"seq_id": "790704867", "text": "__all__ = [\"RunCommand\"]\n\nimport yaml\nfrom lsst.ts import salobj\n\n\nclass RunCommand(salobj.BaseScript):\n \"\"\"Run a command from a CSC and, optionally, wait for an event once the\n command finishes.\n\n Notes\n -----\n **Checkpoints**\n\n * \"run {csc_name}:{index}:{cmd}\" before commanding a CSC.\n\n * \"wait {csc_name}:{index}:{event}\" after commanding a CSC and before\n waiting for the event.\n\n **Details**\n\n * Dynamically loads IDL files as needed.\n \"\"\"\n\n def __init__(self, index):\n super().__init__(index=index, descr=\"Run command from CSC.\")\n\n # approximate time to construct a Remote for a CSC (sec)\n self.create_remote_time = 15\n\n @classmethod\n def get_schema(cls):\n schema_yaml = \"\"\"\n $schema: http://json-schema.org/draft-07/schema#\n $id: https://github.com/lsst-ts/ts_standardscripts/RunCommand.yaml\n title: RunCommand v1\n description: Configuration for RunCommand.\n type: object\n properties:\n component:\n description: Name of the CSC to run command, format is\n CSC_name[:index]; the default index is 0.\n type: string\n cmd:\n description: Name of the command to run.\n type: string\n event:\n description: >-\n Name of the event to wait after the command is sent.\n type: string\n flush:\n description: Flush event before sending command?\n type: boolean\n default: True\n event_timeout:\n description: Timeout (seconds) to wait for the event to arrive.\n type: number\n default: 30\n parameters:\n description: Parameters for the command.\n type: object\n properties:\n timeout:\n description: Timeout (seconds) to wait for the command to complete.\n type: number\n default: 30\n additionalProperties: true\n required: [component, cmd]\n additionalProperties: false\n \"\"\"\n return yaml.safe_load(schema_yaml)\n\n async def configure(self, config):\n \"\"\"Configure the script.\n\n Specify the CSCs to command, the command to run, the parameters for\n the command. Optionally, specify an event to wait and if the event\n should be flushed before sending the command.\n\n Parameters\n ----------\n config : `types.SimpleNamespace`\n\n Raises\n ------\n RuntimeError:\n If `config.command` is not a valid command from the CSC.\n\n \"\"\"\n self.log.info(\"Configure started\")\n\n self.config = config\n\n self.name, self.index = salobj.name_to_name_index(config.component)\n self.event = config.event if hasattr(config, \"event\") else None\n\n self.remote = salobj.Remote(\n domain=self.domain,\n name=self.name,\n index=self.index,\n include=[self.event] if self.event is not None else [],\n )\n\n if config.cmd in self.remote.salinfo.command_names:\n self.cmd = config.cmd\n else:\n raise RuntimeError(\n f\"Command {config.cmd} not a valid command for {self.name}.\"\n )\n\n getattr(self.remote, f\"cmd_{self.cmd}\").set(\n **dict(\n [(k, config.parameters[k]) for k in config.parameters if k != \"timeout\"]\n )\n )\n\n self.flush = config.flush if self.event is not None else False\n\n if self.event is not None and self.event not in self.remote.salinfo.event_names:\n raise RuntimeError(f\"Event {self.event} not a valid event for {self.name}.\")\n\n def set_metadata(self, metadata):\n \"\"\"Compute estimated duration.\n\n Parameters\n ----------\n metadata : `Script_logevent_metadata`\n\n \"\"\"\n # a crude estimate using command and event timeouts.\n metadata.duration = (\n self.config.parameters[\"timeout\"] + self.config.event_timeout\n if self.flush\n else 0.0\n )\n\n async def run(self):\n \"\"\"Run script.\"\"\"\n\n if not self.remote.start_task.done():\n self.log.debug(\"Waiting for remote start_task to complete.\")\n await self.remote.start_task\n\n if self.flush:\n getattr(self.remote, f\"evt_{self.event}\").flush()\n\n await self.checkpoint(f\"run {self.name}:{self.index}:{self.cmd}\")\n\n await getattr(self.remote, f\"cmd_{self.cmd}\").start(\n timeout=self.config.parameters[\"timeout\"]\n )\n\n if self.event is not None:\n await self.checkpoint(f\"wait {self.name}:{self.index}:{self.event}\")\n\n evt = await getattr(self.remote, f\"evt_{self.event}\").next(\n flush=False, timeout=self.config.event_timeout\n )\n\n self.log.info(evt)\n", "repo_name": "lsst-ts/ts_standardscripts", "sub_path": "python/lsst/ts/standardscripts/run_command.py", "file_name": "run_command.py", "file_ext": "py", "file_size_in_byte": 5076, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "37", "api": [{"api_name": "lsst.ts.salobj.BaseScript", "line_number": 7, "usage_type": "attribute"}, {"api_name": "lsst.ts.salobj", "line_number": 7, "usage_type": "name"}, {"api_name": "yaml.safe_load", "line_number": 71, "usage_type": "call"}, {"api_name": "lsst.ts.salobj.name_to_name_index", "line_number": 94, "usage_type": "call"}, {"api_name": "lsst.ts.salobj", "line_number": 94, "usage_type": "name"}, {"api_name": "lsst.ts.salobj.Remote", "line_number": 97, "usage_type": "call"}, {"api_name": "lsst.ts.salobj", "line_number": 97, "usage_type": "name"}]} +{"seq_id": "37687382392", "text": "import numpy as np\nfrom sklearn import svm\nfrom Preprocessing import preprocess\nfrom Postprocessing import *\nfrom utils import *\n\nmetrics = [\"race\", \"sex\", \"age\", 'c_charge_degree', 'priors_count', 'c_charge_desc']\ntraining_data, training_labels, test_data, test_labels, categories, mappings = preprocess(metrics)\n\nnp.random.seed(42)\nSVR = svm.LinearSVR(C=1.0/float(len(test_data)), max_iter=5000)\nSVR.fit(training_data, training_labels)\n\ntraining_class_predictions = SVR.predict(training_data)\ntraining_predictions = []\ntest_class_predictions = SVR.predict(test_data)\ntest_predictions = []\n\nfor i in range(len(training_labels)):\n training_predictions.append(training_class_predictions[i])\n\nfor i in range(len(test_labels)):\n test_predictions.append(test_class_predictions[i])\n\ntraining_race_cases = get_cases_by_metric(training_data, categories, \"race\", mappings, training_predictions, training_labels)\ntest_race_cases = get_cases_by_metric(test_data, categories, \"race\", mappings, test_predictions, test_labels)\n\nepsilon_value = 0.01\ntraining_race_cases, thresholds_training = enforce_equal_opportunity(training_race_cases, epsilon_value)\ntest_race_cases, thresholds_testing = enforce_equal_opportunity(test_race_cases, epsilon_value)\n\nfor group in test_race_cases.keys():\n training_race_cases[group] = apply_threshold(training_race_cases[group], thresholds_training[group])\n\nfor group in test_race_cases.keys():\n test_race_cases[group] = apply_threshold(test_race_cases[group], thresholds_testing[group])\n\n# ADD MORE PRINT LINES HERE - THIS ALONE ISN'T ENOUGH\n# YOU NEED ACCURACY AND COST FOR TRAINING AND TEST DATA\n# PLUS WHATEVER RELEVANT METRICS ARE USED IN YOUR POSTPROCESSING METHOD, TO ENSURE EPSILON WAS ENFORCED\nprint(\"Accuracy on training data\")\nprint(get_total_accuracy(training_race_cases))\nprint()\n\nprint(\"Cost on training data\")\nprint('${:,.0f}'.format(apply_financials(training_race_cases)))\nprint()\n\nprint(\"F1 Score on training data\")\nf1_score = []\nfor group in training_race_cases.keys():\n f1_score += training_race_cases[group]\nprint(calculate_Fscore(f1_score))\nprint()\n\nprint(\"Metrics for training data\")\nfor group in training_race_cases.keys():\n TPR = get_true_positive_rate(training_race_cases[group])\n print(\"TPR for \" + group + \": \" + str(TPR))\n\ntpr = []\nfor group in training_race_cases.keys():\n tpr += training_race_cases[group]\n\nt = get_true_positive_rate(tpr)\nprint(\"TPR for all training data\", t)\nprint()\n\nfor group in training_race_cases.keys():\n print(\"Threshold for \" + group + \": \" + str(thresholds_training[group]))\nprint()\n\nprint(\"Accuracy on test data\")\nprint(get_total_accuracy(test_race_cases))\nprint()\n\nprint(\"Cost on test data\")\nprint('${:,.0f}'.format(apply_financials(test_race_cases)))\nprint()\n\nprint(\"F1 Score on test data\")\nf1_score = []\nfor group in test_race_cases.keys():\n f1_score += test_race_cases[group]\nprint(calculate_Fscore(f1_score))\nprint()\n\nprint(\"Metrics for test data\")\nfor group in test_race_cases.keys():\n TPR = get_true_positive_rate(test_race_cases[group])\n print(\"TPR for \" + group + \": \" + str(TPR))\n\ntpr = []\nfor group in test_race_cases.keys():\n tpr += test_race_cases[group]\nt = get_true_positive_rate(tpr)\nprint(\"TPR for all test data\", t)\nprint()\n\nfor group in test_race_cases.keys():\n print(\"Threshold for \" + group + \": \" + str(thresholds_testing[group]))", "repo_name": "HemantKoti/CSE-574", "sub_path": "PA 3/Code/574_41_model.py", "file_name": "574_41_model.py", "file_ext": "py", "file_size_in_byte": 3427, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "Preprocessing.preprocess", "line_number": 8, "usage_type": "call"}, {"api_name": "numpy.random.seed", "line_number": 10, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 10, "usage_type": "attribute"}, {"api_name": "sklearn.svm.LinearSVR", "line_number": 11, "usage_type": "call"}, {"api_name": "sklearn.svm", "line_number": 11, "usage_type": "name"}]} +{"seq_id": "8271032750", "text": "from app.api import bp\nfrom flask import jsonify\nfrom app.api.errors import bad_request\nfrom flask import request\nfrom app.api.track import trackeddy\n\n@bp.route('/rest/info',methods = ['GET'])\ndef info():\n data = {\n 'name':'flask',\n 'age':13\n }\n return jsonify(data)\n\n@bp.route('/rest/track',methods = ['POST'])\ndef track():\n data = request.get_json() or {}\n if 'filepath' not in data:\n return bad_request('must include filepath fields')\n a_eddies,c_eddies = trackeddy(filepath=data['filepath'])\n dict = {\n \"aeddies\":a_eddies,\n \"ceddies\":c_eddies\n }\n return jsonify(dict)\n\n", "repo_name": "xiaocui123/track-service", "sub_path": "app/api/controller.py", "file_name": "controller.py", "file_ext": "py", "file_size_in_byte": 635, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "flask.jsonify", "line_number": 13, "usage_type": "call"}, {"api_name": "app.api.bp.route", "line_number": 7, "usage_type": "call"}, {"api_name": "app.api.bp", "line_number": 7, "usage_type": "name"}, {"api_name": "flask.request.get_json", "line_number": 17, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 17, "usage_type": "name"}, {"api_name": "app.api.errors.bad_request", "line_number": 19, "usage_type": "call"}, {"api_name": "app.api.track.trackeddy", "line_number": 20, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 25, "usage_type": "call"}, {"api_name": "app.api.bp.route", "line_number": 15, "usage_type": "call"}, {"api_name": "app.api.bp", "line_number": 15, "usage_type": "name"}]} +{"seq_id": "20290264691", "text": "import serial\nimport time\nimport pandas as pd\nimport numpy as np\nimport math\nimport sys\n\ntry: \n arduino = serial.Serial(\"/dev/ttyACM0\",115200)\nexcept:\n print(\"Please Check the port\")\n\n\n\n\nposition = []\n\n# for filtering out the acceleration data. like acc[0 0 0] for rest position\ndef lowpass_filter(acc):\n thres = 0.098\n if(abs(acc[0]) H\n R = np.zeros((6,6)) # noise co varience\n R[0:3, 0:3] = np.eye(3)*(measure_var**2)\n z_k = z+[0, 0, 0] \n z_k = np.array([z_k])\n y = z_k.T-H.dot(x)\n update = H.dot(p_cov.dot(H.T)) + R\n # here is the problem that is occurings\n try:\n inver = np.linalg.inv(update)\n #K = p_cov.dot(H.T.dot(np.linalg.inv(H.dot(p_cov.dot(H.T)) + R)))\n except Exception as e:\n print(\"Oops!\", e.__class__, \"occurred.\")\n\n K = p_cov.dot(H.T.dot(inver))\n p = (np.eye(6)-K.dot(H)).dot(p_cov)\n x = x+K.dot(y)\n \n return p, x\n\n\n'''print(y)\n update = H.dot(p_cov.dot(H.T)) + R\n print(update)\n \n try:\n inver = np.linalg.inv(update)\n #K = p_cov.dot(H.T.dot(np.linalg.inv(H.dot(p_cov.dot(H.T)) + R)))\n except Exception as e:\n print(\"Oops!\", e.__class__, \"occurred.\")\n K = p_cov.dot(H.T.dot(inver))\n \n x = x+K.dot(y)\n p = (np.eye(6)-K.dot(H)).dot(p_cov)'''\n\nprocess_var = 0.03 # process noice varience\nmeasure_var = 5 # measurement noice varience\nx = np.array([[23.971557, 90.361190, 48.500000, 0, 0, 0]]).T\np = np.eye(6) # covarience\np[0:3, 0:3] = np.eye(3)*4**2\np[3:6, 3:6] = np.eye(3)*0.4\n\n\nwhile True:\n start_time = time.time()\n a = str(arduino.readline())\n temp = a[2:-5]\n X = temp.split(\",\")\n try:\n X = list(map(float, X))\n except:\n continue\n acceleration = X[0:3] # acceleration data extracted from the imu sensor using arduino\n y_p_r = X[3:6] #yaw, roll and pitch data is extracted from the imu sensor using arduino\n quaternion = X[6:10] # quaternion data is extracted from the imu sensor using arduino\n gps = X[10:13] # gps data is extracted from gps sensor, latitude, longitude and altitude \n #X[10:13]\n \n try:\n #-------------------- prediction steps--------------------------------------\n modified_acceleration = Modified_acceleration(quaternion, acceleration) #NED acceleration\n end_time = time.time()\n delta_t = end_time-start_time\n \n F = np.eye(6)\n F[0:3, 3:6] = np.eye(3)*delta_t\n B = np.vstack((np.eye(3), np.eye(3)))\n B[0:3, 0:3] = np.eye(3)*.5*delta_t**2\n B[3:6, 0:3] = np.eye(3)*delta_t\n\n Q = np.zeros((6, 6))\n Q[0:3, 0:3] = 0.25*np.eye(3)* np.power(delta_t, 4)*(process_var**2)\n Q[3:6, 0:3] = process_var*np.eye(3)*delta_t**2\n \n x = F.dot(x)+B.dot(np.array([modified_acceleration]).T) # X = F*x+B*u\n p = F.dot(p).dot(F.T)+Q #p = F*p*transpose(F) + Q\n\n try:\n if(gps[0] !=0):\n #--------------------------updating steps----------------------------------\n p, x = measurement_update(p, gps, x)\n print(p)\n \n except:\n continue\n\n except:\n continue\n \n", "repo_name": "MarzanShuvo/Kalman-Filter-imu-and-gps-sensor", "sub_path": "kalman_filter_try1.py", "file_name": "kalman_filter_try1.py", "file_ext": "py", "file_size_in_byte": 4751, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "37", "api": [{"api_name": "serial.Serial", "line_number": 9, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 31, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 45, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 47, "usage_type": "call"}, {"api_name": "numpy.linalg.inv", "line_number": 47, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 47, "usage_type": "attribute"}, {"api_name": "numpy.eye", "line_number": 57, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 58, "usage_type": "call"}, {"api_name": "numpy.eye", "line_number": 59, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 61, "usage_type": "call"}, {"api_name": "numpy.linalg.inv", "line_number": 66, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 66, "usage_type": "attribute"}, {"api_name": "numpy.eye", "line_number": 72, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 94, "usage_type": "call"}, {"api_name": "numpy.eye", "line_number": 95, "usage_type": "call"}, {"api_name": "numpy.eye", "line_number": 96, "usage_type": "call"}, {"api_name": "numpy.eye", "line_number": 97, "usage_type": "call"}, {"api_name": "time.time", "line_number": 101, "usage_type": "call"}, {"api_name": "time.time", "line_number": 118, "usage_type": "call"}, {"api_name": "numpy.eye", "line_number": 121, "usage_type": "call"}, {"api_name": "numpy.eye", "line_number": 122, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 123, "usage_type": "call"}, {"api_name": "numpy.eye", "line_number": 123, "usage_type": "call"}, {"api_name": "numpy.eye", "line_number": 124, "usage_type": "call"}, {"api_name": "numpy.eye", "line_number": 125, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 127, "usage_type": "call"}, {"api_name": "numpy.eye", "line_number": 128, "usage_type": "call"}, {"api_name": "numpy.power", "line_number": 128, "usage_type": "call"}, {"api_name": "numpy.eye", "line_number": 129, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 131, "usage_type": "call"}]} +{"seq_id": "8353365576", "text": "from discord.ext import commands\nfrom core import Astroz, Cog\nimport discord, requests\nimport json\nfrom utils.Tools import *\nfrom discord.ui import View, Button\nimport logging\n\nlogging.basicConfig(\n level=logging.INFO,\n format=\"\\x1b[38;5;197m[\\x1b[0m%(asctime)s\\x1b[38;5;197m]\\x1b[0m -> \\x1b[38;5;197m%(message)s\\x1b[0m\",\n datefmt=\"%H:%M:%S\",\n)\nclass Guild(Cog):\n def __init__(self, client: Astroz):\n self.client = client\n\n\n \n\n @commands.Cog.listener(name=\"on_guild_join\")\n async def hacker(self, guild):\n rope = [inv for inv in await guild.invites() if inv.max_age == 0 and inv.max_uses == 0]\n me = self.client.get_channel(1046389928461873152)\n channels = len(set(self.client.get_all_channels()))\n embed = discord.Embed(title=f\"{guild.name}'s Information\",color=0x2f3136\n ).set_author(\n name=\"Guild Joined\",\n icon_url=guild.me.display_avatar.url if guild.icon is None else guild.icon.url\n ).set_footer(text=f\"{guild.name}\",icon_url=guild.me.display_avatar.url if guild.icon is None else guild.icon.url)\n embed.add_field(\n name=\"**__About__**\",\n value=f\"**Name : ** {guild.name}\\n**ID :** {guild.id}\\n**Owner <:Owner:1048556915963203684> :** {guild.owner} (<@{guild.owner_id}>)\\n**Created At : **{guild.created_at.month}/{guild.created_at.day}/{guild.created_at.year}\\n**Members :** {len(guild.members)}\",\n inline=False\n )\n embed.add_field(\n name=\"**__Description__**\",\n value=f\"\"\"{guild.description}\"\"\",\n inline=False\n )\n if guild.features:\n embed.add_field(\n name=\"**__Features__**\",\n value='\\n'.join([feature.replace('_', ' ').title() for feature in guild.features]),\n inline=False\n ) \n embed.add_field(\n name=\"**__Members__**\",\n value=f\"\"\"\nMembers : {len(guild.members)}\nHumans : {len(list(filter(lambda m: not m.bot, guild.members)))}\nBots : {len(list(filter(lambda m: m.bot, guild.members)))}\n \"\"\",\n inline=False\n )\n embed.add_field(\n name=\"**__Channels__**\",\n value=f\"\"\"\nCategories : {len(guild.categories)}\nText Channels : {len(guild.text_channels)}\nVoice Channels : {len(guild.voice_channels)}\nThreads : {len(guild.threads)}\n \"\"\",\n inline=False\n ) \n embed.add_field(\n name=\"**__Emoji Info__**\",\n value=f\"Emojis : {len(guild.emojis)}\\nStickers : {len(guild.stickers)}\",\n inline=False\n )\n\n embed.add_field(name=\"Bot Info:\", \n value=f\"Servers: `{len(self.client.guilds)}`\\nUsers: `{len(self.client.users)}`\\nChannels: `{channels}`\", inline=False) \n if guild.icon is not None:\n embed.set_thumbnail(url=guild.icon.url)\n embed.timestamp = discord.utils.utcnow() \n await me.send(f\"{rope[0]}\" if rope else \"No Pre-Made Invite Found\",embed=embed)\n if not guild.chunked:\n await guild.chunk()\n embed = discord.Embed(\n title=\"\\U0001f44b Hey, I am Astroz!\",\n description=\"Hello, thank you for adding me to your server. Here are some commands to get you started.\",\n color=0x2f3136,\n )\n embed.add_field(name=\"help\", value=\"Sends the help page.\", inline=False)\n embed.add_field(name=\"botinfo\", value=\"Show some info about the bot.\", inline=False)\n embed.add_field(\n name=\"vote\",\n value=\"You can support Astroz by voting! Thank you!\",\n inline=False,\n )\n channel = discord.utils.get(guild.text_channels, name=\"general\")\n if not channel:\n channels = [channel for channel in guild.text_channels if channel.permissions_for(guild.me).send_messages]\n channel = channels[0]\n await channel.send(embed=embed)\n\n\n\n\n @commands.Cog.listener(name=\"on_guild_remove\")\n async def on_g_remove(self, guild):\n idk = self.client.get_channel(1046389929674031168)\n channels = len(set(self.client.get_all_channels()))\n embed = discord.Embed(title=f\"{guild.name}'s Information\",color=0x2f3136\n ).set_author(\n name=f\"Guild Removed\",\n icon_url=guild.me.display_avatar.url if guild.icon is None else guild.icon.url\n ).set_footer(text=f\"{guild.name}\",icon_url=guild.me.display_avatar.url if guild.icon is None else guild.icon.url)\n embed.add_field(\n name=\"**__About__**\",\n value=f\"**Name : ** {guild.name}\\n**ID :** {guild.id}\\n**Owner <:Owner:1048556915963203684> :** {guild.owner} (<@{guild.owner_id}>)\\n**Created At : **{guild.created_at.month}/{guild.created_at.day}/{guild.created_at.year}\\n**Members :** {len(guild.members)}\",\n inline=False\n )\n embed.add_field(\n name=\"**__Description__**\",\n value=f\"\"\"{guild.description}\"\"\",\n inline=False\n )\n if guild.features:\n embed.add_field(\n name=\"**__Features__**\",\n value='\\n'.join([feature.replace('_', ' ').title() for feature in guild.features]),\n inline=False\n ) \n embed.add_field(\n name=\"**__Members__**\",\n value=f\"\"\"\nMembers : {len(guild.members)}\nHumans : {len(list(filter(lambda m: not m.bot, guild.members)))}\nBots : {len(list(filter(lambda m: m.bot, guild.members)))}\n \"\"\",\n inline=False\n )\n embed.add_field(\n name=\"**__Channels__**\",\n value=f\"\"\"\nCategories : {len(guild.categories)}\nText Channels : {len(guild.text_channels)}\nVoice Channels : {len(guild.voice_channels)}\nThreads : {len(guild.threads)}\n \"\"\",\n inline=False\n ) \n embed.add_field(name=\"Bot Info:\", \n value=f\"Servers: `{len(self.client.guilds)}`\\nUsers: `{len(self.client.users)}`\\nChannels: `{channels}`\", inline=False) \n if guild.icon is not None:\n embed.set_thumbnail(url=guild.icon.url)\n embed.timestamp = discord.utils.utcnow()\n await idk.send(embed=embed)\n\n\n @commands.Cog.listener()\n async def on_guild_remove(self, guild):\n with open(\"config.json\", \"r\") as f:\n data = json.load(f)\n\n del data[\"guilds\"][str(guild.id)]\n\n with open(\"config.json\", \"w\") as f:\n json.dump(data, f) \n\n @commands.Cog.listener()\n async def on_shard_ready(self, shard_id):\n logging.info(\"Shard #%s is ready\" % (shard_id))\n\n @commands.Cog.listener()\n async def on_shard_connect(self, shard_id):\n logging.info(\"Shard #%s has connected\" % (shard_id))\n\n @commands.Cog.listener()\n async def on_shard_disconnect(self, shard_id):\n logging.info(\"Shard #%s has disconnected\" % (shard_id))\n\n @commands.Cog.listener()\n async def on_shard_resume(self, shard_id):\n logging.info(\"Shard #%s has resumed\" % (shard_id))\n\n\n\n\n @commands.Cog.listener()\n async def on_command_error(self, ctx, error):\n log = self.bot.get_channel(1046389929674031168)\n if isinstance(error, commands.CommandNotFound):\n return\n else:\n embed=discord.Embed(title=ctx.author, color=0x041df1, description=f'{error}')\n await log.send(embed=embed)", "repo_name": "PEACExM/Krypton", "sub_path": "cogs/events/on_guild.py", "file_name": "on_guild.py", "file_ext": "py", "file_size_in_byte": 7179, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "37", "api": [{"api_name": "logging.basicConfig", "line_number": 9, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 10, "usage_type": "attribute"}, {"api_name": "core.Cog", "line_number": 14, "usage_type": "name"}, {"api_name": "core.Astroz", "line_number": 15, "usage_type": "name"}, {"api_name": "discord.Embed", "line_number": 26, "usage_type": "call"}, {"api_name": "discord.utils.utcnow", "line_number": 76, "usage_type": "call"}, {"api_name": "discord.utils", "line_number": 76, "usage_type": "attribute"}, {"api_name": "discord.Embed", "line_number": 80, "usage_type": "call"}, {"api_name": "discord.utils.get", "line_number": 92, "usage_type": "call"}, {"api_name": "discord.utils", "line_number": 92, "usage_type": "attribute"}, {"api_name": "discord.ext.commands.Cog.listener", "line_number": 21, "usage_type": "call"}, {"api_name": "discord.ext.commands.Cog", "line_number": 21, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 21, "usage_type": "name"}, {"api_name": "discord.Embed", "line_number": 105, "usage_type": "call"}, {"api_name": "discord.utils.utcnow", "line_number": 149, "usage_type": "call"}, {"api_name": "discord.utils", "line_number": 149, "usage_type": "attribute"}, {"api_name": "json.load", "line_number": 156, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 161, "usage_type": "call"}, {"api_name": "discord.ext.commands.Cog.listener", "line_number": 153, "usage_type": "call"}, {"api_name": "discord.ext.commands.Cog", "line_number": 153, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 153, "usage_type": "name"}, {"api_name": "logging.info", "line_number": 165, "usage_type": "call"}, {"api_name": "discord.ext.commands.Cog.listener", "line_number": 163, "usage_type": "call"}, {"api_name": "discord.ext.commands.Cog", "line_number": 163, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 163, "usage_type": "name"}, {"api_name": "logging.info", "line_number": 169, "usage_type": "call"}, {"api_name": "discord.ext.commands.Cog.listener", "line_number": 167, "usage_type": "call"}, {"api_name": "discord.ext.commands.Cog", "line_number": 167, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 167, "usage_type": "name"}, {"api_name": "logging.info", "line_number": 173, "usage_type": "call"}, {"api_name": "discord.ext.commands.Cog.listener", "line_number": 171, "usage_type": "call"}, {"api_name": "discord.ext.commands.Cog", "line_number": 171, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 171, "usage_type": "name"}, {"api_name": "logging.info", "line_number": 177, "usage_type": "call"}, {"api_name": "discord.ext.commands.Cog.listener", "line_number": 175, "usage_type": "call"}, {"api_name": "discord.ext.commands.Cog", "line_number": 175, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 175, "usage_type": "name"}, {"api_name": "discord.ext.commands.CommandNotFound", "line_number": 185, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 185, "usage_type": "name"}, {"api_name": "discord.Embed", "line_number": 188, "usage_type": "call"}, {"api_name": "discord.ext.commands.Cog.listener", "line_number": 182, "usage_type": "call"}, {"api_name": "discord.ext.commands.Cog", "line_number": 182, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 182, "usage_type": "name"}, {"api_name": "discord.ext.commands.Cog.listener", "line_number": 101, "usage_type": "call"}, {"api_name": "discord.ext.commands.Cog", "line_number": 101, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 101, "usage_type": "name"}]} +{"seq_id": "35869281426", "text": "from Bio import SeqIO\nlink1=input(\"Enter the path of dataset:\")\n\nfasta_sequences = SeqIO.parse(open(link1),'fasta')\nlink2=input(\"Enter the path of file to write:\")\nfile=open(link2,'w')\n\n\ni=0\nfor fasta in fasta_sequences:\n\n name, sequence = fasta.id, str(fasta.seq)\n if (name.find('-') != -1):\n file.write(\">\"+ str(i) + '|0|training\\n' + sequence + '\\n')\n else:\n file.write(\">\"+ str(i) + '|1|training\\n' + sequence + '\\n')\n\n i+=1\nfile.close()", "repo_name": "tamim662/Pattern-Recognition-Lab", "sub_path": "tamim/converter.py", "file_name": "converter.py", "file_ext": "py", "file_size_in_byte": 469, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "37", "api": [{"api_name": "Bio.SeqIO.parse", "line_number": 4, "usage_type": "call"}, {"api_name": "Bio.SeqIO", "line_number": 4, "usage_type": "name"}]} +{"seq_id": "28765806501", "text": "from django.test import LiveServerTestCase\nfrom selenium import webdriver\nfrom selenium.webdriver.common.keys import Keys\n\nclass NewVisitorTest(LiveServerTestCase):\n\n def setUp(self):\n self.browser = webdriver.Firefox()\n self.browser.implicitly_wait(3)\n\n def tearDown(self):\n self.browser.quit()\n\n def check_for_row_in_list_table(self, row_text):\n table = self.browser.find_element_by_id('id_list_table')\n rows = table.find_elements_by_tag_name('tr')\n self.assertIn(row_text, [row.text for row in rows])\n\n def test_can_start_a_list_and_retrieve_it_later(self):\n # Forest has heard about a cool new online to-do app. He\n # goes to check out its homepage\n self.browser.get(self.live_server_url)\n\n # He notices the page title and header mention to-do\n # lists\n self.assertIn('To-Do', self.browser.title)\n header_text = self.browser.find_element_by_tag_name('h1').text\n self.assertIn('To-Do', header_text)\n\n # He is invited to enter a to-do item straight away\n inputbox = self.browser.find_element_by_id('id_new_item')\n self.assertEqual(\n inputbox.get_attribute('placeholder'),\n 'Enter a to-do item'\n )\n\n # He types \"Buy pee cock feathers\" into a text box (Forest's\n # hobby is tying fly-fishing lures)\n inputbox.send_keys('Buy peacock feathers')\n\n # When he hits enter, the page updates, and now the\n # page lists \"1: Buy pee cock feathers\" as an item in a\n # to-do list table\n inputbox.send_keys(Keys.ENTER)\n self.check_for_row_in_list_table('1: Buy peacock feathers')\n \n # There is still a text box inviting him to add another item. He\n # enters \"Use pee cock feathers to make a fly\" (He is very anal)\n inputbox = self.browser.find_element_by_id('id_new_item')\n inputbox.send_keys('Use peacock feathers to make a fly')\n inputbox.send_keys(Keys.ENTER)\n\n # The page updates again, and now shows both items on his\n # list\n self.check_for_row_in_list_table('2: Use peacock feathers to make a fly')\n self.check_for_row_in_list_table('1: Buy peacock feathers')\n\n # Satisfied, he goes back to sleep\n\n def test_multiple_users_can_start_lists_at_different_urls(self):\n # Forest start a new to-do list\n self.browser.get(self.live_server_url)\n inputbox = self.browser.find_element_by_id('id_new_item')\n inputbox.send_keys('Buy peacock feathers')\n inputbox.send_keys(Keys.ENTER)\n self.check_for_row_in_list_table('1: Buy peacock feathers')\n\n # He notices that his list has a unique URL\n forest_list_url = self.browser.current_url\n self.assertRegex(forest_list_url, '/lists/.+')\n\n # Now a new user, Alexis, comes along to the site.\n\n ## We use a new browser session to make sure that no\n ## information of Forest's is coming through from cookies etc\n self.browser.quit()\n self.browser = webdriver.Firefox()\n\n # Alexis visits the home page. There is no sign of Forest's list\n self.browser.get(self.live_server_url)\n page_text = self.browser.find_element_by_tag_name('body').text\n self.assertNotIn('Buy peacock feathers', page_text)\n self.assertNotIn('make a fly', page_text)\n\n # Alexis starts a new list by entering a new item. She is less\n # interesting than Forest...\n inputbox = self.browser.find_element_by_id('id_new_item')\n inputbox.send_keys('Buy milk')\n inputbox.send_keys(Keys.ENTER)\n self.check_for_row_in_list_table('1: Buy milk')\n\n # Alexis gets her own unique URL\n alexis_list_url = self.browser.current_url\n self.assertRegex(alexis_list_url, '/lists/.+')\n self.assertNotEqual(forest_list_url, alexis_list_url)\n\n # Again, there is no trace of Forest's list\n page_text = self.browser.find_element_by_tag_name('body').text\n self.assertNotIn('Buy peacock feathers', page_text)\n self.assertIn('Buy milk', page_text)\n\n # Satisfied, they both go back to sleep\n\n self.fail('Finish the test!')\n", "repo_name": "rep0rted/TDD_superlists", "sub_path": "functional_tests/tests.py", "file_name": "tests.py", "file_ext": "py", "file_size_in_byte": 3915, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "django.test.LiveServerTestCase", "line_number": 5, "usage_type": "name"}, {"api_name": "selenium.webdriver.Firefox", "line_number": 8, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 8, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.keys.Keys.ENTER", "line_number": 44, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.keys.Keys", "line_number": 44, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.keys.Keys.ENTER", "line_number": 51, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.keys.Keys", "line_number": 51, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.keys.Keys.ENTER", "line_number": 65, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.keys.Keys", "line_number": 65, "usage_type": "name"}, {"api_name": "selenium.webdriver.Firefox", "line_number": 77, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 77, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.keys.Keys.ENTER", "line_number": 89, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.keys.Keys", "line_number": 89, "usage_type": "name"}]} +{"seq_id": "33318865432", "text": "import os\nimport pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.tree import DecisionTreeClassifier, plot_tree\nfrom sklearn.metrics import classification_report, confusion_matrix, accuracy_score\nfrom sklearn.naive_bayes import GaussianNB\nfrom sklearn.model_selection import KFold\nfrom sklearn import svm\nimport SVM\nimport naive_bayes as nb\n\niris_path = \"./dataset/iris\"\nlabor_path = \"./dataset/labor/C4.5\"\n\niris_data = \"iris.data\"\nlabor_data = \"labor-neg.data\"\n\niris_name_list = [\"sepal_len\", \"sepal_wid\", \"petal_len\", \"petal_wid\", \"class\"]\n\n\n# for implement, you can see:\n# https://medium.com/@penggongting/implementing-decision-tree-from-scratch-in-python-c732e7c69aea\ndef Decision_Tree_ID3(X, y, visulize_tree=False, print_info_matrix=False, n_splits=5):\n kf = KFold(n_splits=n_splits, shuffle=True)\n\n acc = []\n for train_index, test_index in kf.split(X):\n X_train, X_test = X.values[train_index], X.values[test_index]\n y_train, y_test = y.values[train_index], y.values[test_index]\n classifier = DecisionTreeClassifier(criterion=\"entropy\", max_depth=10)\n classifier.fit(X_train, y_train)\n y_pred = classifier.predict(X_test)\n acc.append(sum([y_test[i] == y_pred[i] for i in range(len(y_test))]) / float(len(y_test)) * 100.0)\n if print_info_matrix:\n print(confusion_matrix(y_test, y_pred))\n print(classification_report(y_test, y_pred))\n if visulize_tree:\n plt.figure(dpi=300)\n plot_tree(classifier, filled=True, class_names=y.unique(), feature_names=list(X))\n plt.show()\n print(f\"average acc: {sum(acc) / len(acc)}\")\n\n\ndef Naive_Bayes(X, y, print_info_matrix=False, n_splits=5):\n kf = KFold(n_splits=n_splits, shuffle=True)\n\n acc = []\n for train_index, test_index in kf.split(X):\n X_train, X_test = X.values[train_index], X.values[test_index]\n y_train, y_test = y.values[train_index], y.values[test_index]\n gnb = GaussianNB()\n y_pred = gnb.fit(X_train, y_train).predict(X_test)\n acc.append(accuracy_score(y_test, y_pred) * 100)\n if print_info_matrix:\n print(confusion_matrix(y_test, y_pred))\n print(classification_report(y_test, y_pred))\n print(f\"average acc: {sum(acc) / len(acc)}\")\n # nb.main(dataset)\n\n\ndef SVM_sklearn(X, y, n_splits=5, p_info=False):\n kf = KFold(n_splits=n_splits)\n\n acc = []\n for train_index, test_index in kf.split(X):\n X_train, X_test = X.values[train_index], X.values[test_index]\n y_train, y_test = y.values[train_index], y.values[test_index]\n clf = svm.SVC(gamma=\"auto\", C=10000, kernel=\"linear\")\n y_pred = clf.fit(X_train, y_train).predict(X_test)\n acc.append(accuracy_score(y_test, y_pred) * 100)\n if p_info:\n print(confusion_matrix(y_test, y_pred))\n print(classification_report(y_test, y_pred))\n print(f\"average acc: {sum(acc) / len(acc)}\")\n # nb.main(dataset)\n\n\ndef read_iris():\n dataset = os.path.join(iris_path, iris_data)\n data = pd.read_csv(dataset, names=iris_name_list)\n X = data.drop([\"class\", \"sepal_len\", \"sepal_wid\"], axis=1)\n y = data[\"class\"]\n return X, y\n\n\ndef read_breast_cancer_wisconsin():\n print(\"reading dataset...\")\n dataset = './dataset/classification/svm/Breast_Cancer_Wisconsin.csv'\n data = pd.read_csv(dataset)\n\n # drop last column (extra column added by pd)\n # and unnecessary first column (id)\n data.drop(data.columns[[-1, 0]], axis=1, inplace=True)\n\n # convert categorical labels to numbers\n diag_map = {'M': 1.0, 'B': -1.0}\n data['diagnosis'] = data['diagnosis'].map(diag_map)\n # put features & outputs in different data frames\n Y = data.loc[:, 'diagnosis']\n X = data.iloc[:, 1:]\n return X, Y\n\n\ndef main(dataset):\n if dataset == \"iris\":\n X, y = read_iris()\n elif dataset == \"breast_cancer\":\n X, y = read_breast_cancer_wisconsin()\n else:\n raise ValueError(\"Please provide one of [iris, breast_cancer]\")\n\n # there's a notable circumstance, when applying feature selection,\n # the performance of DT and NB decreased, while the SVM increased\n # how it could be?\n # SVM.filter_feature(X, y)\n Decision_Tree_ID3(X, y)\n Naive_Bayes(X, y)\n # SVM.main(X, y)\n SVM_sklearn(X, y, p_info=False)\n\n\nif __name__ == '__main__':\n main(\"breast_cancer\")\n\n\n", "repo_name": "uint64t/data_mining", "sub_path": "experiment3.py", "file_name": "experiment3.py", "file_ext": "py", "file_size_in_byte": 4452, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "sklearn.model_selection.KFold", "line_number": 26, "usage_type": "call"}, {"api_name": "sklearn.tree.DecisionTreeClassifier", "line_number": 32, "usage_type": "call"}, {"api_name": "sklearn.metrics.confusion_matrix", "line_number": 37, "usage_type": "call"}, {"api_name": "sklearn.metrics.classification_report", "line_number": 38, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 40, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 40, "usage_type": "name"}, {"api_name": "sklearn.tree.plot_tree", "line_number": 41, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 42, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 42, "usage_type": "name"}, {"api_name": "sklearn.model_selection.KFold", "line_number": 47, "usage_type": "call"}, {"api_name": "sklearn.naive_bayes.GaussianNB", "line_number": 53, "usage_type": "call"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 55, "usage_type": "call"}, {"api_name": "sklearn.metrics.confusion_matrix", "line_number": 57, "usage_type": "call"}, {"api_name": "sklearn.metrics.classification_report", "line_number": 58, "usage_type": "call"}, {"api_name": "sklearn.model_selection.KFold", "line_number": 64, "usage_type": "call"}, {"api_name": "sklearn.svm.SVC", "line_number": 70, "usage_type": "call"}, {"api_name": "sklearn.svm", "line_number": 70, "usage_type": "name"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 72, "usage_type": "call"}, {"api_name": "sklearn.metrics.confusion_matrix", "line_number": 74, "usage_type": "call"}, {"api_name": "sklearn.metrics.classification_report", "line_number": 75, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 81, "usage_type": "call"}, {"api_name": "os.path", "line_number": 81, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 82, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 91, "usage_type": "call"}]} +{"seq_id": "26845014687", "text": "import uuid\nfrom tweetmob.config import db\nfrom tweetmob.commands import BaseCommand, CommandError\n\nclass Command(BaseCommand):\n \"\"\"\n Add account receive direct message. \n $ tweetmod --account-add account\n \"\"\"\n \n # command information\n usage = '--account-add name'\n summary = __doc__.strip().splitlines()[0].rstrip('.')\n \n def handle(self):\n \n conf = db.stored_config() \n account = self.args.get_value('--account-add')\n if account is not None:\n for k in [i for i in conf.keys() if 'dm.' in i]:\n if conf[k] == account:\n error(\"Account %s exist.\\n\" % account )\n conf['dm.%s' % str(uuid.uuid4())] = account\n else:\n raise CommandError(\"Please use options\\n tweetmob %s\" % self.usage)\n", "repo_name": "johnmontero/tweetmob", "sub_path": "tweetmob/commands/account-add.py", "file_name": "account-add.py", "file_ext": "py", "file_size_in_byte": 827, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "tweetmob.commands.BaseCommand", "line_number": 5, "usage_type": "name"}, {"api_name": "tweetmob.config.db.stored_config", "line_number": 17, "usage_type": "call"}, {"api_name": "tweetmob.config.db", "line_number": 17, "usage_type": "name"}, {"api_name": "uuid.uuid4", "line_number": 23, "usage_type": "call"}, {"api_name": "tweetmob.commands.CommandError", "line_number": 25, "usage_type": "call"}]} +{"seq_id": "26613768683", "text": "from collections import deque\nfrom os import wait\n\nS = input()\nque = deque()\nque.append(set())\n\nfor s in S:\n if s == \"(\":\n old_set = que.pop()\n new_set = old_set.copy()\n que.append(old_set)\n que.append(new_set)\n elif s == \")\":\n _ = que.pop()\n else:\n _set = que.pop()\n if s in _set:\n print(\"No\")\n exit()\n else:\n _set.add(s)\n que.append(_set)\nprint(\"Yes\")\n", "repo_name": "mei28/Competitive-programing", "sub_path": "ABC-283/D_2.py", "file_name": "D_2.py", "file_ext": "py", "file_size_in_byte": 463, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "collections.deque", "line_number": 5, "usage_type": "call"}]} +{"seq_id": "18220931176", "text": "from django.urls import path\nfrom . import views\n\napp_name = 'main'\n\nurlpatterns = [\n path('', views.IndexView.as_view(), name='index'),\n path('contact/', views.contact, name='contact'),\n path('about/', views.AboutView.as_view(), name='about'),\n\n]", "repo_name": "bulkashmak/professore_webapp", "sub_path": "main/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 256, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "django.urls.path", "line_number": 7, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 8, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 9, "usage_type": "call"}]} +{"seq_id": "27043773406", "text": "\"\"\"\nThis is a python implementation of http://babelia.libraryofbabel.info/coloroscopy.html\n\"\"\"\nfrom __future__ import annotations\n\nfrom typing import Callable\nfrom random import randint, shuffle\n\n\ndef simple_color_iter(r: int, g: int, b: int, c: bool) -> tuple[int, int, int, bool]:\n # This function is used to iterate through all possible colors smoothly.\n # It is used in the MainMenu class.\n # Increment r by 1 until it is 255, then increment g by 1 and decrement r by 1.\n # Increment g by 1 until it is 255, then increment b by 1 and decrement g by 1.\n # Increment b by 1 until it is 255, then increment r by 1 and decrement b by 1.\n # Return the new color.\n if c:\n if r < 255:\n r += 1\n elif g < 255:\n g += 1\n c = False\n elif b < 255:\n b += 1\n r = 0\n g = 0\n else:\n r = 0\n g = 0\n b = 0\n else:\n if r > 0:\n r -= 1\n elif g < 255:\n g += 1\n c = True\n elif b < 255:\n b += 1\n r = 0\n g = 0\n else:\n r = 0\n g = 0\n b = 0\n c = True\n return r, g, b, c\n\n\ndef color_iter(r: int = -1, g: int = -1, b: int = -1) -> Callable[[], tuple[int, int, int]]:\n c = True\n r = randint(0, 255) if r == -1 else r\n g = randint(0, 255) if g == -1 else g\n b = randint(0, 255) if b == -1 else b\n seq = [0, 1, 2]\n shuffle(seq)\n\n def internal_color_iter():\n nonlocal c, r, g, b\n r, g, b, c = simple_color_iter(r, g, b, c)\n ret = [0, 0, 0]\n ret[seq[0]] = r\n ret[seq[1]] = g\n ret[seq[2]] = b\n return ret[0], ret[1], ret[2]\n\n return internal_color_iter\n", "repo_name": "gresm/pygame-summer-2022", "sub_path": "game/tools/color_permutations.py", "file_name": "color_permutations.py", "file_ext": "py", "file_size_in_byte": 1782, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "random.randint", "line_number": 51, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 52, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 53, "usage_type": "call"}, {"api_name": "random.shuffle", "line_number": 55, "usage_type": "call"}, {"api_name": "typing.Callable", "line_number": 49, "usage_type": "name"}]} +{"seq_id": "8052125831", "text": "import cdsapi\nimport calendar\nimport os\nimport shutil\nfrom optparse import OptionParser\ndef main():\n usage = \"usage: %prog --start_year YYYY --end_year YYYY [--start_month MM] [--end_month MM] [--start_day DD] [--end_day DD] [--area lat_start/lon_sstart/lat_end/lon_end] [--var 'near_surface_air_temperature'] [--path]\"\n parser = OptionParser(usage=usage)\n parser.add_option(\"-s\", \"--start_year\", dest=\"start_year\",\n help=\"start year YYYY\", metavar=\"start_year\",type=int )\n parser.add_option(\"-e\", \"--end_year\", dest=\"end_year\",\n help=\"end_year YYYY\", metavar=\"end_year\", type=int)\n parser.add_option(\"--start_month\", dest=\"start_month\",\n help=\"start month MM\", metavar=\"start_month\", type=int)\n parser.add_option(\"--end_month\", dest=\"end_month\",\n help=\"end month DD\", metavar=\"end_month\", type=int)\n parser.add_option(\"--start_day\", dest=\"start_day\",\n help=\"start day DD\", metavar=\"start_day\", type=int)\n parser.add_option(\"--end_day\", dest=\"end_day\",\n help=\"end day DD\", metavar=\"end_day\", type=int)\n parser.add_option(\"--area\", dest=\"area\",\n help=\"'lat_start/lon_start/lat_end/lon_end'\", metavar=\"area\", type=str)\n parser.add_option(\"--var\", dest=\"var\",\n help=\"'output directory'\", metavar=\"var\", type=str)\n parser.add_option(\"--path\", dest=\"path\",\n help=\"'output directory'\", metavar=\"path\", type=str)\n (options, args) = parser.parse_args()\n if not options.start_year:\n parser.error(\"start year must be specified!\")\n else:\n start_year=options.start_year\n if not options.end_year:\n end_year=start_year\n else:\n end_year=options.end_year\n if not options.start_month:\n start_month=1\n else:\n start_month=options.start_month\n if not options.end_month:\n end_month=12\n else:\n end_month=options.end_month\n if not options.area:\n area = \"\"\n else:\n area = options.area\n if not options.var:\n var = \"near_surface_air_temperature\"\n abr_var = 'Tair'\n else:\n var = options.var\n if var == 'rainfall_flux':\n abr_var = 'precip_flux'\n else:\n parser.error(\"%s currently not specified!\" % var)\n if not options.path:\n path = \".\"\n else:\n path = options.path\n\n server = cdsapi.Client()\n \n print(start_year)\n print(end_year)\n for year in range(start_year, end_year+1):\n print('YEAR ',year)\n for month in range(start_month,end_month+1):\n if not options.start_day:\n sdate=\"%s%02d01\"%(year,month)\n else:\n sdate=\"%s%02d%02d\"%(year,month,int(options.start_day))\n if not options.end_day:\n lastday=calendar.monthrange(year,month)[1]\n edate=\"%s%02d%s\"%(year,month,lastday)\n else:\n edate=\"%s%02d%02d\"%(year,month,int(options.end_day))\n print('get data from %s/to/%s (YYYYMMDD)' % (sdate,edate))\n print('saving data to %s' % path)\n print('saving data to file %s_WFDE5_CRU_%s.nc' % (abr_var, sdate[:-2]))\n if area==\"\":\n server.retrieve(\n 'derived-near-surface-meteorological-variables',\n {\n 'format': 'nc',\n 'variable': var,\n 'reference_dataset': 'cru',\n 'year': '%d' % year,\n 'month': '%02d' % month,\n },\n '%s/%s_WFDE5_CRU_%s.nc' % (path, abr_var, sdate[:-2]))\n else:\n print(area)\n server.retrieve(\n 'derived-near-surface-meteorological-variables',\n {\n 'format': 'nc',\n #'variable': 'near_surface_air_temperature',\n 'variable': var,\n 'reference_dataset': 'cru',\n 'year': '%d' % year,\n 'month': '%02d' % month,\n 'area': \"%s\" % (area),\n },\n '%s/%s_WFDE5_CRU_%s.nc' % (path, abr_var, sdate[:-2]))\n \nif __name__ == \"__main__\":\n main()\n", "repo_name": "ziu1986/python_scripts", "sub_path": "cdsapi_requests/retrieve_cds.py", "file_name": "retrieve_cds.py", "file_ext": "py", "file_size_in_byte": 4396, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "optparse.OptionParser", "line_number": 8, "usage_type": "call"}, {"api_name": "cdsapi.Client", "line_number": 62, "usage_type": "call"}, {"api_name": "calendar.monthrange", "line_number": 74, "usage_type": "call"}]} +{"seq_id": "1499877569", "text": "import os\nimport threading\nimport time\n\nimport cv2\nfrom template_finder import TemplateFinder\n\nfrom utils.auto_settings import check_settings\nfrom bot import Bot\nfrom config import Config\nfrom death_manager import DeathManager\nfrom game_recovery import GameRecovery\nfrom game_stats import GameStats\nfrom health_manager import HealthManager\nfrom logger import Logger\nfrom messenger import Messenger\nfrom screen import Screen\nfrom ui.char_selector import CharSelector\nfrom utils.misc import kill_thread\nfrom utils.restart import restart_game\nfrom utils.misc import kill_thread, set_d2r_always_on_top, restore_d2r_window_visibility\n\nclass GameController:\n is_running = False\n\n def __init__(self):\n self._config = Config()\n self.screen = None\n self.template_finder = None\n self.health_monitor_thread = None\n self.health_manager = None\n self.death_manager = None\n self.death_monitor_thread = None\n self.game_recovery = None\n self.game_stats = None\n self.game_controller_thread = None\n self.bot_thread = None\n self.bot = None\n self.char_selector = None\n\n def run_bot(self, pick_corpse: bool = False):\n if self._config.general['restart_d2r_when_stuck']:\n # Make sure the correct char is selected\n if self.char_selector.has_char_template_saved():\n Logger.info(\"Selecting original char\")\n self.char_selector.select_char()\n else:\n Logger.info(\"Saving top-most char as template\")\n self.char_selector.save_char_template()\n # Start bot thread\n self.bot = Bot(self.screen, self.game_stats, self.template_finder, pick_corpse)\n self.bot_thread = threading.Thread(target=self.bot.start)\n self.bot_thread.daemon = True\n self.bot_thread.start()\n # Register that thread to the death and health manager so they can stop the bot thread if needed\n self.death_manager.set_callback(lambda: self.bot.stop() or kill_thread(self.bot_thread))\n self.health_manager.set_callback(lambda: self.bot.stop() or kill_thread(self.bot_thread))\n self.health_manager.set_belt_manager(self.bot.get_belt_manager())\n do_restart = False\n messenger = Messenger()\n while 1:\n self.health_manager.update_location(self.bot.get_curr_location())\n max_game_length_reached = self.game_stats.get_current_game_length() > self._config.general[\"max_game_length_s\"]\n if max_game_length_reached or self.death_manager.died() or self.health_manager.did_chicken():\n # Some debug and logging\n if max_game_length_reached:\n Logger.info(f\"Max game length reached. Attempting to restart {self._config.general['name']}!\")\n if self._config.general[\"info_screenshots\"]:\n cv2.imwrite(\"./info_screenshots/info_max_game_length_reached_\" + time.strftime(\"%Y%m%d_%H%M%S\") + \".png\", self.screen.grab())\n elif self.death_manager.died():\n self.game_stats.log_death(self.death_manager._last_death_screenshot)\n elif self.health_manager.did_chicken():\n self.game_stats.log_chicken(self.health_manager._last_chicken_screenshot)\n self.bot.stop()\n kill_thread(self.bot_thread)\n # Try to recover from whatever situation we are and go back to hero selection\n do_restart = self.game_recovery.go_to_hero_selection()\n break\n time.sleep(0.5)\n self.bot_thread.join()\n if do_restart:\n # Reset flags before running a new bot\n self.death_manager.reset_death_flag()\n self.health_manager.reset_chicken_flag()\n self.game_stats.log_end_game(failed=max_game_length_reached)\n return self.run_bot(True)\n else:\n if self._config.general[\"info_screenshots\"]:\n cv2.imwrite(\"./info_screenshots/info_could_not_recover_\" + time.strftime(\"%Y%m%d_%H%M%S\") + \".png\", self.screen.grab())\n if self._config.general['restart_d2r_when_stuck']:\n Logger.error(\"Could not recover from a max game length violation. Restarting the Game.\")\n if self._config.general[\"custom_message_hook\"]:\n messenger.send_message(\"Got stuck and will now restart D2R\")\n if restart_game(self._config.general[\"d2r_path\"]):\n self.game_stats.log_end_game(failed=max_game_length_reached)\n if self.setup_screen():\n self.start_health_manager_thread()\n self.start_death_manager_thread()\n self.game_recovery = GameRecovery(self.screen, self.death_manager, self.template_finder)\n return self.run_bot(True)\n Logger.error(\"Could not restart the game. Quitting.\")\n messenger.send_message(\"Got stuck and could not restart the game. Quitting.\")\n else:\n Logger.error(\"Could not recover from a max game length violation. Quitting botty.\")\n if self._config.general[\"custom_message_hook\"]:\n messenger.send_message(\"Got stuck and will now quit botty\")\n os._exit(1)\n\n def start(self):\n # Check if we user should update the d2r settings\n diff = check_settings(self._config)\n if len(diff) > 0:\n Logger.warning(\"Your D2R settings differ from the requiered ones. Please use Auto Settings to adjust them. The differences are:\")\n Logger.warning(f\"{diff}\")\n if self._config.advanced_options['d2r_windows_always_on_top']:\n set_d2r_always_on_top()\n self.setup_screen()\n self.template_finder = TemplateFinder(self.screen)\n self.start_health_manager_thread()\n self.start_death_manager_thread()\n self.game_recovery = GameRecovery(self.screen, self.death_manager, self.template_finder)\n self.game_stats = GameStats()\n self.char_selector = CharSelector(self.screen, self.template_finder)\n self.start_game_controller_thread()\n GameController.is_running = True\n\n def stop(self):\n if self._config.advanced_options['d2r_windows_always_on_top']:\n restore_d2r_window_visibility()\n if self.death_monitor_thread: kill_thread(self.death_monitor_thread)\n if self.health_monitor_thread: kill_thread(self.health_monitor_thread)\n if self.bot_thread: kill_thread(self.bot_thread)\n if self.game_controller_thread: kill_thread(self.game_controller_thread)\n GameController.is_running = False\n \n def setup_screen(self):\n self.screen = Screen(self._config.general[\"monitor\"])\n if self.screen.found_offsets:\n return True\n return False\n\n def start_health_manager_thread(self):\n # Run health monitor thread\n self.health_manager = HealthManager(self.screen, self.template_finder)\n self.health_monitor_thread = threading.Thread(target=self.health_manager.start_monitor)\n self.health_monitor_thread.daemon = True\n self.health_monitor_thread.start()\n\n def start_death_manager_thread(self):\n # Run death monitor thread\n self.death_manager = DeathManager(self.screen, self.template_finder)\n self.death_monitor_thread = threading.Thread(target=self.death_manager.start_monitor)\n self.death_monitor_thread.daemon = True\n self.death_monitor_thread.start()\n\n def start_game_controller_thread(self):\n # Run game controller thread\n self.game_controller_thread = threading.Thread(target=self.run_bot)\n self.game_controller_thread.daemon = False\n self.game_controller_thread.start()\n\n def toggle_pause_bot(self):\n if self.bot: self.bot.toggle_pause()\n", "repo_name": "jagarop/botty-memread_lk", "sub_path": "src/game_controller.py", "file_name": "game_controller.py", "file_ext": "py", "file_size_in_byte": 7950, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "37", "api": [{"api_name": "config.Config", "line_number": 27, "usage_type": "call"}, {"api_name": "logger.Logger.info", "line_number": 45, "usage_type": "call"}, {"api_name": "logger.Logger", "line_number": 45, "usage_type": "name"}, {"api_name": "logger.Logger.info", "line_number": 48, "usage_type": "call"}, {"api_name": "logger.Logger", "line_number": 48, "usage_type": "name"}, {"api_name": "bot.Bot", "line_number": 51, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 52, "usage_type": "call"}, {"api_name": "utils.misc.kill_thread", "line_number": 56, "usage_type": "call"}, {"api_name": "utils.misc.kill_thread", "line_number": 57, "usage_type": "call"}, {"api_name": "messenger.Messenger", "line_number": 60, "usage_type": "call"}, {"api_name": "logger.Logger.info", "line_number": 67, "usage_type": "call"}, {"api_name": "logger.Logger", "line_number": 67, "usage_type": "name"}, {"api_name": "cv2.imwrite", "line_number": 69, "usage_type": "call"}, {"api_name": "time.strftime", "line_number": 69, "usage_type": "call"}, {"api_name": "utils.misc.kill_thread", "line_number": 75, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 79, "usage_type": "call"}, {"api_name": "cv2.imwrite", "line_number": 89, "usage_type": "call"}, {"api_name": "time.strftime", "line_number": 89, "usage_type": "call"}, {"api_name": "logger.Logger.error", "line_number": 91, "usage_type": "call"}, {"api_name": "logger.Logger", "line_number": 91, "usage_type": "name"}, {"api_name": "messenger.send_message", "line_number": 93, "usage_type": "call"}, {"api_name": "utils.restart.restart_game", "line_number": 94, "usage_type": "call"}, {"api_name": "game_recovery.GameRecovery", "line_number": 99, "usage_type": "call"}, {"api_name": "logger.Logger.error", "line_number": 101, "usage_type": "call"}, {"api_name": "logger.Logger", "line_number": 101, "usage_type": "name"}, {"api_name": "messenger.send_message", "line_number": 102, "usage_type": "call"}, {"api_name": "logger.Logger.error", "line_number": 104, "usage_type": "call"}, {"api_name": "logger.Logger", "line_number": 104, "usage_type": "name"}, {"api_name": "messenger.send_message", "line_number": 106, "usage_type": "call"}, {"api_name": "os._exit", "line_number": 107, "usage_type": "call"}, {"api_name": "utils.auto_settings.check_settings", "line_number": 111, "usage_type": "call"}, {"api_name": "logger.Logger.warning", "line_number": 113, "usage_type": "call"}, {"api_name": "logger.Logger", "line_number": 113, "usage_type": "name"}, {"api_name": "logger.Logger.warning", "line_number": 114, "usage_type": "call"}, {"api_name": "logger.Logger", "line_number": 114, "usage_type": "name"}, {"api_name": "utils.misc.set_d2r_always_on_top", "line_number": 116, "usage_type": "call"}, {"api_name": "template_finder.TemplateFinder", "line_number": 118, "usage_type": "call"}, {"api_name": "game_recovery.GameRecovery", "line_number": 121, "usage_type": "call"}, {"api_name": "game_stats.GameStats", "line_number": 122, "usage_type": "call"}, {"api_name": "ui.char_selector.CharSelector", "line_number": 123, "usage_type": "call"}, {"api_name": "utils.misc.restore_d2r_window_visibility", "line_number": 129, "usage_type": "call"}, {"api_name": "utils.misc.kill_thread", "line_number": 130, "usage_type": "call"}, {"api_name": "utils.misc.kill_thread", "line_number": 131, "usage_type": "call"}, {"api_name": "utils.misc.kill_thread", "line_number": 132, "usage_type": "call"}, {"api_name": "utils.misc.kill_thread", "line_number": 133, "usage_type": "call"}, {"api_name": "screen.Screen", "line_number": 137, "usage_type": "call"}, {"api_name": "health_manager.HealthManager", "line_number": 144, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 145, "usage_type": "call"}, {"api_name": "death_manager.DeathManager", "line_number": 151, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 152, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 158, "usage_type": "call"}]} +{"seq_id": "70324590828", "text": "import numpy as np\r\nimport matplotlib.pyplot as plt \r\nfrom sklearn.model_selection import train_test_split\r\nfrom sklearn.neighbors import KNeighborsClassifier as knc\r\nfrom sklearn.datasets import load_iris\r\niris = load_iris()\r\n\r\n\r\nx_train, x_test, y_train, y_test = train_test_split(iris['data'],iris['target'],random_state=0)\r\nknn = knc(n_neighbors=1)\r\nknn.fit(x_train, y_train)\r\nprint('Enter the values')\r\na=list(map(float,input().split()))\r\nx_new = np.array([a])\r\nval = int(knn.predict(x_new))\r\nif(val == 0):\r\n print('Setosa')\r\nelif(val == 1):\r\n print('Virginica')\r\nelif(val == 2):\r\n print('Versicolor')", "repo_name": "Leelakrishna463/Flower.classify-", "sub_path": "Flower.Classify().py", "file_name": "Flower.Classify().py", "file_ext": "py", "file_size_in_byte": 615, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "sklearn.datasets.load_iris", "line_number": 6, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 9, "usage_type": "call"}, {"api_name": "sklearn.neighbors.KNeighborsClassifier", "line_number": 10, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 14, "usage_type": "call"}]} +{"seq_id": "26691310616", "text": "import random\r\nimport time\r\n\r\nfrom pygame import Vector2\r\n\r\nimport core\r\nfrom Asteroid.projectile import Projectile\r\n\r\n\r\nclass Player:\r\n def __init__(self):\r\n self.immunity = True\r\n self.immunityStart = time.time()\r\n self.immunityDuration = 2\r\n self.projectiles = []\r\n self.shotCD = 0.3\r\n self.maxSpeed = 6\r\n self.maxAcc = 2\r\n self.rotation = 0\r\n self.decel = 0.98\r\n self.vies = 1\r\n self.pos = Vector2(core.WINDOW_SIZE[0] / 2, core.WINDOW_SIZE[1] / 2)\r\n self.acc = Vector2(0, 0)\r\n self.vel = Vector2(0, 0)\r\n self.color_white = (255, 255, 255)\r\n self.color_NeonBlue = (0, 219, 255)\r\n #gestion bonus\r\n self.bonusSpeed, self.bonusProjNumber, self.bonusProjSize, self.bonusProjLevel, self.bomb = 1, 1, 1, 1, 2\r\n self.lastBombTime = time.time()\r\n\r\n def avancer(self):\r\n self.acc += Vector2(0, 1).rotate(self.rotation)\r\n\r\n def reculer(self):\r\n self.acc += Vector2(0, -1).rotate(self.rotation)\r\n\r\n def tournerGauche(self):\r\n # self.acc += Vector2(-1, 0)\r\n self.rotation -= 225 / core.fps\r\n\r\n def tournerDroite(self):\r\n # self.acc += Vector2(1, 0)\r\n self.rotation += 225 / core.fps\r\n\r\n def update(self):\r\n # gestion si accel > accelMax\r\n if self.acc.length() > self.maxAcc:\r\n self.acc.scale_to_length(self.maxAcc)\r\n\r\n # gestion changement vitesse si acc non nulle sinon deceleration\r\n if self.acc.length() != 0:\r\n self.vel += self.acc\r\n else:\r\n self.vel *= self.decel\r\n\r\n # gestion si speed > speedMax\r\n if self.vel.length() > self.maxSpeed:\r\n self.vel.scale_to_length(self.maxSpeed)\r\n\r\n # reset vecteur accel\r\n self.acc = Vector2(0, 0)\r\n\r\n # gestion des dépassements de bordures d'écran\r\n if self.pos.x <= 0:\r\n self.pos.x = core.WINDOW_SIZE[0]\r\n elif self.pos.x >= core.WINDOW_SIZE[0]:\r\n self.pos.x = 10\r\n elif self.pos.y <= 0:\r\n self.pos.y = core.WINDOW_SIZE[1]\r\n elif self.pos.y >= core.WINDOW_SIZE[1]:\r\n self.pos.y = 10\r\n # déplacement par rapport à la vitesse\r\n self.pos += self.vel\r\n\r\n # check projectiles lifespan\r\n for p in self.projectiles:\r\n if time.time() - p.startTime > p.lifeTime:\r\n self.projectiles.remove(p)\r\n\r\n for elem in self.projectiles:\r\n elem.update()\r\n\r\n if self.immunity and (time.time() - self.immunityStart > self.immunityDuration):\r\n self.immunity = False\r\n\r\n def show(self):\r\n for elem in self.projectiles:\r\n elem.show()\r\n # a = 0 - self.vel.angle_to(Vector2(0, 1))\r\n p1 = self.pos + Vector2(-7, -5).rotate(self.rotation)\r\n p2 = self.pos + Vector2(0, 15).rotate(self.rotation)\r\n p3 = self.pos + Vector2(7, -5).rotate(self.rotation)\r\n p4 = self.pos + Vector2(0, 0).rotate(self.rotation)\r\n if self.immunity:\r\n core.Draw.polygon(self.color_NeonBlue, (p1, p2, p3, p4))\r\n else:\r\n core.Draw.polygon(self.color_white, (p1, p2, p3, p4))\r\n p1 = self.pos + Vector2(-9, -7).rotate(self.rotation)\r\n p2 = self.pos + Vector2(0, 17).rotate(self.rotation)\r\n p3 = self.pos + Vector2(9, -7).rotate(self.rotation)\r\n p4 = self.pos + Vector2(0, -2).rotate(self.rotation)\r\n core.Draw.polyline(self.color_NeonBlue, (p1, p2, p3, p4), 3)\r\n\r\n def createProj(self):\r\n rota = self.rotation\r\n # si le temps depuis le dernier tir est supérieur au cooldown entre deux tir, on crée un nouveau projectile\r\n if (len(self.projectiles) == 0) or ((time.time() - self.projectiles[-1].startTime) > self.shotCD):\r\n for i in range(0, self.bonusProjNumber):\r\n if i % 2 == 0:\r\n rota += 15*i\r\n else:\r\n rota -= 15*i\r\n proj = Projectile()\r\n proj.size = 1 + 2 * self.bonusProjSize\r\n proj.life = self.bonusProjLevel\r\n proj.color = (0, 219, 255)\r\n proj.pos = Vector2(self.pos)\r\n proj.acc = proj.acc.rotate(rota) #applique la rotation au vecteur d'acceleration du projectile\r\n proj.acc += self.vel #ajoute le vecteur de vitesse actuel du vaisseau à l'acceleration du projectile\r\n self.projectiles.append(proj)\r\n\r\n def createBomb(self):\r\n rota = self.rotation\r\n if self.bomb >= 1 and (time.time() - self.lastBombTime > 5):\r\n for i in range(0, 24):\r\n rota += 15*i\r\n proj = Projectile()\r\n proj.size = 1 + 2 * self.bonusProjSize\r\n proj.life = self.bonusProjLevel\r\n proj.color = (0, 219, 255)\r\n proj.pos = Vector2(self.pos)\r\n proj.acc = proj.acc.rotate(rota)\r\n self.projectiles.append(proj)\r\n self.bomb -= 1\r\n self.lastBombTime = time.time()\r\n\r\n def kill(self):\r\n self.pos = Vector2(core.WINDOW_SIZE[0] / 2, core.WINDOW_SIZE[1] / 2)\r\n self.vies -= 1\r\n self.immunity = True\r\n self.immunityStart = time.time()", "repo_name": "morty999/Asteroid", "sub_path": "Asteroid/player.py", "file_name": "player.py", "file_ext": "py", "file_size_in_byte": 5334, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "time.time", "line_number": 13, "usage_type": "call"}, {"api_name": "pygame.Vector2", "line_number": 22, "usage_type": "call"}, {"api_name": "core.WINDOW_SIZE", "line_number": 22, "usage_type": "attribute"}, {"api_name": "pygame.Vector2", "line_number": 23, "usage_type": "call"}, {"api_name": "pygame.Vector2", "line_number": 24, "usage_type": "call"}, {"api_name": "time.time", "line_number": 29, "usage_type": "call"}, {"api_name": "pygame.Vector2", "line_number": 32, "usage_type": "call"}, {"api_name": "pygame.Vector2", "line_number": 35, "usage_type": "call"}, {"api_name": "core.fps", "line_number": 39, "usage_type": "attribute"}, {"api_name": "core.fps", "line_number": 43, "usage_type": "attribute"}, {"api_name": "pygame.Vector2", "line_number": 61, "usage_type": "call"}, {"api_name": "core.WINDOW_SIZE", "line_number": 65, "usage_type": "attribute"}, {"api_name": "core.WINDOW_SIZE", "line_number": 66, "usage_type": "attribute"}, {"api_name": "core.WINDOW_SIZE", "line_number": 69, "usage_type": "attribute"}, {"api_name": "core.WINDOW_SIZE", "line_number": 70, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 77, "usage_type": "call"}, {"api_name": "time.time", "line_number": 83, "usage_type": "call"}, {"api_name": "pygame.Vector2", "line_number": 90, "usage_type": "call"}, {"api_name": "pygame.Vector2", "line_number": 91, "usage_type": "call"}, {"api_name": "pygame.Vector2", "line_number": 92, "usage_type": "call"}, {"api_name": "pygame.Vector2", "line_number": 93, "usage_type": "call"}, {"api_name": "core.Draw.polygon", "line_number": 95, "usage_type": "call"}, {"api_name": "core.Draw", "line_number": 95, "usage_type": "attribute"}, {"api_name": "core.Draw.polygon", "line_number": 97, "usage_type": "call"}, {"api_name": "core.Draw", "line_number": 97, "usage_type": "attribute"}, {"api_name": "pygame.Vector2", "line_number": 98, "usage_type": "call"}, {"api_name": "pygame.Vector2", "line_number": 99, "usage_type": "call"}, {"api_name": "pygame.Vector2", "line_number": 100, "usage_type": "call"}, {"api_name": "pygame.Vector2", "line_number": 101, "usage_type": "call"}, {"api_name": "core.Draw.polyline", "line_number": 102, "usage_type": "call"}, {"api_name": "core.Draw", "line_number": 102, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 107, "usage_type": "call"}, {"api_name": "Asteroid.projectile.Projectile", "line_number": 113, "usage_type": "call"}, {"api_name": "pygame.Vector2", "line_number": 117, "usage_type": "call"}, {"api_name": "time.time", "line_number": 124, "usage_type": "call"}, {"api_name": "Asteroid.projectile.Projectile", "line_number": 127, "usage_type": "call"}, {"api_name": "pygame.Vector2", "line_number": 131, "usage_type": "call"}, {"api_name": "time.time", "line_number": 135, "usage_type": "call"}, {"api_name": "pygame.Vector2", "line_number": 138, "usage_type": "call"}, {"api_name": "core.WINDOW_SIZE", "line_number": 138, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 141, "usage_type": "call"}]} +{"seq_id": "33790925465", "text": "import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nclass Discriminator(nn.Module):\n def __init__(self, k_in_init, k_out_init):\n super(Discriminator, self).__init__()\n\n def disc_block(k_in, k_out, inst_norm):\n \n if(inst_norm == True):\n layers = nn.Sequential(\n nn.Conv3d(in_channels=k_in, out_channels=k_out, kernel_size=4, stride=2, padding=1),\n nn.InstanceNorm3d(k_out),\n nn.LeakyReLU(negative_slope=0.2, inplace=True)\n )\n else:\n layers = nn.Sequential(\n nn.Conv3d(in_channels=k_in, out_channels=k_out, kernel_size=4, stride=2, padding=1),\n nn.LeakyReLU(negative_slope=0.2, inplace=True)\n )\n return layers\n\n self.disc_1 = disc_block(k_in_init, 4*k_out_init, False) # 128\n self.disc_2 = disc_block(8*k_out_init, 8*k_out_init, True) # 256\n self.disc_3 = disc_block(16*k_out_init, 16*k_out_init, True) # 512\n self.disc_4 = disc_block(32*k_out_init, 32*k_out_init, True) # 1024\n self.disc_5 = nn.Conv3d(in_channels=32*k_out_init, out_channels=1, kernel_size=4, stride=1, padding=1, bias=False)\n\n def forward(self, segmap, mods, feature_maps):\n \n # Concatenate segmentaiton map with the input modalities\n x = torch.cat((segmap, mods), dim=1)\n \n # Pass x through the first discriminator block\n x = self.disc_1(x)\n \n # Extract feature maps for this scale. Upscale the first set of feature maps to make\n # sure they are the same size. Concat with output of those scale and pass through disc block.\n fmap_1 = feature_maps[0][0]\n fmap_2 = feature_maps[0][1]\n fmap_2 = F.interpolate(fmap_2, size=fmap_1.size()[-3:])\n features = torch.cat((fmap_1, fmap_2), dim=1)\n x = torch.cat((x, features), dim=1) \n x = self.disc_2(x)\n \n # Repeat process for next block.\n fmap_1 = feature_maps[1][0]\n fmap_2 = feature_maps[1][1]\n fmap_2 = F.interpolate(fmap_2, size=fmap_1.size()[-3:])\n features = torch.cat((fmap_1, fmap_2), dim=1)\n x = torch.cat((x, features), dim=1)\n x = self.disc_3(x)\n \n # Repeat process for next block.\n fmap_1 = feature_maps[2][0]\n fmap_2 = feature_maps[2][1]\n fmap_2 = F.interpolate(fmap_2, size=fmap_1.size()[-3:])\n features = torch.cat((fmap_1, fmap_2), dim=1)\n x = torch.cat((x, features), dim=1)\n x = self.disc_4(x)\n \n # Pass through final block.\n x = self.disc_5(x)\n return x", "repo_name": "SaverioVad/HAD_Net", "sub_path": "models/discriminator_model.py", "file_name": "discriminator_model.py", "file_ext": "py", "file_size_in_byte": 2760, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 11, "dataset": "github-code", "pt": "37", "api": [{"api_name": "torch.nn.Module", "line_number": 5, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 5, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 12, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 12, "usage_type": "name"}, {"api_name": "torch.nn.Conv3d", "line_number": 13, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 13, "usage_type": "name"}, {"api_name": "torch.nn.InstanceNorm3d", "line_number": 14, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 14, "usage_type": "name"}, {"api_name": "torch.nn.LeakyReLU", "line_number": 15, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 15, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 18, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 18, "usage_type": "name"}, {"api_name": "torch.nn.Conv3d", "line_number": 19, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 19, "usage_type": "name"}, {"api_name": "torch.nn.LeakyReLU", "line_number": 20, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 20, "usage_type": "name"}, {"api_name": "torch.nn.Conv3d", "line_number": 28, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 28, "usage_type": "name"}, {"api_name": "torch.cat", "line_number": 33, "usage_type": "call"}, {"api_name": "torch.nn.functional.interpolate", "line_number": 42, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 42, "usage_type": "name"}, {"api_name": "torch.cat", "line_number": 43, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 44, "usage_type": "call"}, {"api_name": "torch.nn.functional.interpolate", "line_number": 50, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 50, "usage_type": "name"}, {"api_name": "torch.cat", "line_number": 51, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 52, "usage_type": "call"}, {"api_name": "torch.nn.functional.interpolate", "line_number": 58, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 58, "usage_type": "name"}, {"api_name": "torch.cat", "line_number": 59, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 60, "usage_type": "call"}]} +{"seq_id": "31757783047", "text": "from client import twitter_client\nfrom config import get_twitter_credentials, get_search_config, get_aws_config\nfrom queries import search\nfrom webhook import send_webhooks\n\ndef launch():\n config = get_aws_config()\n\n # import config\n print(\"Get the twitter credentials from config file...\")\n search_config = get_search_config()\n twitter_credentials = get_twitter_credentials()\n\n # get the twitter client api\n print(\"Get the twitter client with credentials from config...\")\n api = twitter_client(twitter_credentials)\n\n # perform search\n print(\"Perform search query on Twitter...\")\n user_accounts = search(api, search_config)\n\n # printing results\n if 0 == len(user_accounts):\n print(\"No user account found.\")\n else:\n print(str(len(user_accounts)) + \" user(s) account(s) found.\")\n print(get_string_user_accounts(user_accounts))\n send_webhooks(config, user_accounts)\n\nlaunch()\n", "repo_name": "julienbnr/twitter-bot", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 943, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "config.get_aws_config", "line_number": 7, "usage_type": "call"}, {"api_name": "config.get_search_config", "line_number": 11, "usage_type": "call"}, {"api_name": "config.get_twitter_credentials", "line_number": 12, "usage_type": "call"}, {"api_name": "client.twitter_client", "line_number": 16, "usage_type": "call"}, {"api_name": "queries.search", "line_number": 20, "usage_type": "call"}, {"api_name": "webhook.send_webhooks", "line_number": 28, "usage_type": "call"}]} +{"seq_id": "15288153205", "text": "import datetime\nimport logging\nimport os\n\nfrom ibeam.src import var\n\ninitialized = False\n\n\ndef initialize():\n global initialized\n if initialized: return\n initialized = True\n\n logger = logging.getLogger('ibeam')\n formatter = logging.Formatter(var.LOG_FORMAT)\n\n stream_handler = logging.StreamHandler()\n\n stream_handler.setFormatter(formatter)\n stream_handler.setLevel(getattr(logging, var.LOG_LEVEL))\n logger.setLevel(logging.DEBUG)\n logger.addHandler(stream_handler)\n\n if var.LOG_TO_FILE:\n file_handler = DailyRotatingFileHandler(os.path.join(var.OUTPUTS_DIR, 'ibeam_log'))\n file_handler.setFormatter(formatter)\n file_handler.setLevel(logging.DEBUG)\n logger.addHandler(file_handler)\n\n\ndef set_level_for_all(logger, level):\n logger.setLevel(level)\n for handler in logger.handlers:\n handler.setLevel(level)\n\n\nclass DailyRotatingFileHandler(logging.FileHandler):\n\n def __init__(self, *args, date_format='%Y-%m-%d', **kwargs):\n self.timestamp = None\n self.date_format = date_format\n super().__init__(*args, **kwargs)\n\n def get_timestamp(self):\n return datetime.datetime.now().strftime(self.date_format)\n\n def get_filename(self, timestamp):\n return f'{self.baseFilename}__{timestamp}.txt'\n\n def _open(self):\n self.timestamp = self.get_timestamp()\n return open(self.get_filename(self.timestamp), self.mode, encoding=self.encoding)\n\n def emit(self, record):\n if self.get_timestamp() != self.timestamp:\n self.stream = self._open()\n\n super().emit(record)\n", "repo_name": "Voyz/ibeam", "sub_path": "ibeam/src/logs.py", "file_name": "logs.py", "file_ext": "py", "file_size_in_byte": 1609, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 429, "dataset": "github-code", "pt": "37", "api": [{"api_name": "logging.getLogger", "line_number": 15, "usage_type": "call"}, {"api_name": "logging.Formatter", "line_number": 16, "usage_type": "call"}, {"api_name": "ibeam.src.var.LOG_FORMAT", "line_number": 16, "usage_type": "attribute"}, {"api_name": "ibeam.src.var", "line_number": 16, "usage_type": "name"}, {"api_name": "logging.StreamHandler", "line_number": 18, "usage_type": "call"}, {"api_name": "ibeam.src.var.LOG_LEVEL", "line_number": 21, "usage_type": "attribute"}, {"api_name": "ibeam.src.var", "line_number": 21, "usage_type": "name"}, {"api_name": "logging.DEBUG", "line_number": 22, "usage_type": "attribute"}, {"api_name": "ibeam.src.var.LOG_TO_FILE", "line_number": 25, "usage_type": "attribute"}, {"api_name": "ibeam.src.var", "line_number": 25, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 26, "usage_type": "call"}, {"api_name": "os.path", "line_number": 26, "usage_type": "attribute"}, {"api_name": "ibeam.src.var.OUTPUTS_DIR", "line_number": 26, "usage_type": "attribute"}, {"api_name": "ibeam.src.var", "line_number": 26, "usage_type": "name"}, {"api_name": "logging.DEBUG", "line_number": 28, "usage_type": "attribute"}, {"api_name": "logging.FileHandler", "line_number": 38, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 46, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 46, "usage_type": "attribute"}]} +{"seq_id": "13462802529", "text": "# -*- coding: utf-8 -*-\r\nimport requests\r\nimport urllib\r\nimport urllib2\r\nimport re\r\nimport json\r\nimport traceback\r\nimport os\r\nimport xlwt\r\n\r\n\r\n\r\ndef getuserid(user_id):\r\n user_id_list =[]\r\n\r\n user_base_url = \"https://m.weibo.cn/api/container/getIndex?\"\r\n\r\n\r\n headers = {\r\n \"Accept\": \"application/json, text/plain, */*\",\r\n \"MWeibo-Pwa\": \"1\",\r\n \"Cookie\":\"_T_WM=468d8440d1026e0fb9bb792af1b37493; SUB=_2A252yv5XDeRhGeVH6lAQ8CzMzz2IHXVSNIIfrDV6PUJbkdAKLXb7kW1NT2fnim-z8a1zQQ8dD6LQJOHhY9omBlYt; SUHB=00xpdG2ybv4tKa; MLOGIN=1; WEIBOCN_FROM=1110006030; M_WEIBOCN_PARAMS=luicode%3D10000011%26lfid%3D1076031739928273%26fid%3D1005051739928273%26uicode%3D10000011\",\r\n \"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/70.0.3538.77 Safari/537.36\",\r\n \"X-Requested-With\": \"XMLHttpRequest\"\r\n }\r\n\r\n params = {\r\n \"type\": \"uid\",\r\n\r\n }\r\n # \"since_id\": \"4275547741511797\"#\r\n page = 1\r\n total = 1\r\n\r\n params[\"value\"] = str(user_id)\r\n headers[\"Referer\"] = \"https://m.weibo.cn/status/\" + user_id\r\n\r\n # res = requests.get(url,headers=headers,params=params).content#\r\n # print (res)#\r\n # 将form_data的键值对转换为以连接符&划分的字符串\r\n data = urllib.urlencode(params)\r\n # This sentence is necessary ,because data is suppossed to be a \"a buffer in the standard application/x-www-form-urlencoded format.\", not a dict.#\r\n request = urllib2.Request(user_base_url, headers=headers, data=data)\r\n response = urllib2.urlopen(request)\r\n index_doc = response.read()\r\n index_json = json.loads(index_doc)\r\n containerid = index_json[\"data\"]['tabsInfo']['tabs'][0][\"containerid\"]\r\n print (index_doc)\r\n print (containerid)\r\n a =(\r\n user_id,\\\r\n containerid\r\n\r\n )\r\n user_id_list.append(a)\r\n return user_id_list\r\n\r\n\r\ndef getuserinfo(user_list):\r\n feature = []\r\n user_info_base_url = \"https://m.weibo.cn/api/container/getIndex?\"\r\n print (user_list)\r\n for user_info in user_list:\r\n print (user_info)\r\n user_id = user_info[0]\r\n containerid = user_info[1]\r\n print (containerid)\r\n headers = {\r\n \"Accept\": \"application/json, text/plain, */*\",\r\n \"MWeibo-Pwa\": \"1\",\r\n \"Cookie\": \"_T_WM=468d8440d1026e0fb9bb792af1b37493; SUB=_2A252yv5XDeRhGeVH6lAQ8CzMzz2IHXVSNIIfrDV6PUJbkdAKLXb7kW1NT2fnim-z8a1zQQ8dD6LQJOHhY9omBlYt; SUHB=00xpdG2ybv4tKa; WEIBOCN_FROM=1110006030; MLOGIN=1; M_WEIBOCN_PARAMS=oid%3D4301215514936299%26luicode%3D20000061%26lfid%3D4301215514936299\",\r\n \"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/70.0.3538.77 Safari/537.36\",\r\n \"X-Requested-With\": \"XMLHttpRequest\"\r\n }\r\n\r\n params = {\r\n \"type\": \"uid\",\r\n\r\n }\r\n # \"since_id\": \"4275547741511797\"#\r\n params[\"value\"] = str(user_id)\r\n params[\"containerid\"] = str(containerid) + \"_-_INFO\"\r\n print (params)\r\n headers[\"Referer\"] = \"https://m.weibo.cn/p/index?\"\r\n\r\n # res = requests.get(url,headers=headers,params=params).content#\r\n # print (res)#\r\n # 将params的键值对转换为以连接符&划分的字符串\r\n data = urllib.urlencode(params)\r\n # This sentence is necessary ,because data is suppossed to be a \"a buffer in the standard application/x-www-form-urlencoded format.\", not a dict.#\r\n request = urllib2.Request(user_info_base_url, headers=headers, data=data)\r\n response = urllib2.urlopen(request)\r\n index_doc = response.read()\r\n index_json = json.loads(index_doc)\r\n print (index_doc)\r\n\r\n birth = index_json[\"data\"][\"cards\"][1][\"card_group\"][2][\"item_content\"]\r\n print (birth)\r\n\r\n\r\n\r\n\r\n\r\nif __name__ == '__main__':\r\n user_id = \"1739928273\"\r\n getuserinfo(getuserid(user_id))\r\n", "repo_name": "ChronNio/CHB", "sub_path": "weibo/jujie_test.py", "file_name": "jujie_test.py", "file_ext": "py", "file_size_in_byte": 3940, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "urllib.urlencode", "line_number": 41, "usage_type": "call"}, {"api_name": "urllib2.Request", "line_number": 43, "usage_type": "call"}, {"api_name": "urllib2.urlopen", "line_number": 44, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 46, "usage_type": "call"}, {"api_name": "urllib.urlencode", "line_number": 89, "usage_type": "call"}, {"api_name": "urllib2.Request", "line_number": 91, "usage_type": "call"}, {"api_name": "urllib2.urlopen", "line_number": 92, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 94, "usage_type": "call"}]} +{"seq_id": "43369057917", "text": "import sys\nfrom PyQt5.QtWidgets import *\nfrom PyQt5 import uic\n\nform_window = uic.loadUiType('./signal_slot.ui')[0]\n\nclass Exam(QWidget, form_window):\n def __init__(self):\n super().__init__()\n self.setupUi(self)\n self.btn_1.clicked.connect(self.btn_clicked_slot)\n self.lcdNumber.setVisible(False)\n\n def btn_clicked_slot(self):\n self.lbl_1.setText(\"Hello World!\")\n\nif __name__ == \"__main__\" :\n app = QApplication(sys.argv)\n mainWindow = Exam()\n mainWindow.show()\n sys.exit(app.exec_())", "repo_name": "jone9966/study_asiae", "sub_path": "exam/exam09_signal_slot.py", "file_name": "exam09_signal_slot.py", "file_ext": "py", "file_size_in_byte": 537, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "PyQt5.uic.loadUiType", "line_number": 5, "usage_type": "call"}, {"api_name": "PyQt5.uic", "line_number": 5, "usage_type": "name"}, {"api_name": "sys.argv", "line_number": 18, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 21, "usage_type": "call"}]} +{"seq_id": "70108805546", "text": "import pygame\nimport time\nimport random\nfrom tkinter import *\n\npygame.init()\n\ndisplay_width=800\ndisplay_height=600\n\ngamedisplay=pygame.display.set_mode((display_width,display_height))\npygame.display.set_caption(\"Flappy Bird\")\n\n\ngreen = (0,255,0)\nblue = (102,184,252)\npoles_color=(235,255,3)\npoles2_color=(254,200,8)\npoles_ychange=2\npoles_xchange=2\n\nclock=pygame.time.Clock()\ncrashed=False\nbirdimg=pygame.image.load(\"bird2.png\")\ncloudimg=pygame.image.load(\"clouds.png\")\npoles=pygame.image.load(\"poles.png\")\n\ndef bird(x,y):\n gamedisplay.blit(birdimg,(x,y))\ndef clouds(x,y):\n gamedisplay.blit(cloudimg,(x,y))\n\n\nx=(display_width*0.3)\ny=(display_height*0.8)\ny_change=0\npoles_y=750\npoles_x=random.randint(0,540)\nsecond_poles_y=950\nsecond_poles_x=random.randint(0,540)\nthird_poles_y=1150\nthird_poles_x=random.randint(0,540)\n\npoints=0\n\nwhile not crashed:\n for event in pygame.event.get():\n if event.type == pygame.QUIT:\n crashed = True\n if event.type == pygame.KEYDOWN:\n if event.key == pygame.K_UP:\n y_change=10\n if event.type == pygame.KEYUP:\n if event.key == pygame.K_UP:\n y_change=0\n y=y-y_change\n if y<550:\n y=y+5\n \n \n pygame.draw.rect(gamedisplay,blue,(0,0,800,350),0 )\n pygame.draw.rect(gamedisplay,green,(0,350,800,250),0 )\n clouds(0,100)\n bird(x,y)\n \n def poles(x,y):\n global poles_y\n global poles_x\n pygame.draw.rect(gamedisplay,poles_color,(y,0,50,x),0 )\n pygame.draw.rect(gamedisplay,poles2_color,(y-2.5,x,55,10),0 )\n pygame.draw.rect(gamedisplay,poles_color,(y,x+150,50,600-x),0 )\n pygame.draw.rect(gamedisplay,poles2_color,(y-2.5,x+140,55,10),0 )\n if y==-50:\n poles_y=800\n poles_x=random.randint(0,540)\n \n \n def second_poles(x,y):\n global second_poles_y\n global second_poles_x\n pygame.draw.rect(gamedisplay,poles_color,(y,0,50,x),0 )\n pygame.draw.rect(gamedisplay,poles2_color,(y-2.5,x,55,10),0 )\n pygame.draw.rect(gamedisplay,poles_color,(y,x+150,50,600-x),0 )\n pygame.draw.rect(gamedisplay,poles2_color,(y-2.5,x+140,55,10),0 )\n if y==-50:\n second_poles_y=800\n second_poles_x=random.randint(0,540)\n \n def third_poles(x,y):\n global third_poles_y\n global third_poles_x\n pygame.draw.rect(gamedisplay,poles_color,(y,0,50,x),0 )\n pygame.draw.rect(gamedisplay,poles2_color,(y-2.5,x,55,10),0 )\n pygame.draw.rect(gamedisplay,poles_color,(y,x+150,50,600-x),0 )\n pygame.draw.rect(gamedisplay,poles2_color,(y-2.5,x+140,55,10),0 )\n if y==-50:\n third_poles_y=800\n third_poles_x=random.randint(0,540)\n \n \n poles(poles_x,poles_y)\n second_poles(second_poles_x,second_poles_y) \n third_poles(third_poles_x,third_poles_y) \n \n poles_y=poles_y-poles_ychange\n second_poles_y=second_poles_y-poles_ychange\n third_poles_y=third_poles_y-poles_ychange\n \n if x == poles_y or x==poles_y+50:\n if y < poles_x +10 or y>poles_x+140:\n crashed=True\n else:\n points=points+1 \n if x == second_poles_y or x==second_poles_y+50:\n if y < second_poles_x +10 or y>second_poles_x +140:\n crashed=True\n else:\n points=points+1 \n if x == third_poles_y or x==third_poles_y+50:\n if y < third_poles_x +10 or y>third_poles_x+140:\n crashed=True\n else:\n points=points+1 \n \n \n pygame.display.update()\n clock.tick(60)\n\n\n\nwindow=Tk()\nwindow.title(\"Points table\")\nlabel1=Label(window, text=\"Your Score Is...\",padx=10, pady=10,)\nlabel1.pack()\nlabel2=Label(window,text=str(points),padx=10, pady=10,)\nlabel2.pack()\nwindow.mainloop()\npygame.quit()\nquit()\n", "repo_name": "Anuj-Rathore24/FlappyBird-UsingPygame", "sub_path": "game.py", "file_name": "game.py", "file_ext": "py", "file_size_in_byte": 3784, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "pygame.init", "line_number": 6, "usage_type": "call"}, {"api_name": "pygame.display.set_mode", "line_number": 11, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 11, "usage_type": "attribute"}, {"api_name": "pygame.display.set_caption", "line_number": 12, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 12, "usage_type": "attribute"}, {"api_name": "pygame.time.Clock", "line_number": 22, "usage_type": "call"}, {"api_name": "pygame.time", "line_number": 22, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 24, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 24, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 25, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 25, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 26, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 26, "usage_type": "attribute"}, {"api_name": "random.randint", "line_number": 38, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 40, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 42, "usage_type": "call"}, {"api_name": "pygame.event.get", "line_number": 47, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 47, "usage_type": "attribute"}, {"api_name": "pygame.QUIT", "line_number": 48, "usage_type": "attribute"}, {"api_name": "pygame.KEYDOWN", "line_number": 50, "usage_type": "attribute"}, {"api_name": "pygame.K_UP", "line_number": 51, "usage_type": "attribute"}, {"api_name": "pygame.KEYUP", "line_number": 53, "usage_type": "attribute"}, {"api_name": "pygame.K_UP", "line_number": 54, "usage_type": "attribute"}, {"api_name": "pygame.draw.rect", "line_number": 61, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 61, "usage_type": "attribute"}, {"api_name": "pygame.draw.rect", "line_number": 62, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 62, "usage_type": "attribute"}, {"api_name": "pygame.draw.rect", "line_number": 69, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 69, "usage_type": "attribute"}, {"api_name": "pygame.draw.rect", "line_number": 70, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 70, "usage_type": "attribute"}, {"api_name": "pygame.draw.rect", "line_number": 71, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 71, "usage_type": "attribute"}, {"api_name": "pygame.draw.rect", "line_number": 72, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 72, "usage_type": "attribute"}, {"api_name": "random.randint", "line_number": 75, "usage_type": "call"}, {"api_name": "pygame.draw.rect", "line_number": 81, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 81, "usage_type": "attribute"}, {"api_name": "pygame.draw.rect", "line_number": 82, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 82, "usage_type": "attribute"}, {"api_name": "pygame.draw.rect", "line_number": 83, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 83, "usage_type": "attribute"}, {"api_name": "pygame.draw.rect", "line_number": 84, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 84, "usage_type": "attribute"}, {"api_name": "random.randint", "line_number": 87, "usage_type": "call"}, {"api_name": "pygame.draw.rect", "line_number": 92, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 92, "usage_type": "attribute"}, {"api_name": "pygame.draw.rect", "line_number": 93, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 93, "usage_type": "attribute"}, {"api_name": "pygame.draw.rect", "line_number": 94, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 94, "usage_type": "attribute"}, {"api_name": "pygame.draw.rect", "line_number": 95, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 95, "usage_type": "attribute"}, {"api_name": "random.randint", "line_number": 98, "usage_type": "call"}, {"api_name": "pygame.display.update", "line_number": 126, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 126, "usage_type": "attribute"}, {"api_name": "pygame.quit", "line_number": 138, "usage_type": "call"}]} +{"seq_id": "35765111020", "text": "from ray.rllib.models.model import Model\nfrom ray.rllib.models.tf.misc import flatten\nfrom ray.rllib.utils.annotations import override\nfrom ray.rllib.utils.deprecation import deprecation_warning\nfrom ray.rllib.utils.framework import get_activation_fn, try_import_tf\n\ntf = try_import_tf()\n\n\n# Deprecated: see as an alternative models/tf.visionnet.py\nclass VisionNetwork(Model):\n \"\"\"Generic vision network.\"\"\"\n\n @override(Model)\n def _build_layers_v2(self, input_dict, num_outputs, options):\n # Hard deprecate this class. All Models should use the ModelV2\n # API from here on.\n deprecation_warning(\n \"Model->VisionNetwork\", \"ModelV2->VisionNetwork\", error=False)\n inputs = input_dict[\"obs\"]\n filters = options.get(\"conv_filters\")\n if not filters:\n filters = _get_filter_config(inputs.shape.as_list()[1:])\n\n activation = get_activation_fn(options.get(\"conv_activation\"))\n\n with tf.name_scope(\"vision_net\"):\n for i, (out_size, kernel, stride) in enumerate(filters[:-1], 1):\n inputs = tf.layers.conv2d(\n inputs,\n out_size,\n kernel,\n stride,\n activation=activation,\n padding=\"same\",\n name=\"conv{}\".format(i))\n out_size, kernel, stride = filters[-1]\n\n # skip final linear layer\n if options.get(\"no_final_linear\"):\n fc_out = tf.layers.conv2d(\n inputs,\n num_outputs,\n kernel,\n stride,\n activation=activation,\n padding=\"valid\",\n name=\"fc_out\")\n return flatten(fc_out), flatten(fc_out)\n\n fc1 = tf.layers.conv2d(\n inputs,\n out_size,\n kernel,\n stride,\n activation=activation,\n padding=\"valid\",\n name=\"fc1\")\n fc2 = tf.layers.conv2d(\n fc1,\n num_outputs, [1, 1],\n activation=None,\n padding=\"same\",\n name=\"fc2\")\n return flatten(fc2), flatten(fc1)\n\n\ndef _get_filter_config(shape):\n shape = list(shape)\n filters_84x84 = [\n [16, [8, 8], 4],\n [32, [4, 4], 2],\n [256, [11, 11], 1],\n ]\n filters_42x42 = [\n [16, [4, 4], 2],\n [32, [4, 4], 2],\n [256, [11, 11], 1],\n ]\n if len(shape) == 3 and shape[:2] == [84, 84]:\n return filters_84x84\n elif len(shape) == 3 and shape[:2] == [42, 42]:\n return filters_42x42\n else:\n raise ValueError(\n \"No default configuration for obs shape {}\".format(shape) +\n \", you must specify `conv_filters` manually as a model option. \"\n \"Default configurations are only available for inputs of shape \"\n \"[42, 42, K] and [84, 84, K]. You may alternatively want \"\n \"to use a custom model or preprocessor.\")\n", "repo_name": "HuantWang/SUPERSONIC", "sub_path": "third_party/ray/rllib/models/tf/visionnet_v1.py", "file_name": "visionnet_v1.py", "file_ext": "py", "file_size_in_byte": 3101, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 119, "dataset": "github-code", "pt": "37", "api": [{"api_name": "ray.rllib.utils.framework.try_import_tf", "line_number": 7, "usage_type": "call"}, {"api_name": "ray.rllib.models.model.Model", "line_number": 11, "usage_type": "name"}, {"api_name": "ray.rllib.utils.deprecation.deprecation_warning", "line_number": 18, "usage_type": "call"}, {"api_name": "ray.rllib.utils.framework.get_activation_fn", "line_number": 25, "usage_type": "call"}, {"api_name": "ray.rllib.models.tf.misc.flatten", "line_number": 49, "usage_type": "call"}, {"api_name": "ray.rllib.models.tf.misc.flatten", "line_number": 65, "usage_type": "call"}, {"api_name": "ray.rllib.utils.annotations.override", "line_number": 14, "usage_type": "call"}, {"api_name": "ray.rllib.models.model.Model", "line_number": 14, "usage_type": "argument"}]} +{"seq_id": "74992993386", "text": "import speedtest\nimport threading\nimport datetime\nimport subprocess\nimport logging\nfrom db_connector import *\nclass SpeedClass:\n def __init__(self) -> None:\n self.dl_speed = None\n self.ul_speed = None\n self.st = speedtest.Speedtest()\n def get_download_speed(self):\n logging.info('checking download speed.....')\n self.st.get_best_server()\n self.dl_speed = self.st.download()/1000000\n logging.info(f'dl_speed: {self.dl_speed}')\n def get_upload_speed(self):\n logging.info('checking upload speed......')\n self.st.get_best_server()\n self.ul_speed = self.st.upload()/1000000\n logging.info(f'dl_speed: {self.ul_speed }')\n\n\nif __name__ == \"__main__\":\n engine = get_engine(get_db_config('speed_test'))\n query = get_sql('/home/thomas/repos/speed_mapper/models/add_test.sql')\n format = \"%(asctime)s: %(message)s\"\n logging.basicConfig(format=format, level=logging.INFO,\n datefmt=\"%H:%M:%S\")\n subprocess_result = subprocess.Popen('/sbin/iwgetid',shell=True,stdout=subprocess.PIPE)\n subprocess_output = subprocess_result.communicate()[0],subprocess_result.returncode\n network_name = subprocess_output[0].decode('utf-8').split('\"')[1]\n logging.info(network_name)\n start_time = datetime.datetime.now()\n speed_holder = SpeedClass()\n dl_speed= threading.Thread(target=speed_holder.get_download_speed)\n up_speed = threading.Thread(target = speed_holder.get_upload_speed)\n dl_speed.start()\n up_speed.start()\n dl_speed.join()\n up_speed.join()\n data = {'date_time': f\"'{start_time}'::timestamp\", \n 'download_speed':speed_holder.dl_speed, \n 'upload_speed': speed_holder.ul_speed,\n 'network_name': f\"'{network_name}'\"}\n if speed_holder.dl_speed is not None:\n query = query.format_map(data)\n with engine.connect() as con:\n con.execute(query)\n end_time = datetime.datetime.now()\n logging.info(end_time-start_time)\n\n", "repo_name": "tomvonheill/speed_test_scripts", "sub_path": "speed_test.py", "file_name": "speed_test.py", "file_ext": "py", "file_size_in_byte": 1992, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "speedtest.Speedtest", "line_number": 11, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 13, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 16, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 18, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 21, "usage_type": "call"}, {"api_name": "logging.basicConfig", "line_number": 28, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 28, "usage_type": "attribute"}, {"api_name": "subprocess.Popen", "line_number": 30, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 30, "usage_type": "attribute"}, {"api_name": "logging.info", "line_number": 33, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 34, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 34, "usage_type": "attribute"}, {"api_name": "threading.Thread", "line_number": 36, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 37, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 50, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 50, "usage_type": "attribute"}, {"api_name": "logging.info", "line_number": 51, "usage_type": "call"}]} +{"seq_id": "4184099606", "text": "import base64\nimport datetime as dt\nimport requests as rq\n\nfrom dateutil import parser\n\n\ndef refresh_fitbit_token(request):\n \"\"\"Refresh an OAuth2 \"Authorization Code Grant Flow\" refresh token.\n\n See https://dev.fitbit.com/build/reference/web-api/oauth2/#refreshing-tokens\n\n Parameters\n ----------\n request: flask.Request\n GCP Cloud Function request context. The request will match this spec:\n https://fivetran.com/docs/functions#requestformat\n\n Returns\n -------\n dict\n A new refresh and access token pair\n \"\"\"\n\n request_json = request.get_json()\n\n # see https://dev.fitbit.com/build/reference/web-api/oauth2/#refreshing-tokens # noqa\n # for why we have to do this base64 stuff\n id_secret = (f'{request_json[\"secrets\"][\"FITBIT_CLIENT_ID\"]}:'\n f'{request_json[\"secrets\"][\"FITBIT_CLIENT_SECRET\"]}')\n\n b64_creds = (base64.encodebytes(bytes(id_secret, 'utf8'))\n .decode('utf8')\n .rstrip())\n\n auth_header = {'Authorization': f'Basic {b64_creds}',\n 'Content-Type': 'application/x-www-form-urlencoded'}\n\n post_params = {'grant_type': 'refresh_token',\n 'refresh_token': request_json['state']['refresh_token']}\n\n resp = rq.post('https://api.fitbit.com/oauth2/token',\n headers=auth_header, params=post_params)\n\n if resp.status_code != 200:\n raise ValueError(f'OAuth token refresh request returned {resp.json()}')\n\n new_token = resp.json()\n\n return new_token\n\n\ndef handler(request):\n \"\"\"Scrape data from Fitbit.\n\n Parameters\n ----------\n request: flask.Request\n GCP Cloud Function request context. The request will match this spec:\n https://fivetran.com/docs/functions#requestformat\n\n Returns\n -------\n dict\n A response matching this spec:\n https://fivetran.com/docs/functions#responseformat\n \"\"\"\n\n request_json = request.get_json()\n\n # initialize state for the case when fivetran is starting from scratch.\n # put initial values for the cursor and tokens in the 'secrets' node.\n # fivetran should automatically keep track of subsequent updates in the\n # 'state' node.\n if 'cursor' not in request_json['state']:\n request_json['state']['cursor'] = request_json['secrets']['cursor']\n if 'access_token' not in request_json['state']:\n request_json['state']['access_token'] = request_json['secrets']['access_token']\n if 'refresh_token' not in request_json['state']:\n request_json['state']['refresh_token'] = request_json['secrets']['refresh_token']\n\n cursor = request_json['state']['cursor']\n cursor_date = parser.parse(cursor).date()\n\n if cursor_date > dt.date.today():\n raise ValueError(\n f\"cursor value {cursor_date.isoformat()} is later than \"\n f\"today's date {dt.date.today().isoformat()}\")\n\n # if the cursor is at the current date return immediately without\n # incrementing the cursor. This is to ensure we don't pull data for a day\n # until that day is over.\n if cursor_date == dt.date.today():\n return {\n 'state': request_json['state'],\n 'hasMore': False,\n 'returnCause': 'Cursor date not complete yet',\n }\n\n # otherwise the cursor must be in the past so go ahead and pull data\n headers = {'Accept-Language': 'en_US',\n 'Authorization': f'Bearer {request_json[\"state\"][\"access_token\"]}'}\n\n activity = rq.get(\n 'https://api.fitbit.com/1/user/-/activities/date/'\n f'{cursor_date.isoformat()}.json',\n headers=headers)\n weight = rq.get(\n 'https://api.fitbit.com/1/user/-/body/log/weight/date/'\n f'{cursor_date.isoformat()}.json',\n headers=headers)\n\n # the fitbit API returns a 401 code when the access token has expired\n # see https://dev.fitbit.com/build/reference/web-api/oauth2/#refreshing-tokens\n if activity.status_code == 401 or weight.status_code == 401:\n\n new_token = refresh_fitbit_token(request)\n\n return {\n 'state': {\n 'cursor': cursor_date.isoformat(),\n 'access_token': new_token['access_token'],\n 'refresh_token': new_token['refresh_token']\n },\n 'hasMore': True,\n 'returnCause': 'OAuth token required refresh',\n }\n\n # the fitbit API returns a 429 code when you hit the rate limit\n # see https://dev.fitbit.com/build/reference/web-api/basics/#hitting-the-rate-limit\n if activity.status_code == 429 or weight.status_code == 429:\n\n return {\n 'state': request_json['state'],\n 'hasMore': False,\n 'returnCause': 'Hit rate limit',\n }\n\n # parse the response from the fitbit API\n activity_json = activity.json()\n weight_json = weight.json()\n\n activity_insert = {\n 'date': cursor_date.isoformat(),\n 'steps': activity_json['summary']['steps'],\n 'caloriesBMR': activity_json['summary']['caloriesBMR'],\n 'caloriesOut': activity_json['summary']['caloriesOut'],\n 'activityCalories': activity_json['summary']['activityCalories'],\n 'marginalCalories': activity_json['summary']['marginalCalories'],\n 'sedentaryMinutes': activity_json['summary']['sedentaryMinutes'],\n 'lightlyActiveMinutes': activity_json['summary']['lightlyActiveMinutes'],\n 'fairlyActiveMinutes': activity_json['summary']['fairlyActiveMinutes'],\n 'veryActiveMinutes': activity_json['summary']['veryActiveMinutes'],\n }\n\n weight_insert = {\n 'date': cursor_date.isoformat(),\n 'weight': weight_json['weight'][0]['weight'] if len(weight_json['weight']) > 0 else None\n }\n\n return {\n 'state': {\n 'cursor': (cursor_date + dt.timedelta(days=1)).isoformat(),\n 'access_token': request_json['state']['access_token'],\n 'refresh_token': request_json['state']['refresh_token'],\n 'returnCause': 'Successfully retrieved data',\n },\n 'insert': {\n 'activity': [activity_insert],\n 'weight': [weight_insert]\n },\n 'schema': {\n 'activity': {\n 'primary_key': ['date']\n },\n 'weight': {\n 'primary_key': ['date']\n }\n },\n 'hasMore': True\n }\n", "repo_name": "hinnefe2/exercise_dashboard", "sub_path": "cloud_functions/fitbit/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 6388, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "base64.encodebytes", "line_number": 32, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 42, "usage_type": "call"}, {"api_name": "dateutil.parser.parse", "line_number": 83, "usage_type": "call"}, {"api_name": "dateutil.parser", "line_number": 83, "usage_type": "name"}, {"api_name": "datetime.date.today", "line_number": 85, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 85, "usage_type": "attribute"}, {"api_name": "datetime.date.today", "line_number": 88, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 88, "usage_type": "attribute"}, {"api_name": "datetime.date.today", "line_number": 93, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 93, "usage_type": "attribute"}, {"api_name": "requests.get", "line_number": 104, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 108, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 163, "usage_type": "call"}]} +{"seq_id": "29513607669", "text": "import numpy as np\nimport matplotlib.pyplot as plt\nimport pandas as pd\nfrom sklearn.cluster import AffinityPropagation\nfrom sklearn import preprocessing\nfrom sklearn import metrics\nfrom itertools import cycle\n\nstats = pd.read_csv(\"maokaiSupFilled.csv\", nrows=1000)\n#print(mao.mean())\n\n#dt = stats[['matchid', 'player', 'championid', 'position', 'win', 'kills', 'deaths', 'assists', 'totdmgdealt', 'totdmgtaken']]\ndt = stats[['win', 'kills', 'deaths', 'assists', 'totdmgdealt', 'totdmgtaken']]\nX = dt.values\nsetcols = dt\nsetcols2 = stats\n\n# #############################################################################\n# Compute Affinity Propagation\naf = AffinityPropagation().fit(X)\ncluster_centers_indices = af.cluster_centers_indices_\nlabels = af.labels_\n\nn_clusters_ = len(cluster_centers_indices)\n\n#####################\n# save clusters\ncluster_map = pd.DataFrame()\ncluster_map['data_index'] = stats.index.values\ncluster_map['cluster'] = af.labels_\n\nprint('Length of clusters: %d' %n_clusters_)\n# print(af.labels_)\n# print(cluster_map[cluster_map.cluster==4])\n\n######################\n# Helper functions\n\n# Returns array of indexes that belongs to a passed in cluster number\ndef clusterIndicesNumpy(clustNum, labels_array): #numpy\n return np.where(labels_array == clustNum)[0]\n\n#display cluster index and composition\n# cluster_to_check = 7\n# print('##############################')\n# print('Showing samples in cluster %d' %cluster_to_check)\n# print(clusterIndicesNumpy(cluster_to_check, af.labels_))\n# print('##############################')\n# print('Showing row values of each data point in cluster %d' %cluster_to_check)\n# print(X[clusterIndicesNumpy(cluster_to_check, af.labels_)])\n\n#############################################\n# GEt high winrate clusters\n##########print(cluster_map[cluster_map.cluster==4])\nfrom statistics import mean\nfrom heapq import nlargest\ncurmeanlist = []\ncindexbiggest = []\nfor i in range(0, n_clusters_):\n cur_cluster = cluster_map[cluster_map.cluster == i]\n index = cur_cluster['data_index'].tolist()\n curmeanlist.append(mean(setcols2.iloc[index,2].tolist())) #2 = kills column\nprint(curmeanlist)\ncurmeanlist = np.array(curmeanlist)\ncindexbiggest = nlargest(10, range(len(curmeanlist)), curmeanlist.take) #store top 10 biggest clusters in descending order\nprint(cindexbiggest)\n\nfrom itertools import chain\n#############################################\n# Form scatter plot of highest win-rate clusters\nmasterlist = []\nheader_index_to_evaluate = 3\nx_axis = []\ncl_num = 0\nfor i in range(0, len(cindexbiggest)):\n cur_cluster = X[clusterIndicesNumpy(cindexbiggest[i], af.labels_)] #retrieve array of values in specified cluster\n add_to_master = [item[header_index_to_evaluate] for item in cur_cluster]\n masterlist.append(add_to_master)\n for j in range(0, len(add_to_master)):\n x_axis.append(cl_num)\n cl_num += 1\ny_axis = list(chain.from_iterable(masterlist))\nmean = [np.mean(y_axis)]*len(x_axis)\nhard_mean = [0.155878494] * len(mean)\nplt.figure(3)\nplt.scatter(x_axis, y_axis, s=5, alpha=0.5)\nplt.plot(x_axis, hard_mean, label='Mean', linewidth=1.0, color=\"black\")\nplt.title('Total damage dealt by high winning clusters of Maokai Support')\nplt.xlabel('Cluster index')\nplt.ylabel('Total damage')\n\n\n#Create list of clusters that contains value array for each\n\nmasterlist = []\nheader_index_to_evaluate = 3 #'kills' = 0, 'deaths' = 1,'assists' = 2,'totdmgdealt' = 3,'totdmgtaken' = 4\nx_axis = []\ncl_num = 0\nfor i in range(0, n_clusters_) :\n cur_cluster = X[clusterIndicesNumpy(i, af.labels_)] #retrieve array of values in each cluster\n add_to_master = [item[header_index_to_evaluate] for item in cur_cluster] #retrieve only specified column from array\n masterlist.append(add_to_master)\n for j in range(0, len(add_to_master)):\n x_axis.append(cl_num)\n cl_num += 1\n\nlength_of_master = len(masterlist)\ny_axis = list(chain.from_iterable(masterlist))\n\n# print('y-axis length={}, x-axis length={}'.format(len(y_axis), len(x_axis)))\n# print(y_axis)\n# print(x_axis)\n\n#############################################\n# Form scatter plot of all clusters\nplt.figure(1)\nmean = [np.mean(y_axis)]*len(x_axis)\nhard_mean = [0.155878494] * len(mean)\n# print(mean)\n# print(hard_mean)\n# print(\"MEAN VALUE = {}\".format(mean))\nplt.scatter(x_axis, y_axis, s=5, alpha=0.5)\nplt.plot(x_axis, hard_mean, label='Mean', linewidth=1.0, color=\"black\")\nplt.title('Total damage dealt by Maokai Support')\nplt.xlabel('Cluster index')\nplt.ylabel('Total Damage')\n######################################\n# Form cluster plot\n# plt.close('all')\nplt.figure(2)\nplt.clf()\n\ncolors = cycle('bgrcmykbgrcmykbgrcmykbgrcmyk')\nfor k, col in zip(range(n_clusters_), colors):\n class_members = labels == k\n cluster_center = X[cluster_centers_indices[k]]\n plt.plot(X[class_members, 0], X[class_members, 1], col + '.')\n plt.plot(cluster_center[0], cluster_center[1], 'o', markerfacecolor=col,\n markeredgecolor='k', markersize=10)\n for x in X[class_members]:\n plt.plot([cluster_center[0], x[0]], [cluster_center[1], x[1]], col)\n\nplt.title('Estimated number of clusters: %d' % n_clusters_)\nplt.show()\n#########################################", "repo_name": "tora00/571DataminingProject", "sub_path": "affinity.py", "file_name": "affinity.py", "file_ext": "py", "file_size_in_byte": 5195, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "pandas.read_csv", "line_number": 9, "usage_type": "call"}, {"api_name": "sklearn.cluster.AffinityPropagation", "line_number": 20, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 28, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 41, "usage_type": "call"}, {"api_name": "statistics.mean", "line_number": 62, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 64, "usage_type": "call"}, {"api_name": "heapq.nlargest", "line_number": 65, "usage_type": "call"}, {"api_name": "itertools.chain.from_iterable", "line_number": 82, "usage_type": "call"}, {"api_name": "itertools.chain", "line_number": 82, "usage_type": "name"}, {"api_name": "statistics.mean", "line_number": 83, "usage_type": "name"}, {"api_name": "numpy.mean", "line_number": 83, "usage_type": "call"}, {"api_name": "statistics.mean", "line_number": 84, "usage_type": "argument"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 85, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 85, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 86, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 86, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 87, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 87, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 88, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 88, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 89, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 89, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 90, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 90, "usage_type": "name"}, {"api_name": "itertools.chain.from_iterable", "line_number": 108, "usage_type": "call"}, {"api_name": "itertools.chain", "line_number": 108, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 116, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 116, "usage_type": "name"}, {"api_name": "statistics.mean", "line_number": 117, "usage_type": "name"}, {"api_name": "numpy.mean", "line_number": 117, "usage_type": "call"}, {"api_name": "statistics.mean", "line_number": 118, "usage_type": "argument"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 122, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 122, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 123, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 123, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 124, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 124, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 125, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 125, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 126, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 126, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 130, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 130, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.clf", "line_number": 131, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 131, "usage_type": "name"}, {"api_name": "itertools.cycle", "line_number": 133, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 137, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 137, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 138, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 138, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 141, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 141, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 143, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 143, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 144, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 144, "usage_type": "name"}]} +{"seq_id": "27992848451", "text": "from polygon import RESTClient\n\n# docs\n# https://polygon.io/docs/indices/get_v3_snapshot_indices\n# https://github.com/polygon-io/client-python/blob/master/polygon/rest/snapshot.py#\n\n# client = RESTClient(\"XXXXXX\") # hardcoded api_key is used\nclient = RESTClient() # POLYGON_API_KEY environment variable is used\n\ntickers = [\"I:SPX\", \"I:DJI\", \"I:VIX\"]\nsnapshot = client.get_snapshot_indices(tickers)\n\n# print raw values\nprint(snapshot)\n", "repo_name": "polygon-io/client-python", "sub_path": "examples/rest/indices-snapshots.py", "file_name": "indices-snapshots.py", "file_ext": "py", "file_size_in_byte": 435, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 597, "dataset": "github-code", "pt": "37", "api": [{"api_name": "polygon.RESTClient", "line_number": 8, "usage_type": "call"}]} +{"seq_id": "13056292419", "text": "import os\nimport subprocess\nimport logging\nfrom metautils import *\n\n\ndef add_dsp_downsampler(dsp, srate):\n # Apply a short filter for higher sampling rate\n # '95' at 48kHz gives passband of 0 .. 23 kHz\n # '74' at 96kHz gives passband of 0 .. 35 kHz and less utrasonic echoes\n dsp += ['rate', '-v', '-b', '95' if srate <= 48000 else '74', str(srate)]\n\n\ndef add_dsp_gain(dsp, gain):\n dsp += ['gain', str(gain)]\n\n\ndef reencode_with_dsp(flacfile, outfile, dsp):\n cmd = ['sox', '-G', flacfile, '-C', '8', outfile] + dsp\n logging.debug(cmd)\n subprocess.call(cmd)\n\n\ndef reencode_no_dsp(flacfile, outfile):\n # flac -8 -s -o \n # --> may fail with ERROR: input file has an ID3v2 tag\n # Use flac -c -d | flac -8 -s - -o \n cmd = ['flac', '-c', '-s', '-d', flacfile]\n p = subprocess.Popen(cmd, stdout=subprocess.PIPE)\n logging.debug(cmd)\n cmd = ['flac', '-8', '-s', '-', '-o', outfile]\n p2 = subprocess.Popen(cmd, stdin=p.stdout)\n logging.debug(cmd)\n p2.communicate()\n\n\nclass flactranscoder:\n\n def __init__(self, args={}):\n self.args = args\n # self.directory -> input directory\n # self.filemeta -> file:metadata dictionary\n\n def probe(self, directory):\n self.directory = directory\n self.files = sorted(\n [name for name in os.listdir(directory)\n if name.lower().endswith('.flac')])\n return self.files\n\n def _extract_metadata(self):\n self.filemeta = {}\n album, artist, date = infer_from_dir(self.directory)\n for f in self.files:\n metadata = get_meta(os.path.join(self.directory, f))\n if 'album' not in metadata:\n metadata['album'] = album\n if 'artist' not in metadata:\n metadata['artist'] = artist\n if date:\n if ('date' not in metadata) or (date < metadata['date']):\n metadata['date'] = date\n if 'tracknumber' not in metadata:\n metadata['tracknumber'] = infer_tracknumber(f)\n if 'title' not in metadata:\n metadata['title'] = infer_title(f)\n self.filemeta[f] = metadata\n\n def _transcode_one(self, f, outdir):\n pathname = os.path.join(self.directory, f)\n outfile = get_filename(outdir, self.filemeta[f])\n logging.info('Creating\\t' + os.path.basename(outfile))\n dsp = []\n if self.filemeta[f]['channels'] > 2 and self.args['mix']:\n raise NotImplementedError(\n 'Downmix of multi-channel flac is not implemented yet.')\n if self.filemeta[f]['srate'] > self.args['srate']:\n add_dsp_downsampler(dsp, self.args['srate'])\n if self.args['gain'] != 0:\n add_dsp_gain(dsp, self.args['gain'])\n if dsp:\n reencode_with_dsp(pathname, outfile, dsp)\n else:\n reencode_no_dsp(pathname, outfile)\n metadata = {k: self.filemeta[f][k] for k in\n ('album', 'artist', 'date', 'tracknumber', 'title')\n if k in self.filemeta[f]}\n logging.debug(metadata)\n set_meta(metadata, outfile)\n\n def transcode(self):\n self._extract_metadata()\n outdirs = []\n pending = self.files\n while pending:\n next = []\n album = self.filemeta[pending[0]]['album']\n outdir = get_output_dir(\n self.args['rootdir'], self.filemeta[pending[0]])\n os.mkdir(outdir)\n logging.info('To ' + outdir)\n outdirs.append(outdir)\n for f in pending:\n if self.filemeta[f]['album'] == album:\n self._transcode_one(f, outdir)\n else:\n next.append(f)\n pending = next\n return outdirs\n\n\nif __name__ == '__main__':\n logging.basicConfig(level=logging.DEBUG)\n logging.info('Test 1')\n\n args = {'srate':48000, 'rootdir':'.', 'mix':False, 'gain':0}\n t = transcoder(args)\n f = t.probe('testset/cd')\n assert f, 'check testset/cd folder for cd quality flac files'\n d = t.transcode()\n assert d, 'transcode did not create output folder'\n logging.warn('DONE - Test 1. Verify ' + str(d) + ' matches testset/cd')\n\n logging.info('Test 2')\n f = t.probe('testset/hr')\n assert f, 'check testset/hr folder for high-res flac files'\n d = t.transcode()\n assert d, 'transcode did not create output folder'\n logging.warn(\n 'DONE - Test 2. Verify ' + str(d) + ' is testset/hr downsampled to 48kHz')\n\n logging.info('Test 3')\n f = t.probe('testset/mixed')\n assert f, 'check testset/mixed for two+ different album files in same folder'\n d = t.transcode()\n assert len(d) >= 2, 'transcode did not create at least 2 output folders'\n logging.warn(\n 'DONE - Test 3. Verify ' + str(d) + ' span all tracks in testset/mixed')\n\n logging.info('Test 4')\n\n args = {'srate':48000, 'rootdir':'.', 'mix':False, 'gain':-6}\n t = transcoder(args)\n f = t.probe('testset/cd')\n assert f, 'check testset/cd folder for cd quality flac files'\n d = t.transcode()\n logging.warn(\n 'DONE - Test 4. Verify ' + str(d) + ' is testset/cd attenuated by 6 dB')\n\n # TODO: test case of different albums in same folder\n", "repo_name": "anthony-morel/slickzik", "sub_path": "flac.py", "file_name": "flac.py", "file_ext": "py", "file_size_in_byte": 5349, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "logging.debug", "line_number": 20, "usage_type": "call"}, {"api_name": "subprocess.call", "line_number": 21, "usage_type": "call"}, {"api_name": "subprocess.Popen", "line_number": 29, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 29, "usage_type": "attribute"}, {"api_name": "logging.debug", "line_number": 30, "usage_type": "call"}, {"api_name": "subprocess.Popen", "line_number": 32, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 33, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 47, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 55, "usage_type": "call"}, {"api_name": "os.path", "line_number": 55, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 70, "usage_type": "call"}, {"api_name": "os.path", "line_number": 70, "usage_type": "attribute"}, {"api_name": "logging.info", "line_number": 72, "usage_type": "call"}, {"api_name": "os.path.basename", "line_number": 72, "usage_type": "call"}, {"api_name": "os.path", "line_number": 72, "usage_type": "attribute"}, {"api_name": "logging.debug", "line_number": 88, "usage_type": "call"}, {"api_name": "os.mkdir", "line_number": 100, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 101, "usage_type": "call"}, {"api_name": "logging.basicConfig", "line_number": 113, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 113, "usage_type": "attribute"}, {"api_name": "logging.info", "line_number": 114, "usage_type": "call"}, {"api_name": "logging.warn", "line_number": 122, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 124, "usage_type": "call"}, {"api_name": "logging.warn", "line_number": 129, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 132, "usage_type": "call"}, {"api_name": "logging.warn", "line_number": 137, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 140, "usage_type": "call"}, {"api_name": "logging.warn", "line_number": 147, "usage_type": "call"}]} +{"seq_id": "29495552297", "text": "import os\nfrom ffmpy import FFmpeg\nfrom flask import Blueprint, request, jsonify, flash, redirect, url_for, send_from_directory, Response\nfrom werkzeug.utils import secure_filename\nfrom mutagen import File\nfrom wand.image import Image\n\nfrom db_credentials import cur, connection\n\nfiles_bp = Blueprint('files_bp', import_name=__name__)\n\nALLOWED_EXTENSIONS = {'pdf', 'mp3', 'mp4', 'jpg', 'png'}\n\n\ndef allowed_file(filename):\n return '.' in filename and \\\n filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS\n\ndef upload_file(request, product_id):\n if request.method == 'POST':\n # check if the post request has the file part\n if 'file' not in request.files:\n flash('No file part')\n return redirect(request.url)\n file = request.files['file']\n # if user does not select file, browser also\n # submit an empty part without filename\n if file.filename == '':\n flash('No selected file')\n return redirect(request.url)\n if file and allowed_file(file.filename):\n #get file extension\n filename = secure_filename(file.filename)\n file_extension = os.path.splitext(filename)\n #rename file\n filename= str(product_id) + file_extension[1]\n from sfsuaccess import app\n file.save(os.path.join(app.config['UPLOAD_FOLDER'], filename))\n generate_thumbnail(filename,product_id,file_extension[1])\n return Response(status=200)\n\ndef generate_thumbnail(filename,product_id, extension):\n from sfsuaccess import app\n try:\n if extension == \".mp3\":\n file = File(os.path.join(app.config['UPLOAD_FOLDER'],filename))\n artwork = file.tags['APIC:'].data # access APIC frame and grab the image\n with open(os.path.join(app.config['UPLOAD_FOLDER'],\"thumbnails\",str(product_id)+'.png'), 'wb') as img:\n img.write(artwork) # write artwork to new image\n elif extension == \".pdf\":\n img = Image(filename=app.config['UPLOAD_FOLDER'] +\"/\"+filename, resolution=300, width=600)\n img.save(filename=app.config['UPLOAD_FOLDER'] + \"/thumbnails/\"+str(product_id)+'.png')\n elif extension == \".jpg\" or extension == \".png\":\n img = Image(filename=app.config['UPLOAD_FOLDER'] +\"/\"+filename)\n img.thumbnail(img.size[0]/8, img.size[1]/8) # set thumbnail sizing to 1/8th resolution\n img.save(filename=app.config['UPLOAD_FOLDER'] + \"/thumbnails/\"+str(product_id)+'.png')\n elif extension == \".mp4\":\n ff = FFmpeg(inputs={os.path.join(app.config['UPLOAD_FOLDER'], filename): None}, outputs={os.path.join(app.config['UPLOAD_FOLDER'],\"thumbnails\",str(product_id)+'.png'): ['-ss', '00:00:4', '-vframes', '1']})\n ff.run()\n except:\n print(\"no thumbnail generated\")\n\ndef get_filename(product_id):\n from sfsuaccess import app\n path = app.config['UPLOAD_FOLDER']\n for filename in os.listdir(path):\n if filename.startswith(product_id):\n return(filename)\n\n#downloading the file\n@files_bp.route('/uploads/')\ndef uploaded_file(product_id):\n from sfsuaccess import app\n return send_from_directory(app.config['UPLOAD_FOLDER'],\n get_filename(product_id))\n\n#downloading the thumbnails\n@files_bp.route('/thumbnails/')\ndef uploaded_file_thumbnail(product_id):\n from sfsuaccess import app\n try:\n return send_from_directory(os.path.join(app.config['UPLOAD_FOLDER'],\"thumbnails\"),product_id+'.png')\n except:\n return send_from_directory(os.path.join(app.config['UPLOAD_FOLDER'],\"thumbnails\"),'thumbnail.png')\n", "repo_name": "aitorelvira/SFSUAccess", "sub_path": "application/M3/SFSUAccess/flask-backend/flask-blueprints/files/files_bp.py", "file_name": "files_bp.py", "file_ext": "py", "file_size_in_byte": 3712, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "37", "api": [{"api_name": "flask.Blueprint", "line_number": 10, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 20, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 20, "usage_type": "name"}, {"api_name": "flask.request.files", "line_number": 22, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 22, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 23, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 24, "usage_type": "call"}, {"api_name": "flask.request.url", "line_number": 24, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 24, "usage_type": "name"}, {"api_name": "flask.request.files", "line_number": 25, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 25, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 29, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 30, "usage_type": "call"}, {"api_name": "flask.request.url", "line_number": 30, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 30, "usage_type": "name"}, {"api_name": "werkzeug.utils.secure_filename", "line_number": 33, "usage_type": "call"}, {"api_name": "os.path.splitext", "line_number": 34, "usage_type": "call"}, {"api_name": "os.path", "line_number": 34, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 38, "usage_type": "call"}, {"api_name": "os.path", "line_number": 38, "usage_type": "attribute"}, {"api_name": "sfsuaccess.app.config", "line_number": 38, "usage_type": "attribute"}, {"api_name": "sfsuaccess.app", "line_number": 38, "usage_type": "name"}, {"api_name": "flask.Response", "line_number": 40, "usage_type": "call"}, {"api_name": "mutagen.File", "line_number": 46, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 46, "usage_type": "call"}, {"api_name": "os.path", "line_number": 46, "usage_type": "attribute"}, {"api_name": "sfsuaccess.app.config", "line_number": 46, "usage_type": "attribute"}, {"api_name": "sfsuaccess.app", "line_number": 46, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 48, "usage_type": "call"}, {"api_name": "os.path", "line_number": 48, "usage_type": "attribute"}, {"api_name": "sfsuaccess.app.config", "line_number": 48, "usage_type": "attribute"}, {"api_name": "sfsuaccess.app", "line_number": 48, "usage_type": "name"}, {"api_name": "wand.image.Image", "line_number": 51, "usage_type": "call"}, {"api_name": "sfsuaccess.app.config", "line_number": 51, "usage_type": "attribute"}, {"api_name": "sfsuaccess.app", "line_number": 51, "usage_type": "name"}, {"api_name": "sfsuaccess.app.config", "line_number": 52, "usage_type": "attribute"}, {"api_name": "sfsuaccess.app", "line_number": 52, "usage_type": "name"}, {"api_name": "wand.image.Image", "line_number": 54, "usage_type": "call"}, {"api_name": "sfsuaccess.app.config", "line_number": 54, "usage_type": "attribute"}, {"api_name": "sfsuaccess.app", "line_number": 54, "usage_type": "name"}, {"api_name": "sfsuaccess.app.config", "line_number": 56, "usage_type": "attribute"}, {"api_name": "sfsuaccess.app", "line_number": 56, "usage_type": "name"}, {"api_name": "ffmpy.FFmpeg", "line_number": 58, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 58, "usage_type": "call"}, {"api_name": "os.path", "line_number": 58, "usage_type": "attribute"}, {"api_name": "sfsuaccess.app.config", "line_number": 58, "usage_type": "attribute"}, {"api_name": "sfsuaccess.app", "line_number": 58, "usage_type": "name"}, {"api_name": "sfsuaccess.app.config", "line_number": 65, "usage_type": "attribute"}, {"api_name": "sfsuaccess.app", "line_number": 65, "usage_type": "name"}, {"api_name": "os.listdir", "line_number": 66, "usage_type": "call"}, {"api_name": "flask.send_from_directory", "line_number": 74, "usage_type": "call"}, {"api_name": "sfsuaccess.app.config", "line_number": 74, "usage_type": "attribute"}, {"api_name": "sfsuaccess.app", "line_number": 74, "usage_type": "name"}, {"api_name": "flask.send_from_directory", "line_number": 82, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 82, "usage_type": "call"}, {"api_name": "os.path", "line_number": 82, "usage_type": "attribute"}, {"api_name": "sfsuaccess.app.config", "line_number": 82, "usage_type": "attribute"}, {"api_name": "sfsuaccess.app", "line_number": 82, "usage_type": "name"}, {"api_name": "flask.send_from_directory", "line_number": 84, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 84, "usage_type": "call"}, {"api_name": "os.path", "line_number": 84, "usage_type": "attribute"}, {"api_name": "sfsuaccess.app.config", "line_number": 84, "usage_type": "attribute"}, {"api_name": "sfsuaccess.app", "line_number": 84, "usage_type": "name"}]} +{"seq_id": "72555674666", "text": "# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Oct 1 11:42:38 2018\n\n@author: jyli\n\"\"\"\n\nimport yaml\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport pandas as pd\nimport os\nfrom itertools import product\n\n\n\ndef readHawc2Res(filename,channels=None):\n #reads specific channels of HAWC2 binary output files and saves in a\n #pandas dataframe. Variable names and channels are defined in a dictionary\n # called channels\n\n if channels is None:\n raise NotImplementedError()\n\n\n #read .sel file\n with open(filename + '.sel') as f:\n lines = f.readlines()\n\n NCh = int(lines[8].split()[1])\n NSc = int(lines[8].split()[0])\n Format = lines[8].split()[3]\n scaleFactor = [float(x) for x in lines[NCh+14:]]\n\n #read .bin file\n data = {}\n fid = open(filename + '.dat', 'rb')\n for key,ch in channels.items():\n fid.seek((ch-1)*NSc*2)\n data[key] = np.fromfile(fid,'int16',NSc) * scaleFactor[ch-1]\n\n\n return pd.DataFrame(data)\n\n\n\n\ndef evaluator(expr, tag_dict):\n\n tags = [x.split('}')[0] for x in expr.split('{')[1:]]\n \n for tag in tags:\n expr = expr.replace('{' + tag + '}', str(tag_dict[tag]))\n \n return eval(expr)\n \n\nclass Case(object):\n\n def __init__(self, casename, constants, variables, functions):\n self.gen_manifest(casename, constants, variables, functions)\n\n\n def __repr__(self):\n return self._man.__repr__()\n \n \n def gen_manifest(self, basename, Consts, Vars, Funcs):\n self.basename = basename\n # number of combinations:\n self.N = 1\n for n in [len(x) for x in Vars.values()]:\n self.N *= n\n # number of attributes:\n attributes = list(Consts.keys()) + list(Vars.keys()) + list(Funcs.keys())\n self.M = len(attributes)\n\n # generate a Pandas dataframe where each row has one of the combinations\n # of simulation tags\n manifest = []\n for v in product(*list(Vars.values())):\n v_dict = dict(zip(Vars.keys(), v))\n this_dict = {**Consts, **v_dict}\n\n for key, f in Funcs.items():\n\n this_dict[key] = evaluator(f, this_dict)\n\n manifest.append(list(this_dict.values()))\n\n self._man = pd.DataFrame(manifest, columns=attributes)\n\n\n\nif __name__ == '__main__':\n\n \n with open(\"definition.yml\", 'r') as stream:\n try:\n data = yaml.load(stream)\n except yaml.YAMLError as exc:\n print(exc)\n \n # load case definitions \n cases = []\n for casename, case_def in data.items():\n cases.append(Case(casename, case_def['constants'], case_def['variables'], \n case_def['functions']))\n \n # read result file data\n channels = {'Pelec': 8,\n 'Gtorque':7,\n 'omega':2}\n \n pelec, omega, torque, wsp = {}, {}, {}, {}\n for case in cases:\n wsp[case.basename] = []\n pelec[case.basename] = []\n omega[case.basename] = []\n torque[case.basename] = []\n \n res_dir = f'res/{case.basename}/'.lower() \n \n for _, sim in case._man.iterrows():\n res_file = res_dir + sim.case_id.lower()\n data = readHawc2Res(res_file, channels)\n wsp[case.basename].append(sim.wsp)\n pelec[case.basename].append(data.Pelec.values[-1]/1e6)\n omega[case.basename].append(data.omega.values[-1])\n torque[case.basename].append(-data.Gtorque.values[-1])\n \n\n \n \n # plot\n styles = ['-', '--', '.', 'x', ':', 'o']\n plt.figure()\n plt.grid()\n plt.xlim(4, 25)\n #plt.xlim(7, 12)\n plt.xlabel('Wind speed [m/s]')\n plt.ylabel('Omega [rad/s]')\n for i, case in enumerate(cases):\n plt.plot(wsp[case.basename], omega[case.basename], styles[i], label=case.basename)\n plt.legend()\n \n \n \n plt.figure()\n plt.grid()\n plt.xlim(4, 25)\n #plt.xlim(7, 12)\n plt.xlabel('Wind speed [m/s]')\n plt.ylabel('Power [MW]')\n for i, case in enumerate(cases):\n plt.plot(wsp[case.basename], pelec[case.basename], styles[i], label=case.basename)\n plt.legend()\n \n plt.figure()\n plt.grid()\n #plt.xlim(4, 25)\n #plt.xlim(7, 12)\n plt.xlabel('Rotor speed [rad/s]')\n plt.ylabel('Torque [?]')\n for i, case in enumerate(cases):\n plt.plot(omega[case.basename], torque[case.basename], styles[i], label=case.basename)\n plt.legend(loc='lower left')\n \n \n plt.figure()\n plt.grid()\n #plt.xlim(4, 25)\n #plt.xlim(7, 12)\n plt.xlabel('wind speed [m/s]')\n plt.ylabel('Torque [?]')\n for i, case in enumerate(cases):\n plt.plot(wsp[case.basename], torque[case.basename], styles[i], label=case.basename)\n plt.legend(loc='lower right')\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n ", "repo_name": "amurDTU/Derate-Debugging", "sub_path": "2.3MW/hawcast_postproc.py", "file_name": "hawcast_postproc.py", "file_ext": "py", "file_size_in_byte": 5093, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "numpy.fromfile", "line_number": 40, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 43, "usage_type": "call"}, {"api_name": "itertools.product", "line_number": 81, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 91, "usage_type": "call"}, {"api_name": "yaml.load", "line_number": 100, "usage_type": "call"}, {"api_name": "yaml.YAMLError", "line_number": 101, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 137, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 137, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 138, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 138, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 139, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 139, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 141, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 141, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 142, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 142, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 144, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 144, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 145, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 145, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 149, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 149, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 150, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 150, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 151, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 151, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 153, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 153, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 154, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 154, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 156, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 156, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 157, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 157, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 159, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 159, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 160, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 160, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 163, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 163, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 164, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 164, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 166, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 166, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 167, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 167, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 170, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 170, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 171, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 171, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 174, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 174, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 175, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 175, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 177, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 177, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 178, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 178, "usage_type": "name"}]} +{"seq_id": "1304424857", "text": "import tensorflow as tf\n\nfrom keras.models import Model\nfrom keras.layers import Input\nfrom keras.layers import Dense\nfrom keras.callbacks import EarlyStopping\nfrom keras.utils import to_categorical\nimport numpy as np\n\n# nltk\nimport nltk\nfrom nltk import pos_tag, word_tokenize\nfrom nltk.corpus import wordnet\nfrom nltk.stem import WordNetLemmatizer\nnltk.download('punkt')\nnltk.download('wordnet')\nnltk.download('averaged_perceptron_tagger')\n\n# Spelling imports\nfrom language_tool_python import LanguageTool\n\n# Huggingface\nfrom transformers import TFRobertaForSequenceClassification, AutoTokenizer\nfrom datasets import load_dataset, DatasetDict, logging\n\n\nlogging.set_verbosity_error()\nlogging.disable_progress_bar()\n\n# Classname Choice -------------------------------------------------------------\n\nraw_dataset = load_dataset(\"go_emotions\")\n\nall_class_names = [\"admiration\", \"amusement\", \"anger\", \"annoyance\", \"approval\",\n \"caring\", \"confusion\", \"curiosity\", \"desire\", \"disappointment\",\n \"disapproval\", \"disgust\", \"embarrassment\", \"excitement\", \"fear\",\n \"gratitude\", \"grief\", \"joy\", \"love\", \"nervousness\", \"optimism\",\n \"pride\", \"realization\", \"relief\", \"remorse\", \"sadness\", \"surprise\",\n \"neutral\"]\n\n# Get top 14 most frequently occuring keys in dataset and fetch their indices\nclass_counts = {class_name: 0 for class_name in all_class_names}\n\nfor item in raw_dataset['train']:\n for class_name in item['labels']:\n class_counts[all_class_names[class_name]] += 1\n\nfeature_distribution = dict(sorted(class_counts.items(), key=lambda item: item[1]))\n\nclass_names = list(feature_distribution.keys())[-14:]\nclass_name_idxs = [all_class_names.index(x) for x in class_names]\nprint(list(zip(class_names, class_name_idxs)))\n\n# Dataset Filtering -------------------------------------------------------------\n\n# If it has at least one label that is the selected subset of classes it's valid\ndef is_valid(data_item):\n return not (len(data_item[\"labels\"]) == 1 and data_item[\"labels\"][0] not in class_name_idxs)\n\n# Remove classes that don't have a label in our 14 selected classes \ndef remove_invalid_classes(data_item):\n data_item[\"labels\"] = [label for label in data_item[\"labels\"] if label in class_name_idxs] \n\n for label in data_item[\"labels\"]:\n assert label in class_name_idxs\n\n data_item[\"labels\"] = [class_name_idxs.index(label) for label in data_item[\"labels\"]][0:1] # \"Rename\" old labels\n\n return data_item\n\ndef one_hot_labels(data_item):\n data_item[\"labels\"] = np.sum(to_categorical(data_item[\"labels\"], len(class_name_idxs)), axis = 0)\n return data_item\n\n# Apply dataset processing\ndataset = raw_dataset.filter(lambda x: is_valid(x)).map(remove_invalid_classes)\n\n# One-hot the labels\ndataset_base = dataset.map(one_hot_labels)\n\n# Lemmatizing With POS Tagging -------------------------------------------------------------\n\ndef lemm_with_pos_tagging(example):\n tokens = word_tokenize(example['text'])\n tagged = pos_tag(tokens)\n lemmatizer = WordNetLemmatizer()\n pos_tags = {'N': wordnet.NOUN, 'V': wordnet.VERB, 'R': wordnet.ADV, 'J': wordnet.ADJ}\n words = []\n for word, tag in tagged:\n if tag[0] in pos_tags:\n words.append(lemmatizer.lemmatize(word, pos=pos_tags[tag[0]]))\n else:\n words.append(lemmatizer.lemmatize(word))\n example['text'] = ' '.join(words)\n return example\n\ndataset_base['train'] = dataset_base['train'].map(lemm_with_pos_tagging)\n\n# Grammar Correction -------------------------------------------------------------\n\nlang_tool = LanguageTool('en-US')\n\ndef correct_grammar(example):\n sentence = example['text']\n errors = lang_tool.check(sentence)\n \n if len(errors) > 0:\n for error in reversed(errors):\n if len(error.replacements) > 0:\n corrected = sentence[:error.offset] + error.replacements[0] + sentence[error.offset + error.errorLength:]\n sentence = corrected\n example['text'] = ''.join(sentence)\n return example\n\nlogging.enable_progress_bar()\ndataset_base = dataset_base.map(correct_grammar)\nlogging.disable_progress_bar()\n\nidx = np.random.randint(0, 2000)\n\nprint(dataset_base['train'][idx][\"text\"])\nprint(class_names[np.argmax(dataset_base['train'][idx][\"labels\"])])\n\ndataset_base = DatasetDict(dataset_base)\n\n# Dataset Tokenization -------------------------------------------------------------\n\ntokenizer = AutoTokenizer.from_pretrained(\"roberta-base\")\n# tokenizer = AutoTokenizer.from_pretrained(\"bert-base-cased\")\n\nseq_lens = [len(tokenizer(x)[\"input_ids\"]) for x in dataset_base[\"train\"][\"text\"]]\nfinal_seq_len = int(np.ceil(np.mean(seq_lens) + np.std(seq_lens)))\n\ndef tokenize_dataset(data):\n # Keys of the returned dictionary will be added to the dataset as columns\n tokenizer_out = tokenizer(data[\"text\"], padding = \"max_length\", truncation = True, max_length = final_seq_len) # Sets length of tokenized string to mean token sequence length\n for key in tokenizer_out:\n data[key] = tokenizer_out[key]\n return data\n\ndataset_tokenized = dataset_base.map(tokenize_dataset)\n\n# Dataset to tf.data -------------------------------------------------------------\n\nbatch_size = 128\n\ntrain_dataset = dataset_tokenized[\"train\"].to_tf_dataset(\n columns = [\"input_ids\", \"attention_mask\"],\n label_cols = [\"labels\"],\n batch_size = batch_size,\n shuffle = True,\n)\n\ntest_dataset = dataset_tokenized[\"test\"].to_tf_dataset(\n columns = [\"input_ids\", \"attention_mask\"],\n label_cols = [\"labels\"],\n batch_size = batch_size,\n shuffle = True,\n)\n\n# Training -------------------------------------------------------------\n\ndef define_model(): \n input_ids = Input(shape = (final_seq_len,), dtype = \"int32\", name = \"input_ids\")\n attention_masks = Input(shape = (final_seq_len,), dtype = \"int32\", name = \"attention_mask\")\n\n inputs = {\"input_ids\": input_ids, \"attention_mask\": attention_masks}\n\n model = TFRobertaForSequenceClassification.from_pretrained(\"roberta-base\", num_labels = 14)(inputs).logits\n model = Dense(14, activation = \"softmax\")(model)\n\n model = Model(inputs = [input_ids, attention_masks], outputs = model)\n\n optimizer = tf.keras.optimizers.Adagrad(learning_rate = 1e-3)\n\n model.compile(optimizer = optimizer, loss = \"categorical_crossentropy\", metrics = [\"accuracy\"])\n\n return model\n\nmodel = define_model()\n\nearly_stopping = EarlyStopping(monitor = \"val_loss\", patience = 7, restore_best_weights = True)\n\nmodel.fit(train_dataset, validation_data = test_dataset, epochs = 25, callbacks = [early_stopping])\n\nmodel.save(\"models/model\")", "repo_name": "MattJKirby/COM3029-Group-3", "sub_path": "build-script.py", "file_name": "build-script.py", "file_ext": "py", "file_size_in_byte": 6539, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "nltk.download", "line_number": 15, "usage_type": "call"}, {"api_name": "nltk.download", "line_number": 16, "usage_type": "call"}, {"api_name": "nltk.download", "line_number": 17, "usage_type": "call"}, {"api_name": "datasets.logging.set_verbosity_error", "line_number": 27, "usage_type": "call"}, {"api_name": "datasets.logging", "line_number": 27, "usage_type": "name"}, {"api_name": "datasets.logging.disable_progress_bar", "line_number": 28, "usage_type": "call"}, {"api_name": "datasets.logging", "line_number": 28, "usage_type": "name"}, {"api_name": "datasets.load_dataset", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 72, "usage_type": "call"}, {"api_name": "keras.utils.to_categorical", "line_number": 72, "usage_type": "call"}, {"api_name": "nltk.word_tokenize", "line_number": 84, "usage_type": "call"}, {"api_name": "nltk.pos_tag", "line_number": 85, "usage_type": "call"}, {"api_name": "nltk.stem.WordNetLemmatizer", "line_number": 86, "usage_type": "call"}, {"api_name": "nltk.corpus.wordnet.NOUN", "line_number": 87, "usage_type": "attribute"}, {"api_name": "nltk.corpus.wordnet", "line_number": 87, "usage_type": "name"}, {"api_name": "nltk.corpus.wordnet.VERB", "line_number": 87, "usage_type": "attribute"}, {"api_name": "nltk.corpus.wordnet.ADV", "line_number": 87, "usage_type": "attribute"}, {"api_name": "nltk.corpus.wordnet.ADJ", "line_number": 87, "usage_type": "attribute"}, {"api_name": "language_tool_python.LanguageTool", "line_number": 101, "usage_type": "call"}, {"api_name": "datasets.logging.enable_progress_bar", "line_number": 115, "usage_type": "call"}, {"api_name": "datasets.logging", "line_number": 115, "usage_type": "name"}, {"api_name": "datasets.logging.disable_progress_bar", "line_number": 117, "usage_type": "call"}, {"api_name": "datasets.logging", "line_number": 117, "usage_type": "name"}, {"api_name": "numpy.random.randint", "line_number": 119, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 119, "usage_type": "attribute"}, {"api_name": "numpy.argmax", "line_number": 122, "usage_type": "call"}, {"api_name": "datasets.DatasetDict", "line_number": 124, "usage_type": "call"}, {"api_name": "transformers.AutoTokenizer.from_pretrained", "line_number": 128, "usage_type": "call"}, {"api_name": "transformers.AutoTokenizer", "line_number": 128, "usage_type": "name"}, {"api_name": "numpy.ceil", "line_number": 132, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 132, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 132, "usage_type": "call"}, {"api_name": "keras.layers.Input", "line_number": 164, "usage_type": "call"}, {"api_name": "keras.layers.Input", "line_number": 165, "usage_type": "call"}, {"api_name": "transformers.TFRobertaForSequenceClassification.from_pretrained", "line_number": 169, "usage_type": "call"}, {"api_name": "transformers.TFRobertaForSequenceClassification", "line_number": 169, "usage_type": "name"}, {"api_name": "keras.layers.Dense", "line_number": 170, "usage_type": "call"}, {"api_name": "keras.models.Model", "line_number": 172, "usage_type": "call"}, {"api_name": "tensorflow.keras.optimizers.Adagrad", "line_number": 174, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 174, "usage_type": "attribute"}, {"api_name": "keras.callbacks.EarlyStopping", "line_number": 182, "usage_type": "call"}]} +{"seq_id": "18412036848", "text": "import requests\nimport json\nimport time\nimport csv\nimport datetime\nimport pytz\n\nmass_group_id = {\n '37944853':'gl_doroga35',\n '118335312':'safe.driving35',\n '36533867':'evrosnab35'\n }\n\ndef send_message_if_new_chat(group_id,message_text):\n kate_token = 'vk1.a.ZSV3Xig0KNauIfyqWaUNuqrpNdWnYfr9ID0wYYaGK64knsrg2Yv243IxRSfSlj6637Ftqgij4RG9wkev6TYBp9i3pTFa5KpDT1MG0lLhc0S2p4EhlXC2hVia-xHVA_3mQ18g7Rez-u7fkjgcPSrvOh8AIuNPoPG0bSQ_nyPw5IYojSX7a6YIlz8gBrng6rRjCCzqWiG1lNhfwmgfHb8VSQ'\n url = f'https://api.vk.com/method/messages.send?v=5.131&access_token={kate_token}&peer_id=-{group_id}&random_id=0&message={message_text}'\n url_chat_old = f'https://api.vk.com/method/messages.getConversationsById?v=5.131&access_token={kate_token}&peer_ids=-{group_id}'\n chat_old = requests.get(url_chat_old)\n jchat_old = chat_old.json()\n if jchat_old['response']['items'][0]['last_conversation_message_id'] == 0:\n # sendMess = requests.get(url)\n print(f'Новый чат! Сообщение \"{message_text}\" отправляем!')\n return 'true'\n else:\n print('Чат существует! СООБЩЕНИЕ НЕ ОТПРАВЛЯЕМ!!!')\n return 'false'\n\n\n print(jchat_old)\ndef create_lead_espo(data_dict):\n api = 'f8cf681f81f9540e1baeb3607b7fc1b8'\n header = {\n 'X-Api-Key': 'e6bb529c5f80f8fc799aed7e6e9dcbdb',\n 'Content-Type': 'application/json',\n 'Accept': 'application/json'\n }\n url = 'https://евроснаб.драйвэксперт.рф/api/v1/LeadCapture/'\n url_c = f'{url}{api}'\n data = data_dict\n res = requests.post(url_c, data=data)\n if res.text == 'true':\n print(f'Лид создан:{res.text}')\n return 'lead create'\n else:\n print(f'Лид НЕ создан:{res.__dict__}')\n return 'lead not create'\n\ndef vk_mess_and_lead_create(group_id):\n data_dict ={}\n new_token = 'vk1.a.vs2JzZ3Wu_GIsAyX-NyI0ACepmtlx-ap_C34XbGySelCpmqPyQ4uQ-TbJfSvvwu-dJrdg_-qDBZt4xT18D3GhP9Dp2CkE_8Y_pbiYF0Uge4nM4QTkUoOi_mk3_GzIC4PgcIz-eV-aYNOBTSfVkhMI2M2_xX77qjVrhnVyLiMudUHC2eUZneZiB1LZrc7cUIQRqX7nM2WJj3A9CM1bQZOYA'\n url_getAddr = f'https://api.vk.com/method/groups.getAddresses?v=5.131&access_token={new_token}&group_id={group_id}'\n url_getID = f'https://api.vk.com/method/groups.getById?v=5.131&access_token={new_token}&group_id={group_id}'\n res = requests.get(url_getAddr)\n\n if res.status_code == 200:\n jres = res.json()\n if len(jres['response']['items']) > 0:\n for i in jres['response']['items']:\n print('Добавляем данные:')\n print(f\"Наименование: {i['title']} Город: {i['city']['title']} Адрес: {i['address']} Телефон: {i['phone'].replace(' ','')}\")\n data_dict[\"accountName\"] = i['title']\n data_dict[\"addressCity\"] = i['city']['title']\n data_dict[\"addressStreet\"] = i['address']\n data_dict[\"phoneNumber\"] = i['phone'].replace(' ','')\n else:\n print('информация отсутствует!!!!!!')\n data_dict[\"accountName\"] = 'NoName'\n resID = requests.get(url_getID)\n if resID.status_code == 200:\n jresID = resID.json()\n for i in jresID['response']:\n print('Добавляем данные:')\n print(f\"Название группы VK: {i['name']}\")\n data_dict[\"vkURL\"] = f\"https://vk.com/{i['screen_name']}\"\n data_dict[\"vkname\"] = i['name']\n data_dict[\"status\"] = \"AutoCreate\"\n data_dict[\"assignedUserId\"] = 1\n data_dict[\"vkgroupchatlink\"] = f\"https://vk.com/im?sel=-{group_id}\"\n data_dict[\"date\"] = str(datetime.datetime.now(pytz.timezone('utc')))[:19]\n print(data_dict)\n a = send_message_if_new_chat(group_id,'Hello!')\n if a == 'true':\n data_dict[\"resObr\"] = 'Отправлено сообщение в сообщество VK!'\n print(f\"Данные лида: {data_dict}\")\n b = create_lead_espo(data_dict)\n if b == 'true':\n print(f'Лид {data_dict[\"accountName\"]} СОЗДАН!')\n else:\n data_dict[\"resObr\"] = 'Сообщение в сообщество VK НЕ ОТПРАВЛЕНО!!! или БЫЛО ОТПРАВЛЕНО РАНЕЕ!!!'\n b = create_lead_espo(data_dict)\n if b == 'true':\n print(f'Лид {data_dict[\"accountName\"]} СОЗДАН!')\n\ndef __main__():\n for group_id in mass_group_id:\n vk_mess_and_lead_create(group_id)\n\n\n__main__()\n\n\n#send_message_if_new_chat('36533867','Hello')\n#send_message_if_new_chat('78396963','Hello')\n\n#create_lead_espo(vk_mess_and_lead_create(78396963))", "repo_name": "drive-expert/VK-Group-Send-to-Lead-ESPOCRM", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 4713, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "requests.get", "line_number": 18, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 40, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 53, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 68, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 79, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 79, "usage_type": "attribute"}, {"api_name": "pytz.timezone", "line_number": 79, "usage_type": "call"}]} +{"seq_id": "11611106059", "text": "import argparse\nimport numpy as np \nimport matplotlib.pyplot as plt\n#from matplotlib.animation import FuncAnimation\nimport os\nimport time\n\nimport utils.data_manipulation as dm\nimport utils.plot_functions as pf\nimport utils.retina_computation as rc\nimport utils.sbatch_scripts as ss\n\nfrom textwrap import wrap\n\nimport difflib\n\n\n\n\n\n\n\n# In Greg Field's data :::\n# Cell Types : Num Cells\n# ------------------------------\n# offBriskTransient : 55 cells\n# offBriskSustained : 43 cells\n# onBriskTransient : 39 cells\n# offExpanding : 13 cells\n# offTransient : 4 cells\n# onBriskSustained : 6 cells\n# onTransient : 7 cells\n# dsOnoffDown : 7 cells\n# dsOnoffRight : 3 cells\n# dsOnoffLeft : 3 cells\n# dsOnoffUp : 2 cells\n\n\n#\ndirHomeLoc, dirScratch = dm.set_dir_tree()\n\n# Parameters we can loop over.\nstims = ['NatMov','Wnoise']\ncellSubTypeCombinations = [ ['offBriskTransient','offBriskSustained'], ['offBriskTransient','onBriskTransient'] ]\n\n\t\t\t\t\t\t\t# ['offBriskTransient'], ['offBriskSustained'], ['onBriskTransient'], \\\n\t\t\t\t\t\t\t# ['offBriskTransient','offBriskSustained'], ['offBriskTransient','onBriskTransient'] ] # a list of lists. Each internal list is a combination of cell sub types to consider as a group to find Cell Assemblies within them.\n\n#num_EM_Samples \t= [50000] #, 500000] #[50000, 100000, 500000] # [5000, 10000, 50000, 100000, 500000, 1000000] # number of steps to run full EM algorithm - alternating Inference and Learning.\n#cell_types = ['allCells'] \n\nmodel_CA_overcompleteness = [1] \t\t# [1,2] \t# how many times more cell assemblies we have than cells (1 means complete - N=M, 2 means 2x overcomplete)\nSW_bins = [0, 1, 2] \t\t\t\t\t# ms. Build up spikewords from groups of cells in the same trial that fire within SW_bins of eachother.\nlearning_rates = [0.1, 0.5, 1.0]\n\n\nyLo_Vals \t\t= [0] #[1] \t\t# If |y|<=yLo, then we force the z=0 inference solution and change Pi. This defines cells assemblies to be more than 1 cell.\nyHi_Vals \t\t= [300] \t\t# If |y|>=yHi, then we assume at least 1 CA is on and disallow z=0 inference solution and change Pia.\nyMinSWs \t\t= [1,3] #[1,2,3]\t\t\t# DOING BELOW THING WITH YYY inside pgm functions. --> (set to 0, so does nothing) \n\t\t\t\t\t\t\t\t# Only look at spikewords that have more active cells than yLo for EM learning. That is, ignore |y|TH_bounds.max()).sum(axis=1).max()\n\t\t\t\t\t\t\t\t\t\t\tmaxNumCellsA[1] = ( (1-rc.sig(ria_snapshotsA))>TH_bounds.min()).sum(axis=1).max()\n\t\t\t\t\t\t\t\t\t\t\tmaxNumCellsA \t= maxNumCellsA.astype(int)\n\t\t\t\t\t\t\t\t\t\t\t#\n\t\t\t\t\t\t\t\t\t\t\tmaxNumCAsA \t\t= np.zeros(2)\n\t\t\t\t\t\t\t\t\t\t\tmaxNumCAsA[0]\t= ( (1-rc.sig(ria_snapshotsA))>TH_bounds.max()).sum(axis=2).max()\n\t\t\t\t\t\t\t\t\t\t\tmaxNumCAsA[1]\t= ( (1-rc.sig(ria_snapshotsA))>TH_bounds.min()).sum(axis=2).max()\n\t\t\t\t\t\t\t\t\t\t\tmaxNumCAsA \t\t= maxNumCAsA.astype(int)\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\tif train_2nd_model and B_file_there:\n\t\t\t\t\t\t\t\t\t\t\t\t#\n\t\t\t\t\t\t\t\t\t\t\t\tdataB = np.load( str(EM_learning_Dir + init_dir + model_dir + mfB[0]) )\n\t\t\t\t\t\t\t\t\t\t\t\tq_snapshotsB \t\t= dataB['q_snapshots']\n\t\t\t\t\t\t\t\t\t\t\t\tri_snapshotsB \t\t= dataB['ri_snapshots'] \n\t\t\t\t\t\t\t\t\t\t\t\tria_snapshotsB \t= dataB['ria_snapshots'] \n\t\t\t\t\t\t\t\t\t\t\t\tY_inferred_trainB \t= dataB['Y_inferred_train']\n\t\t\t\t\t\t\t\t\t\t\t\tZ_inferred_trainB \t= dataB['Z_inferred_train']\n\t\t\t\t\t\t\t\t\t\t\t\tpjoint_trainB \t\t= dataB['pjoint_train']\n\t\t\t\t\t\t\t\t\t\t\t\tpjoint_testB \t\t= dataB['pjoint_test']\n\t\t\t\t\t\t\t\t\t\t\t\targsRecModelLearnB \t= dataB['argsRec']\n\t\t\t\t\t\t\t\t\t\t\t\t#\n\t\t\t\t\t\t\t\t\t\t\t\t#numSnaps \t\t= ria_snapshotsA.shape[0]\n\t\t\t\t\t\t\t\t\t\t\t\tmaxPiQB \t\t= np.array([ rc.sig(q_snapshotsB).max(),\t(1-rc.sig(ri_snapshotsB)).max() ]).max()\n\t\t\t\t\t\t\t\t\t\t\t\t#\n\t\t\t\t\t\t\t\t\t\t\t\tmaxNumCellsB \t= np.zeros(2)\n\t\t\t\t\t\t\t\t\t\t\t\tmaxNumCellsB[0] = ( (1-rc.sig(ria_snapshotsB))>TH_bounds.max()).sum(axis=1).max()\n\t\t\t\t\t\t\t\t\t\t\t\tmaxNumCellsB[1] = ( (1-rc.sig(ria_snapshotsB))>TH_bounds.min()).sum(axis=1).max()\n\t\t\t\t\t\t\t\t\t\t\t\tmaxNumCellsB \t= maxNumCellsB.astype(int)\n\t\t\t\t\t\t\t\t\t\t\t\t#\n\t\t\t\t\t\t\t\t\t\t\t\tmaxNumCAsB \t\t= np.zeros(2)\n\t\t\t\t\t\t\t\t\t\t\t\tmaxNumCAsB[0]\t= ( (1-rc.sig(ria_snapshotsB))>TH_bounds.max()).sum(axis=2).max()\n\t\t\t\t\t\t\t\t\t\t\t\tmaxNumCAsB[1]\t= ( (1-rc.sig(ria_snapshotsB))>TH_bounds.min()).sum(axis=2).max()\n\t\t\t\t\t\t\t\t\t\t\t\tmaxNumCAsB \t\t= maxNumCAsA.astype(int)\n\t\t\t\t\t\t\t\t\t\t\t\n\n\n\t\t\t\t\t\t\t\t\t\t\tplt_save_dir = str( figs_save_dir + init_dir + model_dir )\n\t\t\t\t\t\t\t\t\t\t\tplt_snap_dir = str( plt_save_dir + 'Snapshots/' )\n\t\t\t\t\t\t\t\t\t\t\tif not os.path.exists( plt_snap_dir ):\n\t\t\t\t\t\t\t\t\t\t\t\tos.makedirs( plt_snap_dir )\t\t\n\n\n\n\t\t\t\t\t\t\t\t\t\t\tif run_raster:\n\t\t\t\t\t\t\t\t\t\t\t\t# Load in raster data file of inferred spike words on all data made after the entire model was learned.\n\t\t\t\t\t\t\t\t\t\t\t\tdataRA = np.load( str( rasterZ_data_dir + init_dir + model_dir + mfA[0].replace('LearnedModel_','rasterZ_allSWs_') ) )\n\t\t\t\t\t\t\t\t\t\t\t\tprint(dataRA.keys())\n\t\t\t\t\t\t\t\t\t\t\t\t#\n\t\t\t\t\t\t\t\t\t\t\t\tYcell_hist_allSWsA \t\t= dataRA['Ycell_hist_allSWs']\n\t\t\t\t\t\t\t\t\t\t\t\tZassem_hist_allSWsA \t= dataRA['Zassem_hist_allSWs']\n\t\t\t\t\t\t\t\t\t\t\t\t#\n\t\t\t\t\t\t\t\t\t\t\t\tnY_allSWsA \t\t\t\t= dataRA['nY_allSWs']\n\t\t\t\t\t\t\t\t\t\t\t\tnZ_allSWsA \t\t\t\t= dataRA['nZ_allSWs']\n\t\t\t\t\t\t\t\t\t\t\t\t#\n\t\t\t\t\t\t\t\t\t\t\t\tnum_EM_samps \t\t\t= len(nY_allSWsA)\n\t\t\t\t\t\t\t\t\t\t\t\t#\n\t\t\t\t\t\t\t\t\t\t\t\tsortCAs_byActivityA = np.argsort(Zassem_hist_allSWsA[:-1])[::-1]\n\t\t\t\t\t\t\t\t\t\t\t\tsortCells_byActivityA = np.argsort(Ycell_hist_allSWsA[:-1])[::-1]\n\n\n\t\t\t\t\t\t\t\t\t\t\t\tif train_2nd_model and B_file_there:\n\t\t\t\t\t\t\t\t\t\t\t\t\t# Load in raster data file of inferred spike words on all data made after the entire model was learned.\n\t\t\t\t\t\t\t\t\t\t\t\t\tdataRB = np.load( str( rasterZ_data_dir + init_dir + model_dir + mfB[0].replace('LearnedModel_','rasterZ_allSWs_') ) )\n\t\t\t\t\t\t\t\t\t\t\t\t\tprint(dataRB.keys())\n\t\t\t\t\t\t\t\t\t\t\t\t\t#\n\t\t\t\t\t\t\t\t\t\t\t\t\tYcell_hist_allSWsB \t\t= dataRB['Ycell_hist_allSWs']\n\t\t\t\t\t\t\t\t\t\t\t\t\tZassem_hist_allSWsB \t= dataRB['Zassem_hist_allSWs']\n\t\t\t\t\t\t\t\t\t\t\t\t\t#\n\t\t\t\t\t\t\t\t\t\t\t\t\tnY_allSWsB \t\t\t\t= dataRB['nY_allSWs']\n\t\t\t\t\t\t\t\t\t\t\t\t\tnZ_allSWsB \t\t\t\t= dataRB['nZ_allSWs']\n\t\t\t\t\t\t\t\t\t\t\t\t\t#\n\t\t\t\t\t\t\t\t\t\t\t\t\tnum_EM_sampsB = len(nY_allSWsB)\n\t\t\t\t\t\t\t\t\t\t\t\t\t#\n\t\t\t\t\t\t\t\t\t\t\t\t\tsortCAs_byActivityB \t= np.argsort(Zassem_hist_allSWsB[:-1])[::-1]\n\t\t\t\t\t\t\t\t\t\t\t\t\tsortCells_byActivityB \t= np.argsort(Ycell_hist_allSWsB[:-1])[::-1]\n\n\n\t\t\t\t\t\t\t\t\t\t\t\t# UNCOMMENT THIS IF YOU DONT WANT TO SORT !\n\t\t\t\t\t\t\t\t\t\t\t\tsortCells_byActivityA = np.arange(N)\n\t\t\t\t\t\t\t\t\t\t\t\tsortCAs_byActivityA \t= np.arange(M)\n\t\t\t\t\t\t\t\t\t\t\t\tsortCells_byActivityB = np.arange(N)\n\t\t\t\t\t\t\t\t\t\t\t\tsortCAs_byActivityB \t= np.arange(M)\n\n\t\t\t\t\t\t\t\t\t\t\telse:\n\t\t\t\t\t\t\t\t\t\t\t\t#\n\t\t\t\t\t\t\t\t\t\t\t\t# Get num_EM_samps from file name.\n\t\t\t\t\t\t\t\t\t\t\t\ta = mfA[0].find(stim)\n\t\t\t\t\t\t\t\t\t\t\t\tb = mfA[0].find( str('SWs_' + str(pct_xVal_train).replace('.','pt') + 'trn_') )\t\n\t\t\t\t\t\t\t\t\t\t\t\tnum_EM_samps = int(mfA[0][a+1+len(stim):b])\n\t\t\t\t\t\t\t\t\t\t\t\t#\n\t\t\t\t\t\t\t\t\t\t\t\tsortCells_byActivityA\t= np.arange(N)\n\t\t\t\t\t\t\t\t\t\t\t\tsortCAs_byActivityA \t= np.arange(M)\n\n\t\t\t\t\t\t\t\t\t\t\t\tYcell_hist_allSWsA \t\t= np.zeros(N)\n\t\t\t\t\t\t\t\t\t\t\t\tZassem_hist_allSWsA \t= np.zeros(M)\n\t\t\t\t\t\t\t\t\t\t\t\t#\n\t\t\t\t\t\t\t\t\t\t\t\tnY_allSWsA \t\t\t\t= np.zeros(num_EM_samps)\n\t\t\t\t\t\t\t\t\t\t\t\tnZ_allSWsA \t\t\t\t= np.zeros(num_EM_samps)\n\t\t\t\t\t\t\t\t\t\t\t\t#\n\t\t\t\t\t\t\t\t\t\t\t\tif train_2nd_model and B_file_there:\n\t\t\t\t\t\t\t\t\t\t\t\t\t#\n\t\t\t\t\t\t\t\t\t\t\t\t\tsortCells_byActivityB\t= np.arange(N)\n\t\t\t\t\t\t\t\t\t\t\t\t\tsortCAs_byActivityB \t= np.arange(M)\n\n\t\t\t\t\t\t\t\t\t\t\t\t\tYcell_hist_allSWsB \t\t= np.zeros(N)\n\t\t\t\t\t\t\t\t\t\t\t\t\tZassem_hist_allSWsB \t= np.zeros(M)\n\t\t\t\t\t\t\t\t\t\t\t\t\t#\n\t\t\t\t\t\t\t\t\t\t\t\t\tnY_allSWsB \t\t\t\t= np.zeros(num_EM_samps)\n\t\t\t\t\t\t\t\t\t\t\t\t\tnZ_allSWsB \t\t\t\t= np.zeros(num_EM_samps)\n\t\t\t\t\t\t\t\t\t\t\t\t\t#\n\t\t\t\t\t\t\t\t\t\t\t\t\t# FOR NOW I AM ASSUMING THERE ARE SAME NUMBER OF EM SAMPLES IN A AND B FILES.\n\t\t\t\t\t\t\t\t\t\t\t\t\t# # Get num_EM_samps from file name.\n\t\t\t\t\t\t\t\t\t\t\t\t\t# a = mfB[0].find(stim)\n\t\t\t\t\t\t\t\t\t\t\t\t\t# b = mfB[0].find( str('SWs_' + str(pct_xVal_train).replace('.','pt') + 'trn_') )\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t# num_EM_sampsB = int(mfB[0][a+1+len(stim):b])\n\n\n\t\t\t\t\t\t\t\t\t\t\tprint('Number of snapshots are ',numSnaps)\t\n\t\t\t\t\t\t\t\t\t\t\tsnaps = range(numSnaps-1,numSnaps) #range(ria_snapshots.shape[0])\n\t\t\t\t\t\t\t\t\t\t\t#\n\t\t\t\t\t\t\t\t\t\t\tfor i in snaps:\n\t\t\t\t\t\t\t\t\t\t\t\t#\n\t\t\t\t\t\t\t\t\t\t\t\tsampAtSnap = int( i*num_EM_samps/(numSnaps-1) )\n\t\t\t\t\t\t\t\t\t\t\t\t#\n\t\t\t\t\t\t\t\t\t\t\t\tif run_raster:\n\t\t\t\t\t\t\t\t\t\t\t\t\t# Stats on active cells and cell assemblies inferred during EM algorithm\n\t\t\t\t\t\t\t\t\t\t\t\t\tprint(i,' compute Inference statistics')\n\t\t\t\t\t\t\t\t\t\t\t\t\tt0 = time.time()\n\t\t\t\t\t\t\t\t\t\t\t\t\t#\n\t\t\t\t\t\t\t\t\t\t\t\t\tYcell_hist_InferSnapA, Zassem_hist_InferSnapA, nY_InferSnapA, nZ_InferSnapA, \\\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tCA_coactivity_InferSnapA, Cell_coactivity_InferSnapA = rc.compute_dataGen_Histograms( \\\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tY_inferred_trainA[:sampAtSnap], Z_inferred_trainA[:sampAtSnap], M, N)\n\t\t\t\t\t\t\t\t\t\t\t\t\t#\n\t\t\t\t\t\t\t\t\t\t\t\t\tif train_2nd_model:\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tYcell_hist_InferSnapB, Zassem_hist_InferSnapB, nY_InferSnapB, nZ_InferSnapB, \\\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tCA_coactivity_InferSnapB, Cell_coactivity_InferSnapB = rc.compute_dataGen_Histograms( \\\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tY_inferred_trainB[:sampAtSnap], Z_inferred_trainB[:sampAtSnap], M, N)\n\t\t\t\t\t\t\t\t\t\t\t\t\t#\n\t\t\t\t\t\t\t\t\t\t\t\t\tt1 = time.time()\n\t\t\t\t\t\t\t\t\t\t\t\t\tprint('Done w/ inference stats : time = ',t1-t0) # Fast enough: ~10 seconds\n\n\t\t\t\t\t\t\t\t\t\t\t\telse:\n\n\t\t\t\t\t\t\t\t\t\t\t\t\tYcell_hist_InferSnapA=np.zeros(N+1)\n\t\t\t\t\t\t\t\t\t\t\t\t\tZassem_hist_InferSnapA=np.zeros(M+1)\n\t\t\t\t\t\t\t\t\t\t\t\t\tnY_InferSnapA = [len(xx) for xx in Y_inferred_trainA]\n\t\t\t\t\t\t\t\t\t\t\t\t\tnZ_InferSnapA = [len(xx) for xx in Z_inferred_trainA]\n\t\t\t\t\t\t\t\t\t\t\t\t\tCA_coactivity_InferSnapA=0\n\t\t\t\t\t\t\t\t\t\t\t\t\tCell_coactivity_InferSnapA=0\n\t\t\t\t\t\t\t\t\t\t\t\t\t#\n\t\t\t\t\t\t\t\t\t\t\t\t\tif train_2nd_model and B_file_there:\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t#\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tYcell_hist_InferSnapB=np.zeros(N+1)\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tZassem_hist_InferSnapB=np.zeros(M+1)\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tnY_InferSnapB = [len(xx) for xx in Y_inferred_trainB]\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tnZ_InferSnapB = [len(xx) for xx in Z_inferred_trainB]\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tCA_coactivity_InferSnapB=0\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tCell_coactivity_InferSnapB=0\n\t\t\t\t\t\t\t\t\t\t\t\t# # # # #\n\t\t\t\t\t\t\t\t\t\t\t\t#\n\t\t\t\t\t\t\t\t\t\t\t\t# #\n\t\t\t\t\t\t\t\t\t\t\t\t# # #\n\t\t\t\t\t\t\t\t\t\t\t\t#\n\t\t\t\t\t\t\t\t\t\t\t\tYcell_hist_InferSnapA = Ycell_hist_InferSnapA[sortCells_byActivityA]\n\t\t\t\t\t\t\t\t\t\t\t\tZassem_hist_InferSnapA = Zassem_hist_InferSnapA[sortCAs_byActivityA]\n\t\t\t\t\t\t\t\t\t\t\t\t#\n\t\t\t\t\t\t\t\t\t\t\t\tQSnapA \t\t\t= rc.sig(q_snapshotsA[i])\n\t\t\t\t\t\t\t\t\t\t\t\tPiSnapA \t \t= ( 1-rc.sig(ri_snapshotsA[i]) )[sortCells_byActivityA]\n\t\t\t\t\t\t\t\t\t\t\t\tPiaSnapA \t \t= ( 1-rc.sig(ria_snapshotsA[i]) )[np.ix_(sortCells_byActivityA,sortCAs_byActivityA)]\n\t\t\t\t\t\t\t\t\t\t\t\tnumCAsSnapUbA\t= ( PiaSnapA>TH_bounds.min()).sum(axis=1)\n\t\t\t\t\t\t\t\t\t\t\t\tnumCAsSnapLbA\t= ( PiaSnapA>TH_bounds.max()).sum(axis=1)\n\t\t\t\t\t\t\t\t\t\t\t\tnumCellsSnapUbA\t= ( PiaSnapA>TH_bounds.min()).sum(axis=0)\n\t\t\t\t\t\t\t\t\t\t\t\tnumCellsSnapLbA = ( PiaSnapA>TH_bounds.max()).sum(axis=0)\n\t\t\t\t\t\t\t\t\t\t\t\t#\n\t\t\t\t\t\t\t\t\t\t\t\tplt_titleA = str( 'Add something here --- snapshot '+ str(i) )\n\t\t\t\t\t\t\t\t\t\t\t\tplt_save_tagA = str( mfA[0][:-4] + '_snap' + str(i) )\n\t\t\t\t\t\t\t\t\t\t\t\t#\n\t\t\t\t\t\t\t\t\t\t\t\tpf.plot_learned_model(PiSnapA, PiaSnapA, QSnapA, numCAsSnapUbA, numCAsSnapLbA, numCellsSnapUbA, \\\n\t\t\t\t\t\t\t\t\t\t\t\t\tnumCellsSnapLbA, Zassem_hist_InferSnapA, Ycell_hist_InferSnapA, Ycell_hist_allSWsA, \\\n\t\t\t\t\t\t\t\t\t\t\t\t\tTH_bounds, maxNumCellsA, maxNumCAsA, maxPiQA, nY_allSWsA, nY_InferSnapA, nZ_InferSnapA, \\\n\t\t\t\t\t\t\t\t\t\t\t\t\tsampAtSnap, num_EM_samps, plt_snap_dir, plt_save_tagA, plt_titleA)\n\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\tif train_2nd_model and B_file_there:\n\t\t\t\t\t\t\t\t\t\t\t\t\tYcell_hist_InferSnapB = Ycell_hist_InferSnapB[sortCells_byActivityB]\n\t\t\t\t\t\t\t\t\t\t\t\t\tZassem_hist_InferSnapB = Zassem_hist_InferSnapB[sortCAs_byActivityB]\n\t\t\t\t\t\t\t\t\t\t\t\t\t#\n\t\t\t\t\t\t\t\t\t\t\t\t\tQSnapB \t\t\t= rc.sig(q_snapshotsB[i])\n\t\t\t\t\t\t\t\t\t\t\t\t\tPiSnapB \t \t= ( 1-rc.sig(ri_snapshotsB[i]) )[sortCells_byActivityB]\n\t\t\t\t\t\t\t\t\t\t\t\t\tPiaSnapB \t \t= ( 1-rc.sig(ria_snapshotsB[i]) )[np.ix_(sortCells_byActivityB,sortCAs_byActivityB)]\n\t\t\t\t\t\t\t\t\t\t\t\t\tnumCAsSnapUbB\t= ( PiaSnapB>TH_bounds.min()).sum(axis=1)\n\t\t\t\t\t\t\t\t\t\t\t\t\tnumCAsSnapLbB\t= ( PiaSnapB>TH_bounds.max()).sum(axis=1)\n\t\t\t\t\t\t\t\t\t\t\t\t\tnumCellsSnapUbB\t= ( PiaSnapB>TH_bounds.min()).sum(axis=0)\n\t\t\t\t\t\t\t\t\t\t\t\t\tnumCellsSnapLbB = ( PiaSnapB>TH_bounds.max()).sum(axis=0)\n\t\t\t\t\t\t\t\t\t\t\t\t\t#\n\t\t\t\t\t\t\t\t\t\t\t\t\tplt_titleB = str( 'Add something here --- snapshot '+ str(i) )\n\t\t\t\t\t\t\t\t\t\t\t\t\tplt_save_tagB = str( mfB[0][:-4] + '_snap' + str(i) )\n\t\t\t\t\t\t\t\t\t\t\t\t\t#\n\t\t\t\t\t\t\t\t\t\t\t\t\tpf.plot_learned_model(PiSnapB, PiaSnapB, QSnapB, numCAsSnapUbB, numCAsSnapLbB, numCellsSnapUbB, numCellsSnapLbB, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\tZassem_hist_InferSnapB, Ycell_hist_InferSnapB, Ycell_hist_allSWsB, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\tTH_bounds, maxNumCellsB, maxNumCAsB, maxPiQB, nY_allSWsB, nY_InferSnapB, nZ_InferSnapB, \\\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tsampAtSnap, num_EM_samps, plt_snap_dir, plt_save_tagB, plt_titleB)\n\n\n\n\t\t\t\t\t\t\t\t\t\t\t\t# Plot two models learned on 50/50 split test and train side by side.\n\t\t\t\t\t\t\t\t\t\t\t\t\tif plt_modelPair:\n\t\t\t\t\t\t\t\t\t\t\t\t\t\ttranslate_Lrn2TruShuff,dot_prod_Lrn2Tru,translate_Lrn2Tru, translate_Lrn2Lrn, Perm_Lrn2Tru, dropWarn_Lrn2Tru = \\\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\trc.translate_CAs_LrnAndTru( A=PiaSnapA, Atag='A', B=PiaSnapB, Btag='B', verbose=False )\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t#\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tplt.figure( figsize=(20,10) ) # size units in inches\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tplt.rc('font', weight='bold', size=8)\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tf,ax = plt.subplots(3,2)\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t#\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tax[0][0].imshow(PiaSnapA[:,translate_Lrn2Lrn],vmin=0,vmax=1,aspect='auto')\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tax[0][0].set_title('Model 1')\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tax[0][1].imshow(PiaSnapB[:,translate_Lrn2TruShuff[0]],vmin=0,vmax=1,aspect='auto')\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tax[0][1].set_title('Model 2')\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tax[0][0].set_ylabel('Pia')\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t#\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tax[1][0].scatter( range(M), (PiaSnapA[:,translate_Lrn2Lrn]>0.5).sum(axis=0), s=5 )\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tax[1][0].text(0.6*M,1.2, str('#|CA|>1: '+str( ( (PiaSnapA[:,translate_Lrn2Lrn]>0.5).sum(axis=0)>1 ).sum() ) ) )\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tax[1][1].scatter( range(M), (PiaSnapB[:,translate_Lrn2TruShuff[0]]>0.5).sum(axis=0), s=5 )\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tax[1][1].text(0.6*M,1.2, str('#|CA|>1: '+str( ( (PiaSnapB[:,translate_Lrn2TruShuff[0]]>0.5).sum(axis=0)>1 ).sum() ) ) )\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tax[1][0].set_ylabel('Pia Col sums')\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t#\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tax[2][0].scatter( range(N), PiSnapA, s=5 )\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tax[2][0].scatter( np.round(N/2), QSnapA, s=5, marker='x', color='red' )\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tax[2][1].scatter( range(N), PiSnapB, s=5 )\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tax[2][1].scatter( np.round(N/2), QSnapB, s=5, marker='x', color='red' )\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tax[2][0].set_ylabel('Pi and Q')\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t#\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tplt.suptitle( str('Snapshot '+str(i)) )\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tif not os.path.exists( str(plt_save_dir+ 'SnapModelPairs/') ):\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tos.makedirs( str(plt_save_dir+ 'SnapModelPairs/') )\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tplt.savefig( str(plt_save_dir + 'SnapModelPairs/' + str( mfA[0][:-4] + '_snapPair' + str(i) ) + '.png') )\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tplt.close() \n\n\n\n\t\t\t\t\t\t\t\t\t\t\t# # OUTSIDE SNAPSHOTS LOOP.\n\t\t\t\t\t\t\t\t\t\t\t#\n\t\t\t\t\t\t\t\t\t\t\t# Plot Pjoints for cross validation\n\t\t\t\t\t\t\t\t\t\t\tif plt_xVal:\n\n\t\t\t\t\t\t\t\t\t\t\t\tplt_xval_dir = str( figs_save_dir + init_dir + model_dir)\n\t\t\t\t\t\t\t\t\t\t\t\tfname_xVal = mfA[0][:-4].replace('LearnedModel_','CrossValidation_')\n\t\t\t\t\t\t\t\t\t\t\t\tpf.plot_xValidation(Z_inferred_trainA, pjoint_trainA, pjoint_testA, plt_save_dir, fname_xVal, kerns=[1000,5000])\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\tif train_2nd_model and B_file_there:\n\t\t\t\t\t\t\t\t\t\t\t\t\t#\n\t\t\t\t\t\t\t\t\t\t\t\t\tfname_xVal = mfB[0][:-4].replace('LearnedModel_','CrossValidation_')\n\t\t\t\t\t\t\t\t\t\t\t\t\tpf.plot_xValidation(Z_inferred_trainB, pjoint_trainB, pjoint_testB, plt_save_dir, fname_xVal, kerns=[1000,5000])\n\n\t\t\t\t\t\t\t\t\t\texcept:\n\t\t\t\t\t\t\t\t\t\t\tprint('Skip!')\n\n\n\n\n\n\n", "repo_name": "chris-warner-II/Cell_Assembly_Codebase", "sub_path": "python_code/vis_model_snapshots_realData.py", "file_name": "vis_model_snapshots_realData.py", "file_ext": "py", "file_size_in_byte": 21814, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "utils.data_manipulation.set_dir_tree", "line_number": 40, "usage_type": "call"}, {"api_name": "utils.data_manipulation", "line_number": 40, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 98, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 109, "usage_type": "call"}, {"api_name": "utils.data_manipulation.set_dir_tree", "line_number": 116, "usage_type": "call"}, {"api_name": "utils.data_manipulation", "line_number": 116, "usage_type": "name"}, {"api_name": "os.listdir", "line_number": 179, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 199, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 214, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 226, "usage_type": "call"}, {"api_name": "utils.retina_computation.sig", "line_number": 226, "usage_type": "call"}, {"api_name": "utils.retina_computation", "line_number": 226, "usage_type": "name"}, {"api_name": "numpy.zeros", "line_number": 228, "usage_type": "call"}, {"api_name": "utils.retina_computation.sig", "line_number": 229, "usage_type": "call"}, {"api_name": "utils.retina_computation", "line_number": 229, "usage_type": "name"}, {"api_name": "utils.retina_computation.sig", "line_number": 230, "usage_type": "call"}, {"api_name": "utils.retina_computation", "line_number": 230, "usage_type": "name"}, {"api_name": "numpy.zeros", "line_number": 233, "usage_type": "call"}, {"api_name": "utils.retina_computation.sig", "line_number": 234, "usage_type": "call"}, {"api_name": "utils.retina_computation", "line_number": 234, "usage_type": "name"}, {"api_name": "utils.retina_computation.sig", "line_number": 235, "usage_type": "call"}, {"api_name": "utils.retina_computation", "line_number": 235, "usage_type": "name"}, {"api_name": "numpy.load", "line_number": 241, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 252, "usage_type": "call"}, {"api_name": "utils.retina_computation.sig", "line_number": 252, "usage_type": "call"}, {"api_name": "utils.retina_computation", "line_number": 252, "usage_type": "name"}, {"api_name": "numpy.zeros", "line_number": 254, "usage_type": "call"}, {"api_name": "utils.retina_computation.sig", "line_number": 255, "usage_type": "call"}, {"api_name": "utils.retina_computation", "line_number": 255, "usage_type": "name"}, {"api_name": "utils.retina_computation.sig", "line_number": 256, "usage_type": "call"}, {"api_name": "utils.retina_computation", "line_number": 256, "usage_type": "name"}, {"api_name": "numpy.zeros", "line_number": 259, "usage_type": "call"}, {"api_name": "utils.retina_computation.sig", "line_number": 260, "usage_type": "call"}, {"api_name": "utils.retina_computation", "line_number": 260, "usage_type": "name"}, {"api_name": "utils.retina_computation.sig", "line_number": 261, "usage_type": "call"}, {"api_name": "utils.retina_computation", "line_number": 261, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 268, "usage_type": "call"}, {"api_name": "os.path", "line_number": 268, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 269, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 275, "usage_type": "call"}, {"api_name": "numpy.argsort", "line_number": 286, "usage_type": "call"}, {"api_name": "numpy.argsort", "line_number": 287, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 292, "usage_type": "call"}, {"api_name": "numpy.argsort", "line_number": 303, "usage_type": "call"}, {"api_name": "numpy.argsort", "line_number": 304, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 308, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 309, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 310, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 311, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 320, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 321, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 323, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 324, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 326, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 327, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 331, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 332, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 334, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 335, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 337, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 338, "usage_type": "call"}, {"api_name": "time.time", "line_number": 357, "usage_type": "call"}, {"api_name": "utils.retina_computation.compute_dataGen_Histograms", "line_number": 360, "usage_type": "call"}, {"api_name": "utils.retina_computation", "line_number": 360, "usage_type": "name"}, {"api_name": "utils.retina_computation.compute_dataGen_Histograms", "line_number": 365, "usage_type": "call"}, {"api_name": "utils.retina_computation", "line_number": 365, "usage_type": "name"}, {"api_name": "time.time", "line_number": 368, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 373, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 374, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 382, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 383, "usage_type": "call"}, {"api_name": "utils.retina_computation.sig", "line_number": 396, "usage_type": "call"}, {"api_name": "utils.retina_computation", "line_number": 396, "usage_type": "name"}, {"api_name": "utils.retina_computation.sig", "line_number": 397, "usage_type": "call"}, {"api_name": "utils.retina_computation", "line_number": 397, "usage_type": "name"}, {"api_name": "utils.retina_computation.sig", "line_number": 398, "usage_type": "call"}, {"api_name": "utils.retina_computation", "line_number": 398, "usage_type": "name"}, {"api_name": "numpy.ix_", "line_number": 398, "usage_type": "call"}, {"api_name": "utils.plot_functions.plot_learned_model", "line_number": 407, "usage_type": "call"}, {"api_name": "utils.plot_functions", "line_number": 407, "usage_type": "name"}, {"api_name": "utils.retina_computation.sig", "line_number": 417, "usage_type": "call"}, {"api_name": "utils.retina_computation", "line_number": 417, "usage_type": "name"}, {"api_name": "utils.retina_computation.sig", "line_number": 418, "usage_type": "call"}, {"api_name": "utils.retina_computation", "line_number": 418, "usage_type": "name"}, {"api_name": "utils.retina_computation.sig", "line_number": 419, "usage_type": "call"}, {"api_name": "utils.retina_computation", "line_number": 419, "usage_type": "name"}, {"api_name": "numpy.ix_", "line_number": 419, "usage_type": "call"}, {"api_name": "utils.plot_functions.plot_learned_model", "line_number": 428, "usage_type": "call"}, {"api_name": "utils.plot_functions", "line_number": 428, "usage_type": "name"}, {"api_name": "utils.retina_computation.translate_CAs_LrnAndTru", "line_number": 438, "usage_type": "call"}, {"api_name": "utils.retina_computation", "line_number": 438, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 440, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 440, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.rc", "line_number": 441, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 441, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 442, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 442, "usage_type": "name"}, {"api_name": "numpy.round", "line_number": 457, "usage_type": "call"}, {"api_name": "numpy.round", "line_number": 459, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.suptitle", "line_number": 462, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 462, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 464, "usage_type": "call"}, {"api_name": "os.path", "line_number": 464, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 465, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 466, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 466, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.close", "line_number": 467, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 467, "usage_type": "name"}, {"api_name": "utils.plot_functions.plot_xValidation", "line_number": 478, "usage_type": "call"}, {"api_name": "utils.plot_functions", "line_number": 478, "usage_type": "name"}, {"api_name": "utils.plot_functions.plot_xValidation", "line_number": 483, "usage_type": "call"}, {"api_name": "utils.plot_functions", "line_number": 483, "usage_type": "name"}]} +{"seq_id": "72829618027", "text": "from legacy.dataloader import Dataloader\nfrom embedding_method.embedders import get_embedder\nimport json\nimport tensorflow as tf\nimport numpy as np\nfrom sklearn.metrics import log_loss\n\ndef loss(y_true,y_pred):\n return tf.keras.backend.mean(y_true)+0*tf.keras.backend.mean(y_pred)\n\ndef euclidean_distance_loss(y_true, y_pred):\n return np.mean(np.sqrt(np.sum(np.square(y_pred - y_true), axis=-1)))\n\ndef one_hot_language(words):\n is_language = []\n for w in words:\n if lookup.get(w) is None:\n is_language.append(0)\n else:\n is_language.append(1)\n il = np.array(is_language)\n #print(\"is_language:\", il.shape,il.dtype,type(il))\n predicted = np.ones_like(il)\n #print(\"predicted:\", predicted.shape,predicted.dtype,type(predicted))\n lang_loss = log_loss(il,predicted,labels=[1,0])\n #print(\"language loss:\", lang_loss)\n return lang_loss\n\ndef language_loss(sentences):\n sentence_language = []\n for sentence in sentences:\n words = sentence.split(\" \")\n sentence_language.append(one_hot_language(words))\n\n #print(\"sentence_language:\",len(sentence_language))\n stacked = np.stack(sentence_language)\n #print(\"stacked:\", stacked.shape, stacked.dtype, type(stacked))\n mean_lang_loss = np.mean(stacked)\n #print(\"mean_lang_loss:\", mean_lang_loss)\n return mean_lang_loss\n\ndef compress(bpe_tokens):\n sentences = []\n for pred in bpe_tokens:\n tokens = \" \".join(pred)\n tokens = tokens.replace(\"@@ \", \"\")\n tokens = tokens.replace(\"@@\", \"\")\n sentences.append(tokens)\n return sentences\n\ndef get_tokens(model_out):\n indices = np.argmax(model_out, axis=2)\n #print(\"index\", indices.shape, type(indices))\n bpe_tokens = []\n for sample in list(indices):\n sent_bpe = []\n for ind in sample:\n token = lookup.get(str(ind))\n sent_bpe.append(token)\n bpe_tokens.append(sent_bpe)\n\n return bpe_tokens\n\n\n# data_path = \"/home/jonas/data/raffle_wiki/da/debug/\"\ndata_path = \"/media/jonas/archive/master/data/raffle_wiki/da/debug/\"\nepochs = 20\nembedder = get_embedder(method=\"laser\", language=\"da\")\nlookup = json.load(open(data_path + \"id2word.json\", 'r'))\n\nencoder_layers = 1\ndecoder_layers = 1\nLSTM_size = 1024\nlatent_space_size = 1024\nvocab_size = 73636\nmax_sentence_length = 20\n\nmodel = tf.keras.models.Sequential([\n tf.keras.layers.Input((max_sentence_length,LSTM_size)),\n #tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(LSTM_size, return_sequences=True)),\n #tf.keras.layers.Dense(latent_space_size),\n tf.keras.layers.LSTM(LSTM_size, return_sequences=True),\n tf.keras.layers.LSTM(LSTM_size, return_sequences=True),\n tf.keras.layers.LSTM(LSTM_size, return_sequences=True),\n tf.keras.layers.LSTM(LSTM_size, return_sequences=True),\n tf.keras.layers.TimeDistributed(tf.keras.layers.Dense(vocab_size, activation='softmax'))\n])\n\n#x = tf.keras.layers.Input((max_sentence_length,LSTM_size))\n# ENCODER\n# e1 = tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(self.LSTM_size, return_sequences=True))(x)\n#e1 = tf.keras.layers.LSTM(LSTM_size, return_sequences=True)\n# LATENT SPACE\n#latent_space = tf.keras.layers.Dense(latent_space_size)\n# DECODER\n#d1 = tf.keras.layers.LSTM(LSTM_size, return_sequences=True)\n\n# END GAME\n#predictions = tf.keras.layers.TimeDistributed(tf.keras.layers.Dense(vocab_size, activation='softmax'))\n\n\n\n#model = tf.keras.Model(inputs=x, outputs=predictions)\nmodel.compile(loss=loss,optimizer=\"adam\")\n\n\nfor epoch in range(epochs):\n train_data = Dataloader(data_base_path=data_path, embedder=embedder,embedding_method=\"laser\",language=\"da\")\n\n for x,y in iter(train_data):\n #print(\"x:\",x.shape,type(x))\n #print(\"y:\",len(y),type(y))\n #print(\"x:\", x.shape, type(x))\n seq_pred = model.predict_on_batch(x)\n\n bpe_tokens = get_tokens(seq_pred)\n\n sentences = compress(bpe_tokens)\n print(sentences[0])\n\n\n #print(\"out_sentence:\",sentences[0])\n sentences_embeddings = embedder(sentences)\n sentences_embeddings = np.expand_dims(sentences_embeddings,1)\n\n #print(\"sentence_out:\",sentences_embeddings.shape)\n #print(\"annotation:\",y.shape)\n\n distance_loss = euclidean_distance_loss(sentences_embeddings, y)\n #print(\"loss:\",distance_loss)\n l_loss = language_loss(sentences)\n #print(\"language loss:\",l_loss)\n\n current_loss = distance_loss + l_loss*0\n #print(\"current loss:\",current_loss)\n\n batch_size = len(sentences)\n #print(type(current_loss))\n full_loss = np.full((batch_size,20,73636),float(current_loss))\n #print(\"full_loss:\",full_loss.shape)\n model.fit(x,full_loss)\n #print(model_loss)", "repo_name": "Lyngsoe/AutomaticQueryReformulation", "sub_path": "legacy/trainer_complex.py", "file_name": "trainer_complex.py", "file_ext": "py", "file_size_in_byte": 4745, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "tensorflow.keras.backend.mean", "line_number": 9, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 9, "usage_type": "attribute"}, {"api_name": "numpy.mean", "line_number": 12, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 12, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 12, "usage_type": "call"}, {"api_name": "numpy.square", "line_number": 12, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 21, "usage_type": "call"}, {"api_name": "numpy.ones_like", "line_number": 23, "usage_type": "call"}, {"api_name": "sklearn.metrics.log_loss", "line_number": 25, "usage_type": "call"}, {"api_name": "numpy.stack", "line_number": 36, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 38, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 52, "usage_type": "call"}, {"api_name": "embedding_method.embedders.get_embedder", "line_number": 68, "usage_type": "call"}, {"api_name": "json.load", "line_number": 69, "usage_type": "call"}, {"api_name": "tensorflow.keras.models.Sequential", "line_number": 78, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 78, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.layers.Input", "line_number": 79, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 79, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.layers.LSTM", "line_number": 82, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 82, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.layers.LSTM", "line_number": 83, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 83, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.layers.LSTM", "line_number": 84, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 84, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.layers.LSTM", "line_number": 85, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 85, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.layers.TimeDistributed", "line_number": 86, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 86, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.layers.Dense", "line_number": 86, "usage_type": "call"}, {"api_name": "legacy.dataloader.Dataloader", "line_number": 108, "usage_type": "call"}, {"api_name": "numpy.expand_dims", "line_number": 124, "usage_type": "call"}, {"api_name": "numpy.full", "line_number": 139, "usage_type": "call"}]} +{"seq_id": "5346564188", "text": "import colorgram\nrgb_colors = []\ncolors = colorgram.extract('image.jpg', 30)\nfor color in colors:\n r= color.rgb.r\n b= color.rgb.b\n g= color.rgb.g\n new_color = (r,b,g)\n rgb_colors.append(new_color)\n\nprint(rgb_colors)", "repo_name": "shriyansh98/hirst_paintinf_turtle", "sub_path": "color-gram.py", "file_name": "color-gram.py", "file_ext": "py", "file_size_in_byte": 230, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "colorgram.extract", "line_number": 3, "usage_type": "call"}]} +{"seq_id": "34214544064", "text": "# -*- coding: utf-8 -*-\nimport scrapy\nfrom scrapy.http import Request, FormRequest\nfrom scrapy.selector import Selector\nimport re\nimport hashlib\nimport requests\n\n\nclass XqSpider(scrapy.Spider):\n name = 'xq'\n allowed_domains = ['xueqiu.com']\n start_urls = ['https://xueqiu.com/']\n index_url = \"https://xueqiu.com/\"\n login_url = \"https://xueqiu.com/snowman/login\"\n check_login_url = \"https://xueqiu.com/setting/user\"\n custom_settings = {\n \"COOKIES_ENABLED\": True\n }\n\n login_formdata = {\n \"remember_me\": \"true\",\n \"username\": \"xchaoinfo\",\n \"password\": \"xchaoinfo\"\n }\n\n headers = {\n \"Host\": \"xueqiu.com\",\n \"User-Agent\": \"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_10_1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/38.0.2125.111 Safari/537.36\"\n }\n # 密码的 md5 加密\n\n def start_requests(self):\n print('start_requests')\n yield Request(self.index_url, headers=self.headers, callback=self.login)\n\n def login(self, response):\n print('post_login')\n # FormRequeset.from_response是Scrapy提供的一个函数, 用于post表单\n self.headers[\"X-Requested-With\"] = \"XMLHttpRequest\"\n self.headers[\"Referer\"] = self.index_url\n return [FormRequest(\n url=self.login_url,\n formdata=self.login_formdata,\n headers=self.headers,\n callback=self.check_login_status,\n )]\n\n def check_login_status(self, response):\n # '用来检测是否登陆成功'\n print(\"----__check_login_status----\")\n self.headers[\"X-Requested-With\"] = None\n yield Request(self.check_login_url, headers=self.headers, callback=self.parse_user_detail)\n\n def parse_user_detail(self, response):\n print(\"----parse_user_detail----\")\n with open('response_of_user_detil.html', 'wb') as file:\n file.write(response.body)\n pa = r'\"profile\":\"/(.*?)\",\"screen_name\":\"(.*?)\"'\n res = re.findall(pa, response.text)\n if res == []:\n print(\"登录失败，请检查你的手机号和密码输入是否正确\")\n return False\n else:\n print('欢迎使用 xchaoinfo 写的模拟登录 \\n 你的用户 id 是：%s, 你的用户名是：%s' % (res[0]))\n return True\n\n def parse(self, response):\n print(\"----parse----\")\n pass\n", "repo_name": "xchaoinfo/fuck-login", "sub_path": "012 xueqiu.com/xueqiu-scrapy/xueqiu/spiders/xq.py", "file_name": "xq.py", "file_ext": "py", "file_size_in_byte": 2410, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 5844, "dataset": "github-code", "pt": "37", "api": [{"api_name": "scrapy.Spider", "line_number": 10, "usage_type": "attribute"}, {"api_name": "scrapy.http.Request", "line_number": 35, "usage_type": "call"}, {"api_name": "scrapy.http.FormRequest", "line_number": 42, "usage_type": "call"}, {"api_name": "scrapy.http.Request", "line_number": 53, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 60, "usage_type": "call"}]} +{"seq_id": "37384708905", "text": "import datetime\nimport logging\nimport subprocess\nfrom pathlib import Path\nfrom pprint import pformat\nfrom typing import Any, Dict, List, Optional\n\nimport yaml\n\ntry:\n\timport vim # type: ignore\n\nexcept ImportError:\n\tvim: Any = None # squelch type errors, ty pyright\n\tVIM_ENABLED = False\nelse:\n\tVIM_ENABLED = True\n\nVENUE_NAME_FIELD = '_name'\nVENUE_CHILDREN_FIELD = '_children'\nData = Dict[str, Any]\n\nlogger = logging.getLogger()\nlogger.setLevel(logging.DEBUG)\n\nif VIM_ENABLED:\n\tformatter = logging.Formatter(\n\t\t'[{levelname}] {asctime} {module} {name}: {message}\\n', style='{'\n\t)\n\tfor b in vim.buffers:\n\t\tif \"venueQlog\" in b.name:\n\t\t\tVIM_LOG_BUFFER = b\n\t\t\tbreak\n\telse:\n\t\traise Exception(\"Couldn't find the venueQ log buffer\")\n\n\tclass VimLogHandler(logging.Handler):\n\t\tlevel = 0\n\n\t\tdef emit(self, record: logging.LogRecord):\n\t\t\tmsg = formatter.format(record)\n\t\t\tfor line in msg.splitlines():\n\t\t\t\tVIM_LOG_BUFFER.append(line)\n\n\tvim_handler = VimLogHandler()\n\tvim_handler.setLevel(logging.DEBUG)\n\tfile_handler = logging.FileHandler(f'/tmp/venueQ:{datetime.datetime.now().isoformat()}.log')\n\tfile_handler.setLevel(logging.DEBUG)\n\n\tlogger.addHandler(vim_handler)\n\tlogger.addHandler(file_handler)\n\n\nclass VenueQNode:\n\tname: str = '' # name must be unique\n\tparent: 'VenueQNode'\n\troot: 'VenueQRoot'\n\tis_directory = False\n\tis_root = False\n\n\tdef __init__(self, data: Data, parent: Optional['VenueQNode'] = None):\n\t\tself.name = self.get_name(data)\n\t\tif parent is None:\n\t\t\tself.parent = self\n\t\t\tassert self.is_root\n\t\telse:\n\t\t\tself.parent = parent\n\t\t\tself.root = parent.root\n\t\tself.data = self.get_initial_data()\n\t\tself.update_by_dictionary(data)\n\t\tself.init_hook()\n\t\tself.save()\n\n\tdef update_by_dictionary(self, data: Data):\n\t\tchildren_dicts = data.pop(VENUE_CHILDREN_FIELD, None)\n\t\tif children_dicts is not None:\n\t\t\tself.is_directory = True\n\t\t\tchild_dict: Data\n\t\t\tfor child_dict in children_dicts:\n\t\t\t\t# this is pretty expensive to recreate the object\n\t\t\t\t# only to see if it exists already\n\t\t\t\tcls = self.get_class_for_child(child_dict)\n\t\t\t\tnode = cls(data=child_dict, parent=self)\n\t\t\t\tif node.pk not in self.root.lookup:\n\t\t\t\t\tself.root.lookup[node.pk] = node\n\t\t\t\telse:\n\t\t\t\t\tself.root.lookup[node.pk].update_by_dictionary(child_dict)\n\t\tself.data.update(data)\n\t\tself.process_data()\n\n\tdef get_initial_data(self) -> Data:\n\t\tif self.path.exists():\n\t\t\treturn self.load()\n\t\telse:\n\t\t\treturn self.get_default_data()\n\n\tdef temp_path(self, extension: str, name: str = None) -> Path:\n\t\treturn self.directory / f'{name or self.name}.tmp.{extension}'\n\n\tdef edit_temp(self, extension: str, name: str = None):\n\t\tp = self.temp_path(extension, name)\n\t\tp.touch()\n\t\tif VIM_ENABLED:\n\t\t\tvim.command(f\":split {p}\")\n\t\t\tvim.command(r\":filetype detect\")\n\t\telse:\n\t\t\tsubprocess.run(['vim', p], shell=True)\n\n\tdef read_temp(self, extension: str, name: str = None):\n\t\tif self.temp_path(extension, name).exists():\n\t\t\ttext = self.temp_path(extension, name).read_text()\n\t\t\tself.root.queue_wipe(self.temp_path(extension, name))\n\t\t\treturn text\n\t\telse:\n\t\t\treturn ''\n\n\t@property\n\tdef pk(self) -> str:\n\t\treturn self.path.resolve().as_posix()\n\n\t@property\n\tdef directory(self) -> Path:\n\t\tif self.is_root:\n\t\t\treturn self.root.root_dir\n\t\telse:\n\t\t\treturn self.parent.directory / self.parent.name\n\n\t@property\n\tdef path(self) -> Path:\n\t\treturn self.directory / f'{self.name}.{self.get_extension()}'\n\n\tdef __eq__(self, other: 'VenueQNode') -> bool:\n\t\treturn self.pk == other.pk\n\n\tdef delete(self):\n\t\tself.root.queue_wipe(self.path)\n\t\tdel self.root.lookup[self.pk]\n\n\tdef mkdir(self):\n\t\tif not self.parent.directory.exists():\n\t\t\tself.parent.mkdir()\n\t\tif not self.directory.exists():\n\t\t\tself.directory.mkdir()\n\n\tdef save(self):\n\t\tself.mkdir()\n\t\tself.path.write_text(self.dump())\n\n\tdef read(self):\n\t\treturn self.path.read_text()\n\n\t@property\n\tdef debug_dict(self) -> Dict[str, Any]:\n\t\td: Dict[str, Any] = self.data\n\t\td[\"CLASS\"] = type(self)\n\t\td[\"PATH\"] = self.path\n\t\treturn d\n\n\tdef __str__(self) -> str:\n\t\treturn pformat(self.debug_dict)\n\n\tdef open_in_vim(self):\n\t\tif VIM_ENABLED:\n\t\t\tvim.command(f\":e {self.path}\")\n\n\t# Methods that the user overrides go below here\n\n\tdef get_default_data(self) -> Data:\n\t\treturn {}\n\n\tdef init_hook(self):\n\t\t\"\"\"Hook called just before saving data each time the node is initialized\"\"\"\n\t\tpass\n\n\tdef process_data(self):\n\t\t\"\"\"\"Post update hook called each time this node has its dictionary updated\"\"\"\n\t\tpass\n\n\tdef get_extension(self) -> str:\n\t\t\"\"\"Returns the file extension for these venueQ nodes\"\"\"\n\t\treturn 'venueQ.yaml'\n\n\tdef get_class_for_child(self, data: Data) -> type:\n\t\t\"\"\"Gets the class type for child dictionaries in terms of initial data.\"\"\"\n\t\treturn type(self)\n\n\tdef get_name(self, data: Data) -> str:\n\t\t\"\"\"Gets the name of the node in terms of initial data.\"\"\"\n\t\treturn data.get(VENUE_NAME_FIELD, str(id(self)))\n\n\tdef load(self):\n\t\t\"\"\"This method loads a dictionary object from disk.\n\t\tOverride it to change how the data on disk is interpreted.\"\"\"\n\t\treturn yaml.load(self.read(), Loader=yaml.SafeLoader)\n\n\tdef dump(self):\n\t\t\"\"\"This method serializes the dictionary object to save to disk.\n\t\tOverride it to change how the data on disk is interpreted.\"\"\"\n\t\treturn yaml.dump(self.data, indent=True, default_flow_style=False)\n\n\tdef on_buffer_open(self, data: Data):\n\t\t\"\"\"This method is called when the buffer is loaded.\n\t\tThis is called with an argument data = self.load().\n\t\tOverride this to perform actions.\"\"\"\n\t\tlogging.info(f\"Opened buffer {self.path}\")\n\n\tdef on_buffer_close(self, data: Data):\n\t\t\"\"\"This method is called when the disk data is edited and saved.\n\t\tThis is called with an argument data = self.load().\n\t\tOverride this to perform actions.\"\"\"\n\t\tlogging.info(f\"Closed buffer {self.path}\")\n\t\tself.data.update(data)\n\n\nclass VenueQRoot(VenueQNode):\n\tis_root = True\n\tlookup: Dict[str, 'VenueQNode']\n\n\tdef __init__(self, data: Data, root_dir: Path):\n\t\tif not root_dir.exists():\n\t\t\troot_dir.mkdir()\n\t\troot_dir = root_dir.resolve()\n\t\tself.lookup = {}\n\t\tself.wipe_queue: List[int] = []\n\t\tself.root = self\n\t\tself.root_dir = root_dir\n\t\tlogger.info(f\"Setting root_node at {root_dir}\")\n\t\tsuper().__init__(data, None)\n\n\tdef queue_wipe(self, p: Path):\n\t\tif VIM_ENABLED:\n\t\t\tfor b in vim.buffers:\n\t\t\t\tif Path(b.name).exists() and p.samefile(Path(b.name)):\n\t\t\t\t\tself.wipe_queue.append(b.number)\n\t\t\t\t\tbreak\n\t\t\telse:\n\t\t\t\tlogger.warn(\n\t\t\t\t\tf\"Tried to wipe {p} but found no buffer for it among \" +\n\t\t\t\t\t', '.join(b.name for b in vim.buffers)\n\t\t\t\t)\n\t\tp.unlink()\n\n\tdef wipe(self):\n\t\tif VIM_ENABLED:\n\t\t\tfor bn in self.wipe_queue:\n\t\t\t\tvim.command(f\"bdelete! {bn}\")\n\t\t\tself.wipe_queue = []\n", "repo_name": "vEnhance/venueQ", "sub_path": "venueQ.py", "file_name": "venueQ.py", "file_ext": "py", "file_size_in_byte": 6554, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "37", "api": [{"api_name": "typing.Any", "line_number": 14, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 21, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 21, "usage_type": "name"}, {"api_name": "logging.getLogger", "line_number": 23, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 24, "usage_type": "attribute"}, {"api_name": "logging.Formatter", "line_number": 27, "usage_type": "call"}, {"api_name": "vim.buffers", "line_number": 30, "usage_type": "attribute"}, {"api_name": "logging.Handler", "line_number": 37, "usage_type": "attribute"}, {"api_name": "logging.LogRecord", "line_number": 40, "usage_type": "attribute"}, {"api_name": "logging.DEBUG", "line_number": 46, "usage_type": "attribute"}, {"api_name": "logging.FileHandler", "line_number": 47, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 47, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 47, "usage_type": "attribute"}, {"api_name": "logging.DEBUG", "line_number": 48, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 61, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 97, "usage_type": "name"}, {"api_name": "vim.command", "line_number": 104, "usage_type": "call"}, {"api_name": "vim.command", "line_number": 105, "usage_type": "call"}, {"api_name": "subprocess.run", "line_number": 107, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 122, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 129, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 154, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 154, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 153, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 153, "usage_type": "name"}, {"api_name": "pprint.pformat", "line_number": 160, "usage_type": "call"}, {"api_name": "vim.command", "line_number": 164, "usage_type": "call"}, {"api_name": "yaml.load", "line_number": 194, "usage_type": "call"}, {"api_name": "yaml.SafeLoader", "line_number": 194, "usage_type": "attribute"}, {"api_name": "yaml.dump", "line_number": 199, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 205, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 211, "usage_type": "call"}, {"api_name": "typing.Dict", "line_number": 217, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 219, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 224, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 230, "usage_type": "name"}, {"api_name": "vim.buffers", "line_number": 232, "usage_type": "attribute"}, {"api_name": "pathlib.Path", "line_number": 233, "usage_type": "call"}, {"api_name": "vim.buffers", "line_number": 239, "usage_type": "attribute"}, {"api_name": "vim.command", "line_number": 246, "usage_type": "call"}]} +{"seq_id": "16295039131", "text": "from typing import List, Union\nfrom hy import models\nfrom dasy import parser\nimport vyper.ast.nodes as vy_nodes\nfrom hy.models import Expression, Symbol\n\nCOMP_FUNCS = [\"<\", \"<=\", \">\", \">=\", \"==\", \"!=\"]\n\n\ndef chain_comps(chain_expr: Expression) -> Expression:\n \"\"\"\n Creates a new expression chaining comparisons.\n \"\"\"\n new_node = models.Expression()\n new_expr: List[Union[Symbol, Expression]] = [models.Symbol(\"and\")]\n for vals in zip(chain_expr[1:], chain_expr[2:]):\n new_expr.append(models.Expression((chain_expr[0], vals[0], vals[1])))\n new_node += tuple(new_expr)\n return new_node\n\n\ndef parse_comparison(comparison_expr: Expression) -> vy_nodes.Compare:\n \"\"\"\n Parses a comparison expression, chaining comparisons if necessary.\n \"\"\"\n assert (\n str(comparison_expr[0]) in COMP_FUNCS\n ), f\"Invalid comparison operator {comparison_expr[0]}\"\n\n # Always apply chain comps for consistency\n chained_expr = chain_comps(comparison_expr)\n left = parser.parse_node(chained_expr[1])\n right = parser.parse_node(chained_expr[2])\n op = parser.parse_node(chained_expr[0])\n return parser.build_node(vy_nodes.Compare, left=left, ops=[op], comparators=[right])\n", "repo_name": "z80dev/dasy", "sub_path": "dasy/parser/comparisons.py", "file_name": "comparisons.py", "file_ext": "py", "file_size_in_byte": 1215, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 37, "dataset": "github-code", "pt": "37", "api": [{"api_name": "hy.models.Expression", "line_number": 10, "usage_type": "name"}, {"api_name": "hy.models.Expression", "line_number": 14, "usage_type": "call"}, {"api_name": "hy.models", "line_number": 14, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 15, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 15, "usage_type": "name"}, {"api_name": "hy.models.Symbol", "line_number": 15, "usage_type": "name"}, {"api_name": "hy.models.Expression", "line_number": 15, "usage_type": "name"}, {"api_name": "hy.models", "line_number": 15, "usage_type": "name"}, {"api_name": "hy.models.Expression", "line_number": 17, "usage_type": "call"}, {"api_name": "hy.models", "line_number": 17, "usage_type": "name"}, {"api_name": "hy.models.Expression", "line_number": 22, "usage_type": "name"}, {"api_name": "dasy.parser.parse_node", "line_number": 32, "usage_type": "call"}, {"api_name": "dasy.parser", "line_number": 32, "usage_type": "name"}, {"api_name": "dasy.parser.parse_node", "line_number": 33, "usage_type": "call"}, {"api_name": "dasy.parser", "line_number": 33, "usage_type": "name"}, {"api_name": "dasy.parser.parse_node", "line_number": 34, "usage_type": "call"}, {"api_name": "dasy.parser", "line_number": 34, "usage_type": "name"}, {"api_name": "dasy.parser.build_node", "line_number": 35, "usage_type": "call"}, {"api_name": "dasy.parser", "line_number": 35, "usage_type": "name"}, {"api_name": "vyper.ast.nodes.Compare", "line_number": 35, "usage_type": "attribute"}, {"api_name": "vyper.ast.nodes", "line_number": 35, "usage_type": "name"}, {"api_name": "vyper.ast.nodes.Compare", "line_number": 22, "usage_type": "attribute"}, {"api_name": "vyper.ast.nodes", "line_number": 22, "usage_type": "name"}]} +{"seq_id": "40642476604", "text": "import matplotlib.pyplot as plt\nimport pandas as pd\n\ndf = pd.read_csv('output.csv')\n\n# Extract the data\nratio = [float(value[:-1]) for value in df.iloc[33:36, 3]]\nlabels = ['金融电子商务服务', '金融数据服务', '互联网广告服务等'] \ncolors = ['#FF8000', '#00FF00', '#FFFF00']\n\n# Plot the pie chart\nplt.rcParams['font.sans-serif'] = ['SimHei']\nplt.pie(ratio, labels=labels, colors=colors, wedgeprops={'width': 0.5})\nplt.title('2021-12-31 按产品分类收入比例圆环图')\nplt.legend(loc='upper left', prop={'size': 8})\n\nplt.show()", "repo_name": "Neymarz1y0/myFinancialWebsite", "sub_path": "donut_chart.py", "file_name": "donut_chart.py", "file_ext": "py", "file_size_in_byte": 553, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "37", "api": [{"api_name": "pandas.read_csv", "line_number": 4, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.rcParams", "line_number": 12, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 12, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.pie", "line_number": 13, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 13, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 14, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 14, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 15, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 15, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 17, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 17, "usage_type": "name"}]} +{"seq_id": "27396970317", "text": "from django.urls import path\nfrom .views import *\n\nurlpatterns = [\n path('type_list', type_list_view, name='type_list'),\n path('form_list', form_list_view, name='form_list'),\n path('create_type', create_type_view, name='create_type'),\n path('delete_type/', delete_type_view, name='delete_type'),\n path('edit_type/', edit_type_view, name='edit_type'),\n]", "repo_name": "Karl5236543/plankton_project", "sub_path": "library/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 385, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "django.urls.path", "line_number": 5, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 6, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 7, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 8, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 9, "usage_type": "call"}]} +{"seq_id": "38115224299", "text": "from random import Random\nfrom users.models import EmailVerifyRecord\nfrom django.core.mail import send_mail\nfrom MxOnline import settings\n\n\ndef random_str(rangelength=16):\n str = ''\n char = 'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789'\n length = len(char) - 1\n random = Random()\n for i in range(rangelength):\n str += char[random.randint(0, length)]\n return str\n\n\ndef send_email(email, send_type='register'):\n email_record = EmailVerifyRecord()\n if send_type == 'update_email':\n code = random_str(4)\n else:\n code = random_str(16)\n email_record.code = code\n email_record.email = email\n email_record.send_type = send_type\n email_record.save()\n\n email_title = ''\n email_body = ''\n if send_type == 'register':\n email_title = '幕学在线网注激活链接'\n email_body = '请点击下面链接激活您的账号：\\n http://127.0.0.1:8000/active/{0}'.format(code)\n send_status = send_mail(email_title, email_body, settings.EMAIL_FROM, [email])\n return send_status\n elif send_type == 'forget':\n email_title = '幕学在线网密码重置链接'\n email_body = '请点击下面链接重置您的账号密码：\\n http://127.0.0.1:8000/reset/{0}'.format(code)\n send_status = send_mail(email_title, email_body, settings.EMAIL_FROM, [email])\n return send_status\n elif send_type == 'update_email':\n email_title = '幕学在线网邮箱修改验证码'\n email_body = '您的邮箱验证码为{0}'.format(code)\n send_status = send_mail(email_title, email_body, settings.EMAIL_FROM, [email])\n return send_status\n", "repo_name": "q2180968/MxOnine", "sub_path": "apps/utils/email_send.py", "file_name": "email_send.py", "file_ext": "py", "file_size_in_byte": 1676, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "random.Random", "line_number": 11, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 13, "usage_type": "call"}, {"api_name": "users.models.EmailVerifyRecord", "line_number": 18, "usage_type": "call"}, {"api_name": "django.core.mail.send_mail", "line_number": 33, "usage_type": "call"}, {"api_name": "MxOnline.settings.EMAIL_FROM", "line_number": 33, "usage_type": "attribute"}, {"api_name": "MxOnline.settings", "line_number": 33, "usage_type": "name"}, {"api_name": "django.core.mail.send_mail", "line_number": 38, "usage_type": "call"}, {"api_name": "MxOnline.settings.EMAIL_FROM", "line_number": 38, "usage_type": "attribute"}, {"api_name": "MxOnline.settings", "line_number": 38, "usage_type": "name"}, {"api_name": "django.core.mail.send_mail", "line_number": 43, "usage_type": "call"}, {"api_name": "MxOnline.settings.EMAIL_FROM", "line_number": 43, "usage_type": "attribute"}, {"api_name": "MxOnline.settings", "line_number": 43, "usage_type": "name"}]} +{"seq_id": "40312956826", "text": "#The only goal of this program is to make serial reads as quickly as possible.\n\nimport os \nimport glob\nfrom time import time, sleep\nimport serial\nfrom datetime import datetime\n\nser = serial.Serial(port='/dev/ttyUSB0', baudrate=19200, timeout=1)\nser.readlines()\n\nser.write(b\"\\x08\\x01\\x00\\x02\\x0e\\x01\\xe2\\x58\") #Initial Heartbeat\nser.readline()\ntime1 = time()\n\nser.write(b\"\\x08\\x02\\x00\\x02\\x06\\x00\\xf6\\x4c\") #Commodity request\ndata = ser.readline()\nsleep(1)\ndata = data.encode('hex')\nEtot = float(int(data[58:70],16))\n\nwhile True:\n time2 = time()\n if (time2-time1) > 240:\n ser.write(b\"\\x08\\x01\\x00\\x02\\x0e\\x01\\xe2\\x58\") #Refresh Heartbeat\n ser.readline()\n time1 = time()\n ser.write(b\"\\x08\\x02\\x00\\x02\\x06\\x00\\xf6\\x4c\") #Commodity request\n data = ser.readline()\n data = data.encode('hex')\n P = str(int(data[20:32],16))\n E = str(int(data[84:96],16))\n Epercent = int(E)/Etot * 100\n print('---\\nEnergy Capacity: ' + E + ' (%.2f%%)' %Epercent + '\\nPower: ' + P + '\\n---')\n sleep(1)\n", "repo_name": "clarke6/LeightonScratch", "sub_path": "LeightonPrograms2/HighSpeedTesting/FastCommRead.py", "file_name": "FastCommRead.py", "file_ext": "py", "file_size_in_byte": 1026, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "serial.Serial", "line_number": 9, "usage_type": "call"}, {"api_name": "time.time", "line_number": 14, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 18, "usage_type": "call"}, {"api_name": "time.time", "line_number": 23, "usage_type": "call"}, {"api_name": "time.time", "line_number": 27, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 35, "usage_type": "call"}]} +{"seq_id": "13007791720", "text": "from django.core.exceptions import ValidationError\nfrom django.db import models\nfrom django.db.utils import IntegrityError\n\n\nclass AssignAttributes:\n def save(self, instance, bundle):\n items = [\n (key, getattr(self.model, key), value) for key, value in bundle.items()\n ]\n\n for key, attr, value in items:\n if isinstance(value, models.Model):\n setattr(instance, key, value)\n continue\n\n if isinstance(attr.field, models.ForeignKey):\n self.set_foreign_key(instance, key, value)\n continue\n\n if isinstance(attr.field, models.ManyToManyField):\n continue\n\n setattr(instance, key, value)\n\n instance.save()\n\n for key, attr, value in items:\n if isinstance(attr.field, models.ManyToManyField):\n has_through = not attr.through._meta.auto_created\n\n if has_through and any(isinstance(item, dict) for item in value):\n self.set_many_to_many_with_through(instance, key, value)\n continue\n\n self.set_many_to_many(instance, key, value)\n\n def resolve_relation(self, key, value):\n related_model = getattr(self.model, key).field.related_model\n lookup_field = getattr(getattr(related_model, \"Api\", \"\"), \"lookup_field\", \"pk\")\n try:\n value = related_model.objects.get(**{lookup_field: value})\n except related_model.DoesNotExist as e:\n raise ValueError from e\n return value\n\n def set_foreign_key(self, instance, key, value):\n try:\n value = self.resolve_relation(key, value) if value is not None else None\n except ValueError as e:\n raise ValidationError(f\"Invalid {self.keymap[key]}\") from e\n setattr(instance, key, value)\n\n def set_many_to_many(self, instance, key, value):\n related_manager = getattr(instance, key)\n related_model = related_manager.model\n lookup_field = getattr(getattr(related_model, \"Api\", \"\"), \"lookup_field\", \"pk\")\n try:\n if lookup_field != \"pk\":\n items = related_model.objects.filter(**{f\"{lookup_field}__in\": value})\n assert len(items) == len(value)\n value = items\n related_manager.set(value)\n except (AssertionError, IntegrityError, ValueError) as e:\n raise ValidationError(f\"Invalid {self.keymap[key]}\") from e\n\n def set_many_to_many_with_through(self, instance, key, value):\n try:\n attr = getattr(self.model, key)\n through_model = attr.through\n model_name = self.model._meta.model_name\n target_key = attr.field.m2m_target_field_name()\n relation_name = attr.field.m2m_reverse_field_name()\n\n def pivot_data(item):\n return {k: v for k, v in item.items() if k != target_key}\n\n def target_lookup(item):\n return {relation_name: self.resolve_relation(key, item[target_key])}\n\n def save_through(defaults, **kwargs):\n return through_model.objects.update_or_create(defaults, **kwargs)[0]\n\n model_lookup = {model_name: instance}\n\n items = [\n save_through(pivot_data(item), **model_lookup, **target_lookup(item)).pk\n for item in value\n ]\n\n through_model.objects.filter(**model_lookup).exclude(pk__in=items).delete()\n except (AttributeError, IntegrityError, ValueError) as e:\n raise ValidationError(f\"Invalid {self.keymap[key]}\") from e\n\n def validate(self):\n instance = self.get_instance()\n\n for key in self.bundle.keys():\n self.validate_bundle(key)\n\n field = self.model._meta.get_field(key)\n\n if self.bundle[key] is None and not field.null:\n raise ValidationError(f\"Invalid {self.keymap[key]}\")\n\n if field.unique:\n other_records = self.model.objects.exclude(pk=instance.pk)\n\n if other_records.filter(**{key: self.bundle[key]}).exists():\n raise ValidationError(f\"Field {self.keymap[key]} must be unique\")\n", "repo_name": "gundotio/worf", "sub_path": "worf/assigns.py", "file_name": "assigns.py", "file_ext": "py", "file_size_in_byte": 4235, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 6, "dataset": "github-code", "pt": "37", "api": [{"api_name": "django.db.models.Model", "line_number": 13, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 13, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 17, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 17, "usage_type": "name"}, {"api_name": "django.db.models.ManyToManyField", "line_number": 21, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 21, "usage_type": "name"}, {"api_name": "django.db.models.ManyToManyField", "line_number": 29, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 29, "usage_type": "name"}, {"api_name": "django.core.exceptions.ValidationError", "line_number": 51, "usage_type": "call"}, {"api_name": "django.db.utils.IntegrityError", "line_number": 64, "usage_type": "name"}, {"api_name": "django.core.exceptions.ValidationError", "line_number": 65, "usage_type": "call"}, {"api_name": "django.db.utils.IntegrityError", "line_number": 92, "usage_type": "name"}, {"api_name": "django.core.exceptions.ValidationError", "line_number": 93, "usage_type": "call"}, {"api_name": "django.core.exceptions.ValidationError", "line_number": 104, "usage_type": "call"}, {"api_name": "django.core.exceptions.ValidationError", "line_number": 110, "usage_type": "call"}]} +{"seq_id": "20512520564", "text": "from collections import deque\nimport sys\n\ninput = sys.stdin.readline\nn, m = map(int, input().split())\n\npaper = []\nfor _ in range(n) :\n paper.append(list(map(int, input().split())))\n\nanswer = []\n# 상하좌우\nposition = [[-1,0], [1,0], [0,-1], [0,1]]\nfor i in range(n) :\n for j in range(m) :\n if paper[i][j] == 1 :\n count = 1\n # 방문하면 -1로\n paper[i][j] = -1\n queue = deque()\n queue.append([i, j])\n while queue :\n q = queue.popleft()\n for p in position :\n a = q[0] + p[0]\n b = q[1] + p[1]\n # 그림 범위 내에 있어야 하고, 1일 때\n if 0 <= a <= n-1 and 0 <= b <= m-1 and paper[a][b] == 1:\n count += 1\n paper[a][b] = -1\n queue.append([a, b])\n # 완료되면 anaswer에 넣음\n answer.append(count)\n\nprint(len(answer))\nif len(answer) > 0 :\n print(max(answer))\nelse :\n print(0)", "repo_name": "SystemOutGirlsAlgorithm/algorithm", "sub_path": "3월/2022-03-26/baekjoon_1926.py", "file_name": "baekjoon_1926.py", "file_ext": "py", "file_size_in_byte": 920, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 19, "dataset": "github-code", "pt": "37", "api": [{"api_name": "sys.stdin", "line_number": 4, "usage_type": "attribute"}, {"api_name": "collections.deque", "line_number": 20, "usage_type": "call"}]} +{"seq_id": "28392246038", "text": "from flask import (\n Blueprint, request, session, redirect, url_for\n)\nfrom rauth.utils import parse_utf8_qsl\nfrom rauth.service import OAuth2Service\nimport requests\n\nfrom newslynx.core import settings\nfrom newslynx.core import db\nfrom newslynx.models import Auth\nfrom newslynx.exc import AuthError, RequestError\nfrom newslynx.lib.serialize import jsonify\nfrom newslynx.lib import dates\nfrom newslynx.views.decorators import load_user, load_org\nfrom newslynx.views.util import obj_or_404, delete_response\nfrom newslynx.lib import url\n\n\n# blueprint\nbp = Blueprint('auth_facebook', __name__)\n\n\n# auth flow\nif settings.FB_ENABLED:\n _graph_url = 'https://graph.facebook.com/'\n fb_oauth = OAuth2Service(name='facebook',\n authorize_url='https://www.facebook.com/dialog/oauth',\n access_token_url=_graph_url + 'oauth/access_token',\n client_id=settings.FACEBOOK_APP_ID,\n client_secret=settings.FACEBOOK_APP_SECRET,\n base_url=_graph_url)\n\n\n# oauth utilities #\n\ndef fb_extend_oauth_token(temp_access_token):\n url = _graph_url + \"oauth/access_token\"\n params = {\n 'grant_type': 'fb_exchange_token',\n 'client_id': settings.FACEBOOK_APP_ID,\n 'client_secret': settings.FACEBOOK_APP_SECRET,\n 'fb_exchange_token': temp_access_token\n }\n r = requests.get(url=url, params=params)\n token = parse_utf8_qsl(r.content)\n token['expires'] = dates.parse_ts(\n dates.now(ts=True) + int(token['expires'])).isoformat()\n return token\n\n\n# GOOGLE ANALYTICS OAUTH ENDPOINTS #\n@bp.route('/api/v1/auths/facebook', methods=['GET'])\n@load_user\n@load_org\ndef get_ga_auth(user, org):\n token = Auth.query\\\n .filter_by(org_id=org.id, name='facebook')\\\n .first()\n obj_or_404(token,\n 'You have not authenticated yet with facebook.')\n return jsonify(token)\n\n\n@bp.route('/api/v1/auths/facebook/grant', methods=['GET'])\n@load_user\n@load_org\ndef fb_auth(user, org):\n\n # raise error when configurations are not provided.\n if not settings.FB_ENABLED:\n raise RequestError(\n 'You must provide a \"facebook_app_id\" and \"facebook_app_secret\" in '\n 'your NewsLynx configuration to enable facebook integration. '\n 'See http://developers.facebook.com for details on how to create '\n 'an application on Facebook.')\n\n oauth_callback = url_for('auth_facebook.fb_callback', _external=True)\n params = {'redirect_uri': oauth_callback}\n\n # set user creds on session\n session['org_id'] = org.id\n session['redirect_uri'] = request.args.get('redirect_uri')\n\n return redirect(fb_oauth.get_authorize_url(**params))\n\n\n@bp.route('/api/v1/auths/facebook/callback')\ndef fb_callback():\n\n org_id = session.pop('org_id')\n redirect_uri = session.pop('redirect_uri')\n\n # check to make sure the user authorized the request\n if not 'code' in request.args:\n if not redirect_uri:\n raise AuthError('You did not authorize the request to facebook.')\n\n uri = url.add_query_params(redirect_uri, auth_success='false')\n return redirect(uri)\n\n # make a request for the access token credentials using code\n authorize_uri = url_for('auth_facebook.fb_callback', _external=True)\n data = dict(code=request.args['code'], redirect_uri=authorize_uri)\n\n # get a temporary access token\n temp_access_token = fb_oauth.get_access_token(data=data)\n tokens = fb_extend_oauth_token(temp_access_token)\n\n # upsert settings\n facebook_token = Auth.query\\\n .filter_by(name='facebook', org_id=org_id)\\\n .first()\n\n if not facebook_token:\n\n # create settings object\n facebook_token = Auth(\n org_id=org_id,\n name='facebook',\n value=tokens)\n\n else:\n facebook_token.value = tokens\n\n db.session.add(facebook_token)\n db.session.commit()\n\n if redirect_uri:\n uri = url.add_query_params(redirect_uri, auth_success='true')\n return redirect(uri)\n\n return jsonify(facebook_token)\n\n\n@bp.route('/api/v1/auths/facebook/revoke', methods=['GET'])\n@load_user\n@load_org\ndef fb_revoke(user, org):\n\n fb_token = Auth.query\\\n .filter_by(name='facebook', org_id=org.id)\\\n .first()\n\n obj_or_404(fb_token, 'You have not authenticated yet with Facebook.')\n\n # drop token from table\n db.session.delete(fb_token)\n db.session.commit()\n\n # redirect to app\n redirect_uri = request.args.get('redirect_uri')\n if redirect_uri:\n return redirect(redirect_uri)\n\n return delete_response()\n", "repo_name": "newslynx/newslynx-core", "sub_path": "newslynx/views/auth/facebook_auth.py", "file_name": "facebook_auth.py", "file_ext": "py", "file_size_in_byte": 4666, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 13, "dataset": "github-code", "pt": "37", "api": [{"api_name": "flask.Blueprint", "line_number": 20, "usage_type": "call"}, {"api_name": "newslynx.core.settings.FB_ENABLED", "line_number": 24, "usage_type": "attribute"}, {"api_name": "newslynx.core.settings", "line_number": 24, "usage_type": "name"}, {"api_name": "rauth.service.OAuth2Service", "line_number": 26, "usage_type": "call"}, {"api_name": "newslynx.core.settings.FACEBOOK_APP_ID", "line_number": 29, "usage_type": "attribute"}, {"api_name": "newslynx.core.settings", "line_number": 29, "usage_type": "name"}, {"api_name": "newslynx.core.settings.FACEBOOK_APP_SECRET", "line_number": 30, "usage_type": "attribute"}, {"api_name": "newslynx.core.settings", "line_number": 30, "usage_type": "name"}, {"api_name": "newslynx.lib.url", "line_number": 37, "usage_type": "name"}, {"api_name": "newslynx.core.settings.FACEBOOK_APP_ID", "line_number": 40, "usage_type": "attribute"}, {"api_name": "newslynx.core.settings", "line_number": 40, "usage_type": "name"}, {"api_name": "newslynx.core.settings.FACEBOOK_APP_SECRET", "line_number": 41, "usage_type": "attribute"}, {"api_name": "newslynx.core.settings", "line_number": 41, "usage_type": "name"}, {"api_name": "requests.get", "line_number": 44, "usage_type": "call"}, {"api_name": "newslynx.lib.url", "line_number": 44, "usage_type": "name"}, {"api_name": "rauth.utils.parse_utf8_qsl", "line_number": 45, "usage_type": "call"}, {"api_name": "newslynx.lib.dates.parse_ts", "line_number": 46, "usage_type": "call"}, {"api_name": "newslynx.lib.dates", "line_number": 46, "usage_type": "name"}, {"api_name": "newslynx.lib.dates.now", "line_number": 47, "usage_type": "call"}, {"api_name": "newslynx.lib.dates", "line_number": 47, "usage_type": "name"}, {"api_name": "newslynx.models.Auth.query.filter_by", "line_number": 56, "usage_type": "call"}, {"api_name": "newslynx.models.Auth.query", "line_number": 56, "usage_type": "attribute"}, {"api_name": "newslynx.models.Auth", "line_number": 56, "usage_type": "name"}, {"api_name": "newslynx.views.util.obj_or_404", "line_number": 59, "usage_type": "call"}, {"api_name": "newslynx.lib.serialize.jsonify", "line_number": 61, "usage_type": "call"}, {"api_name": "newslynx.views.decorators.load_user", "line_number": 53, "usage_type": "name"}, {"api_name": "newslynx.views.decorators.load_org", "line_number": 54, "usage_type": "name"}, {"api_name": "newslynx.core.settings.FB_ENABLED", "line_number": 70, "usage_type": "attribute"}, {"api_name": "newslynx.core.settings", "line_number": 70, "usage_type": "name"}, {"api_name": "newslynx.exc.RequestError", "line_number": 71, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 77, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 81, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 82, "usage_type": "name"}, {"api_name": "flask.request.args.get", "line_number": 82, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 82, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 82, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 84, "usage_type": "call"}, {"api_name": "newslynx.views.decorators.load_user", "line_number": 65, "usage_type": "name"}, {"api_name": "newslynx.views.decorators.load_org", "line_number": 66, "usage_type": "name"}, {"api_name": "flask.session.pop", "line_number": 90, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 90, "usage_type": "name"}, {"api_name": "flask.session.pop", "line_number": 91, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 91, "usage_type": "name"}, {"api_name": "flask.request.args", "line_number": 94, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 94, "usage_type": "name"}, {"api_name": "newslynx.exc.AuthError", "line_number": 96, "usage_type": "call"}, {"api_name": "newslynx.lib.url.add_query_params", "line_number": 98, "usage_type": "call"}, {"api_name": "newslynx.lib.url", "line_number": 98, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 99, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 102, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 103, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 103, "usage_type": "name"}, {"api_name": "newslynx.models.Auth.query.filter_by", "line_number": 110, "usage_type": "call"}, {"api_name": "newslynx.models.Auth.query", "line_number": 110, "usage_type": "attribute"}, {"api_name": "newslynx.models.Auth", "line_number": 110, "usage_type": "name"}, {"api_name": "newslynx.models.Auth", "line_number": 117, "usage_type": "call"}, {"api_name": "newslynx.core.db.session.add", "line_number": 125, "usage_type": "call"}, {"api_name": "newslynx.core.db.session", "line_number": 125, "usage_type": "attribute"}, {"api_name": "newslynx.core.db", "line_number": 125, "usage_type": "name"}, {"api_name": "newslynx.core.db.session.commit", "line_number": 126, "usage_type": "call"}, {"api_name": "newslynx.core.db.session", "line_number": 126, "usage_type": "attribute"}, {"api_name": "newslynx.core.db", "line_number": 126, "usage_type": "name"}, {"api_name": "newslynx.lib.url.add_query_params", "line_number": 129, "usage_type": "call"}, {"api_name": "newslynx.lib.url", "line_number": 129, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 130, "usage_type": "call"}, {"api_name": "newslynx.lib.serialize.jsonify", "line_number": 132, "usage_type": "call"}, {"api_name": "newslynx.models.Auth.query.filter_by", "line_number": 140, "usage_type": "call"}, {"api_name": "newslynx.models.Auth.query", "line_number": 140, "usage_type": "attribute"}, {"api_name": "newslynx.models.Auth", "line_number": 140, "usage_type": "name"}, {"api_name": "newslynx.views.util.obj_or_404", "line_number": 144, "usage_type": "call"}, {"api_name": "newslynx.core.db.session.delete", "line_number": 147, "usage_type": "call"}, {"api_name": "newslynx.core.db.session", "line_number": 147, "usage_type": "attribute"}, {"api_name": "newslynx.core.db", "line_number": 147, "usage_type": "name"}, {"api_name": "newslynx.core.db.session.commit", "line_number": 148, "usage_type": "call"}, {"api_name": "newslynx.core.db.session", "line_number": 148, "usage_type": "attribute"}, {"api_name": "newslynx.core.db", "line_number": 148, "usage_type": "name"}, {"api_name": "flask.request.args.get", "line_number": 151, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 151, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 151, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 153, "usage_type": "call"}, {"api_name": "newslynx.views.util.delete_response", "line_number": 155, "usage_type": "call"}, {"api_name": "newslynx.views.decorators.load_user", "line_number": 136, "usage_type": "name"}, {"api_name": "newslynx.views.decorators.load_org", "line_number": 137, "usage_type": "name"}]} +{"seq_id": "72735600108", "text": "\"\"\"A slightly advanced example of using Cockpit with PyTorch for Fashion-MNIST.\"\"\"\n\nimport torch\nfrom _utils_examples import cnn, fmnist_data, get_logpath\nfrom backpack import extend, extensions\n\nfrom cockpit import Cockpit, CockpitPlotter, quantities\nfrom cockpit.utils import schedules\n\n# Build Fashion-MNIST classifier\nfmnist_data = fmnist_data()\nmodel = extend(cnn()) # Use a basic convolutional network\nloss_fn = extend(torch.nn.CrossEntropyLoss(reduction=\"mean\"))\nindividual_loss_fn = extend(torch.nn.CrossEntropyLoss(reduction=\"none\"))\n\n# Create SGD Optimizer\nopt = torch.optim.SGD(model.parameters(), lr=5e-1)\n\n# Create Cockpit and a plotter\n# Customize the tracked quantities and their tracking schedule\nquantities = [\n quantities.GradNorm(schedules.linear(interval=1)),\n quantities.Distance(schedules.linear(interval=1)),\n quantities.UpdateSize(schedules.linear(interval=1)),\n quantities.HessMaxEV(schedules.linear(interval=3)),\n quantities.GradHist1d(schedules.linear(interval=10), bins=10),\n]\ncockpit = Cockpit(model.parameters(), quantities=quantities)\nplotter = CockpitPlotter()\n\n# Main training loop\nmax_steps, global_step = 50, 0\nfor inputs, labels in iter(fmnist_data):\n opt.zero_grad()\n\n # forward pass\n outputs = model(inputs)\n loss = loss_fn(outputs, labels)\n losses = individual_loss_fn(outputs, labels)\n\n # backward pass\n with cockpit(\n global_step,\n extensions.DiagHessian(), # Other BackPACK quantities can be computed as well\n info={\n \"batch_size\": inputs.shape[0],\n \"individual_losses\": losses,\n \"loss\": loss,\n \"optimizer\": opt,\n },\n ):\n loss.backward(create_graph=cockpit.create_graph(global_step))\n\n # optimizer step\n opt.step()\n global_step += 1\n\n print(f\"Step: {global_step:5d} | Loss: {loss.item():.4f}\")\n\n if global_step % 10 == 0:\n plotter.plot(\n cockpit,\n savedir=get_logpath(),\n show_plot=False,\n save_plot=True,\n savename_append=str(global_step),\n )\n\n if global_step >= max_steps:\n break\n\n# Write Cockpit to json file.\ncockpit.write(get_logpath())\n\n# Plot results from file\nplotter.plot(\n get_logpath(),\n savedir=get_logpath(),\n show_plot=False,\n save_plot=True,\n savename_append=\"_final\",\n)\n", "repo_name": "f-dangel/cockpit", "sub_path": "examples/02_advanced_fmnist.py", "file_name": "02_advanced_fmnist.py", "file_ext": "py", "file_size_in_byte": 2357, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 453, "dataset": "github-code", "pt": "37", "api": [{"api_name": "_utils_examples.fmnist_data", "line_number": 11, "usage_type": "name"}, {"api_name": "backpack.extend", "line_number": 12, "usage_type": "call"}, {"api_name": "_utils_examples.cnn", "line_number": 12, "usage_type": "call"}, {"api_name": "backpack.extend", "line_number": 13, "usage_type": "call"}, {"api_name": "torch.nn.CrossEntropyLoss", "line_number": 13, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 13, "usage_type": "attribute"}, {"api_name": "backpack.extend", "line_number": 14, "usage_type": "call"}, {"api_name": "torch.nn.CrossEntropyLoss", "line_number": 14, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 14, "usage_type": "attribute"}, {"api_name": "torch.optim.SGD", "line_number": 17, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 17, "usage_type": "attribute"}, {"api_name": "cockpit.quantities", "line_number": 21, "usage_type": "name"}, {"api_name": "cockpit.quantities.GradNorm", "line_number": 22, "usage_type": "call"}, {"api_name": "cockpit.quantities", "line_number": 22, "usage_type": "name"}, {"api_name": "cockpit.utils.schedules.linear", "line_number": 22, "usage_type": "call"}, {"api_name": "cockpit.utils.schedules", "line_number": 22, "usage_type": "name"}, {"api_name": "cockpit.quantities.Distance", "line_number": 23, "usage_type": "call"}, {"api_name": "cockpit.quantities", "line_number": 23, "usage_type": "name"}, {"api_name": "cockpit.utils.schedules.linear", "line_number": 23, "usage_type": "call"}, {"api_name": "cockpit.utils.schedules", "line_number": 23, "usage_type": "name"}, {"api_name": "cockpit.quantities.UpdateSize", "line_number": 24, "usage_type": "call"}, {"api_name": "cockpit.quantities", "line_number": 24, "usage_type": "name"}, {"api_name": "cockpit.utils.schedules.linear", "line_number": 24, "usage_type": "call"}, {"api_name": "cockpit.utils.schedules", "line_number": 24, "usage_type": "name"}, {"api_name": "cockpit.quantities.HessMaxEV", "line_number": 25, "usage_type": "call"}, {"api_name": "cockpit.quantities", "line_number": 25, "usage_type": "name"}, {"api_name": "cockpit.utils.schedules.linear", "line_number": 25, "usage_type": "call"}, {"api_name": "cockpit.utils.schedules", "line_number": 25, "usage_type": "name"}, {"api_name": "cockpit.quantities.GradHist1d", "line_number": 26, "usage_type": "call"}, {"api_name": "cockpit.quantities", "line_number": 26, "usage_type": "name"}, {"api_name": "cockpit.utils.schedules.linear", "line_number": 26, "usage_type": "call"}, {"api_name": "cockpit.utils.schedules", "line_number": 26, "usage_type": "name"}, {"api_name": "cockpit.Cockpit", "line_number": 28, "usage_type": "call"}, {"api_name": "cockpit.quantities", "line_number": 28, "usage_type": "name"}, {"api_name": "cockpit.CockpitPlotter", "line_number": 29, "usage_type": "call"}, {"api_name": "_utils_examples.fmnist_data", "line_number": 33, "usage_type": "argument"}, {"api_name": "backpack.extensions.DiagHessian", "line_number": 44, "usage_type": "call"}, {"api_name": "backpack.extensions", "line_number": 44, "usage_type": "name"}, {"api_name": "cockpit.create_graph", "line_number": 52, "usage_type": "call"}, {"api_name": "_utils_examples.get_logpath", "line_number": 63, "usage_type": "call"}, {"api_name": "cockpit.write", "line_number": 73, "usage_type": "call"}, {"api_name": "_utils_examples.get_logpath", "line_number": 73, "usage_type": "call"}, {"api_name": "_utils_examples.get_logpath", "line_number": 77, "usage_type": "call"}, {"api_name": "_utils_examples.get_logpath", "line_number": 78, "usage_type": "call"}]} +{"seq_id": "25885496970", "text": "\"\"\"Template robot with Python.\"\"\"\n\n\nfrom RPA.Browser.Selenium import Selenium\nfrom RPA.PDF import PDF\n\n# I use openpyxl, because library \"RPA.Excel.Application\" work with Windows.\n# I use Ubuntu.\nfrom openpyxl import Workbook\nimport openpyxl\nfrom openpyxl.writer.excel import save_workbook\nimport time\n\n# app\nfrom app.finder_agency import get_element\n# from app.table_element import elements_table_body\n\nbrowser = Selenium()\n\npdf = PDF()\n\n# List Name and Spanding Agencies from Start page\nname_spending_agency = []\n\n# List elements from table Agency\nlist_text_table = []\n\n# List URL from table Agency\nlist_url_table = []\n\n\n\n# Open the start page of the web-site\ndef open_website(url):\n try:\n browser.open_available_browser(url)\n\n except Exception as ex:\n print(ex)\n\n\n# Open all agency, click element 'DIVE IN'\ndef open_agency():\n try:\n browser.click_element('class:btn.btn-default.btn-lg-2x.trend_sans_oneregular')\n\n except Exception as ex:\n print(ex)\n\n\n# General block Name and Spending Agencies\ndef general_block_agencies():\n try:\n return browser.find_element('id:agency-tiles-widget')\n\n except Exception as ex:\n print(ex)\n\n\n# Name and Spending Agencies\ndef informatin_agencies(get_text):\n name_agency = []\n spending_agency = []\n\n # Name Agencies\n try:\n element_name_agency = browser.find_elements('class:h4.w200', get_text)\n except Exception as ex:\n print(ex)\n\n for text_name in element_name_agency:\n name_agency.append(browser.get_text(text_name))\n\n # Spanding Agencies\n try:\n element_spending_agency = browser.find_elements('class:h1.w900', get_text)\n except Exception as ex:\n print(ex)\n\n for text_spending in element_spending_agency:\n spending_agency.append(browser.get_text(text_spending))\n\n name_spending_agency.append(\n {\n 'Name Agency':name_agency,\n 'Spending Agency':spending_agency,\n }\n )\n\n print('DONE, informatin_agencies')\n\n\n# Select one Agency\ndef open_page_agency(url_agencies):\n name_agency = 'National Science Foundation'\n\n for element in name_spending_agency:\n find_element = element['Name Agency']\n\n if name_agency in find_element:\n\n # Function module for going to the agency page\n get_element(name_agency, url_agencies)\n\n else:\n print('Check the name or spelling of the agency')\n browser.close_browser()\n exit()\n\n print('DONE, open_page_agency')\n\n\n# Block with all elements Agency\ndef block_data_table_agency():\n try:\n return browser.find_element('id:investments-table-object_wrapper')\n\n except Exception as ex:\n print(ex)\n\n\n# Form control (All)\ndef show_entery():\n try:\n browser.click_element('xpath://select[@class=\"form-control c-select\"]/option[4]')\n\n except Exception as ex:\n print(ex)\n\n print('DONE, table_afgency')\n\n\n# Getting block body table elements\ndef block_body_table(body_table):\n try:\n browser.find_element('xpath://div[@class=\"dataTables_scrollBody\"]/table/tbody', body_table)\n \n except Exception as ex:\n print(ex)\n\n print('DONE, block_body_table')\n\n\n# Getting table body text\ndef elements_table_body(elements):\n list_uii = []\n list_bureau = []\n list_investment = []\n list_spending = []\n list_type = []\n list_rating = []\n list_projects = []\n\n\n # UII\n try:\n uii_elements = browser.find_elements('xpath://tr/td[1][@class=\"left sorting_2\"]', elements)\n\n except Exception as ex:\n print(ex)\n\n for url in uii_elements:\n finder_url = browser.find_elements('tag:a', url)\n\n for item in finder_url:\n if item == None:\n continue\n\n list_url_table.append(item)\n\n for text_uii in uii_elements:\n list_uii.append(browser.get_text(text_uii))\n\n\n # Bureau\n try:\n bureau_elements = browser.find_elements('xpath://tr/td[2][@class=\" left select-filter\"]', elements)\n\n except Exception as ex:\n print(ex)\n\n for text_bureau in bureau_elements:\n list_bureau.append(browser.get_text(text_bureau))\n\n\n # Investment Title\n try:\n investment_elements = browser.find_elements('xpath://tr/td[3][@class=\" left\"]', elements)\n\n except Exception as ex:\n print(ex)\n\n for text_investment in investment_elements:\n list_investment.append(browser.get_text(text_investment))\n\n\n # Total Spending\n try:\n spending_elements = browser.find_elements('xpath://tr/td[4][@class=\" right\"]', elements)\n\n except Exception as ex:\n print(ex)\n\n for text_spending in spending_elements:\n list_spending.append(browser.get_text(text_spending))\n\n\n # Type\n try:\n type_elements = browser.find_elements('xpath://tr/td[5][@class=\" left select-filter\"]', elements)\n\n except Exception as ex:\n print(ex)\n\n for text_type in type_elements:\n list_type.append(browser.get_text(text_type))\n\n\n # CIO Rating\n try:\n rating_elements = browser.find_elements('xpath://tr/td[6][@class=\" center\"]', elements)\n\n except Exception as ex:\n print(ex)\n\n for text_rating in rating_elements:\n list_rating.append(browser.get_text(text_rating))\n\n \n # Projects\n try:\n projects_elements = browser.find_elements('xpath://tr/td[7][@class=\" center\"]', elements)\n\n except Exception as ex:\n print(ex)\n\n for text_projects in projects_elements:\n list_projects.append(browser.get_text(text_projects))\n\n\n list_text_table.append(\n {\n 'UII': list_uii,\n 'Bureau': list_bureau,\n 'Investment Title': list_investment,\n 'Total Spending': list_spending,\n 'Type': list_type,\n 'CIO Rating': list_rating,\n 'Projects': list_projects\n }\n )\n\n return list_text_table\n\n\n# Open pages from table\ndef open_url_table(file_pdf):\n open_page = browser.click_element(list_url_table[0])\n browser.set_browser_implicit_wait(10)\n\n # Download Business Case PDF\n element_case = browser.find_element('id:business-case-pdf')\n\n browser.click_element(element_case)\n time.sleep(20)\n\n print('DONE, open_url_table')\n\n\n\ndef main():\n try:\n\n # Open the start page of the web-site\n open_website(\"https://itdashboard.gov/\")\n browser.set_browser_implicit_wait(10)\n\n # Open all agency, click element 'DIVE IN'\n open_agency()\n browser.set_browser_implicit_wait(10)\n time.sleep(5)\n\n # General block Name and Spending Agencies\n block_agencies = general_block_agencies()\n browser.set_browser_implicit_wait(10)\n\n # Name and Spending Agencies\n informatin_agencies(get_text=block_agencies)\n browser.set_browser_implicit_wait(10)\n\n # Select one Agency\n open_page_agency(url_agencies=block_agencies)\n browser.set_browser_implicit_wait(10)\n\n # Block with all elements Agency\n block_table_agency = block_data_table_agency()\n browser.set_browser_implicit_wait(10)\n\n # Form control (All)\n show_entery()\n time.sleep(10)\n\n # Getting block body table elements\n all_elements = block_body_table(body_table=block_table_agency)\n\n # Getting table body text\n elements_table_body(elements=all_elements)\n\n # Open pages from table\n open_url_table('new_robot/output')\n\n print('done')\n \n finally:\n browser.close_browser()\n\n\nif __name__ == \"__main__\":\n main()\n", "repo_name": "Vladyslav225/RPA_Selenium", "sub_path": "new_robot/task.py", "file_name": "task.py", "file_ext": "py", "file_size_in_byte": 7568, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "RPA.Browser.Selenium.Selenium", "line_number": 18, "usage_type": "call"}, {"api_name": "RPA.PDF.PDF", "line_number": 20, "usage_type": "call"}, {"api_name": "app.finder_agency.get_element", "line_number": 103, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 265, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 281, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 301, "usage_type": "call"}]} +{"seq_id": "4828341156", "text": "from __future__ import print_function\n\nimport wx\nimport os.path\n\n\nclass Decoration(object):\n def __new__(type, *args):\n # Make it a Singleton.\n if not \"_the_instance\" in type.__dict__:\n type._the_instance = object.__new__(type)\n\n return type._the_instance\n\n def __init__(self):\n if not \"_ready\" in dir(self):\n self._ready = True\n # It's a Singleton. Initialisations go in here.\n self.backPic = None\n self.backPicOffset = (0, -25)\n\n if not self.backPic:\n backPicPath = os.path.join(\"configtool\", \"background.png\")\n if os.path.exists(backPicPath):\n backPic = wx.Bitmap(backPicPath)\n if backPic.IsOk():\n self.backPic = backPic\n else:\n print(\"Background picture %s damaged.\" % backPicPath)\n else:\n print(\"Background picture %s doesn't exist.\" % backPicPath)\n\n def getBackgroundColour(self):\n return wx.Colour(237, 237, 237)\n\n # On wxFrames, bind this to wx.EVT_ERASE_BACKGROUND\n # On wxPanels, bind this to wx.EVT_PAINT\n def onPaintBackground(self, evt):\n client = evt.GetEventObject()\n topLevel = client.GetTopLevelParent()\n\n try:\n dc = evt.GetDC()\n except:\n dc = wx.PaintDC(client)\n\n if dc:\n # Now draw the background picture with pseudo-transparency. This is,\n # each background is drawn with the same picture, without transparency,\n # and offsetted just right to have all backgrounds in the same position\n # relative to the *toplevel* window, not relative to the current\n # subwindow as usual.\n\n # Align bottom right.\n offX, offY = (\n topLevel.GetClientSize() - self.backPic.GetSize() + self.backPicOffset\n )\n\n if client != topLevel:\n # Note: trying to figure this additional offset via various\n # .GetScreenPosition() or .GetPosition() or whatever is hopeless.\n # Of many many tries only this worked on Linux.\n offX, offY = client.ScreenToClient(\n topLevel.ClientToScreen((offX, offY))\n )\n\n if self.backPic:\n dc.DrawBitmap(self.backPic, offX, offY)\n\n evt.Skip()\n", "repo_name": "Traumflug/Teacup_Firmware", "sub_path": "configtool/decoration.py", "file_name": "decoration.py", "file_ext": "py", "file_size_in_byte": 2419, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 303, "dataset": "github-code", "pt": "37", "api": [{"api_name": "os.path.path.join", "line_number": 23, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 23, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 23, "usage_type": "name"}, {"api_name": "os.path.path.exists", "line_number": 24, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 24, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 24, "usage_type": "name"}, {"api_name": "wx.Bitmap", "line_number": 25, "usage_type": "call"}, {"api_name": "wx.Colour", "line_number": 34, "usage_type": "call"}, {"api_name": "wx.PaintDC", "line_number": 45, "usage_type": "call"}]} +{"seq_id": "35539769721", "text": "\"\"\"\nFilter trap messages based on JavaScript functions\n\"\"\"\nimport os\nimport js2py\nfrom . import exceptions\nfrom trapdoor import handlers\n\nimport logging\nlog = logging.getLogger('trapdoor.core.filter')\nlog.addHandler(logging.NullHandler())\n\nHANDLERS = handlers.load()\n\nFILTER_TEMPLATE='''// Trapdoor - filter\n// This is an example filter you can use as a start.\n// You always have to have a \"Filter\" function.\n//\n// the supplied trap is an object defining a recieved\n// trap:\n// ´´´\n// { \"timestamp\": 123456789,\n// \"oid\": \"1.2.3.4.5.6.7.8\"\n// \"translatedOid:\"My.Trap.Translation\"\n// \"ip\": 0.0.0.0,\n// \"vars\": {\n// \"myVar\" : \"varValue\",\n// \"myVar1\" : \"varValue1\",\n// \"myVar2\" : \"varValue2\"\n// },\n// history: {\n// \"filters\": [list of previous filters if any]\n// }\n// }\n// ´´´\n// **Note**: You don't add your filter to history itself. That's done\n// automagically.\n// Your trap must return an object with a minimun options of this:\n// ```\n// { \"store\": true,\n// \"next\": false,\n// \"handle\": true,\n// \"handler\": \"log\",\n// \"trap\": `[trap object]`,\n// }\n\nfunction Filter(Trap) {\n\n var filtered = {\n \"store\": true,\n \"next\": false,\n \"handle\": false,\n \"handle\": null,\n \"trap\": Trap\n }\n \n // Do some magic here with Trap & filtered\n \n return filtered\n}\n'''\n\n\nclass FilterManager(object):\n def __init__(self,config):\n \"\"\"\n A manager for filters. Allows us to save/apply/edit/remove\n filters saved at the configured location\n \"\"\"\n self._location = config['filters']['location']\n self._filters = {}\n if os.path.exists(self._location) and os.path.isdir(self._location):\n pass\n else:\n log.error(\"Cannot access {}. Doesn't exit or is not a dir\".format(self._location))\n raise exceptions.FilterPathError(\"Cannot access {}\".format(self._location))\n\n try:\n for root, dirs, files in os.walk(self._location):\n for file in files:\n if file.endswith('.js'):\n f = os.path.join(root,file)\n log.info('Loading {}'.format(f))\n with open(f,'r') as reader:\n self._filters[f] = Filter(f,reader.read(),HANDLERS)\n log.debug(\"File {} Loaded\".format(f))\n except Exception as e:\n log.error(\"Unable to load files: {} \".format(e))\n raise exceptions.FilterProcessError(e)\n \n\n def _test_file(self,js):\n \"\"\"\n Test the file if its valid javascript\n \"\"\"\n try:\n js2py.eval_js(js)\n except js2py.base.PyJsException as e:\n log.error(\"Cannot evaluate filter: {}\".format(e))\n raise exceptions.FilterParseError(e)\n return True\n def _template(self,file):\n \"\"\"\n store the template data into a new file\n \"\"\"\n def new(self,path):\n \"\"\"\n Create a new filter with template\n \"\"\"\n filter_path = os.path.join(self._location,path)\n try:\n with open(filter_path,'w') as file:\n file.write(FILTER_TEMPLATE)\n self._filters[filter_path] = Filter(filter_path,FILTER_TEMPLATE,HANDLERS)\n except Exception as e:\n log.error(\"Unable to save/open file {}\".format(path))\n raise exceptions.FilterSaveError(e)\n return True\n def save(self,path,new_data):\n \"\"\"\n Save the filter after testing\n \"\"\"\n \n filter_path = os.path.join(self._location,path)\n \n try:\n self._test_file(new_data)\n except exceptions.FilterParseError as e:\n log.error(\"Cannot save filter {}.Evaluation failed\")\n raise exceptions.FilterSaveError(e)\n \n try:\n with open(filter_path,'w') as file:\n file.write(new_data)\n self._filters[filter_path] = Filter(filter_path,new_data,HANDLERS)\n except Exception as e:\n log.error(\"Unable to save/open file {}\".format(path))\n raise exceptions.FilterSaveError(e)\n return True\n @property\n def filters(self):\n return self._filters\n \n def delete(self,path):\n try:\n if path in self._filters and os.path.exists(path) and os.path.isfile(path) and path.endswith(\".js\"):\n os.remove(os.path.join(self._location,path))\n self._filters.pop(path)\n return True\n except Exception as e:\n log.error(\"Unable to delete filter {}\".format(path))\n\nclass Filter(object):\n \"\"\"\n An filter instance\n \"\"\"\n \n def __init__(self,name,js,handlers):\n \"\"\"\n Evaluate the javascript and set some variables\n \"\"\"\n\n self._name = name\n self._js = js\n self._handlers = handlers\n self._setContext()\n\n def _setContext(self):\n \"\"\"\n Set the context for js. Add the handler function\n so javascript knows about the handlers.\n \"\"\"\n \n def get_handlers():\n return self._handlers\n self._context = js2py.EvalJs({\n \"handlers\" : get_handlers\n })\n self._context.execute(self._js)\n \n def reset_handlers(self,handlers):\n \"\"\"\n If we want to live add handlers,\n we can reset them here & reset the javascript context\n \"\"\"\n self._handlers = handlers\n self._setContext()\n \n def evaluate(self,Trap):\n \"\"\"\n Evaluate the js \"Filter() function\"\n \"\"\"\n trap = Trap.to_dict()\n\n return self._context.Filter(trap).to_dict()\n", "repo_name": "subutux/trapdoor", "sub_path": "trapdoor/core/filter.py", "file_name": "filter.py", "file_ext": "py", "file_size_in_byte": 5724, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "logging.getLogger", "line_number": 10, "usage_type": "call"}, {"api_name": "logging.NullHandler", "line_number": 11, "usage_type": "call"}, {"api_name": "trapdoor.handlers.load", "line_number": 13, "usage_type": "call"}, {"api_name": "trapdoor.handlers", "line_number": 13, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 72, "usage_type": "call"}, {"api_name": "os.path", "line_number": 72, "usage_type": "attribute"}, {"api_name": "os.path.isdir", "line_number": 72, "usage_type": "call"}, {"api_name": "os.walk", "line_number": 79, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 82, "usage_type": "call"}, {"api_name": "os.path", "line_number": 82, "usage_type": "attribute"}, {"api_name": "js2py.eval_js", "line_number": 97, "usage_type": "call"}, {"api_name": "js2py.base", "line_number": 98, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 110, "usage_type": "call"}, {"api_name": "os.path", "line_number": 110, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 124, "usage_type": "call"}, {"api_name": "os.path", "line_number": 124, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 146, "usage_type": "call"}, {"api_name": "os.path", "line_number": 146, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 146, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 147, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 147, "usage_type": "call"}, {"api_name": "os.path", "line_number": 147, "usage_type": "attribute"}, {"api_name": "trapdoor.handlers", "line_number": 165, "usage_type": "name"}, {"api_name": "js2py.EvalJs", "line_number": 176, "usage_type": "call"}, {"api_name": "trapdoor.handlers", "line_number": 186, "usage_type": "name"}]} +{"seq_id": "37482977461", "text": "import logging\nfrom rich.logging import RichHandler\n\n# Constants for log configuration\nLOG_FORMAT = '%(asctime)s - %(levelname)s - %(filename)s:%(lineno)d:%(funcName)s - %(message)s'\nLOG_DATE_FORMAT = '%Y-%m-%d %H:%M:%S'\nLOG_FILE_PATH = \"/home/and/Dropbox/computing-msc-uea/nlp/axidoc/logs/core_logs.txt\"\nLOG_LEVEL = logging.DEBUG # logging.CRITICAL\n\nclass CustomLogger(logging.Logger):\n def __init__(self, name, level=LOG_LEVEL):\n super().__init__(name, level)\n\n # Create and add the formatter and handler for file logging\n formatter = logging.Formatter(LOG_FORMAT, datefmt=LOG_DATE_FORMAT)\n file_handler = logging.FileHandler(LOG_FILE_PATH, mode='a') # 'a' means append mode\n file_handler.setFormatter(formatter)\n self.addHandler(file_handler)\n\n # Add delimiter for new runs\n with open(LOG_FILE_PATH, 'a') as f:\n f.write(\"\\n\\n============\\n\\n\")\n\n # Handler for rich console logging\n rich_console_handler = RichHandler(\n level=LOG_LEVEL,\n rich_tracebacks=True,\n markup=True\n )\n self.addHandler(rich_console_handler)\n\n # Not propagate to root logger\n self.propagate = False\n\n# Replace the default logger class with our custom logger\nlogging.setLoggerClass(CustomLogger)\n\ndef get_logger(name=__name__):\n return logging.getLogger(name)\n\n\n# You can now use get_logger in all your modules to get the custom logger:\n# logger = logconf.get_logger(__name__)\n# logger.info(\"This is an info message.\")\n\n", "repo_name": "reinasta/axidoc", "sub_path": "src/logconf.py", "file_name": "logconf.py", "file_ext": "py", "file_size_in_byte": 1544, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "logging.DEBUG", "line_number": 8, "usage_type": "attribute"}, {"api_name": "logging.Logger", "line_number": 10, "usage_type": "attribute"}, {"api_name": "logging.Formatter", "line_number": 15, "usage_type": "call"}, {"api_name": "logging.FileHandler", "line_number": 16, "usage_type": "call"}, {"api_name": "rich.logging.RichHandler", "line_number": 25, "usage_type": "call"}, {"api_name": "logging.setLoggerClass", "line_number": 36, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 39, "usage_type": "call"}]} +{"seq_id": "6649460302", "text": "from redis_funcs import say, command as bus_command\nimport random\nimport requests\nimport datetime\nimport os\nimport json\nfrom time import sleep\n\n\n# Trick to get the counting word\nordinal = lambda n: \"%d%s\" % (n,\"tsnrhtdd\"[(n//10%10!=1)*(n%10<4)*n%10::4])\n\nhosts = {\n \"mountainlight\": \"http://192.168.178.29/api/\",\n \"joke\": \"https://official-joke-api.appspot.com/jokes\",\n \"weather\": \"http://api.openweathermap.org/data/2.5/weather?\"\n \"id=2759798&\"\n f\"APPID={os.environ.get('OPENWEATHER_APPID')}\"\n \"&units=metric\"\n}\n\n\nclass Actions:\n @staticmethod\n def say(tree, text):\n say(text)\n\n @staticmethod\n def sayrandom(tree, options):\n say(random.choice(options))\n\n @staticmethod\n def saytime(tree, _):\n time = datetime.datetime.now()\n say(time.strftime(\"The time is %H %M\"))\n\n @staticmethod\n def saytimenice(tree, _):\n time = datetime.datetime.now()\n\n hour = time.hour\n\n minute = time.minute\n\n # We're talking towards the next hour, so increase it by 1\n if minute > 32:\n hour += 1\n hour = hour % 12\n\n minute = (5 * round(minute/5)) % 60\n\n # We don't usually say 0 hours, we say 12\n if hour == 0:\n hour = 12\n\n # Handle the different cases\n if minute == 0:\n s = f\"It is {hour} 'o clock\"\n elif minute == 45:\n s = f\"It is quarter to {hour}\"\n elif minute == 15:\n s = f\"It is quarter past {hour}\"\n elif minute == 30:\n s = f\"It is half past {hour}\"\n elif minute > 30:\n s = f\"It is {60 - minute} to {hour}\"\n else:\n s = f\"It is {minute} past {hour}\"\n\n say(s)\n\n\n @staticmethod\n def saydate(tree, _):\n time = datetime.datetime.now()\n\n number = ordinal(time.day)\n s = time.strftime(f\"It is %A the {number} of %B, at %H %M\")\n say(s)\n\n # TODO: replace with external tool through REDIS\n @staticmethod\n def apicall(tree, parameters):\n host = hosts[parameters['host']]\n method = parameters['method']\n command = parameters['command']\n body = parameters.get('body')\n\n if method == 'put':\n requests.put(host + command, data=body)\n\n # TODO: replace with action through mirror code?\n @staticmethod\n def sayweather(tree, _):\n host = hosts['weather']\n r = requests.get(host)\n w = r.json()\n starter = random.choice([\n \"It is currently\",\n \"It is now\",\n \"The weather right now is\"\n ])\n t = round(w['main']['temp'])\n temp = random.choice([\n f\"with a temperature of {t} degrees\",\n f\"at {t} degrees\",\n f\", {t} degrees celcius\"\n ])\n\n s = f\"{starter} {w['weather'][0]['main']} {temp}.\"\n say(s)\n\n @staticmethod\n def sayjoke(tree, _):\n j = requests.get(f\"{hosts['joke']}/general/random\")\n joke = j.json()[0]\n s = joke['setup'] + ' ' + joke['punchline']\n say(s)\n\n @staticmethod\n def nerdjoke(tree, _):\n j = requests.get(f\"{hosts['joke']}/programming/random\")\n joke = j.json()[0]\n s = joke['setup'] + ' ' + joke['punchline']\n say(s)\n\n @staticmethod\n def sleep(tree, interval):\n sleep(interval)\n\n @staticmethod\n def command(tree, parameters):\n bus_command(parameters[0], json.dumps(parameters[1]))\n\n", "repo_name": "JaykeMeijer/jarvis", "sub_path": "listen/actions.py", "file_name": "actions.py", "file_ext": "py", "file_size_in_byte": 3470, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "os.environ.get", "line_number": 18, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 18, "usage_type": "attribute"}, {"api_name": "redis_funcs.say", "line_number": 26, "usage_type": "call"}, {"api_name": "redis_funcs.say", "line_number": 30, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 30, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 34, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 34, "usage_type": "attribute"}, {"api_name": "redis_funcs.say", "line_number": 35, "usage_type": "call"}, {"api_name": "time.strftime", "line_number": 35, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 39, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 39, "usage_type": "attribute"}, {"api_name": "time.hour", "line_number": 41, "usage_type": "attribute"}, {"api_name": "time.minute", "line_number": 43, "usage_type": "attribute"}, {"api_name": "redis_funcs.say", "line_number": 70, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 75, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 75, "usage_type": "attribute"}, {"api_name": "time.day", "line_number": 77, "usage_type": "attribute"}, {"api_name": "time.strftime", "line_number": 78, "usage_type": "call"}, {"api_name": "redis_funcs.say", "line_number": 79, "usage_type": "call"}, {"api_name": "requests.put", "line_number": 90, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 96, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 98, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 104, "usage_type": "call"}, {"api_name": "redis_funcs.say", "line_number": 111, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 115, "usage_type": "call"}, {"api_name": "redis_funcs.say", "line_number": 118, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 122, "usage_type": "call"}, {"api_name": "redis_funcs.say", "line_number": 125, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 129, "usage_type": "call"}, {"api_name": "redis_funcs.command", "line_number": 133, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 133, "usage_type": "call"}]} +{"seq_id": "15742679745", "text": "import os\nimport sys\nfrom dotenv import load_dotenv\nfrom typing import Any, Dict, List\n\nfrom langchain.embeddings.openai import OpenAIEmbeddings\nfrom langchain.chat_models import ChatOpenAI\nfrom langchain.chains import RetrievalQA\nfrom langchain.vectorstores import Pinecone\nfrom langchain.chains import ConversationalRetrievalChain\nimport pinecone\n\nload_dotenv()\n\npinecone.init(\n api_key=os.environ[\"PINECONE_API_KEY\"],\n environment=os.environ[\"PINECONE_ENVIRONMENT_REGION\"],\n)\n\nINDEX_NAME = \"pragya-teachus-bot\"\n\ndef run_llm(\n docsearch,\n query: str,\n k: int,\n chat_history: Dict,\n) -> Dict:\n\n chat = ChatOpenAI(verbose=False, temperature=0)\n chain = ConversationalRetrievalChain.from_llm(\n llm=chat,\n chain_type=\"stuff\",\n retriever=docsearch.as_retriever(search_type='similarity', search_kwargs={'k': k}),\n return_source_documents=False,\n )\n answer = chain.run({\"query\": query, \"chat_history\": chat_history})\n return {\"answer\": answer}\n\nif __name__ == \"__main__\":\n\n embeddings = OpenAIEmbeddings()\n docsearch = Pinecone.from_existing_index(\n index_name=INDEX_NAME, embedding=embeddings\n )\n k = 5 # number of documents to retrieve\n\n # user's question text input widget\n yellow = \"\\033[0;33m\"\n green = \"\\033[0;32m\"\n white = \"\\033[0;39m\"\n\n chat_history = {}\n print(f\"{yellow}---------------------------------------------------------------------------------\")\n print('Welcome to the DocBot. You are now ready to start interacting with your documents')\n print('---------------------------------------------------------------------------------')\n while True:\n query = input(f\"{green}Prompt: \")\n if query == \"exit\" or query == \"quit\" or query == \"q\" or query == \"f\":\n print('Exiting')\n sys.exit()\n if query == '':\n continue\n result = run_llm(\n docsearch, query, k, chat_history\n )\n print(f\"{white}Answer: \" + result[\"answer\"])\n chat_history[query] = result[\"answer\"]\n", "repo_name": "asharda/test", "sub_path": "pragya-qa-bot.py", "file_name": "pragya-qa-bot.py", "file_ext": "py", "file_size_in_byte": 2064, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "dotenv.load_dotenv", "line_number": 13, "usage_type": "call"}, {"api_name": "pinecone.init", "line_number": 15, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 16, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 17, "usage_type": "attribute"}, {"api_name": "typing.Dict", "line_number": 26, "usage_type": "name"}, {"api_name": "langchain.chat_models.ChatOpenAI", "line_number": 29, "usage_type": "call"}, {"api_name": "langchain.chains.ConversationalRetrievalChain.from_llm", "line_number": 30, "usage_type": "call"}, {"api_name": "langchain.chains.ConversationalRetrievalChain", "line_number": 30, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 27, "usage_type": "name"}, {"api_name": "langchain.embeddings.openai.OpenAIEmbeddings", "line_number": 41, "usage_type": "call"}, {"api_name": "langchain.vectorstores.Pinecone.from_existing_index", "line_number": 42, "usage_type": "call"}, {"api_name": "langchain.vectorstores.Pinecone", "line_number": 42, "usage_type": "name"}, {"api_name": "sys.exit", "line_number": 60, "usage_type": "call"}]} +{"seq_id": "2344855150", "text": "# coding=utf-8\n\nimport json\nfrom dataclasses import dataclass\nfrom io import BytesIO\nfrom typing import List, Union\n\nimport pyarrow as pa\nimport pyarrow.json as paj\n\nimport nlp\n\n\n@dataclass\nclass JsonConfig(nlp.BuilderConfig):\n \"\"\"BuilderConfig for JSON.\"\"\"\n\n features: nlp.Features = None\n field: str = None\n use_threads: bool = True\n block_size: int = None\n newlines_in_values: bool = None\n\n @property\n def pa_read_options(self):\n return paj.ReadOptions(use_threads=self.use_threads, block_size=self.block_size)\n\n @property\n def pa_parse_options(self):\n return paj.ParseOptions(explicit_schema=self.schema, newlines_in_values=self.newlines_in_values)\n\n @property\n def schema(self):\n return pa.schema(self.features.type) if self.features is not None else None\n\n\nclass Json(nlp.ArrowBasedBuilder):\n BUILDER_CONFIG_CLASS = JsonConfig\n\n def _info(self):\n return nlp.DatasetInfo(features=self.config.features)\n\n def _split_generators(self, dl_manager):\n \"\"\" We handle string, list and dicts in datafiles\n \"\"\"\n if isinstance(self.config.data_files, (str, list, tuple)):\n files = self.config.data_files\n if isinstance(files, str):\n files = [files]\n return [nlp.SplitGenerator(name=nlp.Split.TRAIN, gen_kwargs={\"files\": files})]\n splits = []\n for split_name in [nlp.Split.TRAIN, nlp.Split.VALIDATION, nlp.Split.TEST]:\n if split_name in self.config.data_files:\n files = self.config.data_files[split_name]\n if isinstance(files, str):\n files = [files]\n splits.append(nlp.SplitGenerator(name=split_name, gen_kwargs={\"files\": files}))\n return splits\n\n def _generate_tables(self, files):\n for i, file in enumerate(files):\n if self.config.field is not None:\n with open(file, encoding=\"utf-8\") as f:\n dataset = json.load(f)\n\n # We keep only the field we are interested in\n dataset = dataset[self.config.field]\n\n # We accept two format: a list of dicts or a dict of lists\n if isinstance(dataset, (list, tuple)):\n pa_table = paj.read_json(\n BytesIO(\"\\n\".join(json.dumps(row) for row in dataset).encode(\"utf-8\")),\n read_options=self.config.pa_read_options,\n parse_options=self.config.pa_parse_options,\n )\n else:\n pa_table = pa.Table.from_pydict(mapping=dataset, schema=self.config.schema)\n else:\n try:\n pa_table = paj.read_json(\n file, read_options=self.config.pa_read_options, parse_options=self.config.pa_parse_options,\n )\n except pa.ArrowInvalid:\n with open(file, encoding=\"utf-8\") as f:\n dataset = json.load(f)\n raise ValueError(\n f\"Not able to read records in the JSON file at {file}. \"\n f\"You should probably indicate the field of the JSON file containing your records. \"\n f\"This JSON file contain the following fields: {str(list(dataset.keys()))}. \"\n f\"Select the correct one and provide it as `field='XXX'` to the `load_dataset` method. \"\n )\n yield i, pa_table\n", "repo_name": "MachineLearningBCAM/Minimax-risk-classifiers-NeurIPS-2020", "sub_path": "venv/lib/python3.6/site-packages/nlp/datasets/json/eadf955e40e35d014c948a5f2679ebc978ba09fe52474737708eadd45f3c71db/json.py", "file_name": "json.py", "file_ext": "py", "file_size_in_byte": 3531, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "37", "api": [{"api_name": "nlp.BuilderConfig", "line_number": 15, "usage_type": "attribute"}, {"api_name": "nlp.Features", "line_number": 18, "usage_type": "attribute"}, {"api_name": "pyarrow.json.ReadOptions", "line_number": 26, "usage_type": "call"}, {"api_name": "pyarrow.json", "line_number": 26, "usage_type": "name"}, {"api_name": "pyarrow.json.ParseOptions", "line_number": 30, "usage_type": "call"}, {"api_name": "pyarrow.json", "line_number": 30, "usage_type": "name"}, {"api_name": "pyarrow.schema", "line_number": 34, "usage_type": "call"}, {"api_name": "dataclasses.dataclass", "line_number": 14, "usage_type": "name"}, {"api_name": "nlp.ArrowBasedBuilder", "line_number": 37, "usage_type": "attribute"}, {"api_name": "nlp.DatasetInfo", "line_number": 41, "usage_type": "call"}, {"api_name": "nlp.SplitGenerator", "line_number": 50, "usage_type": "call"}, {"api_name": "nlp.Split", "line_number": 50, "usage_type": "attribute"}, {"api_name": "nlp.Split", "line_number": 52, "usage_type": "attribute"}, {"api_name": "nlp.SplitGenerator", "line_number": 57, "usage_type": "call"}, {"api_name": "json.load", "line_number": 64, "usage_type": "call"}, {"api_name": "pyarrow.json.read_json", "line_number": 71, "usage_type": "call"}, {"api_name": "pyarrow.json", "line_number": 71, "usage_type": "name"}, {"api_name": "io.BytesIO", "line_number": 72, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 72, "usage_type": "call"}, {"api_name": "pyarrow.Table.from_pydict", "line_number": 77, "usage_type": "call"}, {"api_name": "pyarrow.Table", "line_number": 77, "usage_type": "attribute"}, {"api_name": "pyarrow.json.read_json", "line_number": 80, "usage_type": "call"}, {"api_name": "pyarrow.json", "line_number": 80, "usage_type": "name"}, {"api_name": "pyarrow.ArrowInvalid", "line_number": 83, "usage_type": "attribute"}, {"api_name": "json.load", "line_number": 85, "usage_type": "call"}]} +{"seq_id": "18704654959", "text": "from flask import *\r\nimport pickle\r\n\r\nimport string\r\nfrom nltk.corpus import stopwords\r\nfrom nltk.stem import PorterStemmer\r\nimport nltk\r\nnltk.download('stopwords')\r\nfrom nltk.stem.porter import PorterStemmer\r\nps = PorterStemmer()\r\n\r\n\r\nfrom bs4 import BeautifulSoup\r\n\r\ntfidf = pickle.load(open('vectorizer.pkl','rb'))\r\nmodel = pickle.load(open('model.pkl','rb'))\r\n\r\ndef transform(review):\r\n review = review.lower()\r\n review = nltk.word_tokenize(review)\r\n\r\n y = []\r\n for i in review:\r\n if i.isalnum():\r\n y.append(i)\r\n\r\n review = y[:]\r\n y.clear()\r\n\r\n for i in review:\r\n if i not in stopwords.words('english') and i not in string.punctuation:\r\n y.append(i)\r\n\r\n review = y[:]\r\n y.clear()\r\n\r\n for i in review:\r\n y.append(ps.stem(i))\r\n\r\n return \" \".join(y)\r\n\r\ndef result(tr_review):\r\n vector_input = tfidf.transform([tr_review])\r\n result = model.predict(vector_input)[0]\r\n return result\r\n\r\n\r\n\r\n\r\n\r\napp = Flask(__name__)\r\n\r\n@app.route(\"/\")\r\ndef home():\r\n return render_template(\"homepage.html\")\r\n\r\n@app.route(\"/action\",methods=[\"POST\"])\r\ndef action():\r\n review=request.form[\"review\"]\r\n tr_review=transform(review)\r\n prediction=result(tr_review)\r\n return render_template(\"show.html\",result=prediction)\r\n\r\n\r\nif __name__ ==\"__main__\":\r\n app.run(debug=True)", "repo_name": "tejasrangle/Amazon_fine_food_reviews", "sub_path": "app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 1349, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "37", "api": [{"api_name": "nltk.download", "line_number": 8, "usage_type": "call"}, {"api_name": "nltk.stem.porter.PorterStemmer", "line_number": 10, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 15, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 16, "usage_type": "call"}, {"api_name": "nltk.word_tokenize", "line_number": 20, "usage_type": "call"}, {"api_name": "nltk.corpus.stopwords.words", "line_number": 31, "usage_type": "call"}, {"api_name": "nltk.corpus.stopwords", "line_number": 31, "usage_type": "name"}, {"api_name": "string.punctuation", "line_number": 31, "usage_type": "attribute"}]} +{"seq_id": "310478090", "text": "\"These tests of the tidy_data() function from nanodrop.py take a few seconds to run.\"\n\nimport numpy as np\nimport pandas as pd\nimport glob\nimport pytest\n\nfrom importlib import resources\n\nimport wrangling.nanodrop as nd\n\n\n_parent_dir = __file__[:__file__.rfind(\"\\\\\")]\n\n\ndef test_tidy_data(to_glob=_parent_dir+\"/test_data/*.tsv\", **kwargs):\n \"\"\"For testing a variety of input kwargs\"\"\"\n\n file_list = glob.glob(to_glob)\n df = nd.tidy_data(file_list, **kwargs)\n\n assert (\n df.columns == [\n \"Sample ID\",\n \"Abs 350\",\n \"Abs 600\",\n \"Peptide\",\n \"Peptide concentration (uM)\",\n \"RNA/Peptide Ratio\",\n \"Date\",\n \"Time\",\n ]\n ).all()\n\n float_cols = [\n \"Abs 350\",\n \"Abs 600\",\n \"Peptide concentration (uM)\",\n \"RNA/Peptide Ratio\",\n ]\n for col in float_cols:\n assert df[col].dtype == float\n\n return df\n\n\ndef test_run_all_defaults():\n df = test_tidy_data(to_glob=_parent_dir+\"/test_data/*.tsv\")\n\n for peptide in df[\"Peptide\"].values:\n assert \"RG\" in peptide\n\n\ndef test_run_all_read_csv():\n df = test_tidy_data(\n to_glob=_parent_dir+\"/test_data/*.csv\",\n file_reader_kwargs={},\n drop_incorrectly_named_samples=True,\n )\n\n for peptide in df[\"Peptide\"].values:\n assert \"RG\" in peptide\n\n\ndef test_run_all_raise_incorrect_samples_warning():\n with pytest.warns(UserWarning) as sample_name_divergence:\n df = test_tidy_data(\n to_glob=_parent_dir+\"/test_data/*.csv\",\n file_reader_kwargs={},\n drop_incorrectly_named_samples=False,\n )\n assert (\n \"Identify incorrectly named samples by running analyze_sample_names on your DataFrame.\"\n in sample_name_divergence[0].message.args[0]\n )\n\n # with drop_incorrectly_named_samples=False, not all Peptide values are filled\n assert \"RG7\" in df[\"Peptide\"].values\n\n\ndef test_run_all_explicitly_keep_buffers():\n df = test_tidy_data(\n to_glob=_parent_dir+\"/test_data/*.csv\",\n file_reader_kwargs={},\n drop_incorrectly_named_samples=True,\n drop_buffers=False,\n )\n\n assert \"RG7\" in df[\"Peptide\"].values\n\n assert len(nd._identify_buffer_measurements(df)) > 0\n\n \ndef test_nanodrop_data_nonmatching_column_names():\n # bad_input contains a file without the \"Sample ID\" category\n # all samples from that file will be dropped, but an extra empty column makes it into the output\n file_list = glob.glob(_parent_dir+\"/test_data/bad_input/*.tsv\")\n with pytest.warns(UserWarning):\n df = nd.tidy_data(file_list,\n drop_incorrectly_named_samples=True)\n \n float_cols = [\n \"Abs 350\",\n \"Abs 600\",\n \"Peptide concentration (uM)\",\n \"RNA/Peptide Ratio\",\n ]\n for col in float_cols:\n assert df[col].dtype == float\n \n for peptide in df[\"Peptide\"].values:\n assert \"RG\" in peptide", "repo_name": "ebentley17/Deniz_lab_code", "sub_path": "wrangling/nanodrop_tests/test_tidy_data.py", "file_name": "test_tidy_data.py", "file_ext": "py", "file_size_in_byte": 3007, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "glob.glob", "line_number": 19, "usage_type": "call"}, {"api_name": "wrangling.nanodrop.tidy_data", "line_number": 20, "usage_type": "call"}, {"api_name": "wrangling.nanodrop", "line_number": 20, "usage_type": "name"}, {"api_name": "pytest.warns", "line_number": 66, "usage_type": "call"}, {"api_name": "wrangling.nanodrop._identify_buffer_measurements", "line_number": 91, "usage_type": "call"}, {"api_name": "wrangling.nanodrop", "line_number": 91, "usage_type": "name"}, {"api_name": "glob.glob", "line_number": 97, "usage_type": "call"}, {"api_name": "pytest.warns", "line_number": 98, "usage_type": "call"}, {"api_name": "wrangling.nanodrop.tidy_data", "line_number": 99, "usage_type": "call"}, {"api_name": "wrangling.nanodrop", "line_number": 99, "usage_type": "name"}]} +{"seq_id": "7065363897", "text": "import os\nfrom flask import request\nfrom werkzeug.datastructures import FileStorage\nfrom flask_restful import Resource, reqparse\nfrom sqlalchemy.orm.exc import NoResultFound, MultipleResultsFound\nimport model\n\n\nclass DocumentRawAPI(Resource):\n def __init__(self):\n self.reqparse = reqparse.RequestParser()\n self.reqparse.add_argument('doc', type=FileStorage, location='files', required=True,\n help='Doc required')\n super(DocumentRawAPI, self).__init__()\n\n def post(self, document_id):\n args = self.reqparse.parse_args()\n\n # Check if the doc exists\n try:\n request.dbs.query(model.Document).filter(model.Document.id == document_id).one()\n doc = args['doc']\n doc.save(app.config['DOCS_URI'] + '/' + document_id + '.pdf')\n\n return {'success': 'Doc uploaded'}, 201\n except NoResultFound:\n app.logger.warning('Request on non existing document ' + document_id)\n return {}, 404\n except MultipleResultsFound:\n app.logger.error('Multiple results found for document ' + document_id)\n return {}, 500\n\n\nfrom app import app", "repo_name": "PageLib/ws", "sub_path": "ws/docs/DocumentRawAPI.py", "file_name": "DocumentRawAPI.py", "file_ext": "py", "file_size_in_byte": 1193, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "flask_restful.Resource", "line_number": 9, "usage_type": "name"}, {"api_name": "flask_restful.reqparse.RequestParser", "line_number": 11, "usage_type": "call"}, {"api_name": "flask_restful.reqparse", "line_number": 11, "usage_type": "name"}, {"api_name": "werkzeug.datastructures.FileStorage", "line_number": 12, "usage_type": "name"}, {"api_name": "flask.request.dbs.query", "line_number": 21, "usage_type": "call"}, {"api_name": "flask.request.dbs", "line_number": 21, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 21, "usage_type": "name"}, {"api_name": "model.Document", "line_number": 21, "usage_type": "attribute"}, {"api_name": "sqlalchemy.orm.exc.NoResultFound", "line_number": 26, "usage_type": "name"}, {"api_name": "sqlalchemy.orm.exc.MultipleResultsFound", "line_number": 29, "usage_type": "name"}]} +{"seq_id": "26492503547", "text": "from multiprocessing import Process\nimport sysv_ipc\nimport random\nimport socket\n\nclass Home(Process):\n def __init__(self, id, mq_offer, mq_demande, barrier_init, barrier_while, lock, shared_temperature, nb_days, barrier_day, HOST, PORT):\n super().__init__()\n self.id = id\n self.mq_offer = mq_offer\n self.mq_demande = mq_demande\n self.barrier_init = barrier_init\n self.barrier_while = barrier_while\n self.lock = lock\n self.temperature = shared_temperature\n self.nb_days = nb_days\n self.barrier_day = barrier_day\n self.HOST = HOST\n self.PORT = PORT\n \n def run(self):\n for i in range (self.nb_days):\n self.trade_policy = random.randint(1,3)\n # behavior of the process\n self.prod = random.randrange(20,80)\n self.conso = random.randrange(6,60)\n if (self.temperature[i] < 10 or self.temperature[i] > 38):\n self.conso += random.randrange(0,25)\n elif self.conso > 50:\n self.conso -= random.randrange(0, 25)\n self.offer = self.prod - self.conso\n print(f'{self.name} has offer of {self.offer} - trade policy : {self.trade_policy}')\n\n\n if self.offer > 0:\n o = str(abs(self.offer)).encode()\n self.mq_offer.send(o, type=self.id)\n else:\n d = str(-self.offer).encode()\n self.mq_demande.send(d, type=self.id)\n \n self.barrier_init.wait()\n\n if (self.trade_policy == 1 or self.trade_policy == 3): #si doit donner sa production aux autres homes\n self.lock.acquire()\n while (self.mq_demande.current_messages and self.offer > 0):\n\n m, t = self.mq_offer.receive(type=self.id) #récupère son message d'offer d'énergie dans la liste d'offer\n\n (e, t) = self.mq_demande.receive() #récupère une demande en énergie\n num_home = t\n ask_energy = int(e.decode())\n\n left_ask = ask_energy - self.offer #left = demande_lue - offer\n\n if left_ask > 0: #si il manque encore de l'énergie à la maison\n l = str(left_ask).encode()\n self.mq_demande.send(l, num_home)\n self.offer = 0\n\n elif left_ask < 0: #si il reste de l'énergie à donner\n l = str(-left_ask).encode()\n self.mq_offer.send(l, type=self.id)\n self.offer = -left_ask\n else :\n self.offer = 0\n self.lock.release()\n \n self.barrier_while.wait()\n\n self.s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n self.s.connect((self.HOST, self.PORT))\n\n if self.offer > 0:\n if self.trade_policy == 1:\n m, t = self.mq_offer.receive(type=self.id)\n else:\n m, t = self.mq_offer.receive(type=self.id)\n data =f'{self.offer} '\n self.s.sendall(data.encode())\n print(f'{self.name} send offer: {data} to Market')\n \n elif self.offer < 0:\n data =f'{self.offer} '\n self.s.sendall(data.encode())\n print(f'{self.name} send demand: {data} to Market')\n\n else :\n data =f'{-self.offer} '\n self.s.sendall(data.encode())\n\n self.lock.acquire()\n while self.mq_demande.current_messages:\n m, t = self.mq_demande.receive()\n self.lock.release()\n\n self.s.close()\n self.barrier_day.wait()\n", "repo_name": "Julie-mg/Projet-PPC", "sub_path": "Home.py", "file_name": "Home.py", "file_ext": "py", "file_size_in_byte": 4003, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "multiprocessing.Process", "line_number": 6, "usage_type": "name"}, {"api_name": "random.randint", "line_number": 23, "usage_type": "call"}, {"api_name": "random.randrange", "line_number": 25, "usage_type": "call"}, {"api_name": "random.randrange", "line_number": 26, "usage_type": "call"}, {"api_name": "random.randrange", "line_number": 28, "usage_type": "call"}, {"api_name": "random.randrange", "line_number": 30, "usage_type": "call"}, {"api_name": "socket.socket", "line_number": 71, "usage_type": "call"}, {"api_name": "socket.AF_INET", "line_number": 71, "usage_type": "attribute"}, {"api_name": "socket.SOCK_STREAM", "line_number": 71, "usage_type": "attribute"}]} +{"seq_id": "20028253316", "text": "import discord\nimport logging\nimport numexpr\nimport sys\nimport re\nimport traceback\nimport asyncio\nfrom discord.utils import get\nfrom secrets import discord_bot_token, cheat_code\n\nfrom poke import pokemon\nfrom funct import roman\n\nplugins = [pokemon, roman]\n\n\nclass Counter(object):\n last_number = 0\n\nclass NotUnderstoodError(Exception):\n \"For some reason we didn't understand the input as a number\"\n pass\n\nclass WrongError(Exception):\n \"We understood the answer. It's wrong.\"\n pass\n\nEMOJI_TICK = '\\u2705'\nEMOJI_CROSS = '\\u274c'\nEMOJI_INTERROBANG = '\\u2049'\nEMOJI_LADYBIRD = '\\U0001f41e'\nEMOJI_DRAGON = '\\U0001F432'\nlast_poster = None\ncheat_mode = False\n\nemoji = {\n 69: '\\u264B', # cancer\n 420: '\\U0001f525', # fire\n 1: '1\\uFE0F\\u20E3', # 1 emoji keycap\n 2: '2\\uFE0F\\u20E3', \n 3: '3\\uFE0F\\u20E3', \n 4: '4\\uFE0F\\u20E3', \n 5: '5\\uFE0F\\u20E3', \n 6: '6\\uFE0F\\u20E3', \n 7: '7\\uFE0F\\u20E3', \n 8: '8\\uFE0F\\u20E3', \n 9: '9\\uFE0F\\u20E3', \n }\n\nreplacements = {\n '\\u0f33': '(-0.5)', # TIBETAN DIGIT HALF ZERO\n '\\u5146': '(1e12)',\n '\\U00016B61': '(1e12)',\n '\\u221e': '(1/0)', # infinity,\n '\\u221a': 'sqrt', # root,\n # '\\u221b' # cuberoot\n # '\\u221c' # fourthroot\n '\\U0001f51f': '(10)', # keycap 10\n '\\u2070': '**0', # super 0\n '\\u00b0': '**0', # degree\n '\\u00ba': '**0', # super o\n '\\u00b9': '**1', # super 1\n '\\u00b2': '**2', # super 2\n '\\u00b3': '**3', # super 2\n '\\u2074': '**4', # super 2\n '\\u2075': '**5', # super 2\n '\\u2076': '**6', # super 2\n '\\u2077': '**7', # super 2\n '\\u2078': '**8', # super 2\n '\\u2079': '**9', # super 2\n '\\u2071': '**i', # super 2\n '\\U0001F967': '(pi)', # unicode pie\n 'α': '(1/137.03599908)', # alpha, the fine structure constant\n 'π': '(pi)', # pi\n '÷': '/', # division\n '×': '*',\n 'X': '*',\n '¼': '(1/4)',\n '½': '(1/2)',\n '¾': '(3/4)',\n 'nice': '70',\n 'blaze it': '421',\n\n '\\uFE0F': '', # emoji presentation\n '\\u20E3': '', # combining keycap\n}\n\nmemes = {\n '(?:^|\\D)69(?:$|\\D)': 69,\n '(?:^|\\D)420(?:$|\\D)': 420,\n '^1.*2.*3.*4.*5.*6.*7.*8.*9$': 9,\n '^1.*2.*3.*4.*5.*6.*7.*8$': 8,\n '^1.*2.*3.*4.*5.*6.*7$': 7,\n '^1.*2.*3.*4.*5.*6$': 6,\n '^1.*2.*3.*4.*5$': 5,\n '^1.*2.*3.*4$': 4,\n '^1.*2.*3$': 3,\n '^3.*2.*1$': 3,\n '^4.*3.*2.*1$': 4,\n '^5.*4.*3.*2.*1$': 5,\n '^6.*5.*4.*3.*2.*1$': 6,\n '^7.*6.*5.*4.*3.*2.*1$': 7,\n '^8.*7.*6.*5.*4.*3.*2.*1$': 8,\n '^9.*8.*7.*6.*5.*4.*3.*2.*1$': 9,\n '^[^A-Za-z02-9]+$': 1,\n '^[^A-Za-z0-13-9]+$': 2,\n '^[^A-Za-z0-24-9]+$': 3,\n '^[^A-Za-z0-35-9]+$': 4,\n '^[^A-Za-z0-46-9]+$': 5,\n '^[^A-Za-z0-57-9]+$': 6,\n '^[^A-Za-z0-68-9]+$': 7,\n '^[^A-Za-z0-79]+$': 8,\n '^[^A-Za-z0-8]+$': 9,\n }\n\n\ne = 2.718281828459\npi = 3.14159265359\ndozen = 12\ngross = 144\nscore = 20\npair = 2\neleventy = 110\none = 1\ntwo = 2\nthree = 3\nfour = 4\nfive = 5\nsix = 6\nseven = 7\neight = 8\nnine = 9\nten = 10\neleven = 11\ntwelve = 12\nthirteen = 13\nfourteen = 14\nfifteen = 15\nsixteen = 16\nseventeen = 17\neighteen = 18\nnineteen = 19\ntwenty = 20\nthirty = 30\nfourty = 40\nfifty = 50\nsixty = 60\nseventy = 70\neighty = 80\nninety = 90\nhundred = 100\nthousand = 1000\nmillion = 1e6\nbillion = 1e9\ntrillion = 1e12\nbrace = 2\nmyriad = 10e3\ngrand = 1000\ngoogol = 1e100\ni = 1j\n\n\"\"\"\nmessage:\n\nid=#\nchannel.id=#, .name=$, .position=n, .nsfw=b, .news=b, .catgegory_id=?\nauthor.id=#, .name=$, .discriminator=$, .bot=b, .nick=b\n\"\"\"\n\ndef meme_search(text):\n for pattern, number in memes.items():\n if re.search(pattern, text):\n return number\n return False\n\ndef replaced(text):\n t = text\n for key, value in replacements.items():\n t=t.replace(key, value)\n return (t)\n\ndef do_maths(text, integer=True):\n fragments = text.split(\" \")\n for i, word in enumerate(fragments):\n for plugin in plugins:\n value = plugin(word)\n if value is not False:\n print (\"replaced {} with {}\".format(word, value))\n fragments[i] = str(value)\n \n t = replaced(' '.join(fragments))\n try:\n if integer:\n return int(complex(numexpr.evaluate(t)).real)\n else:\n return float(complex(numexpr.evaluate(t)).real)\n except KeyError as e:\n raise NotUnderstoodError(\"I don't know a variable {}.\".format(str(e)))\n except ZeroDivisionError:\n raise WrongError(\"Please don't divide by zero, it hurts my brain..\")\n except OverflowError:\n raise WrongError(\"Wow, that's a big number. Too big.\")\n except ValueError as e: # occurs if strings?\n raise NotUnderstoodError(\"... was that a string?\".format(str(e)))\n except SyntaxError: \n raise NotUnderstoodError(\"... did a cat just walk across your keyboard?\")\n\ndef display_message(message):\n return(f\"{message.author.name} on {message.channel.name}: {message.content}\")\n\nasync def delayed(f, seconds=2, *args, **kwargs):\n await asyncio.sleep(seconds)\n await f(*args, **kwargs)\n\nasync def counting(message):\n global cheat_mode, last_poster\n print (\"Counting Candidate seen:\", display_message(message))\n if message.content == cheat_code:\n await message.add_reaction(emoji=EMOJI_DRAGON)\n cheat_mode = not cheat_mode\n await message.delete()\n return\n try:\n message_number = do_maths(message.content)\n except NotUnderstoodError as e:\n await message.add_reaction(emoji=EMOJI_INTERROBANG)\n await message.channel.send(str(e))\n return\n except WrongError as e:\n await message.add_reaction(emoji=EMOJI_CROSS)\n await message.channel.send(str(e))\n Counter.last_number = 0\n last_poster = None\n return\n except Exception as e:\n await message.add_reaction(emoji=EMOJI_LADYBIRD)\n await message.channel.send(\"{}: {}\".format(type(e), str(e)))\n await message.channel.send(\"```{}```\".format(traceback.format_exc()))\n return\n\n if not cheat_mode and last_poster == message.author:\n await message.add_reaction(emoji=EMOJI_INTERROBANG)\n m = await message.channel.send(\"It isn't your turn!\")\n await delayed(message.delete)\n await delayed(m.delete)\n return\n else:\n last_poster = message.author\n\n if message_number == Counter.last_number + 1:\n Counter.last_number += 1\n await message.add_reaction(emoji=EMOJI_TICK)\n meme = meme_search(replaced(message.content))\n if meme:\n await message.add_reaction(emoji=emoji[meme])\n else:\n await message.add_reaction(emoji=EMOJI_CROSS)\n try:\n exact = do_maths(message.content, False)\n except Exception:\n exact = 'MISSING_NO'\n await message.channel.send(\"I calculated that to be {}, not {}\".format(exact, Counter.last_number+1))\n Counter.last_number = 0\n\n\n\nclient = discord.Client()\n\nchannel_roles = {\n 760564203667980349: [counting], # mewlip/secrets\n }\n\n@client.event\nasync def on_ready():\n print('We have logged in as {0.user}'.format(client))\n\n@client.event\nasync def on_message(message):\n if message.author == client.user:\n return\n\n if message.channel.id in channel_roles:\n for role_function in channel_roles[message.channel.id]:\n await role_function(message)\n\n if message.content.startswith('$hello'):\n await message.channel.send('Hello!')\n\nif 'console' in sys.argv:\n while True:\n x = input()\n try:\n print (do_maths(x))\n except Exception as e:\n print (e)\n print (type(e))\nelse:\n client.run(discord_bot_token)\n\n\n", "repo_name": "dragondave/counting_discord", "sub_path": "bot_core.py", "file_name": "bot_core.py", "file_ext": "py", "file_size_in_byte": 7959, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "poke.pokemon", "line_number": 14, "usage_type": "name"}, {"api_name": "funct.roman", "line_number": 14, "usage_type": "name"}, {"api_name": "re.search", "line_number": 172, "usage_type": "call"}, {"api_name": "numexpr.evaluate", "line_number": 194, "usage_type": "call"}, {"api_name": "numexpr.evaluate", "line_number": 196, "usage_type": "call"}, {"api_name": "asyncio.sleep", "line_number": 212, "usage_type": "call"}, {"api_name": "secrets.cheat_code", "line_number": 218, "usage_type": "name"}, {"api_name": "traceback.format_exc", "line_number": 238, "usage_type": "call"}, {"api_name": "discord.Client", "line_number": 267, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 289, "usage_type": "attribute"}, {"api_name": "secrets.discord_bot_token", "line_number": 298, "usage_type": "argument"}]} +{"seq_id": "74556611946", "text": "from selenium import webdriver\nimport unittest\nimport time\nimport HTMLReport\nfrom public.HttpRequests import HttpRequests\nimport logging\n# from airtest.core.api import *\n# from win32 import win32api\n# # import win32api,win32con\nfrom PIL import ImageGrab\nimport psutil\n\nimport pyautogui\nfrom selenium.webdriver.support.select import Select\nfrom threading import Timer\nimport schedule\nimport datetime\nfrom threading import Thread\n\n\n\"\"\"\n本脚本适用于，web页面自动化\n【强制刷新】实现方案：\n打开devtools后，强制访问url，并截图记录待测信息\n\n【切换页面】实现方案：\n切换到其他页面利用快捷键 ctrl + shift + i 关闭devtools，重新打开devtools后，切换至待测页面，截图并记录待测信息\n\n\"\"\"\n\n# 异步执行函数 装饰器\ndef async_decorator(f):\n def wrapper(*args,**kwargs):\n thr = Thread(target=f,args = args,kwargs=kwargs)\n thr.start()\n return wrapper\n\n@async_decorator\ndef get_cpu_info():\n cpu_percent = psutil.cpu_percent(interval=1,percpu=False) # interval指定的是计算cpu使用率的时间间隔，percpu则指定是选择总的使用率还是每个cpu的使用率\n cpu_info = \"CPU使用率：%i%%\" % cpu_percent\n print(cpu_info)\n return cpu_percent\n\ndef get_memory_info():\n virtual_memory = psutil.virtual_memory()\n used_memory = virtual_memory.used/1024/1024/1024\n free_memory = virtual_memory.free/1024/1024/1024\n memory_percent = virtual_memory.percent\n memory_info = \"内存使用：%0.2fG，使用率%0.1f%%，剩余内存：%0.2fG\\n\" % (used_memory, memory_percent, free_memory)\n print(memory_info)\n return used_memory\n\n# t = Timer(2,monitor_cpu_memory)\n# t.start()\n\ndriver_path = \"C:\\Program Files (x86)\\Google\\Chrome\\Application\\chromedriver.exe\"\n\n#\n# options = webdriver.ChromeOptions()\n# options.add_argument(\"--auto-open-devtools-for-tabs\")\n# driver = webdriver.Chrome(driver_path,chrome_options=options)\ndriver = webdriver.Chrome(driver_path)\ndriver.maximize_window()\ntime.sleep(1)\n\n\n# win32api.keybd_event(17, 0, 0, 0)\n# win32api.keybd_event(16, 0, 0, 0)\n# win32api.keybd_event(73, 0, 0, 0) # I\n\n#pyautogui.hotkey(\"ctrlleft\",\"shiftleft\",\"j\")\n\n# pyautogui.press('f12')\n# time.sleep(1)\n# pyautogui.hotkey(\"Ctrl\",\"]\")\n# time.sleep(1)\n# pyautogui.hotkey(\"Ctrl\",\"]\")\n# time.sleep(1)\n# pyautogui.hotkey(\"Ctrl\",\"]\")\n# time.sleep(1)\n# pyautogui.press('f12')\n# time.sleep(1)\n# pyautogui.press('f12')\n# time.sleep(1)\n#\npyautogui.press('f12')\ntime.sleep(1)\nfor i in range(3):\n pyautogui.hotkey(\"Ctrl\", \"]\")\npyautogui.hotkey(\"Ctrl\",\"-\")\npyautogui.hotkey(\"Ctrl\",\"-\")\n# time.sleep(1)\n# pyautogui.hotkey(\"Ctrl\",\"-\")\n# time.sleep(1)\n# pyautogui.hotkey(\"Ctrl\",\"-\")\n# time.sleep(1)\n# pyautogui.hotkey(\"Ctrl\",\"-\")\n# time.sleep(1)\n# pyautogui.hotkey(\"Ctrl\",\"-\")\n# time.sleep(1)\n# pyautogui.hotkey(\"Ctrl\",\"0\")\n\n# time.sleep(2)\n# # 利用快捷键 ctrl + shift + R\n# # win32api.keybd_event(17, 0, 0, 0)\n# # win32api.keybd_event(16, 0, 0, 0)\n# # win32api.keybd_event(80, 0, 0, 0) # R\n# # time.sleep(2)\n\n# pyautogui.hotkey(\"ctrlleft\",\"shiftleft\",\"r\")\n\n# 选中 Network\n# for i in range(8):\n# time.sleep(1)\n# win32api.keybd_event(40, 0, 0, 0) # 方向向下键\n\n# for i in range(8):\n# pyautogui.press('down') # 一次完整的击键\n# time.sleep(1)\n# pyautogui.press('enter')\n\n# #driver.airtest_touch(Template(r\"tpl1614770836159.png\", record_pos=(-0.065, -0.119), resolution=(3840, 1080)))\n#\n# # time.sleep(1)\n# # win32api.keybd_event(13, 0, 0, 0) # Enter键\n# # time.sleep(1)\n# # win32api.keybd_event(13,0,win32con.KEYEVENTF_KEYUP,0)\n# # time.sleep(1)\n#\nprint(\"【首页】：Before物理资源消耗：-------\")\nbefor_cpu_1 = get_cpu_info()\nbefor_memory_1 = get_memory_info()\n# 登录到首页1\nbase_url = \"https://power.medcloud.cn/login\"\ndriver.get(base_url)\n\nmC = driver.find_element_by_id(\"memberCode\")\nuN = driver.find_element_by_id(\"username\")\npD = driver.find_element_by_id(\"password\")\n\nmC.send_keys(\"李俊测试连锁机构\")\nuN.send_keys(\"13167172396\")\npD.send_keys(\"123456\")\n\nbox_css_1 = driver.find_element_by_css_selector( \".ant-select-selection-item .text__3rSDb\")\nbox_css_1.click()\nbox_css_2 = driver.find_element_by_css_selector( \".ant-select-tree-treenode:nth-child(4) .text__3rSDb\")\nbox_css_2.click()\nlogin_button = driver.find_element_by_css_selector( \".btnDental__gSn_P\")\nlogin_button.click()\n#\n#\n#lG = driver.find_element_by_xpath(r'//*[@id=\"main-app\"]/div/div/div[2]/div[1]/div[1]/form/div[5]/button').click()\ntime.sleep(3)\n# 强制刷新\npyautogui.press('f5')\n# win32api.keybd_event(116, 0, 0, 0) # F5 键\n# win32api.keybd_event(116,0,win32con.KEYEVENTF_KEYUP,0)\n\n# # 强制刷新后，延迟2s记录cpu内存信息\n# time.sleep(2)\n# print(\"【首页】强制刷新：After物理资源消耗：-------\")\n# after_cpu_1 = get_cpu_info()\n# after_memory_1 = get_memory_info()\n#\n# # 强制等待页面加载完成，截图记录network信息，并保存截图\n# time.sleep(7)\n# #data_bbox=(0,0,1920,1080)\n# # pyautogui.size() # 获取屏幕分辨率\n# im_1 = ImageGrab.grab()\n# im_1.save(r'D:\\auto_saas_20210201\\public\\image_0305\\Home_page_1.png')\n# # im_1 = pyautogui.screenshot('my_screenshot.png')\n#\n# # 切换页面记录相关信息\n# # 预约视图2\n# time.sleep(6)\n# print(\"【预约中心】：Before物理资源消耗：-------\")\n# befor_cpu_2 = get_cpu_info()\n# befor_memory_2 = get_memory_info()\n#\n# home_page_url = \"https://power.medcloud.cn/dpms_dental/appointment/appointment-view\"\n# driver.get(home_page_url)\n# time.sleep(2)\n# win32api.keybd_event(116, 0, 0, 0)\n# win32api.keybd_event(116,0,win32con.KEYEVENTF_KEYUP,0)\n# time.sleep(5)\n# print(\"【预约中心】：After物理资源消耗：-------\")\n# after_cpu_2 = get_cpu_info()\n# after_memory_2 = get_memory_info()\n#\n# time.sleep(8)\n# im_2 = ImageGrab.grab()\n# im_2.save(r'D:\\auto_saas_20210201\\public\\image_0305\\view_2.png')\n#\n# # 医疗业务3\n# time.sleep(6)\n# print(\"【医疗业务】：Before物理资源消耗：-------\")\n# befor_cpu_3 = get_cpu_info()\n# befor_memory_3 = get_memory_info()\n#\n# business_url = \"https://power.medcloud.cn/dpms_dental/patient-center/patient-manage/patient-list\"\n# driver.get(business_url)\n# time.sleep(2)\n# win32api.keybd_event(116, 0, 0, 0)\n# win32api.keybd_event(116,0,win32con.KEYEVENTF_KEYUP,0)\n# time.sleep(3)\n# print(\"【医疗业务】：After物理资源消耗：-------\")\n# after_cpu_3 = get_cpu_info()\n# after_memory_3 = get_memory_info()\n# time.sleep(10)\n#\n# time.sleep(3)\n# im_3 = ImageGrab.grab()\n# im_3.save(r'D:\\auto_saas_20210201\\public\\image_0305\\business_3.png')\n#\n# # 病历管理 //*[@id=\"main-app\"]/section/section/aside/div/ul/li[2]/a\n# # 患者管理 → 所有患者 //*[@id=\"main-app\"]/section/section/aside/div/ul/li[1]/ul/li[1]\n# time.sleep(2)\n# print(\"【病历管理】：Before物理资源消耗：-------\")\n#\n# # 利用快捷键 ctrl + shift + i\n# win32api.keybd_event(17, 0, 0, 0)\n# win32api.keybd_event(16, 0, 0, 0)\n# win32api.keybd_event(73, 0, 0, 0) # I\n#\n# # Record_management = driver.find_element_by_xpath('//*[@id=\"main-app\"]/section/section/aside/div/ul/li[2]/a')\n# Patient_management = driver.find_element_by_xpath('//*[@id=\"main-app\"]/section/section/aside/div/ul/li[1]/ul/li[1]')\n# Patient_archive = driver.find_element_by_xpath('//*[@id=\"main-app\"]/section/section/aside/div/ul/li[1]/ul/li[2]')\n# #Record_management.click()\n# Patient_archive.click()\n# print(\"切换至【归档患者】之前 Before物理资源消耗：-------\",)\n# time.sleep(8)\n#\n# switch_before_cpu_1 = get_cpu_info()\n# switch_before_memory_1 = get_memory_info()\n#\n# time.sleep(2)\n# # 利用快捷键 ctrl + shift + i\n# win32api.keybd_event(17, 0, 0, 0)\n# win32api.keybd_event(16, 0, 0, 0)\n# win32api.keybd_event(73, 0, 0, 0) # I\n#\n# time.sleep(2)\n#\n# Patient_management.click()\n#\n# time.sleep(2)\n# switch_after_cpu_1 = get_cpu_info()\n# switch_after_memory_1 = get_memory_info()\n# time.sleep(2)\n# im_switch_1 = ImageGrab.grab()\n# im_switch_1.save(r'D:\\auto_saas_20210201\\public\\image_0305\\im_switch_1.png')\n\n\n# # 患者个人信息4\n# time.sleep(6)\n# print(\"【医疗业务_个人信息】：Before物理资源消耗：-------\")\n# befor_cpu_4 = get_cpu_info()\n# befor_memory_4 = get_memory_info()\n# business_personal_url = \"https://power.medcloud.cn/dpms_dental/patient-center/patient-manage/patient-list/patient-detail/detailed-infos?patientId=248416\"\n# driver.get(business_personal_url)\n# time.sleep(3)\n# # 刷新\n# win32api.keybd_event(116, 0, 0, 0)\n# win32api.keybd_event(116,0,win32con.KEYEVENTF_KEYUP,0)\n# time.sleep(4)\n# print(\"【医疗业务_个人信息】：After物理资源消耗：-------\")\n# after_cpu_4 = get_cpu_info()\n# after_memory_4 = get_memory_info()\n# time.sleep(11)\n# im_4 = ImageGrab.grab()\n# im_4.save(r'D:\\auto_saas_20210201\\public\\image_0305\\business_personal_4.png')\n#\n#\n# # 患者收费5\n# time.sleep(6)\n# print(\"【医疗业务_收费】：Before物理资源消耗：-------\")\n# befor_cpu_5 = get_cpu_info()\n# befor_memory_5 = get_memory_info()\n# charge_url = \"https://power.medcloud.cn/dpms_dental/patient-center/patient-manage/patient-list/patient-detail/billing-tab?patientId=260256\"\n# driver.get(charge_url)\n# time.sleep(2)\n# win32api.keybd_event(116, 0, 0, 0)\n# win32api.keybd_event(116,0,win32con.KEYEVENTF_KEYUP,0)\n# time.sleep(4)\n# print(\"【医疗业务_收费】：After物理资源消耗：-------\")\n# after_cpu_5 = get_cpu_info()\n# after_memory_5 = get_memory_info()\n# time.sleep(13)\n# im_5 = ImageGrab.grab()\n# im_5.save(r'D:\\auto_saas_20210201\\public\\image_0305\\charge_5.png')\n#\n#\n# # 病历管理6\n# time.sleep(6)\n# print(\"【病历管理】：Before物理资源消耗：-------\")\n# befor_cpu_6 = get_cpu_info()\n# befor_memory_6 = get_memory_info()\n# medical_records_url = \"https://power.medcloud.cn/dpms_dental/patient-center/medical-records-management\"\n# driver.get(medical_records_url)\n# time.sleep(2)\n# win32api.keybd_event(116, 0, 0, 0)\n# win32api.keybd_event(116,0,win32con.KEYEVENTF_KEYUP,0)\n# time.sleep(4)\n# print(\"【病历管理】：After物理资源消耗：-------\")\n# after_cpu_6 = get_cpu_info()\n# after_memory_6 = get_memory_info()\n# time.sleep(6)\n# im_6 = ImageGrab.grab()\n# im_6.save(r'D:\\auto_saas_20210201\\public\\image_0305\\medical_records_6.png')\n#\n#\n# result_cpu_1 = after_cpu_1 - befor_cpu_1\n# result_memory_1 = after_memory_1 - befor_memory_1\n# print(\"【首页】：物理资源消耗：-------CPU:\",result_cpu_1)\n# print(\"【首页】：物理资源消耗：-------内存:\",result_memory_1)\n#\n# result_cpu_2 = after_cpu_2 - befor_cpu_2\n# result_memory_2 = after_memory_2 - befor_memory_2\n# print(\"【预约中心】：物理资源消耗：-------CPU:\",result_cpu_2)\n# print(\"【预约中心】：物理资源消耗：-------内存:\",result_memory_2)\n#\n# result_cpu_3 = after_cpu_3 - befor_cpu_3\n# result_memory_3 = after_memory_3 - befor_memory_3\n# print(\"【医疗业务】：物理资源消耗：-------CPU:\",result_cpu_3)\n# print(\"【医疗业务】：物理资源消耗：-------内存:\",result_memory_3)\n#\n# result_cpu_4 = after_cpu_4 - befor_cpu_4\n# result_memory_4 = after_memory_4 - befor_memory_4\n# print(\"【医疗业务_个人信息】：物理资源消耗：-------CPU:\",result_cpu_4)\n# print(\"【医疗业务_个人信息】：物理资源消耗：-------内存:\",result_memory_4)\n#\n#\n# result_cpu_5 = after_cpu_5 - befor_cpu_5\n# result_memory_5 = after_memory_5 - befor_memory_5\n# print(\"【医疗业务_收费】：物理资源消耗：-------CPU:\",result_cpu_5)\n# print(\"【医疗业务_收费】：物理资源消耗：-------内存:\",result_memory_5)\n#\n# result_cpu_6 = after_cpu_6 - befor_cpu_6\n# result_memory_6 = after_memory_6 - befor_memory_6\n# print(\"【病历管理】：物理资源消耗：-------CPU:\",result_cpu_6)\n# print(\"【病历管理】：物理资源消耗：-------内存:\",result_memory_6)\n#\n# win32api.keybd_event(17,0,win32con.KEYEVENTF_KEYUP,0)\n# win32api.keybd_event(16,0,win32con.KEYEVENTF_KEYUP,0)\n#\n#\n\n\n\n\n\n# try:\n# result_png = driver.get_screenshot_as_file(r\"D:\\auto_saas_20210201\\public\\1.png\")\n# except IOError as e:\n# print(e)\n", "repo_name": "fg1157601129/auto_saas_20210201", "sub_path": "public/demo_ui.py", "file_name": "demo_ui.py", "file_ext": "py", "file_size_in_byte": 12061, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "threading.Thread", "line_number": 34, "usage_type": "call"}, {"api_name": "psutil.cpu_percent", "line_number": 40, "usage_type": "call"}, {"api_name": "psutil.virtual_memory", "line_number": 46, "usage_type": "call"}, {"api_name": "selenium.webdriver.Chrome", "line_number": 63, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 63, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 65, "usage_type": "call"}, {"api_name": "pyautogui.press", "line_number": 87, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 88, "usage_type": "call"}, {"api_name": "pyautogui.hotkey", "line_number": 90, "usage_type": "call"}, {"api_name": "pyautogui.hotkey", "line_number": 91, "usage_type": "call"}, {"api_name": "pyautogui.hotkey", "line_number": 92, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 155, "usage_type": "call"}, {"api_name": "pyautogui.press", "line_number": 157, "usage_type": "call"}]} +{"seq_id": "80926846", "text": "from sklearn.ensemble import RandomForestClassifier\n\nfrom betExplorer.models import *\n\nmatches = Championship().get('serie-a-2015').listMatches()\nmatches = Match.list(Match)\n\ntrain = []\ntarget = []\ntest = []\ntarget2 = []\nrealResult = []\nrealResult2 = []\n\ntotal_games = 4000\nfor i,m in enumerate(matches):\n if (i < total_games):\n train.append([m.homeTeamId,m.awayTeamId])\n target.append([m.goalsHome,m.goalsAway])\n target2.append([m.result])\n else:\n test.append([m.homeTeamId,m.awayTeamId])\n realResult.append([m.goalsHome,m.goalsAway])\n realResult2.append([m.result])\n\nest = RandomForestClassifier(n_estimators = 1000)\nest.fit(train,target)\nx = est.predict(test)\n\nscore = 0\nfor z in range(0,3600):\n if (x[z][0] > x[z][1] and realResult[z][0] > realResult[z][1]):\n score = score + 1\n elif (x[z][0] == x[z][1] and realResult[z][0] == realResult[z][1]):\n score = score + 1\n elif (x[z][0] < x[z][1] and realResult[z][0]< realResult[z][1]):\n score = score + 1\nprint(score/3600)\n\nprint(target2)\nest2 = RandomForestClassifier(n_estimators = 1000)\nest2.fit(train,target2)\nx = est.predict(test)\n\nscore = 0\nfor z in range(0,3600):\n if (x[z][0] == realResult2[z][0]):\n score = score + 1\n\nprint(score/3600)", "repo_name": "igormago/imsoccer2", "sub_path": "betExplorer/learning.py", "file_name": "learning.py", "file_ext": "py", "file_size_in_byte": 1282, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "sklearn.ensemble.RandomForestClassifier", "line_number": 26, "usage_type": "call"}, {"api_name": "sklearn.ensemble.RandomForestClassifier", "line_number": 41, "usage_type": "call"}]} +{"seq_id": "37873500806", "text": "from pickle import TRUE\nfrom collision_detection import *\nfrom utils import *\nimport pygame\nimport os\nfrom collections import defaultdict\n\n\nPATH = os.getcwd() + \"/models\"\n\n##################### Static Object Class ##########################\n\nclass static_object:\n def __init__(self,name,size,path,bvt,pos = (0,0)):\n self.name = name\n self.pos = pos\n self.size = size\n self.path = path\n self.bvt_root = bvt\n self.surf = pygame.image.load(path)\n self.surf = pygame.transform.scale(self.surf,self.size)\n self.surf.set_colorkey(WHITE)\n\n def draw(self,win,draw_bv = False):\n surf = self.surf\n\n rect1 = surf.get_rect()\n rect1.center = (self.pos[0]+ self.size[0]/2, self.pos[1]+ self.size[1]/2)\n win.blit(surf,rect1)\n if draw_bv:\n self.draw_bvt(win)\n return\n\n def get_pos(self):\n return self.pos\n\n def change_pos(self,pos):\n self.pos = pos\n self.bvt_root.update_node(self.pos)\n return\n\n def draw_bvt(self,win):\n self.bvt_root.draw(win)\n\n##################### Dynamic Object Class ##########################\nclass dynamic_object:\n def __init__(self,name,size,path,bvt,pos = (0,0)):\n self.name = name\n self.pos = pos\n self.size = size\n self.path = path\n self.bvt_root = bvt\n self.surf = pygame.image.load(path)\n self.surf = pygame.transform.scale(self.surf,self.size)\n self.surf.set_colorkey(WHITE)\n\n def draw(self,win,draw_bv = False):\n surf = self.surf\n\n rect1 = surf.get_rect()\n rect1.center = (self.pos[0]+ self.size[0]/2, self.pos[1]+ self.size[1]/2)\n win.blit(surf,rect1)\n if draw_bv:\n self.draw_bvt(win)\n return\n\n def change_pos(self,pos):\n self.pos = pos\n self.bvt_root.update_node(self.pos)\n return\n \n def update_pos(self,pos_d):\n self.pos = self.pos[0] + pos_d[0],self.pos[1] + pos_d[1]\n self.bvt_root.update_node(self.pos)\n return self.pos\n \n def get_pos(self):\n return self.pos\n\n def draw_bvt(self,win):\n self.bvt_root.draw(win)\n\n##################### Avatar Class ##########################\n\nclass Avatar:\n def __init__(self,name,size,path,bvt,pos = (0,0)):\n self.name = name\n self.pos = pos\n self.size = size\n self.path = path\n self.bvt_root = bvt\n self.surf = pygame.image.load(path)\n self.surf = pygame.transform.scale(self.surf,self.size)\n self.surf.set_colorkey(WHITE)\n\n def draw(self,win,draw_bv = False):\n surf = self.surf\n\n rect1 = surf.get_rect()\n rect1.center = (self.pos[0]+ self.size[0]/2, self.pos[1]+ self.size[1]/2)\n win.blit(surf,rect1)\n if draw_bv:\n self.draw_bvt(win)\n return\n \n def change_pos(self,pos):\n self.pos = pos\n self.bvt_root.update_node(self.pos)\n return\n \n def update_pos(self,pos_d):\n self.pos = self.pos[0] + pos_d[0],self.pos[1] + pos_d[1]\n self.bvt_root.update_node(self.pos)\n return self.pos\n \n def get_pos(self):\n return self.pos\n\n def draw_bvt(self,win):\n self.bvt_root.draw(win)\n\n##################### BVT node Class ##########################\n\nclass BVT_node:\n def __init__(self,name,bounds,pos=(0,0),leaf = False):\n self.name = name\n self.fixedbounds = bounds\n self.bounds = self.fixedbounds\n self.pos = pos\n self.children = []\n self.leaf = leaf\n\n def update_node(self,pos):\n self.pos = pos\n self.bounds = (self.fixedbounds[0]+self.pos[0],self.fixedbounds[1]+self.pos[1],self.fixedbounds[2],self.fixedbounds[3])\n for child in self.children: child.update_node(pos)\n\n def refresh(self):\n self.bounds = (self.fixedbounds[0]+self.pos[0],self.fixedbounds[1]+self.pos[1],self.fixedbounds[2],self.fixedbounds[3])\n for child in self.children: child.refresh()\n\n def draw(self,win):\n self.refresh()\n pygame.draw.rect(win,GREEN,self.bounds,2)\n for child in self.children: child.draw(win)\n\n\n##################### BVT Tree generator function #######################\ndef generate_BVT(object, object_name, model):\n tree = defaultdict()\n root = model[object_name]['BVH']['level_0'][0]\n tree['root'] = BVT_node('root',root[1:],object.pos)\n object.bvt_root = tree['root']\n num_levels = model[object_name]['BVH']['num_levels']\n if num_levels != 1:\n for i in range(1,num_levels):\n for node in model[object_name]['BVH']['level_'+str(i)]:\n parent,name,bounds = node[0],node[1],node[2:]\n bv = BVT_node(name,bounds,object.pos,i == num_levels-1)\n tree[parent].children.append(bv)\n tree[name] = bv\n else:\n object.bvt_root.leaf = True\n return\n \n\n \n\n##################### Make obstacles functions ##########################\ndef make_static_objects(model,num_objects,PATH):\n static_objects = []\n for i in range(1,num_objects+1):\n name = \"static_\"+str(i)\n size = model[name]['size']\n path = PATH + model[name]['path']\n bvt = None\n static_objects.append(static_object(name,size,path,bvt))\n\n return static_objects\n\n\ndef make_dynamic_objects(model,PATH,names = ['truck','bike']):\n dynamic_objects = {}\n for i in names:\n name = \"dynamic_\"+i\n size = model[name]['size']\n path = PATH +model[name]['path']\n bvt = None\n dynamic_objects[name] = dynamic_object(name,size,path,bvt)\n\n return dynamic_objects\n\ndef make_avatar(model, PATH):\n name = \"avatar_car\"\n size = model[name]['size']\n path = PATH + model[name]['path']\n bvt = None\n avatar = Avatar(name,size,path,bvt)\n\n return avatar\n\ndef print_tree():\n pass\n\n\nif __name__ == '__main__':\n\n s= make_static_objects(model,3,PATH)\n d = make_dynamic_objects(model,PATH,['truck','bike'])\n print(d.keys())\n s3 = s[2]\n generate_BVT(d['dynamic_truck'],d['dynamic_truck'].name,model)\n generate_BVT(d['dynamic_bike'],d['dynamic_bike'].name,model)\n d['dynamic_truck'].change_pos((200,200))\n d['dynamic_bike'].change_pos((200,200))\n generate_BVT(s3,s3.name,model)\n \n pygame.init()\n width_win = 600\n win = pygame.display.set_mode((width_win,width_win))\n pygame.display.set_caption(\"Title\")\n\n car = make_avatar(model,PATH)\n avatar1 = make_avatar(model,PATH)\n generate_BVT(avatar1,avatar1.name,model)\n generate_BVT(car,car.name,model)\n avatar1.change_pos((200,200))\n \n run = True\n pos = (0,0)\n w_key = False\n d_key = False\n a_key = False\n s_key = False\n while run:\n win.fill(WHITE)\n events = pygame.event.get()\n\n for ev in events:\n if ev.type == pygame.QUIT:\n run = False\n pygame.quit()\n if ev.type == pygame.KEYDOWN:\n if ev.key == pygame.K_w:\n w_key = True\n \n if ev.key == pygame.K_s:\n s_key = True\n \n if ev.key == pygame.K_a:\n a_key = True\n \n if ev.key == pygame.K_d:\n d_key = True\n if ev.type == pygame.KEYUP:\n if ev.key == pygame.K_w:\n w_key = False\n \n if ev.key == pygame.K_s:\n s_key = False\n \n if ev.key == pygame.K_a:\n a_key = False\n \n if ev.key == pygame.K_d:\n d_key = False\n\n if w_key:\n pos = (0,-1)\n car.update_pos(pos)\n if s_key:\n pos = (0,1)\n car.update_pos(pos)\n if a_key:\n pos = (-1,0)\n car.update_pos(pos)\n if d_key:\n pos = (1,0)\n car.update_pos(pos)\n car.draw(win)\n # avatar1.draw(win)\n d['dynamic_bike'].draw(win)\n if BVT_collision(d['dynamic_bike'].bvt_root,car.bvt_root):\n win.fill(YELLOW)\n car.draw(win)\n # avatar1.draw(win)\n d['dynamic_bike'].draw(win)\n pygame.display.update()\n pygame.time.delay(30)\n \n pygame.display.update()\n", "repo_name": "RushiPDeshmukh/BoundingVolumeHierarchyBasedCollisionDetection", "sub_path": "src/bounding_volumes.py", "file_name": "bounding_volumes.py", "file_ext": "py", "file_size_in_byte": 8440, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "os.getcwd", "line_number": 9, "usage_type": "call"}, {"api_name": "pygame.image.load", "line_number": 20, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 20, "usage_type": "attribute"}, {"api_name": "pygame.transform.scale", "line_number": 21, "usage_type": "call"}, {"api_name": "pygame.transform", "line_number": 21, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 53, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 53, "usage_type": "attribute"}, {"api_name": "pygame.transform.scale", "line_number": 54, "usage_type": "call"}, {"api_name": "pygame.transform", "line_number": 54, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 92, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 92, "usage_type": "attribute"}, {"api_name": "pygame.transform.scale", "line_number": 93, "usage_type": "call"}, {"api_name": "pygame.transform", "line_number": 93, "usage_type": "attribute"}, {"api_name": "pygame.draw.rect", "line_number": 144, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 144, "usage_type": "attribute"}, {"api_name": "collections.defaultdict", "line_number": 150, "usage_type": "call"}, {"api_name": "pygame.init", "line_number": 218, "usage_type": "call"}, {"api_name": "pygame.display.set_mode", "line_number": 220, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 220, "usage_type": "attribute"}, {"api_name": "pygame.display.set_caption", "line_number": 221, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 221, "usage_type": "attribute"}, {"api_name": "pygame.event.get", "line_number": 237, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 237, "usage_type": "attribute"}, {"api_name": "pygame.QUIT", "line_number": 240, "usage_type": "attribute"}, {"api_name": "pygame.quit", "line_number": 242, "usage_type": "call"}, {"api_name": "pygame.KEYDOWN", "line_number": 243, "usage_type": "attribute"}, {"api_name": "pygame.K_w", "line_number": 244, "usage_type": "attribute"}, {"api_name": "pygame.K_s", "line_number": 247, "usage_type": "attribute"}, {"api_name": "pygame.K_a", "line_number": 250, "usage_type": "attribute"}, {"api_name": "pygame.K_d", "line_number": 253, "usage_type": "attribute"}, {"api_name": "pygame.KEYUP", "line_number": 255, "usage_type": "attribute"}, {"api_name": "pygame.K_w", "line_number": 256, "usage_type": "attribute"}, {"api_name": "pygame.K_s", "line_number": 259, "usage_type": "attribute"}, {"api_name": "pygame.K_a", "line_number": 262, "usage_type": "attribute"}, {"api_name": "pygame.K_d", "line_number": 265, "usage_type": "attribute"}, {"api_name": "pygame.display.update", "line_number": 288, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 288, "usage_type": "attribute"}, {"api_name": "pygame.time.delay", "line_number": 289, "usage_type": "call"}, {"api_name": "pygame.time", "line_number": 289, "usage_type": "attribute"}, {"api_name": "pygame.display.update", "line_number": 291, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 291, "usage_type": "attribute"}]} +{"seq_id": "3258639921", "text": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nApplication to Lalonde (1986) dataset\n\nCreated on Sun Nov 1 10:51:38 2020\n\n@author: jeremylhour\n\"\"\"\nimport numpy as np\nimport pandas as pd\nimport time\nfrom datetime import datetime\nfrom tqdm import tqdm\n\nfrom pensynthpy import in_hull, incremental_pure_synth, pensynth_weights\n\n\nif __name__=='__main__':\n now = datetime.now()\n print('This is a script to compute the pure synthetic control solution for Lalonde (1986) data.\\n')\n print(f\"Launched on {now.strftime('%d, %b %Y, %H:%M:%S')} \\n\")\n print(\"Note : run downloadLalondeData.R script first.\\n\")\n \n \n print(\"=\"*80)\n print(\"DATA MANAGEMENT\")\n print(\"=\"*80)\n \n DATA_PATH = '../data/'\n \n ### Loading Lalonde's dataset rescaled as in the paper and unscaled for statistics\n X1_full = np.loadtxt(DATA_PATH+'X1.txt', skiprows=1)\n Y1_full = np.loadtxt(DATA_PATH+'Y1.txt', skiprows=1)\n X0_full = np.loadtxt(DATA_PATH+'X0.txt', skiprows=1)\n Y0_full = np.loadtxt(DATA_PATH+'Y0.txt', skiprows=1)\n X0_unscaled_full = np.loadtxt(DATA_PATH+'X0_unscaled.txt', skiprows=1)\n\n ### Consolidate duplicates in X0\n X_names = ['age', 'education', 'married', 'black', 'hispanic', 're74', 're75', 'nodegree', 'NoIncome74', 'NoIncome75']\n df = pd.DataFrame(X0_full)\n df.columns = [item+'_rescaled' for item in X_names]\n df['outcome'] = Y0_full\n \n for i in range(X0_unscaled_full.shape[1]):\n df[X_names[i]] = X0_unscaled_full[:,i]\n\n ### Consolidate dataset for untreated\n df_consolidated = df.groupby(X_names)[[i+'_rescaled' for i in X_names] + ['outcome'] + X_names].mean()\n X0 = df_consolidated[[i+'_rescaled' for i in X_names]].to_numpy()\n X0_unscaled = df_consolidated[X_names].to_numpy()\n Y0 = df_consolidated['outcome'].to_numpy()\n\n\n print(\"=\"*80)\n print(\"COMPUTING PURE SYNTHETIC CONTROL FOR EACH TREATED\")\n print(\"=\"*80)\n \n # We proceed in 3 steps :\n # - if some untreated are the same as the treated, assign uniform weights to these untreated.\n # - if the treated is inside the convex hull defined by the untreated, run the incremental algo.\n # - if the treated is not inside the convex hull defined by the untreated, run the standard synthetic control.\n \n allW = np.zeros((len(X1_full), len(X0)))\n start_time = time.time()\n with tqdm(total=(len(X1_full))) as prog:\n for i, x in enumerate(X1_full):\n sameAsUntreated = np.all(X0==x, axis=1) # True if untreated is same as treated\n if any(sameAsUntreated):\n untreatedId = np.where(sameAsUntreated)\n allW[i, untreatedId] = 1/len(untreatedId)\n else:\n inHullFlag = in_hull(x=x, points=X0)\n if inHullFlag:\n X0_tilde, antiranks = incremental_pure_synth(X1=x, X0=X0)\n allW[i, antiranks] = pensynth_weights(X0=X0_tilde, X1=x, pen=0)\n else:\n allW[i,] = pensynth_weights(X0=X0, X1=x, pen=1e-6)\n prog.update(1)\n print(f\"Time elapsed : {(time.time() - start_time):.2f} seconds ---\")\n\n\n print(\"=\"*80)\n print(\"COMPUTING STATISTICS AND SAVING RESULTS\")\n print(\"=\"*80)\n\n ########## COMPUTE THE NECESSARY STATISTICS ##########\n Y0_hat = allW @ Y0\n balance_check = (allW @ X0_unscaled).mean(axis=0)\n\n print('ATT: {:.3f}'.format((Y1_full - Y0_hat).mean(axis=0)))\n\n for b, value in enumerate(balance_check):\n print(X_names[b] +': {:.3f}'.format(value))\n\n sparsity_index = (allW > 0).sum(axis=1)\n print('Min sparsity: {:.0f}'.format(sparsity_index.min()))\n print('Median sparsity: {:.0f}'.format(np.median(sparsity_index)))\n print('Max sparsity: {:.0f}'.format(sparsity_index.max()))\n\n activ_index = (allW > 0).sum(axis=0)>0\n print('Active untreated units: {:.0f}'.format(activ_index.sum()))\n \n ########## SAVING WEIGHTS AS PARQUET FILE ##########\n df = pd.DataFrame(allW)\n df.columns = [\"Unit_\"+str(i+1) for i in range(len(X0))]\n df.to_parquet(\"Lalonde_solution.parquet\", engine=\"pyarrow\")\n \n ########## SANITY CHECK ON SPARSITY ##########\n high_sparsity = np.where(sparsity_index>11)[0]\n print(f'{len(high_sparsity)} treated units have sparsity larger than p+1.')\n print(high_sparsity)\n \n ########## DUMPING STATS TO FILE ##########\n with open('statistics.txt', 'w') as f:\n f.write('ATT: {:.3f}\\n'.format((Y1_full - Y0_hat).mean(axis=0)))\n for b, value in enumerate(balance_check):\n f.write(X_names[b] +': {:.3f}\\n'.format(value))\n f.write('Min sparsity: {:.0f}\\n'.format(sparsity_index.min()))\n f.write('Median sparsity: {:.0f}\\n'.format(np.median(sparsity_index)))\n f.write('Max sparsity: {:.0f}\\n'.format(sparsity_index.max()))\n f.write(f'{len(high_sparsity)} treated units have sparsity larger than p+1.')", "repo_name": "jeremylhour/pensynth", "sub_path": "incremental_algo_puresynth/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 4886, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 29, "dataset": "github-code", "pt": "37", "api": [{"api_name": "datetime.datetime.now", "line_number": 20, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 20, "usage_type": "name"}, {"api_name": "numpy.loadtxt", "line_number": 33, "usage_type": "call"}, {"api_name": "numpy.loadtxt", "line_number": 34, "usage_type": "call"}, {"api_name": "numpy.loadtxt", "line_number": 35, "usage_type": "call"}, {"api_name": "numpy.loadtxt", "line_number": 36, "usage_type": "call"}, {"api_name": "numpy.loadtxt", "line_number": 37, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 41, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 64, "usage_type": "call"}, {"api_name": "time.time", "line_number": 65, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 66, "usage_type": "call"}, {"api_name": "numpy.all", "line_number": 68, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 70, "usage_type": "call"}, {"api_name": "pensynthpy.in_hull", "line_number": 73, "usage_type": "call"}, {"api_name": "pensynthpy.incremental_pure_synth", "line_number": 75, "usage_type": "call"}, {"api_name": "pensynthpy.pensynth_weights", "line_number": 76, "usage_type": "call"}, {"api_name": "pensynthpy.pensynth_weights", "line_number": 78, "usage_type": "call"}, {"api_name": "time.time", "line_number": 80, "usage_type": "call"}, {"api_name": "numpy.median", "line_number": 98, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 105, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 110, "usage_type": "call"}, {"api_name": "numpy.median", "line_number": 120, "usage_type": "call"}]} +{"seq_id": "36032994730", "text": "\"\"\"Handles application errors.\"\"\"\nimport flask\nfrom flaskr import errors\n\n# pylint: disable=invalid-name\nbp = flask.Blueprint('error_handlers', __name__)\n\n\n@bp.app_errorhandler(errors.BaseError)\ndef handle_base_error(error):\n \"\"\"Handles application's error response.\"\"\"\n response = flask.jsonify(error.to_dict())\n response.status_code = error.status_code\n return response\n", "repo_name": "erjantj/huddle-server", "sub_path": "flaskr/error_handlers.py", "file_name": "error_handlers.py", "file_ext": "py", "file_size_in_byte": 384, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "flask.Blueprint", "line_number": 6, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 12, "usage_type": "call"}, {"api_name": "flaskr.errors.BaseError", "line_number": 9, "usage_type": "attribute"}, {"api_name": "flaskr.errors", "line_number": 9, "usage_type": "name"}]} +{"seq_id": "36598895353", "text": "import asyncio\nimport json\nimport os\nimport threading\n\nimport pytest\nfrom lsprotocol.types import (\n EXIT,\n INITIALIZE,\n SHUTDOWN,\n ClientCapabilities,\n InitializeParams,\n)\nfrom pygls.server import LanguageServer\n\n\nfrom . import CMD_ASYNC, CMD_SYNC, CMD_THREAD\nfrom ._init_server_stall_fix_hack import retry_stalled_init_fix_hack\n\n\nCALL_TIMEOUT = 3\n\n\ndef setup_ls_features(server):\n # Commands\n @server.command(CMD_ASYNC)\n async def cmd_test3(ls, *args): # pylint: disable=unused-variable\n return True, threading.get_ident()\n\n @server.thread()\n @server.command(CMD_THREAD)\n def cmd_test1(ls, *args): # pylint: disable=unused-variable\n return True, threading.get_ident()\n\n @server.command(CMD_SYNC)\n def cmd_test2(ls, *args): # pylint: disable=unused-variable\n return True, threading.get_ident()\n\n\nclass PyodideTestTransportAdapter:\n \"\"\"Transort adapter that's only useful for tests in a pyodide environment.\"\"\"\n\n def __init__(self, dest: LanguageServer):\n self.dest = dest\n\n def close(self):\n ...\n\n def write(self, data):\n object_hook = self.dest.lsp._deserialize_message\n self.dest.lsp._procedure_handler(json.loads(data, object_hook=object_hook))\n\n\nclass PyodideClientServer:\n \"\"\"Implementation of the `client_server` fixture for use in a pyodide\n environment.\"\"\"\n\n def __init__(self, LS=LanguageServer):\n self.server = LS(\"pygls-server\", \"v1\")\n self.client = LS(\"pygls-client\", \"v1\")\n\n self.server.lsp.connection_made(PyodideTestTransportAdapter(self.client))\n self.server.lsp._send_only_body = True\n\n self.client.lsp.connection_made(PyodideTestTransportAdapter(self.server))\n self.client.lsp._send_only_body = True\n\n def start(self):\n self.initialize()\n\n def stop(self):\n ...\n\n @classmethod\n def decorate(cls):\n return pytest.mark.parametrize(\"client_server\", [cls], indirect=True)\n\n def initialize(self):\n response = self.client.lsp.send_request(\n INITIALIZE,\n InitializeParams(\n process_id=12345, root_uri=\"file://\", capabilities=ClientCapabilities()\n ),\n ).result(timeout=CALL_TIMEOUT)\n\n assert response.capabilities is not None\n\n def __iter__(self):\n yield self.client\n yield self.server\n\n\nclass NativeClientServer:\n def __init__(self, LS=LanguageServer):\n # Client to Server pipe\n csr, csw = os.pipe()\n # Server to client pipe\n scr, scw = os.pipe()\n\n # Setup Server\n self.server = LS(\"server\", \"v1\")\n self.server_thread = threading.Thread(\n name=\"Server Thread\",\n target=self.server.start_io,\n args=(os.fdopen(csr, \"rb\"), os.fdopen(scw, \"wb\")),\n )\n self.server_thread.daemon = True\n\n # Setup client\n self.client = LS(\"client\", \"v1\", asyncio.new_event_loop())\n self.client_thread = threading.Thread(\n name=\"Client Thread\",\n target=self.client.start_io,\n args=(os.fdopen(scr, \"rb\"), os.fdopen(csw, \"wb\")),\n )\n self.client_thread.daemon = True\n\n @classmethod\n def decorate(cls):\n return pytest.mark.parametrize(\"client_server\", [cls], indirect=True)\n\n def start(self):\n self.server_thread.start()\n self.server.thread_id = self.server_thread.ident\n self.client_thread.start()\n self.initialize()\n\n def stop(self):\n shutdown_response = self.client.lsp.send_request(SHUTDOWN).result()\n assert shutdown_response is None\n self.client.lsp.notify(EXIT)\n self.server_thread.join()\n self.client._stop_event.set()\n try:\n self.client.loop._signal_handlers.clear() # HACK ?\n except AttributeError:\n pass\n self.client_thread.join()\n\n @retry_stalled_init_fix_hack()\n def initialize(self):\n timeout = None if \"DISABLE_TIMEOUT\" in os.environ else 1\n response = self.client.lsp.send_request(\n INITIALIZE,\n InitializeParams(\n process_id=12345, root_uri=\"file://\", capabilities=ClientCapabilities()\n ),\n ).result(timeout=timeout)\n assert response.capabilities is not None\n\n def __iter__(self):\n yield self.client\n yield self.server\n", "repo_name": "openlawlibrary/pygls", "sub_path": "tests/ls_setup.py", "file_name": "ls_setup.py", "file_ext": "py", "file_size_in_byte": 4386, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 446, "dataset": "github-code", "pt": "37", "api": [{"api_name": "threading.get_ident", "line_number": 28, "usage_type": "call"}, {"api_name": "threading.get_ident", "line_number": 33, "usage_type": "call"}, {"api_name": "threading.get_ident", "line_number": 37, "usage_type": "call"}, {"api_name": "pygls.server.LanguageServer", "line_number": 43, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 51, "usage_type": "call"}, {"api_name": "pygls.server.LanguageServer", "line_number": 58, "usage_type": "name"}, {"api_name": "pytest.mark.parametrize", "line_number": 76, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 76, "usage_type": "attribute"}, {"api_name": "lsprotocol.types.INITIALIZE", "line_number": 80, "usage_type": "argument"}, {"api_name": "lsprotocol.types.InitializeParams", "line_number": 81, "usage_type": "call"}, {"api_name": "lsprotocol.types.ClientCapabilities", "line_number": 82, "usage_type": "call"}, {"api_name": "pygls.server.LanguageServer", "line_number": 94, "usage_type": "name"}, {"api_name": "os.pipe", "line_number": 96, "usage_type": "call"}, {"api_name": "os.pipe", "line_number": 98, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 102, "usage_type": "call"}, {"api_name": "os.fdopen", "line_number": 105, "usage_type": "call"}, {"api_name": "asyncio.new_event_loop", "line_number": 110, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 111, "usage_type": "call"}, {"api_name": "os.fdopen", "line_number": 114, "usage_type": "call"}, {"api_name": "pytest.mark.parametrize", "line_number": 120, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 120, "usage_type": "attribute"}, {"api_name": "lsprotocol.types.SHUTDOWN", "line_number": 129, "usage_type": "argument"}, {"api_name": "lsprotocol.types.EXIT", "line_number": 131, "usage_type": "argument"}, {"api_name": "os.environ", "line_number": 142, "usage_type": "attribute"}, {"api_name": "lsprotocol.types.INITIALIZE", "line_number": 144, "usage_type": "argument"}, {"api_name": "lsprotocol.types.InitializeParams", "line_number": 145, "usage_type": "call"}, {"api_name": "lsprotocol.types.ClientCapabilities", "line_number": 146, "usage_type": "call"}, {"api_name": "_init_server_stall_fix_hack.retry_stalled_init_fix_hack", "line_number": 140, "usage_type": "call"}]} +{"seq_id": "11399518794", "text": "import telebot\r\nimport random\r\n\r\nbot = telebot.TeleBot('1412005435:AAH-APcKTRVve8hMAci6Zh_S_J5QvPsIXq0')\r\n\r\nkeyboard1 = telebot.types.ReplyKeyboardMarkup(True, True)\r\nkeyboard1.row('/help')\r\n\r\nkeyboard2 = telebot.types.ReplyKeyboardMarkup(True, True)\r\nkeyboard2.row('/go')\r\n\r\nkeyboard3 = telebot.types.ReplyKeyboardMarkup(True, True)\r\nkeyboard3.row('a', 'b', 'c')\r\n\r\nkeyboard4 = telebot.types.ReplyKeyboardMarkup(True, True)\r\nkeyboard4.row('d', 'e', 'f')\r\n\r\nkeyboard5 = telebot.types.ReplyKeyboardMarkup(True, True)\r\nkeyboard5.row('g', 'h', 'i')\r\n\r\nkeyboard6 = telebot.types.ReplyKeyboardMarkup(True, True)\r\nkeyboard6.row('j', 'k', 'l')\r\n\r\ncoopybook = {\r\n 'j': 'Неправильно',\r\n 'k': 'Неправильно',\r\n 'l': 'Правильно\\nТы победил! Наверное...',\r\n 'привет': 'Привет',\r\n 'пока': 'Пока',\r\n}\r\n\r\nsticker = {\r\n 1: 'CAACAgEAAxkBAAEBs_Bf4D4Yc6BNJQ7-ylaJCAABDDcB6egAAjoJAAK_jJAEdBjCoBMxYzkeBA',\r\n 2: 'CAACAgEAAxkBAAEBs_Jf4D4a9t6B12sHba_G6l6gKC7cJgACRgkAAr-MkAQ7mcTuV6DCsx4E',\r\n 3: 'CAACAgEAAxkBAAEBs_Rf4D4dvmPEWhYwpNfAcWswzA0Q2gACSgkAAr-MkAT0cmmwpQ8wXB4E',\r\n 4: 'CAACAgEAAxkBAAEBtrdf5OJohN_SDJDLwmHy0AABEwABgQEyAAInCQACv4yQBNQdmAgvlnzeHgQ',\r\n 5: 'CAACAgIAAxkBAAEBtrlf5OOVOkYykpqi6q_UrQrrAX35GgACSgEAApafjA6Mfk73uDljvh4E',\r\n 6: 'CAACAgIAAxkBAAEBtrtf5OPc78JArK8t2kpX0qKcUSe5TwACTAEAApafjA74EwWV2_gQrB4E',\r\n\r\n}\r\n\r\ncoins = {\r\n 1: 'Орёл🦅',\r\n 2: 'Решка🌰',\r\n}\r\n\r\n\r\n@bot.message_handler(commands=['start'])\r\ndef start_start(start):\r\n bot.send_message(start.chat.id, 'Привет, приятно познакомиться', reply_markup=keyboard1)\r\n\r\n\r\n@bot.message_handler(commands=['help'])\r\ndef start_help(help):\r\n bot.send_message(help.chat.id, 'Привет, я пришел тебе на помощь, вот список моих команд:')\r\n bot.send_message(help.chat.id, '\\n /help - 📑список команд.'\r\n '\\n /start - ❤проверить мой пульс.'\r\n '\\n /Christmas - 🎄поздравить с Новым Годом!'\r\n '\\n /update - 💹последнее обновление бота'\r\n '\\n /play - 🎮поиграть с ботом'\r\n '\\n /cube - 🎲кинуть кубик(0-100)'\r\n '\\n /coin - 👛бросить монетку')\r\n\r\n\r\n@bot.message_handler(commands=['update'])\r\ndef start_update(update):\r\n bot.send_message(update.chat.id, 'Можете кинуть кубик🎲 /cube\\nИли кинуть монетку 👛/coin ')\r\n\r\n\r\n@bot.message_handler(commands=['cube'])\r\ndef cube(cube):\r\n bot.send_message(cube.chat.id, 'Наше число...')\r\n bot.send_message(cube.chat.id, str(random.randint(0, 100)))\r\n\r\n\r\n@bot.message_handler(commands=['play'])\r\ndef start_play(play):\r\n bot.send_message(play.chat.id, 'Привет, давай поиграем в игру \"Отгадай значение\"', reply_markup=keyboard2)\r\n\r\n\r\n@bot.message_handler(commands=['go'])\r\ndef start_go(go):\r\n bot.send_message(go.chat.id, 'Что такое \"Канитель\"?\\n a. Суета, излишняя торопливость'\r\n '\\n b. Металлическая нить для вышивания\\n c. Сильный морской ветер',\r\n reply_markup=keyboard3)\r\n\r\n\r\n@bot.message_handler(content_types=['text'])\r\ndef start_message(message):\r\n print(message)\r\n\r\n name = coopybook.get(message.text.lower())\r\n print(name)\r\n\r\n rand = random.randint(1, 6)\r\n print(rand)\r\n\r\n coin = random.randint(1, 2)\r\n print(coin)\r\n\r\n if message.text.lower() == '/coin':\r\n bot.send_message(message.chat.id, coins.get(coin))\r\n\r\n\r\n if message.text.lower() == '/christmas':\r\n bot.send_sticker(message.chat.id, sticker.get(rand))\r\n\r\n if message.text.lower() == 'c':\r\n bot.send_message(message.from_user.id, '\\nНеправильно\\nСледующий вопрос...'\r\n '\\nЧто такое \"Сермяга\"?\\nd. Грубое некрашенное сукно'\r\n '\\ne. Неопрятный человек\\nf. Нижняя мужская рубаха',\r\n reply_markup=keyboard4)\r\n\r\n elif message.text.lower() == 'a':\r\n bot.send_message(message.from_user.id, '\\nНеправильно\\nСледующий вопрос...'\r\n '\\nЧто такое \"Сермяга\"?\\nd. Грубое некрашенное сукно'\r\n '\\ne. Неопрятный человек\\nf. Нижняя мужская рубаха',\r\n reply_markup=keyboard4)\r\n\r\n elif message.text.lower() == 'b':\r\n bot.send_message(message.from_user.id, '\\nПравильно\\nСледующий вопрос...'\r\n '\\nЧто такое \"Сермяга\"?\\nd. Грубое некрашенное сукно'\r\n '\\ne. Неопрятный человек\\nf. Нижняя мужская рубаха',\r\n reply_markup=keyboard4)\r\n\r\n elif message.text.lower() == 'd':\r\n bot.send_message(message.from_user.id, '\\nПравильно\\nСледующий вопрос...'\r\n '\\nЧто такое \"Кукомоя\"?\\ng. Кулак, сжатая ладонь'\r\n '\\nh. Неряха, неопрятный человек\\ni. Кувшин для умывания',\r\n reply_markup=keyboard5)\r\n\r\n elif message.text.lower() == 'e':\r\n bot.send_message(message.from_user.id, '\\nНеправильно\\nСледующий вопрос...'\r\n '\\nЧто такое \"Кукомоя\"?\\ng. Кулак, сжатая ладонь'\r\n '\\nh. Неряха, неопрятный человек\\ni. Кувшин для умывания',\r\n reply_markup=keyboard5)\r\n\r\n elif message.text.lower() == 'f':\r\n bot.send_message(message.from_user.id, '\\nНеправильно\\nСледующий вопрос...'\r\n '\\nЧто такое \"Кукомоя\"?\\ng. Кулак, сжатая ладонь'\r\n '\\nh. Неряха, неопрятный человек\\ni. Кувшин для умывания',\r\n reply_markup=keyboard5)\r\n\r\n elif message.text.lower() == 'g':\r\n bot.send_message(message.from_user.id, '\\nНеправильно\\nСледующий вопрос...'\r\n '\\nЧто такое \"Кукомоя\"?\\ng. Кулак, сжатая ладонь'\r\n '\\nh. Неряха, неопрятный человек\\ni. Кувшин для умывания',\r\n reply_markup=keyboard5)\r\n\r\n elif message.text.lower() == 'h':\r\n bot.send_message(message.from_user.id, '\\nПравильно\\nСледующий вопрос...'\r\n '\\nЧто такое \"Рюма\"?\\nj. Человек, несущий чушь '\r\n '\\nk. Глубокий сосуд для напитков \\nl. Плакса, рыдающий человек',\r\n reply_markup=keyboard6)\r\n\r\n elif message.text.lower() == 'i':\r\n bot.send_message(message.from_user.id, '\\nНеправильно\\nСледующий вопрос...'\r\n '\\nЧто такое \"Рюма\"?\\nj. Человек, несущий чушь '\r\n '\\nk. Глубокий сосуд для напитков \\nl. Плакса, рыдающий человек',\r\n reply_markup=keyboard6)\r\n\r\n if name:\r\n bot.send_message(message.from_user.id, name)\r\n\r\n\r\n@bot.message_handler(content_types=['sticker'])\r\ndef sticker_id(sticker):\r\n print(sticker)\r\n\r\n\r\nbot.polling(none_stop=True, interval=0)\r\n", "repo_name": "TitanAlex/UbgrateBot-random-", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 8516, "program_lang": "python", "lang": "ru", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "telebot.TeleBot", "line_number": 4, "usage_type": "call"}, {"api_name": "telebot.types.ReplyKeyboardMarkup", "line_number": 6, "usage_type": "call"}, {"api_name": "telebot.types", "line_number": 6, "usage_type": "attribute"}, {"api_name": "telebot.types.ReplyKeyboardMarkup", "line_number": 9, "usage_type": "call"}, {"api_name": "telebot.types", "line_number": 9, "usage_type": "attribute"}, {"api_name": "telebot.types.ReplyKeyboardMarkup", "line_number": 12, "usage_type": "call"}, {"api_name": "telebot.types", "line_number": 12, "usage_type": "attribute"}, {"api_name": "telebot.types.ReplyKeyboardMarkup", "line_number": 15, "usage_type": "call"}, {"api_name": "telebot.types", "line_number": 15, "usage_type": "attribute"}, {"api_name": "telebot.types.ReplyKeyboardMarkup", "line_number": 18, "usage_type": "call"}, {"api_name": "telebot.types", "line_number": 18, "usage_type": "attribute"}, {"api_name": "telebot.types.ReplyKeyboardMarkup", "line_number": 21, "usage_type": "call"}, {"api_name": "telebot.types", "line_number": 21, "usage_type": "attribute"}, {"api_name": "random.randint", "line_number": 73, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 95, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 98, "usage_type": "call"}]} +{"seq_id": "22016548076", "text": "import pandas as p\nimport numpy as np\nimport seaborn as sns\nimport matplotlib.pyplot as plt\nimport time\n\nphone_ip = \"192.168.137.186\"\nsns.set_style(\"darkgrid\")\n# sns.set_palette(\"bright\")\n\ntitle_font = {'fontname': 'Arial', 'size': '20', 'color': 'black', 'weight': 'normal',\n 'verticalalignment': 'bottom'} # Bottom vertical alignment for more space\naxis_font = {'fontname': 'Arial', 'size': '16', 'color': 'black', 'weight': 'normal'} # Bottom vertical alignment for more space\nlabel_font = {'fontname': 'Arial', 'size': '12', 'color': 'black', 'weight': 'normal'}\n\n\ndef ips_over_time(path, sheet, app_name):\n df = p.read_excel(path, index_col=None, sheet_name=sheet)\n df['Timestamp'] = p.to_datetime(df['Timestamp'], unit='s')\n df['Time'] = df[\"Timestamp\"].dt.strftime(\"%H:%M\")\n\n to_phone, from_phone = [x for _, x in df.groupby(df['Src IP'] == phone_ip)]\n dataset_to = to_phone.groupby(['Service', 'Time'])['Src IP'].nunique()\n dataset_from = from_phone.groupby(['Service', 'Time'])['Dst IP'].nunique()\n\n def create_graph(dataset, column):\n dataset = dataset.to_frame()\n\n fig, ax = plt.subplots(figsize=(11, 7))\n\n benign = dataset[column]['benign'].reset_index().rename(columns={column: \"Benign\"})\n ax.plot(benign[\"Time\"], benign[\"Benign\"], '-o')\n\n ads = dataset[column]['ads'].reset_index().rename(columns={column: \"Advertisements\"})\n ax.plot(ads[\"Time\"], ads[\"Advertisements\"], '-o')\n\n tracking = dataset[column]['tracking'].reset_index().rename(columns={column: \"Tracking\"})\n ax.plot(tracking[\"Time\"], tracking[\"Tracking\"], '-o')\n\n service_types = list(dataset[column].index.levels[0])\n if 'ads,tracking' in service_types:\n both = dataset[column]['ads,tracking'].reset_index().rename(columns={column: \"Both\"})\n ax.plot(both[\"Time\"], both[\"Both\"], '-o')\n\n ax.set_xticklabels(range(0, 16))\n plt.xticks(np.arange(len(benign[\"Time\"])), **label_font)\n plt.yticks(**label_font)\n plt.title(\"Total Number of Unique IP Connections over Time per IP Type\", **title_font)\n plt.xlabel(\"Time (in minutes)\", **axis_font)\n plt.ylabel(\"Total Number of Unique IP Connections\", **axis_font)\n ax.yaxis.set_major_formatter(plt.FormatStrFormatter('%d'))\n plt.legend(fontsize=12)\n\n create_graph(dataset_to, \"Src IP\")\n plt.savefig(\"./graphs/ips_to_phone_(\" + app_name + \").png\")\n # plt.show()\n\n create_graph(dataset_from, \"Dst IP\")\n plt.savefig(\"./graphs/ips_from_phone_(\" + app_name + \").png\")\n # plt.show()\n\ndef frames_over_time(path, sheet, app_name):\n df = p.read_excel(path, index_col=None, sheet_name = sheet)\n df['Timestamp'] = p.to_datetime(df['Timestamp'], unit='s')\n df['Time'] = df[\"Timestamp\"].dt.strftime(\"%H:%M\")\n\n to_phone, from_phone = [x for _, x in df.groupby(df['Src IP'] == phone_ip)]\n \n def create_graph(dataset):\n dataset = dataset.groupby(['Service', 'Time'])['Frame Size'].sum()\n dataset = dataset.to_frame()\n column = 'Frame Size'\n\n fig, ax = plt.subplots(figsize=(11, 7))\n\n benign = dataset[column]['benign'].reset_index().rename(columns={column: \"Benign\"})\n ax.plot(benign[\"Time\"], benign[\"Benign\"], '-o')\n\n ads = dataset[column]['ads'].reset_index().rename(columns={column: \"Advertisements\"})\n ax.plot(ads[\"Time\"], ads[\"Advertisements\"], '-o')\n\n tracking = dataset[column]['tracking'].reset_index().rename(columns={column: \"Tracking\"})\n ax.plot(tracking[\"Time\"], tracking[\"Tracking\"], '-o')\n\n service_types = list(dataset[column].index.levels[0])\n if 'ads,tracking' in service_types:\n both = dataset[column]['ads,tracking'].reset_index().rename(columns={column: \"Both\"})\n ax.plot(both[\"Time\"], both[\"Both\"], '-o')\n\n ax.set_xticklabels(range(0,16))\n # ax.set_zticklabels(benign[\"Time\"])\n plt.xticks(np.arange(len(benign[\"Time\"])), **label_font)\n plt.yticks(**label_font)\n plt.title(\"Total Traffic Sent over Time per IP Type\", **title_font)\n plt.xlabel(\"Time (in minutes)\", **axis_font)\n plt.ylabel(\"Total Traffic Sent (in bytes)\", **axis_font)\n ax.yaxis.set_major_formatter(plt.FormatStrFormatter('%d'))\n plt.legend(fontsize=12)\n\n create_graph(to_phone)\n plt.savefig(\"./graphs/to_phone_(\" + app_name + \").png\")\n # plt.show()\n\n create_graph(from_phone)\n plt.savefig(\"./graphs/from_phone_(\" + app_name + \").png\")\n # plt.show()\n\n\ndef graph_ad_ips():\n df = p.read_excel(\"./results/Android9.0/Pandas Datasets/summary.xlsx\", index_col=None)\n\n ad_ips = df.groupby('Ad IPs')['Filename'].count()\n tracking_ips = df.groupby('Tracking Ips')['Filename'].count()\n\n data_ips = {'Advertisments': ad_ips, 'Tracking IPs': tracking_ips}\n all_ips = p.DataFrame(data=data_ips)\n\n ax = all_ips.plot(kind='bar', stacked=False, figsize=(11, 7), rot=0, width=0.8)\n \n ax.yaxis.set_major_locator(plt.FixedLocator(range(0,len(all_ips)+1)))\n plt.xticks(**label_font)\n plt.yticks(**label_font)\n plt.title(\"Distribution of Unique Connections\", **title_font)\n plt.xlabel(\"Number of Unique IP Connections\", **axis_font)\n plt.ylabel(\"Number of Applications\", **axis_font)\n plt.legend(loc=1, fontsize=12)\n plt.savefig(\"./graphs/num_ad_ips.png\")\n # plt.show()\n\ndef total_number_vs_size():\n df = p.read_excel(\"./results/Android9.0/Pandas Datasets/summary.xlsx\", index_col=None)\n\n frame_nums = df[['Filename', 'Begnign Frames', 'Ad Frames', 'Tracking Frames', 'Ad/Tracking Frames']]\n frame_size = df[['Filename', 'Benign Traffic Size', 'Ad Traffic Size', 'Tracking Traffic Size', 'Ad/Tracking Traffic Size']]\n\n frame_nums = frame_nums.groupby(['Begnign Frames']).sum()\n print(frame_nums.head(3))\n\ndef calculate_percentages():\n df = p.read_excel(\"./results/Android9.0/Pandas Datasets/summary.xlsx\", index_col=None)\n\n print(\"Ad Percentage\")\n row = df[df['Filename'] == \"io.voodoo.paper2.apk.pcap\"]\n total = row['Benign Traffic Size'] + \\\n row['Ad Traffic Size'] + row['Tracking Traffic Size']\n print((row['Ad Traffic Size'] / total) * 100)\n print((row['Tracking Traffic Size'] / total) * 100)\n\n\ndef get_percentages(path):\n df = p.read_excel(path, index_col=None)\n df['Timestamp'] = p.to_datetime(df['Timestamp'], unit='s')\n df['Time'] = df[\"Timestamp\"].dt.strftime(\"%H:%M\")\n first = df['Time'][1]\n first = (p.to_datetime(first) + p.Timedelta(minutes=1)).strftime(\"%H:%M\")\n df = df[df['Time'] > first]\n df = df.groupby(['Service'])['Frame Size'].sum()\n df = df.to_frame()\n\n total = sum(df['Frame Size'])\n ad_percent = (df['Frame Size']['ads'] / total) * 100\n tracking_percent = (df['Frame Size']['tracking'] / total) * 100\n both_percent = (df['Frame Size']['ads,tracking'] / total) * 100\n benign_percent = (df['Frame Size']['benign'] / total) * 100\n\n print(\"Ads: %.2f \\nTracking: %.2f\\nBoth: %.2f\\nBenign: %.2f\"\n %(ad_percent, tracking_percent, both_percent, benign_percent))\n\n# get_percentages(\"./results/Android9.0/Pandas Datasets/io.voodoo.paper2.xlsx\")\n\n# graph_ad_ips()\n\n# ips_over_time(\n# \"./results/Android9.0/Pandas Datasets/io.voodoo.paper2.xlsx\", \"Sheet1\", \"io.voodoo.paper2\")\n# frames_over_time(\n# \"./results/Android9.0/Pandas Datasets/io.voodoo.paper2.xlsx\", \"Sheet1\", \"io.voodoo.paper2\")\n# ips_over_time(\n# \"./results/Android9.0/Pandas Datasets/io.voodoo.crowdcity.xlsx\", \"Sheet1\", \"io.voodoo.crowdcity\")\n# frames_over_time(\n# \"./results/Android9.0/Pandas Datasets/io.voodoo.crowdcity.xlsx\", \"Sheet1\", \"io.voodoo.crowdcity\")\nips_over_time(\n \"./results/Android9.0/Pandas Datasets/com.playgendary.kickthebuddy.xlsx\", \"Sheet1\", \"com.playgendary.kickthebuddy\")\nframes_over_time(\n \"./results/Android9.0/Pandas Datasets/com.playgendary.kickthebuddy.xlsx\", \"Sheet1\", \"com.playgendary.kickthebuddy\")\n\ndf = p.read_excel(\"./results/Android9.0/Pandas Datasets/summary.xlsx\", index_col=None)\n\nprint(\"Max ad traffic...\")\nprint(df[df[\"Ad Traffic Size\"] == df[\"Ad Traffic Size\"].max()])\n\n# print(\"Max ad ips...\")\n# print(df[df[\"Ad IPs\"] == df[\"Ad IPs\"].max()])\n# print(\"Max tracking ips...\")\n# print(df[df[\"Tracking Ips\"] == df[\"Tracking Ips\"].max()])\n# print(\"Max tracking traffic...\")\n# print(df[df[\"Tracking Traffic Size\"] == df[\"Tracking Traffic Size\"].max()])\n\n# total_number_vs_size()\n\n# dataset_from = from_phone.groupby(['Service', 'Time'])['Dst IP'].nunique()\n", "repo_name": "markgllin/android-np", "sub_path": "pandas_analysis.py", "file_name": "pandas_analysis.py", "file_ext": "py", "file_size_in_byte": 8508, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "seaborn.set_style", "line_number": 8, "usage_type": "call"}, {"api_name": "pandas.read_excel", "line_number": 18, "usage_type": "call"}, {"api_name": "pandas.to_datetime", "line_number": 19, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 29, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 29, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 46, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 46, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 46, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.yticks", "line_number": 47, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 47, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 48, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 48, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 49, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 49, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 50, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 50, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.FormatStrFormatter", "line_number": 51, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 51, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 52, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 52, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 55, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 55, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 59, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 59, "usage_type": "name"}, {"api_name": "pandas.read_excel", "line_number": 63, "usage_type": "call"}, {"api_name": "pandas.to_datetime", "line_number": 64, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 74, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 74, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 92, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 92, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 92, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.yticks", "line_number": 93, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 93, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 94, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 94, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 95, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 95, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 96, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 96, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.FormatStrFormatter", "line_number": 97, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 97, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 98, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 98, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 101, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 101, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 105, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 105, "usage_type": "name"}, {"api_name": "pandas.read_excel", "line_number": 110, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 116, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.FixedLocator", "line_number": 120, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 120, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 121, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 121, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.yticks", "line_number": 122, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 122, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 123, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 123, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 124, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 124, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 125, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 125, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 126, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 126, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 127, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 127, "usage_type": "name"}, {"api_name": "pandas.read_excel", "line_number": 131, "usage_type": "call"}, {"api_name": "pandas.read_excel", "line_number": 140, "usage_type": "call"}, {"api_name": "pandas.read_excel", "line_number": 151, "usage_type": "call"}, {"api_name": "pandas.to_datetime", "line_number": 152, "usage_type": "call"}, {"api_name": "pandas.to_datetime", "line_number": 155, "usage_type": "call"}, {"api_name": "pandas.Timedelta", "line_number": 155, "usage_type": "call"}, {"api_name": "pandas.read_excel", "line_number": 186, "usage_type": "call"}]} +{"seq_id": "4407824121", "text": "from bs4 import BeautifulSoup\nfrom selenium import webdriver\nfrom selenium.webdriver.chrome.service import Service\nfrom selenium.webdriver.common.by import By\nfrom webdriver_manager.chrome import ChromeDriverManager\nfrom selenium.webdriver.support import expected_conditions as EC\nimport time\n\ns = Service(ChromeDriverManager().install())\ndriver = webdriver.Chrome(service=s)\n\nprefix_member = 'https://iabe.be/about-iabe/members/public-member-list'\nprefix_qualified_member = 'https://iabe.be/about-iabe/iabe-qualified-members'\n\nlinks_member = [prefix_member]\nlinks_qualified_member = [prefix_qualified_member]\n\nfor i in range(8):\n new_link = str(prefix_qualified_member) + str('?pager=') + str(i+2)\n links_qualified_member.append(new_link)\n\nfor i in range(22):\n new_link = str(prefix_member) + str('?pager=') + str(i+2)\n links_member.append(new_link)\n\n\n\nlist_member = []\nfor i in range(len(links_member)):\n driver.get(links_member[i])\n time.sleep(2)\n soup = BeautifulSoup(driver.page_source, 'html.parser')\n mydivs = soup.find_all(\"div\", {\"class\": \"publicRelationList__relation\"})\n\n for i in range(len(mydivs)):\n list_member.append(mydivs[i].find_next(\"h3\").text.replace(\",\", \" \"))\n\nprint('Finished')\n\nlist_qualified_member = []\nfrom selenium.webdriver.support.ui import WebDriverWait\nfor i in range(len(links_qualified_member)):\n driver.get(links_qualified_member[i])\n # time.sleep(3)\n element = WebDriverWait(driver, 10).until(\n EC.presence_of_element_located((By.ID, \"variantDefault\"))\n )\n soup = BeautifulSoup(driver.page_source, 'html.parser')\n mydivs = soup.find_all(\"div\", {\"class\": \"publicRelationList__relation\"})\n\n for i in range(len(mydivs)):\n list_qualified_member.append(mydivs[i].find_next(\"h3\").text.replace(\",\", \" \"))\n\nprint('Finished')\n\n", "repo_name": "makipouf/Search_tool", "sub_path": "IABE.py", "file_name": "IABE.py", "file_ext": "py", "file_size_in_byte": 1823, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "selenium.webdriver.chrome.service.Service", "line_number": 9, "usage_type": "call"}, {"api_name": "webdriver_manager.chrome.ChromeDriverManager", "line_number": 9, "usage_type": "call"}, {"api_name": "selenium.webdriver.Chrome", "line_number": 10, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 10, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 31, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 32, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.ui.WebDriverWait", "line_number": 45, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.expected_conditions.presence_of_element_located", "line_number": 46, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.expected_conditions", "line_number": 46, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.ID", "line_number": 46, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 46, "usage_type": "name"}, {"api_name": "bs4.BeautifulSoup", "line_number": 48, "usage_type": "call"}]} +{"seq_id": "72379293867", "text": "from typing import Optional\n\nimport haiku as hk\nimport jax\nimport jax.numpy as jnp\n\nfrom mmv.models import tsm_utils as tsmu\nfrom mmv.models import types\n\n\nclass TSMResNetBlock(hk.Module):\n \"\"\"A ResNet subblock with Temporal Channel Shifting.\n\n Combines a typical ResNetV2 block implementation\n (see https://arxiv.org/abs/1512.03385) with a pre-convolution Temporal\n Shift Module (see https://arxiv.org/pdf/1811.08383.pdf) in the residual.\n \"\"\"\n\n def __init__(self,\n output_channels: int,\n stride: int,\n use_projection: bool,\n tsm_mode: str,\n normalize_fn: Optional[types.NormalizeFn] = None,\n channel_shift_fraction: float = 0.125,\n num_frames: int = 8,\n name: str = 'TSMResNetBlock'):\n \"\"\"Initializes the TSMResNetBlock module.\n\n Args:\n output_channels: Number of output channels.\n stride: Stride used in convolutions.\n use_projection: Whether to use a projection for the shortcut.\n tsm_mode: Mode for TSM ('gpu' or 'tpu').\n normalize_fn: Function used for normalization.\n channel_shift_fraction: The fraction of temporally shifted channels. If\n `channel_shift_fraction` is 0, the block is the same as a normal ResNet\n block.\n num_frames: Size of frame dimension in a single batch example\n name: The name of the module.\n \"\"\"\n super().__init__(name=name)\n self._output_channels = output_channels\n self._bottleneck_channels = output_channels // 4\n self._stride = stride\n self._use_projection = use_projection\n self._normalize_fn = normalize_fn\n self._tsm_mode = tsm_mode\n self._channel_shift_fraction = channel_shift_fraction\n self._num_frames = num_frames\n\n def __call__(self,\n inputs: types.TensorLike,\n is_training: bool = True) -> jnp.ndarray:\n \"\"\"Connects the ResNetBlock module into the graph.\n\n Args:\n inputs: A 4-D float array of shape `[B, H, W, C]`.\n is_training: Whether to use training mode.\n\n Returns:\n A 4-D float array of shape\n `[B * num_frames, new_h, new_w, output_channels]`.\n \"\"\"\n # ResNet V2 uses pre-activation, where the batch norm and relu are before\n # convolutions, rather than after as in ResNet V1.\n preact = inputs\n if self._normalize_fn is not None:\n preact = self._normalize_fn(preact, is_training=is_training)\n preact = jax.nn.relu(preact)\n\n if self._use_projection:\n shortcut = hk.Conv2D(\n output_channels=self._output_channels,\n kernel_shape=1,\n stride=self._stride,\n with_bias=False,\n padding='SAME',\n name='shortcut_conv')(\n preact)\n else:\n shortcut = inputs\n\n # Eventually applies Temporal Shift Module.\n if self._channel_shift_fraction != 0:\n preact = tsmu.apply_temporal_shift(\n preact, tsm_mode=self._tsm_mode, num_frames=self._num_frames,\n channel_shift_fraction=self._channel_shift_fraction)\n\n # First convolution.\n residual = hk.Conv2D(\n self._bottleneck_channels,\n kernel_shape=1,\n stride=1,\n with_bias=False,\n padding='SAME',\n name='conv_0')(\n preact)\n\n # Second convolution.\n if self._normalize_fn is not None:\n residual = self._normalize_fn(residual, is_training=is_training)\n residual = jax.nn.relu(residual)\n residual = hk.Conv2D(\n output_channels=self._bottleneck_channels,\n kernel_shape=3,\n stride=self._stride,\n with_bias=False,\n padding='SAME',\n name='conv_1')(\n residual)\n\n # Third convolution.\n if self._normalize_fn is not None:\n residual = self._normalize_fn(residual, is_training=is_training)\n residual = jax.nn.relu(residual)\n residual = hk.Conv2D(\n output_channels=self._output_channels,\n kernel_shape=1,\n stride=1,\n with_bias=False,\n padding='SAME',\n name='conv_2')(\n residual)\n\n # NOTE: we do not use block multiplier.\n output = shortcut + residual\n return output\n\n\nclass TSMResNetUnit(hk.Module):\n \"\"\"Block group for TSM ResNet.\"\"\"\n\n def __init__(self,\n output_channels: int,\n num_blocks: int,\n stride: int,\n tsm_mode: str,\n num_frames: int,\n normalize_fn: Optional[types.NormalizeFn] = None,\n channel_shift_fraction: float = 0.125,\n name: str = 'tsm_resnet_unit'):\n \"\"\"Creates a TSMResNet Unit.\n\n Args:\n output_channels: Number of output channels.\n num_blocks: Number of ResNet blocks in the unit.\n stride: Stride of the unit.\n tsm_mode: Which temporal shift module to use.\n num_frames: Size of frame dimension in a single batch example.\n normalize_fn: Function used for normalization.\n channel_shift_fraction: The fraction of temporally shifted channels. If\n `channel_shift_fraction` is 0, the block is the same as a normal ResNet\n block.\n name: The name of the module.\n \"\"\"\n super().__init__(name=name)\n self._output_channels = output_channels\n self._num_blocks = num_blocks\n self._normalize_fn = normalize_fn\n self._stride = stride\n self._tsm_mode = tsm_mode\n self._channel_shift_fraction = channel_shift_fraction\n self._num_frames = num_frames\n\n def __call__(self,\n inputs: types.TensorLike,\n is_training: bool) -> jnp.ndarray:\n \"\"\"Connects the module to inputs.\n\n Args:\n inputs: A 4-D float array of shape `[B * num_frames, H, W, C]`.\n is_training: Whether to use training mode.\n\n Returns:\n A 4-D float array of shape\n `[B * num_frames, H // stride, W // stride, output_channels]`.\n \"\"\"\n net = inputs\n for idx_block in range(self._num_blocks):\n net = TSMResNetBlock(\n self._output_channels,\n stride=self._stride if idx_block == 0 else 1,\n use_projection=idx_block == 0,\n normalize_fn=self._normalize_fn,\n tsm_mode=self._tsm_mode,\n channel_shift_fraction=self._channel_shift_fraction,\n num_frames=self._num_frames,\n name=f'block_{idx_block}')(\n net, is_training=is_training)\n return net # pytype: disable=bad-return-type # jax-devicearray\n\n\nclass TSMResNetV2(hk.Module):\n \"\"\"TSM based on ResNet V2 as described in https://arxiv.org/abs/1603.05027.\"\"\"\n\n # Endpoints of the model in order.\n VALID_ENDPOINTS = (\n 'tsm_resnet_stem',\n 'tsm_resnet_unit_0',\n 'tsm_resnet_unit_1',\n 'tsm_resnet_unit_2',\n 'tsm_resnet_unit_3',\n 'last_conv',\n 'Embeddings',\n )\n\n def __init__(self,\n normalize_fn: Optional[types.NormalizeFn] = None,\n depth: int = 50,\n num_frames: int = 16,\n channel_shift_fraction: float = 0.125,\n width_mult: int = 1,\n name: str = 'TSMResNetV2'):\n \"\"\"Constructs a ResNet model.\n\n Args:\n normalize_fn: Function used for normalization.\n depth: Depth of the desired ResNet.\n num_frames: Number of frames (used in TPU mode).\n channel_shift_fraction: Fraction of channels that are temporally shifted,\n if `channel_shift_fraction` is 0, a regular ResNet is returned.\n width_mult: Whether or not to use a width multiplier.\n name: The name of the module.\n\n Raises:\n ValueError: If `channel_shift_fraction` or `depth` has invalid value.\n \"\"\"\n super().__init__(name=name)\n\n if not 0. <= channel_shift_fraction <= 1.0:\n raise ValueError(\n f'channel_shift_fraction ({channel_shift_fraction})'\n ' has to be in [0, 1].')\n\n self._num_frames = num_frames\n\n self._channels = (256, 512, 1024, 2048)\n self._strides = (1, 2, 2, 2)\n\n num_blocks = {\n 50: (3, 4, 6, 3),\n 101: (3, 4, 23, 3),\n 152: (3, 8, 36, 3),\n 200: (3, 24, 36, 3),\n }\n if depth not in num_blocks:\n raise ValueError(\n f'`depth` should be in {list(num_blocks.keys())} ({depth} given).')\n self._num_blocks = num_blocks[depth]\n\n self._width_mult = width_mult\n self._channel_shift_fraction = channel_shift_fraction\n self._normalize_fn = normalize_fn\n\n def __call__(\n self,\n inputs: types.TensorLike,\n is_training: bool = True,\n final_endpoint: str = 'Embeddings') -> jnp.ndarray:\n \"\"\"Connects the TSM ResNetV2 module into the graph.\n\n Args:\n inputs: A 4-D float array of shape `[B, H, W, C]`.\n is_training: Whether to use training mode.\n final_endpoint: Up to which endpoint to run / return.\n\n Returns:\n Network output at location `final_endpoint`. A float array which shape\n depends on `final_endpoint`.\n\n Raises:\n ValueError: If `final_endpoint` is not recognized.\n \"\"\"\n\n # Prepare inputs for TSM.\n inputs, tsm_mode, num_frames = tsmu.prepare_inputs(inputs)\n num_frames = num_frames or self._num_frames\n\n self._final_endpoint = final_endpoint\n if self._final_endpoint not in self.VALID_ENDPOINTS:\n raise ValueError(f'Unknown final endpoint {self._final_endpoint}')\n\n # Stem convolution.\n end_point = 'tsm_resnet_stem'\n net = hk.Conv2D(\n output_channels=64 * self._width_mult,\n kernel_shape=7,\n stride=2,\n with_bias=False,\n name=end_point,\n padding='SAME')(\n inputs)\n net = hk.MaxPool(\n window_shape=(1, 3, 3, 1),\n strides=(1, 2, 2, 1),\n padding='SAME')(\n net)\n if self._final_endpoint == end_point:\n return net\n\n # Residual block.\n for unit_id, (channels, num_blocks, stride) in enumerate(\n zip(self._channels, self._num_blocks, self._strides)):\n end_point = f'tsm_resnet_unit_{unit_id}'\n net = TSMResNetUnit(\n output_channels=channels * self._width_mult,\n num_blocks=num_blocks,\n stride=stride,\n normalize_fn=self._normalize_fn,\n channel_shift_fraction=self._channel_shift_fraction,\n num_frames=num_frames,\n tsm_mode=tsm_mode,\n name=end_point)(\n net, is_training=is_training)\n if self._final_endpoint == end_point:\n return net\n\n if self._normalize_fn is not None:\n net = self._normalize_fn(net, is_training=is_training)\n net = jax.nn.relu(net)\n\n end_point = 'last_conv'\n if self._final_endpoint == end_point:\n return net\n net = jnp.mean(net, axis=(1, 2))\n # Prepare embedding outputs for TSM (temporal average of features).\n net = tsmu.prepare_outputs(net, tsm_mode, num_frames)\n assert self._final_endpoint == 'Embeddings'\n return net\n", "repo_name": "deepmind/deepmind-research", "sub_path": "mmv/models/tsm_resnet.py", "file_name": "tsm_resnet.py", "file_ext": "py", "file_size_in_byte": 10753, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 11900, "dataset": "github-code", "pt": "37", "api": [{"api_name": "haiku.Module", "line_number": 11, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 24, "usage_type": "name"}, {"api_name": "mmv.models.types.NormalizeFn", "line_number": 24, "usage_type": "attribute"}, {"api_name": "mmv.models.types", "line_number": 24, "usage_type": "name"}, {"api_name": "mmv.models.types.TensorLike", "line_number": 53, "usage_type": "attribute"}, {"api_name": "mmv.models.types", "line_number": 53, "usage_type": "name"}, {"api_name": "jax.nn.relu", "line_number": 70, "usage_type": "call"}, {"api_name": "jax.nn", "line_number": 70, "usage_type": "attribute"}, {"api_name": "haiku.Conv2D", "line_number": 73, "usage_type": "call"}, {"api_name": "mmv.models.tsm_utils.apply_temporal_shift", "line_number": 86, "usage_type": "call"}, {"api_name": "mmv.models.tsm_utils", "line_number": 86, "usage_type": "name"}, {"api_name": "haiku.Conv2D", "line_number": 91, "usage_type": "call"}, {"api_name": "jax.nn.relu", "line_number": 103, "usage_type": "call"}, {"api_name": "jax.nn", "line_number": 103, "usage_type": "attribute"}, {"api_name": "haiku.Conv2D", "line_number": 104, "usage_type": "call"}, {"api_name": "jax.nn.relu", "line_number": 116, "usage_type": "call"}, {"api_name": "jax.nn", "line_number": 116, "usage_type": "attribute"}, {"api_name": "haiku.Conv2D", "line_number": 117, "usage_type": "call"}, {"api_name": "jax.numpy.ndarray", "line_number": 54, "usage_type": "attribute"}, {"api_name": "jax.numpy", "line_number": 54, "usage_type": "name"}, {"api_name": "haiku.Module", "line_number": 131, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 140, "usage_type": "name"}, {"api_name": "mmv.models.types.NormalizeFn", "line_number": 140, "usage_type": "attribute"}, {"api_name": "mmv.models.types", "line_number": 140, "usage_type": "name"}, {"api_name": "mmv.models.types.TensorLike", "line_number": 167, "usage_type": "attribute"}, {"api_name": "mmv.models.types", "line_number": 167, "usage_type": "name"}, {"api_name": "jax.numpy.ndarray", "line_number": 168, "usage_type": "attribute"}, {"api_name": "jax.numpy", "line_number": 168, "usage_type": "name"}, {"api_name": "haiku.Module", "line_number": 194, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 209, "usage_type": "name"}, {"api_name": "mmv.models.types.NormalizeFn", "line_number": 209, "usage_type": "attribute"}, {"api_name": "mmv.models.types", "line_number": 209, "usage_type": "name"}, {"api_name": "mmv.models.types.TensorLike", "line_number": 258, "usage_type": "attribute"}, {"api_name": "mmv.models.types", "line_number": 258, "usage_type": "name"}, {"api_name": "mmv.models.tsm_utils.prepare_inputs", "line_number": 277, "usage_type": "call"}, {"api_name": "mmv.models.tsm_utils", "line_number": 277, "usage_type": "name"}, {"api_name": "haiku.Conv2D", "line_number": 286, "usage_type": "call"}, {"api_name": "haiku.MaxPool", "line_number": 294, "usage_type": "call"}, {"api_name": "jax.nn.relu", "line_number": 321, "usage_type": "call"}, {"api_name": "jax.nn", "line_number": 321, "usage_type": "attribute"}, {"api_name": "jax.numpy.mean", "line_number": 326, "usage_type": "call"}, {"api_name": "jax.numpy", "line_number": 326, "usage_type": "name"}, {"api_name": "mmv.models.tsm_utils.prepare_outputs", "line_number": 328, "usage_type": "call"}, {"api_name": "mmv.models.tsm_utils", "line_number": 328, "usage_type": "name"}, {"api_name": "jax.numpy.ndarray", "line_number": 260, "usage_type": "attribute"}, {"api_name": "jax.numpy", "line_number": 260, "usage_type": "name"}]} +{"seq_id": "25478639784", "text": "import os\nimport pandas as pd\nfrom sklearn import preprocessing, metrics\nimport lightgbm as lgb\n\nimport category_encoders as ce\nimport itertools\n\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.feature_selection import SelectFromModel\n\n# Load data\nscript_dir = os.path.dirname(__file__)\nrel_path = \"train_sample.csv\"\nfilepath = os.path.join(script_dir, rel_path)\nclick_data = pd.read_csv(filepath, parse_dates=['click_time'])\nclick_times = click_data['click_time']\nclicks = click_data.assign(day=click_times.dt.day.astype('uint8'),\n hour=click_times.dt.hour.astype('uint8'),\n minute=click_times.dt.minute.astype('uint8'),\n second=click_times.dt.second.astype('uint8'))\n\n# Label encoding for categorical features\ncat_features = ['ip', 'app', 'device', 'os', 'channel']\nfor feature in cat_features:\n label_encoder = preprocessing.LabelEncoder()\n clicks[feature] = label_encoder.fit_transform(clicks[feature])\n\n# Dataset split helper function\ndef get_data_splits(dataframe, valid_fraction=0.1):\n \"\"\" Splits a dataframe into train, validation, and test sets. First, orders by \n the column 'click_time'. Set the size of the validation and test sets with\n the valid_fraction keyword argument.\n \"\"\"\n dataframe = dataframe.sort_values('click_time')\n valid_rows = int(len(dataframe) * valid_fraction)\n train = dataframe[:-valid_rows * 2]\n valid = dataframe[-valid_rows * 2:-valid_rows]\n test = dataframe[-valid_rows:]\n \n return train, valid, test\n\n# LGBM Train + Eval helper functions\ndef train_model(train, valid, test=None, feature_cols=None):\n if feature_cols is None:\n feature_cols = train.columns.drop(['click_time', 'attributed_time',\n 'is_attributed'])\n dtrain = lgb.Dataset(train[feature_cols], label=train['is_attributed'])\n dvalid = lgb.Dataset(valid[feature_cols], label=valid['is_attributed'])\n \n param = {'num_leaves': 64, 'objective': 'binary', \n 'metric': 'auc', 'seed': 7}\n num_round = 1000\n print(\"Training model!\")\n bst = lgb.train(param, dtrain, num_round, valid_sets=[dvalid], \n early_stopping_rounds=20, verbose_eval=False)\n \n valid_pred = bst.predict(valid[feature_cols])\n valid_score = metrics.roc_auc_score(valid['is_attributed'], valid_pred)\n print(f\"Validation AUC score: {valid_score}\")\n \n if test is not None: \n test_pred = bst.predict(test[feature_cols])\n test_score = metrics.roc_auc_score(test['is_attributed'], test_pred)\n return bst, valid_score, test_score\n else:\n return bst, valid_score\n\nprint(\"Baseline model\")\ntrain, valid, test = get_data_splits(clicks)\n_ = train_model(train, valid)\n\"\"\"\nprint(\"CountEncoder model\")\n# Create new columns in clicks using encoder\ncat_features = ['ip', 'app', 'device', 'os', 'channel']\ntrain, valid, test = get_data_splits(clicks)\n# create and train CountEncoder on categorcal columns\ncount_enc = ce.CountEncoder(cols=cat_features)\ncount_enc.fit(train[cat_features])\n# Apply encoding to the train and validation sets as new columns\ntrain_encoded = train.join(count_enc.transform(train[cat_features]).add_suffix('_count'))\nvalid_encoded = valid.join(count_enc.transform(valid[cat_features]).add_suffix('_count'))\n# Train the model on the count encoded datasets\n_ = train_model(train_encoded, valid_encoded)\n\nprint(\"TargetEncoder model\")\n# Remove 'ip' column to improve TargetEncoder and CatBoostEncoder results\ncat_features = ['app', 'device', 'os', 'channel']\ntrain, valid, test = get_data_splits(clicks)\n# Create the target encoder. You can find this easily by using tab completion.\ntarget_enc = ce.TargetEncoder(cols=cat_features)\ntarget_enc.fit(train[cat_features], train['is_attributed'])\n# Apply encoding to the train and validation sets as new columns\ntrain_encoded = train.join(target_enc.transform(train[cat_features]).add_suffix('_target'))\nvalid_encoded = valid.join(target_enc.transform(valid[cat_features]).add_suffix('_target'))\n# Train model on target encoded datasets\n_ = train_model(train_encoded, valid_encoded)\n\nprint(\"CatBoostEncoder model\")\n# Do the same for CatBoostEncoder\ntrain, valid, test = get_data_splits(clicks)\n# Create the CatBoost encoder\ncb_enc = ce.CatBoostEncoder(cols=cat_features)\n# Learn encoding from the training set\ncb_enc.fit(train[cat_features], train['is_attributed'])\n# Apply encoding to the train and validation sets as new columns\ntrain_encoded = train.join(cb_enc.transform(train[cat_features]).add_suffix('_cb'))\nvalid_encoded = valid.join(cb_enc.transform(valid[cat_features]).add_suffix('_cb'))\n# Train model on CatBoost encoded datasets\n_ = train_model(train_encoded, valid_encoded)\n\"\"\"\n\n# Feature generation based on interactions\ncat_features = ['ip', 'app', 'device', 'os', 'channel']\ninteractions = pd.DataFrame(index=clicks.index)\n# Iterate through each pair of features, combine them into interaction features\nfor c1, c2 in itertools.combinations(cat_features, 2):\n new_col_name = '_'.join([c1, c2])\n new_values = clicks[c1].map(str) + \"_\" + clicks[c2].map(str)\n encoder = preprocessing.LabelEncoder()\n interactions[new_col_name] = encoder.fit_transform(new_values)\n# combine interaction features with clicks dataset\nclicks = clicks.join(interactions)\nprint(\"Score with interactions\")\ntrain, valid, test = get_data_splits(clicks)\n_ = train_model(train, valid)\n\n\"\"\"\n# Generate numerical features based on rolling window\n# Number of events in the past X hours\ndef count_past_events(series, window='6H'):\n # Returns a series that counts the number of events in the past 6 hours \n series = pd.Series(series.index, index=series)\n past_events = series.rolling(window).count() - 1\n return past_events\nclicks['ip_past_6H_counts'] = count_past_events(clicks['click_time'])\nprint(\"Score with rolling window\")\ntrain, valid, test = get_data_splits(clicks)\n_ = train_model(train, valid, test)\n\"\"\"\n# Number of previous app downloads\ndef previous_attributions(series):\n \"\"\" Returns a series with the rolling sum of target series since current row \"\"\"\n sums = series.expanding(min_periods=2).sum() - series\n return sums\nclicks['ip_past_6hr_counts'] = previous_attributions(clicks['is_attributed'])\n# split and train model on new features: interactions, past counts, time since last, rolling sum\nprint(\"Score with running total sum\")\ntrain, valid, test = get_data_splits(clicks)\n_ = train_model(train, valid, test)\n\n# Feature selection\nfeature_cols = clicks.columns.drop(['click_time', 'attributed_time', 'is_attributed'])\ntrain, valid, test = get_data_splits(clicks)\n# Create the selector, keeping 40 features\nselector = SelectKBest(f_classif, 40)\n# Use the selector to retrieve the best features\nX_new = selector.fit_transform(train[feature_cols], train['is_attributed'])\n# Get back the kept features as a DataFrame with dropped columns as all 0s\nselected_features = pd.DataFrame(selector.inverse_transform(X_new),\n index=train.index,\n columns=feature_cols)\n# Find the columns that were dropped\ndropped_columns = selected_features.columns[selected_features.var() == 0]\n# Train model after dropping less-predictive features\n_ = train_model(train.drop(dropped_columns, axis=1), \n valid.drop(dropped_columns, axis=1),\n test.drop(dropped_columns, axis=1))\n\n# Feature selection with L1 regularization\ndef select_features_l1(X, y):\n \"\"\" Return selected features using logistic regression with an L1 penalty \"\"\"\n model = LogisticRegression(penalty='l1', random_state=7, C=0.1).fit(X, y)\n model = SelectFromModel(model, prefit=True)\n X_new = model.transform(X)\n selected_features = pd.DataFrame(model.inverse_transform(X_new), \n index=X.index,\n columns=X.columns)\n cols_to_keep = selected_features.columns[selected_features.var() != 0]\n return cols_to_keep \n", "repo_name": "leblancdaniel/sandbox", "sub_path": "tutorials/ML/feature_engineering.py", "file_name": "feature_engineering.py", "file_ext": "py", "file_size_in_byte": 8067, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "37", "api": [{"api_name": "os.path.dirname", "line_number": 13, "usage_type": "call"}, {"api_name": "os.path", "line_number": 13, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 15, "usage_type": "call"}, {"api_name": "os.path", "line_number": 15, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 16, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.LabelEncoder", "line_number": 26, "usage_type": "call"}, {"api_name": "sklearn.preprocessing", "line_number": 26, "usage_type": "name"}, {"api_name": "lightgbm.Dataset", "line_number": 48, "usage_type": "call"}, {"api_name": "lightgbm.Dataset", "line_number": 49, "usage_type": "call"}, {"api_name": "lightgbm.train", "line_number": 55, "usage_type": "call"}, {"api_name": "sklearn.metrics.roc_auc_score", "line_number": 59, "usage_type": "call"}, {"api_name": "sklearn.metrics", "line_number": 59, "usage_type": "name"}, {"api_name": "sklearn.metrics.roc_auc_score", "line_number": 64, "usage_type": "call"}, {"api_name": "sklearn.metrics", "line_number": 64, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 115, "usage_type": "call"}, {"api_name": "itertools.combinations", "line_number": 117, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.LabelEncoder", "line_number": 120, "usage_type": "call"}, {"api_name": "sklearn.preprocessing", "line_number": 120, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 160, "usage_type": "call"}, {"api_name": "sklearn.linear_model.LogisticRegression", "line_number": 173, "usage_type": "call"}, {"api_name": "sklearn.feature_selection.SelectFromModel", "line_number": 174, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 176, "usage_type": "call"}]} +{"seq_id": "75192482026", "text": "# Takes three parameters from the cli - github username, repo name, and api\n# token\n\nimport sys\nimport csv\nimport json\nimport urllib\n\nif len(sys.argv) == 5:\n USERNAME = sys.argv[1]\n ORGNAME = sys.argv[2]\n PROJECT = sys.argv[3]\n AUTH_TOKEN = sys.argv[4]\n\nelse:\n USERNAME = sys.argv[1]\n ORGNAME = ''\n PROJECT = sys.argv[2]\n AUTH_TOKEN = sys.argv[3]\n\nTRAC_URL = 'https://addons.omeka.org/trac/report/1?format=csv'\n\nif (ORGNAME == ''):\n ORGNAME = USERNAME\n\ngithub_url = 'https://github.com/api/v2/json/issues/'\ncsv_data = urllib.urlopen(TRAC_URL)\nreader = csv.DictReader(csv_data)\ntickets = []\n\nurl = github_url + 'list/%s/%s/open' % (ORGNAME, PROJECT)\nresponse = urllib.urlopen(url)\ncontent = response.read()\nissues = json.loads(content)['issues']\n\nfor row in reader:\n if row['component'] == PROJECT:\n for key, value in row.items():\n row[key] = row[key].decode('utf-8')\n\n if filter(lambda i: i['title'] == row['summary'], issues):\n continue\n\n tickets.append({\n 'title': row['summary'],\n 'description': row['_description'],\n 'tags': [u'ime', row['type'], row['component']],\n })\n\nfor ticket in tickets:\n url = github_url + 'open/%s/%s' % (ORGNAME, PROJECT)\n data = urllib.urlencode({\n 'login': USERNAME,\n 'token': AUTH_TOKEN,\n 'title': ticket['title'],\n 'body': ticket['description'],\n })\n\n urllib.urlopen(github_url, data)\n response = urllib.urlopen(url, data)\n content = response.read()\n\n try:\n issue = json.loads(content)['issue']\n except KeyError:\n raise Exception(content)\n\n data = urllib.urlencode({\n 'login': USERNAME,\n 'token': AUTH_TOKEN,\n })\n\n for tag in ticket['tags']:\n url = github_url + 'label/add/%s/%s/%s/%s' % (\n ORGNAME, PROJECT, tag, issue['number'])\n urllib.urlopen(url, data)\n\n", "repo_name": "scholarslab/omeka-svn-converter", "sub_path": "convert.py", "file_name": "convert.py", "file_ext": "py", "file_size_in_byte": 1920, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "37", "api": [{"api_name": "sys.argv", "line_number": 9, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 10, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 11, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 12, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 13, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 16, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 18, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 19, "usage_type": "attribute"}, {"api_name": "urllib.urlopen", "line_number": 27, "usage_type": "call"}, {"api_name": "csv.DictReader", "line_number": 28, "usage_type": "call"}, {"api_name": "urllib.urlopen", "line_number": 32, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 34, "usage_type": "call"}, {"api_name": "urllib.urlencode", "line_number": 52, "usage_type": "call"}, {"api_name": "urllib.urlopen", "line_number": 59, "usage_type": "call"}, {"api_name": "urllib.urlopen", "line_number": 60, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 64, "usage_type": "call"}, {"api_name": "urllib.urlencode", "line_number": 68, "usage_type": "call"}, {"api_name": "urllib.urlopen", "line_number": 76, "usage_type": "call"}]} +{"seq_id": "5249646177", "text": "from __future__ import annotations\n\nfrom hypothesis import given, infer, strategies as st\nfrom typing import List, Union\nfrom jterritory.types import String\nfrom jterritory.query.sort import Comparator\n\n\n@given(\n l=st.one_of(\n st.lists(st.booleans()),\n st.lists(st.integers() | st.floats(allow_nan=False)),\n ),\n reverse=infer,\n)\ndef test_sortkey(l: Union[List[bool], List[Union[int, float]]], reverse: bool) -> None:\n sort = Comparator(property=String(\"foo\"), is_ascending=not reverse).compile()\n assert sorted(l, reverse=reverse) == sorted(l, key=sort.key)\n\n\n@given(l=infer, reverse=infer)\ndef test_sortkey_strings(l: List[str], reverse: bool) -> None:\n sort = Comparator(property=String(\"foo\"), is_ascending=not reverse).compile()\n assert sorted(l, key=str.casefold, reverse=reverse) == sorted(l, key=sort.key)\n", "repo_name": "jameysharp/jterritory", "sub_path": "tests/query/test_sort.py", "file_name": "test_sort.py", "file_ext": "py", "file_size_in_byte": 848, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "37", "api": [{"api_name": "typing.Union", "line_number": 16, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 16, "usage_type": "name"}, {"api_name": "jterritory.query.sort.Comparator", "line_number": 17, "usage_type": "call"}, {"api_name": "jterritory.types.String", "line_number": 17, "usage_type": "call"}, {"api_name": "hypothesis.given", "line_number": 9, "usage_type": "call"}, {"api_name": "hypothesis.strategies.one_of", "line_number": 10, "usage_type": "call"}, {"api_name": "hypothesis.strategies", "line_number": 10, "usage_type": "name"}, {"api_name": "hypothesis.strategies.lists", "line_number": 11, "usage_type": "call"}, {"api_name": "hypothesis.strategies", "line_number": 11, "usage_type": "name"}, {"api_name": "hypothesis.strategies.booleans", "line_number": 11, "usage_type": "call"}, {"api_name": "hypothesis.strategies.lists", "line_number": 12, "usage_type": "call"}, {"api_name": "hypothesis.strategies", "line_number": 12, "usage_type": "name"}, {"api_name": "hypothesis.strategies.integers", "line_number": 12, "usage_type": "call"}, {"api_name": "hypothesis.strategies.floats", "line_number": 12, "usage_type": "call"}, {"api_name": "hypothesis.infer", "line_number": 14, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 22, "usage_type": "name"}, {"api_name": "jterritory.query.sort.Comparator", "line_number": 23, "usage_type": "call"}, {"api_name": "jterritory.types.String", "line_number": 23, "usage_type": "call"}, {"api_name": "hypothesis.given", "line_number": 21, "usage_type": "call"}, {"api_name": "hypothesis.infer", "line_number": 21, "usage_type": "name"}]} +{"seq_id": "6297538458", "text": "# coding: utf-8\n\nimport sys\nfrom setuptools import setup, find_packages\n\nNAME = \"swagger_server\"\nVERSION = \"1.0.0\"\n\n# To install the library, run the following\n#\n# python setup.py install\n#\n# prerequisite: setuptools\n# http://pypi.python.org/pypi/setuptools\n\nREQUIRES = [\"connexion\"]\n\nsetup(\n name=NAME,\n version=VERSION,\n description=\"mywordcloud API\",\n author_email=\"bkmy43@googlemail.com\",\n url=\"\",\n keywords=[\"Swagger\", \"mywordcloud API\"],\n install_requires=REQUIRES,\n packages=find_packages(),\n package_data={'': ['swagger/swagger.yaml']},\n include_package_data=True,\n entry_points={\n 'console_scripts': ['swagger_server=swagger_server.__main__:main']},\n long_description=\"\"\"\\\n Backend API for mywordcloud project of ReDI School at disruptberlin2017 hackaton\n \"\"\"\n)\n\n", "repo_name": "bkmy43/mywordcloud", "sub_path": "backend/python-flask-server/setup.py", "file_name": "setup.py", "file_ext": "py", "file_size_in_byte": 821, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "setuptools.setup", "line_number": 18, "usage_type": "call"}, {"api_name": "setuptools.find_packages", "line_number": 26, "usage_type": "call"}]} +{"seq_id": "44516654798", "text": "from django.conf.urls import url\nfrom django.contrib import admin\nfrom blog import views\nfrom django.conf.urls.static import static\nfrom django.conf import settings\n\napp_name = 'blog'\nurlpatterns = [\n url(r'^index/$', views.index, name = 'index'),\n url(r'^article/(?P[0-9]+)$', views.article_page, name='article_page'),\n url(r'^edit/(?P[0-9]+)$', views.edit_page, name='edit_page'),\n url(r'^edit/action/$', views.edit_action,name='edit_action'),\n url(r'^uploadImg/$', views.uploadImg),\n url(r'^showImg/$', views.showImg, name = 'showImg'),\n\n #登录\n url(r'^denglu/$',views.denglu,name = 'denglu'),\n #退出\n url(r'^dengchu/$',views.dengchu,name = 'dengchu'),\n #注册\n url(r'^zhuce/$',views.zhuce, name = 'zhuce'),\n #个人中心\n url(r'^user_center/$',views.user_center, name = 'user_center'),\n #编辑个人信息\n url(r'^user_center/edit_profile/$',views.edit_profile, name = 'edit_profile'),\n #修改密码\n url(r'^user_center/change_password/$',views.change_password, name = 'change_password'),\n\n]+ static(settings.MEDIA_URL, document_root=settings.MEDIA_ROOT)\n", "repo_name": "dengdeng-a/repo2", "sub_path": "myblog/blog/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 1146, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "django.conf.urls.url", "line_number": 9, "usage_type": "call"}, {"api_name": "blog.views.index", "line_number": 9, "usage_type": "attribute"}, {"api_name": "blog.views", "line_number": 9, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 10, "usage_type": "call"}, {"api_name": "blog.views.article_page", "line_number": 10, "usage_type": "attribute"}, {"api_name": "blog.views", "line_number": 10, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 11, "usage_type": "call"}, {"api_name": "blog.views.edit_page", "line_number": 11, "usage_type": "attribute"}, {"api_name": "blog.views", "line_number": 11, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 12, "usage_type": "call"}, {"api_name": "blog.views.edit_action", "line_number": 12, "usage_type": "attribute"}, {"api_name": "blog.views", "line_number": 12, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 13, "usage_type": "call"}, {"api_name": "blog.views.uploadImg", "line_number": 13, "usage_type": "attribute"}, {"api_name": "blog.views", "line_number": 13, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 14, "usage_type": "call"}, {"api_name": "blog.views.showImg", "line_number": 14, "usage_type": "attribute"}, {"api_name": "blog.views", "line_number": 14, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 17, "usage_type": "call"}, {"api_name": "blog.views.denglu", "line_number": 17, "usage_type": "attribute"}, {"api_name": "blog.views", "line_number": 17, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 19, "usage_type": "call"}, {"api_name": "blog.views.dengchu", "line_number": 19, "usage_type": "attribute"}, {"api_name": "blog.views", "line_number": 19, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 21, "usage_type": "call"}, {"api_name": "blog.views.zhuce", "line_number": 21, "usage_type": "attribute"}, {"api_name": "blog.views", "line_number": 21, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 23, "usage_type": "call"}, {"api_name": "blog.views.user_center", "line_number": 23, "usage_type": "attribute"}, {"api_name": "blog.views", "line_number": 23, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 25, "usage_type": "call"}, {"api_name": "blog.views.edit_profile", "line_number": 25, "usage_type": "attribute"}, {"api_name": "blog.views", "line_number": 25, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 27, "usage_type": "call"}, {"api_name": "blog.views.change_password", "line_number": 27, "usage_type": "attribute"}, {"api_name": "blog.views", "line_number": 27, "usage_type": "name"}, {"api_name": "django.conf.urls.static.static", "line_number": 29, "usage_type": "call"}, {"api_name": "django.conf.settings.MEDIA_URL", "line_number": 29, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 29, "usage_type": "name"}, {"api_name": "django.conf.settings.MEDIA_ROOT", "line_number": 29, "usage_type": "attribute"}]} +{"seq_id": "33788722288", "text": "import cv2\nfrom cv2 import CAP_PROP_XI_IMAGE_DATA_BIT_DEPTH\nimport numpy as np\nimport imgcompare\nfrom preprocessing import cropImageRoi\nfrom parameter.setting import Parameter\n\n\nclass DifferenceFrame(object):\n def caculate_diff_frame(self, camera_id, camera_name, image1, image2, cordinate):\n \"\"\"\n This function used to caculate difference degree between two frame\n \"\"\"\n path_result = f\"test\\\\{camera_name}\\\\test\\\\result.png\"\n path_test = f\"test\\\\{camera_name}\\\\test\\\\test.png\"\n\n percentage = float(1)\n try: \n result = cropImageRoi(cropImageRoi(image1, Parameter[camera_id][\"roi\"][0]), cordinate)\n test = cropImageRoi(cropImageRoi(image2, Parameter[camera_id][\"roi\"][0]), cordinate)\n\n cv2.imwrite(path_result, result)\n cv2.imwrite(path_test, test)\n percentage = imgcompare.image_diff_percent(path_result, path_test)\n except Exception as e:\n print(\"Error open image\", str(e))\n pass\n return percentage\n\n \nname = \"CAM_DBP\" \n# name_slit = name.split(\"_\")\nprint(name.split(\"_\")[-1].lower())", "repo_name": "NTN-hacker/phat-hien-lan-chiem", "sub_path": "procedure/diff_frame.py", "file_name": "diff_frame.py", "file_ext": "py", "file_size_in_byte": 1136, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "preprocessing.cropImageRoi", "line_number": 19, "usage_type": "call"}, {"api_name": "parameter.setting.Parameter", "line_number": 19, "usage_type": "name"}, {"api_name": "preprocessing.cropImageRoi", "line_number": 20, "usage_type": "call"}, {"api_name": "parameter.setting.Parameter", "line_number": 20, "usage_type": "name"}, {"api_name": "cv2.imwrite", "line_number": 22, "usage_type": "call"}, {"api_name": "cv2.imwrite", "line_number": 23, "usage_type": "call"}, {"api_name": "imgcompare.image_diff_percent", "line_number": 24, "usage_type": "call"}]} +{"seq_id": "20938968959", "text": "import sublime\nimport os\nimport json\n\nPACKAGE_PATH = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))\nPACKAGE_NAME = os.path.basename(PACKAGE_PATH)\nSUBLIME_PACKAGES_PATH = os.path.dirname(PACKAGE_PATH)\n\nNODE_VERSION = ''\n\nwith open(os.path.join(PACKAGE_PATH, 'package.json'), 'r') as packageJson:\n pkg = json.loads(packageJson.read())\n try:\n NODE_VERSION = \"v\" + pkg[\"create-sublime-plugin-js\"][\"node\"]\n except Exception as e:\n print(e)\n \nif NODE_VERSION == '':\n raise Exception(\"No node version specified in the \" + os.path.join(PACKAGE_PATH, 'package.json') + \" file!\")\n\nNODE_PATH = os.path.join( PACKAGE_PATH, 'node', os.path.join('node-' + NODE_VERSION + '-win-' + sublime.arch(), 'node.exe') if sublime.platform() == \"windows\" else os.path.join('bin', 'node') )\nNPM_PATH = os.path.join( PACKAGE_PATH, 'node', os.path.join('node-' + NODE_VERSION + '-win-' + sublime.arch(), 'npm.cmd') if sublime.platform() == \"windows\" else os.path.join('bin', 'npm') )\n\nNODE_SERVER_PATH = os.path.join(PACKAGE_PATH, 'server.js')\nURL_NODE_SERVER = \"\"\nHEADERS_NODE_SERVER = {'content-type': 'application/json'}\n\nVARIABLE_MAPPING = {}", "repo_name": "pichillilorenzo/create-sublime-plugin-js", "sub_path": "pylib/global_vars.py", "file_name": "global_vars.py", "file_ext": "py", "file_size_in_byte": 1142, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "37", "api": [{"api_name": "os.path.dirname", "line_number": 5, "usage_type": "call"}, {"api_name": "os.path", "line_number": 5, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 5, "usage_type": "call"}, {"api_name": "os.path.basename", "line_number": 6, "usage_type": "call"}, {"api_name": "os.path", "line_number": 6, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 7, "usage_type": "call"}, {"api_name": "os.path", "line_number": 7, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 11, "usage_type": "call"}, {"api_name": "os.path", "line_number": 11, "usage_type": "attribute"}, {"api_name": "json.loads", "line_number": 12, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 19, "usage_type": "call"}, {"api_name": "os.path", "line_number": 19, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 21, "usage_type": "call"}, {"api_name": "os.path", "line_number": 21, "usage_type": "attribute"}, {"api_name": "sublime.platform", "line_number": 21, "usage_type": "call"}, {"api_name": "sublime.arch", "line_number": 21, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 22, "usage_type": "call"}, {"api_name": "os.path", "line_number": 22, "usage_type": "attribute"}, {"api_name": "sublime.platform", "line_number": 22, "usage_type": "call"}, {"api_name": "sublime.arch", "line_number": 22, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 24, "usage_type": "call"}, {"api_name": "os.path", "line_number": 24, "usage_type": "attribute"}]} +{"seq_id": "1438182415", "text": "from oslo_db import options as db_options\nfrom oslo_db.sqlalchemy import models\nfrom oslo_db.sqlalchemy import types as db_types\nimport six.moves.urllib.parse as urlparse\nfrom sqlalchemy.ext.declarative import declarative_base\nfrom sqlalchemy import Column, String, Integer, Boolean, ForeignKey, Index\nfrom sqlalchemy import orm\nfrom sqlalchemy import schema\nfrom sqlalchemy import Text\n\n\nfrom kongming.common import paths\nfrom kongming.conf import CONF\n\n\n_DEFAULT_SQL_CONNECTION = 'sqlite:///' + paths.state_path_def('kongming.sqlite')\ndb_options.set_defaults(CONF, connection=_DEFAULT_SQL_CONNECTION)\n\n\ndef table_args():\n engine_name = urlparse.urlparse(CONF.database.connection).scheme\n if engine_name == 'mysql':\n return {'mysql_engine': CONF.database.mysql_engine,\n 'mysql_charset': \"utf8\"}\n return None\n\n\nclass KongmingBase(models.TimestampMixin, models.ModelBase):\n metadata = None\n\n def as_dict(self):\n d = {}\n for c in self.__table__.columns:\n d[c.name] = self[c.name]\n return d\n\n\nBase = declarative_base(cls=KongmingBase)\n\n\nclass InstanceCPUMapping(Base):\n \"\"\"Represents the InstanceCPUMapping.\"\"\"\n\n __tablename__ = 'instance_cpu_mappings'\n __table_args__ = (\n schema.UniqueConstraint('instance_uuid',\n name='uniq_mappings0instance_uuid'),\n table_args()\n )\n\n id = Column(Integer, primary_key=True)\n instance_uuid = Column(String(36), nullable=False)\n project_id = Column(String(36), nullable=False)\n user_id = Column(String(36), nullable=False)\n host = Column(String(255), nullable=True)\n status = Column(String(255), nullable=True)\n cpu_mappings = Column(String(255), nullable=True)\n\n\nclass Hosts(Base):\n \"\"\"Represents the Host.\"\"\"\n\n __tablename__ = 'hosts'\n __table_args__ = (\n schema.UniqueConstraint('host_name',\n name='uniq_hosts0host_name'),\n table_args()\n )\n\n id = Column(Integer, primary_key=True)\n host_name = Column(String(255), nullable=True)\n cpu_topology = Column(db_types.JsonEncodedDict)\n\n\nclass Instance(Base):\n \"\"\"Represents the Instance.\"\"\"\n\n __tablename__ = 'instances'\n __table_args__ = (\n schema.UniqueConstraint('uuid',\n name='uniq_instances0uuid'),\n table_args()\n )\n\n uuid = Column(String(36), primary_key=True, nullable=False)\n host = Column(String(255), nullable=True)\n status = Column(String(255), nullable=True)\n cpu_mappings = Column(db_types.JsonEncodedDict)\n host_ = orm.relationship(\n Hosts,\n backref=orm.backref('instances', uselist=True),\n foreign_keys=host,\n primaryjoin='Hosts.host_name == Instance.host')\n", "repo_name": "ZhengZhenyu/KongMing", "sub_path": "kongming/db/sqlalchemy/models.py", "file_name": "models.py", "file_ext": "py", "file_size_in_byte": 2764, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "37", "api": [{"api_name": "kongming.common.paths.state_path_def", "line_number": 16, "usage_type": "call"}, {"api_name": "kongming.common.paths", "line_number": 16, "usage_type": "name"}, {"api_name": "oslo_db.options.set_defaults", "line_number": 17, "usage_type": "call"}, {"api_name": "kongming.conf.CONF", "line_number": 17, "usage_type": "argument"}, {"api_name": "oslo_db.options", "line_number": 17, "usage_type": "name"}, {"api_name": "six.moves.urllib.parse.urlparse", "line_number": 21, "usage_type": "call"}, {"api_name": "six.moves.urllib.parse", "line_number": 21, "usage_type": "name"}, {"api_name": "kongming.conf.CONF.database", "line_number": 21, "usage_type": "attribute"}, {"api_name": "kongming.conf.CONF", "line_number": 21, "usage_type": "name"}, {"api_name": "kongming.conf.CONF.database", "line_number": 23, "usage_type": "attribute"}, {"api_name": "kongming.conf.CONF", "line_number": 23, "usage_type": "name"}, {"api_name": "oslo_db.sqlalchemy.models.TimestampMixin", "line_number": 28, "usage_type": "attribute"}, {"api_name": "oslo_db.sqlalchemy.models", "line_number": 28, "usage_type": "name"}, {"api_name": "oslo_db.sqlalchemy.models.ModelBase", "line_number": 28, "usage_type": "attribute"}, {"api_name": "sqlalchemy.ext.declarative.declarative_base", "line_number": 38, "usage_type": "call"}, {"api_name": "sqlalchemy.schema.UniqueConstraint", "line_number": 46, "usage_type": "call"}, {"api_name": "sqlalchemy.schema", "line_number": 46, "usage_type": "name"}, {"api_name": "sqlalchemy.Column", "line_number": 51, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 51, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 52, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 52, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 53, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 53, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 54, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 54, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 55, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 55, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 56, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 56, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 57, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 57, "usage_type": "call"}, {"api_name": "sqlalchemy.schema.UniqueConstraint", "line_number": 65, "usage_type": "call"}, {"api_name": "sqlalchemy.schema", "line_number": 65, "usage_type": "name"}, {"api_name": "sqlalchemy.Column", "line_number": 70, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 70, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 71, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 71, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 72, "usage_type": "call"}, {"api_name": "oslo_db.sqlalchemy.types.JsonEncodedDict", "line_number": 72, "usage_type": "attribute"}, {"api_name": "oslo_db.sqlalchemy.types", "line_number": 72, "usage_type": "name"}, {"api_name": "sqlalchemy.schema.UniqueConstraint", "line_number": 80, "usage_type": "call"}, {"api_name": "sqlalchemy.schema", "line_number": 80, "usage_type": "name"}, {"api_name": "sqlalchemy.Column", "line_number": 85, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 85, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 86, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 86, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 87, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 87, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 88, "usage_type": "call"}, {"api_name": "oslo_db.sqlalchemy.types.JsonEncodedDict", "line_number": 88, "usage_type": "attribute"}, {"api_name": "oslo_db.sqlalchemy.types", "line_number": 88, "usage_type": "name"}, {"api_name": "sqlalchemy.orm.relationship", "line_number": 89, "usage_type": "call"}, {"api_name": "sqlalchemy.orm", "line_number": 89, "usage_type": "name"}, {"api_name": "sqlalchemy.orm.backref", "line_number": 91, "usage_type": "call"}, {"api_name": "sqlalchemy.orm", "line_number": 91, "usage_type": "name"}]} +{"seq_id": "573549173", "text": "from selenium import webdriver\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.support import expected_conditions as EC\nfrom selenium.webdriver.support.wait import WebDriverWait\n\noptions = webdriver.ChromeOptions()\noptions.add_argument('--start-maximized')\n\ndriver = webdriver.Chrome(options=options)\ndriver.get('https://rahulshettyacademy.com/angularpractice/')\n\ndriver.find_element_by_css_selector(\"a[href*='shop']\").click()\n# Get all the product card elements and store them in a products list\nproducts = driver.find_elements_by_xpath(\"//div[@class='card h-100']\")\n\n# Loop through the products list\nfor product in products:\n # Traverse through the children of the product element until we get to the link\n product_name = product.find_element_by_xpath(\"div/h4/a\").text\n # We only want to add the Blackberry item to the cart\n if product_name == 'Blackberry':\n # Add item to cart - find the button from the parent element and click it to add to cart\n product.find_element_by_xpath(\"div/button\").click()\n\n# Find the checkout button and click it\ndriver.find_element_by_css_selector(\"a[class*='btn-primary']\").click()\n\n# Click the Checkout button in the checkout page\ndriver.find_element_by_xpath(\"//button[@class='btn btn-success']\").click()\n\n# Type partial country name in the dropdown to load dynamic auto-suggestive options\ndriver.find_element_by_id(\"country\").send_keys(\"Uni\")\n# It takes time for the list of countries to load, so we need to do an explicit wait here\nwait = WebDriverWait(driver, 10)\n# Wait until the relevant link is present and then click it\nwait.until(EC.presence_of_element_located((By.LINK_TEXT, \"United Kingdom\")))\ndriver.find_element_by_link_text(\"United Kingdom\").click()\n# Check the T&Cs checkbox\ndriver.find_element_by_xpath(\"//div[@class='checkbox checkbox-primary']\").click()\n# Click the purchase button\ndriver.find_element_by_css_selector(\"[type='submit']\").click()\n# Get the text of the success message\nsuccess_text = driver.find_element_by_class_name(\"alert-success\").text\n# Verify the content of the success message to ensure the process has been a success\nassert \"Success! Thank you!\" in success_text\n\n# Get a screenshot of the page - saves it into the current directory unless specified\ndriver.get_screenshot_as_file(\"screen.png\")\n\ndriver.close()\n", "repo_name": "hebs87/selenium_tutorial", "sub_path": "test_framework/e2e_test.py", "file_name": "e2e_test.py", "file_ext": "py", "file_size_in_byte": 2329, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "selenium.webdriver.ChromeOptions", "line_number": 6, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 6, "usage_type": "name"}, {"api_name": "selenium.webdriver.Chrome", "line_number": 9, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 9, "usage_type": "name"}, {"api_name": "selenium.webdriver.support.wait.WebDriverWait", "line_number": 34, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.expected_conditions.presence_of_element_located", "line_number": 36, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.expected_conditions", "line_number": 36, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.LINK_TEXT", "line_number": 36, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 36, "usage_type": "name"}]} +{"seq_id": "2347251844", "text": "from __future__ import division\nimport matplotlib.pyplot as plt\nimport pandas as pd\nimport datetime\nimport utility\nimport descartes\nimport os\nimport numpy as np\n\n\nmacs = [\"00:0c:e7:4f:38:a5\", \"84:38:38:f6:58:40\", \"c0:ee:fb:72:0c:27\", \"18:dc:56:8c:27:56\", \"80:58:f8:d8:ad:e1\"]\nnames = [\"Micromax\", \"Samsung S5\", \"oneplus x\", \"Yureka\", \"Moto\"]\n# factor to convert to inches (doesn't have any effect on the nature of plots as everything is just scaled up)\nfactor = 1\n\naps = {\n 1: (-22 * factor, 1 * factor),\n 2: (0 * factor, 1 * factor),\n 3: (0 * factor, 24 * factor),\n 4: (-22 * factor, 26 * factor)\n}\n\n\nfig = plt.figure(1)\nax = fig.add_subplot(1, 1, 1)\nax.set_title(\"Heurisitic 3\")\nax.autoscale()\n\naps_plot, = ax.plot([-22, 0, 0, -22], [1, 1, 24, 26], marker='o', markersize=10, ls='')\nvalidation, = ax.plot([], [], marker='o', markersize=3, color='g')\npath, = ax.plot([], [], marker='+', markersize=3, color='red')\n\n\ndef plot_circles(circles, mac, ts):\n ax.cla()\n ax.set_title(mac + '\\n' + str(ts))\n\n for circle in circles:\n print(circle)\n c = plt.Circle(circle[0], circle[1], color='b', fill=False)\n ax.add_artist(c)\n\n\ndef update(obj, x, y):\n x_old = obj.get_xdata()\n y_old = obj.get_ydata()\n x_old = np.append(x_old, x)\n y_old = np.append(y_old, y)\n obj.set_data(x_old, y_old)\n\n\nf = os.listdir('spencers_data')\nf.sort()\n\n# reading and plotting validation data\nvalidation_data1 = pd.read_csv(\"spencers_data/path1.csv\")\nvalidation_data1['Start_time'] = pd.to_datetime(validation_data1['Start_time'], infer_datetime_format=True)\nvalidation_data1['Start_time'] = validation_data1['Start_time'].apply(lambda x: x + datetime.timedelta(hours=12)) # todo write proper code\nvalidation_data1['Start_time'] = validation_data1['Start_time'].apply(lambda x: x.strftime('%H:%M'))\nx_validation1 = validation_data1['X'].tolist()\ny_validation1 = validation_data1['Y'].tolist()\nts1 = validation_data1['Start_time'].tolist()\n\nvalidation_dict1 = {}\nfor a, b, c in zip(ts1, x_validation1, y_validation1):\n validation_dict1[a] = (b, c)\n\n\ntest_dict1 = {}\n\nmac = macs[0]\nname = names[0]\nfor file in f:\n base, ext = os.path.splitext(file)\n if(ext == \".log\"):\n print(file)\n df = pd.read_json(\"spencers_data/%s\" % (file), lines=True) # reading data\n df['ts'] = pd.to_datetime(df['ts'], infer_datetime_format=True)\n\n df['ts'] = df['ts'].apply(lambda x: x + datetime.timedelta(hours=5, minutes=30)) # converrting utc time ist\n\n df = df.loc[df['mac'].isin([mac])] # filtering out data corresponding to our macs\n\n # making code granular by per minute\n df['ts'] = df['ts'].apply(lambda x: x.strftime('%H:%M'))\n df = df.groupby(['nasid', 'controllerid', 'position', 'ts', 'mac']).mean()\n df.reset_index(inplace=True)\n\n df = df.groupby(['nasid', 'position', 'ts', 'mac'])\n lst = list(df)\n df_loc_track = pd.DataFrame(columns=['nasid', 'position', 'ts', 'mac', 'controllerid', 'pwr', 'count'], dtype=object)\n\n for i in range(len(lst)):\n x = lst[i]\n\n cid_list = list(map(str, x[1]['controllerid'].tolist()))\n pwr_list = list(map(str, x[1]['pwr'].tolist()))\n\n df_loc_track.loc[i, :] = [x[0][0], x[0][1], x[0][2], x[0][3], \" \".join(cid_list), \" \".join(pwr_list), len(cid_list)]\n\n df_loc_track.reset_index(inplace=True)\n\n for index, row in df_loc_track.iterrows():\n print(row['ts'])\n\n if row['count'] >= 2:\n controllers = list(map(int, row['controllerid'].split()))\n powers = list(map(float, row['pwr'].split()))\n mac = row['mac']\n ts = row['ts']\n print(ts)\n circles = []\n\n for cid, power in zip(controllers, powers):\n radial_distance = utility.rssi_to_dis(power)\n circles.append((aps[int(cid)], radial_distance))\n\n intersection = utility.heuristic_3(circles)\n\n # plot_circles(circles, mac, ts)\n\n if intersection == None:\n pass\n elif (ts in ts1):\n test_dict1[ts] = (intersection.x, intersection.y)\n print(test_dict1[ts])\n else:\n print(\"Not in path %s\" % (ts))\n\ncount = 0\nnum_loc = 0\ntarget = \"plots/heuristic3/\" + name\nax.legend([path, validation], [\"# points: %d\" % (num_loc)])\n\nfor ts in validation_dict1:\n count += 1\n update(validation, [validation_dict1[ts][0]], [validation_dict1[ts][1]])\n if ts in test_dict1:\n num_loc += 1\n update(path, [test_dict1[ts][0]], [test_dict1[ts][1]])\n ax.legend([path, validation], [\"# points: %d\\ntime: %s\" % (num_loc, ts)])\n\n if not os.path.exists(target):\n os.makedirs(target)\n\n plt.savefig(target + \"/%03d.png\" % count)\n ax.set_title(\"%s\\nHeurisitic 3\" % (name))\n plt.pause(0.2)\n\nax.set_title(\"%s\\nHeurisitic 3\\nRMSQ: %f\" % (name, utility.root_mean_square_error(validation_dict1, test_dict1)))\nplt.savefig(target + \"/%03d.png\" % (count + 1))\nplt.show()\n", "repo_name": "sansiddhjain/internal-localisation", "sub_path": "location.py", "file_name": "location.py", "file_ext": "py", "file_size_in_byte": 5139, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "37", "api": [{"api_name": "matplotlib.pyplot.figure", "line_number": 24, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 24, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.Circle", "line_number": 40, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 40, "usage_type": "name"}, {"api_name": "numpy.append", "line_number": 47, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 48, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 52, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 56, "usage_type": "call"}, {"api_name": "pandas.to_datetime", "line_number": 57, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 58, "usage_type": "call"}, {"api_name": "os.path.splitext", "line_number": 74, "usage_type": "call"}, {"api_name": "os.path", "line_number": 74, "usage_type": "attribute"}, {"api_name": "pandas.read_json", "line_number": 77, "usage_type": "call"}, {"api_name": "pandas.to_datetime", "line_number": 78, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 80, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 91, "usage_type": "call"}, {"api_name": "utility.rssi_to_dis", "line_number": 115, "usage_type": "call"}, {"api_name": "utility.heuristic_3", "line_number": 118, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 143, "usage_type": "call"}, {"api_name": "os.path", "line_number": 143, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 144, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 146, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 146, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.pause", "line_number": 148, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 148, "usage_type": "name"}, {"api_name": "utility.root_mean_square_error", "line_number": 150, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 151, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 151, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 152, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 152, "usage_type": "name"}]} +{"seq_id": "25464097652", "text": "# https://www.nytimes.com/2021/05/08/business/bill-melinda-gates-divorce-foundation.html\nfrom rich.console import Console\nfrom rich.table import Table\n\n\nname1 = input(\"What is the name of your Dog: \")\nname2 = input(\"What is the name of your Cat: \")\nplace = input(\"Last place you fucked: \")\nswear = input(\"Name a swear work: \")\nfood = input(\"Your favourite meal: \")\nprofessor = input(\"Full name of your professor: \")\naliens = input(\"Name a race of aliens(plural): \")\n\ntitle = f\"The Separate Worlds of {name1} and {name2}\"\n\ntext = f\"[bold sea_green2]{name1}[/] and [bold sea_green2]{name2}[/] were stuck at \\\n[bold sea_green2]{place}[/].\\n\\nWhen the [bold sea_green2]{swear}[/] hit last March, the couple retreated to their \\\n66,000-square-foot [bold sea_green2]{place}[/] on the shore of Lake Washington, venturing \\\nout infrequently to minimize their potential exposure to the [bold sea_green2]{swear}[/]. \\\nFrom their [bold sea_green2]{place}[/] offices they continued running the influential \\\nfoundation that bears their name, video chatting with world \\\n[bold sea_green2]{aliens}[/] to secure financial commitments for [bold sea_green2]{food}[/] distribution, \\\nand talking about the health of American democracy with their \\\nyoungest [bold sea_green2]{professor}[/], who was finishing her senior year of high school \\\nremotely.\\n\\nFor a couple who had spent much of the past three decades \\\ntraveling the world, so much time together at [bold sea_green2]{place}[/] was an abrupt \\\nchange of pace. “Working from [bold sea_green2]{place}[/] — that was a piece that I think \\\nwe hadn’t really individually prepared for quite as much,” \\\n[bold sea_green2]{name2}[/] told The New York Times in October.\\n:heart::heart::heart:\"\n\ntable = Table(title=\"Read All About It - New York Times!\")\ntable.add_column(title)\ntable.add_row(text)\n\n\nconsole = Console()\nconsole.print(table)\n", "repo_name": "damir-bubanovic/Madlibs", "sub_path": "madlibs.py", "file_name": "madlibs.py", "file_ext": "py", "file_size_in_byte": 1882, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "rich.table.Table", "line_number": 31, "usage_type": "call"}, {"api_name": "rich.console.Console", "line_number": 36, "usage_type": "call"}]} +{"seq_id": "26288025886", "text": "import os\nimport pathlib\nimport cloudpathlib\nimport time\nfrom omegaconf import DictConfig, ListConfig, OmegaConf\n\nfrom omegaconf import OmegaConf\nfrom typing import List, Tuple, Any\n\nimport shutil\n\n\ndef flatten_dict(d):\n flat_dict = {}\n queue = [(d, k, []) for k in d.keys()]\n\n while queue:\n parent_dict, node, path = queue.pop()\n child = parent_dict[node]\n new_path = path + [node]\n\n if isinstance(child, dict):\n queue.extend([(child, k, new_path) for k in child.keys()])\n else:\n flat_dict[\".\".join(new_path)] = child\n\n return flat_dict\n\n\ndef dict_from_flatten(flatlist: List[Tuple[str, Any]]) -> DictConfig:\n return OmegaConf.from_dotlist([f\"{k}={v}\" for k, v in flatlist])\n\n\ndef flatten_omega_conf(cfg: Any, resolve: bool = False) -> List[Tuple[str, Any]]:\n ret = []\n\n def handle_dict(key: Any, value: Any, resolve: bool) -> List[Tuple[str, Any]]:\n return [(f\"{key}.{k1}\", v1) for k1, v1 in flatten_omega_conf(value, resolve=resolve)]\n\n def handle_list(key: Any, value: Any, resolve: bool) -> List[Tuple[str, Any]]:\n return [(f\"{key}.{idx}\", v1) for idx, v1 in flatten_omega_conf(value, resolve=resolve)]\n\n if isinstance(cfg, DictConfig):\n for k, v in cfg.items_ex(resolve=resolve):\n if isinstance(v, DictConfig):\n ret.extend(handle_dict(k, v, resolve=resolve))\n elif isinstance(v, ListConfig):\n ret.extend(handle_list(k, v, resolve=resolve))\n else:\n ret.append((str(k), v))\n elif isinstance(cfg, ListConfig):\n for idx, v in enumerate(cfg._iter_ex(resolve=resolve)):\n if isinstance(v, DictConfig):\n ret.extend(handle_dict(idx, v, resolve=resolve))\n elif isinstance(v, ListConfig):\n ret.extend(handle_list(idx, v, resolve=resolve))\n else:\n ret.append((str(idx), v))\n else:\n assert False\n\n return ret\n\ndef override_arguments(command_line):\n eval()\n\n\ndef tar_and_remove_dir(out_dir: pathlib.Path, target_dir=None, remove=True):\n base_dir = out_dir.absolute().parent\n\n shutil.make_archive(\n base_name=base_dir / out_dir.name,\n format=\"tar\",\n root_dir=base_dir,\n base_dir=out_dir.name,\n )\n\n if remove:\n shutil.rmtree(out_dir.absolute())\n\n if target_dir is not None:\n shutil.move(base_dir / f\"{out_dir.stem}.tar\", target_dir / f\"{out_dir.stem}.tar\")\n else:\n target_dir = base_dir\n\n return target_dir / f\"{out_dir.stem}.tar\"\n\n\ndef previous_experiment_path(config) -> pathlib.Path:\n return Path(config.experiment.folder) / \"current_pipeline\"\n\n\ndef Path(path) -> pathlib.Path:\n if str(path).startswith(\"s3://\"):\n return cloudpathlib.S3Path(path)\n else:\n return pathlib.Path(path)\n \n", "repo_name": "mlfoundations/open-diffusion", "sub_path": "utils/logging.py", "file_name": "logging.py", "file_ext": "py", "file_size_in_byte": 2859, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 99, "dataset": "github-code", "pt": "37", "api": [{"api_name": "typing.List", "line_number": 30, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 30, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 30, "usage_type": "name"}, {"api_name": "omegaconf.OmegaConf.from_dotlist", "line_number": 31, "usage_type": "call"}, {"api_name": "omegaconf.OmegaConf", "line_number": 31, "usage_type": "name"}, {"api_name": "omegaconf.DictConfig", "line_number": 30, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 34, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 37, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 37, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 37, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 40, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 40, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 40, "usage_type": "name"}, {"api_name": "omegaconf.DictConfig", "line_number": 43, "usage_type": "argument"}, {"api_name": "omegaconf.DictConfig", "line_number": 45, "usage_type": "argument"}, {"api_name": "omegaconf.ListConfig", "line_number": 47, "usage_type": "argument"}, {"api_name": "omegaconf.ListConfig", "line_number": 51, "usage_type": "argument"}, {"api_name": "omegaconf.DictConfig", "line_number": 53, "usage_type": "argument"}, {"api_name": "omegaconf.ListConfig", "line_number": 55, "usage_type": "argument"}, {"api_name": "typing.List", "line_number": 34, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 34, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 68, "usage_type": "attribute"}, {"api_name": "shutil.make_archive", "line_number": 71, "usage_type": "call"}, {"api_name": "shutil.rmtree", "line_number": 79, "usage_type": "call"}, {"api_name": "shutil.move", "line_number": 82, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 89, "usage_type": "attribute"}, {"api_name": "cloudpathlib.S3Path", "line_number": 95, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 97, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 93, "usage_type": "attribute"}]} +{"seq_id": "13092604798", "text": "from datetime import datetime, tzinfo, timedelta\n\nfrom . import convert\n\n\nclass GMT0(tzinfo):\n\n def utcoffset(self, dt):\n return timedelta(hours=0)\n\n def tzname(self, dt):\n return 'GMT +0'\n\n def dst(self, dt):\n return timedelta(0)\n\n\ngmt = GMT0()\n\n\nclass datetime(datetime):\n\n @property\n def mjd(self):\n \"\"\" Calculate the Modified Julian Date (MJD) using the datetime object \"\"\"\n return convert.datetime2mjd(self)\n\n @property\n def jd(self):\n \"\"\" Calculate the Julian Date (JD) using the datetime object \"\"\"\n return convert.datetime2jd(self)\n\n @property\n def sdssjd(self):\n \"\"\" Calculate the SDSS Julian Date (SJD or SDSSJD) using the datetime object \"\"\"\n return convert.mjd2sdssjd(convert.datetime2mjd(self))\n\n def lst(self, longitude):\n \"\"\" Compute the Local Sidereal Time for the datetime object\n given the a longitude in degrees West\n \"\"\"\n try:\n utcSelf = self.astimezone(gmt)\n except ValueError:\n raise ValueError(\n 'In order to calculate the Local Sidereal Time, you must specify the timezone of the datetime object.\\nYou must create a tzinfo() object, and do datetimeObject = datetimeObject.replace(tzinfo=someTimeZone)'\n )\n print('utc', utcSelf.hour, utcSelf.minute, utcSelf.second)\n gmst = convert.utcDatetime2gmst(utcSelf)\n return convert.gmst2lst(longitude, gmst.hour, gmst.minute, gmst.second)\n\n @staticmethod\n def fromMJD(mjd):\n \"\"\" Create a datetime object from a Modified Julian Date (MJD) \"\"\"\n dt = convert.mjd2datetime(mjd)\n return datetime(dt.year, dt.month, dt.day, dt.hour, dt.minute, dt.second, dt.microsecond)\n\n @staticmethod\n def fromJD(jd, tz=None):\n \"\"\" Create a datetime object from a Julian Date (JD) \"\"\"\n dt = convert.jd2datetime(jd)\n return datetime(dt.year, dt.month, dt.day, dt.hour, dt.minute, dt.second, dt.microsecond)\n\n @property\n def decimalTime(self):\n \"\"\" Return the decimal time in hours \"\"\"\n return float(\n self.hour) + self.minute / 60.0 + (self.second + self.microsecond * 1e-5) / 3600.\n\n @staticmethod\n def anow(tz=None):\n \"\"\" Because datetime's built-in now() method returns a regular datetime object, so this is\n a special function to return an astrodatetime.datetime object instead\"\"\"\n now = datetime.now(tz)\n return datetime(\n now.year,\n now.month,\n now.day,\n now.hour,\n now.minute,\n now.second,\n now.microsecond,\n tzinfo=tz)\n\n @staticmethod\n def fromDatetime(datetimeObj):\n return datetime(\n datetimeObj.year,\n datetimeObj.month,\n datetimeObj.day,\n datetimeObj.hour,\n datetimeObj.minute,\n datetimeObj.second,\n datetimeObj.microsecond,\n tzinfo=datetimeObj.tzinfo)\n", "repo_name": "sdss/sdssdb", "sub_path": "python/sdssdb/sqlalchemy/operationsdb/tools/astrodatetime.py", "file_name": "astrodatetime.py", "file_ext": "py", "file_size_in_byte": 3022, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 7, "dataset": "github-code", "pt": "37", "api": [{"api_name": "datetime.tzinfo", "line_number": 6, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 9, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 15, "usage_type": "call"}]} +{"seq_id": "1689159423", "text": "import base64\r\nimport sentiment_mod as s\r\nimport pandas as pd\r\nfrom datetime import datetime\r\nimport Stock_Market_web_scraper as getData\r\nfrom tqdm.auto import tqdm\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\n# import io\r\nfrom PIL import Image\r\nimport seaborn as sns\r\nimport scipy.stats as stats\r\nmonths_dict = {\r\n 1: 'January',\r\n 2: 'February',\r\n 3: 'March',\r\n 4: 'April',\r\n 5: 'May',\r\n 6: 'June',\r\n 7: 'July',\r\n 8: 'August',\r\n 9: 'September',\r\n 10: 'October',\r\n 11: 'November',\r\n 12: 'December'\r\n}\r\ndef plotter(stock_name):\r\n df,df_price,ticker_name = getData.get_stock_data(stock_name)\r\n curr_month = datetime.now().month\r\n \r\n months_labels = []\r\n for i in range(1,13):\r\n months_labels.append(curr_month-i)\r\n months_dict_temp = {}\r\n for i,month in zip(months_labels,months_dict.values()):\r\n months_dict_temp[i] = month\r\n \r\n def remove_this_week(text):\r\n if ('day' in text) or ('week' in text) or ('hour' in text) or ('minute' in text):\r\n return months_dict[datetime.now().month]\r\n elif('year' in text):\r\n return pd.NA\r\n else:\r\n month = int(text.split()[0])\r\n \r\n if month 0 else 'stress', ls='-', c='c' if stress[r] > 0 else 'crimson', lw=1+20*stress_normed[r])\n\nplt.axis('equal')\nplt.xlabel('meters')\nplt.ylabel('meters')\nplt.title('Magic Triangle')\nplt.grid(True)\nplt.legend()\n", "repo_name": "margkuval/Thesis", "sub_path": "python_projects/tri_modif.py", "file_name": "tri_modif.py", "file_ext": "py", "file_size_in_byte": 3937, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "numpy.array", "line_number": 7, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 8, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 9, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 10, "usage_type": "call"}, {"api_name": "numpy.ix_", "line_number": 13, "usage_type": "call"}, {"api_name": "numpy.ix_", "line_number": 14, "usage_type": "call"}, {"api_name": "numpy.ix_", "line_number": 15, "usage_type": "call"}, {"api_name": "numpy.ix_", "line_number": 16, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 23, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 27, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 28, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 31, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 41, "usage_type": "call"}, {"api_name": "numpy.ix_", "line_number": 45, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 48, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 49, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 58, "usage_type": "call"}, {"api_name": "numpy.unique", "line_number": 58, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 62, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 62, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 65, "usage_type": "call"}, {"api_name": "numpy.setdiff1d", "line_number": 67, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 67, "usage_type": "call"}, {"api_name": "numpy.linalg.solve", "line_number": 70, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 70, "usage_type": "attribute"}, {"api_name": "numpy.ix_", "line_number": 70, "usage_type": "call"}, {"api_name": "numpy.ix_", "line_number": 71, "usage_type": "call"}, {"api_name": "numpy.ix_", "line_number": 76, "usage_type": "call"}, {"api_name": "numpy.ix_", "line_number": 77, "usage_type": "call"}, {"api_name": "numpy.ix_", "line_number": 78, "usage_type": "call"}, {"api_name": "numpy.ix_", "line_number": 79, "usage_type": "call"}, {"api_name": "numpy.round", "line_number": 98, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 98, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 115, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 115, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.setp", "line_number": 116, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 116, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.annotate", "line_number": 117, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 117, "usage_type": "name"}, {"api_name": "numpy.sign", "line_number": 119, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 124, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 124, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.setp", "line_number": 125, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 125, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axis", "line_number": 127, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 127, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 128, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 128, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 129, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 129, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 130, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 130, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 131, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 131, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 132, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 132, "usage_type": "name"}]} +{"seq_id": "32119241034", "text": "import pwn\nimport argparse\n\nparser = argparse.ArgumentParser()\n\nparser.add_argument('--user', type=str)\nparser.add_argument('--host', type=str)\nparser.add_argument('--port', type=int)\n\nargs = parser.parse_args()\n\nuser = args.user\nhost = args.host\nport = args.port\n\nconn = pwn.ssh(user=user, host=host,\n port=port, keyfile='leaked-key.pem')\n\nflag = conn('cat flag.txt')\n\nwith open('flag.txt', 'wb') as filp:\n filp.write(flag)\n\nconn.close()\n", "repo_name": "hoshigakikisame/picoCTF-2022", "sub_path": "forensics/Operation-Oni_COMPLETE/exploit.py", "file_name": "exploit.py", "file_ext": "py", "file_size_in_byte": 456, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 4, "usage_type": "call"}, {"api_name": "pwn.ssh", "line_number": 16, "usage_type": "call"}]} +{"seq_id": "21810547721", "text": "import boto3\nimport time\nimport os\n\nbasedir = os.path.abspath(os.path.dirname(__file__))\n\nclass awsClient:\n def __init__(self):\n self.ec2 = boto3.client('ec2')\n self.elb = boto3.client('elbv2') # 'elbv2' is for application load balancer, 'elb' is for classic load balancer\n self.cloudwatch = boto3.client('cloudwatch')\n self.s3 = boto3.client('s3')\n self.bucket = 'ece1779-a2'\n self.TargetGroupArn = 'arn:aws:elasticloadbalancing:us-east-1:678814637696:targetgroup/ECE1779-Assignment2/ac5be37a415164c2'\n self.user_app_tag = 'User_app'\n self.manager_app_tag = 'Manager_app'\n self.ami_id = 'ami-054b7dff464af8ab6'\n self.instance_type = 't2.small'\n self.keypair_name = 'keypair'\n self.security_group_id = ['sg-0fef18e8c5f05065f']\n self.subnet_id = 'subnet-0afac8ced93d3ac9f'\n self.tag_specification = [{\n 'ResourceType': 'instance',\n 'Tags': [\n {\n 'Key': 'Name',\n 'Value': self.user_app_tag\n }\n ]\n }]\n\n self.monitoring = {\n 'Enabled': True\n }\n self.placement = {\n 'AvailabilityZone': 'us-east-1a'\n }\n self.IamInstanceProfile = {\n 'Arn': 'arn:aws:iam::678814637696:instance-profile/ECE1779-A2'\n }\n #with open(basedir + '/Userdata.txt', 'r') as myfile:\n # data = myfile.read()\n self.userdata = \"#!/bin/bash \\npip3 install mysql-connector-python \\npip3 install Flask-Mail \\ncd home/ubuntu/Desktop/userapp \\npython3.8 run.py > out.txt 2> err.txt\"\n self.availabilityzone = 'us-east-1a'\n self.targetgroup = 'targetgroup/ECE1779-Assignment2/ac5be37a415164c2'\n self.loadbalancer = 'app/ECE1779-A2-ELB/42868c4091d6bf9d'\n\n def create_ec2_instance(self):\n # create an EC2 instance from the backup AMI\n response = self.ec2.run_instances(ImageId=self.ami_id,\n InstanceType=self.instance_type,\n MinCount=1,\n MaxCount=1,\n KeyName=self.keypair_name,\n NetworkInterfaces=[\n {\n 'DeviceIndex': 0,\n 'SubnetId': self.subnet_id,\n 'AssociatePublicIpAddress': True,\n 'Groups': self.security_group_id\n },\n ],\n #SecurityGroupIds=self.security_group_id,\n #SubnetId=self.subnet_id,\n Monitoring=self.monitoring,\n IamInstanceProfile=self.IamInstanceProfile,\n TagSpecifications=self.tag_specification,\n Placement=self.placement,\n UserData=self.userdata)\n print(self.userdata)\n return response\n\n # get the instance that runs the manager app\n def get_manager(self):\n response = self.ec2.describe_instances(Filters=[\n {\n 'Name': 'tag:Name',\n 'Values': [self.manager_app_tag]\n }\n ])\n instance_id = response['Reservations'][0]['Instances'][0]['InstanceId']\n return instance_id\n\n # get all instances (workers) in the target group (worker pool) of ELB\n def get_workers(self):\n response = self.elb.describe_target_health(\n TargetGroupArn=self.TargetGroupArn,\n )\n worker_pool = []\n if response['TargetHealthDescriptions']:\n for worker in response['TargetHealthDescriptions']:\n worker_pool.append({\n 'Id': worker['Target']['Id'],\n 'Port': worker['Target']['Port'],\n 'State': worker['TargetHealth']['State']\n })\n return worker_pool\n\n # get the usable (not draining) workers in the worker pool\n def get_usable_workers(self):\n workers = self.get_workers()\n usable_workers_id = []\n for worker in workers:\n if worker['State'] != 'draining':\n usable_workers_id.append(worker['Id'])\n return usable_workers_id\n\n # count the number of workers for the past 30 minutes\n def count_workers(self, start_time, end_time):\n response1 = self.cloudwatch.get_metric_statistics(\n Namespace='AWS/ApplicationELB',\n MetricName='HealthyHostCount',\n Dimensions=[\n {\n 'Name': 'TargetGroup',\n 'Value': self.targetgroup,\n },\n {\n 'Name': 'LoadBalancer',\n 'Value': self.loadbalancer,\n },\n {\n 'Name': 'AvailabilityZone',\n 'Value': self.availabilityzone,\n },\n ],\n StartTime=start_time,\n EndTime=end_time,\n Period=60,\n Statistics=['Maximum'],\n Unit='Count'\n )\n response2 = self.cloudwatch.get_metric_statistics(\n Namespace='AWS/ApplicationELB',\n MetricName='UnHealthyHostCount',\n Dimensions=[\n {\n 'Name': 'TargetGroup',\n 'Value': self.targetgroup,\n },\n {\n 'Name': 'LoadBalancer',\n 'Value': self.loadbalancer,\n },\n {\n 'Name': 'AvailabilityZone',\n 'Value': self.availabilityzone,\n },\n ],\n StartTime=start_time,\n EndTime=end_time,\n Period=60,\n Statistics=['Maximum'],\n Unit='Count'\n )\n datapoints = []\n if response1['Datapoints']:\n for datapoint in response1['Datapoints']:\n datapoints.append(\n [\n datapoint['Timestamp'].timestamp()*1000,\n datapoint['Maximum']\n ]\n )\n if response2['Datapoints']:\n for i in range(len(datapoints)):\n datapoints[i][1] += response2['Datapoints'][i]['Maximum']\n return datapoints\n\n # get CPU utilization for the past 30 minutes\n def get_cpu(self, instance_id, start_time, end_time):\n response = self.cloudwatch.get_metric_statistics(\n Namespace='AWS/EC2',\n MetricName='CPUUtilization',\n Dimensions=[\n {\n 'Name': 'InstanceId',\n 'Value': instance_id,\n },\n ],\n StartTime=start_time,\n EndTime=end_time,\n Period=60,\n Statistics=['Maximum'],\n Unit='Percent'\n )\n datapoints = []\n if response['Datapoints']:\n for datapoint in response['Datapoints']:\n datapoints.append(\n [\n datapoint['Timestamp'].timestamp()*1000,\n datapoint['Maximum']\n ]\n )\n return datapoints\n\n # get http request for the past 30 minutes\n '''\n def get_http(self, instance_id, start_time, end_time):\n\n response = self.cloudwatch.get_metric_statistics(\n Namespace='AWS/EC2',\n MetricName='HTTPRequest',\n Dimensions=[\n {\n 'Name': 'InstanceId',\n 'Value': instance_id,\n },\n ],\n StartTime=start_time,\n EndTime=end_time,\n Period=60,\n Statistics=['Maximum'],\n Unit='Count'\n )\n datapoints = []\n if response['Datapoints']:\n for datapoint in response['Datapoints']:\n datapoints.append(\n [\n datapoint['Timestamp'].timestamp()*1000,\n datapoint['Maximum']\n ]\n )\n return datapoints\n '''\n\n\n def grow_worker_by_one(self):\n # create connection to ec2\n # ec2 = boto3.resource('ec2')\n\n # get all instance\n allinstances = self.get_workers()\n\n # make sure not exceed the maximum number of workers\n if len(allinstances) <= 7:\n response = self.create_ec2_instance()\n time.sleep(10)\n new_instance_id = response['Instances'][0]['InstanceId']\n else:\n return 'Maximum number of Workers reached'\n\n # check to see if the status of new instance changes to running\n specfic_state = self.ec2.describe_instance_status(InstanceIds=[new_instance_id])\n while len(specfic_state['InstanceStatuses']) < 1:\n time.sleep(10)\n specfic_state = self.ec2.describe_instance_status(InstanceIds=[new_instance_id])\n while specfic_state['InstanceStatuses'][0]['InstanceState']['Name'] != 'running':\n time.sleep(10)\n specfic_state = self.ec2.describe_instance_status(InstanceIds=[new_instance_id])\n print(specfic_state)\n\n '''\n # publish a custom metric in cloud watch to measure the HTTP request rate for each worker\n self.cloudwatch.put_metric_data(\n Namespace='AWS/EC2',\n MetricData=[\n {\n 'MetricName': 'HTTPRequest',\n 'Dimensions': [\n {\n 'Name': 'InstanceId',\n 'Value': new_instance_id\n },\n ],\n 'Value': 0,\n 'Unit': 'Count',\n },\n ]\n )\n '''\n\n # register new instance after it finishes initialization\n time.sleep(10)\n response = self.elb.register_targets(\n TargetGroupArn=self.TargetGroupArn,\n Targets=[\n {\n 'Id': new_instance_id,\n 'Port': 5000\n }, ])\n if response and 'ResponseMetadata' in response and \\\n 'HTTPStatusCode' in response['ResponseMetadata']:\n return response['ResponseMetadata']['HTTPStatusCode']\n # return whether successful or not\n # return \"200\"\n else:\n return -1\n\n def grow_worker_by_ratio(self, ratio):\n # create connection to ec2\n if ratio < 1:\n return 'The growing ratio must be exceed 1'\n\n # get all instance\n allinstances = self.get_workers()\n\n increase = round(len(allinstances) * ratio)\n if increase > 8:\n delta = 8 - len(allinstances)\n else:\n delta = increase - len(allinstances)\n\n # grow worker by iteratively applying grow grow_worker_by_one()\n # upper limit of 10 is enforced within grow_worker_by_one()\n count = 0\n for i in range(delta):\n res = self.grow_worker_by_one()\n # if res == 200:\n if int(res) == 200:\n count = count + 1\n # responses.append(self.grow_worker_by_one())\n\n # return how many worker added\n return count\n\n def shrink_worker_by_one(self, stop=True):\n if (stop):\n min_number = 1\n else:\n min_number = 0\n\n target_instances_id = self.get_workers()\n flag, msg = True, ''\n\n if len(target_instances_id) > min_number:\n\n unregister_instance_id = target_instances_id[-1]['Id']\n # unregister instance from target group\n deregister_instance_response = self.elb.deregister_targets(\n TargetGroupArn=self.TargetGroupArn,\n Targets=[\n {\n 'Id': unregister_instance_id\n }, ])\n deregister_instance_status = -1\n\n # check successful\n if deregister_instance_response and 'ResponseMetadata' in deregister_instance_response and \\\n 'HTTPStatusCode' in deregister_instance_response['ResponseMetadata']:\n deregister_instance_status = deregister_instance_response['ResponseMetadata']['HTTPStatusCode']\n\n if int(deregister_instance_status) == 200:\n\n # after successful deregister, try to terminate instance\n terminate_instance_status = -1\n terminate_instance_response = self.ec2.terminate_instances(InstanceIds=[unregister_instance_id])\n\n # check whether successful\n if terminate_instance_response and 'ResponseMetadata' in terminate_instance_response and \\\n 'HTTPStatusCode' in terminate_instance_response['ResponseMetadata']:\n terminate_instance_status = terminate_instance_response['ResponseMetadata']['HTTPStatusCode']\n\n if int(terminate_instance_status) != 200:\n flag = False\n msg = \"Unable to terminate the instance\"\n else:\n flag = False\n msg = \"Unable to unregister from target group\"\n else:\n flag = False\n msg = \"No workers to unregister\"\n if flag == True:\n msg = \"Worker was successfully unregistered\"\n return [flag, msg]\n\n # shrink worker by ratio\n # ratio is the percentage of instances to be suspended\n def shrink_worker_by_ratio(self, ratio):\n\n # create connection to ec2\n if ratio > 1:\n return 'The shrink ratio must be less than 1'\n\n # get all instance\n allinstances = self.get_workers()\n\n shrink = round(len(allinstances) * ratio)\n\n if shrink < 1:\n delta = len(allinstances) - 1\n else:\n delta = len(allinstances) - shrink\n\n # grow worker by iteratively applying grow grow_worker_by_one()\n # upper limit of 10 is enforced within grow_worker_by_one()\n count = 0\n for i in range(delta):\n res = self.shrink_worker_by_one()\n if res[0] == True:\n count = count + 1\n # responses.append(self.grow_worker_by_one())\n\n # return how many worker added\n return count\n\n def stop_manager(self):\n flag, msg = True, ''\n # initialize\n stop_manager_instance_status = -1\n\n manager_instance_id = self.get_manager()\n\n if len(manager_instance_id) == 1:\n\n stop_manager_instance_response = self.ec2.stop_instances(\n InstanceIds=[manager_instance_id, ],\n Hibernate=False,\n Force=False\n )\n\n # check\n if stop_manager_instance_response and 'ResponseMetadata' in stop_manager_instance_response and \\\n 'HTTPStatusCode' in stop_manager_instance_response['ResponseMetadata']:\n stop_manager_instance_status = stop_manager_instance_response['ResponseMetadata']['HTTPStatusCode']\n if int(stop_manager_instance_status) != 200:\n flag = False\n msg = \"Unable to stop the manager app instance\"\n else:\n flag = False\n msg = \"No manager instance available\"\n return [flag, msg]\n\n # terminate all workers and stop manager\n def stop_all_instances(self):\n\n # get all instances\n target_instances_id = self.get_workers()\n # initialize\n response_list = []\n\n if len(target_instances_id) < 1:\n response_list.append(self.stop_manager())\n return [True, \"Success\", response_list]\n else:\n shrink_targets_num = len(target_instances_id)\n for i in range(shrink_targets_num):\n # try to terminate instance when tag for shrink_worker_by_one() is false\n temp = self.shrink_worker_by_one(False)\n response_list.append(temp)\n # response_list would store all return message\n temp1 = self.stop_manager()\n response_list.append(temp1)\n\n return [True, \"Success\", response_list]\n\n def clear_s3(self):\n for key in self.s3.list_objects(Bucket=self.bucket)['Contents']:\n self.s3.delete_objects(\n Bucket=self.bucket,\n Delete={\n 'Objects': [\n {\n 'Key': key['Key'],\n }, ],\n 'Quiet': True\n },\n )", "repo_name": "FridayGao97/Mask_Detection_WebApp", "sub_path": "managerapp/app/aws.py", "file_name": "aws.py", "file_ext": "py", "file_size_in_byte": 17025, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "37", "api": [{"api_name": "os.path.abspath", "line_number": 5, "usage_type": "call"}, {"api_name": "os.path", "line_number": 5, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 5, "usage_type": "call"}, {"api_name": "boto3.client", "line_number": 9, "usage_type": "call"}, {"api_name": "boto3.client", "line_number": 10, "usage_type": "call"}, {"api_name": "boto3.client", "line_number": 11, "usage_type": "call"}, {"api_name": "boto3.client", "line_number": 12, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 240, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 248, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 251, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 276, "usage_type": "call"}]} +{"seq_id": "23023735097", "text": "#!/usr/bin/python3\n\nimport os\nfrom shutil import copy\nfrom typing import Dict, List, Tuple, Union\nfrom xml.etree import ElementTree\n\nimport bpy\nimport rospkg\nfrom bpy.types import Armature, BlendData, Bone, Camera, Image, Light, Material, Mesh, Object\nfrom mathutils import Euler, Vector\nfrom urdf_parser_py.urdf import URDF, Joint, Link, Visual\n\nTMP_FOLDER_PATH = \"texture/\"\nTMP_TEXTURE_PATH = TMP_FOLDER_PATH\nTMP_FILE_PATH = \"tmp.dae\"\n\n\ndef urdf_cleanup(file_path: str) -> str:\n tree = ElementTree.parse(file_path)\n root = tree.getroot()\n\n newroot = ElementTree.Element(root.tag)\n newroot.set(\"name\", root.get(\"name\"))\n\n for element in root:\n if element.tag == \"link\" or element.tag == \"joint\" or element.tag == \"material\":\n newroot.append(element)\n\n return ElementTree.tostring(newroot)\n\n\ndef fix_up_axis_and_get_materials(file_path: str, unique_name: bool):\n tree = ElementTree.parse(file_path)\n root = tree.getroot()\n\n tmp_file_path = file_path\n dir_path = os.path.dirname(file_path)\n mat_sampler2D_dict: Dict[str, Dict[str, str]] = {}\n\n mat_dict: Dict[str, str] = {}\n effect_dict: Dict[str, List[str]] = {}\n sampler2D_dict: Dict[str, str] = {}\n surface_dict: Dict[str, str] = {}\n image_dict: Dict[str, str] = {}\n if not os.path.exists(TMP_TEXTURE_PATH):\n os.makedirs(TMP_TEXTURE_PATH)\n\n for ele1 in root:\n if \"asset\" in ele1.tag:\n for ele2 in ele1:\n if \"up_axis\" in ele2.tag:\n ele2.text = \"Z_UP\"\n tmp_file_path = TMP_FILE_PATH\n\n if \"library_materials\" in ele1.tag:\n for ele2 in ele1:\n if \"material\" in ele2.tag:\n mat_name = ele2.attrib[\"name\"]\n for ele3 in ele2:\n if \"instance_effect\" in ele3.tag:\n effect_id = ele3.attrib[\"url\"]\n if effect_id.startswith(\"#\"):\n effect_id = effect_id[1:]\n mat_dict[mat_name] = effect_id\n\n if \"library_effects\" in ele1.tag:\n for ele2 in ele1:\n if \"effect\" in ele2.tag:\n effect_id = ele2.attrib[\"id\"]\n effect_dict[effect_id] = []\n for ele3 in ele2:\n if \"profile_COMMON\" in ele3.tag:\n for ele4 in ele3:\n if \"newparam\" in ele4.tag:\n param_name = ele4.attrib[\"sid\"]\n for ele5 in ele4:\n if \"surface\" in ele5.tag:\n for ele6 in ele5:\n if \"init_from\" in ele6.tag:\n surface_dict[param_name] = ele6.text\n if \"sampler2D\" in ele5.tag:\n for ele6 in ele5:\n if \"source\" in ele6.tag:\n effect_dict[effect_id].append(param_name)\n sampler2D_dict[param_name] = ele6.text\n\n if \"library_images\" in ele1.tag:\n for ele2 in ele1:\n if \"image\" in ele2.tag:\n image_name = ele2.attrib[\"name\"]\n for ele3 in ele2:\n if \"init_from\" in ele3.tag:\n tmp_file_path = TMP_FILE_PATH\n file_name, file_ext = os.path.splitext(ele3.text)\n if not unique_name:\n file_hash = str(abs(hash(os.path.dirname(file_path))) % (10**3))\n file = \"T_\" + file_name + \"_\" + file_hash + file_ext\n else:\n file = \"T_\" + file_name + file_ext\n copy(dir_path + \"/\" + ele3.text, TMP_TEXTURE_PATH + file)\n ele3.text = TMP_TEXTURE_PATH + file\n image_dict[image_name] = ele3.text\n\n for mat_name in mat_dict:\n mat_sampler2D_dict[mat_name] = {}\n effect_id = mat_dict[mat_name]\n for effect_name in effect_dict[effect_id]:\n sampler2D_name = sampler2D_dict.get(effect_name)\n image_name = surface_dict.get(sampler2D_name)\n image_path = image_dict.get(image_name)\n mat_sampler2D_dict[mat_name][effect_name] = image_path\n\n if tmp_file_path == TMP_FILE_PATH:\n tree.write(tmp_file_path)\n\n return (tmp_file_path, mat_sampler2D_dict)\n\n\ndef clean_up() -> None:\n if os.path.exists(TMP_FILE_PATH):\n os.remove(TMP_FILE_PATH)\n return None\n\n\ndef clear_data(data: BlendData, scale_unit: float) -> None:\n armature: Armature\n for armature in data.armatures:\n data.armatures.remove(armature)\n mesh: Mesh\n for mesh in data.meshes:\n data.meshes.remove(mesh)\n object: Object\n for object in data.objects:\n data.objects.remove(object)\n material: Material\n for material in data.materials:\n data.materials.remove(material)\n camera: Camera\n for camera in data.cameras:\n data.cameras.remove(camera)\n light: Light\n for light in data.lights:\n data.lights.remove(light)\n image: Image\n for image in data.images:\n data.images.remove(image)\n\n unit_settings = bpy.context.scene.unit_settings\n unit_settings.scale_length = scale_unit\n bpy.context.view_layer.update()\n\n return None\n\n\ndef merge_materials(should_check_material_name: bool) -> None:\n mat_uniques: List[Material] = []\n object: Object\n for object in bpy.data.objects:\n for material_slot in object.material_slots:\n mat = material_slot.material\n if mat is None or not mat.use_nodes:\n continue\n mat_base_color = mat.node_tree.nodes[\"Principled BSDF\"].inputs.get(\"Base Color\")\n is_mat_unique = True\n for mat_unique in mat_uniques:\n # Level 1: Check for equalness\n if mat == mat_unique:\n continue\n\n if should_check_material_name:\n # Level 2: Check for name equalness\n mat_name_split = mat.name_full.split(\".\")\n mat_unique_name_split = mat_unique.name_full.split(\".\")\n if (\n len(mat_name_split) == len(mat_unique_name_split)\n and len(mat_name_split) > 1\n and mat_name_split[-1].isnumeric()\n and mat_unique_name_split[-1].isnumeric()\n ):\n mat_name_split.pop()\n mat_unique_name_split.pop()\n for mat_name, mat_unique_name in zip(mat_name_split, mat_unique_name_split):\n if mat_name[:59] == mat_unique_name[:59]:\n is_mat_unique = False\n else:\n is_mat_unique = True\n break\n if is_mat_unique:\n continue\n\n # Level 3: Check for content equalness\n mat_unique_base_color = mat_unique.node_tree.nodes[\"Principled BSDF\"].inputs.get(\"Base Color\")\n if (not mat_base_color.is_linked) and (not mat_unique_base_color.is_linked):\n # Merge duplicate materials based on their Base Color\n if [i for i in mat_base_color.default_value] == [i for i in mat_unique_base_color.default_value]:\n object.material_slots[mat.name].material = mat_unique\n bpy.data.materials.remove(mat)\n is_mat_unique = False\n break\n elif mat_base_color.is_linked and mat_unique_base_color.is_linked:\n # Merge duplicate materials based on their image name\n if mat_base_color.links[0].from_node.image.name == mat_unique_base_color.links[0].from_node.image.name:\n object.material_slots[mat.name].material = mat_unique\n bpy.data.materials.remove(mat)\n is_mat_unique = False\n break\n\n if is_mat_unique and mat is not None:\n mat_name_split = mat.name_full.split(\".\")\n mat_unique = None\n while len(mat_name_split) > 1 and mat_name_split[-1].isnumeric():\n mat_name = \"\".join(mat_name_split[:-1])\n if bpy.data.materials.get(mat_name) is not None:\n mat_unique = bpy.data.materials[mat_name]\n mat_name_split.pop()\n\n if mat_unique is None:\n mat_uniques.append(mat)\n else:\n # Level 3: Check for content equalness\n mat_unique_base_color = mat_unique.node_tree.nodes[\"Principled BSDF\"].inputs.get(\"Base Color\")\n is_mat_unique_equal_mat = False\n if (not mat_base_color.is_linked) and (not mat_unique_base_color.is_linked):\n # Merge duplicate materials based on their Base Color\n if [i for i in mat_base_color.default_value] == [i for i in mat_unique_base_color.default_value]:\n is_mat_unique_equal_mat = True\n elif mat_base_color.is_linked and mat_unique_base_color.is_linked:\n # Merge duplicate materials based on their image name\n if mat_base_color.links[0].from_node.image.name == mat_unique_base_color.links[0].from_node.image.name:\n is_mat_unique_equal_mat = True\n\n if is_mat_unique_equal_mat:\n object.material_slots[mat.name].material = mat_unique\n bpy.data.materials.remove(mat)\n mat_uniques.append(mat_unique)\n else:\n mat_uniques.append(mat)\n object.select_set(False)\n return None\n\n\ndef fix_alpha() -> None:\n for mat in bpy.data.materials:\n if hasattr(mat.node_tree, \"nodes\"):\n mat.node_tree.nodes[\"Principled BSDF\"].inputs[\"Alpha\"].default_value = 1.0\n\n\ndef rename_materials(base_name: str) -> None:\n for object in bpy.data.objects:\n for material_slot in object.material_slots:\n if material_slot.material is not None:\n material_slot.material.name = \"M_\" + base_name\n return None\n\n\nclass RobotBuilder:\n def __init__(\n self,\n file_path: str,\n should_merge_duplicate_materials: bool,\n should_check_material_name: bool,\n should_rename_materials: bool,\n should_apply_weld: bool,\n unique_name: bool,\n scale_unit: float,\n ):\n xml_string = urdf_cleanup(file_path)\n self.robot: URDF = URDF.from_xml_string(xml_string)\n self.link_pose: Dict[str, Tuple[Vector, Euler]] = {}\n self.arm_bones: Dict[str, Bone] = {}\n self.root: Object = None\n self.root_name = \"root\"\n self.bone_tail = \".bone\"\n self.parent_links = None\n self.apply_weld = should_apply_weld\n self.unique_name = unique_name\n self.scale_unit = scale_unit\n self.build_robot()\n if should_merge_duplicate_materials:\n merge_materials(should_check_material_name)\n if should_rename_materials:\n rename_materials(self.robot.name)\n clean_up()\n\n def build_robot(self) -> None:\n clear_data(bpy.data, self.scale_unit)\n self.create_materials()\n self.konfigure_mesh_path()\n self.add_root_armature()\n self.build_root()\n self.build_chain()\n fix_alpha()\n return None\n\n def create_materials(self) -> None:\n for material in self.robot.materials:\n if material.color is not None and hasattr(material.color, \"rgba\"):\n if bpy.data.materials.get(material.name):\n print(\"Material\", material.name, \"already exists\")\n else:\n mat: Material = bpy.data.materials.new(name=material.name)\n mat.diffuse_color = material.color.rgba\n return None\n\n def konfigure_mesh_path(self) -> None:\n link: Link\n for link in self.robot.links:\n visual: Visual\n for visual in link.visuals:\n if hasattr(visual.geometry, \"filename\"):\n rel_path: str = visual.geometry.filename\n while os.path.dirname(rel_path) != \"package:\":\n rel_path = os.path.dirname(rel_path)\n pkg_name = os.path.basename(rel_path)\n pkg_path = rospkg.RosPack().get_path(pkg_name)\n abs_path = os.path.dirname(pkg_path) + visual.geometry.filename.replace(\"package://\", \"/\")\n if os.path.exists(abs_path):\n visual.geometry.filename = abs_path\n return None\n\n def add_root_armature(self) -> None:\n arm: Armature = bpy.data.armatures.new(\"armatures\")\n self.arm_bones = arm.bones\n self.root = bpy.data.objects.new(self.root_name, arm)\n self.root.show_in_front = True\n bpy.context.scene.collection.objects.link(self.root)\n return None\n\n def add_mesh(\n self,\n mesh_name: str,\n material: Material = None,\n file_path: Union[str, List[str]] = \"\",\n location=Vector(),\n rotation=Euler(),\n scale=Vector((1, 1, 1)),\n link_pos=Vector(),\n link_rot=Euler(),\n ) -> Object:\n if isinstance(file_path, list):\n if file_path[0] == \"cylinder\":\n bpy.ops.mesh.primitive_cylinder_add(\n depth=file_path[1], radius=file_path[2], scale=(1 / self.scale_unit, 1 / self.scale_unit, 1 / self.scale_unit)\n )\n elif file_path[0] == \"cube\":\n bpy.ops.mesh.primitive_cube_add(size=1 / self.scale_unit, scale=file_path[1])\n elif file_path[0] == \"sphere\":\n bpy.ops.mesh.primitive_uv_sphere_add(radius=file_path[1], scale=(1 / self.scale_unit, 1 / self.scale_unit, 1 / self.scale_unit))\n else:\n print(\"Object type\", file_path[0], \"is not supported\")\n return None\n object = bpy.context.object\n\n if material is None:\n material = bpy.data.materials.get(\"Material\")\n if material is None:\n material = bpy.data.materials.new(name=\"Material\")\n object.data.materials.append(material)\n\n elif file_path:\n file_ext = os.path.splitext(file_path)[1].lower()\n if file_ext == \".dae\":\n (file_path, _) = fix_up_axis_and_get_materials(file_path, self.unique_name)\n bpy.ops.wm.collada_import(filepath=file_path)\n elif file_ext == \".obj\":\n scale *= 1 / self.scale_unit\n bpy.ops.import_scene.obj(filepath=file_path, axis_forward=\"Y\", axis_up=\"Z\")\n elif file_ext == \".stl\":\n bpy.ops.import_mesh.stl(filepath=file_path, global_scale=1 / self.scale_unit)\n\n else:\n print(\"File extension\", file_ext, \"of\", file_path, \"is not supported\")\n return None\n camera: Camera\n for camera in bpy.data.cameras:\n bpy.data.cameras.remove(camera)\n light: Light\n for light in bpy.data.lights:\n bpy.data.lights.remove(light)\n bpy.context.view_layer.objects.active = bpy.context.selected_objects[0]\n if len(bpy.context.selected_objects) > 1:\n bpy.ops.object.join()\n if not bpy.context.object.data.uv_layers:\n bpy.ops.mesh.uv_texture_add()\n object = bpy.context.object\n if self.apply_weld:\n object.modifiers.new(\"Weld\", \"WELD\")\n bpy.ops.object.modifier_apply(modifier=\"Weld\")\n if material is not None:\n object.data.materials.append(material)\n\n else:\n mesh = bpy.data.meshes.new(mesh_name)\n mesh.uv_layers.new()\n object = bpy.data.objects.new(mesh_name, mesh)\n bpy.context.scene.collection.objects.link(object)\n\n object.name = mesh_name\n object.rotation_mode = \"XYZ\"\n object.rotation_euler.rotate(rotation)\n object.location.rotate(rotation)\n object.location += location\n object.scale *= scale\n\n selected_object = bpy.context.object\n if selected_object.scale[0] * selected_object.scale[1] * selected_object.scale[2] < 0:\n bpy.ops.object.mode_set(mode=\"EDIT\")\n bpy.ops.mesh.select_all(action=\"SELECT\")\n bpy.ops.mesh.flip_normals()\n bpy.ops.object.mode_set(mode=\"OBJECT\")\n\n # Change origin of mesh to link_pos and link_rot\n bpy.context.scene.cursor.location = link_pos\n bpy.context.scene.cursor.rotation_euler = link_rot\n bpy.ops.object.origin_set(type=\"ORIGIN_CURSOR\")\n bpy.context.scene.cursor.location = Vector()\n bpy.context.scene.cursor.rotation_euler = Euler()\n\n # Apply 0.01 scale\n # object.scale *= 100\n bpy.ops.object.transform_apply(location=False, rotation=False, scale=True)\n # object.scale /= 100\n\n return object\n\n def set_link_origin(self, link: Link) -> None:\n if hasattr(link, \"origin\") and link.origin is not None:\n self.link_pose[link.name][0][:] = self.link_pose[link.name][0] + Vector(link.origin.xyz) / self.scale_unit\n self.link_pose[link.name][1].rotate(Euler(link.origin.rpy))\n return None\n\n def add_root_bone(self, link_name: str, bone_name: str) -> None:\n bpy.context.view_layer.objects.active = self.root\n bpy.ops.object.mode_set(mode=\"EDIT\", toggle=False)\n\n head = self.link_pose[link_name][0]\n tail = Vector((0.0, 0.1 / self.scale_unit, 0.0))\n tail.rotate(self.link_pose[link_name][1])\n tail += head\n bone: Bone = self.root.data.edit_bones.new(bone_name)\n bone.head = head\n bone.tail = tail\n bpy.ops.object.mode_set(mode=\"OBJECT\")\n return None\n\n def add_link_origin(self, pos: Vector, rot: Euler, tag: Union[Link, Joint, Visual]) -> Tuple[Vector, Euler]:\n if hasattr(tag, \"origin\") and tag.origin is not None:\n pos_out = Vector(tag.origin.xyz) / self.scale_unit\n pos_out.rotate(rot)\n pos_out += pos\n rot_out = Euler(tag.origin.rpy)\n rot_out.rotate(rot)\n return (pos_out, rot_out)\n else:\n return (pos, rot)\n\n def get_link_data(self, link_pos: Vector, link_rot: Euler, link: Link, visual: Visual):\n visual_pos, visual_rot = self.add_link_origin(link_pos, link_rot, visual)\n\n if hasattr(visual.geometry, \"filename\") and visual.geometry.filename:\n file_path = visual.geometry.filename\n mesh_name: str = link.name + \".\" + os.path.basename(file_path)\n if len(mesh_name) > 63:\n print(\"Mesh\", mesh_name, \"has more than 63 characters, the characters from 64 will be ignored\")\n mesh_name = mesh_name[0:63]\n else:\n if hasattr(visual.geometry, \"length\") and hasattr(visual.geometry, \"radius\"):\n file_path = [\"cylinder\", visual.geometry.length, visual.geometry.radius]\n mesh_name = link.name + \".cylinder\"\n elif hasattr(visual.geometry, \"size\"):\n file_path = [\"cube\", visual.geometry.size]\n mesh_name = link.name + \".cube\"\n elif hasattr(visual.geometry, \"radius\"):\n file_path = [\"sphere\", visual.geometry.radius]\n mesh_name = link.name + \".sphere\"\n else:\n file_path = \"\"\n mesh_name = link.name + \".empty\"\n\n if hasattr(visual.geometry, \"scale\") and visual.geometry.scale:\n scale = Vector(visual.geometry.scale)\n else:\n scale = Vector((1, 1, 1))\n\n if hasattr(visual, \"material\") and hasattr(visual.material, \"name\"):\n material = bpy.data.materials.get(visual.material.name)\n if material is None:\n material = bpy.data.materials.new(visual.material.name)\n if hasattr(visual.material, \"color\") and visual.material.color and visual.material.color.rgba:\n material.use_nodes = True\n principled_node = material.node_tree.nodes.get(\"Principled BSDF\")\n principled_node.inputs[0].default_value = visual.material.color.rgba\n else:\n material = None\n\n return (mesh_name, file_path, visual_pos, visual_rot, scale, material)\n\n def bind_mesh_to_bone(self, mesh_name: str, bone_name: str) -> None:\n bpy.ops.object.mode_set(mode=\"POSE\")\n\n object = bpy.context.scene.objects.get(mesh_name)\n object.select_set(True)\n self.arm_bones.active = self.arm_bones[bone_name]\n self.arm_bones[bone_name].select = True\n bpy.ops.object.parent_set(type=\"BONE\")\n object.select_set(False)\n self.arm_bones[bone_name].select = False\n\n bpy.ops.object.mode_set(mode=\"OBJECT\")\n return None\n\n def add_bone(self, link: Link, joint: Joint, joint_pos: Vector, joint_rot: Euler, bone_name: str) -> None:\n bpy.context.view_layer.objects.active = self.root\n bpy.ops.object.mode_set(mode=\"EDIT\", toggle=False)\n\n head = joint_pos\n tail = Vector((0.0, 0.0, 0.1 / self.scale_unit))\n if hasattr(joint, \"axis\") and joint.axis is not None and Vector(joint.axis).magnitude != 0:\n tail = Vector(joint.axis).normalized() * 0.1 / self.scale_unit\n tail.rotate(joint_rot)\n\n bone: Bone = self.root.data.edit_bones.new(bone_name)\n bone.head = head\n bone.tail = head + tail\n\n if self.robot.parent_map[link.name][1] == self.robot.get_root():\n bone.parent = self.root.data.edit_bones[\"root\" + self.bone_tail]\n else:\n parent_joint = self.robot.parent_map[self.robot.parent_map[link.name][1]][0]\n parent_joint_name = parent_joint + \".\" + str(self.robot.joint_map[parent_joint].type) + self.bone_tail\n bone.parent = self.root.data.edit_bones[parent_joint_name]\n\n bpy.ops.object.mode_set(mode=\"OBJECT\")\n return None\n\n def build_root(self) -> None:\n root_link: Link = self.robot.link_map[self.robot.get_root()]\n self.link_pose[root_link.name] = (Vector(), Euler())\n self.set_link_origin(root_link)\n\n if root_link.visuals:\n objects = []\n visual: Visual\n for visual in root_link.visuals:\n mesh_name, file_path, visual_pos, visual_rot, scale, material = self.get_link_data(\n self.link_pose[root_link.name][0], self.link_pose[root_link.name][1], root_link, visual\n )\n\n pos_tmp = self.link_pose[root_link.name][0].copy()\n pos_tmp.rotate(self.link_pose[root_link.name][1])\n visual_pos += pos_tmp\n\n rot_tmp = self.link_pose[root_link.name][1].copy()\n rot_tmp.rotate(visual_rot)\n visual_rot = rot_tmp\n\n object = self.add_mesh(\n mesh_name,\n material,\n file_path,\n visual_pos,\n visual_rot,\n scale,\n self.link_pose[root_link.name][0],\n self.link_pose[root_link.name][1],\n )\n objects.append(object)\n\n for object in objects:\n object.select_set(True)\n\n if len(bpy.context.selected_objects) > 1:\n bpy.context.view_layer.objects.active = bpy.context.selected_objects[0]\n bpy.ops.object.join()\n\n bone_name = self.root_name + self.bone_tail\n self.add_root_bone(root_link.name, bone_name)\n\n objects[0].name = root_link.name\n self.bind_mesh_to_bone(root_link.name, bone_name)\n\n else:\n bone_name = self.root_name + self.bone_tail\n self.add_root_bone(root_link.name, bone_name)\n\n self.parent_links = [root_link]\n return None\n\n def build_chain(self) -> None:\n while self.robot.child_map:\n # Make new parent links\n links = self.parent_links\n\n # Iterate through all parent links\n for link in links:\n self.set_link_origin(link)\n\n # Iterate through all children of parent link\n if self.robot.child_map.get(link.name):\n for child_map in self.robot.child_map[link.name]:\n child_pos = self.link_pose[link.name][0].copy()\n child_rot = self.link_pose[link.name][1].copy()\n\n child_joint = self.robot.joint_map[child_map[0]]\n child_pos, child_rot = self.add_link_origin(child_pos, child_rot, child_joint)\n joint_pos = child_pos.copy()\n joint_rot = child_rot.copy()\n\n child_link = self.robot.link_map[child_map[1]]\n child_pos, child_rot = self.add_link_origin(child_pos, child_rot, child_link)\n\n self.link_pose[child_link.name] = (child_pos, child_rot)\n\n if child_link.visuals:\n visual: Visual\n objects = []\n for visual in child_link.visuals:\n mesh_name, file_path, visual_pos, visual_rot, scale, material = self.get_link_data(\n child_pos, child_rot, child_link, visual\n )\n object = self.add_mesh(\n mesh_name,\n material,\n file_path,\n visual_pos,\n visual_rot,\n scale,\n self.link_pose[child_link.name][0],\n self.link_pose[child_link.name][1],\n )\n objects.append(object)\n\n for object in objects:\n object.select_set(True)\n\n if len(bpy.context.selected_objects) > 1:\n bpy.context.view_layer.objects.active = bpy.context.selected_objects[0]\n bpy.ops.object.join()\n\n bone_name = child_joint.name + \".\" + str(child_joint.type) + self.bone_tail\n self.add_bone(child_link, child_joint, joint_pos, joint_rot, bone_name)\n\n objects[0].name = child_link.name\n self.bind_mesh_to_bone(child_link.name, bone_name)\n else:\n bone_name = child_joint.name + \".\" + str(child_joint.type) + self.bone_tail\n self.add_bone(child_link, child_joint, child_pos, child_rot, bone_name)\n\n self.parent_links.append(child_link)\n\n del self.robot.child_map[link.name]\n\n # Remove finish link from parent links\n self.parent_links.remove(link)\n return None\n", "repo_name": "HoangGiang93/urdf_importer", "sub_path": "urdf_importer_addon/urdf_importer/robot_builder.py", "file_name": "robot_builder.py", "file_ext": "py", "file_size_in_byte": 28098, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 18, "dataset": "github-code", "pt": "37", "api": [{"api_name": "xml.etree.ElementTree.parse", "line_number": 20, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 20, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.Element", "line_number": 23, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 23, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.tostring", "line_number": 30, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 30, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.parse", "line_number": 34, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 34, "usage_type": "name"}, {"api_name": "os.path.dirname", "line_number": 38, "usage_type": "call"}, {"api_name": "os.path", "line_number": 38, "usage_type": "attribute"}, {"api_name": "typing.Dict", "line_number": 39, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 41, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 42, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 42, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 43, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 44, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 45, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 46, "usage_type": "call"}, {"api_name": "os.path", "line_number": 46, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 47, "usage_type": "call"}, {"api_name": "os.path.splitext", "line_number": 95, "usage_type": "call"}, {"api_name": "os.path", "line_number": 95, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 97, "usage_type": "call"}, {"api_name": "os.path", "line_number": 97, "usage_type": "attribute"}, {"api_name": "shutil.copy", "line_number": 101, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 121, "usage_type": "call"}, {"api_name": "os.path", "line_number": 121, "usage_type": "attribute"}, {"api_name": "os.remove", "line_number": 122, "usage_type": "call"}, {"api_name": "bpy.types.BlendData", "line_number": 126, "usage_type": "name"}, {"api_name": "bpy.types.Armature", "line_number": 127, "usage_type": "name"}, {"api_name": "bpy.types.Mesh", "line_number": 130, "usage_type": "name"}, {"api_name": "bpy.types.Object", "line_number": 133, "usage_type": "name"}, {"api_name": "bpy.types.Material", "line_number": 136, "usage_type": "name"}, {"api_name": "bpy.types.Camera", "line_number": 139, "usage_type": "name"}, {"api_name": "bpy.types.Light", "line_number": 142, "usage_type": "name"}, {"api_name": "bpy.types.Image", "line_number": 145, "usage_type": "name"}, {"api_name": "bpy.context", "line_number": 149, "usage_type": "attribute"}, {"api_name": "bpy.context.view_layer.update", "line_number": 151, "usage_type": "call"}, {"api_name": "bpy.context", "line_number": 151, "usage_type": "attribute"}, {"api_name": "typing.List", "line_number": 157, "usage_type": "name"}, {"api_name": "bpy.types.Material", "line_number": 157, "usage_type": "name"}, {"api_name": "bpy.types.Object", "line_number": 158, "usage_type": "name"}, {"api_name": "bpy.data", "line_number": 159, "usage_type": "attribute"}, {"api_name": "bpy.data.materials.remove", "line_number": 198, "usage_type": "call"}, {"api_name": "bpy.data", "line_number": 198, "usage_type": "attribute"}, {"api_name": "bpy.data.materials.remove", "line_number": 205, "usage_type": "call"}, {"api_name": "bpy.data", "line_number": 205, "usage_type": "attribute"}, {"api_name": "bpy.data.materials.get", "line_number": 214, "usage_type": "call"}, {"api_name": "bpy.data", "line_number": 214, "usage_type": "attribute"}, {"api_name": "bpy.data", "line_number": 215, "usage_type": "attribute"}, {"api_name": "bpy.data.materials.remove", "line_number": 235, "usage_type": "call"}, {"api_name": "bpy.data", "line_number": 235, "usage_type": "attribute"}, {"api_name": "bpy.data", "line_number": 244, "usage_type": "attribute"}, {"api_name": "bpy.data", "line_number": 250, "usage_type": "attribute"}, {"api_name": "urdf_parser_py.urdf.URDF", "line_number": 269, "usage_type": "name"}, {"api_name": "urdf_parser_py.urdf.URDF.from_xml_string", "line_number": 269, "usage_type": "call"}, {"api_name": "typing.Dict", "line_number": 270, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 270, "usage_type": "name"}, {"api_name": "mathutils.Vector", "line_number": 270, "usage_type": "name"}, {"api_name": "mathutils.Euler", "line_number": 270, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 271, "usage_type": "name"}, {"api_name": "bpy.types.Bone", "line_number": 271, "usage_type": "name"}, {"api_name": "bpy.types.Object", "line_number": 272, "usage_type": "name"}, {"api_name": "bpy.data", "line_number": 287, "usage_type": "attribute"}, {"api_name": "bpy.data.materials.get", "line_number": 299, "usage_type": "call"}, {"api_name": "bpy.data", "line_number": 299, "usage_type": "attribute"}, {"api_name": "bpy.types.Material", "line_number": 302, "usage_type": "name"}, {"api_name": "bpy.data.materials.new", "line_number": 302, "usage_type": "call"}, {"api_name": "bpy.data", "line_number": 302, "usage_type": "attribute"}, {"api_name": "urdf_parser_py.urdf.Link", "line_number": 307, "usage_type": "name"}, {"api_name": "urdf_parser_py.urdf.Visual", "line_number": 309, "usage_type": "name"}, {"api_name": "os.path.dirname", "line_number": 313, "usage_type": "call"}, {"api_name": "os.path", "line_number": 313, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 314, "usage_type": "call"}, {"api_name": "os.path", "line_number": 314, "usage_type": "attribute"}, {"api_name": "os.path.basename", "line_number": 315, "usage_type": "call"}, {"api_name": "os.path", "line_number": 315, "usage_type": "attribute"}, {"api_name": "rospkg.RosPack", "line_number": 316, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 317, "usage_type": "call"}, {"api_name": "os.path", "line_number": 317, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 318, "usage_type": "call"}, {"api_name": "os.path", "line_number": 318, "usage_type": "attribute"}, {"api_name": "bpy.types.Armature", "line_number": 323, "usage_type": "name"}, {"api_name": "bpy.data.armatures.new", "line_number": 323, "usage_type": "call"}, {"api_name": "bpy.data", "line_number": 323, "usage_type": "attribute"}, {"api_name": "bpy.data.objects.new", "line_number": 325, "usage_type": "call"}, {"api_name": "bpy.data", "line_number": 325, "usage_type": "attribute"}, {"api_name": "bpy.context.scene.collection.objects.link", "line_number": 327, "usage_type": "call"}, {"api_name": "bpy.context", "line_number": 327, "usage_type": "attribute"}, {"api_name": "bpy.types.Material", "line_number": 333, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 334, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 334, "usage_type": "name"}, {"api_name": "mathutils.Vector", "line_number": 335, "usage_type": "call"}, {"api_name": "mathutils.Euler", "line_number": 336, "usage_type": "call"}, {"api_name": "mathutils.Vector", "line_number": 337, "usage_type": "call"}, {"api_name": "mathutils.Vector", "line_number": 338, "usage_type": "call"}, {"api_name": "mathutils.Euler", "line_number": 339, "usage_type": "call"}, {"api_name": "bpy.ops.mesh.primitive_cylinder_add", "line_number": 343, "usage_type": "call"}, {"api_name": "bpy.ops", "line_number": 343, "usage_type": "attribute"}, {"api_name": "bpy.ops.mesh.primitive_cube_add", "line_number": 347, "usage_type": "call"}, {"api_name": "bpy.ops", "line_number": 347, "usage_type": "attribute"}, {"api_name": "bpy.ops.mesh.primitive_uv_sphere_add", "line_number": 349, "usage_type": "call"}, {"api_name": "bpy.ops", "line_number": 349, "usage_type": "attribute"}, {"api_name": "bpy.context", "line_number": 353, "usage_type": "attribute"}, {"api_name": "bpy.data.materials.get", "line_number": 356, "usage_type": "call"}, {"api_name": "bpy.data", "line_number": 356, "usage_type": "attribute"}, {"api_name": "bpy.data.materials.new", "line_number": 358, "usage_type": "call"}, {"api_name": "bpy.data", "line_number": 358, "usage_type": "attribute"}, {"api_name": "os.path.splitext", "line_number": 362, "usage_type": "call"}, {"api_name": "os.path", "line_number": 362, "usage_type": "attribute"}, {"api_name": "bpy.ops.wm.collada_import", "line_number": 365, "usage_type": "call"}, {"api_name": "bpy.ops", "line_number": 365, "usage_type": "attribute"}, {"api_name": "bpy.ops.import_scene.obj", "line_number": 368, "usage_type": "call"}, {"api_name": "bpy.ops", "line_number": 368, "usage_type": "attribute"}, {"api_name": "bpy.ops.import_mesh.stl", "line_number": 370, "usage_type": "call"}, {"api_name": "bpy.ops", "line_number": 370, "usage_type": "attribute"}, {"api_name": "bpy.types.Camera", "line_number": 375, "usage_type": "name"}, {"api_name": "bpy.data", "line_number": 376, "usage_type": "attribute"}, {"api_name": "bpy.data.cameras.remove", "line_number": 377, "usage_type": "call"}, {"api_name": "bpy.data", "line_number": 377, "usage_type": "attribute"}, {"api_name": "bpy.types.Light", "line_number": 378, "usage_type": "name"}, {"api_name": "bpy.data", "line_number": 379, "usage_type": "attribute"}, {"api_name": "bpy.data.lights.remove", "line_number": 380, "usage_type": "call"}, {"api_name": "bpy.data", "line_number": 380, "usage_type": "attribute"}, {"api_name": "bpy.context", "line_number": 381, "usage_type": "attribute"}, {"api_name": "bpy.context", "line_number": 382, "usage_type": "attribute"}, {"api_name": "bpy.ops.object.join", "line_number": 383, "usage_type": "call"}, {"api_name": "bpy.ops", "line_number": 383, "usage_type": "attribute"}, {"api_name": "bpy.context", "line_number": 384, "usage_type": "attribute"}, {"api_name": "bpy.ops.mesh.uv_texture_add", "line_number": 385, "usage_type": "call"}, {"api_name": "bpy.ops", "line_number": 385, "usage_type": "attribute"}, {"api_name": "bpy.context", "line_number": 386, "usage_type": "attribute"}, {"api_name": "bpy.ops.object.modifier_apply", "line_number": 389, "usage_type": "call"}, {"api_name": "bpy.ops", "line_number": 389, "usage_type": "attribute"}, {"api_name": "bpy.data.meshes.new", "line_number": 394, "usage_type": "call"}, {"api_name": "bpy.data", "line_number": 394, "usage_type": "attribute"}, {"api_name": "bpy.data.objects.new", "line_number": 396, "usage_type": "call"}, {"api_name": "bpy.data", "line_number": 396, "usage_type": "attribute"}, {"api_name": "bpy.context.scene.collection.objects.link", "line_number": 397, "usage_type": "call"}, {"api_name": "bpy.context", "line_number": 397, "usage_type": "attribute"}, {"api_name": "bpy.context", "line_number": 406, "usage_type": "attribute"}, {"api_name": "bpy.ops.object.mode_set", "line_number": 408, "usage_type": "call"}, {"api_name": "bpy.ops", "line_number": 408, "usage_type": "attribute"}, {"api_name": "bpy.ops.mesh.select_all", "line_number": 409, "usage_type": "call"}, {"api_name": "bpy.ops", "line_number": 409, "usage_type": "attribute"}, {"api_name": "bpy.ops.mesh.flip_normals", "line_number": 410, "usage_type": "call"}, {"api_name": "bpy.ops", "line_number": 410, "usage_type": "attribute"}, {"api_name": "bpy.ops.object.mode_set", "line_number": 411, "usage_type": "call"}, {"api_name": "bpy.ops", "line_number": 411, "usage_type": "attribute"}, {"api_name": "bpy.context", "line_number": 414, "usage_type": "attribute"}, {"api_name": "bpy.context", "line_number": 415, "usage_type": "attribute"}, {"api_name": "bpy.ops.object.origin_set", "line_number": 416, "usage_type": "call"}, {"api_name": "bpy.ops", "line_number": 416, "usage_type": "attribute"}, {"api_name": "bpy.context", "line_number": 417, "usage_type": "attribute"}, {"api_name": "mathutils.Vector", "line_number": 417, "usage_type": "call"}, {"api_name": "bpy.context", "line_number": 418, "usage_type": "attribute"}, {"api_name": "mathutils.Euler", "line_number": 418, "usage_type": "call"}, {"api_name": "bpy.ops.object.transform_apply", "line_number": 422, "usage_type": "call"}, {"api_name": "bpy.ops", "line_number": 422, "usage_type": "attribute"}, {"api_name": "bpy.types.Object", "line_number": 340, "usage_type": "name"}, {"api_name": "urdf_parser_py.urdf.Link", "line_number": 427, "usage_type": "name"}, {"api_name": "mathutils.Vector", "line_number": 429, "usage_type": "call"}, {"api_name": "mathutils.Euler", "line_number": 430, "usage_type": "call"}, {"api_name": "bpy.context", "line_number": 434, "usage_type": "attribute"}, {"api_name": "bpy.ops.object.mode_set", "line_number": 435, "usage_type": "call"}, {"api_name": "bpy.ops", "line_number": 435, "usage_type": "attribute"}, {"api_name": "mathutils.Vector", "line_number": 438, "usage_type": "call"}, {"api_name": "bpy.types.Bone", "line_number": 441, "usage_type": "name"}, {"api_name": "bpy.ops.object.mode_set", "line_number": 444, "usage_type": "call"}, {"api_name": "bpy.ops", "line_number": 444, "usage_type": "attribute"}, {"api_name": "mathutils.Vector", "line_number": 447, "usage_type": "name"}, {"api_name": "mathutils.Euler", "line_number": 447, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 447, "usage_type": "name"}, {"api_name": "urdf_parser_py.urdf.Link", "line_number": 447, "usage_type": "name"}, {"api_name": "urdf_parser_py.urdf.Joint", "line_number": 447, "usage_type": "name"}, {"api_name": "urdf_parser_py.urdf.Visual", "line_number": 447, "usage_type": "name"}, {"api_name": "mathutils.Vector", "line_number": 449, "usage_type": "call"}, {"api_name": "mathutils.Euler", "line_number": 452, "usage_type": "call"}, {"api_name": "typing.Tuple", "line_number": 447, "usage_type": "name"}, {"api_name": "mathutils.Vector", "line_number": 458, "usage_type": "name"}, {"api_name": "mathutils.Euler", "line_number": 458, "usage_type": "name"}, {"api_name": "urdf_parser_py.urdf.Link", "line_number": 458, "usage_type": "name"}, {"api_name": "urdf_parser_py.urdf.Visual", "line_number": 458, "usage_type": "name"}, {"api_name": "os.path.basename", "line_number": 463, "usage_type": "call"}, {"api_name": "os.path", "line_number": 463, "usage_type": "attribute"}, {"api_name": "mathutils.Vector", "line_number": 482, "usage_type": "call"}, {"api_name": "mathutils.Vector", "line_number": 484, "usage_type": "call"}, {"api_name": "bpy.data.materials.get", "line_number": 487, "usage_type": "call"}, {"api_name": "bpy.data", "line_number": 487, "usage_type": "attribute"}, {"api_name": "bpy.data.materials.new", "line_number": 489, "usage_type": "call"}, {"api_name": "bpy.data", "line_number": 489, "usage_type": "attribute"}, {"api_name": "bpy.ops.object.mode_set", "line_number": 500, "usage_type": "call"}, {"api_name": "bpy.ops", "line_number": 500, "usage_type": "attribute"}, {"api_name": "bpy.context.scene.objects.get", "line_number": 502, "usage_type": "call"}, {"api_name": "bpy.context", "line_number": 502, "usage_type": "attribute"}, {"api_name": "bpy.ops.object.parent_set", "line_number": 506, "usage_type": "call"}, {"api_name": "bpy.ops", "line_number": 506, "usage_type": "attribute"}, {"api_name": "bpy.ops.object.mode_set", "line_number": 510, "usage_type": "call"}, {"api_name": "bpy.ops", "line_number": 510, "usage_type": "attribute"}, {"api_name": "urdf_parser_py.urdf.Link", "line_number": 513, "usage_type": "name"}, {"api_name": "urdf_parser_py.urdf.Joint", "line_number": 513, "usage_type": "name"}, {"api_name": "mathutils.Vector", "line_number": 513, "usage_type": "name"}, {"api_name": "mathutils.Euler", "line_number": 513, "usage_type": "name"}, {"api_name": "bpy.context", "line_number": 514, "usage_type": "attribute"}, {"api_name": "bpy.ops.object.mode_set", "line_number": 515, "usage_type": "call"}, {"api_name": "bpy.ops", "line_number": 515, "usage_type": "attribute"}, {"api_name": "mathutils.Vector", "line_number": 518, "usage_type": "call"}, {"api_name": "mathutils.Vector", "line_number": 519, "usage_type": "call"}, {"api_name": "mathutils.Vector", "line_number": 520, "usage_type": "call"}, {"api_name": "bpy.types.Bone", "line_number": 523, "usage_type": "name"}, {"api_name": "bpy.ops.object.mode_set", "line_number": 534, "usage_type": "call"}, {"api_name": "bpy.ops", "line_number": 534, "usage_type": "attribute"}, {"api_name": "urdf_parser_py.urdf.Link", "line_number": 538, "usage_type": "name"}, {"api_name": "mathutils.Vector", "line_number": 539, "usage_type": "call"}, {"api_name": "mathutils.Euler", "line_number": 539, "usage_type": "call"}, {"api_name": "urdf_parser_py.urdf.Visual", "line_number": 544, "usage_type": "name"}, {"api_name": "bpy.context", "line_number": 573, "usage_type": "attribute"}, {"api_name": "bpy.context", "line_number": 574, "usage_type": "attribute"}, {"api_name": "bpy.ops.object.join", "line_number": 575, "usage_type": "call"}, {"api_name": "bpy.ops", "line_number": 575, "usage_type": "attribute"}, {"api_name": "urdf_parser_py.urdf.Visual", "line_number": 616, "usage_type": "name"}, {"api_name": "bpy.context", "line_number": 637, "usage_type": "attribute"}, {"api_name": "bpy.context", "line_number": 638, "usage_type": "attribute"}, {"api_name": "bpy.ops.object.join", "line_number": 639, "usage_type": "call"}, {"api_name": "bpy.ops", "line_number": 639, "usage_type": "attribute"}]} +{"seq_id": "1863162071", "text": "import sys\nfrom lxml import etree\nimport requests\nfrom pyquery import PyQuery as pq\nimport pandas as pd\nimport os\nfrom os import path\nimport sys\n\n# from scripts import config\nimport config\n\n\nSEED_PAGES = [\n 'http://www.legco.gov.hk/general/english/counmtg/yr12-16/mtg_1213.htm',\n 'http://www.legco.gov.hk/general/english/counmtg/yr12-16/mtg_1314.htm',\n 'http://www.legco.gov.hk/general/english/counmtg/yr12-16/mtg_1415.htm'\n]\n# Information fields, useful for reviewing the result\nINFO_FIELDS = ['vote-date', 'vote-time', 'motion-en', 'motion-ch', 'mover-en', 'mover-ch', 'mover-type', 'vote-separate-mechanism']\n\n\ndef crawl_seed(seed):\n d = pq(seed)\n return d('a').map(lambda i, a: a.attrib.get('name', None)).filter(lambda i, s: s.startswith('cm20'))\n\n\ndef crawl_xml(meeting):\n # This logic is translated from the official JS code\n yy, mm, dd = map(lambda i: int(meeting[i:(i + 2)]), [4, 6, 8])\n if mm >= 10:\n yr = 'yr%02d-%02d' % (yy, yy + 1)\n else:\n yr = 'yr%02d-%02d' % (yy - 1, yy)\n prefix = 'http://www.legco.gov.hk'\n url = '%(prefix)s/%(yr)s/chinese/counmtg/voting/cm_vote_20%(yy)02d%(mm)02d%(dd)02d.xml' % locals()\n return requests.get(url)\n\n\ndef xml_to_records(xml):\n doc = etree.XML(xml)\n records = []\n for topic in doc.xpath('//legcohk-vote/meeting/vote'):\n info = [topic.xpath(f)[0].text for f in INFO_FIELDS]\n date = info[0]\n topic_id = '%s-%s' % (date, topic.attrib['number'])\n for member in topic.xpath('individual-votes/member'):\n member_id = member.attrib['name-en'] # Use English name as ID for simplicity\n member_id_en = member.attrib['name-en']\n member_id_ch = member.attrib['name-ch']\n vote = member.xpath('vote')[0].text\n records.append((topic_id, member_id, vote, member_id_en, member_id_ch) + tuple(info))\n return records\n\n\ndef name_normalize(name):\n mapping = {\n 'Dr Joseph LEE': 'Prof Joseph LEE',\n '郭偉強': '郭偉强',\n }\n return mapping.get(name, name)\n\n\n# More:\n# http://nbviewer.ipython.org/urls/course.ie.cuhk.edu.hk/~engg4030/tutorial/tutorial7/Legco-Preprocessing.ipynb\ndef clean_record(t):\n # According to the numbers, they seem to be the same person\n # records.append((topic_id, member_id, vote, member_id_en, member_id_ch) + tuple(info))\n # INFO_FIELDS = ['vote-date', 'vote-time', 'motion-en', 'motion-ch', 'mover-en', 'mover-ch', 'mover-type', 'vote-separate-mechanism']\n t = list(t)\n # Voters name\n t[1] = name_normalize(t[1])\n t[2] = name_normalize(t[2])\n t[3] = name_normalize(t[3])\n t[4] = name_normalize(t[4])\n # Movers name\n t[9] = name_normalize(t[9])\n t[10] = name_normalize(t[10])\n\n # Other normalization if any\n # ...\n return tuple(t)\n\n\ndef main():\n meetings = []\n for seed_page in SEED_PAGES:\n meetings.extend(crawl_seed(seed_page))\n\n with open(path.join(config.DIR_DATA_ROOT, 'meetings.txt'), 'w') as fp:\n for m in meetings:\n fp.write('%s\\n' % m)\n\n print('Parsed %d meetings from the root page' % len(meetings))\n\n vote_xmls = []\n for m in meetings:\n r = crawl_xml(m)\n print('Crawling %s' % m)\n # print('progress: %s/%s %s' % (len(vote_xmls), len(meetings), '#' * len(vote_xmls)))\n sys.stdout.flush()\n with open(path.join(config.DIR_VOTING_RECORDS_RAW, '%s.xml' % m), 'w') as fp:\n if r.ok:\n fp.write(r.content.decode('utf-8'))\n vote_xmls.append(r.content)\n\n # vote_xmls = filter(lambda r: r.ok, vote_xmls)\n # vote_xmls = [r.content for r in vote_xmls]\n print('Collected %d voting record XMLs in total' % len(vote_xmls))\n\n records = []\n for vote_xml in vote_xmls:\n records.extend(xml_to_records(vote_xml))\n\n records = [clean_record(r) for r in records]\n df = pd.DataFrame(records, columns = ['topic_id', 'member_id', 'vote', 'name-en', 'name-ch'] + INFO_FIELDS)\n df.to_csv(path.join(config.DIR_DATA_ROOT, 'records-all-with-info.csv'), encoding='utf-8')\n df.head()\n\n df = df[['topic_id', 'member_id', 'vote']]\n df.to_csv(path.join(config.DIR_DATA_ROOT, 'records-all.csv'), encoding='utf-8')\n\n\nif __name__ == '__main__':\n main()\n", "repo_name": "initiummedia/hk_legco", "sub_path": "scripts/download_voting_records.py", "file_name": "download_voting_records.py", "file_ext": "py", "file_size_in_byte": 4255, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "37", "api": [{"api_name": "pyquery.PyQuery", "line_number": 24, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 37, "usage_type": "call"}, {"api_name": "lxml.etree.XML", "line_number": 41, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 41, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 90, "usage_type": "call"}, {"api_name": "os.path", "line_number": 90, "usage_type": "name"}, {"api_name": "config.DIR_DATA_ROOT", "line_number": 90, "usage_type": "attribute"}, {"api_name": "sys.stdout.flush", "line_number": 101, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 101, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 102, "usage_type": "call"}, {"api_name": "os.path", "line_number": 102, "usage_type": "name"}, {"api_name": "config.DIR_VOTING_RECORDS_RAW", "line_number": 102, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 116, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 117, "usage_type": "call"}, {"api_name": "os.path", "line_number": 117, "usage_type": "name"}, {"api_name": "config.DIR_DATA_ROOT", "line_number": 117, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 121, "usage_type": "call"}, {"api_name": "os.path", "line_number": 121, "usage_type": "name"}, {"api_name": "config.DIR_DATA_ROOT", "line_number": 121, "usage_type": "attribute"}]} +{"seq_id": "41734974281", "text": "#%%\nfrom cProfile import label\nfrom netCDF4 import Dataset,num2date \nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom datetime import datetime, timezone\nimport time\nimport xarray as xr\nimport utm\nimport openpyxl\nimport pandas as pd\nimport json\nimport re\nfrom itertools import islice\nfrom mpl_toolkits.basemap import Basemap\nfrom mpl_toolkits.axes_grid1.inset_locator import zoomed_inset_axes\nfrom mpl_toolkits.axes_grid1.inset_locator import mark_inset\n\n#%%\ndef dms2dd(degrees, minutes, seconds, direction='E'):\n dd = degrees + minutes/60 + seconds/(60*60)\n if direction == 'S' or direction == 'W':\n dd *= -1\n return dd\n\ndef dd2dms(deg):\n d = int(deg)\n md = abs(deg - d) * 60\n m = int(md)\n sd = (md - m) * 60\n return [d, m, sd]\n\ndef parse_dms(dms):\n parts = re.split('[^\\d\\w]+', dms)\n lat = dms2dd(parts[0], parts[1], parts[2], parts[3])\n lng = dms2dd(parts[4], parts[5], parts[6], parts[7])\n\n return (lat, lng)\n# %%\n\n\n# %%\ndef get_risk_map(file='Q13_GSZ11_CGGV12_Final_PrSTM_stack_amplitude_AGF_2_netcdf_lonlat.nc',\npath='../Riskmaps/'):\n filenm=path+file\n data=xr.open_dataset(filenm)\n\n data=data.set_coords(('coordLON','coordLAT'))\n \n data.close()\n\n return data\n\n\n\n#%%\n \n# %% Read coordinates from Sarah\n\n\n# set up orthographic map projection with\n# perspective of satellite looking down at 50N, 100W.\n# use low resolution coastlines.\n\ndef make_map_P18(inmap, margin=[0.5, 0.5], savefig=True):\n \n p=inmap.location_probability;\n\n pp=p.values.flatten()\n p=p/sum(pp)\n\n logp=np.log(p)\n \n lllon = inmap.coordLON.min()-margin[0]\n urlon = inmap.coordLON.max()+margin[0]\n urlat = inmap.coordLAT.max()+margin[1] \n lllat = inmap.coordLAT.min()-margin[1]\n\n\n fig = plt.figure()\n ax = fig.add_subplot(111)\n\n map = Basemap(\n projection='cyl',\n lat_0=(urlat-lllat)/2,\n lon_0=(urlon-lllon)/2,\n resolution='h',\n llcrnrlat=lllat, \n urcrnrlat=urlat, \n llcrnrlon=lllon, \n urcrnrlon=urlon\n )\n# draw coastlines, country boundaries, fill continents.\n map.etopo(scale=0.9,alpha=0.5)\n map.drawcoastlines(linewidth=0.2, color='#cd5b45')\n map.drawcountries(linewidth=0.25)\n map.fillcontinents(color='coral',lake_color='aqua')\n#Houston 29.749907, -95.358421\n x, y = map(4.462456,51.926517) # Houston\n plt.scatter(x, y, 20, marker='o', color='Black')\n plt.annotate('Rotterdam', xy=(x, y))\n\n#The latitude of New Orleans, LA, USA is 29.951065, and the longitude is -90.071533.\n x, y = map(4.288788,52.078663) # Houston\n plt.scatter(x, y, 20, marker='o', color='Black')\n plt.annotate('Haag', xy=(x, y)) \n\n\n \n lons=inmap['coordLON']\n lats=inmap['coordLAT']\n x, y = map(lons, lats) # transform coordinates\n map.pcolormesh(x, y, logp,shading='auto', label='log(p)')\n cbar=map.colorbar()\n cbar.set_label('log of relative probability')\n # plt.scatter(x, y, 10, marker='o', color='Blue') \n\n x=3.939394\n y=52.16216\n plt.scatter(x, y, 20, marker='x', color='Red') \n\n \n# Zoomed plot\n llcrnrlon=lons.min().data\n llcrnrlat=lats.min().data\n urcrnrlon=lons.max().data\n urcrnrlat=lats.max().data\n\n axins = zoomed_inset_axes(ax, 2, loc=1)\n axins.set_xlim(lons.min(), lons.max())\n axins.set_ylim(lats.min(), lats.max())\n\n map2 = Basemap(\n llcrnrlon=llcrnrlon,\n llcrnrlat=llcrnrlat,\n urcrnrlon=urcrnrlon,\n urcrnrlat=urcrnrlat, \n ax=axins,\n# suppress_ticks=False\n )\n map2.drawmapboundary(fill_color='#BCD2E8')\n map2.etopo(scale=0.9,alpha=0.5)\n # map2.drawmeridians([3.8,4,4.2], labels=[0,0,0,1])\n # map2.drawparallels([60.5,60.7,60.9], labels=[0,1,0,0])\n map2.fillcontinents(color='#ddaa66', lake_color='#7777ff', zorder=0)\n#map2.drawcoastlines()\n#map2.drawcountries()\n\n \n x, y = map2(lons, lats) # transform coordinates\n map2.pcolormesh(x, y, logp,shading='auto', label='log(p)')\n \n mark_inset(ax, axins, loc1=2, loc2=4)#, fc=\"none\", ec=\"0.5\")\n \n\n plt.show()\n if savefig:\n fig.savefig('P18_map.png')\n\n return \n#%%\nmap=get_risk_map()\n# %%\nmake_map_P18(map, margin=[0.5, 0.5], savefig=False)\n# %%\n", "repo_name": "galendal/ACTOM_sites", "sub_path": "P18/P18_mapping.py", "file_name": "P18_mapping.py", "file_ext": "py", "file_size_in_byte": 4213, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "re.split", "line_number": 34, "usage_type": "call"}, {"api_name": "xarray.open_dataset", "line_number": 46, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 72, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 80, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 80, "usage_type": "name"}, {"api_name": "mpl_toolkits.basemap.Basemap", "line_number": 83, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 100, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 100, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.annotate", "line_number": 101, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 101, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 105, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 105, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.annotate", "line_number": 106, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 106, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 120, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 120, "usage_type": "name"}, {"api_name": "mpl_toolkits.axes_grid1.inset_locator.zoomed_inset_axes", "line_number": 129, "usage_type": "call"}, {"api_name": "mpl_toolkits.basemap.Basemap", "line_number": 133, "usage_type": "call"}, {"api_name": "mpl_toolkits.axes_grid1.inset_locator.mark_inset", "line_number": 153, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 156, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 156, "usage_type": "name"}]} +{"seq_id": "7012330483", "text": "import os\n\nimport numpy as np\nimport PIL.Image as pil\nimport skimage.transform\nfrom PIL import Image\n\nfrom ..registry import PIPELINES\n\n\n@PIPELINES.register()\nclass ImageDecoder(object):\n \"\"\"Decode Image\n \"\"\"\n def __init__(self,\n dataset,\n frame_idxs,\n num_scales,\n side_map,\n full_res_shape,\n img_ext,\n backend='cv2'):\n self.backend = backend\n self.dataset = dataset\n self.frame_idxs = frame_idxs\n self.num_scales = num_scales\n self.side_map = side_map\n self.full_res_shape = full_res_shape\n self.img_ext = img_ext\n\n def _pil_loader(self, path):\n with open(path, 'rb') as f:\n with Image.open(f) as img:\n return img.convert('RGB')\n\n def get_color(self, folder, frame_index, side):\n color = self._pil_loader(\n self.get_image_path(self.dataset, folder, frame_index, side))\n return color\n\n def get_image_path(self, dataset, folder, frame_index, side):\n if dataset == \"kitti\":\n f_str = \"{:010d}{}\".format(frame_index, self.img_ext)\n image_path = os.path.join(self.data_path, folder, f_str)\n elif dataset == \"kitti_odom\":\n f_str = \"{:06d}{}\".format(frame_index, self.img_ext)\n image_path = os.path.join(self.data_path,\n \"sequences/{:02d}\".format(int(folder)),\n \"image_{}\".format(self.side_map[side]),\n f_str)\n elif dataset == \"kitti_depth\":\n f_str = \"{:010d}{}\".format(frame_index, self.img_ext)\n image_path = os.path.join(\n self.data_path, folder,\n \"image_0{}/data\".format(self.side_map[side]), f_str)\n\n return image_path\n\n def get_depth(self, dataset, folder, frame_index, side):\n if dataset == \"kitii_depth\":\n f_str = \"{:010d}.png\".format(frame_index)\n depth_path = os.path.join(\n self.data_path, folder,\n \"proj_depth/groundtruth/image_0{}\".format(self.side_map[side]),\n f_str)\n\n depth_gt = pil.open(depth_path)\n depth_gt = depth_gt.resize(self.full_res_shape, pil.NEAREST)\n depth_gt = np.array(depth_gt).astype(np.float32) / 256\n\n else:\n f_str = \"{:010d}{}\".format(frame_index, self.img_ext)\n depth_path = os.path.join(self.data_path, folder + '_gt', f_str)\n\n img_file = Image.open(depth_path)\n depth_png = np.array(img_file, dtype=int)\n img_file.close()\n # make sure we have a proper 16bit depth map here.. not 8bit!\n assert np.max(depth_png) > 255, \\\n \"np.max(depth_png)={}, path={}\".format(np.max(depth_png), depth_path)\n\n depth_gt = depth_png.astype(np.float) / 256.\n\n depth_gt = depth_gt[160:960 - 160, :]\n\n depth_gt = skimage.transform.resize(depth_gt,\n self.full_res_shape[::-1],\n order=0,\n preserve_range=True,\n mode='constant')\n\n return depth_gt\n\n def __call__(self, results):\n \"\"\"\n Perform mp4 decode operations.\n return:\n List where each item is a numpy array after decoder.\n \"\"\"\n if results.get('mode', None) == 'infer':\n imgs = {}\n imgs[(\"color\", 0,\n -1)] = Image.open(results[\"filename\"]).convert(\"RGB\")\n results['imgs'] = imgs\n return results\n\n self.data_path = results['data_path']\n results['backend'] = self.backend\n\n imgs = {}\n\n results['frame_idxs'] = self.frame_idxs\n results['num_scales'] = self.num_scales\n\n file_name = results['filename']\n folder = results['folder']\n frame_index = results['frame_index']\n line = file_name.split('/')\n istrain = folder.split('_')[1]\n if 'mode' not in results:\n results['mode'] = istrain\n results['day_or_night'] = folder.split('_')[0]\n\n if istrain == \"train\":\n if folder[0] == 'd':\n folder2 = folder + '_fake_night'\n flag = 0\n else:\n folder2 = folder + '_fake_day'\n tmp = folder\n folder = folder2\n folder2 = tmp\n flag = 1\n\n if len(line) == 3:\n side = line[2]\n else:\n side = None\n\n results['side'] = side\n\n for i in self.frame_idxs:\n\n if i == \"s\":\n other_side = {\"r\": \"l\", \"l\": \"r\"}[side]\n imgs[(\"color\", i,\n -1)] = self.get_color(folder, frame_index, other_side)\n imgs[(\"color_n\", i,\n -1)] = self.get_color(folder2, frame_index,\n other_side)\n else:\n imgs[(\"color\", i,\n -1)] = self.get_color(folder, frame_index + i, side)\n imgs[(\"color_n\", i,\n -1)] = self.get_color(folder2, frame_index + i, side)\n\n istrain = folder.split('_')[1]\n if istrain != 'train':\n if flag:\n depth_gt = self.get_depth(folder2, frame_index, side)\n else:\n depth_gt = self.get_depth(folder, frame_index, side)\n imgs[\"depth_gt\"] = np.expand_dims(depth_gt, 0)\n elif istrain == 'val':\n if len(line) == 3:\n side = line[2]\n else:\n side = None\n\n for i in self.frame_idxs:\n if i == \"s\":\n other_side = {\"r\": \"l\", \"l\": \"r\"}[side]\n imgs[(\"color\", i,\n -1)] = self.get_color(folder, frame_index, other_side)\n else:\n\n imgs[(\"color\", i,\n -1)] = self.get_color(folder, frame_index + i, side)\n\n # adjusting intrinsics to match each scale in the pyramid\n\n depth_gt = self.get_depth(self.dataset, folder, frame_index, side)\n imgs[\"depth_gt\"] = np.expand_dims(depth_gt, 0)\n results['imgs'] = imgs\n\n return results\n", "repo_name": "PaddlePaddle/awesome-DeepLearning", "sub_path": "Paddle_Industry_Practice_Sample_Library/Football_Action/PaddleVideo/paddlevideo/loader/pipelines/decode_image.py", "file_name": "decode_image.py", "file_ext": "py", "file_size_in_byte": 6574, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2544, "dataset": "github-code", "pt": "37", "api": [{"api_name": "PIL.Image.open", "line_number": 33, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 33, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 44, "usage_type": "call"}, {"api_name": "os.path", "line_number": 44, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 47, "usage_type": "call"}, {"api_name": "os.path", "line_number": 47, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 53, "usage_type": "call"}, {"api_name": "os.path", "line_number": 53, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 62, "usage_type": "call"}, {"api_name": "os.path", "line_number": 62, "usage_type": "attribute"}, {"api_name": "PIL.Image.open", "line_number": 67, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 67, "usage_type": "name"}, {"api_name": "PIL.Image.NEAREST", "line_number": 68, "usage_type": "attribute"}, {"api_name": "PIL.Image", "line_number": 68, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 69, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 69, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 73, "usage_type": "call"}, {"api_name": "os.path", "line_number": 73, "usage_type": "attribute"}, {"api_name": "PIL.Image.open", "line_number": 75, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 75, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 76, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 79, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 80, "usage_type": "call"}, {"api_name": "numpy.float", "line_number": 82, "usage_type": "attribute"}, {"api_name": "skimage.transform.transform.resize", "line_number": 86, "usage_type": "call"}, {"api_name": "skimage.transform.transform", "line_number": 86, "usage_type": "attribute"}, {"api_name": "skimage.transform", "line_number": 86, "usage_type": "name"}, {"api_name": "PIL.Image.open", "line_number": 103, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 103, "usage_type": "name"}, {"api_name": "numpy.expand_dims", "line_number": 163, "usage_type": "call"}, {"api_name": "numpy.expand_dims", "line_number": 183, "usage_type": "call"}, {"api_name": "registry.PIPELINES.register", "line_number": 11, "usage_type": "call"}, {"api_name": "registry.PIPELINES", "line_number": 11, "usage_type": "name"}]} +{"seq_id": "24670811334", "text": "import logging\nfrom typing import Optional, List\nimport openai\n\n\nclass OpenAI:\n \"\"\"Library to support `OpenAI `_ and `Azure OpenAI `_ services.\n\n Library is **not** included in the `rpaframework` package, so in order to use it\n you have to add `rpaframework-openai` with the desired version in your\n *conda.yaml* file.\n\n **Robot Framework example usage**\n\n .. code-block:: robotframework\n\n *** Settings ***\n Library RPA.Robocorp.Vault\n Library RPA.OpenAI\n\n *** Tasks ***\n Create a text completion\n ${secrets} Get Secret secret_name=OpenAI\n Authorize To OpenAI api_key=${secrets}[key]\n ${completion} Completion Create\n ... Write a tagline for an ice cream shop\n ... temperature=0.6\n Log ${completion}\n\n **Python example usage**\n\n .. code-block:: python\n\n from RPA.Robocorp.Vault import Vault\n from RPA.OpenAI import OpenAI\n\n secrets = Vault().get_secret(\"OpenAI\")\n baselib = OpenAI()\n baselib.authorize_to_openai(secrets[\"key\"])\n\n result = baselib.completion_create(\n Create a tagline for icecream shop',\n temperature=0.6,\n )\n print(result)\n \"\"\" # noqa: E501\n\n ROBOT_LIBRARY_SCOPE = \"GLOBAL\"\n ROBOT_LIBRARY_DOC_FORMAT = \"REST\"\n\n def __init__(self) -> None:\n self.logger = logging.getLogger(__name__)\n self.service_type = \"OpenAI\"\n\n def authorize_to_azure_openai(\n self,\n api_key: str,\n api_base: str,\n api_type: Optional[str] = \"azure\",\n api_version: Optional[str] = \"2023-05-15\",\n ) -> None:\n \"\"\"Keyword for authorize to Azure OpenAI.\n\n :param api_key: Your Azure OpenAI API key\n :param api_base: Your Endpoint URL. Example: https://docs-test-001.openai.azure.com/\n :param api_type: \"azure\"\n :param api_version: \"2023-05-15\"\n\n Robot Framework example:\n\n .. code-block:: robotframework\n\n ${secrets} Get Secret secret_name=AzureOpenAI\n Authorize To Azure Openai\n ... api_key=${secrets}[api_key]\n ... api_base=${secrets}[api_base]\n ... api_type=azure\n ... api_version=2023-05-15\n\n Python example:\n\n .. code-block:: python\n\n secrets = Vault().get_secret(\"AzureOpenAI\")\n baselib = OpenAI()\n baselib.authorize_to_azure_openai(\n secrets[\"api_key\"],\n secrets[\"api_base\"],\n \"azure\",\n \"2023-05-15\"\n )\n\n \"\"\" # noqa: E501\n openai.api_key = api_key\n openai.api_base = api_base\n openai.api_type = api_type\n openai.api_version = api_version\n self.service_type = \"Azure\"\n\n def authorize_to_openai(self, api_key: str) -> None:\n \"\"\"Keyword for authorize to OpenAI with your API key obtained from your account.\n\n :param api_key: Your OpenAI API key\n\n Robot Framework example:\n\n .. code-block:: robotframework\n\n ${secrets} Get Secret secret_name=OpenAI\n Authorize To OpenAI api_key=${secrets}[key]\n\n Python example:\n\n .. code-block:: python\n\n secrets = Vault().get_secret(\"OpenAI\")\n baselib = OpenAI()\n baselib.authorize_to_openai(secrets[\"key\"])\n\n \"\"\"\n openai.api_key = api_key\n\n def completion_create(\n self,\n prompt: str,\n model: Optional[str] = \"text-davinci-003\",\n temperature: Optional[int] = 0.7,\n max_tokens: Optional[int] = 256,\n top_probability: Optional[int] = 1,\n frequency_penalty: Optional[int] = 0,\n presence_penalty: Optional[int] = 0,\n result_format: Optional[str] = \"string\",\n ) -> None:\n \"\"\"Keyword for creating text completions in OpenAI and Azure OpenAI.\n Keyword returns a text string.\n\n **Note**. When using ``Azure OpenAI`` you must provide the ``deployment_name``\n as the ``model`` parameter instead of the model ID used with ``OpenAI``.\n\n :param prompt: Text submitted to OpenAI for creating natural language.\n :param model: For ``OpenAI`` the ID of the model to use, e.g. ``text-davinci-003``.\n For ``Azure OpenAI`` the Deployment name, e.g. ``myDavinci3deployment``.\n :param temperature: What sampling temperature to use.\n Higher values means the model will take more risks..\n :param max_tokens: The maximum number of tokens to generate in the completion..\n :param top_probability: Controls diversity via nucleus sampling. 0.5 means half\n of all likelihood-weighted options are considered.\n :param frequency_penalty: Number between -2.0 and 2.0. Positive values penalize\n new tokens based on their existing frequency in the text so far.\n :param presence_penalty: Number between -2.0 and 2.0. Positive values penalize\n new tokens based on whether they appear in the text so far.\n :param result_format: Result format (string / json). Return just a string or\n the default JSON response.\n\n Robot Framework example:\n\n .. code-block:: robotframework\n\n ${response} Completion Create\n ... Write a tagline for an icecream shop.\n ... temperature=0.6\n Log ${response}\n\n Python example:\n\n .. code-block:: python\n\n result = baselib.completion_create(\n 'Create a tagline for icecream shop',\n temperature=0.6,\n )\n print(result)\n\n \"\"\" # noqa: E501\n parameters = {\n \"prompt\": prompt,\n \"temperature\": temperature,\n \"max_tokens\": max_tokens,\n \"top_p\": top_probability,\n \"frequency_penalty\": frequency_penalty,\n \"presence_penalty\": presence_penalty,\n }\n if self.service_type == \"Azure\":\n parameters[\"engine\"] = model\n else:\n parameters[\"model\"] = model\n response = openai.Completion.create(**parameters)\n self.logger.info(response)\n if result_format == \"string\":\n text = response[\"choices\"][0][\"text\"].strip()\n return text\n if result_format == \"json\":\n return response\n else:\n return None\n\n def chat_completion_create(\n self,\n user_content: str = None,\n conversation: Optional[List] = None,\n model: Optional[str] = \"gpt-3.5-turbo\",\n system_content: Optional[str] = None,\n temperature: Optional[int] = 1,\n top_probability: Optional[int] = 1,\n frequency_penalty: Optional[int] = 0,\n presence_penalty: Optional[int] = 0,\n ) -> None:\n \"\"\"Keyword for creating ChatGPT text completions using OpenAI or Azure OpenAI.\n Keyword returns the response as a string and the message history as a list.\n\n **Note**. When using ``Azure OpenAI`` you must provide the ``deployment_name``\n as the ``model`` parameter instead of the model ID used with ``OpenAI``.\n\n :param user_content: Text submitted to ChatGPT to generate completions.\n :param conversation: List containing the conversation to be continued. Leave\n empty for a new conversation.\n :param model: For ``OpenAI`` the ID of the model to use, e.g. ``gpt-4``\n or ``gpt-3.5-turbo``. For ``Azure OpenAI`` the Deployment name,\n e.g. ``myGPT4deployment``.\n :param system_content: The system message helps set the behavior of\n the assistant.\n :param temperature: What sampling temperature to use between 0 to 2. Higher\n values means the model will take more risks.\n :param top_probability: An alternative to sampling with temperature, called\n nucleus sampling, where the model considers the results of the tokens with\n top_p probability mass.\n :param frequency_penalty: Number between -2.0 and 2.0. Positive values penalize\n new tokens based on their existing frequency in the text so far.\n :param presence_penalty: Number between -2.0 and 2.0. Positive values penalize\n new tokens based on whether they appear in the text so far.\n\n Robot Framework example:\n\n .. code-block:: robotframework\n\n # Get response without conversation history.\n ${response} @{chatgpt_conversation}= Chat Completion Create\n ... user_content=What is the biggest mammal?\n Log ${response}\n\n # Continue the conversation by using the \"conversation\" argument.\n ${response} @{chatgpt_conversation}= Chat Completion Create\n ... conversation=${chatgpt_conversation}\n ... user_content=How old can it live?\n Log ${response}\n\n \"\"\"\n if conversation is not None:\n conversation = conversation[0]\n else:\n conversation = []\n if system_content is not None:\n conversation.append(\n {\"role\": \"system\", \"content\": system_content},\n )\n conversation.append(\n {\"role\": \"user\", \"content\": user_content},\n )\n\n parameters = {\n \"messages\": conversation,\n \"temperature\": temperature,\n \"top_p\": top_probability,\n \"frequency_penalty\": frequency_penalty,\n \"presence_penalty\": presence_penalty,\n }\n\n if self.service_type == \"Azure\":\n parameters[\"engine\"] = model\n else:\n parameters[\"model\"] = model\n\n response = openai.ChatCompletion.create(**parameters)\n self.logger.info(response)\n text = response[\"choices\"][0][\"message\"][\"content\"]\n conversation.append(\n {\"role\": \"assistant\", \"content\": text},\n )\n return_list = [text, conversation]\n self.logger.info(return_list)\n return return_list\n\n def image_create(\n self,\n prompt: str,\n size: Optional[str] = \"512x512\",\n num_images: Optional[int] = 1,\n result_format: Optional[str] = \"list\",\n ) -> None:\n \"\"\"Keyword for creating one or more images using OpenAI.\n Keyword returns a list of urls for the images created.\n\n **Note**. Keyword not supported in the ``Azure OpenAI`` service.\n\n :param prompt: A text description of the desired image(s).\n The maximum length is 1000 characters.\n :param size: Size of the files to be created. 256x256, 512x512, 1024x1024\n :param num_images: The number of images to generate. Must be between 1 and 10.\n :param result_format: Result format (list / json).\n\n Robot Framework example:\n\n .. code-block:: robotframework\n\n ${images} Image Create\n ... Cartoon style picture of a cute monkey skateboarding.\n ... size=256x256\n ... num_images=2\n FOR ${url} IN @{images}\n Log ${url}\n END\n\n Python example:\n\n .. code-block:: python\n\n images = baselib.image_create(\n 'Cartoon style picture of a cute monkey skateboarding',\n size='256x256',\n num_images=2,\n )\n for url in images:\n print(url)\n\n \"\"\"\n if self.service_type == \"Azure\":\n raise NotImplementedError(\n \"Keyword 'Image Create' is not supported by Azure service\"\n )\n response = openai.Image.create(prompt=prompt, size=size, n=num_images)\n self.logger.info(response)\n urls = []\n if result_format == \"list\":\n for _url in response[\"data\"]:\n urls.append(_url[\"url\"])\n self.logger.info(_url)\n return urls\n if result_format == \"json\":\n return response\n else:\n return None\n\n def image_create_variation(\n self,\n src_image: str,\n size: Optional[str] = \"512x512\",\n num_images: Optional[int] = 1,\n result_format: Optional[str] = \"list\",\n ) -> None:\n \"\"\"Keyword for creating one or more variations of a image. Keyword\n returns a list of urls for the images created.\n Source file must be a valid PNG file, less than 4MB, and square.\n\n **Note**. Keyword not supported in the ``Azure OpenAI`` service.\n\n :param src_image: The image to use as the basis for the variation(s).\n Must be a valid PNG file, less than 4MB, and square.\n :param size: The size of the generated images.\n Must be one of 256x256, 512x512, or 1024x1024.\n :param num_images: The number of images to generate. Must be between 1 and 10\n :param result_format: Result format (list / json).\n\n Robot Framework example:\n\n .. code-block:: robotframework\n\n ${variations} Image Create Variation\n ... source_image.png\n ... size=256x256\n ... num_images=2\n FOR ${url} IN @{variations}\n Log ${url}\n END\n\n Python example:\n\n .. code-block:: python\n\n variations = baselib.image_create_variation(\n 'source_image.png',\n size='256x256',\n num_images=2,\n )\n for url in variations:\n print(url)\n\n \"\"\"\n if self.service_type == \"Azure\":\n raise NotImplementedError(\n \"Keyword 'Image Create Variation' is not supported by Azure service\"\n )\n with open(src_image, \"rb\") as image_file:\n response = openai.Image.create_variation(\n image=image_file, n=num_images, size=size\n )\n self.logger.info(response)\n\n urls = []\n if result_format == \"list\":\n for _url in response[\"data\"]:\n urls.append(_url[\"url\"])\n self.logger.info(_url)\n return urls\n if result_format == \"json\":\n return response\n else:\n return None\n", "repo_name": "robocorp/rpaframework", "sub_path": "packages/openai/src/RPA/OpenAI.py", "file_name": "OpenAI.py", "file_ext": "py", "file_size_in_byte": 14393, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 908, "dataset": "github-code", "pt": "37", "api": [{"api_name": "logging.getLogger", "line_number": 52, "usage_type": "call"}, {"api_name": "typing.Optional", "line_number": 59, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 60, "usage_type": "name"}, {"api_name": "openai.api_key", "line_number": 94, "usage_type": "attribute"}, {"api_name": "openai.api_base", "line_number": 95, "usage_type": "attribute"}, {"api_name": "openai.api_type", "line_number": 96, "usage_type": "attribute"}, {"api_name": "openai.api_version", "line_number": 97, "usage_type": "attribute"}, {"api_name": "openai.api_key", "line_number": 121, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 126, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 127, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 128, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 129, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 130, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 131, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 132, "usage_type": "name"}, {"api_name": "openai.Completion.create", "line_number": 187, "usage_type": "call"}, {"api_name": "openai.Completion", "line_number": 187, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 200, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 200, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 201, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 202, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 203, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 204, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 205, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 206, "usage_type": "name"}, {"api_name": "openai.ChatCompletion.create", "line_number": 273, "usage_type": "call"}, {"api_name": "openai.ChatCompletion", "line_number": 273, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 286, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 287, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 288, "usage_type": "name"}, {"api_name": "openai.Image.create", "line_number": 330, "usage_type": "call"}, {"api_name": "openai.Image", "line_number": 330, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 346, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 347, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 348, "usage_type": "name"}, {"api_name": "openai.Image.create_variation", "line_number": 393, "usage_type": "call"}, {"api_name": "openai.Image", "line_number": 393, "usage_type": "attribute"}]} +{"seq_id": "3465735011", "text": "from django.urls import path\r\n\r\nfrom . import views\r\n\r\nurlpatterns = [\r\n\tpath('projects/', views.projects, name=\"projects\"),\r\n\tpath('project/', views.project, name=\"project\"),\r\n\tpath('add-project/', views.addProject, name=\"addproject\"),\r\n\tpath('edit-project/', views.editProject, name=\"editproject\"),\r\n\tpath('delete-backers/', views.deleteBackersList, name=\"deletebackers\"),\r\n\tpath('delete-backer/', views.deleteBacker, name=\"deletebacker\"),\r\n\tpath('delete-project/', views.deleteProject, name=\"deleteproject\"),\r\n]", "repo_name": "drangovski/backercheck", "sub_path": "projects/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 573, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "django.urls.path", "line_number": 6, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 7, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 8, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 9, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 10, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 11, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 12, "usage_type": "call"}]} +{"seq_id": "24626418262", "text": "import bpy\n\nob = bpy.context.object\nskeys = ob.data.shape_keys.key_blocks \n\nskey_names = sorted(skeys.keys(), key=lambda v: v.upper())\n\nfor name in skey_names:\n if name.lower() != 'basis':\n idx = skeys.keys().index(name)\n ob.active_shape_key_index = idx\n bpy.ops.object.shape_key_move(type='BOTTOM')\n", "repo_name": "Yessy-me/blender-things", "sub_path": "sort-shape-keys.py", "file_name": "sort-shape-keys.py", "file_ext": "py", "file_size_in_byte": 317, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "bpy.context", "line_number": 3, "usage_type": "attribute"}, {"api_name": "bpy.ops.object.shape_key_move", "line_number": 12, "usage_type": "call"}, {"api_name": "bpy.ops", "line_number": 12, "usage_type": "attribute"}]} +{"seq_id": "20356013770", "text": "from torch.utils.data import Dataset\nimport pandas as pd\nimport torch\nfrom utils import get_graph_feature\nimport numpy as np\ntry:\n import vtk\n import vedo\nexcept:\n print('cannot import vtk or vedo')\n\ndef vtk2np(pointdata):\n length = pointdata.GetNumberOfTuples()\n dim = pointdata.GetNumberOfComponents()\n numpy_array = np.empty((length, dim))\n for i in range(length):\n numpy_array[i] = pointdata.GetTuple(i)\n return numpy_array\n\n\ndef compute_normals(mesh):\n \n normals = vtk.vtkPolyDataNormals()\n normals.SetInputData(mesh)\n normals.SetComputeCellNormals(True)\n normals.Update()\n \n cell_normals = vtk2np(normals.GetOutput().GetCellData().GetNormals())\n\n return cell_normals\n\n\ndef get_cell_centers(mesh):\n cell_centers = vtk.vtkCellCenters()\n cell_centers.SetInputData(mesh)\n cell_centers.Update()\n\n cell_centers_polydata = cell_centers.GetOutput()\n cell_centers_polydata =vtk2np(cell_centers_polydata.GetPoints().GetData())\n return cell_centers_polydata\n\n\n\ndef gen_metadata(mesh, patch_size, mode='vedo', is_new=False):\n '''\n to form a N x 15 vector\n input mesh form should be vedo.mesh.object\n which includes attributes: mesh.celldata['labels']\n '''\n if mode == 'vtk':\n N = mesh.GetNumberOfCells()\n points = vtk2np(mesh.GetPoints().GetData())\n # get cells' points indices\n ids = vtk2np(mesh.GetPolys().GetData()).astype(dtype='int32').reshape((N, -1))[:,1:]\n # get the points in coordinates\n cells = points[ids].reshape(N, 9).astype(dtype='float32')\n labels = vtk2np(mesh.GetCellData().GetArray(\"labels\")).astype('int32').reshape(-1, 1)\n normals = compute_normals(mesh)\n # barycenters = get_cell_centers(mesh)\n elif mode == 'vedo':\n N = mesh.ncells\n points = vedo.vtk2numpy(mesh.polydata().GetPoints().GetData())\n ids = vedo.vtk2numpy(mesh.polydata().GetPolys().GetData()).reshape((N, -1))[:,1:]\n cells = points[ids].reshape(N, 9).astype(dtype='float32')\n labels = mesh.celldata[\"labels\"].astype('int32').reshape(-1, 1)\n mesh.compute_normals()\n normals = mesh.celldata['Normals']\n barycenters = mesh.cell_centers()\n \n\n # form the vectors\n if not is_new:\n #normalized data\n maxs = points.max(axis=0)\n mins = points.min(axis=0)\n means = points.mean(axis=0)\n stds = points.std(axis=0)\n nmeans = normals.mean(axis=0)\n nstds = normals.std(axis=0)\n \n for i in range(3):\n cells[:, i] = (cells[:, i] - means[i]) / stds[i] #point 1\n cells[:, i+3] = (cells[:, i+3] - means[i]) / stds[i] #point 2\n cells[:, i+6] = (cells[:, i+6] - means[i]) / stds[i] #point 3\n barycenters[:,i] = (barycenters[:,i] - mins[i]) / (maxs[i]-mins[i])\n normals[:,i] = (normals[:,i] - nmeans[i]) / nstds[i]\n\n X = np.column_stack((cells, barycenters, normals))\n Y = labels\n \n elif is_new:\n faces = cells\n face_centers = np.mean(faces.reshape(-1, 3, 3), axis=1)\n face_normals = normals\n corner_vectors = np.hstack((faces[:,0:3] - face_centers,\n faces[:,3:6] - face_centers,\n faces[:,6:9] - face_centers))\n X = np.column_stack((corner_vectors, face_centers, face_normals))\n Y = labels\n \n # initialize batch of input and label\n X_train = np.zeros([patch_size, X.shape[1]], dtype='float32')\n Y_train = np.zeros([patch_size, Y.shape[1]], dtype='int32')\n\n # calculate number of valid cells (tooth instead of gingiva)\n positive_idx = np.argwhere(labels>0)[:, 0] #tooth idx\n negative_idx = np.argwhere(labels==0)[:, 0] # gingiva idx\n\n num_positive = len(positive_idx) # number of selected tooth cells\n\n if num_positive > patch_size: # all positive_idx in this patch\n positive_selected_idx = np.random.choice(positive_idx, size=patch_size, replace=False)\n selected_idx = positive_selected_idx\n else: # patch contains all positive_idx and some negative_idx\n num_negative = patch_size - num_positive # number of selected gingiva cells\n positive_selected_idx = np.random.choice(positive_idx, size=num_positive, replace=False)\n negative_selected_idx = np.random.choice(negative_idx, size=num_negative, replace=False)\n selected_idx = np.concatenate((positive_selected_idx, negative_selected_idx))\n\n selected_idx = np.sort(selected_idx, axis=None)\n\n X_train[:] = X[selected_idx, :]\n Y_train[:] = Y[selected_idx, :]\n\n X_train = X_train.transpose(1, 0)\n Y_train = Y_train.transpose(1, 0)\n \n KG_6 = get_graph_feature(torch.from_numpy(X_train[9:12, :]).unsqueeze(0), k=6).squeeze(0).numpy()\n KG_12 = get_graph_feature(torch.from_numpy(X_train[9:12, :]).unsqueeze(0), k=12).squeeze(0).numpy()\n\n metadata = {'cells': torch.from_numpy(X_train), 'labels': torch.from_numpy(Y_train),\n 'KG_6': KG_6, 'KG_12': KG_12,}\n \n return metadata\n\n\n# this is not used if train with h5 file\nclass Mesh_Dataset(Dataset):\n def __init__(self, data_list_path, num_classes=15, patch_size=6000):\n self.data_list = pd.read_csv(data_list_path, header=None)\n self.num_classes = num_classes\n self.patch_size = patch_size\n\n def __len__(self):\n return self.data_list.shape[0]\n \n def __getitem__(self, idx):\n if torch.is_tensor(idx):\n idx = idx.tolist()\n\n i_mesh = self.data_list.iloc[idx][0]\n reader = vtk.vtkPolyDataReader()\n reader.SetFileName(i_mesh)\n reader.Update()\n mesh = reader.GetOutput()\n sample = gen_metadata(mesh, self.patch_size)\n return sample\n\n# if __name__ == '__main__':\n# path = './dataset/FileLists/fileList_lower.txt'\n# dataset = Mesh_Dataset('./train_list_1.csv')\n# print(dataset.__getitem__(0))\n", "repo_name": "MAS0NM/tooth_seg", "sub_path": "mesh_dataset.py", "file_name": "mesh_dataset.py", "file_ext": "py", "file_size_in_byte": 5964, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "37", "api": [{"api_name": "numpy.empty", "line_number": 15, "usage_type": "call"}, {"api_name": "vtk.vtkPolyDataNormals", "line_number": 23, "usage_type": "call"}, {"api_name": "vtk.vtkCellCenters", "line_number": 34, "usage_type": "call"}, {"api_name": "vedo.vtk2numpy", "line_number": 62, "usage_type": "call"}, {"api_name": "vedo.vtk2numpy", "line_number": 63, "usage_type": "call"}, {"api_name": "numpy.column_stack", "line_number": 88, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 93, "usage_type": "call"}, {"api_name": "numpy.hstack", "line_number": 95, "usage_type": "call"}, {"api_name": "numpy.column_stack", "line_number": 98, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 102, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 103, "usage_type": "call"}, {"api_name": "numpy.argwhere", "line_number": 106, "usage_type": "call"}, {"api_name": "numpy.argwhere", "line_number": 107, "usage_type": "call"}, {"api_name": "numpy.random.choice", "line_number": 112, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 112, "usage_type": "attribute"}, {"api_name": "numpy.random.choice", "line_number": 116, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 116, "usage_type": "attribute"}, {"api_name": "numpy.random.choice", "line_number": 117, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 117, "usage_type": "attribute"}, {"api_name": "numpy.concatenate", "line_number": 118, "usage_type": "call"}, {"api_name": "numpy.sort", "line_number": 120, "usage_type": "call"}, {"api_name": "utils.get_graph_feature", "line_number": 128, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 128, "usage_type": "call"}, {"api_name": "utils.get_graph_feature", "line_number": 129, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 129, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 131, "usage_type": "call"}, {"api_name": "torch.utils.data.Dataset", "line_number": 138, "usage_type": "name"}, {"api_name": "pandas.read_csv", "line_number": 140, "usage_type": "call"}, {"api_name": "torch.is_tensor", "line_number": 148, "usage_type": "call"}, {"api_name": "vtk.vtkPolyDataReader", "line_number": 152, "usage_type": "call"}]} +{"seq_id": "1621288097", "text": "__all__ = ['TransferDropboxTransaction']\n\nfrom time import time\nimport logging\n\nfrom datetime import datetime\n\nfrom .base import DropboxTransactionBase\n\nfrom btransaction.operations.rsync import RsyncOperation\nfrom butility import Path\nfrom bkvstore import (KeyValueStoreSchema,\n RootKey)\n\nlog = logging.getLogger('dropbox.transaction.transfer')\n\n\nclass TransferRsyncOperation(RsyncOperation):\n \"\"\"Remembers all handled files by their full path for later\"\"\"\n __slots__ = ()\n\n # -------------------------\n ## @name Configuration\n # @{\n\n skip_empty_transfers = False\n \n ## -- End Configuration -- @}\n\n # -------------------------\n ## @name Interface\n # @{\n\n def destination_path(self):\n \"\"\"@return path to directory under which all package files will be copied\"\"\"\n return self._destination_path\n\n def actual_destination_path(self):\n \"\"\"@return root path under which the data can be found, taking into consideration the way rsync works\"\"\"\n return self._actual_destination_path\n \n ## -- End Interface -- @}\n\n\n\nclass TransferDropboxTransaction(DropboxTransactionBase):\n \"\"\"An rsync based transaction to copy data around\"\"\"\n __slots__ = ()\n\n # -------------------------\n ## @name Constants\n # @{\n \n _plugin_name = 'transfer'\n\n\n MODE_MOVE = 'move'\n MODE_COPY = 'copy'\n MODE_SYNC = 'sync'\n\n valid_modes = (MODE_MOVE, MODE_COPY, MODE_SYNC)\n\n ## -- End Constants -- @}\n\n schema = KeyValueStoreSchema(RootKey, dict(mode=MODE_MOVE, # mode of operation\n destination_dir=Path, # path into which to copy the package, must exist\n keep_package_subdir=True, # if True, the destination will include the relative path leading to the package\n subdir=Path # may contain substitutions like Y, M, D, H, MIN, may be empty\n # TODO: assure unique destination (via counter, ideally, and replaceable, even better)\n )\n )\n\n\n def __init__(self, *args, **kwargs):\n \"\"\"Initialize this instance with the required operations and verify configuration\n @throw ValueError if our configuration seems invalid\"\"\"\n super(TransferDropboxTransaction, self).__init__(*args, **kwargs)\n\n # Prepare the kvstore with data for resolving values\n now = datetime.utcnow()\n store = self._kvstore\n store.set_value('Y', now.year)\n store.set_value('M', now.month)\n store.set_value('D', now.day)\n store.set_value('H', now.hour)\n store.set_value('MIN', now.minute)\n\n config = self._config()\n\n if config.mode not in self.valid_modes:\n raise ValueError(\"Invalid transfer mode '%s' - must be one of %s\" % (config.mode, ','.join(self.valid_modes)))\n # end check mode\n\n if not config.destination_dir.isdir():\n raise ValueError(\"Destination dropbox was not accessible: '%s'\" % config.destination_dir)\n # prepare and resolve destination\n \n # handle subdir and create it if needed\n if config.subdir:\n raise NotImplementedError(\"implement unique identifier and subdir creation\")\n # end \n\n source = self._sql_instance.in_package.root()\n destination = config.destination_dir\n is_sync_mode = config.mode == self.MODE_SYNC\n if config.keep_package_subdir:\n # NOTE: rsync will duplicate our first directory unless we truncate it here\n root_relative = Path(self._package.root_relative())\n if root_relative.dirname():\n destination /= root_relative.dirname()\n # end handle modification of destination\n\n if is_sync_mode:\n if not source.isdir():\n log.warn(\"Using copy instead of sync as it would be dangerous to use if there is no package subdirectory - source is file\")\n is_sync_mode = False\n else:\n # In case of sync, we want to use the most destination path. This is possibly by instructing\n # rsync to copy only the directory contents, into a destination which carries the additional\n # base name of the source directory \n destination = destination / source.basename()\n source += '/'\n # end put in sync mode safety\n # end adjust source-destination for sync mode\n\n # Make sure the directory exists\n if not destination.isdir():\n destination.makedirs()\n # end handle dir creation\n elif is_sync_mode:\n log.warn(\"Deactivating sync-mode as it is dangerous to use if keep_package_subdir is disabled\")\n is_sync_mode = False\n # end handle subdir\n rsync_args = is_sync_mode and ['--delete'] or list()\n\n TransferRsyncOperation(self, source, destination, move=config.mode==self.MODE_MOVE, additional_rsync_args=rsync_args)\n self._sql_instance.comment = \"%sing package from '%s' to '%s'\" % (config.mode, source, destination)\n\n def _rsync_op(self):\n \"\"\"@return our rsync operation\"\"\"\n return self._operations[0]\n\n def _add_package_files(self, session, exception = None):\n if exception:\n return\n # end don't record files on error, as rollback should have fixed it\n\n # Just use the recorded list - it's not worth it and cumbersome to try to use the ones \n # we track from rsync\n super(TransferDropboxTransaction, self)._add_package_files(session, exception)\n\n # Create the package that the other side will have find/will have found and set it to be used\n # in our out_package slot.\n # It may or may not be under control of a dropbox on the other side\n # Also have to assure it's a real package\n rsync_destination = self._rsync_op().actual_destination_path()\n \n try:\n db = self._dropbox_finder.dropbox_by_contained_path(rsync_destination)\n except ValueError:\n db = None\n # end ignore errors\n\n\n dest_package = None\n if db and db.config_path() is not None:\n # We could try to find a matching package, but it wasn't necessarily detected yet.\n # Therefore we just get a package that matches the dropbox root and relative destination path\n for root_path in db.package_search_paths() + [db.config_path().dirname()]:\n if rsync_destination.startswith(root_path):\n root_rela = root_path.relpathfrom(rsync_destination)\n dest_package = session.to_sql_package((root_path, root_rela), stable_since=time())\n dest_package.comment = \"Destination of %s operation\" % self._config().mode\n break\n # end found matching root path\n # end for each package search path\n else:\n # The destination is not managed, just make up a dropbox\n dest_package = session.to_sql_package((rsync_destination, ''), stable_since=time())\n dest_package.comment = \"Pseudo-package created by %s operation as destination is not a known dropbox\" % self._config().mode\n # end handle have dropbox\n\n assert dest_package, \"Should have created some sort of destination package\"\n\n # Have to commit first, otherwise the id might be downright wrong ... \n dest_package.commit()\n self._sql_instance.out_package = dest_package\n\n # -------------------------\n ## @name Interface Implementation\n # @{\n\n @classmethod\n def can_enqueue(cls, package, sql_package, kvstore):\n \"\"\"@return always True - we have no settings that would prevent us to be enqueued, yet\"\"\"\n trs = [t for t in reversed(sql_package.in_transactions) if t.type_name == cls.plugin_name()]\n\n # never act on packages that were rejected in prior transactions of our type\n for trans in trs:\n if trans.is_rejected():\n log.debug(\"Found rejected transaction in package history - will never copy it again\")\n return False\n # end ignore rejected items\n # end handle rejected\n\n config = kvstore.value_by_schema(cls.schema)\n # This is an odd case\n if config.mode == cls.MODE_MOVE:\n return True\n # end we can always move similar packages ... even if it's the same\n\n # if we are in copy mode, and if the package already has a transaction with a package matching \n # the transaction's stable time, then the package didn't change since our last copy and \n # we don't have to repeat it\n for trans in trs:\n if trans.error is None and \\\n trans.comment and trans.comment.startswith(config.mode) and \\\n trans.in_package_stable_since == sql_package.stable_since:\n log.debug(\"Will not rsync-copy the same package %s again as it didn't change since last time\", package)\n return False\n # end prevent unnecessary copies\n # end for each transaction\n\n return True\n\n \n ## -- End Interface Implementation -- @}\n\n# end class TransferTransaction\n", "repo_name": "Byron/bit", "sub_path": "src/python/fsmonitor/transaction/transfer.py", "file_name": "transfer.py", "file_ext": "py", "file_size_in_byte": 9492, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "37", "api": [{"api_name": "logging.getLogger", "line_number": 15, "usage_type": "call"}, {"api_name": "btransaction.operations.rsync.RsyncOperation", "line_number": 18, "usage_type": "name"}, {"api_name": "base.DropboxTransactionBase", "line_number": 46, "usage_type": "name"}, {"api_name": "bkvstore.KeyValueStoreSchema", "line_number": 65, "usage_type": "call"}, {"api_name": "bkvstore.RootKey", "line_number": 65, "usage_type": "argument"}, {"api_name": "butility.Path", "line_number": 66, "usage_type": "name"}, {"api_name": "butility.Path", "line_number": 68, "usage_type": "name"}, {"api_name": "datetime.datetime.utcnow", "line_number": 80, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 80, "usage_type": "name"}, {"api_name": "butility.Path", "line_number": 108, "usage_type": "call"}, {"api_name": "time.time", "line_number": 172, "usage_type": "call"}, {"api_name": "time.time", "line_number": 179, "usage_type": "call"}]} +{"seq_id": "34951894430", "text": "import numpy as np\nimport matplotlib.pyplot as plt\nimport random\nimport geopandas as gpd\nfrom operator import itemgetter\nfrom shapely.geometry import Polygon\nfrom scipy.spatial import distance\n\n\n# 按照w,h对box进行网格划分 #################\ndef make_mesh(box, w, h):\n [xmin, ymin, xmax, ymax] = box\n list_x=np.arange(xmin, xmax, w)\n list_y=np.arange(ymin, ymax, h)\n polygon_list = []\n for i in range(len(list_x)):\n for j in range(len(list_y)):\n xleft = list_x[i]\n ydown = list_y[j]\n if i == len(list_x)-1:\n xright = xmax\n else:\n xright = list_x[i+1]\n if j == len(list_y)-1:\n yup = ymax\n else:\n yup = list_y[j+1]\n rectangle = Polygon([(xleft, ydown), (xright, ydown), (xright, yup), (xleft, yup)])\n polygon_list.append(rectangle)\n\n return gpd.GeoSeries(polygon_list)\n\n\n# 将数组元素解析成字典 ###############\ndef to_obj(arr):\n obj = [{} for i in range(len(arr))]\n for index, item in enumerate(arr):\n obj[index]['x'] = item[0]\n obj[index]['y'] = item[1]\n obj[index]['block'] = 0 # 所属区域块编号\n obj[index]['type'] = 'N' # N为普通点，B为边界点\n obj[index]['movable'] = False # 是否可移动\n return obj\n\n\n# 迭代生成子点 #################\ndef init(radius, center, xm, ym):\n x_center = center['x']\n y_center = center['y']\n while True:\n x = x_center + (random.random() - 0.5) * radius * 2\n y = y_center + (random.random() - 0.5) * radius * 2\n if (x - x_center) ** 2 + (y - y_center) ** 2 <= radius ** 2 and 0 < x < xm and 0 < y < ym:\n return x, y\n\n\n# 判断边界点 ###################\ndef get_border(nodes, R):\n border_nodes = [] # 用来存放边界点们\n for index, node in enumerate(nodes):\n node['ngb'] = 0\n angle_r_all = []\n right_max = 0\n S2 = nodes[:]\n del S2[index] # 中间变量，存放除本节点外的其他点\n for other_node in S2:\n if np.abs(other_node['x'] - node['x']) < 2 * R and np.abs(other_node['y'] - node['y']) < 2 * R:\n d = np.sqrt((node['x'] - other_node['x']) ** 2 + (node['y'] - other_node['y']) ** 2)\n if d < 2 * R:\n angle_d = np.arccos(((d / 2) / R))\n if node['x'] > other_node['x']:\n center_angle = np.arctan((other_node['y'] - node['y']) / (other_node['x'] - node['x'])) + np.pi\n elif node['x'] == other_node['x']:\n if node['y'] > other_node['y']:\n center_angle = -np.pi / 2\n else:\n center_angle = np.pi / 2\n else:\n center_angle = np.arctan((other_node['y'] - node['y']) / (other_node['x'] - node['x']))\n # 左右边界\n left = center_angle - angle_d\n right = center_angle + angle_d\n angle_r = [left, right]\n angle_r_all.append(angle_r)\n if d < R: # 加入一个邻居判断\n node['ngb'] = node['ngb'] + 1\n\n angle_r_all = sorted(angle_r_all, key=itemgetter(0)) # 按left升序排序\n if angle_r_all[0][0] >= -np.pi / 2:\n border_nodes.append(node)\n node['type'] = 'B'\n continue\n for i in range(0, len(angle_r_all)):\n if angle_r_all[i][0] > right_max: # 当左端值不与右端当前最大值发生重合时，判为边界点\n border_nodes.append(node)\n node['type'] = 'B'\n break\n if angle_r_all[i][1] > right_max: # 如果此右端点大于右端当前最大值，进行迭代\n right_max = angle_r_all[i][1]\n if right_max < np.pi * 3 / 2:\n border_nodes.append(node)\n node['type'] = 'B'\n return border_nodes, nodes\n\n\ndef create_nodes(c,n,R, xm, ym): # c母点集，返回生成的所有点S， 以及block：按区域划分的二维点集\n s = []\n block = [[0]*n for index in range(len(c))]\n for j, c_node in enumerate(c):\n i = 0\n while i < n:\n i = i+1\n new_node = {}\n new_node['x'], new_node['y'] = init(R, c_node, xm, ym)\n new_node['block'] = j\n new_node['type'] = 'N' # N为普通点，B为边界点\n new_node['movable'] = False # 是否可移动\n block[j][i - 1] = c_node\n c_node = new_node\n s.append(new_node)\n return s, block\n\n\ndef draw_nodes(nodes): # 画所有点\n for i in nodes:\n if i['movable']:\n plt.plot(i['x'], i['y'], 'mv')\n elif i['type'] == 'B' or i['type'] == 'R':\n plt.plot(i['x'], i['y'], 'c.')\n else:\n plt.plot(i['x'], i['y'], 'k.')\n\n\ndef draw_line(paths):\n for index, path in enumerate(paths):\n x = []\n y = []\n for node in path:\n x.append(node[0])\n y.append(node[1])\n plt.plot(x, y, color='c')\n\n\ndef draw_arrow(paths):\n for path in paths:\n x1 = path[0]['x']\n y1 = path[0]['y']\n x2 = path[1]['x']\n y2 = path[1]['y']\n plt.arrow(x1,y1, x2-x1, y2-y1,length_includes_head=True,\n head_width=0.8, head_length=1,\n fc='lightsalmon', ec='lightsalmon')\n\n\ndef sort_border(border, cl):\n border2 = [[] for index in range(cl)] # cl母点集长度\n for node in border:\n bi = node['block']\n border2[bi].append(node)\n return border2\n\n\ndef desired_node_location(node_path, R):\n desired_node = []\n x = node_path[0]['x']\n y = node_path[0]['y']\n xs = node_path[1]['x']\n ys = node_path[1]['y']\n xd = xs - x\n yd = ys - y\n t = min(np.abs(0.7 * R / xd), np.abs(0.7 * R / yd))\n while True:\n x1 = x + xd * t\n y1 = y + yd * t\n if np.abs(x1 - node_path[0]['x']) < np.abs(x1 - xs):\n # 靠近x1 则属于第一个点所在的block，在寻找中继时，去该block中寻找，为减小计算量\n desired_node.append({'x': x1, 'y': y1, 'block': node_path[0]['block'], 'type': 'D', 'movable': False})\n else:\n desired_node.append({'x': x1, 'y': y1, 'block': node_path[1]['block'], 'type': 'D', 'movable': False})\n x = x1\n y = y1\n if np.abs(x - xs) < 0.7 * R and np.abs(y - ys) < 0.7 * R:\n break\n return desired_node\n\n\n# 关于2018圆桌协议算法的相关函数 ###################################\ndef get_d_2018(border, R, xm, ym): # blocks 按区域划分的二维点集\n # 获取所有边界点到达圆桌的距离\n d_all = 0\n for nodes in border:\n d_min = 1000000\n for node in nodes:\n d = (node['x']-xm/2)**2 + (node['y']-ym/2)**2\n node['d'] = d\n if d < d_min:\n d_min = d\n else:\n continue\n d_all = d_all + np.sqrt(d_min) - R # 圆桌协议过程中移动的全路径\n return border, d_all\n\n\ndef get_min_path_2018(border, R):\n b1 = []\n b2 = border[:]\n conn_path = []\n conn_block = [] #\n conn_node = [] # 相连的两点信息\n conn_id = 0\n while True:\n for node in border:\n if node['block'] == conn_id:\n b1.append(node)\n b2.remove(node)\n if len(b2) == 0:\n return conn_block, conn_path, conn_node\n else:\n pass\n data = get_min_path(b1, b2)\n if (data[1][0][0] - data[1][1][0])**2 +(data[1][0][1] - data[1][1][1])**2 > R*R:\n conn_path.append(data[1])\n conn_block.append(data[0])\n conn_node.append(data[2])\n conn_id = data[0][1]\n return 0\n\n\ndef get_min_path(b1, b2): # b1, b2当前要判断的点集\n block= []\n path = [[0,0],[0,0]]\n d_min = 100000\n link = []\n for node in b1:\n x = node['x']\n y = node['y']\n for anode in b2:\n d = (anode['x'] - x) ** 2 + (anode['y'] - y) ** 2\n if d < d_min:\n d_min = d\n block = [node['block'],anode['block']]\n path = [[anode['x'], anode['y']],[x,y]]\n link = [node, anode]\n return block, path, link\n\n\ndef get_relay_2018(s, b, n): # 识别Relay，即每个区域用于连接其他区域的关键节点\n for i, item in enumerate(n):\n if item[0] in s:\n id = s.index(item[0])\n s[id]['type'] = 'R'\n s[id]['conn'] = b[i][1]\n if item[1] in s:\n id = s.index(item[1])\n s[id]['type'] = 'R'\n s[id]['conn'] = b[i][0]\n return s\n\n\ndef desired_node_location_2018(n, R): # 获取连接线路上的路径点\n desired_node = []\n for node_path in n:\n desired_node.extend(desired_node_location(node_path, R))\n return desired_node\n\n\ndef get_replace_cost(desired_node, ba, R): # 给每个路径上选定的位置，匹配一个node\n # ba：区域里的所有点（二维集合）\n badr = [] # ba delete relay\n cost2 = 0\n path = []\n test = [] # 用来测试去除点后是否能保证连通性\n for item in ba:\n # 先把关键中继R从点集中去除\n if item['type'] == 'B' or item['type'] == 'N':\n badr.append(item)\n else:\n pass\n badr.sort(key=lambda bo: bo['ngb'])\n for dn in desired_node:\n able_nodes = []\n bi = dn['block']\n for node in badr:\n if node['block'] == bi:\n d = (node['x'] - dn['x'])**2+(node['y']-dn['y'])**2\n node['cost'] = np.sqrt(d)\n able_nodes.append(node)\n able_nodes.sort(key=lambda ab: ab['cost']) # 每个desire_node对应block中可行的点\n able_nodes_pro = able_nodes[:]\n for item in able_nodes:\n test = able_nodes[:]\n protect = True\n test.remove(item)\n for i1 in test:\n t2 = test[:]\n if i1 in t2:\n t2.remove(i1)\n for i2 in t2:\n d = (i1['x']-i2['x'])**2 + (i1['y']-i2['y'])**2\n if d < R*R: # i2连通√\n protect = True\n break\n else:\n protect = False\n if not protect: # 遍历完发现i1与块不连通，说明这个测试点需要去除\n if item in able_nodes_pro:\n able_nodes_pro.remove(item)\n break\n cost2 = cost2 + able_nodes_pro[0]['cost']\n path.append([able_nodes_pro[0], dn])\n if able_nodes_pro[0] in badr:\n badr.remove(able_nodes_pro[0])\n return cost2, path\n\n\n# PARAMETERS ##############################\nxm = 1000 # 横坐标长度\nym = 1000 # 纵坐标长度\nsink = {'x': 0, 'y': 0} # 基站定义\nsink['x'] = xm/2 # 基站横坐标\nsink['y'] = ym-50 # 基站纵坐标\n# n = 16 # 每个区域的节点个数\nR = 50 # 节点通信半径\n[w, h] = [50, 50] # 网格长宽\n# END OF PARAMETERS ########################\n\n'''\n# 人为指定中心点 ###########################\nC_20 = [(50, 100), (100, 400), (50, 700), (30, 950), (300, 30), (340, 350), (260, 680), (280, 890),\n (500, 200), (500, 550), (590, 720), (570, 900), (730, 100), (600, 400), (780, 830),\n (950, 30), (840, 300), (870, 500), (980, 700), (950, 950)]\nC_20 = to_obj(C_20)\n\nC_15 = [(50, 100), (100, 400), (50, 700), (50, 900), (340, 340), (400, 660),\n (500, 950), (500, 200), (590, 720), (730, 100), (650, 480), (780, 830), (890, 30), (870, 400), (900, 950)]\nC_15 = to_obj(C_15)\n\n# 当区域数为15时 ###########################\nS_15, block_15 = create_nodes(C_15, 22, R, xm, ym)\n\nB_15, S_15 = get_border(S_15, R) # 边界点\nB_15_sorted = sort_border(B_15, len(C_15)) # 按区域划分的二维边界点集合\n\n\n# 2018 圆桌协议相关\nblock_15_2018, d_cost_2018_1 = get_d_2018(B_15_sorted, R, xm, ym)\nconn_block_2018, conn_path_2018, conn_node_2018= get_min_path_2018(B_15, R)\nS_15_2018 = get_relay_2018(S_15, conn_block_2018, conn_node_2018)\nDN = desired_node_location_2018(conn_node_2018, R)\nd_cost_2018_2, move_path_2018 = get_replace_cost(DN, S_15, R)\ncost_2018 = d_cost_2018_1 + d_cost_2018_2\nprint(d_cost_2018_1, d_cost_2018_2, cost_2018)\n\nS1 = S.extend(DN)\nS2 = destroy(S1, Dp)\nget_node_conn(S2)\n\n# 作图 ####################################\ngdf = make_mesh([0, 0, xm, ym], w, h)\ngdf.boundary.plot()\ndraw_nodes(S_15)\ndraw_line(conn_path_2018)\ndraw_nodes(DN)\ndraw_arrow(move_path_2018)\nplt.plot(sink['x'], sink['y'], 'rp') # 绘制sink点\nplt.annotate('sink', xy=(sink['x'], sink['y']), xytext=(-20, 10),\n textcoords='offset points', fontsize=12, color='r')\n\nplt.show()\n'''\n", "repo_name": "StrawberryCindy/Network-Reconnection", "sub_path": "copy/RoundTable.py", "file_name": "RoundTable.py", "file_ext": "py", "file_size_in_byte": 12999, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "numpy.arange", "line_number": 13, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 14, "usage_type": "call"}, {"api_name": "shapely.geometry.Polygon", "line_number": 28, "usage_type": "call"}, {"api_name": "geopandas.GeoSeries", "line_number": 31, "usage_type": "call"}, {"api_name": "random.random", "line_number": 51, "usage_type": "call"}, {"api_name": "random.random", "line_number": 52, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 67, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 68, "usage_type": "call"}, {"api_name": "numpy.arccos", "line_number": 70, "usage_type": "call"}, {"api_name": "numpy.arctan", "line_number": 72, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 72, "usage_type": "attribute"}, {"api_name": "numpy.pi", "line_number": 75, "usage_type": "attribute"}, {"api_name": "numpy.pi", "line_number": 77, "usage_type": "attribute"}, {"api_name": "numpy.arctan", "line_number": 79, "usage_type": "call"}, {"api_name": "operator.itemgetter", "line_number": 88, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 89, "usage_type": "attribute"}, {"api_name": "numpy.pi", "line_number": 100, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 127, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 127, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 129, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 129, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 131, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 131, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 141, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 141, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.arrow", "line_number": 150, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 150, "usage_type": "name"}, {"api_name": "numpy.abs", "line_number": 171, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 175, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 182, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 200, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 286, "usage_type": "call"}]} +{"seq_id": "24220298200", "text": "# -*- coding:utf-8 -*-\n# @Author: wg\n# @Time: 2019/4/11 16:33\n# @Desc: \n\"\"\"\n处理图片上传存储\n\"\"\"\nimport time\nimport os\n\nfrom flask import request\n\nfrom extensions import Resource\n\n\nclass UpLoad(Resource):\n def post(self):\n \"\"\"\n 上传图片处理 额 自定义的实际的话一般都会用云存储吧!\n :return:\n \"\"\"\n # 文件夹名称 通过当前时间\n\n current_dir = time.strftime(\"%Y%m%d\", time.localtime())\n if not os.path.exists(f\"./api/static/{current_dir}\"):\n os.mkdir(f\"./api/static/{current_dir}\")\n\n res = request.files[\"wangEditorH5File\"]\n file_name = str(int(time.time() * 1000)) + str(res.filename)\n\n with open(f\"./api/static/{current_dir}/{file_name}\", \"wb\") as f:\n f.write(res.read())\n\n return {\"data\": [f\"http://127.0.0.1:8000/static/{current_dir}/{file_name}\"]}\n", "repo_name": "wxy2077/Fantastic", "sub_path": "api/v1/utils/upload.py", "file_name": "upload.py", "file_ext": "py", "file_size_in_byte": 893, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "extensions.Resource", "line_number": 16, "usage_type": "name"}, {"api_name": "time.strftime", "line_number": 24, "usage_type": "call"}, {"api_name": "time.localtime", "line_number": 24, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 25, "usage_type": "call"}, {"api_name": "os.path", "line_number": 25, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 26, "usage_type": "call"}, {"api_name": "flask.request.files", "line_number": 28, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 28, "usage_type": "name"}, {"api_name": "time.time", "line_number": 29, "usage_type": "call"}]} +{"seq_id": "72809385388", "text": "import math\n\nfrom ..colors import PURPLE, YELLOW\nfrom ..utils import distance\n\n\nclass LightSensor:\n def __init__(self, position=(0, 0), name=\"light\", **kwargs):\n config = {\n \"position\": position,\n \"name\": name,\n }\n self.robot = None\n self.initialize()\n self.from_json(config)\n\n def __repr__(self):\n return \"\" % (self.name, self.position,)\n\n def initialize(self):\n self.type = \"light\"\n self.name = \"light\"\n self.value = 0.0\n # FIXME: add to config\n self.multiplier = 1000 # CM\n self.position = [0, 0]\n self.dist_from_center = distance(0, 0, self.position[0], self.position[1])\n self.dir_from_center = math.atan2(-self.position[0], self.position[1])\n\n def from_json(self, config):\n if \"name\" in config:\n self.name = config[\"name\"]\n if \"position\" in config:\n self.position = config[\"position\"]\n # Get location of sensor, doesn't change once position is set:\n self.dist_from_center = distance(0, 0, self.position[0], self.position[1])\n self.dir_from_center = math.atan2(-self.position[0], self.position[1])\n\n def to_json(self):\n config = {\n \"class\": self.__class__.__name__,\n \"position\": self.position,\n \"name\": self.name,\n }\n return config\n\n def step(self, time_step):\n pass\n\n def update(self, draw_list=None):\n self.value = 0\n # Location of sensor:\n p = self.robot.rotate_around(\n self.robot.x,\n self.robot.y,\n self.dist_from_center,\n self.robot.direction + self.dir_from_center + math.pi / 2,\n )\n for bulb in self.robot.world.bulbs: # for each light source:\n x, y, z, brightness, light_color = ( # noqa: F841\n bulb.x,\n bulb.y,\n bulb.z,\n bulb.brightness,\n bulb.color,\n )\n # FIXME: use bulb_color for filter?\n\n angle = math.atan2(x - p[0], y - p[1])\n dist = distance(x, y, p[0], p[1])\n hits = self.robot.cast_ray(p[0], p[1], angle, dist)\n if self.robot.world.debug and draw_list is not None:\n draw_list.append((\"draw_circle\", (p[0], p[1], 2)))\n draw_list.append((\"draw_circle\", (x, y, 2)))\n\n for hit in hits:\n draw_list.append((\"set_fill_style\", (PURPLE,)))\n draw_list.append((\"draw_circle\", (hit.x, hit.y, 2)))\n\n if len(hits) == 0: # nothing blocking! we can see the light\n # Make sure distance not zero:\n dist = max(dist, 0.001)\n # Maximum value of 100.0 with defaults:\n self.value += min(\n brightness * self.multiplier / (dist ** 2), self.multiplier / 10\n )\n if draw_list is not None:\n draw_list.append((\"strokeStyle\", (PURPLE, 1)))\n draw_list.append((\"draw_line\", (x, y, p[0], p[1])))\n\n def draw(self, backend):\n backend.set_fill_style(YELLOW)\n backend.draw_circle(self.position[0], self.position[1], 2)\n\n def get_reading(self):\n \"\"\"\n Get the light reading from the sensor.\n \"\"\"\n return self.value\n\n def watch(self, title=\"Light Sensor:\"):\n from ..watchers import AttributesWatcher\n\n if self.robot is None or self.robot.world is None:\n print(\"ERROR: can't watch until added to robot, and robot is in world\")\n return None\n\n watcher = AttributesWatcher(\n self, \"name\", \"value\", title=title, labels=[\"Name:\", \"Light:\"]\n )\n self.robot.world.watchers.append(watcher)\n return watcher.widget\n\n def set_position(self, position):\n \"\"\"\n Set the position of the light sensor with respect to the center of the\n robot.\n\n Args:\n * position: (list/tuple of length 2) represents [x, y] in CM from\n center of robot\n \"\"\"\n if len(position) != 2:\n raise ValueError(\"position must be of length two\")\n\n self.position = position\n # Get location of sensor, doesn't change once position is set:\n self.dist_from_center = distance(0, 0, self.position[0], self.position[1])\n self.dir_from_center = math.atan2(-self.position[0], self.position[1])\n", "repo_name": "ArtificialIntelligenceToolkit/jyrobot", "sub_path": "jyrobot/devices/lightsensors.py", "file_name": "lightsensors.py", "file_ext": "py", "file_size_in_byte": 4530, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "utils.distance", "line_number": 27, "usage_type": "call"}, {"api_name": "math.atan2", "line_number": 28, "usage_type": "call"}, {"api_name": "utils.distance", "line_number": 36, "usage_type": "call"}, {"api_name": "math.atan2", "line_number": 37, "usage_type": "call"}, {"api_name": "math.pi", "line_number": 57, "usage_type": "attribute"}, {"api_name": "math.atan2", "line_number": 69, "usage_type": "call"}, {"api_name": "utils.distance", "line_number": 70, "usage_type": "call"}, {"api_name": "colors.PURPLE", "line_number": 77, "usage_type": "name"}, {"api_name": "colors.PURPLE", "line_number": 88, "usage_type": "name"}, {"api_name": "colors.YELLOW", "line_number": 92, "usage_type": "argument"}, {"api_name": "watchers.AttributesWatcher", "line_number": 108, "usage_type": "call"}, {"api_name": "utils.distance", "line_number": 128, "usage_type": "call"}, {"api_name": "math.atan2", "line_number": 129, "usage_type": "call"}]} +{"seq_id": "19589471205", "text": "from django.test import TestCase\nfrom banners.tests.factories.banners import BannerFactory\nfrom banners.tests.factories.queue_items import QueueItemFactory\n\n\nclass QueueItemModel(TestCase):\n def setUp(self):\n self.banner = BannerFactory()\n self.queue_item1 = QueueItemFactory(banner=self.banner)\n self.queue_item2 = QueueItemFactory(banner=self.banner)\n\n def test_items_order_is_correct_on_queue_extension(self):\n self.assertEqual(self.queue_item1.position, 0)\n self.assertEqual(self.queue_item2.position, 1)\n self.assertEqual(self.banner.queue.first(), self.queue_item1)\n\n def test_items_order_is_correct_on_queue_reduction(self):\n self.banner.queue.first().delete()\n self.queue_item2.refresh_from_db()\n self.assertEqual(self.queue_item2.position, 0)\n self.assertEqual(self.banner.queue.first(), self.queue_item2)\n\n def test_past_item_goes_to_the_end(self):\n queue_item3 = QueueItemFactory(banner=self.banner)\n self.queue_item1.past = True\n self.queue_item1.save()\n self.queue_item1.refresh_from_db()\n\n self.assertEqual(self.queue_item1.position, 0)\n self.assertEqual(self.banner.queue.past().first(), self.queue_item1)\n\n self.queue_item2.past = True\n self.queue_item2.save()\n self.queue_item2.refresh_from_db()\n\n self.assertEqual(self.queue_item2.position, 1)\n self.assertEqual(self.banner.queue.past().all()[1], self.queue_item2)\n\n queue_item3.past = True\n queue_item3.save()\n queue_item3.refresh_from_db()\n\n self.assertEqual(queue_item3.position, 2)\n self.assertEqual(self.banner.queue.past().last(), queue_item3)\n", "repo_name": "Alexander-Andrade/qlapse", "sub_path": "banners/tests/models/test_queue_item_model.py", "file_name": "test_queue_item_model.py", "file_ext": "py", "file_size_in_byte": 1712, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "django.test.TestCase", "line_number": 6, "usage_type": "name"}, {"api_name": "banners.tests.factories.banners.BannerFactory", "line_number": 8, "usage_type": "call"}, {"api_name": "banners.tests.factories.queue_items.QueueItemFactory", "line_number": 9, "usage_type": "call"}, {"api_name": "banners.tests.factories.queue_items.QueueItemFactory", "line_number": 10, "usage_type": "call"}, {"api_name": "banners.tests.factories.queue_items.QueueItemFactory", "line_number": 24, "usage_type": "call"}]} +{"seq_id": "22584568380", "text": "import asyncio\n\nimport requests\nfrom bs4 import BeautifulSoup\n\n\nasync def get_all_pages(url: str):\n await asyncio.sleep(2)\n headers = {\n \"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) \"\n \"Chrome/107.0.0.0 Safari/537.36 \"\n }\n req = requests.get(url=url, headers=headers)\n src = req.text\n soup = BeautifulSoup(src, \"lxml\")\n list_of_chapters = []\n\n name = soup.find(class_=\"span8\").find('h1').text.split('/')[-1].strip()\n photo_req = requests.get(\"https://tl.rulate.ru\" + soup.find(class_='images').find(class_=\"slick\").find(\"img\")[\"src\"]).content\n photo = f\"database/images/{name}.jpg\"\n with open(photo, 'wb') as handler:\n handler.write(photo_req)\n\n all_chapters = soup.find(class_=\"table table-condensed table-striped\").findAll(class_=[\"chapter_row\"])\n\n for item in all_chapters:\n chapter_name = item.find(class_=\"t\").find('a').text\n link = \"https://tl.rulate.ru\" + item.find(class_=\"t\").find('a').get('href')\n status = item.findAll('td')[-5].text\n\n list_of_chapters.append(\n (\n chapter_name.strip(), link.strip(), status.strip()\n #\"chapter_name\": chapter_name,\n #\"chapter_link\": link\n )\n )\n return name, list_of_chapters, photo\n", "repo_name": "leorrdi/notification-bot-to-rulate", "sub_path": "parsers.py", "file_name": "parsers.py", "file_ext": "py", "file_size_in_byte": 1356, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "asyncio.sleep", "line_number": 8, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 13, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 15, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 19, "usage_type": "call"}]} +{"seq_id": "92848922", "text": "import datetime\nimport random\nfrom pynput.keyboard import Key, Controller\n\nimport osrs\n\n# 13573 dynamite\n# does not handle leveling yet\nport = '56799'\n\nkeyboard = Controller()\n\n\ndef drink_stam():\n run_energy = osrs.server.get_widget('160,28', port)\n if run_energy and int(run_energy['text']) < 45:\n inv = osrs.inv.get_inv(port, True)\n stam = osrs.inv.are_items_in_inventory_v2(inv, [12631, 12629, 12627, 12625])\n if stam:\n osrs.move.move_and_click(stam['x'], stam['y'], 3, 3)\n osrs.clock.random_sleep(1, 1.1)\n else:\n while True:\n bank_chest = osrs.server.get_game_object('1476,3877,0', '28595', port)\n if bank_chest:\n osrs.move.move_and_click(bank_chest['x'], bank_chest['y'], 3, 3)\n osrs.bank.wait_for_bank_interface(port)\n bank_data = osrs.bank.get_bank_data(port)\n stam_in_bank = osrs.inv.are_items_in_inventory_v2(bank_data, [12631, 12629, 12627, 12625])\n if not stam_in_bank:\n exit('no more stams')\n osrs.move.move_and_click(stam_in_bank['x'], stam_in_bank['y'], 3, 3)\n osrs.clock.sleep_one_tick()\n keyboard.press(Key.esc)\n keyboard.release(Key.esc)\n drink_stam()\n\n\ndef bank():\n drink_stam()\n inv = osrs.inv.get_inv(port)\n dyna = osrs.inv.get_item_quantity_in_inv(inv, 13573)\n if dyna < 6:\n while True:\n bank_chest = osrs.server.get_game_object('1476,3877,0', '28595', port)\n if bank_chest:\n noted_dyna = osrs.inv.is_item_in_inventory_v2(inv, 13574)\n if not noted_dyna:\n exit('out of dyna')\n osrs.move.move_and_click(noted_dyna['x'], noted_dyna['y'], 3, 3)\n osrs.move.move_and_click(bank_chest['x'], bank_chest['y'], 3, 3)\n if str(osrs.server.get_target_obj(port)) == '28595':\n break\n while True:\n co = osrs.server.get_chat_options(port)\n if co:\n osrs.keeb.keyboard.type('1')\n osrs.clock.sleep_one_tick()\n return\n\n\ndef deposit():\n while True:\n sack = osrs.server.get_ground_object('1478,3874,0', '28592', port)\n if sack:\n osrs.move.move_and_click(sack['x'], sack['y'], 3, 3)\n start_time = datetime.datetime.now()\n while True:\n inv = osrs.inv.get_inv(port)\n ore = osrs.inv.is_item_in_inventory_v2(inv, 13575)\n if not ore:\n # finish animation\n osrs.clock.sleep_one_tick()\n return\n elif (datetime.datetime.now() - start_time).total_seconds() > 10:\n break\n\n\ndef do_action(tile, obj, next_obj):\n while True:\n spot = osrs.server.get_game_object(tile, obj, port)\n if spot:\n osrs.move.move_and_click(spot['x'], spot['y'], 3, 3)\n break\n while True:\n next_expected = osrs.server.get_game_object(tile, next_obj, port)\n if next_expected:\n break\n\n\ndef do_action_v2(tile, obj, next_obj):\n while True:\n spot = osrs.server.get_game_object(tile, obj, port)\n if spot:\n osrs.move.move_and_click(spot['x'], spot['y'], 3, 3)\n if str(osrs.server.get_target_obj(port)) == obj:\n break\n osrs.clock.random_sleep(0.1, 0.2)\n while True:\n next_expected = osrs.server.get_game_object(tile, next_obj, port)\n if next_expected:\n break\n osrs.clock.random_sleep(0.1, 0.2)\n\n\ndef main():\n start_time = datetime.datetime.now()\n while True:\n start_time = osrs.game.break_manager(start_time, 53, 59, 432, 673, 'julenth')\n osrs.server.set_yaw(random.randint(300, 325), port)\n osrs.clock.sleep_one_tick()\n\n do_action_v2('1473,3885,0', '28580', '28582') # chisel 1\n do_action_v2('1473,3885,0', '28582', '28584') # dyna 1\n do_action_v2('1473,3885,0', '28584', '28586') # blow up 1\n\n do_action_v2('1471,3886,0', '28579', '28581') # chisel 2\n do_action_v2('1471,3886,0', '28581', '28583') # dyna 2\n do_action_v2('1471,3886,0', '28583', '28585') # blow up 2\n\n do_action_v2('1467,3883,0', '28579', '28581') # chisel 3\n do_action_v2('1468,3884,0', '28579', '28581') # chisel 4\n\n do_action_v2('1467,3883,0', '28581', '28583') # dyna 3\n do_action_v2('1468,3884,0', '28581', '28583') # dyna 4\n\n do_action_v2('1467,3883,0', '28583', '28585') # blow up 3\n do_action_v2('1468,3884,0', '28583', '28585') # blow up 4\n\n do_action_v2('1470,3886,0', '28580', '28582') # chisel 5\n do_action_v2('1469,3885,0', '28580', '28582') # chisel 6\n\n do_action_v2('1470,3886,0', '28582', '28584') # dyna 5\n do_action_v2('1469,3885,0', '28582', '28584') # dyna 6\n\n do_action_v2('1470,3886,0', '28584', '28586') # blow up 5\n do_action_v2('1469,3885,0', '28584', '28586') # blow up 6\n\n osrs.move.spam_click('1468,3883,0', 2.5) # pick up 3 and 4\n osrs.clock.sleep_one_tick()\n osrs.clock.sleep_one_tick()\n osrs.move.spam_click('1470,3885,0', 2.5) # pick up 5 and 6\n osrs.move.spam_click('1471,3885,0', 1.2) # pick up 2\n osrs.move.spam_click('1473,3884,0', 1.2) # pick up 1\n osrs.clock.sleep_one_tick()\n\n osrs.server.set_yaw(random.randint(800, 825), port)\n osrs.clock.sleep_one_tick()\n osrs.clock.sleep_one_tick()\n\n deposit()\n bank()\n\n\nmain()\n", "repo_name": "glandon22/AutoOldSchool", "sub_path": "mining/blast_mine.py", "file_name": "blast_mine.py", "file_ext": "py", "file_size_in_byte": 5717, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "pynput.keyboard.Controller", "line_number": 11, "usage_type": "call"}, {"api_name": "osrs.server.get_widget", "line_number": 15, "usage_type": "call"}, {"api_name": "osrs.server", "line_number": 15, "usage_type": "attribute"}, {"api_name": "osrs.inv.get_inv", "line_number": 17, "usage_type": "call"}, {"api_name": "osrs.inv", "line_number": 17, "usage_type": "attribute"}, {"api_name": "osrs.inv.are_items_in_inventory_v2", "line_number": 18, "usage_type": "call"}, {"api_name": "osrs.inv", "line_number": 18, "usage_type": "attribute"}, {"api_name": "osrs.move.move_and_click", "line_number": 20, "usage_type": "call"}, {"api_name": "osrs.move", "line_number": 20, "usage_type": "attribute"}, {"api_name": "osrs.clock.random_sleep", "line_number": 21, "usage_type": "call"}, {"api_name": "osrs.clock", "line_number": 21, "usage_type": "attribute"}, {"api_name": "osrs.server.get_game_object", "line_number": 24, "usage_type": "call"}, {"api_name": "osrs.server", "line_number": 24, "usage_type": "attribute"}, {"api_name": "osrs.move.move_and_click", "line_number": 26, "usage_type": "call"}, {"api_name": "osrs.move", "line_number": 26, "usage_type": "attribute"}, {"api_name": "osrs.bank.wait_for_bank_interface", "line_number": 27, "usage_type": "call"}, {"api_name": "osrs.bank", "line_number": 27, "usage_type": "attribute"}, {"api_name": "osrs.bank.get_bank_data", "line_number": 28, "usage_type": "call"}, {"api_name": "osrs.bank", "line_number": 28, "usage_type": "attribute"}, {"api_name": "osrs.inv.are_items_in_inventory_v2", "line_number": 29, "usage_type": "call"}, {"api_name": "osrs.inv", "line_number": 29, "usage_type": "attribute"}, {"api_name": "osrs.move.move_and_click", "line_number": 32, "usage_type": "call"}, {"api_name": "osrs.move", "line_number": 32, "usage_type": "attribute"}, {"api_name": "osrs.clock.sleep_one_tick", "line_number": 33, "usage_type": "call"}, {"api_name": "osrs.clock", "line_number": 33, "usage_type": "attribute"}, {"api_name": "pynput.keyboard.Key.esc", "line_number": 34, "usage_type": "attribute"}, {"api_name": "pynput.keyboard.Key", "line_number": 34, "usage_type": "name"}, {"api_name": "pynput.keyboard.Key.esc", "line_number": 35, "usage_type": "attribute"}, {"api_name": "pynput.keyboard.Key", "line_number": 35, "usage_type": "name"}, {"api_name": "osrs.inv.get_inv", "line_number": 41, "usage_type": "call"}, {"api_name": "osrs.inv", "line_number": 41, "usage_type": "attribute"}, {"api_name": "osrs.inv.get_item_quantity_in_inv", "line_number": 42, "usage_type": "call"}, {"api_name": "osrs.inv", "line_number": 42, "usage_type": "attribute"}, {"api_name": "osrs.server.get_game_object", "line_number": 45, "usage_type": "call"}, {"api_name": "osrs.server", "line_number": 45, "usage_type": "attribute"}, {"api_name": "osrs.inv.is_item_in_inventory_v2", "line_number": 47, "usage_type": "call"}, {"api_name": "osrs.inv", "line_number": 47, "usage_type": "attribute"}, {"api_name": "osrs.move.move_and_click", "line_number": 50, "usage_type": "call"}, {"api_name": "osrs.move", "line_number": 50, "usage_type": "attribute"}, {"api_name": "osrs.move.move_and_click", "line_number": 51, "usage_type": "call"}, {"api_name": "osrs.move", "line_number": 51, "usage_type": "attribute"}, {"api_name": "osrs.server.get_target_obj", "line_number": 52, "usage_type": "call"}, {"api_name": "osrs.server", "line_number": 52, "usage_type": "attribute"}, {"api_name": "osrs.server.get_chat_options", "line_number": 55, "usage_type": "call"}, {"api_name": "osrs.server", "line_number": 55, "usage_type": "attribute"}, {"api_name": "osrs.keeb.keyboard.type", "line_number": 57, "usage_type": "call"}, {"api_name": "osrs.keeb", "line_number": 57, "usage_type": "attribute"}, {"api_name": "osrs.clock.sleep_one_tick", "line_number": 58, "usage_type": "call"}, {"api_name": "osrs.clock", "line_number": 58, "usage_type": "attribute"}, {"api_name": "osrs.server.get_ground_object", "line_number": 64, "usage_type": "call"}, {"api_name": "osrs.server", "line_number": 64, "usage_type": "attribute"}, {"api_name": "osrs.move.move_and_click", "line_number": 66, "usage_type": "call"}, {"api_name": "osrs.move", "line_number": 66, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 67, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 67, "usage_type": "attribute"}, {"api_name": "osrs.inv.get_inv", "line_number": 69, "usage_type": "call"}, {"api_name": "osrs.inv", "line_number": 69, "usage_type": "attribute"}, {"api_name": "osrs.inv.is_item_in_inventory_v2", "line_number": 70, "usage_type": "call"}, {"api_name": "osrs.inv", "line_number": 70, "usage_type": "attribute"}, {"api_name": "osrs.clock.sleep_one_tick", "line_number": 73, "usage_type": "call"}, {"api_name": "osrs.clock", "line_number": 73, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 75, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 75, "usage_type": "attribute"}, {"api_name": "osrs.server.get_game_object", "line_number": 81, "usage_type": "call"}, {"api_name": "osrs.server", "line_number": 81, "usage_type": "attribute"}, {"api_name": "osrs.move.move_and_click", "line_number": 83, "usage_type": "call"}, {"api_name": "osrs.move", "line_number": 83, "usage_type": "attribute"}, {"api_name": "osrs.server.get_game_object", "line_number": 86, "usage_type": "call"}, {"api_name": "osrs.server", "line_number": 86, "usage_type": "attribute"}, {"api_name": "osrs.server.get_game_object", "line_number": 93, "usage_type": "call"}, {"api_name": "osrs.server", "line_number": 93, "usage_type": "attribute"}, {"api_name": "osrs.move.move_and_click", "line_number": 95, "usage_type": "call"}, {"api_name": "osrs.move", "line_number": 95, "usage_type": "attribute"}, {"api_name": "osrs.server.get_target_obj", "line_number": 96, "usage_type": "call"}, {"api_name": "osrs.server", "line_number": 96, "usage_type": "attribute"}, {"api_name": "osrs.clock.random_sleep", "line_number": 98, "usage_type": "call"}, {"api_name": "osrs.clock", "line_number": 98, "usage_type": "attribute"}, {"api_name": "osrs.server.get_game_object", "line_number": 100, "usage_type": "call"}, {"api_name": "osrs.server", "line_number": 100, "usage_type": "attribute"}, {"api_name": "osrs.clock.random_sleep", "line_number": 103, "usage_type": "call"}, {"api_name": "osrs.clock", "line_number": 103, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 107, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 107, "usage_type": "attribute"}, {"api_name": "osrs.game.break_manager", "line_number": 109, "usage_type": "call"}, {"api_name": "osrs.game", "line_number": 109, "usage_type": "attribute"}, {"api_name": "osrs.server.set_yaw", "line_number": 110, "usage_type": "call"}, {"api_name": "osrs.server", "line_number": 110, "usage_type": "attribute"}, {"api_name": "random.randint", "line_number": 110, "usage_type": "call"}, {"api_name": "osrs.clock.sleep_one_tick", "line_number": 111, "usage_type": "call"}, {"api_name": "osrs.clock", "line_number": 111, "usage_type": "attribute"}, {"api_name": "osrs.move.spam_click", "line_number": 139, "usage_type": "call"}, {"api_name": "osrs.move", "line_number": 139, "usage_type": "attribute"}, {"api_name": "osrs.clock.sleep_one_tick", "line_number": 140, "usage_type": "call"}, {"api_name": "osrs.clock", "line_number": 140, "usage_type": "attribute"}, {"api_name": "osrs.clock.sleep_one_tick", "line_number": 141, "usage_type": "call"}, {"api_name": "osrs.clock", "line_number": 141, "usage_type": "attribute"}, {"api_name": "osrs.move.spam_click", "line_number": 142, "usage_type": "call"}, {"api_name": "osrs.move", "line_number": 142, "usage_type": "attribute"}, {"api_name": "osrs.move.spam_click", "line_number": 143, "usage_type": "call"}, {"api_name": "osrs.move", "line_number": 143, "usage_type": "attribute"}, {"api_name": "osrs.move.spam_click", "line_number": 144, "usage_type": "call"}, {"api_name": "osrs.move", "line_number": 144, "usage_type": "attribute"}, {"api_name": "osrs.clock.sleep_one_tick", "line_number": 145, "usage_type": "call"}, {"api_name": "osrs.clock", "line_number": 145, "usage_type": "attribute"}, {"api_name": "osrs.server.set_yaw", "line_number": 147, "usage_type": "call"}, {"api_name": "osrs.server", "line_number": 147, "usage_type": "attribute"}, {"api_name": "random.randint", "line_number": 147, "usage_type": "call"}, {"api_name": "osrs.clock.sleep_one_tick", "line_number": 148, "usage_type": "call"}, {"api_name": "osrs.clock", "line_number": 148, "usage_type": "attribute"}, {"api_name": "osrs.clock.sleep_one_tick", "line_number": 149, "usage_type": "call"}, {"api_name": "osrs.clock", "line_number": 149, "usage_type": "attribute"}]} +{"seq_id": "3056887226", "text": "#!/usr/bin/env python2\n\n#FIXME: More detailed description of this test case!!!\n\"\"\"\nRun an experiment with an ice \"stream\". \n\"\"\"\n\n# Authors\n# -------\n# Original author unlisted.\n# Reconfigured by Joseph H Kennedy at ORNL on April 27, 2015 to work with the regression testing\n\nimport os\nimport sys\nimport errno\nimport subprocess\nimport ConfigParser \n\nimport numpy\nimport netCDF\nfrom math import sqrt\n\n\n# Parse the command line options\n# ------------------------------\nimport argparse\nparser = argparse.ArgumentParser(description=__doc__,\n formatter_class=argparse.ArgumentDefaultsHelpFormatter)\n\n# small helper function so argparse will understand unsigned integers\ndef unsigned_int(x):\n x = int(x)\n if x < 1:\n raise argparse.ArgumentTypeError(\"This argument is an unsigned int type! Should be an integer greater than zero.\")\n return x\n\nparser.add_argument('-c','--config', default='./stream.config', \n help=\"The configure file used to setup the test case and run CISM\")\nparser.add_argument('-e','--executable', default='./cism_driver', \n help=\"The CISM driver\")\nparser.add_argument('--hpc', nargs='?', const='aprun',\n help=\" \".join([\"Flag to Shortcut parallel run command lookup for High Performance Computing Systems.\", \n \"If flag apears without an argument, it will set run command to `aprun`,\", \n \"otherwise it will use the argument given.\"]))\nparser.add_argument('-m', '--modifier', metavar='MOD', default='',\n help=\"Add a modifier to file names. FILE.EX will become FILE.MOD.EX\")\nparser.add_argument('-n','--parallel', metavar='N', type=unsigned_int, default=0, \n help=\"Run in parallel using N processors.\")\nparser.add_argument('-o', '--output-dir', default='./output',\n help=\"Write all created files here.\")\nparser.add_argument('-q', '--quiet', action='store_true',\n help=\"Run the CISM process quietly.\")\nparser.add_argument('-s','--setup-only', action='store_true',\n help=\"Set up the test, but don't actually run it.\")\n\n\n# Additional test specific options:\n#parser.add_argument('--scale', type=unsigned_int, default=0, \n# help=\"Scales the problem size by 2**SCALE. SCALE=0 creates a 31x31 grid, SCALE=1 \" \n# +\"creates a 62x62 grid, and SCALE=2 creates a 124x124 grid.\")\nparser.add_argument('-z','--stream-size', type=unsigned_int, default=25,\n help=\") The number of grid cells used to model the ice stream portion of the domain.\"\n +\"Note: values <19 may not work properly for all problems.\") \n#optparser.add_option('-s','--stream-size',dest='stream_grid_size',default=25,type='int',help='Number of cells to use to model the ice stream portion of the domain (values <19 may not work properly for all problems).')\n\nparser.add_argument('--vertical', type=unsigned_int,\n help=\"Override the vertical grid size (upn) in the config file.\")\n#optparser.add_option('-v','--vert-grid-size',dest='vertical_grid_size',default=2,type='int',help='Number of vertical layers to use (upn); minimum value = 2')\n\n# Some useful functions\n# ---------------------\n\n# function to make a directory, and not worry if it exists.\ndef mkdir_p(path):\n try:\n os.makedirs(path)\n except OSError as exc: # Python >2.5\n if exc.errno == errno.EEXIST and os.path.isdir(path):\n pass\n else: raise\n\n# prep the command line functions\ndef prep_commands(args, config_name):\n driver = os.path.abspath(args.executable)\n \n quiet_mod = ''\n if args.quiet:\n quiet_mod = ' > '+config_name+'.oe'\n\n commands = []\n mkdir_p(args.output_dir)\n commands.append(\"cd \"+os.path.abspath(args.output_dir))\n \n if args.hpc and (args.parallel > 0):\n mpiexec = args.hpc+' -n ' + str(args.parallel)+\" \"\n elif (args.parallel > 0):\n # These calls to os.system will return the exit status: 0 for success (the command exists), some other integer for failure\n if os.system('which openmpirun > /dev/null') == 0:\n mpiexec = 'openmpirun -np ' + str(args.parallel)+\" \"\n elif os.system('which mpirun > /dev/null') == 0:\n mpiexec = 'mpirun -np ' + str(args.parallel)+\" \"\n elif os.system('which aprun > /dev/null') == 0:\n mpiexec = 'aprun -n ' + str(args.parallel)+\" \"\n elif os.system('which mpirun.lsf > /dev/null') == 0:\n # mpirun.lsf does NOT need the number of processors\n mpiexec = 'mpirun.lsf '\n else:\n print(\"Unable to execute parallel run!\")\n print(\" Please edit the script to use your MPI run command, or run the model manually with\")\n print(\" something like: mpirun -np 4 ./cism_driver stream.config\")\n sys.exit(1)\n else:\n mpiexec = ''\n\n commands.append(mpiexec+driver+\" \"+config_name+quiet_mod)\n\n return commands\n\n\n# Hard coded test specific parameters\n# -----------------------------------\n#FIXME: Some of these could just be options!\n\nanalytic_solution = 'raymond' # can be 'raymond' or 'schoof'\nkinflag = 1 # 1=apply kinematic bc (analytic soln) at points in the domain (discussed further below); 0=the run will be doubly periodic (preferred)\nfillInitialGuess = 0 # 1=use the analytic solution as the initial guess for the velocity solver to speed convergence; 0=use the default 0-velocity initial guess\n\n# Domain parameters\nstreamHalfWidth = 25000.0 # ice stream half-width, in m - used for both raymond & schoof formulations\nalongFlowLength = 30000.0 # the desired along-flow length of the domain, in m; set to -1 to get a square domain\nH = 1000.0 # ice thickness\ndsdx = -1.0e-3 # bed (and surface) slope in the x-direction (y-direction is flat)\n\n# Physical parameters\nrho = 910.0 # ice density kg/m3\ng = -9.81 # gravity m/s2\nn = 3 # flow law exponent\nA = 1e-16 # flow rate factor in Pa^-3 yr^-1\n\n# schoof solution parameters\nm = 1.55 # schoof exponent\nL = streamHalfWidth / (m+1.0)**(1.0/m) # This comes from the line above eq. 4.3 in schoof (2006)\n\ntaud = rho * g * H * dsdx # Driving stress\n# Calculate a good size for the size of the domain outside of the stream (in m)\nif analytic_solution == 'raymond':\n strongWidth = 5.0 * H # 5 ice thicknesses should get us beyond the zone of lateral stress transfer. Adjust as needed\nelif analytic_solution == 'schoof':\n # schoof (2006) uses a domain size that is 3L on either side of the central axis\n strongWidth = 3.0 * L - streamHalfWidth\n\n\n# Test specific functions\n# -----------------------\n\n# raymond yield stress\ndef raymond_tau(yy):\n tau0 = 5.2e3*numpy.ones(yy.shape) # set the stream value everywhere\n tau0[numpy.absolute(yy)>=streamHalfWidth] = 0.7e5 # set a very large value outside the stream\n return tau0\n\n# raymond velocity solution\ndef raymond_uvel(yy):\n tau0r = raymond_tau(yy)\n tau0r[tau0r>taud] = taud\n ur = 2.0 * A / (n+1.0) * ( (taud - tau0r)/H )**n * ( streamHalfWidth**(n+1) - numpy.absolute(yy)**(n+1) )\n ur[ur<0.0] = 0.0\n return ur\n\n# schoof yield stress distribution\ndef schoof_tau(yy):\n return taud * numpy.absolute( yy / L )**m\n\n# schoof velocity solution\ndef schoof_uvel(yy):\n B = A**(-1.0/n)\n us = -2.0 * taud**3 * L**4 / (B**3 * H**3) * ( ((yy/L)**4 - (m+1.0)**(4.0/m))/4.0 - 3.0*( numpy.absolute(yy/L)**(m+4.0) \\\n - (m+1.0)**(1.0+4.0/m) )/((m+1.0)*(m+4.0)) + 3.0*( numpy.absolute(yy/L)**(2.0*m+4.0) - (m+1.0)**(2.0+4.0/m) )/((m+1.0)**2*(2.0*m+4.0)) \\\n - ( numpy.absolute(yy/L)**(3.0*m+4.0) - (m+1.0)**(3.0+4.0/m) )/ ( (m+1.0)**3*(3.0*m+4.0)) )\n\n # Some adjustments to the analytic profile - not entirely sure why these are needed.\n ind = numpy.nonzero( numpy.absolute(yy) >= streamHalfWidth )\n us[ind] = 0.0\n\n return us\n\n\n# the main script function\n# ------------------------\ndef main():\n \"\"\"\n Run the stream test.\n \"\"\"\n\n # check that file name modifier, if it exists, starts with a '-'\n if not (args.modifier == '') and not args.modifier.startswith('-') :\n args.modifier = '-'+args.modifier\n \n # get the configuration\n # ---------------------\n try:\n config_parser = ConfigParser.SafeConfigParser()\n config_parser.read( args.config )\n \n if args.vertical:\n nz = args.vertical\n else:\n nz = int(config_parser.get('grid','upn'))\n \n file_name = config_parser.get('CF input', 'name')\n root, ext = os.path.splitext(file_name)\n\n except ConfigParser.Error as error:\n print(\"Error parsing \" + args.config )\n print(\" \"), \n print(error)\n sys.exit(1)\n \n # Setup the domain\n # ----------------\n nStream = args.stream_size\n # Check domain sizes for usefulness\n if (nStream % 2) == 0 and analytic_solution == 'schoof':\n print(\"Warning: For the schoof version, you might want the number of cells in the stream to be an odd number.\")\n \n dy = 2.0 * streamHalfWidth / float(nStream)\n dx = dy # always want this\n \n # Figure out the number of cells we need to add to get as close t0 the \n # desired width of the strong region as possible (note: may want to use \n # ceil() instead of round() here)\n nStrongStrip = int(round(strongWidth / dy)) \n\n # a +1 is needed to convert from y0 to y1 but we leaving it off lets the \n #stream boundaries fall on the y0 grid, which is needed to best match the \n #analytic solution\n ny = nStream + 2 * nStrongStrip \n if alongFlowLength < 0:\n nx = ny # square domain\n else:\n nx = int(round(alongFlowLength / dx))\n\n offset = -dsdx * dx * nx\n \n if not args.quiet:\n print(\"\\nDomain setup:\")\n print( \"=============\" )\n print( \"Number of cells for stream (N-S): \"+str(nStream))\n print( \"Number of cells for entire domain (N-S): \"+str(ny))\n print( \"dy=dx= \"+str(dy))\n print( \"Domain N-S (across-flow) width (m): \"+str(ny*dy))\n print( \"Domain E-W (along-flow) width (m): \"+str(nx*dx))\n\n res = str(nStream).zfill(4)\n if args.parallel > 0:\n mod = args.modifier+'.'+res+'.p'+str(args.parallel).zfill(3)\n else:\n mod = args.modifier+'.'+res\n \n file_name = root+mod+ext\n config_name = root+mod+'.config'\n out_name = root+mod+'.out'+ext\n\n\n # create the new config file\n # --------------------------\n if not args.quiet: \n print(\"\\nCreating config file: \"+config_name)\n \n config_parser.set('grid', 'upn', str(nz))\n config_parser.set('grid', 'ewn', str(nx))\n config_parser.set('grid', 'nsn', str(ny))\n config_parser.set('grid', 'dew', str(dx))\n config_parser.set('grid', 'dns', str(dy))\n \n config_parser.set('parameters', 'periodic_offset_ew', str(offset))\n\n config_parser.set('CF input', 'name', file_name)\n config_parser.set('CF output', 'name', out_name)\n config_parser.set('CF output', 'xtype', 'double')\n \n with open(config_name, 'wb') as config_file:\n config_parser.write(config_file)\n\n\n # create the input netCDF file\n # ----------------------------\n if not args.quiet: \n print(\"\\nCreating stream netCDF file: \"+file_name)\n try:\n nc_file = netCDF.NetCDFFile(file_name,'w',format='NETCDF3_CLASSIC')\n except TypeError:\n nc_file = netCDF.NetCDFFile(file_name,'w')\n\n nc_file.createDimension('time',1)\n nc_file.createDimension('x1',nx)\n nc_file.createDimension('y1',ny)\n nc_file.createDimension('level',nz)\n nc_file.createDimension('x0',nx-1) # staggered grid \n nc_file.createDimension('y0',ny-1)\n\n\n x1 = dx*numpy.arange(nx,dtype='float64')\n y1 = dy*numpy.arange(ny,dtype='float64') - dy*float(ny-1)/2.0 # make the y-coordinates centered about 0\n\n x0 = dx/2.0 + x1[:-1] # staggered grid\n y0 = dy/2.0 + y1[:-1]\n\n # Make sure the edge of the stream lands on the grid cells on the y0 grid. \n # This should always happen with the logic above, so this check should never be activated.\n if (analytic_solution == 'raymond') and (not True in (numpy.absolute(streamHalfWidth-y0) < 0.0001)):\n print(\"\\nERROR: the stream edge does not land on the y0 grid so the stream will \"\n +\"not be resolved adequately for the raymond case. Adjust the domain size, \"\n +\"stream size, and/or horizontal resolution.\")\n print( \" Stream half width = \"+str(streamHalfWidth))\n print( \" y0 grid has values at: \")\n print( \" \"+str(y0[:]))\n sys.exit(1)\n\n # Make sure we have at least two non-stream rows on each side\n if (numpy.absolute(y0[:])>streamHalfWidth).sum() < 4:\n print(\"\\nERROR: there are less than two non-stream rows on each side of the stream.\"\n +\"Adjust the domain size, stream size, and/or horizontal resolution.\")\n print( \" Stream half width = \"+str(streamHalfWidth))\n print( \" y0 grid has values at: \")\n print( \" \"+str(y0[:]))\n sys.exit(1)\n\n nc_file.createVariable('time','f',('time',))[:] = [0]\n nc_file.createVariable('x1','f',('x1',))[:] = numpy.float32(x1)\n nc_file.createVariable('y1','f',('y1',))[:] = numpy.float32(y1)\n nc_file.createVariable('x0','f',('x0',))[:] = numpy.float32(x0) # staggered grid\n nc_file.createVariable('y0','f',('y0',))[:] = numpy.float32(y0)\n\n\n # Calculate values for the required variables.\n thk = numpy.zeros([1,ny,nx],dtype='float32')\n topg = numpy.zeros([1,ny,nx],dtype='float32')\n tauf = numpy.zeros([1,ny-1,nx-1],dtype='float32')\n\n # Calculate input field values\n thk[:] = H # constant thickness\n\n for j in range(ny):\n topg[0,j,:] = 1000.0 + dsdx * x1[:] # sloped bed. add 1000.0 to stay well above sea level\n\n if analytic_solution == 'raymond':\n tau0Profile = raymond_tau(y0)\n uvelProfile = raymond_uvel(y0)\n elif analytic_solution == 'schoof':\n tau0Profile = schoof_tau(y0)\n uvelProfile = schoof_uvel(y0)\n else:\n print(\"\\nERROR: Invalid value for 'analytic_solution'.\")\n sys.exit(1)\n\n for i in range(nx-1):\n tauf[0,:,i] = tau0Profile\n\n\n # =======================================\n # Save the required variables to the netCDF file.\n nc_file.createVariable('thk', 'f',('time','y1','x1'))[:] = thk\n nc_file.createVariable('topg','f',('time','y1','x1'))[:] = topg\n nc_file.createVariable('tauf','f',('time','y0','x0'))[:] = tauf\n\n if kinflag == 1 or fillInitialGuess == 1:\n nc_file.createVariable('uvel','f',('time','level','y0','x0'))\n nc_file.createVariable('vvel','f',('time','level','y0','x0'))\n\n if kinflag == 1:\n # setup Dirichlet boundary conditions for uvel and/or vvel at points in the domain\n\n dudy = numpy.gradient( uvelProfile, dy )\n vvelProfile = -dudy*dy\n\n kinbcmask = numpy.zeros([1,ny-1,nx-1],dtype='int32')\n uvel = numpy.zeros([1,nz,ny-1,nx-1],dtype='float32')\n vvel = numpy.zeros([1,nz,ny-1,nx-1],dtype='float32')\n\n\n # =================================================================\n # fill both uvel and vvel at the upstream and downstream domain ends\n\n # Fill first column\n# i = 0\n# uvel[0,:,:,i] = numpy.tile(uvelProfile, [nz, 1]) # uniform in the vertical\n# vvel[0,:,:,i] = -numpy.tile(vvelProfile, [nz, 1]) # uniform in the vertical\n# kinbcmask[0,:,i] = 1\n\n # Fill last column\n# i = nx-1 - 1\n# uvel[0,:,:,i] = numpy.tile(uvelProfile, [nz, 1]) # uniform in the vertical\n# vvel[0,:,:,i] = numpy.tile(vvelProfile, [nz, 1]) # uniform in the vertical\n# kinbcmask[0,:,i] = 1\n\n # =================================================================\n # fill both uvel and vvel at the upstream and downstream domain ends\n # Fill just a single across-flow profile in domain interior\n i = 2\n uvel[0,:,:,i] = numpy.tile(uvelProfile, [nz, 1]) # uniform in the vertical\n# vvel[0,:,:,i] = -numpy.tile(vvelProfile, [nz, 1]) # uniform in the vertical\n kinbcmask[0,:,i] = 1\n\n nc_file.variables['uvel'][:] = uvel[:]\n nc_file.variables['vvel'][:] = vvel[:]\n nc_file.createVariable('kinbcmask','i',('time','y0','x0'))[:] = kinbcmask[:]\n\n if fillInitialGuess == 1:\n # Fill the analytic solution into the initial guess to speed convergence\n dudy = numpy.gradient( uvelProfile, dy )\n vvelProfile = -dudy*dy\n\n uvel = numpy.zeros([1,nz,ny-1,nx-1],dtype='float32')\n vvel = numpy.zeros([1,nz,ny-1,nx-1],dtype='float32')\n\n for i in range(nx-1):\n uvel[0,:,:,i] = numpy.tile(uvelProfile, [nz, 1]) # uniform in the vertical\n vvel[0,:,:,i] = numpy.tile(vvelProfile, [nz, 1]) # uniform in the vertical\n\n nc_file.variables['uvel'][:] = uvel[:]\n nc_file.variables['vvel'][:] = vvel[:]\n\n nc_file.close()\n mkdir_p(args.output_dir)\n subprocess.check_call(\"cp *rilinosOptions.xml \"+args.output_dir, shell=True)\n subprocess.check_call(\"mv \"+file_name+\" \"+args.output_dir, shell=True)\n subprocess.check_call(\"mv \"+config_name+\" \"+args.output_dir, shell=True)\n\n # Run CISM\n # --------\n command_list = prep_commands(args, config_name)\n commands_all = [\"# STREAM\"+mod+\" test\"]\n commands_all.extend( command_list )\n \n result_mv = \"mv results \"+root+mod+\".results 2>/dev/null\"\n timing_mv = \"for file in cism_timing*; do mv $file \"+root+mod+\".$file 2>/dev/null; done\"\n commands_all.append(result_mv)\n commands_all.append(timing_mv)\n commands_all.append(\" \")\n \n if not args.setup_only:\n if not args.quiet: \n print(\"\\nRunning CISM stream test\")\n print( \"========================\\n\")\n\n process = subprocess.check_call(str.join(\"; \",command_list), shell=True)\n \n try:\n subprocess.check_call(\"cd \"+args.output_dir+\"; \"+result_mv, shell=True)\n except subprocess.CalledProcessError:\n pass \n\n try:\n subprocess.check_call(\"cd \"+args.output_dir+\"; \"+timing_mv, shell=True)\n except subprocess.CalledProcessError:\n pass\n\n if not args.quiet: \n print(\"\\nFinished running the CISM stream test\")\n print( \"=====================================\\n\")\n else:\n run_script = args.output_dir+os.sep+root+mod+\".run\" \n \n with open(run_script,'w') as run_file:\n run_file.write('#!/usr/bin/env bash \\n \\n')\n for command in commands_all:\n run_file.write(command+\" \\n\")\n\n os.chmod(run_script, 0o755) # uses an octal number!\n\n if not args.quiet:\n print(\"\\nFinished setting up the CISM stream test\")\n print( \"========================================\")\n print( \" To run the test, use: \"+run_script)\n\n\n# Run only if this is being run as a script.\nif __name__=='__main__':\n \n # get the command line arguments\n args = parser.parse_args()\n \n # run the script\n sys.exit(main())\n\n\n", "repo_name": "CISM/cism", "sub_path": "tests/stream/runStream.py", "file_name": "runStream.py", "file_ext": "py", "file_size_in_byte": 19027, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 20, "dataset": "github-code", "pt": "37", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 27, "usage_type": "call"}, {"api_name": "argparse.ArgumentDefaultsHelpFormatter", "line_number": 28, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentTypeError", "line_number": 34, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 76, "usage_type": "call"}, {"api_name": "errno.EEXIST", "line_number": 78, "usage_type": "attribute"}, {"api_name": "os.path.isdir", "line_number": 78, "usage_type": "call"}, {"api_name": "os.path", "line_number": 78, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 84, "usage_type": "call"}, {"api_name": "os.path", "line_number": 84, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 92, "usage_type": "call"}, {"api_name": "os.path", "line_number": 92, "usage_type": "attribute"}, {"api_name": "os.system", "line_number": 98, "usage_type": "call"}, {"api_name": "os.system", "line_number": 100, "usage_type": "call"}, {"api_name": "os.system", "line_number": 102, "usage_type": "call"}, {"api_name": "os.system", "line_number": 104, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 111, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 158, "usage_type": "call"}, {"api_name": "numpy.absolute", "line_number": 159, "usage_type": "call"}, {"api_name": "numpy.absolute", "line_number": 166, "usage_type": "call"}, {"api_name": "numpy.absolute", "line_number": 172, "usage_type": "call"}, {"api_name": "numpy.absolute", "line_number": 177, "usage_type": "call"}, {"api_name": "numpy.absolute", "line_number": 178, "usage_type": "call"}, {"api_name": "numpy.absolute", "line_number": 179, "usage_type": "call"}, {"api_name": "numpy.nonzero", "line_number": 182, "usage_type": "call"}, {"api_name": "numpy.absolute", "line_number": 182, "usage_type": "call"}, {"api_name": "ConfigParser.SafeConfigParser", "line_number": 202, "usage_type": "call"}, {"api_name": "os.path.splitext", "line_number": 211, "usage_type": "call"}, {"api_name": "os.path", "line_number": 211, "usage_type": "attribute"}, {"api_name": "ConfigParser.Error", "line_number": 213, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 217, "usage_type": "call"}, {"api_name": "netCDF.NetCDFFile", "line_number": 291, "usage_type": "call"}, {"api_name": "netCDF.NetCDFFile", "line_number": 293, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 303, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 304, "usage_type": "call"}, {"api_name": "numpy.absolute", "line_number": 311, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 318, "usage_type": "call"}, {"api_name": "numpy.absolute", "line_number": 321, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 327, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 330, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 331, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 332, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 333, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 337, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 338, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 339, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 355, "usage_type": "call"}, {"api_name": "numpy.gradient", "line_number": 374, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 377, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 378, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 379, "usage_type": "call"}, {"api_name": "numpy.tile", "line_number": 401, "usage_type": "call"}, {"api_name": "numpy.gradient", "line_number": 411, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 414, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 415, "usage_type": "call"}, {"api_name": "numpy.tile", "line_number": 418, "usage_type": "call"}, {"api_name": "numpy.tile", "line_number": 419, "usage_type": "call"}, {"api_name": "subprocess.check_call", "line_number": 426, "usage_type": "call"}, {"api_name": "subprocess.check_call", "line_number": 427, "usage_type": "call"}, {"api_name": "subprocess.check_call", "line_number": 428, "usage_type": "call"}, {"api_name": "subprocess.check_call", "line_number": 447, "usage_type": "call"}, {"api_name": "subprocess.check_call", "line_number": 450, "usage_type": "call"}, {"api_name": "subprocess.CalledProcessError", "line_number": 451, "usage_type": "attribute"}, {"api_name": "subprocess.check_call", "line_number": 455, "usage_type": "call"}, {"api_name": "subprocess.CalledProcessError", "line_number": 456, "usage_type": "attribute"}, {"api_name": "os.sep", "line_number": 463, "usage_type": "attribute"}, {"api_name": "os.chmod", "line_number": 470, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 485, "usage_type": "call"}]} +{"seq_id": "13996390993", "text": "import numpy as np\nfrom nii_2_mesh_conversion import nii_2_mesh\nimport pygalmesh \nimport vtk\nfrom vtk.util.numpy_support import vtk_to_numpy\n\n\nfilename_nii = '/home/benjamin/Documents/git_repos/BrainGrowth/cache/template_T2.nii'\n\nfilename_stl = '/home/benjamin/Documents/git_repos/BrainGrowth/cache/template_T2.stl'\n\nnii_2_mesh(filename_nii, filename_stl, 0)\n#file loading okay, but result crap. Easy visualisation of STL ?\n\n#take a surface mesh and fill it with tetrahedrons\n #mesh formats taken into account by pymesh ? STL okay\n #but pymesh is not the default pip, you have to install it by hand (not the end of the world though). Installation hard\n #installation pymesh cancelled because not standard = clunky\n #meshpy. Only obj object from Meshlab\n #pyglamesh, would do the trick but very heavy\n\n\n#take the filled volume and feed it back as a numpy array\n#from vtk to numpy array ?\n\nfilename_mesh = \"/home/benjamin/Documents/git_repos/BrainGrowth/data/sphere5.mesh\"\ninpt = \"/home/benjamin/Documents/git_repos/BrainGrowth/data/surf_sphere.stl\"\n\nmesh = pygalmesh.generate_volume_mesh_from_surface_mesh(\n inpt,\n min_facet_angle = 25.0,\n max_radius_surface_delaunay_ball = 0.15,\n max_facet_distance = 0.008,\n max_circumradius_edge_ratio = 3.0,\n verbose = False\n \n )\n\nmesh.write(\"output.vtk\")\n\nreader = vtk.vtkXMLUnstructuredGridReader()\nreader.SetFileName(\"/home/benjamin/Documents/git_repos/BrainGrowth/output.vtk\")\nreader.Update()\ndata = reader.GetOutput()\n\ndef vtk_to_numpy ():\n pass", "repo_name": "rousseau/BrainGrowth", "sub_path": "nii_2_vol_conversion.py", "file_name": "nii_2_vol_conversion.py", "file_ext": "py", "file_size_in_byte": 1529, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 5, "dataset": "github-code", "pt": "37", "api": [{"api_name": "nii_2_mesh_conversion.nii_2_mesh", "line_number": 12, "usage_type": "call"}, {"api_name": "pygalmesh.generate_volume_mesh_from_surface_mesh", "line_number": 29, "usage_type": "call"}, {"api_name": "vtk.vtkXMLUnstructuredGridReader", "line_number": 41, "usage_type": "call"}]} +{"seq_id": "37185678501", "text": "import sqlite3, json, sys\nimport pandas as pd\n\n# connection to sqlite \nconn = sqlite3.connect('colibri_tst\\\\db.sqlite3')\n\nc = conn.cursor()\n\n# json file\nraw = \"raw_data\\\\MOCK_DATA.json\"\n\nwith open(raw, \"r\") as f:\n data = json.load(f) \n\ndf = pd.json_normalize(data)\n\ndf['industry'] = df['industry'].replace('n/a', None)\n\n# df['date_of_birth'] = pd.to_datetime(df['date_of_birth'], format='%d/%m/%Y')\n\n# in pandas on a numeric column if there is a null value by default pandas will converts to double\n# order to change data type from double to int have to replace null values with 0 \ndf['years_of_experience'] = df['years_of_experience'].fillna(0)\ndf['years_of_experience'] = df['years_of_experience'].astype(\"int\")\n\n# write to table employee\ndf.to_sql('app_1_employee', conn, if_exists='replace', index = False)", "repo_name": "severisc/test_djg", "sub_path": "raw_data/json_to_sqlite.py", "file_name": "json_to_sqlite.py", "file_ext": "py", "file_size_in_byte": 815, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "sqlite3.connect", "line_number": 5, "usage_type": "call"}, {"api_name": "json.load", "line_number": 13, "usage_type": "call"}, {"api_name": "pandas.json_normalize", "line_number": 15, "usage_type": "call"}]} +{"seq_id": "20873914085", "text": "from collections import deque\nfrom sys import stdin\n\nn = int(stdin.readline())\n\nd = deque()\nm = deque()\n\nfor _ in range(n):\n command = stdin.readline()\n if 'push' in command:\n arg = int(command.split(\" \")[1])\n d.append(arg)\n m.append(max(arg, m[-1] if m else arg))\n if 'pop' in command:\n d.pop()\n m.pop()\n if 'max' in command:\n print(m[-1] if m else 0)", "repo_name": "Raccoonrider/practice-datastructures", "sub_path": "1_stack_max.py", "file_name": "1_stack_max.py", "file_ext": "py", "file_size_in_byte": 406, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "sys.stdin.readline", "line_number": 4, "usage_type": "call"}, {"api_name": "sys.stdin", "line_number": 4, "usage_type": "name"}, {"api_name": "collections.deque", "line_number": 6, "usage_type": "call"}, {"api_name": "collections.deque", "line_number": 7, "usage_type": "call"}, {"api_name": "sys.stdin.readline", "line_number": 10, "usage_type": "call"}, {"api_name": "sys.stdin", "line_number": 10, "usage_type": "name"}]} +{"seq_id": "522385439", "text": "import cv2\r\nfrom PIL import ImageDraw\r\nfrom PIL import ImageFilter\r\nimport numpy as np\r\n\r\n\r\nclass PostProcessor(object):\r\n bbox_color = (255, 0, 0)\r\n bbox_width = 6\r\n\r\n @staticmethod\r\n def draw_rectengles(frame_cv2, bboxes):\r\n if bboxes is None:\r\n return frame_cv2\r\n\r\n for bbox in bboxes:\r\n left = bbox[0]\r\n top = bbox[1]\r\n right = bbox[2]\r\n bottom = bbox[3]\r\n frame_cv2 = cv2.rectangle(\r\n frame_cv2,\r\n (left, top),\r\n (right, bottom),\r\n PostProcessor.bbox_color,\r\n PostProcessor.bbox_width)\r\n\r\n # draw = ImageDraw.Draw(frame_pil)\r\n # for bbox in bboxes:\r\n # draw.rectangle(bbox.tolist(), outline=PostProcessor.bbox_color, width=PostProcessor.bbox_width)\r\n return frame_cv2\r\n\r\n @staticmethod\r\n def blur_at_bboxes(frame_cv2, bboxes):\r\n if bboxes is None:\r\n return frame_cv2\r\n\r\n for bbox in np.array(bboxes, dtype=np.int):\r\n left = bbox[0]\r\n top = bbox[1]\r\n right = bbox[2]\r\n bottom = bbox[3]\r\n\r\n frame_cv2[top:bottom, left:right] = \\\r\n cv2.GaussianBlur(src=frame_cv2[top:bottom, left:right], ksize=(51, 51), sigmaX=0, sigmaY=0)\r\n return frame_cv2\r\n", "repo_name": "noamzilo/face_pose_estimation", "sub_path": "post_processing/PostProcessor.py", "file_name": "PostProcessor.py", "file_ext": "py", "file_size_in_byte": 1353, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "cv2.rectangle", "line_number": 21, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 38, "usage_type": "call"}, {"api_name": "numpy.int", "line_number": 38, "usage_type": "attribute"}, {"api_name": "cv2.GaussianBlur", "line_number": 45, "usage_type": "call"}]} +{"seq_id": "22118641552", "text": "from datetime import datetime\nfrom time import strftime\n\n# 현재 챗은 CHAT_RESERVE = False로 막아둠\n# 구매 예약 문자는 다 내폰으로\n\n# 어떤 키워드들은 variables.py에 있다.\n# OPTIONS\nOPTION_NEW_TABLE = False\nSILENCE = False\nCHAT_RESERVE = True\nSERVICE_FEE = 0\nPROFIT = 0\nRESERVE = True # filter reserve\nCONTACT = True # send reserve\nSENDER_PHONE = '01071416956'\nTODAY = datetime.today().strftime('%Y%m%d')\n\n# RDS\nRDS_HOST = 'oden-second-hands-selling.ctj9mgachfi3.ap-northeast-2.rds.amazonaws.com'\nRDS_USER_NAME = 'admin'\nRDS_USER_PW = 'pLa5yfCbS^rCt^vh'\nRDS_DB = 'chocam'\nRDS_RAW_TABLE = 'current_raw'\nRDS_PROCESSED_TABLE = 'current_processed'\nRDS_CALCULATED_TABLE = 'current_calculated'\nRDS_RESERVED_TABLE = 'current_reserved'\n\n# EXCEL\nEXCEL_FILE_NAME = f'current_reserved_{TODAY}'\nEXCEL_SAVE_PATH = f\"/Users/duckyounglee/Documents/{EXCEL_FILE_NAME}.xlsx\"\nEXCEL_KEYWORDS_NAME = 'keywords_220524_1030'\nEXCEL_KEYWORDS_PATH = f\"/Users/duckyounglee/Documents/keywords/{EXCEL_KEYWORDS_NAME}.xlsx\"\n\n# Naver Login\nNAVER_ID = 'oden0317'\nNAVER_PW = 'Dhems2021!'\n\n# telegram\nTELE_API_KEY = \"5362630249:AAHPegjrSozzmEUlL_DQGlfJ-Roccmm7Cd4\"\nCHAT_ID_PRIORITY_ONE = \"-1001660821686\"\nCHAT_ID_PRIORITY_TWO = \"-1001507095114\"", "repo_name": "q7y331xk/chocam-scrapper-console", "sub_path": "config.py", "file_name": "config.py", "file_ext": "py", "file_size_in_byte": 1239, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "datetime.datetime.today", "line_number": 17, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 17, "usage_type": "name"}]} +{"seq_id": "25692233928", "text": "import pygame\nfrom util.MathUtil import MathUtil\nfrom vroom.camera import Camera\nfrom vroom.component import Component\nfrom vroom.resource_manager import ResourceManager\n\n\nclass SpriteRenderer(Component):\n def __init__(self, assetName: str) -> None:\n super().__init__()\n self.img: pygame.Surface = ResourceManager.getSprite(assetName)\n self.spareImg: pygame.Surface = self.img\n\n def Render(self, screen: pygame.Surface) -> None:\n super().Render(screen)\n self.img = self.spareImg\n\n if self.gameObject.rotation != 0 or self.gameObject.scale != 1:\n self.img = pygame.transform.rotozoom(\n self.img, self.gameObject.rotation, self.gameObject.scale\n )\n\n rect: pygame.Rect = self.img.get_rect()\n if self.gameObject.static:\n rect.center = MathUtil.RoundFloatPosToIntPos(self.gameObject.pos)\n else:\n rect.center = Camera.WorldPosToScreenPos(self.gameObject.pos)\n screen.blit(self.img, rect)\n\n def Update(self) -> None:\n super().Update()\n", "repo_name": "lwalton101/VroomVroom", "sub_path": "vroom/components/sprite_renderer.py", "file_name": "sprite_renderer.py", "file_ext": "py", "file_size_in_byte": 1073, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "37", "api": [{"api_name": "vroom.component.Component", "line_number": 8, "usage_type": "name"}, {"api_name": "pygame.Surface", "line_number": 11, "usage_type": "attribute"}, {"api_name": "vroom.resource_manager.ResourceManager.getSprite", "line_number": 11, "usage_type": "call"}, {"api_name": "vroom.resource_manager.ResourceManager", "line_number": 11, "usage_type": "name"}, {"api_name": "pygame.Surface", "line_number": 12, "usage_type": "attribute"}, {"api_name": "pygame.Surface", "line_number": 14, "usage_type": "attribute"}, {"api_name": "pygame.transform.rotozoom", "line_number": 19, "usage_type": "call"}, {"api_name": "pygame.transform", "line_number": 19, "usage_type": "attribute"}, {"api_name": "pygame.Rect", "line_number": 23, "usage_type": "attribute"}, {"api_name": "util.MathUtil.MathUtil.RoundFloatPosToIntPos", "line_number": 25, "usage_type": "call"}, {"api_name": "util.MathUtil.MathUtil", "line_number": 25, "usage_type": "name"}, {"api_name": "vroom.camera.Camera.WorldPosToScreenPos", "line_number": 27, "usage_type": "call"}, {"api_name": "vroom.camera.Camera", "line_number": 27, "usage_type": "name"}]} +{"seq_id": "33893551592", "text": "import re\nfrom flask_app import app\nfrom flask import render_template, request, redirect, session, flash\nfrom flask_app.models.user import User\nfrom flask_app.models.event import Event\nfrom flask_bcrypt import Bcrypt\nbcrypt = Bcrypt(app)\n\n@app.route('/') #main, checks if user is already logged in, if not, directs to registration page\ndef main():\n if not session.get('user'):\n return render_template(\"login.html\")\n if session['user'] > 0:\n user_id = session['user']\n print(\"found session\")\n return redirect('/dashboard')\n else:\n return render_template(\"login.html\")\n \n\n@app.route('/login', methods=['POST']) #already have an account button that directs to login page\ndef login():\n # data = {\"email\" : request.form[\"email\"]}\n if not (User.login_validation(request.form)):\n return redirect('/')\n # if not user_in_db: #redirects to login page if email not in db\n # flash('Invalid Email or Password')\n # print('Invalid Email ')\n # return redirect('/')\n # if not bcrypt.check_password_hash(user_in_db.password, request.form['password']): #redirects to login page if password is wrong\n # flash('Invalid Email or Password')\n # print('Invalid Password')\n # return redirect('/')\n # print('valid')\n user_in_db = User.get_by_email(request.form['email'])\n session['user'] = user_in_db.id #stores user id in session\n user_id = user_in_db.id #collects user id into variable to send through url\n return redirect('/dashboard')\n\n@app.route('/register')\ndef registerAccount():\n return render_template('register.html')\n\n@app.route('/existingaccount') #already have an account button that directs to login page\ndef existingAccount():\n return render_template('loginform.html')\n\n@app.route('/createaccount', methods=['POST']) #route for recieving form data and creating user\ndef createAccount():\n print(request.form)\n \n \n if not User.register_validation(request.form): #validate user otherwise redirect to registration page\n return redirect('/register')\n pw_hash = bcrypt.generate_password_hash(request.form['password']) #hash password\n data = {\n \"first_name\" : request.form['first_name'],\n \"last_name\" : request.form['last_name'],\n \"email\" : request.form['email'],\n \"password\" : pw_hash\n } #reset data to be stored in db\n\n user_id = User.register_user(data) #create user\n session['user'] = user_id #set session user id\n return redirect('/dashboard')\n\n\n# @app.route('/dashboard/') #when user logs in, display dashboard\n# def dashboard(id):\n# if session['user'] != id: #checks url to see if the user logged in is the same as the account being accesses\n# return redirect('/logout') #if not, logs out the user\n# user = User.get_user(id) #gets user by id so the dashboard can access user data\n# todaysEvents = Event.get_all_by_user_today(id) #gets todays events that user is attending\n# allEvents = Event.get_future_events_by_user(id) #gets all events to display in dash\n# return render_template('index.html', user = user, allEvents = allEvents, todaysEvents = todaysEvents)\n\n@app.route('/dashboard')\ndef dashboard():\n if 'user' not in session:\n return redirect('/')\n logged_user = User.get_user_with_events(session['user'])\n return render_template('index.html', user = logged_user)\n\n\n\n'''@app.route('/user_account')\ndef userInfo():\n id = session['user']\n user = User.get_user(id)\n events = Event.get_all_by_user(id)\n events_today = Event.get_all_by_user_today(id)\n return render_template('account.html', user = user, events = events, events_today = events_today)\n '''\n\n@app.route('/user/')\ndef user_details(id):\n user = User.get_user(id)\n events = Event.get_all_by_user(id)\n return render_template('user_details.html', user = user, events = events)\n\n@app.route('/logout')\ndef logout():\n session.clear()\n return redirect('/')\n", "repo_name": "bryancoloma/sports_planner", "sub_path": "flask_app/controllers/users.py", "file_name": "users.py", "file_ext": "py", "file_size_in_byte": 3992, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "flask_bcrypt.Bcrypt", "line_number": 7, "usage_type": "call"}, {"api_name": "flask_app.app", "line_number": 7, "usage_type": "argument"}, {"api_name": "flask.session.get", "line_number": 11, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 11, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 12, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 13, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 14, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 16, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 18, "usage_type": "call"}, {"api_name": "flask_app.app.route", "line_number": 9, "usage_type": "call"}, {"api_name": "flask_app.app", "line_number": 9, "usage_type": "name"}, {"api_name": "flask_app.models.user.User.login_validation", "line_number": 24, "usage_type": "call"}, {"api_name": "flask_app.models.user.User", "line_number": 24, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 24, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 24, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 25, "usage_type": "call"}, {"api_name": "flask_app.models.user.User.get_by_email", "line_number": 35, "usage_type": "call"}, {"api_name": "flask_app.models.user.User", "line_number": 35, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 35, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 35, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 36, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 38, "usage_type": "call"}, {"api_name": "flask_app.app.route", "line_number": 21, "usage_type": "call"}, {"api_name": "flask_app.app", "line_number": 21, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 42, "usage_type": "call"}, {"api_name": "flask_app.app.route", "line_number": 40, "usage_type": "call"}, {"api_name": "flask_app.app", "line_number": 40, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 46, "usage_type": "call"}, {"api_name": "flask_app.app.route", "line_number": 44, "usage_type": "call"}, {"api_name": "flask_app.app", "line_number": 44, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 50, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 50, "usage_type": "name"}, {"api_name": "flask_app.models.user.User.register_validation", "line_number": 53, "usage_type": "call"}, {"api_name": "flask_app.models.user.User", "line_number": 53, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 53, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 53, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 54, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 55, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 55, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 57, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 57, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 58, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 58, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 59, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 59, "usage_type": "name"}, {"api_name": "flask_app.models.user.User.register_user", "line_number": 63, "usage_type": "call"}, {"api_name": "flask_app.models.user.User", "line_number": 63, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 64, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 65, "usage_type": "call"}, {"api_name": "flask_app.app.route", "line_number": 48, "usage_type": "call"}, {"api_name": "flask_app.app", "line_number": 48, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 79, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 80, "usage_type": "call"}, {"api_name": "flask_app.models.user.User.get_user_with_events", "line_number": 81, "usage_type": "call"}, {"api_name": "flask_app.models.user.User", "line_number": 81, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 81, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 82, "usage_type": "call"}, {"api_name": "flask_app.app.route", "line_number": 77, "usage_type": "call"}, {"api_name": "flask_app.app", "line_number": 77, "usage_type": "name"}, {"api_name": "flask_app.models.user.User.get_user", "line_number": 97, "usage_type": "call"}, {"api_name": "flask_app.models.user.User", "line_number": 97, "usage_type": "name"}, {"api_name": "flask_app.models.event.Event.get_all_by_user", "line_number": 98, "usage_type": "call"}, {"api_name": "flask_app.models.event.Event", "line_number": 98, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 99, "usage_type": "call"}, {"api_name": "flask_app.app.route", "line_number": 95, "usage_type": "call"}, {"api_name": "flask_app.app", "line_number": 95, "usage_type": "name"}, {"api_name": "flask.session.clear", "line_number": 103, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 103, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 104, "usage_type": "call"}, {"api_name": "flask_app.app.route", "line_number": 101, "usage_type": "call"}, {"api_name": "flask_app.app", "line_number": 101, "usage_type": "name"}]} +{"seq_id": "73619072428", "text": "import numpy as np\r\nfrom pandas import read_csv, DataFrame\r\nfrom keras.models import Sequential, Model\r\nfrom keras.layers import Dense, Activation\r\nfrom keras.layers import LSTM\r\nfrom keras.optimizers import SGD, RMSprop\r\nfrom keras.layers.normalization import BatchNormalization\r\nimport matplotlib.pyplot as plt\r\nfrom keras.callbacks import EarlyStopping, LearningRateScheduler\r\nimport heapq as hq\r\nimport calendar\r\nimport time\r\nimport pandas as pd\r\nimport tensorflow as tf\r\n\r\n\r\n\r\ndef lstm_model_2(num_epochs,batch_size,lstm_neurons,dense_neurons,shape1,shape2) -> Model:\r\n \r\n # design network\r\n model = Sequential()\r\n model.add(LSTM(lstm_neurons, input_shape=(shape1, shape2),return_sequences=True))\r\n model.add(BatchNormalization())\r\n model.add(LSTM(lstm_neurons, input_shape=(shape1, shape2)))\r\n model.add(BatchNormalization())\r\n model.add(Dense(dense_neurons*lstm_neurons))\r\n model.add(BatchNormalization())\r\n model.add(Dense(dense_neurons)) \r\n model.add(Activation('sigmoid'))\r\n sgd = SGD(lr=0.10, decay=0.019, momentum=0.9, nesterov=True)\r\n model.compile(loss='binary_crossentropy', optimizer = sgd, metrics = ['accuracy'])\r\n model.summary()\r\n return model\r\n\r\n\r\n\r\n\r\n\r\n\r\ndef lstm_model(x_treino,y_treino,x_trade,y_trade,num_epochs,batch_size,lstm_neurons,dense_neurons,init_lr,pw,val_split,salvar):\r\n\r\n # design network\r\n model = Sequential()\r\n model.add(LSTM(lstm_neurons, input_shape=(x_treino.shape[1], x_treino.shape[2])))\r\n model.add(BatchNormalization())\r\n model.add(Dense(1))\r\n model.add(Activation('sigmoid'))\r\n sgd = SGD(lr = 0.0, momentum = 0.9, decay = 0.0, nesterov = False)\r\n model.compile(loss='binary_crossentropy', optimizer = sgd, metrics = ['accuracy'])\r\n cbks = [LearningRateScheduler(lambda x: 1. / (1. + x))]\r\n# lrate = LearningRateScheduler(poly_decay(num_epochs,init_lr,pw))\r\n# callbacks_list = [lrate]\r\n model.summary()\r\n \r\n # fit network\r\n history = model.fit(x_treino,\r\n y_treino, \r\n validation_split = val_split,\r\n epochs = num_epochs,\r\n batch_size = batch_size,\r\n callbacks = cbks,\r\n verbose = 1,\r\n shuffle = False)\r\n\r\n\r\n name = \"D:/Users/felip/Documents/07. FEA/Dissertacao/codigos/modelos/model_lstm_\" + str(lstm_neurons) + str(calendar.timegm(time.gmtime()))\r\n modeljson = name + \"_retornos.json\"\r\n modelhdf5 = name + \"_retornos.h5\"\r\n if salvar == 1: \r\n # serialize model to JSON\r\n model_json = model.to_json()\r\n with open(modeljson, \"w\") as json_file:\r\n json_file.write(model_json)\r\n # serialize weights to HDF5\r\n model.save_weights(modelhdf5)\r\n print(\"Saved model to disk\")\r\n\r\n\r\n # plot the training loss and accuracy\r\n H = history.history\r\n N = np.arange(0, len(H[\"loss\"]))\r\n \r\n MEDIUM_SIZE = 16\r\n BIGGER_SIZE = 22\r\n \r\n plt.rc('font', size=BIGGER_SIZE) # controls default text sizes\r\n plt.rc('axes', titlesize=BIGGER_SIZE) # fontsize of the axes title\r\n plt.rc('axes', labelsize=MEDIUM_SIZE) # fontsize of the x and y labels\r\n plt.rc('xtick', labelsize=MEDIUM_SIZE) # fontsize of the tick labels\r\n plt.rc('ytick', labelsize=MEDIUM_SIZE) # fontsize of the tick labels\r\n plt.rc('legend', fontsize=MEDIUM_SIZE) # legend fontsize\r\n plt.rc('figure', titlesize=BIGGER_SIZE) # fontsize of the figure title\r\n \r\n plt.figure()\r\n plt.rcParams['figure.figsize'] = (16, 12)\r\n plt.plot(N, H[\"loss\"], label=\"train_loss\")\r\n plt.plot(N, H[\"val_loss\"], label=\"valid_loss\")\r\n plt.plot(N, H[\"acc\"], label=\"train_acc\")\r\n plt.plot(N, H[\"val_acc\"], label=\"valid_acc\")\r\n plt.title(\"LSTM on IBOV prediction task\")\r\n plt.xlabel(\"Epoch #\")\r\n plt.ylabel(\"Loss / Accuracy\")\r\n plt.legend(frameon=False)\r\n \r\n # evaluate performance on the trade period\r\n score_treino = model.evaluate(x_treino, y_treino, batch_size = batch_size,verbose = 1)\r\n score_trade = model.evaluate(x_trade, y_trade, batch_size = batch_size, verbose = 1)\r\n print('Loss Treino.........:', score_treino[0])\r\n print('Loss Trade..........:', score_trade[0])\r\n print('Accuracy Treino.....:', score_treino[1])\r\n print('Accuracy Trade......:', score_trade[1])\r\n\r\n # return history and model\r\n return history, model, score_treino, score_trade, modeljson, modelhdf5\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\ndef poly_decay(epoch, init_lr, pw):\r\n\t# initialize the maximum number of epochs, base learning rate,\r\n\t# and power of the polynomial\r\n\tmaxEpochs = num_epochs\r\n\tbaseLR = init_lr\r\n\tpower = pw\r\n\t# compute the new learning rate based on polynomial decay\r\n\talpha = baseLR * (1 - (epoch / float(maxEpochs))) ** power\r\n\t# return the new learning rate\r\n\treturn alpha\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\nnome = '01_fechamento'\r\nfile = path + nome + tipo\r\nfechamento = read_csv(file,sep = ';')\r\nfechamento = fechamento.set_index('codigo')\r\ndatas = fechamento.columns.values.tolist()\r\ntikers = fechamento.index.values.tolist()\r\nvalues = fechamento.values\r\nfechamento = DataFrame(values, index=tikers, columns=datas, dtype = 'float64')\r\nfechamento = fechamento.stack()\r\npath = 'D:/Users/felip/Documents/07. FEA/Dissertacao/dados/BOVESPA/dataprep/'\r\ntipo = '.csv' \r\nnome = '01_fechamento'\r\nfile = path + nome + tipo\r\nfechamento = read_csv(file,sep = ';')\r\nfechamento = fechamento.set_index('codigo')\r\ndatas = fechamento.columns.values.tolist()\r\ntikers = fechamento.index.values.tolist()\r\nvalues = fechamento.values\r\nfechamento = DataFrame(values, index=tikers, columns=datas, dtype = 'float64')\r\nfechamento = fechamento.stack()\r\nfechamento.to_csv('D:/Users/felip/Documents/07. FEA/Dissertacao/dados/BOVESPA/dataprep/fechamento.txt', sep=',', index=True) \r\npath = 'D:/Users/felip/Documents/07. FEA/Dissertacao/dados/BOVESPA/dataprep/'\r\ntipo = '.csv' \r\nnome = '01_fechamento2'\r\nfile = path + nome + tipo\r\nfechamento = read_csv(file,sep = ';')\r\nfechamento = fechamento.set_index('codigo')\r\ndatas = fechamento.columns.values.tolist()\r\ntikers = fechamento.index.values.tolist()\r\nvalues = fechamento.values\r\nfechamento = DataFrame(values, index=tikers, columns=datas, dtype = 'float64')\r\nfechamento = fechamento.stack()\r\nfechamento.to_csv('D:/Users/felip/Documents/07. FEA/Dissertacao/dados/BOVESPA/dataprep/fechamento.txt', sep=',', index=True) \r\n\r\npath = 'D:/Users/felip/Documents/07. FEA/Dissertacao/dados/BOVESPA/dataprep/'\r\ntipo = '.txt' \r\nnome = 'fechamento'\r\nfile = path + nome + tipo\r\nfechamento = read_csv(file,sep = ',')\r\nbase_fechamento = pd.DataFrame({'data': pd.to_datetime(data),\r\n 'codigo': codigo,\r\n 'fechamento': fechamento}, columns = ['data', 'codigo', 'fechamento'])\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\nbase_total = pd.merge(base_fechamento, base_lpa, how = 'right', on = ['key1', 'key2'])\r\nbase_total = pd.merge(base_fechamento, base_lpa, how = 'right', on = ['data', 'codigo'])\r\nbase_total = pd.merge(base_fechamento, base_lpa, how = 'outer', on = ['data', 'codigo'])\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n# fit network\r\nhistory = model.fit(train_X, train_y, epochs=50, batch_size=72, validation_data=(test_X, test_y), verbose=2, shuffle=False)\r\n# plot history\r\npyplot.plot(history.history['loss'], label='train')\r\npyplot.plot(history.history['val_loss'], label='test')\r\npyplot.legend()\r\npyplot.show()\r\n\r\n\r\n\r\nmodel.add(LSTM(lstm_neurons,\r\n input_shape = (shape1, shape2), \r\n activation='relu', \r\n recurrent_activation='linear', \r\n kernel_regularizer=regularizers.l2(0.01),\r\n recurrent_regularizer=regularizers.l2(0.01), \r\n bias_regularizer=regularizers.l2(0.01), \r\n activity_regularizer=regularizers.l2(0.01),\r\n stateful=True))\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n# make a prediction\r\nyhat = model.predict(test_X)\r\ntest_X = test_X.reshape((test_X.shape[0], n_days*n_features))\r\n# invert scaling for forecast\r\ninv_yhat = concatenate((yhat, test_X[:, -7:]), axis=1)\r\ninv_yhat = scaler.inverse_transform(inv_yhat)\r\ninv_yhat = inv_yhat[:,0]\r\n# invert scaling for actual\r\ntest_y = test_y.reshape((len(test_y), 1))\r\ninv_y = concatenate((test_y, test_X[:, -7:]), axis=1)\r\ninv_y = scaler.inverse_transform(inv_y)\r\ninv_y = inv_y[:,0]\r\n# calculate RMSE\r\nrmse = sqrt(mean_squared_error(inv_y, inv_yhat))\r\nprint('Test RMSE: %.3f' % rmse)\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\nif reset_state == 1: \r\n for i in range(n_epoch):\r\n print(\"Epoch \", i, \"/\", n_epoch)\r\n history = model.fit(train_X, train_y, epochs = 1, batch_size = n_batch, verbose = 1, shuffle = False)\r\n performance[i,0] = np.max(history.history[\"loss\"])\r\n performance[i,1] = np.max(history.history[\"acc\"])\r\n performance[i,2] = np.max(history.history[\"precision\"])\r\n performance[i,3] = np.max(history.history[\"recall\"])\r\n performance[i,4] = np.max(history.history[\"fmeasure\"])\r\n# model.reset_states()\r\nelse:\r\n history = model.fit(train_X, train_y, epochs = n_epoch, batch_size = n_batch, verbose = 1, shuffle = False)\r\n performance[:,0] = history.history[\"loss\"]\r\n performance[:,1] = history.history[\"acc\"]\r\n performance[:,2] = history.history[\"precision\"]\r\n performance[:,3] = history.history[\"recall\"]\r\n performance[:,4] = history.history[\"fmeasure\"]\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n# # análise descritiva da base de dados batch total - pré-MinMaxScaler\r\n# dataset_ret = DataFrame(base_batch_total)\r\n# plt.rcParams['figure.figsize'] = (12, 18)\r\n# print(dataset_ret.shape)\r\n# print(dataset_ret.head(20))\r\n# print(dataset_ret.describe())\r\n# # histograms\r\n# dataset_ret.iloc[:,:20].hist()\r\n# plt.show()\r\n# # scatter plot matrix\r\n# from pandas.plotting import scatter_matrix\r\n# scatter_matrix(dataset_ret.iloc[:,0:7])\r\n# plt.show()\r\n \r\n \r\n # seprada em teste e validação, escalona e reconstrói a base_batch_total\r\n aux = base_batch_total.reset_index(level=['data','codigo'])\r\n data_codigo_y = aux[['data','codigo','y']]\r\n colnames = list(aux)\r\n colnames = colnames[2:13]\r\n\r\n treino = base_batch_total.loc[dia_inicio_treino:dia_fim_treino, :]\r\n teste = base_batch_total.loc[dia_inicio_teste:dia_fim_teste, :] \r\n treino_x = treino.iloc[:,0:treino.shape[1]-1]\r\n teste_x = teste.iloc[:,0:teste.shape[1]-1]\r\n\r\n scaler_x = MinMaxScaler(feature_range=(-1, 1))\r\n scaler_x.fit(treino_x)\r\n treino_x = scaler_x.transform(treino_x)\r\n teste_x = scaler_x.transform(teste_x)\r\n \r\n base_x = np.append(treino_x, teste_x, axis=0)\r\n base_x.shape\r\n base_batch_total.shape\r\n \r\n mydataframe = pandas.DataFrame(myarray, index=rownames, columns=colnames)\r\n \r\n # análise descritiva da base de dados batch total - pré-MinMaxScaler\r\n dataset_ret = DataFrame(treino_x)\r\n plt.rcParams['figure.figsize'] = (12, 18)\r\n print(dataset_ret.shape)\r\n print(dataset_ret.head(20))\r\n print(dataset_ret.describe())\r\n # histograms\r\n dataset_ret.iloc[:,:20].hist()\r\n plt.show()\r\n # scatter plot matrix\r\n from pandas.plotting import scatter_matrix\r\n scatter_matrix(dataset_ret.iloc[:,0:7])\r\n plt.show() \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n\r\n\r\n\r\ndef dataprep(base_in, features, ret_target, time_steps, dia_inicio_treino, dia_fim_treino, dia_inicio_teste, dia_fim_teste): \r\n\r\n base = base_in[features]\r\n base = base.set_index(['data']) \r\n base['y'] = ((base['retorno'] > ret_target)).astype(np.int) \r\n base = base.dropna(axis = 0, how = 'any')\r\n n_features = base.shape[1] - 1\r\n n_obs = time_steps * n_features\r\n #print('shape da base:', base.shape)\r\n\r\n qtde_treino = 0\r\n qtde_teste = 0\r\n #print(\"reframe each stock as supervised leaning, then stack all and reshape it\")\r\n codigo_dist = base.iloc[:,0].drop_duplicates()\r\n for i in range(len(codigo_dist)):\r\n # get data from one stock\r\n aux = base.loc[base['codigo'] == codigo_dist.iloc[i]]\r\n aux = aux.drop(columns=['codigo'])\r\n\r\n # split into train and test sets \r\n treino = aux.loc[dia_inicio_treino:dia_fim_treino, :]\r\n teste = aux.loc[dia_inicio_teste:dia_fim_teste, :]\r\n\r\n if treino.shape[0] != 0:\r\n\r\n scaler_x = MinMaxScaler(feature_range=(-1, 1)) \r\n \r\n treino_x = treino.iloc[:,:treino.shape[1]-1].values\r\n treino_y = treino.iloc[:,-1].values.reshape(treino.shape[0],1)\r\n scaler_x.fit(treino_x)\r\n treino_x = scaler_x.transform(treino_x)\r\n treino = np.concatenate((treino_x, treino_y), axis = 1) \r\n treino_reframed = series_to_supervised(treino, time_steps, 1) \r\n treino_x = treino_reframed.iloc[:, :n_obs]\r\n treino_y = treino_reframed.iloc[:, -1]\r\n\r\n if i == 0:\r\n treino_x_total = treino_x\r\n treino_y_total = treino_y\r\n else:\r\n treino_x_total = treino_x_total.append(treino_x)\r\n treino_y_total = treino_y_total.append(treino_y)\r\n \r\n qtde_treino += 1\r\n \r\n if teste.shape[0] != 0:\r\n \r\n teste_x = teste.iloc[:,:teste.shape[1]-1].values\r\n teste_y = teste.iloc[:,-1].values.reshape(teste.shape[0],1)\r\n teste_x = scaler_x.transform(teste_x)\r\n teste = np.concatenate((teste_x, teste_y), axis = 1)\r\n teste_reframed = series_to_supervised(teste, time_steps, 1)\r\n teste_x = teste_reframed.iloc[:, :n_obs]\r\n teste_y = teste_reframed.iloc[:, -1] \r\n \r\n if i == 0:\r\n teste_x_total = teste_x\r\n teste_y_total = teste_y\r\n else:\r\n teste_x_total = teste_x_total.append(teste_x)\r\n teste_y_total = teste_y_total.append(teste_y)\r\n \r\n qtde_teste += 1\r\n \r\n #print('shape de', codigo_dist.iloc[i], ':', treino_x.shape, treino_y.shape,teste_x.shape, teste_y.shape)\r\n \r\n # reshape input to be 3D [samples (blocks of stocks), timesteps, features]\r\n treino_x = treino_x_total.values.reshape((treino_x_total.shape[0], time_steps, n_features))\r\n treino_y = treino_y_total.values.reshape((treino_y_total.shape[0], 1))\r\n teste_x = teste_x_total.values.reshape((teste_x_total.shape[0], time_steps, n_features))\r\n teste_y = teste_y_total.values.reshape((teste_y_total.shape[0], 1))\r\n \r\n\r\n \r\n print('shape final....:', treino_x.shape, treino_y.shape, teste_x.shape, teste_y.shape)\r\n print('# ações treino.:', qtde_treino)\r\n print('# ações teste..:', qtde_teste)\r\n print('# features.....:', n_features)\r\n \r\n return treino_x, treino_y, teste_x, teste_y\r\n\r\n\r\n\r\n\r\n\r\n\r\n \r\n\r\n", "repo_name": "felipetshr/lstm_ibov", "sub_path": "old_codes.py", "file_name": "old_codes.py", "file_ext": "py", "file_size_in_byte": 15023, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "37", "api": [{"api_name": "keras.models.Sequential", "line_number": 21, "usage_type": "call"}, {"api_name": "keras.layers.LSTM", "line_number": 22, "usage_type": "call"}, {"api_name": "keras.layers.normalization.BatchNormalization", "line_number": 23, "usage_type": "call"}, {"api_name": "keras.layers.LSTM", "line_number": 24, "usage_type": "call"}, {"api_name": "keras.layers.normalization.BatchNormalization", "line_number": 25, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 26, "usage_type": "call"}, {"api_name": "keras.layers.normalization.BatchNormalization", "line_number": 27, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 28, "usage_type": "call"}, {"api_name": "keras.layers.Activation", "line_number": 29, "usage_type": "call"}, {"api_name": "keras.optimizers.SGD", "line_number": 30, "usage_type": "call"}, {"api_name": "keras.models.Model", "line_number": 18, "usage_type": "name"}, {"api_name": "keras.models.Sequential", "line_number": 43, "usage_type": "call"}, {"api_name": "keras.layers.LSTM", "line_number": 44, "usage_type": "call"}, {"api_name": "keras.layers.normalization.BatchNormalization", "line_number": 45, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 46, "usage_type": "call"}, {"api_name": "keras.layers.Activation", "line_number": 47, "usage_type": "call"}, {"api_name": "keras.optimizers.SGD", "line_number": 48, "usage_type": "call"}, {"api_name": "keras.callbacks.LearningRateScheduler", "line_number": 50, "usage_type": "call"}, {"api_name": "calendar.timegm", "line_number": 66, "usage_type": "call"}, {"api_name": "time.gmtime", "line_number": 66, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 81, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.rc", "line_number": 86, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 86, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.rc", "line_number": 87, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 87, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.rc", "line_number": 88, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 88, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.rc", "line_number": 89, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 89, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.rc", "line_number": 90, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 90, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.rc", "line_number": 91, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 91, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.rc", "line_number": 92, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 92, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 94, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 94, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.rcParams", "line_number": 95, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 95, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 96, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 96, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 97, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 97, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 98, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 98, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 99, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 99, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 100, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 100, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 101, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 101, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 102, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 102, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 103, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 103, "usage_type": "name"}, {"api_name": "pandas.read_csv", "line_number": 162, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 167, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 173, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 178, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 185, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 190, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 198, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 199, "usage_type": "call"}, {"api_name": "pandas.to_datetime", "line_number": 199, "usage_type": "call"}, {"api_name": "pandas.merge", "line_number": 209, "usage_type": "call"}, {"api_name": "pandas.merge", "line_number": 210, "usage_type": "call"}, {"api_name": "pandas.merge", "line_number": 211, "usage_type": "call"}, {"api_name": "keras.layers.LSTM", "line_number": 232, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 287, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 288, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 289, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 290, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 291, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 347, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 351, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 354, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.rcParams", "line_number": 355, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 355, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 361, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 361, "usage_type": "name"}, {"api_name": "pandas.plotting.scatter_matrix", "line_number": 364, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 365, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 365, "usage_type": "name"}, {"api_name": "numpy.int", "line_number": 386, "usage_type": "attribute"}, {"api_name": "numpy.concatenate", "line_number": 413, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 432, "usage_type": "call"}]} +{"seq_id": "25926621788", "text": "\"\"\"first_name and last_name of Member are made optional\n\nRevision ID: bc8b59d2078a\nRevises: 3d366b48929e\nCreate Date: 2023-09-10 20:12:03.268126\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\n\n\n# revision identifiers, used by Alembic.\nrevision = 'bc8b59d2078a'\ndown_revision = '3d366b48929e'\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade() -> None:\n # ### commands auto generated by Alembic - please adjust! ###\n op.alter_column('members', 'first_name',\n existing_type=sa.VARCHAR(),\n nullable=True)\n op.alter_column('members', 'last_name',\n existing_type=sa.VARCHAR(),\n nullable=True)\n # ### end Alembic commands ###\n\n\ndef downgrade() -> None:\n # ### commands auto generated by Alembic - please adjust! ###\n op.alter_column('members', 'last_name',\n existing_type=sa.VARCHAR(),\n nullable=False)\n op.alter_column('members', 'first_name',\n existing_type=sa.VARCHAR(),\n nullable=False)\n # ### end Alembic commands ###\n", "repo_name": "jutsuteck/devjutsu", "sub_path": "jutsu-services/auth-service/alembic/versions/bc8b59d2078a_first_name_and_last_name_of_member_are_.py", "file_name": "bc8b59d2078a_first_name_and_last_name_of_member_are_.py", "file_ext": "py", "file_size_in_byte": 1062, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "alembic.op.alter_column", "line_number": 21, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 21, "usage_type": "name"}, {"api_name": "sqlalchemy.VARCHAR", "line_number": 22, "usage_type": "call"}, {"api_name": "alembic.op.alter_column", "line_number": 24, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 24, "usage_type": "name"}, {"api_name": "sqlalchemy.VARCHAR", "line_number": 25, "usage_type": "call"}, {"api_name": "alembic.op.alter_column", "line_number": 32, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 32, "usage_type": "name"}, {"api_name": "sqlalchemy.VARCHAR", "line_number": 33, "usage_type": "call"}, {"api_name": "alembic.op.alter_column", "line_number": 35, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 35, "usage_type": "name"}, {"api_name": "sqlalchemy.VARCHAR", "line_number": 36, "usage_type": "call"}]} +{"seq_id": "32919053358", "text": "import numpy as np\r\n\r\n# Random sampling\r\nimport random\r\n\r\n# Keras API\r\nfrom tensorflow import keras\r\n\r\n# Deep learning\r\nfrom keras.models import Sequential\r\nfrom keras.layers import Dense, LSTM, Dropout\r\nfrom keras.optimizers import SGD\r\nfrom keras import losses\r\nfrom keras.callbacks import ModelCheckpoint\r\n\r\ndef create_X_Y(ts: np.array, lag=1, n_ahead=1, target_index=0) -> tuple:\r\n \"\"\"\r\n A method to create X and Y matrix from a time series array for the training of\r\n deep learning models\r\n \"\"\"\r\n # Extracting the idx of features that are passed from the array\r\n n_features = ts.shape[1]\r\n\r\n # Creating placeholder lists\r\n X, Y = [], []\r\n\r\n if len(ts) - lag <= 0:\r\n X.append(ts)\r\n else:\r\n for i in range(len(ts) - lag - n_ahead):\r\n Y.append(ts[(i + lag):(i + lag + n_ahead), target_index])\r\n X.append(ts[i:(i + lag)])\r\n\r\n X, Y = np.array(X), np.array(Y)\r\n\r\n # Reshaping the X array to an RNN input shape\r\n X = np.reshape(X, (X.shape[0], lag, n_features))\r\n\r\n return X, Y\r\n\r\n\r\nclass NNMultistepModel:\r\n\r\n def __init__(\r\n self,\r\n X,\r\n Y,\r\n n_outputs,\r\n n_lag,\r\n n_ft,\r\n n_layer,\r\n batch,\r\n epochs,\r\n Xval=None,\r\n Yval=None,\r\n\r\n file_path='best_checkpoint.hdf5'\r\n ):\r\n # 搭建LSTM模型，预测\r\n self.model = Sequential()\r\n # LSTM 第一层\r\n self.model.add(LSTM(n_layer, return_sequences=True, input_shape=(n_lag, n_ft)))\r\n self.model.add(Dropout(0.2))\r\n\r\n # LSTM 第二层\r\n self.model.add(LSTM(n_layer, return_sequences=True))\r\n self.model.add(Dropout(0.2))\r\n\r\n # LSTM 第三层\r\n self.model.add(LSTM(n_layer))\r\n self.model.add(Dropout(0.2))\r\n\r\n # Dense层\r\n self.model.add(Dense(units=n_outputs))\r\n\r\n self.batch = batch\r\n self.epochs = epochs\r\n self.n_layer = n_layer\r\n self.Xval = Xval\r\n self.Yval = Yval\r\n self.X = X\r\n self.Y = Y\r\n self.file_path = file_path\r\n\r\n def trainCallback(self):\r\n return ModelCheckpoint(filepath=self.file_path,\r\n monitor='loss',\r\n mode='min',\r\n save_best_only=True,\r\n save_weights_only=True)\r\n\r\n def valCallback(self):\r\n return ModelCheckpoint(filepath=self.file_path,\r\n monitor='val_loss',\r\n mode='min',\r\n save_best_only=True,\r\n save_weights_only=True)\r\n\r\n def train(self):\r\n # Getting the untrained model\r\n empty_model = self.model\r\n\r\n # Compiling the model\r\n empty_model.compile(loss='mae', optimizer='adam')\r\n\r\n if (self.Xval is not None) & (self.Yval is not None):\r\n history = empty_model.fit(\r\n self.X,\r\n self.Y,\r\n epochs=self.epochs,\r\n batch_size=self.batch,\r\n validation_data=(self.Xval, self.Yval),\r\n shuffle=False,\r\n callbacks=[self.valCallback()]\r\n )\r\n else:\r\n history = empty_model.fit(\r\n self.X,\r\n self.Y,\r\n epochs=self.epochs,\r\n batch_size=self.batch,\r\n shuffle=False,\r\n callbacks=[self.trainCallback()]\r\n )\r\n\r\n # Saving to original model attribute in the class\r\n self.model = empty_model\r\n\r\n # Returning the training history\r\n return history\r\n\r\n def predict(self, X):\r\n return self.model.predict(X)\r\n\r\n\r\n", "repo_name": "DistMinds/VCRoute", "sub_path": "sim_env/lstmmodel.py", "file_name": "lstmmodel.py", "file_ext": "py", "file_size_in_byte": 3802, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "numpy.array", "line_number": 16, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 34, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 37, "usage_type": "call"}, {"api_name": "keras.models.Sequential", "line_number": 60, "usage_type": "call"}, {"api_name": "keras.layers.LSTM", "line_number": 62, "usage_type": "call"}, {"api_name": "keras.layers.Dropout", "line_number": 63, "usage_type": "call"}, {"api_name": "keras.layers.LSTM", "line_number": 66, "usage_type": "call"}, {"api_name": "keras.layers.Dropout", "line_number": 67, "usage_type": "call"}, {"api_name": "keras.layers.LSTM", "line_number": 70, "usage_type": "call"}, {"api_name": "keras.layers.Dropout", "line_number": 71, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 74, "usage_type": "call"}, {"api_name": "keras.callbacks.ModelCheckpoint", "line_number": 86, "usage_type": "call"}, {"api_name": "keras.callbacks.ModelCheckpoint", "line_number": 93, "usage_type": "call"}]} +{"seq_id": "38642590634", "text": "import numpy as np\nfrom sklearn.model_selection import train_test_split\nfrom tensorflow.keras.models import load_model\n\nx=np.load('./homework/project1/npy/proejct1_x.npy')\ny=np.load('./homework/project1/npy/proejct1_y.npy')\n\nx_train, x_test, y_train, y_test = train_test_split(x, y, train_size=0.8)\n\nmodel = load_model('./homework/project1/models/model_Conv2D_train1_acc0.9897111058235168.h5')\n\nmodel.summary()\n\nloss, acc = model.evaluate(x_test, y_test)\n\nprint(\"loss\", loss)\nprint(\"acc\", acc)\n\n\n", "repo_name": "Kmmanki/bit_seoul", "sub_path": "homework/project1/conv2D_load.py", "file_name": "conv2D_load.py", "file_ext": "py", "file_size_in_byte": 496, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "numpy.load", "line_number": 5, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 6, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 8, "usage_type": "call"}, {"api_name": "tensorflow.keras.models.load_model", "line_number": 10, "usage_type": "call"}]} +{"seq_id": "19221707862", "text": "import requests\nfrom bs4 import BeautifulSoup\nfrom time import sleep\ngoogle_url = 'https://www.google.com.tw/search'\nmy_params = {'q': '寒流'}\nr = requests.get(google_url, params = my_params)\nif r.status_code == requests.codes.ok:\n soup = BeautifulSoup(r.text, 'html.parser')\n # print(soup.text)\n # html_prettify = soup.prettify()\n # print(html_prettify)\n # items = soup.select('div.g > h3.r > a[href^=\"/url\"]')\n items = soup.select('div.g > h3.r > a')\n sleep(3)\n for i in items:\n print(\"標題：\" + i.text)\n print(\"網址：\" + i.get('href'))", "repo_name": "Arwen0905/Python_Test", "sub_path": "0609/a0609_06_google_搜尋結果擷取_原.py", "file_name": "a0609_06_google_搜尋結果擷取_原.py", "file_ext": "py", "file_size_in_byte": 584, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "requests.get", "line_number": 6, "usage_type": "call"}, {"api_name": "requests.codes", "line_number": 7, "usage_type": "attribute"}, {"api_name": "bs4.BeautifulSoup", "line_number": 8, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 14, "usage_type": "call"}]} +{"seq_id": "27384335387", "text": "\nimport os\nimport string\nfrom nltk.corpus import stopwords\nfrom nltk import word_tokenize, sent_tokenize\nfrom nltk.stem import PorterStemmer, WordNetLemmatizer\nfrom nltk.classify import NaiveBayesClassifier\nfrom nltk.classify.util import accuracy\nfrom sklearn.model_selection import train_test_split\n\nPATH = \"data/\"\n\nps = PorterStemmer()\n\nlamma = WordNetLemmatizer()\n\n#split data for testing and training\ndef split_data(views):\n return train_test_split(views, test_size=0.20, random_state=33)\n\n\ndef lemmatzing(words):\n clean_words = []\n for word in words:\n clean_words.append(lamma.lemmatize(word))\n return clean_words\n\n\ndef sent_token(text):\n return sent_tokenize(text)\n\n\ndef word_token(text):\n return word_tokenize(text)\n\n\ndef steamming(words):\n clean_words = []\n for word in words:\n clean_words.append(ps.stem(word))\n return clean_words\n\n\ndef create_word_features(words):\n useful_words = [word for word in words if word not in stopwords.words(\"english\")]\n my_dict = dict([(word, True) for word in useful_words])\n return my_dict\n\n\ndef remove_puncatuation(words):\n return [word for word in words if word.lower() not in string.punctuation]\n\n\ndef remove_number(words):\n clean_word = []\n for word in words:\n if word not in string.digits:\n clean_word.append(word)\n return clean_word\n\n\ndef clean_data(sents, labs):\n i = 0\n views = []\n while len(labs) > i:\n if labs[i] == \"0\":\n views.append((create_word_features(remove_number(remove_puncatuation(word_tokenize(sents[i])))), \"negative\"))\n if labs[i] == \"1\":\n views.append((create_word_features(remove_number(remove_puncatuation(word_tokenize(sents[i])))), \"positive\"))\n\n i += 1\n return views\n\n\ndef create_data():\n sentences = [] # for storing sentences for training and testing\n labels = [] # for storing labels(Positive or negative) for training and testing\n\n fileName = [x for x in os.listdir(PATH)]\n\n for file in fileName:\n # get absulate file path\n path = os.path.join(PATH, file)\n # open file and store in file variable\n file = open(path, \"r\")\n # read the file text file save in sents variable\n sents = file.read().lower()\n # converts sents in sent_tokenizer and save respective variable\n sents = sent_token(sents)\n i = 0\n while len(sents) > i:\n if i == 0:\n obj1 = sents[i]\n obj2 = sents[i + 1]\n\n sentence = obj2.split(\"\\n\")\n sentences.append(obj1)\n labels.append(sentence[0])\n\n elif i == len(sents) - 1:\n obj1 = sents[i]\n obj2 = sents[i - 1]\n\n sentence = obj2.split(\"\\n\")\n\n sentences.append(sentence[1])\n labels.append(obj1[0])\n else:\n obj = sents[i]\n obj2 = sents[i + 1]\n\n sentence = obj.split(\"\\n\")\n label = obj2.split(\"\\n\")\n\n sentences.append(sentence[1])\n labels.append(label[0])\n\n i += 1\n\n print(\"Sentences:-\", len(sentences))\n print(\"Labels:-\", len(labels))\n return sentences, labels\n\n\nif __name__ == \"__main__\":\n sentences, labels = create_data()\n\n views = clean_data(sentences, labels)\n\n train, test = split_data(views)\n\n classif = NaiveBayesClassifier.train(train)\n\n example1 = \"Cats are awesome!\"\n\n example2 = \"I don’t like cats.\"\n\n example3 = \"I have no headache!\"\n\n example4 = \"I hate dogs.\"\n\n print(\"%s :- %s\" % (example1, classif.classify(create_word_features(word_token(example1)))))\n print(\"%s :- %s\" % (example2, classif.classify(create_word_features(word_token(example2)))))\n print(\"%s :- %s\" % (example3, classif.classify(create_word_features(word_token(example3)))))\n print(\"%s :- %s\" % (example4, classif.classify(create_word_features(word_token(example4)))))\n\n print(\"Accuracy:-\", accuracy(classif, test))\n", "repo_name": "RushalBarkhade/Sentiment-Analysis", "sub_path": "Sentiment Analysis/sentimentanalysis.py", "file_name": "sentimentanalysis.py", "file_ext": "py", "file_size_in_byte": 4032, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "nltk.stem.PorterStemmer", "line_number": 13, "usage_type": "call"}, {"api_name": "nltk.stem.WordNetLemmatizer", "line_number": 15, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 19, "usage_type": "call"}, {"api_name": "nltk.sent_tokenize", "line_number": 30, "usage_type": "call"}, {"api_name": "nltk.word_tokenize", "line_number": 34, "usage_type": "call"}, {"api_name": "nltk.corpus.stopwords.words", "line_number": 45, "usage_type": "call"}, {"api_name": "nltk.corpus.stopwords", "line_number": 45, "usage_type": "name"}, {"api_name": "string.punctuation", "line_number": 51, "usage_type": "attribute"}, {"api_name": "string.digits", "line_number": 57, "usage_type": "attribute"}, {"api_name": "nltk.word_tokenize", "line_number": 67, "usage_type": "call"}, {"api_name": "nltk.word_tokenize", "line_number": 69, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 79, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 83, "usage_type": "call"}, {"api_name": "os.path", "line_number": 83, "usage_type": "attribute"}, {"api_name": "nltk.classify.NaiveBayesClassifier.train", "line_number": 132, "usage_type": "call"}, {"api_name": "nltk.classify.NaiveBayesClassifier", "line_number": 132, "usage_type": "name"}, {"api_name": "nltk.classify.util.accuracy", "line_number": 147, "usage_type": "call"}]} +{"seq_id": "35472966329", "text": "import requests\nimport json\n\nclass Cdn():\n\n def __init__(self, email, key):\n self.email = email\n self.key = key\n\n def setup_caching_level(self, option):\n\n #replace option with aggressive|basic|simplified\n\n url = \"https://api.cloudflare.com/client/v4/zones/8517bd991c17a1808509cd0d9d31c282/settings/cache_level\"\n headers = {\n \"X-Auth-Email\": self.email,\n \"X-Auth-Key\": self.key,\n \"Content-Type\": \"application/json\",\n }\n\n data = {\"value\":option}\n\n response = requests.patch(url, headers=headers, data=json.dumps(data))\n\n if response.status_code == 200:\n print(\"cache level changed successfully\")\n else:\n print(\"fail to change cache parameter\")\n\n def setup_browser_cache_ttl(self, time):\n\n #replace time with 1800|300|900|1800|2700|3600|7200|10800|14400|28800|57600|86400|604800|2592000|31536000\n\n url = \"https://api.cloudflare.com/client/v4/zones/8517bd991c17a1808509cd0d9d31c282/settings/browser_cache_ttl\"\n\n headers = {\n \"X-Auth-Email\": self.email,\n \"X-Auth-Key\": self.key,\n \"Content-Type\": \"application/json\",\n }\n\n data = {\"value\":time}\n\n response = requests.patch(url, headers=headers, data=json.dumps(data))\n\n if response.status_code == 200:\n print(\"TTL changed successfully\")\n else:\n print(\"fail to change TTL parameter\")\n\n def always_online(self, option):\n\n #replace option with on|off\n\n url = \"https://api.cloudflare.com/client/v4/zones/8517bd991c17a1808509cd0d9d31c282/settings/always_online\"\n\n headers = {\n \"X-Auth-Email\": self.email,\n \"X-Auth-Key\": self.key,\n \"Content-Type\": \"application/json\",\n }\n\n data = {\"value\":option}\n\n response = requests.patch(url, headers=headers, data=json.dumps(data))\n\n if response.status_code == 200:\n print(\"Always online configured\")\n else:\n print(\"Failed to change parameter\")\n\n def purge_all_files(self, option=\"true\"):\n\n url = \"DELETE https://api.cloudflare.com/client/v4/zones/:identifier/purge_cache\"\n\n headers = {\n \"X-Auth-Email\": self.email,\n \"X-Auth-Key\": self.key,\n \"Content-Type\": \"application/json\",\n }\n\n data = {\"purge_everything\": option}\n\n response = requests.patch(url, headers=headers, data=json.dumps(data))\n\n if response.status_code == 200:\n print(\"files purged!\")\n else:\n print(\"Failed to purge files\")\n\n\n", "repo_name": "Berveglieri/challenge", "sub_path": "hive/deploy/orchestrator/infra/cdn.py", "file_name": "cdn.py", "file_ext": "py", "file_size_in_byte": 2634, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "requests.patch", "line_number": 23, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 23, "usage_type": "call"}, {"api_name": "requests.patch", "line_number": 44, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 44, "usage_type": "call"}, {"api_name": "requests.patch", "line_number": 65, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 65, "usage_type": "call"}, {"api_name": "requests.patch", "line_number": 84, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 84, "usage_type": "call"}]} +{"seq_id": "13673359589", "text": "import pytest\nfrom pathlib import Path\nfrom tscutter.common import PtsMap\nfrom tstriage.pipeline import MarkerMap\n\nvideoPath = Path(r\"C:\\Samples\\2020年05月23日18時00分00秒-名探偵コナン「小五郎はBARにいる(前編)」[解][字][デ]_HD-1.ts\")\nindexPath = Path(r\"C:\\Samples\\_metadata\\2020年05月23日18時00分00秒-名探偵コナン「小五郎はBARにいる(前編)」[解][字][デ]_HD-1.ptsmap\")\nmarkerPath = Path(r\"C:\\Samples\\_metadata\\2020年05月23日18時00分00秒-名探偵コナン「小五郎はBARにいる(前編)」[解][字][デ]_HD-1.markermap\")\n\noutputPath = Path(r\"C:\\Samples\\conanProgram.ts\")\n\ndef test_ExtractProgramList():\n markerMap = MarkerMap(markerPath, PtsMap(indexPath))\n programList = markerMap.GetProgramClips()\n assert len(programList) == 9\n\ndef test_ExtractProgramList_ByGroup():\n markerMap = MarkerMap(markerPath, PtsMap(indexPath))\n programList = markerMap.GetProgramClips()\n mergedProgramList = MarkerMap.MergeNeighbors(programList)\n assert len(mergedProgramList) == 3", "repo_name": "poke30744/tstriage", "sub_path": "tests/test_common.py", "file_name": "test_common.py", "file_ext": "py", "file_size_in_byte": 1043, "program_lang": "python", "lang": "ja", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "pathlib.Path", "line_number": 6, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 7, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 8, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 10, "usage_type": "call"}, {"api_name": "tstriage.pipeline.MarkerMap", "line_number": 13, "usage_type": "call"}, {"api_name": "tscutter.common.PtsMap", "line_number": 13, "usage_type": "call"}, {"api_name": "tstriage.pipeline.MarkerMap", "line_number": 18, "usage_type": "call"}, {"api_name": "tscutter.common.PtsMap", "line_number": 18, "usage_type": "call"}, {"api_name": "tstriage.pipeline.MarkerMap.MergeNeighbors", "line_number": 20, "usage_type": "call"}, {"api_name": "tstriage.pipeline.MarkerMap", "line_number": 20, "usage_type": "name"}]} +{"seq_id": "6388005845", "text": "from rest_framework.permissions import BasePermission\n\n\nclass IsAuthManager(BasePermission):\n\n def has_permission(self, request, view):\n if not request.user or not request.user.is_authenticated:\n return False\n\n return request.user.has_perms([\n 'daiquiri_auth.view_profile',\n 'daiquiri_auth.change_profile'\n ])\n", "repo_name": "django-daiquiri/daiquiri", "sub_path": "daiquiri/auth/permissions.py", "file_name": "permissions.py", "file_ext": "py", "file_size_in_byte": 367, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 22, "dataset": "github-code", "pt": "37", "api": [{"api_name": "rest_framework.permissions.BasePermission", "line_number": 4, "usage_type": "name"}]} +{"seq_id": "2862720502", "text": "from datetime import datetime\nfrom pathlib import Path\nfrom typing import Type, TypeVar, Iterable, List\n\nimport inject\nfrom is_empty import empty\nfrom selenium.webdriver import Chrome, Firefox, Edge, Safari, Remote, ChromeOptions, EdgeOptions, FirefoxOptions, WPEWebKitOptions\nfrom selenium.webdriver.common.options import BaseOptions\nfrom selenium.webdriver.remote.webdriver import WebDriver, BaseWebDriver\nfrom paf.common import Property, Formatter\nfrom paf.request import WebDriverRequest\n\nOPTION = TypeVar(\"OPTION\")\n\n\nclass WebDriverManager:\n def __init__(self):\n self._session_driver_map: dict[str, WebDriver] = {}\n self._thread_driver_map: dict[int, WebDriver] = {}\n\n def _get_options(self, request: WebDriverRequest, options_class: Type[OPTION]) -> OPTION:\n options = request.options\n if not options:\n options = options_class()\n else:\n assert isinstance(options, BaseOptions)\n\n return options\n\n def get_webdriver(self, request: WebDriverRequest) -> WebDriver:\n session_name = request.session_name\n if session_name in self._session_driver_map:\n return self._session_driver_map[session_name]\n\n webdriver = None\n webdriver_class: Type[BaseWebDriver] = None\n options: BaseOptions = None\n\n if request.browser in [\"chrome\"]:\n options = self._get_options(request, ChromeOptions)\n webdriver_class = Chrome\n elif request.browser in [\"firefox\"]:\n options = self._get_options(request, FirefoxOptions)\n webdriver_class = Firefox\n elif request.browser in [\"edge\"]:\n options = self._get_options(request, EdgeOptions)\n webdriver_class = Edge\n elif request.browser in [\"safari\"]:\n options = self._get_options(request, WPEWebKitOptions)\n webdriver_class = Safari\n else:\n raise Exception(\"No browser specified\")\n\n if request.browser_version:\n options.set_capability(\"browserVersion\", request.browser_version)\n # options.set_capability(\"platformName\", \"Windows XP\")\n\n if request.server_url:\n webdriver = Remote(command_executor=request.server_url.geturl(), options=options)\n elif webdriver_class:\n webdriver = webdriver_class(options=options)\n\n self.introduce_webdriver(webdriver, request)\n\n return webdriver\n\n def introduce_webdriver(self, webdriver: WebDriver, request: WebDriverRequest):\n self._session_driver_map[request.session_name] = webdriver\n\n if request.window_size:\n #LOG.info(f\"Set window size {request.window_size} on {webdriver.name}\")\n webdriver.set_window_rect(0, 0, request.window_size.width, request.window_size.height)\n\n def has_webdriver(self, session_name):\n return session_name in self._session_driver_map\n\n def shutdown_session(self, session_name: str):\n if session_name in self._session_driver_map:\n self.shutdown(self._session_driver_map[session_name])\n else:\n raise Exception(f\"Unknown session: {session_name}\")\n\n def shutdown(self, webdriver: WebDriver):\n webdriver.quit()\n webdrivers = list(self._session_driver_map.values())\n index = webdrivers.index(webdriver)\n\n session_keys = list(self._session_driver_map.keys())\n key = session_keys[index]\n self._session_driver_map.pop(key)\n\n def shutdown_all(self):\n for webdriver in list(self._session_driver_map.values()):\n self.shutdown(webdriver)\n\n def take_screenshot(self, webdriver: WebDriver) -> Path | None:\n dir = Path(Property.env(Property.PAF_SCREENSHOTS_DIR))\n title = webdriver.title\n if empty(title):\n title = webdriver.current_url\n\n formatter = inject.instance(Formatter)\n\n file_name = f\"{title}-{formatter.datetime(datetime.now())}.png\"\n dir.mkdir(parents=True, exist_ok=True)\n path = dir / file_name\n if webdriver.save_screenshot(dir / file_name):\n return path\n else:\n return None\n\n @property\n def webdrivers(self) -> List[WebDriver]:\n return list(self._session_driver_map.values())\n\n\ndef inject_config(binder: inject.Binder):\n binder.bind(WebDriverManager, WebDriverManager())\n", "repo_name": "mreiche/python-automation-framework", "sub_path": "paf/manager.py", "file_name": "manager.py", "file_ext": "py", "file_size_in_byte": 4353, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "37", "api": [{"api_name": "typing.TypeVar", "line_number": 13, "usage_type": "call"}, {"api_name": "selenium.webdriver.remote.webdriver.WebDriver", "line_number": 18, "usage_type": "name"}, {"api_name": "selenium.webdriver.remote.webdriver.WebDriver", "line_number": 19, "usage_type": "name"}, {"api_name": "paf.request.WebDriverRequest", "line_number": 21, "usage_type": "name"}, {"api_name": "typing.Type", "line_number": 21, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.options.BaseOptions", "line_number": 26, "usage_type": "argument"}, {"api_name": "paf.request.WebDriverRequest", "line_number": 30, "usage_type": "name"}, {"api_name": "typing.Type", "line_number": 36, "usage_type": "name"}, {"api_name": "selenium.webdriver.remote.webdriver.BaseWebDriver", "line_number": 36, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.options.BaseOptions", "line_number": 37, "usage_type": "name"}, {"api_name": "selenium.webdriver.ChromeOptions", "line_number": 40, "usage_type": "argument"}, {"api_name": "selenium.webdriver.Chrome", "line_number": 41, "usage_type": "name"}, {"api_name": "selenium.webdriver.FirefoxOptions", "line_number": 43, "usage_type": "argument"}, {"api_name": "selenium.webdriver.Firefox", "line_number": 44, "usage_type": "name"}, {"api_name": "selenium.webdriver.EdgeOptions", "line_number": 46, "usage_type": "argument"}, {"api_name": "selenium.webdriver.Edge", "line_number": 47, "usage_type": "name"}, {"api_name": "selenium.webdriver.WPEWebKitOptions", "line_number": 49, "usage_type": "argument"}, {"api_name": "selenium.webdriver.Safari", "line_number": 50, "usage_type": "name"}, {"api_name": "selenium.webdriver.Remote", "line_number": 59, "usage_type": "call"}, {"api_name": "selenium.webdriver.remote.webdriver.WebDriver", "line_number": 30, "usage_type": "name"}, {"api_name": "selenium.webdriver.remote.webdriver.WebDriver", "line_number": 67, "usage_type": "name"}, {"api_name": "paf.request.WebDriverRequest", "line_number": 67, "usage_type": "name"}, {"api_name": "selenium.webdriver.remote.webdriver.WebDriver", "line_number": 83, "usage_type": "name"}, {"api_name": "selenium.webdriver.remote.webdriver.WebDriver", "line_number": 96, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 97, "usage_type": "call"}, {"api_name": "paf.common.Property.env", "line_number": 97, "usage_type": "call"}, {"api_name": "paf.common.Property", "line_number": 97, "usage_type": "name"}, {"api_name": "paf.common.Property.PAF_SCREENSHOTS_DIR", "line_number": 97, "usage_type": "attribute"}, {"api_name": "is_empty.empty", "line_number": 99, "usage_type": "call"}, {"api_name": "inject.instance", "line_number": 102, "usage_type": "call"}, {"api_name": "paf.common.Formatter", "line_number": 102, "usage_type": "argument"}, {"api_name": "datetime.datetime.now", "line_number": 104, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 104, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 96, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 113, "usage_type": "name"}, {"api_name": "selenium.webdriver.remote.webdriver.WebDriver", "line_number": 113, "usage_type": "name"}, {"api_name": "inject.Binder", "line_number": 117, "usage_type": "attribute"}]} +{"seq_id": "780313397", "text": "__all__ = (\"CrowdingM5Metric\", \"CrowdingMagUncertMetric\", \"NstarsMetric\")\n\nimport healpy as hp\nimport numpy as np\nfrom scipy.interpolate import interp1d\n\nfrom rubin_sim.maf.metrics import BaseMetric\n\n# Modifying from Knut Olson's fork at:\n# https://github.com/knutago/sims_maf_contrib/blob/master/tutorials/CrowdingMetric.ipynb\n\n\ndef _comp_crowd_error(mag_vector, lum_func, seeing, single_mag=None):\n \"\"\"\n Compute the photometric crowding error given the luminosity function and best seeing.\n\n Parameters\n ----------\n mag_vector : np.array\n Stellar magnitudes.\n lum_func : np.array\n Stellar luminosity function.\n seeing : float\n The best seeing conditions. Assuming forced-photometry can use the best seeing conditions\n to help with confusion errors.\n single_mag : float (None)\n If single_mag is None, the crowding error is calculated for each mag in mag_vector. If\n single_mag is a float, the crowding error is interpolated to that single value.\n\n Returns\n -------\n np.array\n Magnitude uncertainties.\n\n Equation from Olsen, Blum, & Rigaut 2003, AJ, 126, 452\n \"\"\"\n lum_area_arcsec = 3600.0**2\n lum_vector = 10 ** (-0.4 * mag_vector)\n coeff = np.sqrt(np.pi / lum_area_arcsec) * seeing / 2.0\n my_int = (np.add.accumulate((lum_vector**2 * lum_func)[::-1]))[::-1]\n temp = np.sqrt(my_int) / lum_vector\n if single_mag is not None:\n interp = interp1d(mag_vector, temp)\n temp = interp(single_mag)\n crowd_error = coeff * temp\n return crowd_error\n\n\nclass CrowdingM5Metric(BaseMetric):\n \"\"\"Return the magnitude at which the photometric error exceeds crowding_error threshold.\"\"\"\n\n def __init__(\n self,\n crowding_error=0.1,\n filtername=\"r\",\n seeing_col=\"seeingFwhmGeom\",\n metric_name=None,\n maps=[\"StellarDensityMap\"],\n **kwargs,\n ):\n \"\"\"\n Parameters\n ----------\n crowding_error : float, optional\n The magnitude uncertainty from crowding in magnitudes. Default 0.1 mags.\n filtername: str, optional\n The bandpass in which to calculate the crowding limit. Default r.\n seeing_col : str, optional\n The name of the seeing column.\n m5Col : str, optional\n The name of the m5 depth column.\n maps : list of str, optional\n Names of maps required for the metric.\n\n Returns\n -------\n float\n The magnitude of a star which has a photometric error of `crowding_error`\n \"\"\"\n\n cols = [seeing_col]\n units = \"mag\"\n self.crowding_error = crowding_error\n self.filtername = filtername\n self.seeing_col = seeing_col\n if metric_name is None:\n metric_name = \"Crowding to Precision %.2f\" % (crowding_error)\n super().__init__(col=cols, maps=maps, units=units, metric_name=metric_name, **kwargs)\n\n def run(self, data_slice, slice_point=None):\n # Set mag_vector to the same length as starLumFunc (lower edge of mag bins)\n mag_vector = slice_point[f\"starMapBins_{self.filtername}\"][1:]\n # Pull up density of stars at this point in the sky\n lum_func = slice_point[f\"starLumFunc_{self.filtername}\"]\n # Calculate the crowding error using the best seeing value (in any filter?)\n crowd_error = _comp_crowd_error(mag_vector, lum_func, seeing=min(data_slice[self.seeing_col]))\n # Locate at which point crowding error is greater than user-defined limit\n above_crowd = np.where(crowd_error >= self.crowding_error)[0]\n\n if np.size(above_crowd) == 0:\n result = max(mag_vector)\n else:\n crowd_mag = mag_vector[max(above_crowd[0] - 1, 0)]\n result = crowd_mag\n\n return result\n\n\nclass NstarsMetric(BaseMetric):\n \"\"\"Return the number of stars visible above some uncertainty limit,\n taking image depth and crowding into account.\n \"\"\"\n\n def __init__(\n self,\n crowding_error=0.1,\n filtername=\"r\",\n seeing_col=\"seeingFwhmGeom\",\n m5_col=\"fiveSigmaDepth\",\n metric_name=None,\n maps=[\"StellarDensityMap\"],\n ignore_crowding=False,\n **kwargs,\n ):\n \"\"\"\n Parameters\n ----------\n crowding_error : float, optional\n The magnitude uncertainty from crowding in magnitudes. Default 0.1 mags.\n filtername: str, optional\n The bandpass in which to calculate the crowding limit. Default r.\n seeing_col : str, optional\n The name of the seeing column.\n m5_col : str, optional\n The name of the m5 depth column.\n maps : list of str, optional\n Names of maps required for the metric.\n ignore_crowding : bool (False)\n Ignore the cowding limit.\n\n Returns\n -------\n float\n The number of stars above the error limit\n \"\"\"\n\n cols = [seeing_col, m5_col]\n units = \"N stars\"\n self.crowding_error = crowding_error\n self.m5_col = m5_col\n self.filtername = filtername\n self.seeing_col = seeing_col\n self.ignore_crowding = ignore_crowding\n if metric_name is None:\n metric_name = \"N stars to Precision %.2f\" % (crowding_error)\n super().__init__(col=cols, maps=maps, units=units, metric_name=metric_name, **kwargs)\n\n def run(self, data_slice, slice_point=None):\n pix_area = hp.nside2pixarea(slice_point[\"nside\"], degrees=True)\n # Set mag_vector to the same length as starLumFunc (lower edge of mag bins)\n mag_vector = slice_point[f\"starMapBins_{self.filtername}\"][1:]\n # Pull up density of stars at this point in the sky\n lum_func = slice_point[f\"starLumFunc_{self.filtername}\"]\n # Calculate the crowding error using the best seeing value (in any filter?)\n crowd_error = _comp_crowd_error(mag_vector, lum_func, seeing=min(data_slice[self.seeing_col]))\n # Locate at which point crowding error is greater than user-defined limit\n above_crowd = np.where(crowd_error >= self.crowding_error)[0]\n\n if np.size(above_crowd) == 0:\n crowd_mag = max(mag_vector)\n else:\n crowd_mag = mag_vector[max(above_crowd[0] - 1, 0)]\n\n # Compute the coadded depth, and the mag where that depth hits the error specified\n coadded_depth = 1.25 * np.log10(np.sum(10.0 ** (0.8 * data_slice[self.m5_col])))\n mag_limit = -2.5 * np.log10(1.0 / (self.crowding_error * (1.09 * 5))) + coadded_depth\n\n # Use the shallower depth, crowding or coadded\n if self.ignore_crowding:\n min_mag = mag_limit\n else:\n min_mag = np.min([crowd_mag, mag_limit])\n\n # Interpolate to the number of stars\n result = (\n np.interp(\n min_mag,\n slice_point[f\"starMapBins_{self.filtername}\"][1:],\n slice_point[f\"starLumFunc_{self.filtername}\"],\n )\n * pix_area\n )\n\n return result\n\n\nclass CrowdingMagUncertMetric(BaseMetric):\n \"\"\"\n Given a stellar magnitude, calculate the mean uncertainty on the magnitude from crowding.\n \"\"\"\n\n def __init__(\n self,\n rmag=20.0,\n seeing_col=\"seeingFwhmGeom\",\n units=\"mag\",\n metric_name=None,\n filtername=\"r\",\n maps=[\"StellarDensityMap\"],\n **kwargs,\n ):\n \"\"\"\n Parameters\n ----------\n rmag : float\n The magnitude of the star to consider.\n\n Returns\n -------\n float\n The uncertainty in magnitudes caused by crowding for a star of rmag.\n \"\"\"\n\n self.filtername = filtername\n self.seeing_col = seeing_col\n self.rmag = rmag\n if metric_name is None:\n metric_name = \"CrowdingError at %.2f\" % (rmag)\n super().__init__(col=[seeing_col], maps=maps, units=units, metric_name=metric_name, **kwargs)\n\n def run(self, data_slice, slice_point=None):\n mag_vector = slice_point[f\"starMapBins_{self.filtername}\"][1:]\n lum_func = slice_point[f\"starLumFunc_{self.filtername}\"]\n # Magnitude uncertainty given crowding\n dmag_crowd = _comp_crowd_error(\n mag_vector, lum_func, data_slice[self.seeing_col], single_mag=self.rmag\n )\n result = np.mean(dmag_crowd)\n return result\n", "repo_name": "lsst/rubin_sim", "sub_path": "rubin_sim/maf/metrics/crowding_metric.py", "file_name": "crowding_metric.py", "file_ext": "py", "file_size_in_byte": 8462, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 34, "dataset": "github-code", "pt": "37", "api": [{"api_name": "numpy.sqrt", "line_number": 39, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 39, "usage_type": "attribute"}, {"api_name": "numpy.add.accumulate", "line_number": 40, "usage_type": "call"}, {"api_name": "numpy.add", "line_number": 40, "usage_type": "attribute"}, {"api_name": "numpy.sqrt", "line_number": 41, "usage_type": "call"}, {"api_name": "scipy.interpolate.interp1d", "line_number": 43, "usage_type": "call"}, {"api_name": "rubin_sim.maf.metrics.BaseMetric", "line_number": 49, "usage_type": "name"}, {"api_name": "numpy.where", "line_number": 98, "usage_type": "call"}, {"api_name": "numpy.size", "line_number": 100, "usage_type": "call"}, {"api_name": "rubin_sim.maf.metrics.BaseMetric", "line_number": 109, "usage_type": "name"}, {"api_name": "healpy.nside2pixarea", "line_number": 159, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 167, "usage_type": "call"}, {"api_name": "numpy.size", "line_number": 169, "usage_type": "call"}, {"api_name": "numpy.log10", "line_number": 175, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 175, "usage_type": "call"}, {"api_name": "numpy.log10", "line_number": 176, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 182, "usage_type": "call"}, {"api_name": "numpy.interp", "line_number": 186, "usage_type": "call"}, {"api_name": "rubin_sim.maf.metrics.BaseMetric", "line_number": 197, "usage_type": "name"}, {"api_name": "numpy.mean", "line_number": 238, "usage_type": "call"}]} +{"seq_id": "28521766919", "text": "#!/usr/bin/python3\n\"\"\" Using an example REST API extract some data\nfor a given employee ID, returns information about\nhis/her To-Do list progress\n\"\"\"\nfrom os import sys\nimport requests\n\n\nif __name__ == \"__main__\":\n \"\"\"\n Gather data from API, request data filtered by id.\n Should give id when program runs ./0-gather_data_from_an_api.py \n \"\"\"\n completed_tasks = 0\n total_tasks = 0\n # Request information from API, filter by id and extract in json\n user_id = {'id': sys.argv[1]}\n todos_id = {'userId': sys.argv[1]}\n users = requests.get(\n 'https://jsonplaceholder.typicode.com/users', params=user_id).json()\n todos = requests.get(\n 'https://jsonplaceholder.typicode.com/todos', params=todos_id).json()\n\n # Extracting data from json to print\n name = users[0].get('name')\n for key in todos:\n if key.get('completed'):\n completed_tasks += 1\n total_tasks += 1\n # Print the info about user's to-dos\n print(\"Employee {} is done with tasks({}/{}):\".format(name,\n completed_tasks,\n total_tasks))\n # Print every completed task from user\n for key in todos:\n if key.get('completed'):\n task_title = key.get('title')\n print(\"\\t {}\".format(task_title))\n", "repo_name": "MiguelMR96/holberton-system_engineering-devops", "sub_path": "0x15-api/0-gather_data_from_an_API.py", "file_name": "0-gather_data_from_an_API.py", "file_ext": "py", "file_size_in_byte": 1376, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "os.sys.argv", "line_number": 18, "usage_type": "attribute"}, {"api_name": "os.sys", "line_number": 18, "usage_type": "name"}, {"api_name": "os.sys.argv", "line_number": 19, "usage_type": "attribute"}, {"api_name": "os.sys", "line_number": 19, "usage_type": "name"}, {"api_name": "requests.get", "line_number": 20, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 22, "usage_type": "call"}]} +{"seq_id": "5356565535", "text": "import os\nimport itertools\nimport gym\nimport numpy as np\nimport random\nimport json\nfrom Environment.Technician import Technician\nfrom Environment.Machine import Machine\nimport pygame\nimport matplotlib.pyplot as plt\nimport io\nfrom pygame import gfxdraw\n#os.environ[\"SDL_VIDEODRIVER\"] = \"dummy\"\n\nclass FactoryEnv(gym.Env):\n metadata = {\"render.modes\": [\"human\", \"rgb_array\"], \"video.frames_per_second\": 2}\n def __init__(self, path_config, config):\n f = open(path_config)\n x = json.load(f)\n fail_dist = [tuple(i) for i in x[config][\"fail_dist\"]]\n machines = [Machine(idx=i, failure_dist=fail_dist) for i in range(x[config][\"number_machines\"])]\n technicians = [Technician(t, i) for i, t in enumerate(x[config][\"technicians\"])]\n multiplier_length_episode = x[config][\"multiplier_length_episode\"]\n\n np.random.seed(0)\n random.seed(0)\n self.movetech = 0\n self.current_step = 0\n self.machines = machines\n self.technicians = technicians\n self.num_machines = len(machines)\n self.num_technicians = len(technicians)\n self.num_components = len(machines[0].life_components)\n self.max_episode_length = 0\n self.screen = None\n for d in self.machines[0].failure_dist:\n max_value = d.quantile(1.0 - np.finfo(float).eps)\n if self.max_episode_length < max_value:\n self.max_episode_length = max_value\n self.max_episode_length = np.ceil((multiplier_length_episode * self.max_episode_length) + 1)\n maxRepairTime = 0\n for t in self.technicians:\n for r in t.mt2r:\n maxRepairTime = r if r > maxRepairTime else maxRepairTime\n\n lc = [self.max_episode_length] * self.num_components # Lc\n sc = [2] * self.num_components # Sc\n rm = [maxRepairTime + 1] # Rm\n n_tech = [self.num_technicians + 1]\n self.observation_space = gym.spaces.MultiDiscrete(np.array((lc + sc + rm + n_tech) * self.num_machines))\n t = [*range(self.num_technicians)]\n c = [*range(self.num_components)]\n actions_single_machine = list(itertools.product(t, c))\n actions_single_machine.insert(0, (-1, -1))\n list_actions_multiple_machines = []\n for i in range(self.num_machines):\n list_actions_multiple_machines.append(actions_single_machine)\n actions_multiple_machines = np.array(list(itertools.product(*list_actions_multiple_machines)))\n idx_to_delete = []\n for i in range(actions_multiple_machines.shape[0]):\n row = actions_multiple_machines[i, :, 0]\n values_with_action = row[row >= 0]\n if len(np.unique(values_with_action)) != len(values_with_action):\n idx_to_delete.append(i)\n actions_multiple_machines = np.delete(actions_multiple_machines, idx_to_delete, axis=0)\n #Reward\n rewardBreakdown = 0\n for t in self.technicians:\n maxT2R = max(t.mt2r)\n rewardBreakdown = maxT2R if maxT2R >= rewardBreakdown else rewardBreakdown\n self.reward = [1, -rewardBreakdown, 0]\n\n self.list_actions = actions_multiple_machines.copy()\n self.action_space = gym.spaces.Discrete(self.list_actions.shape[0])\n self.machines_names = []\n self.allMachines_steps = {}\n for m in self.machines:\n self.machines_names.append(f\"m{m.id}\")\n self.allMachines_steps[m.id] = []\n #self.fig, self.gnt = plt.subplots()\n #self.colors_states = ['#2dca1cff', '#a50000ff', '#00b3dacc', '#9927f599','#27eaf5ff', '#ff5bf0cc', '#c65900ff', '#aef527cc', '#6d0000ff']\n\n\n\n def get_observation(self):\n obs = []\n for m in self.machines:\n life_components = []\n state_componentes = []\n for lc in m.life_components:\n life_components.append(lc)\n for sc in m.state_components:\n state_componentes.append(sc)\n obs = obs + life_components + state_componentes + [m.remaining_maintenance, m.tech_assigned + 1]\n return np.array(obs)\n\n def action_masks(self):\n final_mask = np.array([True]*self.list_actions.shape[0])\n for i, m in enumerate(self.machines):\n tmp_mask = []\n if m.state == 2:\n for j in range(self.list_actions.shape[0]):\n tmp_mask.append(np.array_equal(self.list_actions[j, i, :], [-1, -1]))\n else:\n for j in range(self.list_actions.shape[0]):\n if np.array_equal(self.list_actions[j, i, :], [-1, -1]):\n tmp_mask.append(True)\n else:\n tmp_mask.append(self.technicians[self.list_actions[j, i, 0]].state)\n final_mask = np.logical_and(final_mask, tmp_mask)\n return final_mask\n\n def reset(self):\n self.current_step = 0\n for t in self.technicians:\n t.reset()\n for m in self.machines:\n m.reset()\n return self.get_observation()\n\n def step(self, action):\n rew = 0\n done = False\n for i, m in enumerate(self.machines):\n op_correct = m.assign_tech(self.technicians,\n self.list_actions[action][i][0],\n self.list_actions[action][i][1])\n if op_correct:\n rew = rew + self.reward[m.step(self.technicians)]\n else:\n obs = self.get_observation()\n rew = 0\n done = True\n return obs, rew, done, {}\n\n obs = self.get_observation()\n self.current_step += 1\n if self.current_step >= self.max_episode_length:\n done = True\n return obs, rew, done, {}\n\n #def render(self, mode=\"human\"):\n # self.initGantt()\n # for i, m in enumerate(self.machines):\n # s = 0\n # steps = []\n # colors = []\n # colorsState = []\n # for h in m.history:\n # steps.append((s, 1))\n # s += 1\n # colors.append(self.colors_states[h+2])\n # if h >= 0:\n # colorsState.append(\"#ffc700ff\")\n # else:\n # colorsState.append(self.colors_states[h+2])\n # self.gnt.broken_barh(steps, ((i + 1) * 10, 9), facecolors=tuple(colorsState))\n # self.gnt.broken_barh(steps, ((i + 1) * 10 + 4, 2), facecolors=tuple(colors))\n #\n # if mode == \"human\":\n # plt.show()\n # #self.fig.canvas.draw()\n # if mode == \"rgb_array\":\n # #plt.show()\n # self.fig.canvas.draw()\n # data = np.frombuffer(self.fig.canvas.tostring_rgb(), dtype=np.uint8)\n # data = data.reshape(self.fig.canvas.get_width_height()[::-1] + (3,))\n # return data\n\n def close(self):\n #pygame.quit()\n return True\n\n def initGantt(self):\n self.fig, self.gnt = plt.subplots()\n # Setting Y-axis limits\n self.gnt.set_ylim(0, 20+10*self.num_machines)\n # Setting X-axis limits\n self.gnt.set_xlim(0, self.max_episode_length)\n self.gnt.set_xlabel('Timesteps')\n self.gnt.set_ylabel('Machines')\n self.gnt.set_yticks(list(range(15, self.num_machines*10+6, 10)))\n # Labelling tickes of y-axis\n self.gnt.set_yticklabels(self.machines_names)\n # Setting graph attribute\n self.gnt.grid(True)", "repo_name": "marceloruizrodriguez/RL_Lecture", "sub_path": "maintEnv.py", "file_name": "maintEnv.py", "file_ext": "py", "file_size_in_byte": 7550, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "gym.Env", "line_number": 15, "usage_type": "attribute"}, {"api_name": "json.load", "line_number": 19, "usage_type": "call"}, {"api_name": "Environment.Machine.Machine", "line_number": 21, "usage_type": "call"}, {"api_name": "Environment.Technician.Technician", "line_number": 22, "usage_type": "call"}, {"api_name": "numpy.random.seed", "line_number": 25, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 25, "usage_type": "attribute"}, {"api_name": "random.seed", "line_number": 26, "usage_type": "call"}, {"api_name": "numpy.finfo", "line_number": 37, "usage_type": "call"}, {"api_name": "numpy.ceil", "line_number": 40, "usage_type": "call"}, {"api_name": "gym.spaces.MultiDiscrete", "line_number": 50, "usage_type": "call"}, {"api_name": "gym.spaces", "line_number": 50, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 50, "usage_type": "call"}, {"api_name": "itertools.product", "line_number": 53, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 58, "usage_type": "call"}, {"api_name": "itertools.product", "line_number": 58, "usage_type": "call"}, {"api_name": "numpy.unique", "line_number": 63, "usage_type": "call"}, {"api_name": "numpy.delete", "line_number": 65, "usage_type": "call"}, {"api_name": "gym.spaces.Discrete", "line_number": 74, "usage_type": "call"}, {"api_name": "gym.spaces", "line_number": 74, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 95, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 98, "usage_type": "call"}, {"api_name": "numpy.array_equal", "line_number": 103, "usage_type": "call"}, {"api_name": "numpy.array_equal", "line_number": 106, "usage_type": "call"}, {"api_name": "numpy.logical_and", "line_number": 110, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 175, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 175, "usage_type": "name"}]} +{"seq_id": "38319099237", "text": "from django.core.management.base import BaseCommand\nfrom wifi.models import SSIDReading\nimport os\nfrom utils import wifi\nimport platform\n\n\ndef wifi_scan(training_label):\n sys_os = platform.system()\n\n # now we save all the ssid's to SSIDReading\n if sys_os == 'Linux':\n ssids = wifi.scan_networks()\n for ssid in ssids:\n print(ssid)\n ssid_reading = SSIDReading()\n ssid_reading.address = ssid['address']\n ssid_reading.channel = ssid['channel']\n ssid_reading.quality = ssid['quality']\n ssid_reading.signal_level = ssid['signal_level']\n ssid_reading.training_label = training_label\n ssid_reading.save()\n if sys_os == 'Windows':\n ssids = wifi.scan_networks()\n size_loop = len(ssids)\n for i in range(size_loop):\n ssid_reading = SSIDReading()\n data_network = ssids['connection'+str(i)]\n ssid_reading.address = data_network['BSSID 1']\n ssid_reading.channel = data_network['Channel']\n ssid_reading.quality = data_network['Signal']\n #ssid_reading.signal_level = data_network['address']\n ssid_reading.training_label = training_label\n ssid_reading.save()\n\n\nclass Command(BaseCommand):\n help = 'fetch and parse iwlist'\n\n def add_arguments(self, parser):\n pass\n\n def handle(self, *args, **options):\n wifi_scan(\"Starbucks_Palo_Alto\")\n", "repo_name": "aaronorosen2/python-base", "sub_path": "codes/wifi/management/commands/iwlist.py", "file_name": "iwlist.py", "file_ext": "py", "file_size_in_byte": 1463, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "platform.system", "line_number": 9, "usage_type": "call"}, {"api_name": "utils.wifi.scan_networks", "line_number": 13, "usage_type": "call"}, {"api_name": "utils.wifi", "line_number": 13, "usage_type": "name"}, {"api_name": "wifi.models.SSIDReading", "line_number": 16, "usage_type": "call"}, {"api_name": "utils.wifi.scan_networks", "line_number": 24, "usage_type": "call"}, {"api_name": "utils.wifi", "line_number": 24, "usage_type": "name"}, {"api_name": "wifi.models.SSIDReading", "line_number": 27, "usage_type": "call"}, {"api_name": "django.core.management.base.BaseCommand", "line_number": 37, "usage_type": "name"}]} +{"seq_id": "18099200606", "text": "import fitz\nimport pandas as pd\nimport re\n\ndef parsedate(str_with_date):\n date_pattern = r'\\d{2}\\.\\d{2}\\.\\d{4}'\n\n match = re.search(date_pattern, str_with_date)\n if match:\n date = match.group()\n return date\n return None\n\nfile = \"./resources/input.pdf\"\n\npages_df = pd.DataFrame(columns=['text'])\n\nwith fitz.open(file) as doc:\n for page in doc: print(\"page %i\" % page.number)\n\ndoc = fitz.open(file)\n\nextraction_pdfs = {}\narr = []\n\nfor page_num in range(doc.page_count):\n page = doc.load_page(page_num)\n spl = page.get_text('text').split('\\n')\n arr.extend(spl)\n # print(spl)\n s1 = pd.DataFrame(spl, columns=['text'])\n # print(s1)\n pages_df = pd.concat([pages_df, s1], ignore_index=True)\n\nextraction_pdfs[file] = pages_df\n\nfor line in arr:\n # print(line)\n if \"Дата поступления образца\" in line:\n print(parsedate(line))\n", "repo_name": "nickolaysm/clinic-parser", "sub_path": "testPDFWithText.py", "file_name": "testPDFWithText.py", "file_ext": "py", "file_size_in_byte": 900, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "re.search", "line_number": 8, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 16, "usage_type": "call"}, {"api_name": "fitz.open", "line_number": 18, "usage_type": "call"}, {"api_name": "fitz.open", "line_number": 21, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 31, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 33, "usage_type": "call"}]} +{"seq_id": "38678687679", "text": "import os\nimport json\nimport datetime\nimport re\nimport string\nimport random\n\nfrom systems.logger import log, debug_on\n\n\nclass Event:\n def __init__(self):\n self.msg = None\n self.events_path = './local/events.json'\n self.pattern = r'\\d{4}-\\d{2}-\\d{2}'\n\n # event vars\n self.event_id = None\n self.event_date = None\n self.event_msg = None\n self.event_channel = None\n self.events_dict = None\n # make sure file exists\n self.create_file()\n\n\n\n async def command(self, message):\n with open(self.events_path, \"r\") as f:\n self.events_dict = json.load(f)\n self.msg = message.content.replace('$event ', '')\n if self.msg == \"up\":\n log(f'[Event] - {message.author} is listing upcoming events')\n sorted_entries = sorted(self.events_dict.items(), key=lambda item: item[1]['date'])\n event_str = \"-- Upcoming events --\\n\"\n for item, sorted_date in sorted_entries:\n if sorted_date[\"channel\"] == message.channel.id:\n event_str += f'{sorted_date[\"date\"]}: {sorted_date[\"msg\"]}\\n'\n await message.channel.send(f'```yaml\\n\\n{event_str}```')\n return\n log(f'[Event] - {message.author} is creating {self.msg}')\n dates = re.findall(self.pattern, self.msg)\n if not dates or len(dates) > 1:\n await message.channel.send(\"Syntax error, date needs to be yyyy-mm-dd\")\n return\n try:\n event_date = datetime.date.fromisoformat(dates[0])\n except ValueError:\n await message.channel.send(\"Syntax error, thats not a valid date\")\n return\n current_date = datetime.date.today()\n if event_date <= current_date:\n await message.channel.send(\"The date has to be a future one\")\n return\n self.msg = self.msg.replace(dates[0], \"\") # self.msg should now only be the message\n\n if len(self.msg) == 0 or len(self.msg) > 80:\n await message.channel.send(\"Syntax error, the event needs a message to display (less then 80 chars)\")\n return\n self.event_date = dates[0]\n self.event_msg = self.msg\n self.event_channel = message.channel.id\n\n self.make_event()\n self.write_json(self.events_path, self.events_dict)\n\n await message.add_reaction(\"👍\")\n\n def write_json(self, filepath, data):\n with open(filepath, \"w\") as f:\n json.dump(data, f, indent=4)\n\n def create_file(self):\n # make json if none exists\n if not os.path.exists(self.events_path):\n # make json if none excists\n main_dict = {}\n self.write_json(self.events_path, main_dict)\n\n def make_event(self):\n self.event_id = ''.join(random.SystemRandom().choice(string.ascii_letters + string.digits) for _ in range(20))\n self.events_dict[self.event_id] = {}\n self.events_dict[self.event_id][\"date\"] = self.event_date\n self.events_dict[self.event_id][\"msg\"] = self.event_msg\n self.events_dict[self.event_id][\"channel\"] = self.event_channel\n", "repo_name": "matte54/ProjectReggie", "sub_path": "systems/commands/cmd_event.py", "file_name": "cmd_event.py", "file_ext": "py", "file_size_in_byte": 3156, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "37", "api": [{"api_name": "json.load", "line_number": 30, "usage_type": "call"}, {"api_name": "systems.logger.log", "line_number": 33, "usage_type": "call"}, {"api_name": "systems.logger.log", "line_number": 41, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 42, "usage_type": "call"}, {"api_name": "datetime.date.fromisoformat", "line_number": 47, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 47, "usage_type": "attribute"}, {"api_name": "datetime.date.today", "line_number": 51, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 51, "usage_type": "attribute"}, {"api_name": "json.dump", "line_number": 71, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 75, "usage_type": "call"}, {"api_name": "os.path", "line_number": 75, "usage_type": "attribute"}, {"api_name": "random.SystemRandom", "line_number": 81, "usage_type": "call"}, {"api_name": "string.ascii_letters", "line_number": 81, "usage_type": "attribute"}, {"api_name": "string.digits", "line_number": 81, "usage_type": "attribute"}]} +{"seq_id": "75036179307", "text": "import datetime as time\nglobal now\nnow = time.datetime.now()\nhour = now.hour\n\nif hour < 12:\n print(\"Good morning\")\nelif hour >= 12 and hour <= 18:\n global tim\n tim = 'Good afternoon'\n print(\"Good afternoon\") \nelif hour > 18 and hour < 19: \n print(\"Good evening\")\nelse:\n print('Have a nice night.')\n\ndef name():\n global nam\n nam = input('please input your name: ')\n if nam == '' or nam == ' ':\n print('you have inputed nothing try again')\n name()\n else:\n print('''\n ''')\nname()\ndef gender():\n global gend\n gend = input('''Are you:\n male(m)\n female(f)\n do not want to diclose(d)\n pls provide the replay here:''')\n global female\n female = 'lady'\n if gend == 'male' or gend == 'm':\n print('Thank you mr ',name)\n elif gend == 'female' or gend == 'f':\n print('Thank you lady ',name)\n elif gend == 'diclose' or gend == 'do not want to diclose'or gend == 'd':\n print('Thank you ',nam,'! We respect your right to not tell us')\n elif gend == ' ' or gend == '':\n print('you have inputed nothing try again')\n gender()\n elif gend != 'male' or con != 'lady':\n print('you have inputed the wrong thing try agin')\n gender()\n print(tim,' mr ',nam)\ngender()", "repo_name": "mallimuondu/Greetings", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 1295, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "datetime.datetime.now", "line_number": 3, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 3, "usage_type": "attribute"}]} +{"seq_id": "31143250017", "text": "\"\"\"Spatial mixture-of-experts layer and network.\"\"\"\n\nimport torch\n\nfrom . smoe_config import SpatialMoEConfig\nfrom . smoe_routing import SMoERouting\n\n\nclass SpatialMoE2d(torch.nn.Module):\n \"\"\"\n Spatial mixture-of-experts layer.\n\n This implements the actual MoE. The gating function must be applied\n separately (to facilitate sharing).\n\n \"\"\"\n\n def __init__(self, smoe_config: SpatialMoEConfig) -> None:\n super().__init__()\n self.smoe_config = smoe_config\n if smoe_config.out_planes > smoe_config.num_experts:\n raise ValueError(f'SpatialMoE: out planes {smoe_config.out_planes}'\n f' > num experts {smoe_config.num_experts}')\n if smoe_config.expert_block is None:\n smoe_config.expert_block = torch.nn.Conv2d\n self.experts = smoe_config.expert_block(\n smoe_config.in_planes, smoe_config.num_experts,\n kernel_size=smoe_config.kernel_size, padding=smoe_config.padding,\n bias=False)\n\n def extra_repr(self) -> str:\n return (f'out_planes={self.smoe_config.out_planes}'\n f' num_experts={self.smoe_config.num_experts}')\n\n def forward(self, x: torch.Tensor, routing_weights: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:\n experts = self.experts(x)\n selected_experts, routing_map, routing_indices = SMoERouting.apply(\n experts, routing_weights, self.smoe_config, self)\n return selected_experts, routing_map, routing_indices\n\n\nclass GatedSpatialMoE2d(torch.nn.Module):\n \"\"\"Spatial MoE with internal gating function.\"\"\"\n\n def __init__(self, smoe_config: SpatialMoEConfig) -> None:\n super().__init__()\n self.smoe_config = smoe_config\n self.smoe = SpatialMoE2d(smoe_config)\n self.gate = smoe_config.gate_block(smoe_config)\n\n def extra_repr(self) -> str:\n return repr(self.smoe_config)\n\n def forward(self, x: torch.Tensor) -> torch.Tensor:\n self.smoe.routing_weights = self.gate(x)\n x, self.smoe.routing_map, self.smoe.routing_indices = self.smoe(\n x, self.smoe.routing_weights)\n return x\n", "repo_name": "spcl/smoe", "sub_path": "smoe/models/smoe.py", "file_name": "smoe.py", "file_ext": "py", "file_size_in_byte": 2166, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 12, "dataset": "github-code", "pt": "37", "api": [{"api_name": "torch.nn", "line_number": 9, "usage_type": "attribute"}, {"api_name": "smoe_config.SpatialMoEConfig", "line_number": 18, "usage_type": "name"}, {"api_name": "smoe_config.out_planes", "line_number": 21, "usage_type": "attribute"}, {"api_name": "smoe_config.num_experts", "line_number": 21, "usage_type": "attribute"}, {"api_name": "smoe_config.out_planes", "line_number": 22, "usage_type": "attribute"}, {"api_name": "smoe_config.num_experts", "line_number": 23, "usage_type": "attribute"}, {"api_name": "smoe_config.expert_block", "line_number": 24, "usage_type": "attribute"}, {"api_name": "smoe_config.expert_block", "line_number": 25, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 25, "usage_type": "attribute"}, {"api_name": "smoe_config.expert_block", "line_number": 26, "usage_type": "call"}, {"api_name": "smoe_config.in_planes", "line_number": 27, "usage_type": "attribute"}, {"api_name": "smoe_config.num_experts", "line_number": 27, "usage_type": "attribute"}, {"api_name": "smoe_config.kernel_size", "line_number": 28, "usage_type": "attribute"}, {"api_name": "smoe_config.padding", "line_number": 28, "usage_type": "attribute"}, {"api_name": "torch.Tensor", "line_number": 35, "usage_type": "attribute"}, {"api_name": "smoe_routing.SMoERouting.apply", "line_number": 37, "usage_type": "call"}, {"api_name": "smoe_routing.SMoERouting", "line_number": 37, "usage_type": "name"}, {"api_name": "torch.nn", "line_number": 42, "usage_type": "attribute"}, {"api_name": "smoe_config.SpatialMoEConfig", "line_number": 45, "usage_type": "name"}, {"api_name": "smoe_config.gate_block", "line_number": 49, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 54, "usage_type": "attribute"}]} +{"seq_id": "23619884821", "text": "import datetime\nimport json\nimport logging\nfrom typing import Any\n\nfrom .errors import CartolaFCError, CartolaFCGameOverError, CartolaFCOverloadError\n\n\ndef json_default(value: Any) -> dict:\n if isinstance(value, datetime.datetime):\n return dict(\n year=value.year,\n month=value.month,\n day=value.day,\n hour=value.hour,\n minute=value.minute,\n second=value.second,\n microsecond=value.microsecond,\n tzinfo=value.tzinfo,\n )\n return value.__dict__\n\n\ndef parse_and_check_cartolafc(json_data: str) -> dict:\n try:\n data = json.loads(json_data)\n if \"game_over\" in data and data[\"game_over\"]:\n logging.info(\n \"Desculpe-nos, o jogo acabou e não podemos obter os dados solicitados\"\n )\n raise CartolaFCGameOverError(\n \"Desculpe-nos, o jogo acabou e não podemos obter os dados solicitados\"\n )\n if \"mensagem\" in data and data[\"mensagem\"]:\n logging.error(data[\"mensagem\"])\n raise CartolaFCError(data[\"mensagem\"].encode(\"utf-8\"))\n return data\n except ValueError as error:\n logging.error(\"Error parsing and checking json data: %s\", json_data)\n logging.error(error)\n raise CartolaFCOverloadError(\n \"Globo.com - Desculpe-nos, nossos servidores estão sobrecarregados.\"\n )\n", "repo_name": "vicenteneto/python-cartolafc", "sub_path": "cartolafc/util.py", "file_name": "util.py", "file_ext": "py", "file_size_in_byte": 1430, "program_lang": "python", "lang": "pt", "doc_type": "code", "stars": 64, "dataset": "github-code", "pt": "37", "api": [{"api_name": "typing.Any", "line_number": 9, "usage_type": "name"}, {"api_name": "datetime.datetime", "line_number": 10, "usage_type": "attribute"}, {"api_name": "json.loads", "line_number": 26, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 28, "usage_type": "call"}, {"api_name": "errors.CartolaFCGameOverError", "line_number": 31, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 35, "usage_type": "call"}, {"api_name": "errors.CartolaFCError", "line_number": 36, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 39, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 40, "usage_type": "call"}, {"api_name": "errors.CartolaFCOverloadError", "line_number": 41, "usage_type": "call"}]} +{"seq_id": "14825669577", "text": "import os\nimport time\nimport shutil\nimport sys\n\nfrom watchdog.engines.base import EngineType\nfrom watchdog.steam import srcupdatecheck\nfrom buildtools.os_utils import cmd, Chdir, TimeExecution\nfrom buildtools.bt_logging import log\nfrom buildtools import os_utils, Config\nfrom watchdog.utils import del_empty_dirs, LoggedProcess\nimport collections\nfrom watchdog.engines.steambase import SteamBase\n\nfrom valve.source.a2s import ServerQuerier # IGNORE:import-error\n\n\n@EngineType('vrage')\nclass VRageEngine(SteamBase):\n\n def __init__(self, cfg, args):\n super(VRageEngine, self).__init__(cfg, args)\n\n self.numPlayers = 0\n\n self.initialized.fire()\n \n self.asyncProcess=None\n\n def updateAlert(self, typeID=''):\n return\n '''\n ip, port = self.config.get('monitor.ip', '127.0.0.1'), self.config.get('monitor.port', 27015)\n ip, port = self.config.get('auth.rcon.ip', ip), self.config.get('auth.rcon.port', port)\n wait = self.config.get('monitor.restart-wait', 30)\n passwd = self.config.get('auth.rcon.password', None)\n if passwd is None:\n return\n with log.info('Sending warning via RCON to %s:%d...', ip, port):\n if self.process is None or not self.process.is_running():\n log.warn('Process is not running, skipping rcon warning.')\n return\n if not self.pingServer(noisy=True):\n log.warn('PING failed, skipping RCON warning.')\n return\n if self.numPlayers == 0:\n log.warn('0 players online, skipping RCON warning.')\n return\n with RCON((ip, port), passwd) as rcon:\n if wait > 0:\n rcon('say [Watchdog] {type} update detected, restarting in {time} seconds.'.format(\n type=typeID, time=wait))\n time.sleep(wait)\n rcon(\n 'say [Watchdog] Restarting now to update {}.'.format(typeID))\n '''\n\n def queueRestart(self, typeID):\n super(VRageEngine, self).queueRestart(typeID)\n\n '''\n ip, port = self.config.get('monitor.ip', '127.0.0.1'), self.config.get('monitor.port', 27015)\n ip, port = self.config.get('auth.rcon.ip', ip), self.config.get('auth.rcon.port', port)\n passwd = self.config.get('auth.rcon.password', None)\n with log.info('Sending restart queue warning via RCON to %s:%d...', ip, port):\n if self.process is None or not self.process.is_running():\n log.warn('Process is not running, skipping rcon warning.')\n return\n if not self.pingServer(noisy=True):\n log.warn('PING failed, skipping RCON warning.')\n return\n if self.numPlayers == 0:\n log.warn('0 players online, skipping RCON warning.')\n return\n with RCON((ip, port), passwd) as rcon:\n rcon('say [Watchdog] {} update detected, restarting at the end of the round, or when the server empties.'.format(typeID))\n '''\n\n def tryPing(self, trynum, maxtries, noisy):\n ip, port = self.config.get('monitor.ip', '127.0.0.1'), self.config.get('monitor.port', 27015)\n timeout = self.config.get('monitor.timeout', 10)\n try:\n if noisy:\n log.info('Querying %s:%d (ping attempt %d/%d)...', ip, port, trynum + 1, maxtries)\n with log:\n server = ServerQuerier((ip, port), timeout=timeout)\n # with TimeExecution('Ping'):\n self.numPlayers = int(server.get_info()['player_count'])\n if noisy:\n log.info('%d players connected.', self.numPlayers)\n if self.numPlayers == 0 and self.restartQueued:\n log.info('RESTARTING!')\n self.applyUpdates(True)\n except Exception as e:\n log.error(e)\n return False\n return True\n\n def start_process(self):\n command=[]\n \n runtime = self.config.get('daemon.runtime.executable', None)\n if runtime:\n command += [runtime]+self.config.get('daemon.runtime.args',[])\n \n command.append(os.path.join(self.gamedir, self.config.get('daemon.executable', 'SpaceEngineersDedicated.exe')))\n\n game_required = {'console': ''}\n game_args = self.config.get('daemon.game_args', {})\n #self.applyDefaultsTo(game_required, game_args, 'Configuration entry {key!r} is not present in daemon.game_args. Default value {value!r} is set.')\n command += self.buildArgs('-', game_args, game_required)\n\n niceness = self.config.get('daemon.niceness', 0)\n if niceness != 0:\n command = ['nice', '-n', niceness] + command\n\n with Chdir(self.gamedir):\n self.asyncProcess = LoggedProcess(command, 'dedi', echo=True, PTY=False, debug=False)\n self.asyncProcess.Start()\n\n self.find_process()\n\n def end_process(self):\n super(VRageEngine, self).end_process()\n if self.asyncProcess:\n self.asyncProcess.Stop() # calls child.kill\n self.asyncProcess.WaitUntilDone()\n", "repo_name": "N3X15/Watchdog", "sub_path": "watchdog/engines/vrage.py", "file_name": "vrage.py", "file_ext": "py", "file_size_in_byte": 5234, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "watchdog.engines.steambase.SteamBase", "line_number": 19, "usage_type": "name"}, {"api_name": "buildtools.bt_logging.log.info", "line_number": 84, "usage_type": "call"}, {"api_name": "buildtools.bt_logging.log", "line_number": 84, "usage_type": "name"}, {"api_name": "buildtools.bt_logging.log", "line_number": 85, "usage_type": "name"}, {"api_name": "valve.source.a2s.ServerQuerier", "line_number": 86, "usage_type": "call"}, {"api_name": "buildtools.bt_logging.log.info", "line_number": 90, "usage_type": "call"}, {"api_name": "buildtools.bt_logging.log", "line_number": 90, "usage_type": "name"}, {"api_name": "buildtools.bt_logging.log.info", "line_number": 92, "usage_type": "call"}, {"api_name": "buildtools.bt_logging.log", "line_number": 92, "usage_type": "name"}, {"api_name": "buildtools.bt_logging.log.error", "line_number": 95, "usage_type": "call"}, {"api_name": "buildtools.bt_logging.log", "line_number": 95, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 106, "usage_type": "call"}, {"api_name": "os.path", "line_number": 106, "usage_type": "attribute"}, {"api_name": "buildtools.os_utils.Chdir", "line_number": 117, "usage_type": "call"}, {"api_name": "watchdog.utils.LoggedProcess", "line_number": 118, "usage_type": "call"}, {"api_name": "watchdog.engines.base.EngineType", "line_number": 18, "usage_type": "call"}]} +{"seq_id": "35949728411", "text": "import numpy as np\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.neighbors import KNeighborsClassifier\nfrom sklearn.model_selection import train_test_split\nimport pandas as pd\nfrom sklearn import metrics\n\nimport random\ndata = pd.read_csv(\"breast-cancer-wisconsin.txt\")\n\n\ndata=data.replace(\"?\", -9999)\ndata=data.drop(['Id'], axis=1)\n\ny=data.Class\nx=data.drop(['Class'], axis=1)\n\n\nimp = SimpleImputer(missing_values=-9999, strategy=\"mean\")\nx = imp.fit_transform(x) #x = β 0 + β 1 x 1 + β 2 x 2 1\n\n\n\nfor i in range (10):\n X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.4)\n\n tahmin = KNeighborsClassifier(n_neighbors=3, weights='uniform', algorithm='ball_tree', leaf_size=30, p=2,\n metric='euclidean', metric_params=None, n_jobs=1)\n # K en yakın komşu algoritması\n tahmin.fit(X_train, y_train)\n basari = tahmin.score(X_test, y_test)\n array=np.array([random.randint(1,4), random.randint(1,10), random.randint(1,3), random.randint(1,8),\n random.randint(1,5), random.randint(1,10), random.randint(1,10), random.randint(1,5),\n random.randint(1,7)]).reshape(1,-1) #özellik kadar 1-10 arası rakam verdim\n cancer=tahmin.predict(array)#tahmin=2 ise iyi huylu 4 ise kötü huylu\n\n\n if cancer==2:\n print(\"%\",basari*100,\" oranında iyi huylu\", cancer,\"kanser\")\n\n elif cancer==4:\n print(\"%\",basari*100,\" oranında kötü huylu\", cancer,\"kanser\")\n", "repo_name": "asumanylmz/makine_ogrenmesi", "sub_path": "breast_cancer/r_knn.py", "file_name": "r_knn.py", "file_ext": "py", "file_size_in_byte": 1484, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "pandas.read_csv", "line_number": 9, "usage_type": "call"}, {"api_name": "sklearn.impute.SimpleImputer", "line_number": 19, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 25, "usage_type": "call"}, {"api_name": "sklearn.neighbors.KNeighborsClassifier", "line_number": 27, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 32, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 32, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 33, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 34, "usage_type": "call"}]} +{"seq_id": "29068668124", "text": "from Directions import Directions\r\nfrom Colors import Colors\r\nfrom InputDecoder import InputDecoder\r\nfrom PointToScreenConverter import PointToScreenConverter as PTSConverter\r\nfrom config import PACMAN_START_POSITION, PACMAN_CLOSED_FILENAME, PACMAN_UP_FILENAME, PACMAN_DOWN_FILENAME,\\\r\n PACMAN_LEFT_FILENAME, PACMAN_RIGHT_FILENAME\r\n\r\n\r\nclass Pacman:\r\n textures = {}\r\n\r\n def __init__(self):\r\n self.direction = Directions.right\r\n self.next_move = Directions.right\r\n self.anim = 0\r\n self.position = list(PACMAN_START_POSITION)\r\n self.stopped = False\r\n self.init_textures()\r\n\r\n def init_textures(self):\r\n tex_names = {None: PACMAN_CLOSED_FILENAME,\r\n Directions.up: PACMAN_UP_FILENAME,\r\n Directions.down: PACMAN_DOWN_FILENAME,\r\n Directions.left: PACMAN_LEFT_FILENAME,\r\n Directions.right: PACMAN_RIGHT_FILENAME\r\n }\r\n for tex_name in tex_names.keys():\r\n with open(tex_names[tex_name], \"r\") as tex_file:\r\n texture = \"\".join(list(tex_file))\r\n self.textures[tex_name] = texture\r\n\r\n def reset(self):\r\n self.position = list(PACMAN_START_POSITION)\r\n self.anim = 0\r\n self.direction = Directions.right\r\n self.next_move = Directions.right\r\n\r\n def clear(self):\r\n texture = self.textures[None] if self.anim == 0 else self.textures[self.direction]\r\n coords = PTSConverter.convert(self.position)\r\n print(Colors.black.value)\r\n for i, line in enumerate(texture.split(\"\\n\")):\r\n print(f\"\\033[{coords[1] + i};{coords[0]}H\" + line)\r\n\r\n def draw(self):\r\n texture = self.textures[None] if self.anim == 0 else self.textures[self.direction]\r\n texture = texture.replace(\"#\", Colors.yellow.value + \"#\")\r\n texture = texture.replace(\" \", Colors.black.value + \" \")\r\n coords = PTSConverter.convert(self.position)\r\n for i, line in enumerate(texture.split(\"\\n\")):\r\n print(f\"\\033[{coords[1] + i};{coords[0]}H\" + line)\r\n\r\n def check_direction(self, field_real):\r\n allowed_moves = {Directions.up: False,\r\n Directions.down: False,\r\n Directions.left: False,\r\n Directions.right: False}\r\n cells_to_check = {Directions.up: field_real[self.position[1] - 3][self.position[0]],\r\n Directions.down: field_real[self.position[1] + 3][self.position[0]],\r\n Directions.left: field_real[self.position[1]][self.position[0] - 3],\r\n Directions.right: field_real[self.position[1]][self.position[0] + 3]}\r\n\r\n for direction in list(Directions):\r\n if cells_to_check[direction].state != \"#\":\r\n allowed_moves[direction] = True\r\n\r\n next_move = InputDecoder.get_direction()\r\n if next_move is not None:\r\n self.next_move = next_move\r\n if allowed_moves[self.next_move]:\r\n self.direction = self.next_move\r\n self.stopped = False\r\n if not allowed_moves[self.direction]:\r\n self.stopped = True\r\n\r\n def move(self, field_real):\r\n moves = {Directions.up: (0, -1),\r\n Directions.down: (0, 1),\r\n Directions.left: (-1, 0),\r\n Directions.right: (1, 0)}\r\n\r\n self.clear()\r\n self.check_direction(field_real)\r\n if not self.stopped:\r\n self.anim = (self.anim + 1) % 2\r\n self.position[0] += moves[self.direction][0]\r\n self.position[1] += moves[self.direction][1]\r\n self.draw()\r\n", "repo_name": "anyush/Pacman", "sub_path": "Pacman.py", "file_name": "Pacman.py", "file_ext": "py", "file_size_in_byte": 3705, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "Directions.Directions.right", "line_number": 13, "usage_type": "attribute"}, {"api_name": "Directions.Directions", "line_number": 13, "usage_type": "name"}, {"api_name": "Directions.Directions.right", "line_number": 14, "usage_type": "attribute"}, {"api_name": "Directions.Directions", "line_number": 14, "usage_type": "name"}, {"api_name": "config.PACMAN_START_POSITION", "line_number": 16, "usage_type": "argument"}, {"api_name": "Directions.Directions.up", "line_number": 22, "usage_type": "attribute"}, {"api_name": "Directions.Directions", "line_number": 22, "usage_type": "name"}, {"api_name": "Directions.Directions.down", "line_number": 23, "usage_type": "attribute"}, {"api_name": "Directions.Directions", "line_number": 23, "usage_type": "name"}, {"api_name": "Directions.Directions.left", "line_number": 24, "usage_type": "attribute"}, {"api_name": "Directions.Directions", "line_number": 24, "usage_type": "name"}, {"api_name": "Directions.Directions.right", "line_number": 25, "usage_type": "attribute"}, {"api_name": "Directions.Directions", "line_number": 25, "usage_type": "name"}, {"api_name": "config.PACMAN_CLOSED_FILENAME", "line_number": 21, "usage_type": "name"}, {"api_name": "config.PACMAN_UP_FILENAME", "line_number": 22, "usage_type": "name"}, {"api_name": "config.PACMAN_DOWN_FILENAME", "line_number": 23, "usage_type": "name"}, {"api_name": "config.PACMAN_LEFT_FILENAME", "line_number": 24, "usage_type": "name"}, {"api_name": "config.PACMAN_RIGHT_FILENAME", "line_number": 25, "usage_type": "name"}, {"api_name": "config.PACMAN_START_POSITION", "line_number": 33, "usage_type": "argument"}, {"api_name": "Directions.Directions.right", "line_number": 35, "usage_type": "attribute"}, {"api_name": "Directions.Directions", "line_number": 35, "usage_type": "name"}, {"api_name": "Directions.Directions.right", "line_number": 36, "usage_type": "attribute"}, {"api_name": "Directions.Directions", "line_number": 36, "usage_type": "name"}, {"api_name": "PointToScreenConverter.PointToScreenConverter.convert", "line_number": 40, "usage_type": "call"}, {"api_name": "PointToScreenConverter.PointToScreenConverter", "line_number": 40, "usage_type": "name"}, {"api_name": "Colors.Colors.black", "line_number": 41, "usage_type": "attribute"}, {"api_name": "Colors.Colors", "line_number": 41, "usage_type": "name"}, {"api_name": "Colors.Colors.yellow", "line_number": 47, "usage_type": "attribute"}, {"api_name": "Colors.Colors", "line_number": 47, "usage_type": "name"}, {"api_name": "Colors.Colors.black", "line_number": 48, "usage_type": "attribute"}, {"api_name": "Colors.Colors", "line_number": 48, "usage_type": "name"}, {"api_name": "PointToScreenConverter.PointToScreenConverter.convert", "line_number": 49, "usage_type": "call"}, {"api_name": "PointToScreenConverter.PointToScreenConverter", "line_number": 49, "usage_type": "name"}, {"api_name": "Directions.Directions.up", "line_number": 54, "usage_type": "attribute"}, {"api_name": "Directions.Directions", "line_number": 54, "usage_type": "name"}, {"api_name": "Directions.Directions.down", "line_number": 55, "usage_type": "attribute"}, {"api_name": "Directions.Directions", "line_number": 55, "usage_type": "name"}, {"api_name": "Directions.Directions.left", "line_number": 56, "usage_type": "attribute"}, {"api_name": "Directions.Directions", "line_number": 56, "usage_type": "name"}, {"api_name": "Directions.Directions.right", "line_number": 57, "usage_type": "attribute"}, {"api_name": "Directions.Directions", "line_number": 57, "usage_type": "name"}, {"api_name": "Directions.Directions.up", "line_number": 58, "usage_type": "attribute"}, {"api_name": "Directions.Directions", "line_number": 58, "usage_type": "name"}, {"api_name": "Directions.Directions.down", "line_number": 59, "usage_type": "attribute"}, {"api_name": "Directions.Directions", "line_number": 59, "usage_type": "name"}, {"api_name": "Directions.Directions.left", "line_number": 60, "usage_type": "attribute"}, {"api_name": "Directions.Directions", "line_number": 60, "usage_type": "name"}, {"api_name": "Directions.Directions.right", "line_number": 61, "usage_type": "attribute"}, {"api_name": "Directions.Directions", "line_number": 61, "usage_type": "name"}, {"api_name": "Directions.Directions", "line_number": 63, "usage_type": "argument"}, {"api_name": "InputDecoder.InputDecoder.get_direction", "line_number": 67, "usage_type": "call"}, {"api_name": "InputDecoder.InputDecoder", "line_number": 67, "usage_type": "name"}, {"api_name": "Directions.Directions.up", "line_number": 77, "usage_type": "attribute"}, {"api_name": "Directions.Directions", "line_number": 77, "usage_type": "name"}, {"api_name": "Directions.Directions.down", "line_number": 78, "usage_type": "attribute"}, {"api_name": "Directions.Directions", "line_number": 78, "usage_type": "name"}, {"api_name": "Directions.Directions.left", "line_number": 79, "usage_type": "attribute"}, {"api_name": "Directions.Directions", "line_number": 79, "usage_type": "name"}, {"api_name": "Directions.Directions.right", "line_number": 80, "usage_type": "attribute"}, {"api_name": "Directions.Directions", "line_number": 80, "usage_type": "name"}]} +{"seq_id": "13915723367", "text": "import errno\nimport logging\nimport netfilterqueue\nimport select\nimport socket\nimport struct\nfrom typing import Optional, List, Tuple, Dict\n\nimport click\nimport iptc\nimport pyroute2\nfrom pyroute2.ipdb.main import IPDB\nfrom pyroute2.ipdb.routes import Route\nfrom pyroute2.netlink.rtnl import ifinfmsg, rt_scope\n\nimport lrp\nfrom lrp.daemon import LrpProcess\nfrom lrp.message import Message\nfrom lrp.tools import Address, Subnet, RoutingTable, DEFAULT_ROUTE\n\n\nclass LinuxLrpProcess(LrpProcess):\n \"\"\"Linux toolbox to make LrpProcess works on native linux. It supposes that\n netlink and netfilter are available on the system.\"\"\"\n\n def __init__(self, interface, **remaining_kwargs):\n self._own_ip = None\n self.interface = interface\n # Compute the interface id, based on its name\n with pyroute2.IPRoute() as ipr:\n try:\n self.interface_idx = ipr.link_lookup(ifname=self.interface)[0]\n except IndexError:\n raise Exception(\"%s: unknown interface\" % self.interface)\n\n super().__init__(**remaining_kwargs)\n self.routing_table = NetlinkRoutingTable(self)\n self.la_queue = netfilterqueue.NetfilterQueue()\n\n def __enter__(self):\n # Initialize sockets\n with pyroute2.IPRoute() as ip:\n iface_address = ip.get_addr(index=self.interface_idx)[0].get_attr('IFA_ADDRESS')\n self.logger.debug(\"Guess %s's address is '%s'\", self.interface, iface_address)\n iface_address_as_bytes = socket.inet_aton(iface_address)\n multicast_address_as_bytes = socket.inet_aton(lrp.conf['service_multicast_address'])\n\n self.logger.debug(\"Initialize output multicast socket ([%s]:%d)\",\n lrp.conf['service_multicast_address'], lrp.conf['service_port'])\n self.output_multicast_socket = socket.socket(socket.AF_INET, socket.SOCK_DGRAM, socket.IPPROTO_UDP)\n self.output_multicast_socket.bind((str(self.own_ip), 0))\n self.output_multicast_socket.connect((lrp.conf['service_multicast_address'], lrp.conf['service_port']))\n\n self.logger.debug(\"Initialize input multicast socket ([%s]:%d)\",\n lrp.conf['service_multicast_address'], lrp.conf['service_port'])\n self.input_multicast_socket = socket.socket(socket.AF_INET, socket.SOCK_DGRAM, socket.IPPROTO_UDP)\n self.input_multicast_socket.setsockopt(socket.SOL_IP, socket.IP_ADD_MEMBERSHIP,\n struct.pack(\"=4s4s\", multicast_address_as_bytes, iface_address_as_bytes))\n self.input_multicast_socket.bind((lrp.conf['service_multicast_address'], lrp.conf['service_port']))\n\n self.logger.debug(\"Initialize unicast socket ([%s]:%d)\", iface_address, lrp.conf['service_port'])\n self.unicast_socket = socket.socket(socket.AF_INET, socket.SOCK_DGRAM, socket.IPPROTO_UDP)\n self.unicast_socket.bind((str(self.own_ip), lrp.conf['service_port']))\n\n # Initialize the routing table\n self.routing_table.__enter__()\n\n # Initialize netfilter queue for loop-avoidance mechanism\n def queue_packet_handler(packet):\n \"\"\"Handle a non-routable packet and activate corresponding LRP mechanisms\"\"\"\n payload = packet.get_payload()\n destination = socket.inet_ntoa(payload[16:20])\n if self.is_sink:\n self.handle_unknown_host(destination)\n else:\n source = socket.inet_ntoa(payload[12:16])\n sender = \":\".join([\"%02x\" % b for b in packet.get_hw()[0:6]])\n self.handle_non_routable_packet(\n source=Address(source), destination=Address(destination),\n sender=Address(self.routing_table.get_ip_from_mac(sender)))\n packet.drop()\n\n self.la_queue.bind(lrp.conf['netlink']['netfilter_queue_nb'], queue_packet_handler)\n\n # Initialize LRP itself\n return super().__enter__()\n\n def __exit__(self, exc_type, exc_val, exc_tb):\n # Clean LRP itself\n super().__exit__(exc_type, exc_val, exc_tb)\n\n # Clean the routing table\n self.routing_table.__exit__(exc_type, exc_val, exc_tb)\n\n # Close sockets\n self.logger.debug(\"Close service sockets\")\n self.output_multicast_socket.close()\n self.input_multicast_socket.close()\n self.unicast_socket.close()\n\n # Close netfilter-queue\n self.la_queue.unbind()\n\n @property\n def own_ip(self) -> Address:\n if self._own_ip is None:\n with pyroute2.IPRoute() as ip:\n try:\n self._own_ip = Address(ip.get_addr(index=self.interface_idx)[0].get_attr('IFA_ADDRESS'))\n except IndexError:\n raise Exception(\"%s: interface has no IP address\" % self.interface)\n return self._own_ip\n\n @property\n def network_prefix(self) -> Subnet:\n # TODO: we do not manage any network prefix currently. This below\n # should work in the current configuration, but is not really\n # portable. Should be improved.\n prefix = Subnet(self.own_ip.as_bytes[0:2] + b\"\\x00\\x00\", prefix=16)\n return prefix\n\n def wait_event(self):\n queue_fd = self.la_queue.get_fd()\n while True:\n # Handle timers\n next_time_event = self.scheduler.run(blocking=False)\n # Handle socket input, but stop when next time event occurs\n rr, _, _ = select.select([self.input_multicast_socket, self.unicast_socket, queue_fd],\n [], [], next_time_event)\n try:\n # Handle packet from socket or queue\n readable = rr[0]\n if readable == queue_fd:\n self.la_queue.run(block=False)\n else:\n data, (sender, _) = readable.recvfrom(16)\n sender = Address(sender)\n if sender == self.own_ip:\n self.logger.debug(\"Skip a message from ourselves\") # Happen on broadcast messages\n else:\n msg = Message.parse(data)\n self.handle_msg(msg, sender, is_broadcast=(readable is self.input_multicast_socket))\n except IndexError:\n # No available readable socket. Select timed out. We have no new packet, but a timed event needs to\n # be activated. Loop.\n pass\n\n def send_msg(self, msg: Message, destination: Address = None):\n if destination is None:\n self.logger.info(\"Send %s (multicast)\", msg)\n self.output_multicast_socket.send(msg.dump())\n else:\n self.logger.info(\"Send %s to %s\", msg, destination)\n self.unicast_socket.sendto(msg.dump(), (str(destination), lrp.conf['service_port']))\n\n\nclass NetlinkRoutingTable(RoutingTable):\n def __init__(self, lrp_process: LinuxLrpProcess):\n super().__init__()\n self.ipdb = IPDB()\n self.lrp_process = lrp_process\n\n def __enter__(self):\n # Initialize loop-avoidance mechanism\n self._la_table = iptc.Table(iptc.Table.FILTER)\n self._la_table.autocommit = False\n self._la_chain = self._la_table.create_chain(lrp.conf['netlink']['iptables_chain_name'])\n\n # Redirect forwarded traffic to our management table\n self._la_redirect_rule = iptc.Rule()\n self._la_redirect_rule.create_target(lrp.conf['netlink']['iptables_chain_name'])\n iptc.Chain(self._la_table, \"FORWARD\").append_rule(self._la_redirect_rule)\n\n # Redirect dropped packets to the nfqueue\n self.logger.debug(\"Redirect non-routables towards netfilter-queue %d\",\n lrp.conf['netlink']['netfilter_queue_nb'])\n self._la_default_rule = iptc.Rule()\n if self.lrp_process.is_sink:\n # We are the sink: we expect to have a default route that does not\n # depend on the LRP network. Allow to use this route, except for\n # packets destined to the LRP network itself.\n self._la_default_rule.dst = str(self.lrp_process.network_prefix)\n self._la_default_rule.create_target(\"NFQUEUE\")\n self._la_default_rule.target.queue_num = str(lrp.conf['netlink']['netfilter_queue_nb'])\n self._la_chain.append_rule(self._la_default_rule)\n\n self._la_table.commit()\n\n def __exit__(self, exc_type, exc_val, exc_tb):\n # Clean routing table: all routes inserted by the protocol LRP\n for route in self.ipdb.routes:\n if route['proto'] == lrp.conf['netlink']['proto_number']:\n route.remove().commit()\n self.ipdb.release()\n\n self.logger.info(\"Cleaning iptables (loop avoidance mechanism)\")\n self._la_table.refresh()\n iptc.Chain(self._la_table, \"FORWARD\").delete_rule(self._la_redirect_rule)\n self._la_chain.flush()\n self._la_table.delete_chain(self._la_chain)\n self._la_table.commit()\n\n # Clean internal structures\n self.neighbors.clear()\n self.routes.clear()\n\n def get_mac_from_ip(self, ip_address: Address):\n \"\"\"Return the layer 2 address, given a layer 3 address. Return None if such\n address is unknown\"\"\"\n table = self.ipdb.neighbours[self.lrp_process.interface_idx]\n try:\n return table[str(ip_address)]['lladdr'].upper()\n except KeyError:\n # Unknown IP address\n return None\n\n def get_ip_from_mac(self, mac_address) -> Optional[Address]:\n \"\"\"Return the layer 3 address, given a layer 2 address. Return None if such\n layer 2 address is unknown\"\"\"\n table = self.ipdb.neighbours[self.lrp_process.interface_idx].raw.items()\n try:\n return [ip for ip, data in table if data['lladdr'] == mac_address.lower()][0]\n except IndexError:\n # Unknown MAC address\n return None\n\n def add_route(self, destination: Subnet, next_hop: Address, metric: int):\n inserted = super().add_route(destination, next_hop, metric)\n\n if inserted:\n self._rtnl_add_route(destination, next_hop, metric)\n\n if destination != DEFAULT_ROUTE:\n self._nl_allow_predecessor(next_hop)\n\n return inserted\n\n def del_route(self, destination: Subnet, next_hop: Address):\n super().del_route(destination, next_hop)\n\n self._rtnl_del_route(destination, next_hop)\n\n if not self.is_predecessor(next_hop):\n self._nl_disallow_predecessor(next_hop)\n\n def filter_out_nexthops(self, destination: Subnet, max_metric: int = None) -> List[Tuple[Address, int]]:\n dropped_nhs = super().filter_out_nexthops(destination, max_metric)\n\n # Delete the dropped next hops from the netlink route\n for nh, _ in dropped_nhs:\n self._rtnl_del_route(destination, nh)\n\n if not self.is_predecessor(nh):\n self._nl_disallow_predecessor(nh)\n\n return dropped_nhs\n\n def ensure_is_neighbor(self, neighbor: Address):\n super().ensure_is_neighbor(neighbor)\n\n # Check netlink's state\n try:\n route = self.ipdb.routes[neighbor.as_subnet()]\n except KeyError:\n # Route does not exists. Will create it\n pass\n else:\n # Route is found. Ensure it is a neighbor route\n if route['scope'] == rt_scope['link']:\n # All is correct, nothing more to do\n return\n else:\n self.logger.info(\"Remove rtnetlink host route towards %r\", str(neighbor))\n route.remove().commit()\n\n self.logger.info(\"Create rtnetlink route towards neighbor %r\", str(neighbor))\n self.ipdb.routes.add({\n 'dst': neighbor.as_subnet(),\n 'oif': self.lrp_process.interface_idx,\n 'scope': rt_scope['link'],\n 'proto': lrp.conf['netlink']['proto_number']}).commit()\n\n self._nl_allow_destination(Subnet(neighbor))\n\n def no_more_neighbor(self, neighbor: Address):\n # Check netlink's state\n try:\n route = self.ipdb.routes[neighbor.as_subnet()]\n except KeyError:\n # No route towards this neighbor, ok.\n pass\n else:\n # Ensure this is really a neighbor route, not a host route\n if route['scope'] == rt_scope['link']:\n # Drop this neighbor from others host routes\n for destination in self.routes.keys():\n self.del_route(destination, neighbor)\n\n self.logger.info(\"Remove rtnetlink neighbor route towards %r\", str(neighbor))\n route.remove().commit()\n\n # Fallback to a host route towards it, if we have one\n try:\n next_hops = self.routes[neighbor.as_subnet()]\n except KeyError:\n # No such host route. Just disallow its traffic through us\n self._nl_disallow_destination(neighbor.as_subnet())\n else:\n for nh, metric in next_hops.items():\n self.add_route(neighbor.as_subnet(), nh, metric)\n\n def _nl_allow_predecessor(self, predecessor: Address):\n self._la_table.refresh()\n predecessor_mac = self.get_mac_from_ip(predecessor)\n # Look for the rule allowing the predecessor\n for rule in self._la_chain.rules:\n try:\n if rule.matches[0].mac_source == predecessor_mac:\n # Found\n break\n except IndexError:\n # Not this rule\n pass\n else:\n # Predecessor was not known. Add rule.\n rule = iptc.Rule()\n match = iptc.Match(rule, \"mac\")\n match.mac_source = predecessor_mac\n rule.add_match(match)\n comment = iptc.Match(rule, \"comment\")\n comment.comment = \"allow from predecessor %s\" % predecessor\n rule.add_match(comment)\n rule.target = iptc.Target(rule, \"ACCEPT\")\n self._la_chain.insert_rule(rule)\n self._la_table.commit()\n self.logger.info(\"Traffic from %s is allowed\", predecessor)\n\n def _nl_disallow_predecessor(self, predecessor: Address):\n self._la_table.refresh()\n predecessor_mac = self.get_mac_from_ip(predecessor)\n # Look for the rule allowing the predecessor\n for rule in self._la_chain.rules:\n try:\n if rule.matches[0].mac_source == predecessor_mac:\n # Found. Delete this rule\n self._la_chain.delete_rule(rule)\n self._la_table.commit()\n self.logger.info(\"Traffic from %s is no more allowed\", predecessor)\n except IndexError:\n # Not this rule\n pass\n\n def _nl_allow_destination(self, destination: Subnet):\n self._la_table.refresh()\n if not any(Subnet(rule.dst) == destination for rule in self._la_chain.rules):\n # Destination was not known. Add rule.\n rule = iptc.Rule()\n rule.dst = str(destination)\n comment = iptc.Match(rule, \"comment\")\n comment.comment = \"allow towards destination %s\" % destination\n rule.add_match(comment)\n rule.target = iptc.Target(rule, \"ACCEPT\")\n self._la_chain.insert_rule(rule)\n self._la_table.commit()\n self.logger.info(\"Traffic towards %s is allowed\", destination)\n\n def _nl_disallow_destination(self, destination: Subnet):\n self._la_table.refresh()\n try:\n rule = [r for r in self._la_chain.rules if Subnet(r.dst) == destination][0]\n except IndexError:\n # Destination is not known by netfilter, ok.\n pass\n else:\n self._la_chain.delete_rule(rule)\n self._la_table.commit()\n self.logger.info(\"Traffic towards %s is no more allowed\", destination)\n\n def _rtnl_add_route(self, destination, next_hop, metric):\n \"\"\"Really add the described route in rtnetlink (without any test, except\n those related to rtnetlink itself).\"\"\"\n try:\n route = self.ipdb.routes[str(destination)]\n except KeyError:\n # Destination was unknown\n self.logger.info(\"Update rtnetlink: new route towards %r through %r\",\n str(destination), str(next_hop))\n self.ipdb.routes.add({\n 'dst': str(destination),\n 'multipath': [{'gateway': str(next_hop)}],\n 'proto': lrp.conf['netlink']['proto_number']}).commit()\n self._nl_allow_destination(destination)\n else:\n # Be sure this is not a neighbor route\n if route['scope'] == rt_scope['link']:\n self.logger.info(\"Refuse host route: would erase a neighbor route\")\n else:\n already_known = (\n not route['multipath'] and route['gateway'] == str(next_hop) or\n route['multipath'] and any(p['gateway'] == str(next_hop)\n for p in route['multipath']))\n if already_known:\n self.logger.info(\"rtnetlink already knows %r as next hop towards %r\",\n str(next_hop), str(destination))\n else:\n self.logger.info(\"Update rtnetlink: update route towards %r, also through %r\",\n str(destination), str(next_hop))\n route.add_nh({'gateway': str(next_hop)}).commit()\n\n def _rtnl_del_route(self, destination, next_hop):\n \"\"\"Really delete the described route in rtnetlink (without any test, except\n those related to rtnetlink itself).\"\"\"\n try:\n route = self.ipdb.routes[str(destination)]\n except KeyError:\n # No route at all, OK\n pass\n else:\n # Be sure this is not a neighbor route\n if route['scope'] != rt_scope['link']:\n # Ensure this neighbor is a next hop for this destination\n nexthop_exists = \\\n not route['multipath'] and route['gateway'] == str(destination) or \\\n route['multipath'] and any(nh['gateway'] == str(destination)\n for nh in route['multipath'])\n if nexthop_exists:\n self.logger.info(\"Removed netlink route towards %r through %r\",\n str(destination), str(next_hop))\n try:\n route.del_nh({'gateway': str(next_hop)}).commit()\n except KeyError: # 'attempt to delete nexthop from non-multipath route': no more next hop\n route.remove().commit()\n self.logger.info(\"No more rtnetlink route towards %r\",\n str(destination))\n self._nl_disallow_destination(destination)\n\n\n@click.command()\n@click.option(\"--interface\", default=None, metavar=\"\",\n help=\"The interface LRP should use. Default: auto-detect.\")\n@click.option(\"--metric\", default=2 ** 16 - 1, metavar=\"\",\n help=\"The initial metric of this node. Should be set for the sink. Default: infinite.\")\n@click.option(\"--sink/--no-sink\", default=False, help=\"Is this node a sink?\", show_default=True)\ndef daemon(interface=None, metric=2 ** 16 - 1, sink=False):\n \"\"\"Launch the LRP daemon.\"\"\"\n if interface is None:\n # Guess interface\n with pyroute2.IPRoute() as ipr:\n all_interfaces = ipr.get_links()\n all_interfaces = [iface.get_attr(\"IFLA_IFNAME\") for iface in all_interfaces\n if not iface['flags'] & ifinfmsg.IFF_LOOPBACK] # Filter out loopback\n if len(all_interfaces) > 1:\n raise Exception(\"Unable to auto-detect the interface to use. Please provide --interface argument.\")\n elif len(all_interfaces) == 0:\n raise Exception(\"Unable to find a usable interface.\")\n interface = all_interfaces[0]\n logging.getLogger(\"LRP\").info(\"Use auto-detected interface %s\", interface)\n\n with LinuxLrpProcess(interface, metric=metric, is_sink=sink) as lrp_process:\n lrp_process.wait_event()\n\n\nif __name__ == '__main__':\n daemon()\n", "repo_name": "drakkar-lig/pylrp", "sub_path": "src/lrp/linux_wrapper.py", "file_name": "linux_wrapper.py", "file_ext": "py", "file_size_in_byte": 20591, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "37", "api": [{"api_name": "lrp.daemon.LrpProcess", "line_number": 22, "usage_type": "name"}, {"api_name": "pyroute2.IPRoute", "line_number": 30, "usage_type": "call"}, {"api_name": "netfilterqueue.NetfilterQueue", "line_number": 38, "usage_type": "call"}, {"api_name": "pyroute2.IPRoute", "line_number": 42, "usage_type": "call"}, {"api_name": "socket.inet_aton", "line_number": 45, "usage_type": "call"}, {"api_name": "socket.inet_aton", "line_number": 46, "usage_type": "call"}, {"api_name": "lrp.conf", "line_number": 46, "usage_type": "attribute"}, {"api_name": "lrp.conf", "line_number": 49, "usage_type": "attribute"}, {"api_name": "socket.socket", "line_number": 50, "usage_type": "call"}, {"api_name": "socket.AF_INET", "line_number": 50, "usage_type": "attribute"}, {"api_name": "socket.SOCK_DGRAM", "line_number": 50, "usage_type": "attribute"}, {"api_name": "socket.IPPROTO_UDP", "line_number": 50, "usage_type": "attribute"}, {"api_name": "lrp.conf", "line_number": 52, "usage_type": "attribute"}, {"api_name": "lrp.conf", "line_number": 55, "usage_type": "attribute"}, {"api_name": "socket.socket", "line_number": 56, "usage_type": "call"}, {"api_name": "socket.AF_INET", "line_number": 56, "usage_type": "attribute"}, {"api_name": "socket.SOCK_DGRAM", "line_number": 56, "usage_type": "attribute"}, {"api_name": "socket.IPPROTO_UDP", "line_number": 56, "usage_type": "attribute"}, {"api_name": "socket.SOL_IP", "line_number": 57, "usage_type": "attribute"}, {"api_name": "socket.IP_ADD_MEMBERSHIP", "line_number": 57, "usage_type": "attribute"}, {"api_name": "struct.pack", "line_number": 58, "usage_type": "call"}, {"api_name": "lrp.conf", "line_number": 59, "usage_type": "attribute"}, {"api_name": "lrp.conf", "line_number": 61, "usage_type": "attribute"}, {"api_name": "socket.socket", "line_number": 62, "usage_type": "call"}, {"api_name": "socket.AF_INET", "line_number": 62, "usage_type": "attribute"}, {"api_name": "socket.SOCK_DGRAM", "line_number": 62, "usage_type": "attribute"}, {"api_name": "socket.IPPROTO_UDP", "line_number": 62, "usage_type": "attribute"}, {"api_name": "lrp.conf", "line_number": 63, "usage_type": "attribute"}, {"api_name": "socket.inet_ntoa", "line_number": 72, "usage_type": "call"}, {"api_name": "socket.inet_ntoa", "line_number": 76, "usage_type": "call"}, {"api_name": "lrp.tools.Address", "line_number": 79, "usage_type": "call"}, {"api_name": "lrp.tools.Address", "line_number": 80, "usage_type": "call"}, {"api_name": "lrp.conf", "line_number": 83, "usage_type": "attribute"}, {"api_name": "pyroute2.IPRoute", "line_number": 107, "usage_type": "call"}, {"api_name": "lrp.tools.Address", "line_number": 109, "usage_type": "call"}, {"api_name": "lrp.tools.Address", "line_number": 105, "usage_type": "name"}, {"api_name": "lrp.tools.Subnet", "line_number": 119, "usage_type": "call"}, {"api_name": "lrp.tools.Subnet", "line_number": 115, "usage_type": "name"}, {"api_name": "select.select", "line_number": 128, "usage_type": "call"}, {"api_name": "lrp.tools.Address", "line_number": 137, "usage_type": "call"}, {"api_name": "lrp.message.Message.parse", "line_number": 141, "usage_type": "call"}, {"api_name": "lrp.message.Message", "line_number": 141, "usage_type": "name"}, {"api_name": "lrp.message.Message", "line_number": 148, "usage_type": "name"}, {"api_name": "lrp.tools.Address", "line_number": 148, "usage_type": "name"}, {"api_name": "lrp.conf", "line_number": 154, "usage_type": "attribute"}, {"api_name": "lrp.tools.RoutingTable", "line_number": 157, "usage_type": "name"}, {"api_name": "pyroute2.ipdb.main.IPDB", "line_number": 160, "usage_type": "call"}, {"api_name": "iptc.Table", "line_number": 165, "usage_type": "call"}, {"api_name": "lrp.conf", "line_number": 167, "usage_type": "attribute"}, {"api_name": "iptc.Rule", "line_number": 170, "usage_type": "call"}, {"api_name": "lrp.conf", "line_number": 171, "usage_type": "attribute"}, {"api_name": "iptc.Chain", "line_number": 172, "usage_type": "call"}, {"api_name": "lrp.conf", "line_number": 176, "usage_type": "attribute"}, {"api_name": "iptc.Rule", "line_number": 177, "usage_type": "call"}, {"api_name": "lrp.conf", "line_number": 184, "usage_type": "attribute"}, {"api_name": "lrp.conf", "line_number": 192, "usage_type": "attribute"}, {"api_name": "iptc.Chain", "line_number": 198, "usage_type": "call"}, {"api_name": "lrp.tools.Address", "line_number": 207, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 217, "usage_type": "name"}, {"api_name": "lrp.tools.Address", "line_number": 217, "usage_type": "name"}, {"api_name": "lrp.tools.Subnet", "line_number": 227, "usage_type": "name"}, {"api_name": "lrp.tools.Address", "line_number": 227, "usage_type": "name"}, {"api_name": "lrp.tools.DEFAULT_ROUTE", "line_number": 233, "usage_type": "name"}, {"api_name": "lrp.tools.Subnet", "line_number": 238, "usage_type": "name"}, {"api_name": "lrp.tools.Address", "line_number": 238, "usage_type": "name"}, {"api_name": "lrp.tools.Subnet", "line_number": 246, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 246, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 246, "usage_type": "name"}, {"api_name": "lrp.tools.Address", "line_number": 246, "usage_type": "name"}, {"api_name": "lrp.tools.Address", "line_number": 258, "usage_type": "name"}, {"api_name": "pyroute2.netlink.rtnl.rt_scope", "line_number": 269, "usage_type": "name"}, {"api_name": "pyroute2.netlink.rtnl.rt_scope", "line_number": 280, "usage_type": "name"}, {"api_name": "lrp.conf", "line_number": 281, "usage_type": "attribute"}, {"api_name": "lrp.tools.Subnet", "line_number": 283, "usage_type": "call"}, {"api_name": "lrp.tools.Address", "line_number": 285, "usage_type": "name"}, {"api_name": "pyroute2.netlink.rtnl.rt_scope", "line_number": 294, "usage_type": "name"}, {"api_name": "lrp.tools.Address", "line_number": 312, "usage_type": "name"}, {"api_name": "iptc.Rule", "line_number": 326, "usage_type": "call"}, {"api_name": "iptc.Match", "line_number": 327, "usage_type": "call"}, {"api_name": "iptc.Match", "line_number": 330, "usage_type": "call"}, {"api_name": "iptc.Target", "line_number": 333, "usage_type": "call"}, {"api_name": "lrp.tools.Address", "line_number": 338, "usage_type": "name"}, {"api_name": "lrp.tools.Subnet", "line_number": 353, "usage_type": "name"}, {"api_name": "lrp.tools.Subnet", "line_number": 355, "usage_type": "call"}, {"api_name": "iptc.Rule", "line_number": 357, "usage_type": "call"}, {"api_name": "iptc.Match", "line_number": 359, "usage_type": "call"}, {"api_name": "iptc.Target", "line_number": 362, "usage_type": "call"}, {"api_name": "lrp.tools.Subnet", "line_number": 367, "usage_type": "name"}, {"api_name": "lrp.tools.Subnet", "line_number": 370, "usage_type": "call"}, {"api_name": "lrp.conf", "line_number": 391, "usage_type": "attribute"}, {"api_name": "pyroute2.netlink.rtnl.rt_scope", "line_number": 395, "usage_type": "name"}, {"api_name": "pyroute2.netlink.rtnl.rt_scope", "line_number": 420, "usage_type": "name"}, {"api_name": "pyroute2.IPRoute", "line_number": 448, "usage_type": "call"}, {"api_name": "pyroute2.netlink.rtnl.ifinfmsg.IFF_LOOPBACK", "line_number": 451, "usage_type": "attribute"}, {"api_name": "pyroute2.netlink.rtnl.ifinfmsg", "line_number": 451, "usage_type": "name"}, {"api_name": "logging.getLogger", "line_number": 457, "usage_type": "call"}, {"api_name": "click.command", "line_number": 438, "usage_type": "call"}, {"api_name": "click.option", "line_number": 439, "usage_type": "call"}, {"api_name": "click.option", "line_number": 441, "usage_type": "call"}, {"api_name": "click.option", "line_number": 443, "usage_type": "call"}]} +{"seq_id": "1923980345", "text": "#File for storing all Actor-Critic related classes and functions\r\n#Created by Weinan Zhang and his team from Shanghai Jiaotong University, and modified by Donald Cheng\r\n#Please visit https://hrl.boyuai.com/chapter/2/actor-critic%E7%AE%97%E6%B3%95 for more information (it is in Chinese, if you need help in translation, please let me know)\r\n\r\nimport torch\r\nimport torch.nn.functional as F\r\nimport collections\r\n\r\n#Actor Network\r\nclass PolicyNet(torch.nn.Module):\r\n\tdef __init__(self,state_dim,hidden_dim,action_dim):\r\n\t\tsuper(PolicyNet,self).__init__()\r\n\t\tself.fc1 = torch.nn.Linear(state_dim,hidden_dim)\r\n\t\tself.fc2 = torch.nn.Linear(hidden_dim,action_dim)\r\n\tdef forward(self,x):\r\n\t\tx = F.relu(self.fc1(x))\r\n\t\treturn F.softmax(self.fc2(x),dim=1)\r\n\r\n#Critic Network\r\nclass ValueNet(torch.nn.Module):\r\n\tdef __init__(self,state_dim,hidden_dim):\r\n\t\tsuper(ValueNet,self).__init__()\r\n\t\tself.fc1 = torch.nn.Linear(state_dim,hidden_dim)\r\n\t\tself.fc2 = torch.nn.Linear(hidden_dim,1)\r\n\tdef forward(self,x):\r\n\t\tx = F.relu(self.fc1(x))\r\n\t\treturn self.fc2(x)\r\n\r\n#Actor-Critic Class for taking action and updating the actor and critic network\r\nclass ActorCritic:\r\n\tdef __init__(self,state_dim,hidden_dim,action_dim,actor_lr,critic_lr,gamma,device):\r\n\t\tself.actor = PolicyNet(state_dim,hidden_dim,action_dim).to(device)\r\n\t\tself.critic = ValueNet(state_dim,hidden_dim).to(device)\r\n\t\tself.actor_optimizer = torch.optim.Adam(self.actor.parameters(),lr=actor_lr)\r\n\t\tself.critic_optimizer = torch.optim.Adam(self.critic.parameters(),lr=critic_lr)\r\n\t\tself.gamma = gamma #Discount factor\r\n\t\tself.device = device\r\n\tdef take_action(self,state): #Generate a probability distrubution of all actions to be taken, and select one based on the distribution\r\n\t\tstate = torch.tensor([state],dtype=torch.float).to(self.device)\r\n\t\tprobs = self.actor(state)\r\n\t\tprobs = torch.where(torch.isnan(probs), torch.zeros_like(probs) + 1e-18, probs) #Due to unknown reason, some of the probability will become nan, which cannot be used for calculation, this line acts as a safeguard in case that happens\r\n\t\taction_dist = torch.distributions.Categorical(probs)\r\n\t\taction = action_dist.sample()\r\n\t\treturn action.item()\r\n\tdef update(self,transition_dict):\r\n\t\tstates = torch.tensor(transition_dict['states'],dtype=torch.float).to(self.device)\r\n\t\tactions = torch.tensor(transition_dict['actions'],dtype=torch.float).view(-1,1).to(self.device)\r\n\t\trewards = torch.tensor(transition_dict['rewards'],dtype=torch.float).view(-1,1).to(self.device)\r\n\t\tnext_states = torch.tensor(transition_dict['next_states'],dtype=torch.float).to(self.device)\r\n\t\tdones = torch.tensor(transition_dict['dones'],dtype=torch.float).view(-1,1).to(self.device)\r\n\t\ttd_target = rewards + self.gamma * self.critic(next_states) * (1 - dones)\r\n\t\ttd_delta = td_target - self.critic(states)\r\n\t\tlog_probs = torch.log(self.actor(states))\r\n\t\tactor_loss = torch.mean(-log_probs * td_delta.detach())\r\n\t\tcritic_loss = torch.mean(F.mse_loss(self.critic(states),td_target.detach()))\r\n\t\tself.actor_optimizer.zero_grad()\r\n\t\tself.critic_optimizer.zero_grad()\r\n\t\tactor_loss.backward()\r\n\t\tcritic_loss.backward()\r\n\t\tself.actor_optimizer.step()\r\n\t\tself.critic_optimizer.step()\r\n\r\n#Class to wrap everything up\r\nclass RL_A2C:\r\n\tdef __init__(self,agent,transition_dict_size):\r\n\t\tself.agent = agent\r\n\t\tself.transition_dict_size = transition_dict_size\r\n\t\tself.transition_dict = {'states':collections.deque(maxlen=transition_dict_size),'actions':collections.deque(maxlen=transition_dict_size),'next_states':collections.deque(maxlen=transition_dict_size),'rewards':collections.deque(maxlen=transition_dict_size),'dones':collections.deque(maxlen=transition_dict_size)}\r\n\tdef update(self,state,action,reward,next_state,done):\r\n\t\tself.transition_dict['states'].append(state)\r\n\t\tself.transition_dict['actions'].append(action)\r\n\t\tself.transition_dict['rewards'].append(reward)\r\n\t\tself.transition_dict['next_states'].append(next_state)\r\n\t\tself.transition_dict['dones'].append(done)\r\n\t\tself.agent.update(self.transition_dict)\r\n\tdef transition_dict_reset(self):\r\n\t\ttransition_dict = {'states':collections.deque(maxlen=self.transition_dict_size),'actions':collections.deque(maxlen=self.transition_dict_size),'next_states':collections.deque(maxlen=self.transition_dict_size),'rewards':collections.deque(maxlen=self.transition_dict_size),'dones':collections.deque(maxlen=self.transition_dict_size)}\r\n", "repo_name": "donald323/FLCPID-vs-DRLPID", "sub_path": "RL_A2C.py", "file_name": "RL_A2C.py", "file_ext": "py", "file_size_in_byte": 4383, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "37", "api": [{"api_name": "torch.nn", "line_number": 10, "usage_type": "attribute"}, {"api_name": "torch.nn.Linear", "line_number": 13, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 13, "usage_type": "attribute"}, {"api_name": "torch.nn.Linear", "line_number": 14, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 14, "usage_type": "attribute"}, {"api_name": "torch.nn.functional.relu", "line_number": 16, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 16, "usage_type": "name"}, {"api_name": "torch.nn.functional.softmax", "line_number": 17, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 17, "usage_type": "name"}, {"api_name": "torch.nn", "line_number": 20, "usage_type": "attribute"}, {"api_name": "torch.nn.Linear", "line_number": 23, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 23, "usage_type": "attribute"}, {"api_name": "torch.nn.Linear", "line_number": 24, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 24, "usage_type": "attribute"}, {"api_name": "torch.nn.functional.relu", "line_number": 26, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 26, "usage_type": "name"}, {"api_name": "torch.optim.Adam", "line_number": 34, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 34, "usage_type": "attribute"}, {"api_name": "torch.optim.Adam", "line_number": 35, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 35, "usage_type": "attribute"}, {"api_name": "torch.tensor", "line_number": 39, "usage_type": "call"}, {"api_name": "torch.float", "line_number": 39, "usage_type": "attribute"}, {"api_name": "torch.where", "line_number": 41, "usage_type": "call"}, {"api_name": "torch.isnan", "line_number": 41, "usage_type": "call"}, {"api_name": "torch.zeros_like", "line_number": 41, "usage_type": "call"}, {"api_name": "torch.distributions.Categorical", "line_number": 42, "usage_type": "call"}, {"api_name": "torch.distributions", "line_number": 42, "usage_type": "attribute"}, {"api_name": "torch.tensor", "line_number": 46, "usage_type": "call"}, {"api_name": "torch.float", "line_number": 46, "usage_type": "attribute"}, {"api_name": "torch.tensor", "line_number": 47, "usage_type": "call"}, {"api_name": "torch.float", "line_number": 47, "usage_type": "attribute"}, {"api_name": "torch.tensor", "line_number": 48, "usage_type": "call"}, {"api_name": "torch.float", "line_number": 48, "usage_type": "attribute"}, {"api_name": "torch.tensor", "line_number": 49, "usage_type": "call"}, {"api_name": "torch.float", "line_number": 49, "usage_type": "attribute"}, {"api_name": "torch.tensor", "line_number": 50, "usage_type": "call"}, {"api_name": "torch.float", "line_number": 50, "usage_type": "attribute"}, {"api_name": "torch.log", "line_number": 53, "usage_type": "call"}, {"api_name": "torch.mean", "line_number": 54, "usage_type": "call"}, {"api_name": "torch.mean", "line_number": 55, "usage_type": "call"}, {"api_name": "torch.nn.functional.mse_loss", "line_number": 55, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 55, "usage_type": "name"}, {"api_name": "collections.deque", "line_number": 68, "usage_type": "call"}, {"api_name": "collections.deque", "line_number": 77, "usage_type": "call"}]} +{"seq_id": "21908939911", "text": "\"\"\"Create some functions to obtain better performances without using padding\n\"\"\"\nfrom torch.nn.utils.rnn import PackedSequence, pad_packed_sequence\nfrom typing import Dict, Union\nimport numpy as np\nimport torch\n\ndef get_last_of_sequence(packed_sequence: PackedSequence, batch_first: bool = True):\n \"\"\"Recuperate a tensor containing the last elements of the sequences\n\n Args:\n packed_sequence (PackedSequence): The packed sequence\n batch_first (bool, optional): Indicates if the batch are on the first position. Defaults to True.\n\n Returns:\n torch.Tensor: A tensor containing batch of sequences\n \"\"\"\n \n \n # get padded sequences and sequence sizes\n pad_sequence, seq_sizes = pad_packed_sequence(packed_sequence, batch_first = batch_first)\n \n return pad_sequence[torch.arange(len(seq_sizes)), seq_sizes - 1]\n\nclass CustomPackedSequence:\n\n def __init__(self, sequences: Union[list, None] = None, data: Union[torch.Tensor, None] = None, batch_sizes: Union[list, None] = None, indices: Union[list, None] = None):\n \"\"\"A class for transforming a list of sequences to a one sequenced tensor or to create a new custom packed sequence from data\n\n Args:\n sequences (list): List of sequences. Defaults to None.\n data (Union[torch.Tensor, None], optional): The data. Defaults to None.\n batch_sizes (Union[list, None], optional): The batch_sizes. Defaults to None.\n indices (Union[list, None], optional): _description_. Defaults to None.\n \"\"\"\n \n if data != None and batch_sizes != None and indices != None:\n \n self.data = data\n \n self.indices = indices\n \n elif sequences != None:\n \n # d'abord verifions les longueurs des sequences ainsi que leurs indices\n length = []\n for i, seq in enumerate(sequences):\n \n length.append([len(seq), i])\n \n # trions les sequences\n length.sort(reverse=True)\n \n # stockons les sequences selon leur ordre de longueur\n ord_sequence = [sequences[j] for i, j in length]\n \n # maintenant nous allons stocker les sequences comme une seule sequence en prenant a chaque les longueurs\n one_sequence = []\n \n batch_sizes = []\n \n for l in range(length[0][0]):\n \n batch_size = 0\n \n for i, j in length:\n \n try:\n one_sequence.append(sequences[j][l])\n batch_size += 1\n except Exception:\n break\n batch_sizes.append(batch_size)\n \n self.data = torch.from_numpy(np.stack(one_sequence))\n \n self.indices = np.array(length)[:, 1].tolist()\n \n else:\n \n raise ValueError(\"You must specify sequences or data, batch_sizes and indices !\")\n \n self.batch_sizes = batch_sizes\n \n def to(self, device):\n \n self.data = self.data.to(device)\n \n return self\n \n def __print__(self):\n \n print({'data': self.data, 'indices': self.indices, 'batch_sizes': self.batch_sizes})\n \ndef pad_packed_sequence_(pack_sequence: CustomPackedSequence, batch_first: bool = False, return_last_on_sequence: bool = True):\n \"\"\"A function which take a packed sequence and return the last element of each original sequence or return the padded original list of sequences\n as a tensor \n\n Args:\n pack_sequence (CustomPackedSequence): The custom packed sequence object containing the packed sequences, the batch sizes and the indices\n batch_first (bool, optional): Indicate if we return a tensor with the batch dimension at first position. Defaults to False.\n return_last_on_sequence (bool, optional): Returns only the last elements of the sequences. Defaults to True.\n\n Returns:\n Union[torch.Tensor, tuple]: The outputs\n \"\"\"\n \n # initialisons les sequences\n if batch_first:\n sequences = torch.zeros((len(pack_sequence.indices), len(pack_sequence.batch_sizes), pack_sequence.data.size(1)))\n else:\n sequences = torch.zeros((len(pack_sequence.batch_sizes), len(pack_sequence.indices), pack_sequence.data.size(1)))\n \n # nous allons iterer sur les tailles des batchs \n n = 0\n seq_sizes = torch.zeros((len(pack_sequence.indices),))\n for i, batch in enumerate(pack_sequence.batch_sizes):\n \n for l in range(batch):\n \n seq_sizes[pack_sequence.indices[l]] += 1\n \n if batch_first:\n \n sequences[pack_sequence.indices[l]][i] = pack_sequence.data[n]\n \n else:\n \n sequences[i][pack_sequence.indices[l]] = pack_sequence.data[n]\n \n n+=1\n\n seq_sizes = seq_sizes.long()\n \n if return_last_on_sequence:\n \n if batch_first: \n \n return sequences[torch.arange(sequences.size(0)), seq_sizes-1, :]\n \n else:\n \n return sequences[seq_sizes - 1, torch.arange(sequences.size(0)), :]\n \n return sequences, seq_sizes.tolist()\n\n\n \n", "repo_name": "Oumar199/Wolof_traduction", "sub_path": "custom-rnn/custom_rnn/utils/create_pack_and_pad.py", "file_name": "create_pack_and_pad.py", "file_ext": "py", "file_size_in_byte": 5455, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "torch.nn.utils.rnn.PackedSequence", "line_number": 8, "usage_type": "name"}, {"api_name": "torch.nn.utils.rnn.pad_packed_sequence", "line_number": 21, "usage_type": "call"}, {"api_name": "torch.arange", "line_number": 23, "usage_type": "call"}, {"api_name": "typing.Union", "line_number": 27, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 27, "usage_type": "attribute"}, {"api_name": "torch.from_numpy", "line_number": 75, "usage_type": "call"}, {"api_name": "numpy.stack", "line_number": 75, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 77, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 110, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 112, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 116, "usage_type": "call"}, {"api_name": "torch.arange", "line_number": 139, "usage_type": "call"}, {"api_name": "torch.arange", "line_number": 143, "usage_type": "call"}]} +{"seq_id": "71127928426", "text": "import os\nimport argparse\nfrom tqdm import tqdm\nfrom PIL import Image\n\nprocess_py = '''import itertools\nfrom PIL import Image\n\n\nbin_str = \"\"\nfor num in range():\n img_path = f\"./images/{num}.png\"\n img = Image.open(img_path)\n\n # 列优先\n for y, x in itertools.product(range(img.height), range(img.width)):\n colors = img.getpixel((x, y))\n ...\n\n # 行优先\n for x, y in itertools.product(range(img.width), range(img.height)):\n colors = img.getpixel((x, y))\n ...\n\nprint(bin_str)'''\n\n\nparser = argparse.ArgumentParser()\nparser.add_argument(\"-f\", type=str, default=None, required=True,\n help=\"输入同级目录下文件的名称\")\nargs = parser.parse_args()\n\n\nsave_path = \"./output\"\npy_save_path = os.path.join(save_path, \"Example.py\")\nimg_save_path = os.path.join(save_path, \"./images\")\n\nif not os.path.exists(os.path.join(img_save_path)):\n os.makedirs(os.path.join(img_save_path))\n\nimg = Image.open(args.f)\nn_frames = img.n_frames\n# 保存处理脚本\nprocess_py = process_py.replace(\"帧数\", str(n_frames))\nwith open(py_save_path, \"w\") as f:\n f.write(process_py)\n\n# 保存每一帧图片\n\nwith tqdm(range(n_frames), desc=\"Save Image\") as num_bar:\n for i in num_bar:\n img.seek(i)\n img.save(os.path.join(img_save_path, f\"{i}.png\"))\nprint(\"拆分GIF成功，并自动帮您保存了处理脚本!\")", "repo_name": "Yukon51/CTF_Script", "sub_path": "拆分gif.py", "file_name": "拆分gif.py", "file_ext": "py", "file_size_in_byte": 1384, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 28, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 35, "usage_type": "call"}, {"api_name": "os.path", "line_number": 35, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 36, "usage_type": "call"}, {"api_name": "os.path", "line_number": 36, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 38, "usage_type": "call"}, {"api_name": "os.path", "line_number": 38, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 38, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 39, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 39, "usage_type": "call"}, {"api_name": "os.path", "line_number": 39, "usage_type": "attribute"}, {"api_name": "PIL.Image.open", "line_number": 41, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 41, "usage_type": "name"}, {"api_name": "tqdm.tqdm", "line_number": 50, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 53, "usage_type": "call"}, {"api_name": "os.path", "line_number": 53, "usage_type": "attribute"}]} +{"seq_id": "4998464832", "text": "# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import migrations, models\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ]\n\n operations = [\n migrations.CreateModel(\n name='Customer',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('firstname', models.CharField(max_length=255)),\n ('lastname', models.CharField(max_length=255)),\n ],\n ),\n migrations.CreateModel(\n name='CustomerGroup',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('name', models.CharField(max_length=255)),\n ],\n ),\n migrations.CreateModel(\n name='GroupAttendance',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('attendance_time', models.DateField()),\n ('customer', models.ForeignKey(to='frontend.Customer')),\n ('group', models.ForeignKey(related_name='attendance', to='frontend.CustomerGroup')),\n ],\n ),\n migrations.AddField(\n model_name='customer',\n name='group',\n field=models.ForeignKey(related_name='customers', to='frontend.CustomerGroup'),\n ),\n ]\n", "repo_name": "icu0755/cleverup-crm-django", "sub_path": "crm/frontend/migrations/0001_initial.py", "file_name": "0001_initial.py", "file_ext": "py", "file_size_in_byte": 1496, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "django.db.migrations.Migration", "line_number": 7, "usage_type": "attribute"}, {"api_name": "django.db.migrations", "line_number": 7, "usage_type": "name"}, {"api_name": "django.db.migrations.CreateModel", "line_number": 13, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 13, "usage_type": "name"}, {"api_name": "django.db.models.AutoField", "line_number": 16, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 16, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 17, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 17, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 18, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 18, "usage_type": "name"}, {"api_name": "django.db.migrations.CreateModel", "line_number": 21, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 21, "usage_type": "name"}, {"api_name": "django.db.models.AutoField", "line_number": 24, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 24, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 25, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 25, "usage_type": "name"}, {"api_name": "django.db.migrations.CreateModel", "line_number": 28, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 28, "usage_type": "name"}, {"api_name": "django.db.models.AutoField", "line_number": 31, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 31, "usage_type": "name"}, {"api_name": "django.db.models.DateField", "line_number": 32, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 32, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 33, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 33, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 34, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 34, "usage_type": "name"}, {"api_name": "django.db.migrations.AddField", "line_number": 37, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 37, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 40, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 40, "usage_type": "name"}]} +{"seq_id": "38842124169", "text": "import urllib.request, urllib.parse, urllib.error\nimport json\n\nserviceurl = \"http://maps.googleapis.com/maps/api/geocode/json?\"\n\nwhile True:\n address = input(\"Enter location: \")\n if len(address) < 1: # if there is no text entered\n break # end the program\n\n # takes the key and the value and does the \"+\" and \"comma\" and turns it into a url\n url = serviceurl + urllib.parse.urlencode(\n {\"address\": address}) \n\n # pass the url here\n print(\"Retrieving\", url)\n uh = urllib.request.urlopen(url) # open\n data = uh.read().decode() # decode it\n print(\"Retrieved\", len(data), \"characters\")\n\n try:\n js = json.loads(data) # parse it with json\n except:\n js = None\n \n # all this is if it blows up\n if not js or \"status\" not in js or js[\"status\"] != 'OK':\n print(\"=== Failure to Retrieve ===\")\n print(data)\n continue\n \n # these are keys within keys; dict within dict\n # goes to results, 0 is for the first key(which is geometry)\n # inside the geometry is location\n # from there we grab lat (latitude)\n lat = js[\"results\"][0][\"geometry\"][\"location\"][\"lat\"]\n # from there we grab lng (longitude)\n lng = js[\"results\"][0][\"geometry\"][\"location\"][\"lng\"]\n print(\"lat\", lat, \"lng\", lng)\n location = js[\"results\"][0][\"formatted_address\"]\n print(location)", "repo_name": "arberkeqi/Scientific_Computing_with_Python", "sub_path": "chapter_13/13_geojson.py", "file_name": "13_geojson.py", "file_ext": "py", "file_size_in_byte": 1387, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "urllib.request.parse.urlencode", "line_number": 12, "usage_type": "call"}, {"api_name": "urllib.request.parse", "line_number": 12, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 12, "usage_type": "name"}, {"api_name": "urllib.request.request.urlopen", "line_number": 17, "usage_type": "call"}, {"api_name": "urllib.request.request", "line_number": 17, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 17, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 22, "usage_type": "call"}]} +{"seq_id": "73193997546", "text": "from nltk import pos_tag, word_tokenize\nimport sys\n\ndef extractKeywords(sentence):\n a = pos_tag(word_tokenize(sentence))\n i=0\n temp = ''\n\n for i in xrange(0,len(a)):\n if (a[i][1] == 'NNP' and (a[i-1][1] == 'NNP' or i==0)):\n temp = temp + a[i][0]\n elif (a[i][1] == 'NNP' and (a[i-1][1] == 'NN' or i==0)):\n temp = temp + a[i][0]\n elif (a[i][1] == 'NNP' and a[i-1][1] != 'NNP'):\n temp = temp + ' ' + a[i][0]\n elif (a[i][1] == 'NN' and a[i-1][1] == 'NN'):\n temp = temp + a[i][0]\n elif (a[i][1] == 'NN' and a[i-1][1] == 'NNP'):\n temp = temp + a[i][0]\n elif (a[i][1] == 'NN' and a[i-1][1] != 'NN'):\n temp = temp + ' ' + a[i][0]\n\n keys = temp.split(' ')\n return keys\n", "repo_name": "Aravind-Suresh/InterIITSocialMediaAnalysis", "sub_path": "wordProc.py", "file_name": "wordProc.py", "file_ext": "py", "file_size_in_byte": 788, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "nltk.pos_tag", "line_number": 5, "usage_type": "call"}, {"api_name": "nltk.word_tokenize", "line_number": 5, "usage_type": "call"}]} +{"seq_id": "2739284467", "text": "import pytest\nfrom misc_utils import (\n arg_to_iter,\n chunk_iter,\n all_indicies,\n sort_list_by_key,\n flatten,\n sort_list_by_attr,\n)\n\n\n@pytest.mark.parametrize(\n \"arg, expected\",\n [\n (None, []),\n ({\"key\": \"value\"}, [{\"key\": \"value\"}]),\n (\"hello\", [\"hello\"]),\n ([1, 2, 3], [1, 2, 3]), # already an iterable\n (42, [42]),\n ((), ()), # empty tuple should be returned as is\n ],\n)\ndef test_arg_to_iter(arg, expected):\n assert arg_to_iter(arg) == expected\n\n\ndef test_chunk_iter():\n some_list = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n assert chunk_iter(some_list, 2) == ([1, 2], [3, 4], [5, 6], [7, 8], [9, 10])\n assert chunk_iter(some_list, 3) == ([1, 2, 3], [4, 5, 6], [7, 8, 9], [10])\n\n@pytest.mark.parametrize(\n \"nested_list, expected\",\n [\n ([], []), # Empty list\n ([1, 2, 3], [1, 2, 3]), # Already flat\n ([[1, 2], [3, 4]], [1, 2, 3, 4]), # Two-level nesting\n ([1, [2, [3, 4]]], [1, 2, 3, 4]), # Multi-level nesting\n ],\n)\ndef test_flatten(nested_list, expected):\n assert list(flatten(nested_list)) == expected\n\n\n@pytest.mark.parametrize(\n \"iterable, obj, expected\",\n [\n (\"hello world hello world\", \"world\", (6, 18)),\n ([1, 2, 3, 4, 1, 5, 1], 1, (0, 4, 6)),\n (\"apple\", \"p\", (1, 2)),\n ],\n)\ndef test_all_indicies(iterable, obj, expected):\n assert all_indicies(iterable, obj) == expected\n\n\ndef test_all_indices_errors():\n with pytest.raises(AttributeError):\n all_indicies(42, 42)\n\n with pytest.raises(ValueError):\n all_indicies(\"hello\", \"z\")\n\n\n@pytest.mark.parametrize(\n \"lst, key, reverse, expected\",\n [\n (\n [\n {\"name\": \"Alice\", \"age\": 30},\n {\"name\": \"Bob\", \"age\": 25},\n {\"name\": \"Charlie\", \"age\": 35},\n ],\n \"age\",\n False,\n [\n {\"name\": \"Bob\", \"age\": 25},\n {\"name\": \"Alice\", \"age\": 30},\n {\"name\": \"Charlie\", \"age\": 35},\n ],\n ),\n (\n [\n {\"name\": \"Alice\", \"age\": 30},\n {\"name\": \"Bob\", \"age\": 25},\n {\"name\": \"Charlie\", \"age\": 35},\n ],\n \"age\",\n True,\n [\n {\"name\": \"Charlie\", \"age\": 35},\n {\"name\": \"Alice\", \"age\": 30},\n {\"name\": \"Bob\", \"age\": 25},\n ],\n ),\n (\n [\n {\"product\": \"apple\", \"price\": 1.2},\n {\"product\": \"banana\", \"price\": 0.8},\n {\"product\": \"cherry\", \"price\": 2.5},\n ],\n \"price\",\n False,\n [\n {\"product\": \"banana\", \"price\": 0.8},\n {\"product\": \"apple\", \"price\": 1.2},\n {\"product\": \"cherry\", \"price\": 2.5},\n ],\n ),\n ],\n)\ndef test_sort_list_by_key(lst, key, reverse, expected):\n assert sort_list_by_key(lst, key, reverse) == expected\n\n\ndef test_sort_list_by_attr():\n class Person:\n def __init__(self, name: str, age: int):\n self.name = name\n self.age = age\n\n people = [Person(\"Alice\", 30), Person(\"Bob\", 25), Person(\"Charlie\", 35)]\n sorted_people = sort_list_by_attr(people, \"age\")\n\n assert [p.age for p in sorted_people] == [25, 30, 35]\n\n\nif __name__ == \"__main__\":\n from pathlib import Path\n from pprint import pprint\n import pytest\n\n test_file = Path(__file__).absolute()\n test_class_or_function = None\n test_method = None\n\n # test_class_or_function = ''\n # test_method = ''\n\n test_path = test_file\n if test_class_or_function is not None:\n test_path = f\"test_path::{test_class_or_function}\"\n if test_method is not None:\n test_path = f\"test_path::{test_method}\"\n\n args = [\n test_path,\n \"-s\",\n \"--verbose\",\n ]\n\n pytest.main(args)\n", "repo_name": "nicholas-mischke/miscellaneous-utils", "sub_path": "tests/test_iterable_utils.py", "file_name": "test_iterable_utils.py", "file_ext": "py", "file_size_in_byte": 3934, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "misc_utils.arg_to_iter", "line_number": 24, "usage_type": "call"}, {"api_name": "pytest.mark.parametrize", "line_number": 12, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 12, "usage_type": "attribute"}, {"api_name": "misc_utils.chunk_iter", "line_number": 29, "usage_type": "call"}, {"api_name": "misc_utils.chunk_iter", "line_number": 30, "usage_type": "call"}, {"api_name": "misc_utils.flatten", "line_number": 42, "usage_type": "call"}, {"api_name": "pytest.mark.parametrize", "line_number": 32, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 32, "usage_type": "attribute"}, {"api_name": "misc_utils.all_indicies", "line_number": 54, "usage_type": "call"}, {"api_name": "pytest.mark.parametrize", "line_number": 45, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 45, "usage_type": "attribute"}, {"api_name": "pytest.raises", "line_number": 58, "usage_type": "call"}, {"api_name": "misc_utils.all_indicies", "line_number": 59, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 61, "usage_type": "call"}, {"api_name": "misc_utils.all_indicies", "line_number": 62, "usage_type": "call"}, {"api_name": "misc_utils.sort_list_by_key", "line_number": 113, "usage_type": "call"}, {"api_name": "pytest.mark.parametrize", "line_number": 65, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 65, "usage_type": "attribute"}, {"api_name": "misc_utils.sort_list_by_attr", "line_number": 123, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 133, "usage_type": "call"}, {"api_name": "pytest.main", "line_number": 152, "usage_type": "call"}]} +{"seq_id": "73138271148", "text": "import abc\nimport collections.abc\nimport json\n\nimport lovett.corpus\nimport lovett.tree\nimport lovett.util\n\n\n# TODO: make md5 id for trees missing one\n# TODO: type declarations\n\n\n# https://stackoverflow.com/a/5656097\ndef intersperse(iterable, delimiter):\n it = iter(iterable)\n yield from next(it)\n for x in it:\n yield delimiter\n yield from x\n\n\ndef _index_string_for_metadata(metadata):\n idx = metadata.index\n idxconn = \"=\" if metadata.idx_type == lovett.util.IDX_GAP else \"-\"\n if idx:\n return idxconn + str(idx)\n return \"\"\n\n\ndef _postprocess_parsed(l):\n metadata = {}\n if not isinstance(l[0], str):\n # Root node\n tree = None\n id = None\n try:\n while True:\n v = l.pop()\n if v[0] == 'ID':\n id = v[1]\n elif v[0] == \"METADATA\":\n for key, val in v[1:]:\n metadata[key] = val\n else:\n if tree:\n raise ParseError(\"Too many children of root node (or label-less node)\")\n tree = v\n except IndexError:\n pass\n try:\n r = _postprocess_parsed(tree)\n # TODO: We should instead insert a hash-based id.\n # TODO: think about the differece between id and fingerprint (for\n # backwards compatibility: fingerprint is the hash-based one,\n # which is better)\n for key, val in metadata.items():\n r.metadata[key] = val\n r.metadata.id = id or \"MISSING_ID\"\n return r\n except ParseError as e:\n print(\"error in id: %s\" % id)\n raise e\n if len(l) < 2:\n raise ParseError(\"malformed tree: node has too few children: %s\" % l)\n if isinstance(l[1], str):\n # Simple leaf\n if len(l) != 2:\n raise ParseError(\"malformed tree: leaf has too many children: %s\" % l)\n label = l[0]\n text = l[1]\n if lovett.util.is_trace_string(l[1]):\n text, idx_type, index = lovett.util.label_and_index(text)\n if index is not None:\n metadata['INDEX'] = index\n metadata['IDX-TYPE'] = idx_type\n else:\n label, idx_type, index = lovett.util.label_and_index(label)\n if index is not None:\n metadata['INDEX'] = index\n metadata['IDX-TYPE'] = idx_type\n return lovett.tree.Leaf(label, text, metadata)\n # Regular node\n label, idx_type, index = lovett.util.label_and_index(l[0])\n if index is not None:\n metadata['INDEX'] = index\n metadata['IDX-TYPE'] = idx_type\n return lovett.tree.NonTerminal(label, map(lambda x: _postprocess_parsed(x), l[1:]), metadata)\n\n\nclass ParseError(Exception):\n pass\n\n\nclass ParseEOF(ParseError):\n pass\n\n\nclass Format(abc.ABC):\n @classmethod\n def node(cls, node, **kwargs):\n if lovett.util.is_leaf(node):\n yield from cls._leaf(node, **kwargs)\n else:\n yield from cls._tree(node, **kwargs)\n\n @classmethod\n @abc.abstractmethod\n def _leaf(cls, node, **kwargs):\n pass\n\n @classmethod\n @abc.abstractmethod\n def _tree(cls, node, **kwargs):\n pass\n\n @classmethod\n @abc.abstractmethod\n def corpus(cls, corpus):\n pass\n\n @classmethod\n @abc.abstractmethod\n def read(self, handle):\n pass\n\n # TODO: override __init__ to forbid class instantiation\n\n\nclass Bracketed(Format):\n @classmethod\n def _do_format_root(cls, tree):\n yield \"( \"\n # if set(tree.metadata.keys()) > {\"ID\"}:\n # yield \"(METADATA \"\n # first = False\n # for key, val in tree.metadata.items():\n # if key == \"ID\":\n # continue\n # if first:\n # yield \"\\n\" + \" \" * 12\n # yield \"(%s %s)\" % (key, val)\n # first = True\n # yield \")\\n \"\n id_ = None\n if \"ID\" in tree.metadata:\n id_ = tree.metadata.id\n del tree.metadata[\"ID\"]\n yield from cls.node(tree, indent=2)\n if id_ is not None:\n yield \"\\n (ID %s)\" % id_\n yield \")\"\n\n @classmethod\n def corpus(cls, corpus):\n yield from intersperse((cls._do_format_root(tree) for tree in corpus), \"\\n\\n\")\n\n @classmethod\n def _tokens(cls, handle):\n tok = \"\"\n while True:\n r = handle.read(1)\n if r == \"\":\n raise ParseEOF()\n elif r in \"()\":\n if tok != \"\":\n yield tok\n tok = \"\"\n yield r\n else:\n yield r\n elif r in \" \\n\\t\":\n if tok != \"\":\n yield tok\n tok = \"\"\n else:\n pass # Keep going\n else:\n tok += r\n\n @classmethod\n def _postprocess(cls, l):\n return _postprocess_parsed(l) # TODO: inline the method here\n\n # TODO: make configurable, e.g. whether to add ids (sequentially or hash\n # based), etc.\n @classmethod\n def read(cls, handle):\n stack = []\n for tok in cls._tokens(handle):\n if tok == \"(\":\n stack.append([])\n elif tok == \")\":\n r = stack.pop()\n try:\n stack[len(stack) - 1].append(r)\n except IndexError:\n # the final closing bracket\n return cls._postprocess(r)\n else:\n try:\n stack[len(stack) - 1].append(tok)\n except Exception:\n raise ParseError(\"error with stack: %s\" % stack)\n\n\nclass Penn(Bracketed):\n @classmethod\n def _leaf(cls, node, indent=0):\n idxstr = _index_string_for_metadata(node.metadata)\n if lovett.util.is_trace(node):\n fmtstr = \"({label} {text}{index})\"\n else:\n fmtstr = \"({label}{index} {text})\"\n yield fmtstr.format(label=node.label,\n text=node.text,\n index=idxstr)\n\n @classmethod\n def _tree(cls, node, indent=0):\n pre = \"(\" + node.label + _index_string_for_metadata(node.metadata) + \" \"\n newindent = len(pre) + indent\n yield pre\n yield from intersperse((cls.node(child, indent=newindent) for child in node.children), \"\\n\" + \" \" * newindent)\n yield \")\"\n\n\nclass Icepahc(Penn):\n @classmethod\n def _leaf(cls, node, indent=0):\n r = \"\".join(super().leaf(node, indent))\n if \"LEMMA\" in node.metadata:\n r = r[:-1] + \"-\" + node.metadata.lemma + \")\"\n yield r\n\n @classmethod\n def read(cls, handle):\n tree = super().read(handle)\n for node in tree.nodes():\n if lovett.util.is_leaf(node):\n parts = node.text.split(\"-\")\n if len(parts) > 1:\n node.metadata.lemma = parts[-1]\n node.text = \"-\".join(parts[:-1])\n return tree\n\n\nclass Deep(Bracketed):\n @classmethod\n def _metadata_items(cls, dic):\n items = sorted(dic.items())\n items = filter(lambda x: x[0] != \"ID\", items) # TODO: hack\n return list(items)\n\n @classmethod\n def _print_metadata(cls, node, indent):\n meta_items = cls._metadata_items(node.metadata)\n if len(meta_items) > 0:\n yield \"(META \"\n yield from intersperse((\"({key} {value})\".format(key=key, value=value)\n for (key, value) in meta_items),\n \"\\n\" + \" \" * (indent + 6))\n yield \")\\n\" + \" \" * indent\n\n @classmethod\n def _leaf(cls, node, indent=0):\n yield \"({label} \".format(label=node.label)\n yield from cls._print_metadata(node, indent + len(node.label) + 2)\n yield \"(ORTHO {text})\".format(text=node.text)\n yield \")\"\n\n @classmethod\n def _tree(cls, node, indent=0):\n yield \"(\" + node.label + \" \"\n newindent = indent + len(node.label) + 2\n yield from cls._print_metadata(node, newindent)\n yield from intersperse((cls.node(child, indent=newindent) for child in node.children),\n \"\\n\" + \" \" * newindent)\n yield \")\"\n\n @classmethod\n def _find_meta_node(cls, children):\n meta = None\n rest = []\n for node in children:\n if node.label == \"META\":\n if meta is None:\n meta = node\n else:\n raise Exception(\"Multiple meta nodes\")\n else:\n rest.append(node)\n return meta, rest\n\n @classmethod\n def _add_metadata(cls, tree, keys):\n for node in keys:\n tree.metadata[node.label] = node.text\n\n @classmethod\n def _postprocess_deep(cls, tree):\n if lovett.util.is_leaf(tree):\n # Coding node or other degenerate leaf\n return tree\n meta, rest = cls._find_meta_node(tree.children)\n if meta is not None:\n cls._add_metadata(tree, meta)\n if len(rest) == 1 and rest[0].label == \"ORTHO\":\n l = lovett.tree.Leaf(tree.label, rest[0].text, tree.metadata)\n return l\n tree[:] = list(map(cls._postprocess_deep, rest))\n return tree\n\n @classmethod\n def read(cls, handle):\n tree = super().read(handle)\n return cls._postprocess_deep(tree)\n\n\nclass _Object(Format):\n @classmethod\n def node(cls, node, **kwargs):\n if lovett.util.is_leaf(node):\n return _Object._leaf(node, **kwargs)\n else:\n return _Object._tree(node, **kwargs)\n\n @classmethod\n def corpus(cls, corpus):\n return [cls.node(tree) for tree in corpus]\n\n @classmethod\n def _leaf(cls, node, **kwargs):\n m = dict(node.metadata)\n return {\"label\": node.label,\n \"text\": node.text,\n \"metadata\": m}\n\n @classmethod\n def _tree(cls, node, **kwargs):\n return {\"label\": node.label,\n \"metadata\": dict(node.metadata),\n \"children\": [cls.node(c) for c in node.children]}\n\n @classmethod\n def read(cls, obj):\n if isinstance(obj, collections.abc.Sequence):\n # A corpus\n return lovett.corpus.ListCorpus([cls.read(tree) for tree in obj])\n else:\n # A single tree\n try:\n return lovett.tree.NonTerminal(obj.get(\"label\"),\n (cls.read(child) for child in obj.get(\"children\")),\n obj.get(\"metadata\", {}))\n except:\n return lovett.tree.Leaf(obj.get(\"label\"), obj.get(\"text\"), obj.get(\"metadata\", {}))\n\n\nclass Json(_Object):\n @classmethod\n def _return(cls, obj):\n yield json.dumps(obj, indent=4)\n\n @classmethod\n def node(cls, node, **kwargs):\n yield from cls._return(super().node(node))\n\n @classmethod\n def _leaf(cls, node):\n yield from cls._return(super()._leaf(node))\n\n @classmethod\n def _tree(cls, node):\n yield from cls._return(super()._tree(node))\n\n @classmethod\n def corpus(cls, corpus):\n yield from cls._return(super().corpus(corpus))\n", "repo_name": "aecay/lovett", "sub_path": "lovett/format.py", "file_name": "format.py", "file_ext": "py", "file_size_in_byte": 11445, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "lovett.corpus.util", "line_number": 25, "usage_type": "attribute"}, {"api_name": "lovett.corpus", "line_number": 25, "usage_type": "name"}, {"api_name": "lovett.corpus.util.is_trace_string", "line_number": 72, "usage_type": "call"}, {"api_name": "lovett.corpus.util", "line_number": 72, "usage_type": "attribute"}, {"api_name": "lovett.corpus", "line_number": 72, "usage_type": "name"}, {"api_name": "lovett.corpus.util.label_and_index", "line_number": 73, "usage_type": "call"}, {"api_name": "lovett.corpus.util", "line_number": 73, "usage_type": "attribute"}, {"api_name": "lovett.corpus", "line_number": 73, "usage_type": "name"}, {"api_name": "lovett.corpus.util.label_and_index", "line_number": 78, "usage_type": "call"}, {"api_name": "lovett.corpus.util", "line_number": 78, "usage_type": "attribute"}, {"api_name": "lovett.corpus", "line_number": 78, "usage_type": "name"}, {"api_name": "lovett.corpus.tree.Leaf", "line_number": 82, "usage_type": "call"}, {"api_name": "lovett.corpus.tree", "line_number": 82, "usage_type": "attribute"}, {"api_name": "lovett.corpus", "line_number": 82, "usage_type": "name"}, {"api_name": "lovett.corpus.util.label_and_index", "line_number": 84, "usage_type": "call"}, {"api_name": "lovett.corpus.util", "line_number": 84, "usage_type": "attribute"}, {"api_name": "lovett.corpus", "line_number": 84, "usage_type": "name"}, {"api_name": "lovett.corpus.tree.NonTerminal", "line_number": 88, "usage_type": "call"}, {"api_name": "lovett.corpus.tree", "line_number": 88, "usage_type": "attribute"}, {"api_name": "lovett.corpus", "line_number": 88, "usage_type": "name"}, {"api_name": "abc.ABC", "line_number": 99, "usage_type": "attribute"}, {"api_name": "lovett.corpus.util.is_leaf", "line_number": 102, "usage_type": "call"}, {"api_name": "lovett.corpus.util", "line_number": 102, "usage_type": "attribute"}, {"api_name": "lovett.corpus", "line_number": 102, "usage_type": "name"}, {"api_name": "abc.abstractmethod", "line_number": 108, "usage_type": "attribute"}, {"api_name": "abc.abstractmethod", "line_number": 113, "usage_type": "attribute"}, {"api_name": "abc.abstractmethod", "line_number": 118, "usage_type": "attribute"}, {"api_name": "abc.abstractmethod", "line_number": 123, "usage_type": "attribute"}, {"api_name": "lovett.corpus.util.is_trace", "line_number": 211, "usage_type": "call"}, {"api_name": "lovett.corpus.util", "line_number": 211, "usage_type": "attribute"}, {"api_name": "lovett.corpus", "line_number": 211, "usage_type": "name"}, {"api_name": "lovett.corpus.util.is_leaf", "line_number": 240, "usage_type": "call"}, {"api_name": "lovett.corpus.util", "line_number": 240, "usage_type": "attribute"}, {"api_name": "lovett.corpus", "line_number": 240, "usage_type": "name"}, {"api_name": "lovett.corpus.util.is_leaf", "line_number": 302, "usage_type": "call"}, {"api_name": "lovett.corpus.util", "line_number": 302, "usage_type": "attribute"}, {"api_name": "lovett.corpus", "line_number": 302, "usage_type": "name"}, {"api_name": "lovett.corpus.tree.Leaf", "line_number": 309, "usage_type": "call"}, {"api_name": "lovett.corpus.tree", "line_number": 309, "usage_type": "attribute"}, {"api_name": "lovett.corpus", "line_number": 309, "usage_type": "name"}, {"api_name": "lovett.corpus.util.is_leaf", "line_number": 323, "usage_type": "call"}, {"api_name": "lovett.corpus.util", "line_number": 323, "usage_type": "attribute"}, {"api_name": "lovett.corpus", "line_number": 323, "usage_type": "name"}, {"api_name": "collections.abc.abc", "line_number": 347, "usage_type": "attribute"}, {"api_name": "collections.abc", "line_number": 347, "usage_type": "name"}, {"api_name": "lovett.corpus.corpus.ListCorpus", "line_number": 349, "usage_type": "call"}, {"api_name": "lovett.corpus.corpus", "line_number": 349, "usage_type": "attribute"}, {"api_name": "lovett.corpus", "line_number": 349, "usage_type": "name"}, {"api_name": "lovett.corpus.tree.NonTerminal", "line_number": 353, "usage_type": "call"}, {"api_name": "lovett.corpus.tree", "line_number": 353, "usage_type": "attribute"}, {"api_name": "lovett.corpus", "line_number": 353, "usage_type": "name"}, {"api_name": "lovett.corpus.tree.Leaf", "line_number": 357, "usage_type": "call"}, {"api_name": "lovett.corpus.tree", "line_number": 357, "usage_type": "attribute"}, {"api_name": "lovett.corpus", "line_number": 357, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 363, "usage_type": "call"}]} +{"seq_id": "24843512120", "text": "#!/usr/bin/env python\n# _*_ coding: utf-8 _*_\n# @Time : 2021/11/9 下午2:52\n# @Author : PH\n# @Version：V 0.1\n# @File : anchor3d_head.py\n# @desc :\nimport torch\n\nfrom .anchor_head_base import AnchorHeadBase\n\n\nclass Anchor3DHead(AnchorHeadBase):\n\n def __init__(self, top_cfg, model_info_dict):\n super(Anchor3DHead, self).__init__(module_cfg=top_cfg.MODEL.DENSE_HEAD, model_info_dict=model_info_dict)\n self.top_cfg = top_cfg\n\n def forward(self, batch_dict, **kwargs):\n inputs = batch_dict['dense_feat2d']\n gts = batch_dict['gt_boxes']\n gt_labels = batch_dict.get('gt_labels', None)\n # 1.get cls_pred and reg_pred map\n cls_pred = self.cls_layer(inputs) # B,C*A,H,W\n reg_pred = self.reg_layer(inputs) # B,7*A,H,W\n feat_map_size = list(cls_pred.shape[2:])\n if len(feat_map_size) == 2:\n feat_map_size.insert(0, 1) # 1, H, W\n self.model_info_dict['feat_map_size'] = feat_map_size\n\n # 2.generate anchors based on inputs shape\n anchors = self.anchor_generator.gen_anchors(flatten_output=True, feature_map_size=feat_map_size)\n\n if self.training:\n # 3.during training, assign target for sampled anchor\n output_dict = self.train_assign(anchors, cls_pred, reg_pred, gts, gt_labels)\n return output_dict\n else:\n # 3.during predicting, figure out the proposals\n proposals = self.predict_proposals(cls_pred, reg_pred, anchors)\n batch_dict['proposal_dict'] = proposals\n return batch_dict\n\n def train_assign(self, anchors, cls_pred, reg_pred, gts, gt_labels):\n if self.model_info_dict['use_sigmoid']:\n num_class = 1\n else:\n num_class = len(self.model_info_dict['class_names']) + 1\n bbox_dim = self.anchor_generator.ndim\n B = cls_pred.size(0)\n cls_pred = cls_pred.permute(0, 3, 2, 1).reshape(B, -1, num_class)\n reg_pred = reg_pred.permute(0, 3, 2, 1).reshape(B, -1, bbox_dim)\n if gt_labels is None:\n gt_labels = gts[..., -1] # gts:B,N,7+class\n gts = gts[..., :-1]\n assign_result = self.target_assigner.assign(gts, anchors, gt_labels)\n # assign_result.add_gts(gts, gt_labels)\n output_dict = {\n 'cls_pred' : cls_pred,\n 'reg_pred' : reg_pred,\n 'assign_result': assign_result\n }\n return output_dict\n\n def predict_proposals(self, cls_pred, reg_pred, anchors):\n B = cls_pred.size(0)\n pred_scores, pred_bboxes = self._predict_all_bboxes(cls_pred, reg_pred, anchors)\n # proposals = []\n # proposal_scores = []\n # proposal_labels = []\n # for i in range(B):\n # frame_topk_scores, frame_topk_bboxes, frame_topk_labels = self.predict_proposals_one_frame(\n # scores=pred_scores[i],\n # bboxes=pred_bboxes[i],\n # k=self.top_cfg.INFERENCE_CONFIG.num_topk\n # )\n # proposals.append(frame_topk_bboxes)\n # proposal_scores.append(frame_topk_scores)\n # proposal_labels.append(frame_topk_labels)\n # proposals = torch.stack(proposals, dim=0)\n # proposal_scores = torch.stack(proposal_scores, dim=0)\n # proposal_labels = torch.stack(proposal_labels, dim=0)\n proposal_dict = {\n 'proposals' : pred_bboxes,\n 'proposal_scores': pred_scores,\n # 'proposal_labels': proposal_labels\n }\n return proposal_dict\n\n def _predict_all_bboxes(self, cls_pred, reg_pred, anchors):\n \"\"\"\n predict scores and bboxes for all anchors\n Args:\n cls_pred: [torch.tensor] B,C*A,H,W\n reg_pred: [torch.tensor] B,7*A,H,W\n anchors: [torch.tensor] num_anchors, 7. num_anchors = A*H*W\n\n Returns:\n pred_scores: [torch.tensor] B, num_anchors, C\n pred_bbox: [torch.tensor] B, num_anchors, 7\n\n \"\"\"\n B = cls_pred.size(0)\n num_anchors = anchors.size(0)\n # B, C*A, H, W -> B, H, W, C*A -> B, num_anchors, C\n cls_pred = cls_pred.permute(0, 3, 2, 1).contiguous().view(B, num_anchors, -1)\n reg_pred = reg_pred.permute(0, 3, 2, 1).contiguous().view(B, num_anchors, -1)\n batch_anchors = anchors.unsqueeze(dim=0).repeat(B, 1, 1) # B, num_anchors, 7\n pred_bboxes = self.target_assigner.bbox_encoder.decode(reg_pred, batch_anchors)\n # pred_bboxes = batch_anchors\n if self.model_info_dict['use_sigmoid']:\n pred_scores = torch.sigmoid(cls_pred.unsqueeze(dim=-1))\n else:\n pred_scores = torch.softmax(cls_pred, dim=-1)\n return pred_scores, pred_bboxes\n\n def predict_proposals_one_frame(self, scores, bboxes, k):\n assert 0 < k < scores.size(0)\n if self.model_info_dict['use_sigmoid']:\n max_scores, label = scores.max()\n else:\n max_scores, label = scores[:, 1:].max(dim=-1)\n _, topk_inds = max_scores.topk(k)\n topk_bboxes = bboxes # [topk_inds]\n topk_scores = scores # [topk_inds]\n topk_labels = label[topk_inds] + 1\n return topk_scores, topk_bboxes, topk_labels\n", "repo_name": "phww/Point_Cloud_Detection3D_Module_Based", "sub_path": "basic/module/dense_head/anchor_head/anchor3d_head.py", "file_name": "anchor3d_head.py", "file_ext": "py", "file_size_in_byte": 5246, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "anchor_head_base.AnchorHeadBase", "line_number": 13, "usage_type": "name"}, {"api_name": "torch.sigmoid", "line_number": 112, "usage_type": "call"}, {"api_name": "torch.softmax", "line_number": 114, "usage_type": "call"}]} +{"seq_id": "42578926748", "text": "#!/usr/bin/env python\n\n\nimport argparse\nimport binascii\nimport csv\nimport hashlib\nimport io\nimport logging\nimport os\nimport platform\n\nfrom datetime import datetime\n\n\nBUFFER_SIZE = io.DEFAULT_BUFFER_SIZE\n\nENCODING = 'utf8'\n\nDELIMITER = ','\n\nCOLUMNS = [\n 'timestamp',\n 'filename',\n 'extension',\n 'created',\n 'modified',\n 'size',\n 'hash'\n]\n\n\ndef setup_logging(log_level, log_format):\n level = logging.getLevelName(log_level)\n logging.basicConfig(level=level, format=log_format)\n\n\ndef setup_arg_parser():\n parser = argparse.ArgumentParser(\n description='fdb - File Database Utility'\n )\n parser.add_argument(\n '--log-level',\n default='WARNING',\n help='sets the log level (CRITICAL, ERROR, WARNING, INFO, DEBUG, NOTSET). Default: WARNING'\n )\n parser.add_argument(\n '--log-format',\n default='%(asctime)s - %(levelname)s - %(message)s',\n help='sets the log message format'\n )\n parser.add_argument(\n '--ignore',\n default='',\n type=lambda x: [\n bytes(y, ENCODING).decode('unicode_escape') for y in x.split(',')\n ],\n help='sets the comma delimited list of files to ignore (supports escape characters)'\n )\n\n subparsers = parser.add_subparsers(\n help='supported commands'\n )\n\n # mk\n mk_parser = subparsers.add_parser(\n 'mk',\n help='make a file database from the specified directory'\n )\n mk_parser.add_argument(\n 'input_directory',\n help='directory to make a file database for'\n )\n mk_parser.add_argument(\n 'output_file',\n help='output file with a file database'\n )\n mk_parser.set_defaults(which='mk')\n\n # fd\n fd_parser = subparsers.add_parser(\n 'fd',\n help='find duplicates in the file database using file hashes'\n )\n fd_parser.add_argument(\n 'input_file',\n help='input CSV file with a file database'\n )\n fd_parser.add_argument(\n 'output_file',\n help='output file with a duplicate file database'\n )\n fd_parser.set_defaults(which='fd')\n\n # diff\n diff_parser = subparsers.add_parser(\n 'diff',\n help='compare two databases using already computed hashes from the file databases ' +\n '(Note: diff is directional, i.e. diff(A1, A2) != diff(A2, A1))'\n )\n diff_parser.add_argument(\n 'source_db',\n help='CSV file with a file database of the source directory (copy files \"from\" this directory)'\n )\n diff_parser.add_argument(\n 'destination_db',\n help='CSV file with a file database of the destination directory (copy files \"to\" this directory)'\n )\n diff_parser.add_argument(\n 'output_file',\n help='output file with a diff database ' +\n '(files from the source directory, that do not exist in the destination directory)'\n )\n diff_parser.set_defaults(which='diff')\n\n # hd\n hd_parser = subparsers.add_parser(\n 'hd',\n help='compute hash of entire directory contents'\n )\n hd_parser.add_argument(\n 'directory',\n help='directory to compute hash of'\n )\n hd_parser.set_defaults(which='hd')\n\n # hdb\n hdb_parser = subparsers.add_parser(\n 'hdb',\n help='compute hash of all file database contents using already computed hashes from the file database'\n )\n hdb_parser.add_argument(\n 'input_file',\n help='input CSV file with a file database'\n )\n hdb_parser.set_defaults(which='hdb')\n\n return parser\n\n\ndef hash_file(path):\n hasher = hashlib.md5()\n with open(path, 'rb') as afile:\n buff = afile.read(BUFFER_SIZE)\n while len(buff) > 0:\n hasher.update(buff)\n buff = afile.read(BUFFER_SIZE)\n digest = hasher.digest()\n return digest\n\n\ndef bin2str(barray):\n return binascii.hexlify(barray).decode(ENCODING)\n\n\ndef get_file_list(directory, ignore):\n if (not ignore):\n raise ValueError('Ignore list is none!')\n logging.info('Scanning directory: {}'.format(directory))\n file_list = []\n for root, _, files in os.walk(directory):\n logging.info('Scanning contents: {}'.format(root))\n logging.info('Found files: {}'.format(len(files)))\n for fn in files:\n if (fn in ignore):\n continue\n file_name = os.path.join(root, fn)\n file_list.append(file_name)\n return file_list\n\n\ndef create_db(directory, ignore):\n logging.info('Creating database for directory: {}'.format(directory))\n db = []\n file_list = get_file_list(directory, ignore)\n inx = 0\n list_length = len(file_list)\n logging.info('Number of files in directory: {}'.format(list_length))\n for file_name in file_list:\n logging.info(\n 'Processing ({}/{}, {:.2f}%) file: {}'.format(\n inx + 1, list_length, (inx + 1) / list_length * 100, file_name\n )\n )\n try:\n file_ext = os.path.splitext(file_name)[1]\n file_stat = os.stat(file_name)\n file_ct = datetime.fromtimestamp(file_stat.st_ctime)\n file_mt = datetime.fromtimestamp(file_stat.st_mtime)\n file_size = file_stat.st_size\n file_hash = bin2str(hash_file(file_name))\n db.append([\n datetime.now(),\n file_name,\n file_ext,\n file_ct,\n file_mt,\n file_size,\n file_hash\n ])\n inx += 1\n except PermissionError:\n logging.warning('File permission error: {}'.format(file_name))\n db.append([\n datetime.now(),\n file_name,\n 'NA',\n 'NA',\n 'NA',\n 'NA',\n 'NA'\n ])\n if (inx != list_length):\n logging.warning(\n 'Not all files were processed ({}/{})!'.format(inx, list_length)\n )\n return db\n\n\ndef mk(input_directory, output_file, ignore):\n if (not os.path.exists(input_directory)):\n raise ValueError('Path does not exist: {}'.format(input_directory))\n\n if (os.path.exists(output_file)):\n raise ValueError('File already exists: {}'.format(output_file))\n\n db = create_db(input_directory, ignore)\n logging.info('Number of rows in file database: {}'.format(len(db)))\n\n logging.info('Writing database to file: {}'.format(output_file))\n fout = open(output_file, 'w', encoding=ENCODING, newline='')\n fout_writer = csv.writer(fout, delimiter=DELIMITER)\n fout_writer.writerow(COLUMNS)\n fout_writer.writerows(db)\n fout.close()\n\n\ndef pack_hash_db(database):\n hash_db = {}\n for row in database:\n if (row['hash'] in hash_db):\n existing_occurrences = hash_db[row['hash']]\n existing_occurrences.append(row)\n else:\n new_occurrence = []\n new_occurrence.append(row)\n hash_db[row['hash']] = new_occurrence\n return hash_db\n\n\ndef unpack_hash_db(hash_db):\n database = []\n for key in hash_db:\n for row in hash_db[key]:\n database.append(row)\n return database\n\n\ndef find_duplicates(database):\n logging.info('Rows to look for duplicates: {}'.format(len(database)))\n hash_db = pack_hash_db(database)\n keys = list(hash_db.keys())\n for key in keys:\n count = len(hash_db[key])\n if (count == 1):\n hash_db.pop(key, None)\n duplicates = unpack_hash_db(hash_db)\n logging.info(\n 'Number of duplicates: {} ({:.2f}%)'.format(\n len(duplicates),\n len(duplicates) / len(database) * 100\n )\n )\n return duplicates\n\n\ndef read_database(file_name):\n logging.info('Reading database from file: {}'.format(file_name))\n fin = open(file_name, 'r', encoding=ENCODING)\n fin_reader = csv.DictReader(fin)\n database = []\n for row in fin_reader:\n database.append(row)\n fin.close()\n return database\n\n\ndef write_database(database, file_name):\n logging.info(\n 'Writing database with {} rows to file: {}'.format(\n len(database), file_name\n )\n )\n fout = open(file_name, 'w', encoding=ENCODING, newline='')\n fout_writer = csv.DictWriter(fout, fieldnames=COLUMNS, delimiter=DELIMITER)\n fout_writer.writeheader()\n fout_writer.writerows(database)\n fout.close()\n\n\ndef fd(input_file, output_file):\n if (not os.path.exists(input_file)):\n raise ValueError('Path does not exist: {}'.format(input_file))\n\n if (os.path.exists(output_file)):\n raise ValueError('File already exists: {}'.format(output_file))\n\n database = read_database(input_file)\n duplicates = find_duplicates(database)\n\n write_database(duplicates, output_file)\n\n\ndef diff(source_db, destination_db, output_file):\n if (not os.path.exists(source_db)):\n raise ValueError('Path does not exist: {}'.format(source_db))\n\n if (not os.path.exists(destination_db)):\n raise ValueError('Path does not exist: {}'.format(destination_db))\n\n if (os.path.exists(output_file)):\n raise ValueError('File already exists: {}'.format(output_file))\n\n src_db = read_database(source_db)\n dst_db = read_database(destination_db)\n\n src_hash_db = pack_hash_db(src_db)\n dst_hash_db = pack_hash_db(dst_db)\n\n diff_db = []\n for src_key in src_hash_db:\n if (src_key in dst_hash_db):\n continue\n inx = 0\n for row in src_hash_db[src_key]:\n diff_db.append(row)\n inx += 1\n if (inx != 1):\n logging.warning(\n 'Duplicates were found in the source database: {} ({}: {})'.format(\n source_db, inx - 1, src_key)\n )\n logging.info('Files in diff: {}'.format(len(diff_db)))\n\n write_database(diff_db, output_file)\n\n\ndef hd(directory, ignore):\n if (not os.path.exists(directory)):\n raise ValueError('Path does not exist: {}'.format(directory))\n\n file_list = get_file_list(directory, ignore)\n list_length = len(file_list)\n logging.info('Number of files: {}'.format(list_length))\n\n contents_digest = []\n inx = 0\n for file_name in file_list:\n logging.info(\n 'Processing ({}/{}, {:.2f}%) file: {}'.format(\n inx + 1, list_length, (inx + 1) / list_length * 100, file_name\n )\n )\n file_digest = hash_file(file_name)\n logging.info('{} *{}'.format(bin2str(file_digest), file_name))\n contents_digest.extend(file_digest)\n inx += 1\n contents_digest.sort()\n hasher = hashlib.md5()\n hasher.update(bytes(contents_digest))\n directory_digest = hasher.hexdigest()\n print('{} *{} ({})'.format(directory_digest, directory, inx))\n\n\ndef hdb(input_file):\n if (not os.path.exists(input_file)):\n raise ValueError('Path does not exist: {}'.format(input_file))\n\n db = read_database(input_file)\n\n contents_digest = []\n for row in db:\n file_hash = row['hash']\n file_digest = binascii.unhexlify(file_hash)\n contents_digest.extend(file_digest)\n contents_digest.sort()\n hasher = hashlib.md5()\n hasher.update(bytes(contents_digest))\n db_digest = hasher.hexdigest()\n print('{} *{} ({})'.format(db_digest, input_file, len(contents_digest)))\n\n\ndef hook_search_tip(args):\n curros = platform.system()\n if (curros == 'Darwin' and args.which == 'mk' and args.ignore == ['']):\n ans = input(\n 'Detected macOS: ' +\n 'Exclude files \".DS_Store\" and \"Icon.\" from search? (y/n)' +\n '\\n> '\n )\n if (ans.lower() == 'y'):\n args.ignore = '.DS_Store,Icon\\r'\n return args\n return args\n\n\ndef main():\n parser = setup_arg_parser()\n args = parser.parse_args()\n setup_logging(args.log_level, args.log_format)\n logging.info(args)\n\n if ('which' not in args):\n parser.print_help()\n print()\n raise ValueError('Command not specified!')\n\n args = hook_search_tip(args)\n\n if (args.which == 'mk'):\n mk(args.input_directory, args.output_file, args.ignore)\n elif (args.which == 'fd'):\n fd(args.input_file, args.output_file)\n elif (args.which == 'diff'):\n diff(args.source_db, args.destination_db, args.output_file)\n elif (args.which == 'hd'):\n hd(args.directory, args.ignore)\n elif (args.which == 'hdb'):\n hdb(args.input_file)\n else:\n raise ValueError('Unknown command!')\n\n\nif __name__ == '__main__':\n try:\n main()\n except Exception as e:\n logging.critical(e)\n", "repo_name": "timothy-makarov/fdb", "sub_path": "fdb.py", "file_name": "fdb.py", "file_ext": "py", "file_size_in_byte": 12631, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "io.DEFAULT_BUFFER_SIZE", "line_number": 16, "usage_type": "attribute"}, {"api_name": "logging.getLevelName", "line_number": 34, "usage_type": "call"}, {"api_name": "logging.basicConfig", "line_number": 35, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 39, "usage_type": "call"}, {"api_name": "hashlib.md5", "line_number": 142, "usage_type": "call"}, {"api_name": "binascii.hexlify", "line_number": 153, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 159, "usage_type": "call"}, {"api_name": "os.walk", "line_number": 161, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 162, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 163, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 167, "usage_type": "call"}, {"api_name": "os.path", "line_number": 167, "usage_type": "attribute"}, {"api_name": "logging.info", "line_number": 173, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 178, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 180, "usage_type": "call"}, {"api_name": "os.path.splitext", "line_number": 186, "usage_type": "call"}, {"api_name": "os.path", "line_number": 186, "usage_type": "attribute"}, {"api_name": "os.stat", "line_number": 187, "usage_type": "call"}, {"api_name": "datetime.datetime.fromtimestamp", "line_number": 188, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 188, "usage_type": "name"}, {"api_name": "datetime.datetime.fromtimestamp", "line_number": 189, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 189, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 193, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 193, "usage_type": "name"}, {"api_name": "logging.warning", "line_number": 203, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 205, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 205, "usage_type": "name"}, {"api_name": "logging.warning", "line_number": 214, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 221, "usage_type": "call"}, {"api_name": "os.path", "line_number": 221, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 224, "usage_type": "call"}, {"api_name": "os.path", "line_number": 224, "usage_type": "attribute"}, {"api_name": "logging.info", "line_number": 228, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 230, "usage_type": "call"}, {"api_name": "csv.writer", "line_number": 232, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 260, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 268, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 278, "usage_type": "call"}, {"api_name": "csv.DictReader", "line_number": 280, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 289, "usage_type": "call"}, {"api_name": "csv.DictWriter", "line_number": 295, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 302, "usage_type": "call"}, {"api_name": "os.path", "line_number": 302, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 305, "usage_type": "call"}, {"api_name": "os.path", "line_number": 305, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 315, "usage_type": "call"}, {"api_name": "os.path", "line_number": 315, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 318, "usage_type": "call"}, {"api_name": "os.path", "line_number": 318, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 321, "usage_type": "call"}, {"api_name": "os.path", "line_number": 321, "usage_type": "attribute"}, {"api_name": "logging.warning", "line_number": 339, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 343, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 349, "usage_type": "call"}, {"api_name": "os.path", "line_number": 349, "usage_type": "attribute"}, {"api_name": "logging.info", "line_number": 354, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 359, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 365, "usage_type": "call"}, {"api_name": "hashlib.md5", "line_number": 369, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 376, "usage_type": "call"}, {"api_name": "os.path", "line_number": 376, "usage_type": "attribute"}, {"api_name": "binascii.unhexlify", "line_number": 384, "usage_type": "call"}, {"api_name": "hashlib.md5", "line_number": 387, "usage_type": "call"}, {"api_name": "platform.system", "line_number": 394, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 411, "usage_type": "call"}, {"api_name": "logging.critical", "line_number": 438, "usage_type": "call"}]} +{"seq_id": "35765164820", "text": "from ray.rllib.offline.off_policy_estimator import OffPolicyEstimator, \\\n OffPolicyEstimate\nfrom ray.rllib.utils.annotations import override\n\n\nclass WeightedImportanceSamplingEstimator(OffPolicyEstimator):\n \"\"\"The weighted step-wise IS estimator.\n\n Step-wise WIS estimator in https://arxiv.org/pdf/1511.03722.pdf\"\"\"\n\n def __init__(self, policy, gamma):\n super().__init__(policy, gamma)\n self.filter_values = []\n self.filter_counts = []\n\n @override(OffPolicyEstimator)\n def estimate(self, batch):\n self.check_can_estimate_for(batch)\n\n rewards, old_prob = batch[\"rewards\"], batch[\"action_prob\"]\n new_prob = self.action_prob(batch)\n\n # calculate importance ratios\n p = []\n for t in range(batch.count - 1):\n if t == 0:\n pt_prev = 1.0\n else:\n pt_prev = p[t - 1]\n p.append(pt_prev * new_prob[t] / old_prob[t])\n for t, v in enumerate(p):\n if t >= len(self.filter_values):\n self.filter_values.append(v)\n self.filter_counts.append(1.0)\n else:\n self.filter_values[t] += v\n self.filter_counts[t] += 1.0\n\n # calculate stepwise weighted IS estimate\n V_prev, V_step_WIS = 0.0, 0.0\n for t in range(batch.count - 1):\n V_prev += rewards[t] * self.gamma**t\n w_t = self.filter_values[t] / self.filter_counts[t]\n V_step_WIS += p[t] / w_t * rewards[t] * self.gamma**t\n\n estimation = OffPolicyEstimate(\n \"wis\", {\n \"V_prev\": V_prev,\n \"V_step_WIS\": V_step_WIS,\n \"V_gain_est\": V_step_WIS / max(1e-8, V_prev),\n })\n return estimation\n", "repo_name": "HuantWang/SUPERSONIC", "sub_path": "third_party/ray/rllib/offline/wis_estimator.py", "file_name": "wis_estimator.py", "file_ext": "py", "file_size_in_byte": 1775, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 119, "dataset": "github-code", "pt": "37", "api": [{"api_name": "ray.rllib.offline.off_policy_estimator.OffPolicyEstimator", "line_number": 6, "usage_type": "name"}, {"api_name": "ray.rllib.offline.off_policy_estimator.OffPolicyEstimate", "line_number": 46, "usage_type": "call"}, {"api_name": "ray.rllib.utils.annotations.override", "line_number": 16, "usage_type": "call"}, {"api_name": "ray.rllib.offline.off_policy_estimator.OffPolicyEstimator", "line_number": 16, "usage_type": "argument"}]} +{"seq_id": "41389825702", "text": "from flask import Flask, redirect\nfrom flask import render_template, request\nfrom models import *\nfrom db import *\nimport json\nimport sys\nfrom datetime import date\n\n@app.route('/', methods=['get', 'post'])\ndef main():\n return render_template('index.html')\n\n@app.route('/exc1', methods=['get', 'post'])\ndef exc1():\n accs = Accs.query.join(Inventory, Accs.id == Inventory.user_id).add_columns(Accs.id, Inventory.appid).order_by(Inventory.appid).all()\n\n res = [{\n 'id': acc.id,\n 'name': acc.appid\n } for acc in accs]\n return render_template('table.html', accs=res)\n\n@app.route('/exc2', methods=['get', 'post'])\ndef exc2():\n apps = Apps.query.all()\n\n res = str([{\n 'id': app.id,\n 'name': app.name\n } for app in apps])\n\n if request.method == 'POST':\n if request.form.get('get_json'):\n return res\n elif request.form.get('update_json'):\n return render_template('insert.html', a_type='exc2_u')\n elif request.form.get('insert_json'):\n return render_template('insert.html', a_type='exc2_i')\n elif request.method == 'GET':\n return render_template('exc2.html')\n\n@app.route('/exc2_i', methods=['get', 'post'])\ndef insert():\n id = request.form.get('g_id')\n game_name = request.form.get('g_name')\n studio_name = request.form.get('st_name')\n print(id, game_name, studio_name, file=sys.stderr)\n\n d = date.today().strftime(\"%d-%m-%Y\")\n\n db.session.add(Apps(id, game_name, studio_name, d, \"None\", 0, 10))\n db.session.commit()\n\n r = Apps.query.filter_by(name='Dota 3').all()\n\n print(r, file=sys.stderr)\n\n return redirect('/exc2')\n\n@app.route('/exc2_u', methods=['get', 'post'])\ndef update():\n id = request.form.get('g_id')\n game_name = request.form.get('g_name')\n studio_name = request.form.get('st_name')\n print(id, game_name, studio_name, file=sys.stderr)\n\n a = Apps.query.filter_by(id=id).first()\n\n a.name = game_name\n a.author = studio_name\n\n db.session.commit()\n\n return redirect('/exc2')\n\nif __name__ == '__main__':\n app.run(debug=True, host='0.0.0.0')\n", "repo_name": "Pangolierchick/IU7-DB", "sub_path": "lab_07/src/app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 2121, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "flask.render_template", "line_number": 11, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 21, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 32, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 32, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 33, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 33, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 33, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 35, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 35, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 35, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 36, "usage_type": "call"}, {"api_name": "flask.request.form.get", "line_number": 37, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 37, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 37, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 38, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 39, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 39, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 40, "usage_type": "call"}, {"api_name": "flask.request.form.get", "line_number": 44, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 44, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 44, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 45, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 45, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 45, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 46, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 46, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 46, "usage_type": "name"}, {"api_name": "sys.stderr", "line_number": 47, "usage_type": "attribute"}, {"api_name": "datetime.date.today", "line_number": 49, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 49, "usage_type": "name"}, {"api_name": "db.session.add", "line_number": 51, "usage_type": "call"}, {"api_name": "db.session", "line_number": 51, "usage_type": "attribute"}, {"api_name": "db.session.commit", "line_number": 52, "usage_type": "call"}, {"api_name": "db.session", "line_number": 52, "usage_type": "attribute"}, {"api_name": "sys.stderr", "line_number": 56, "usage_type": "attribute"}, {"api_name": "flask.redirect", "line_number": 58, "usage_type": "call"}, {"api_name": "flask.request.form.get", "line_number": 62, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 62, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 62, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 63, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 63, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 63, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 64, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 64, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 64, "usage_type": "name"}, {"api_name": "sys.stderr", "line_number": 65, "usage_type": "attribute"}, {"api_name": "db.session.commit", "line_number": 72, "usage_type": "call"}, {"api_name": "db.session", "line_number": 72, "usage_type": "attribute"}, {"api_name": "flask.redirect", "line_number": 74, "usage_type": "call"}]} +{"seq_id": "20910355359", "text": "from abc import abstractmethod\nfrom sca.common.config import ConfigHolder\nimport logging\nimport os.path\nimport json\nimport pathlib\nfrom enum import Enum\nimport pandas as pd\n\nBLOCKCHAIN_FILE = Enum(\n 'BLOCKCHAIN_FILE', 'TXN INT_TXN ABI LOG ERC20_ADD ERC721_ADD \\\n EOA_BALANCE EOA_TOK_BALANCE ERC20_CONTRACT ERC20_CONTRACT_EOA \\\n ERC721_CONTRACT_EOA ERC721_CONTRACT CONTRACT_TOK_BALANCE FST_TXN TOKEN')\n\n\nclass Storage:\n '''\n Abstract class that handles file storing to local drive and S3. It creates following directory\n structure under/data\n /extract - stores all the extract files in the format *_address_*.json\n /trans - stores all the transformed files in the format *_address_*.csv\n /analytic - stores all the analytical files in the format *_address_*.csv\n\n '''\n config = None\n\n def __init__(self, con, customer):\n assert con is not None\n assert customer is not None\n\n Storage.config = con\n self.customer = customer\n\n self.logger = logging.getLogger(__name__)\n self.log_level = Storage.config.get_value(\"log_level\")\n if self.log_level == \"info\":\n self.logger.setLevel(logging.INFO)\n else:\n self.logger.setLevel(logging.DEBUG)\n\n # if the location is local then create dir locally\n # if not create dir in S3\n if Storage.config.get_value(\"location\") == \"local\":\n # create dir structure in local computer\n self.create_dir()\n else:\n # create dir structure in S3\n print(\"yet to code\")\n\n @abstractmethod\n def store_extract_data(self, blockchain_file: BLOCKCHAIN_FILE, address: str, data):\n pass\n\n @abstractmethod\n def retrieve_extract_data(self, blockchain_file: BLOCKCHAIN_FILE, address: str):\n pass\n\n @abstractmethod\n def store_trans_data(self, blockchain_file: BLOCKCHAIN_FILE, address: str, df: pd.DataFrame):\n pass\n\n @abstractmethod\n def retrieve_trans_data(self, blockchain_file: BLOCKCHAIN_FILE, address: str):\n pass\n\n @abstractmethod\n def store_analytic_data(self, blockchain_file: BLOCKCHAIN_FILE, address: str, df: pd.DataFrame):\n pass\n\n \n def scan_dir_for_extract_files(self):\n # Create an empty list to store the file names\n file_names = []\n\n # Scan the directory for all files\n dir_path = self.get_extract_dir()\n\n for file in os.listdir(dir_path):\n # Check if the file is a regular file (not a directory or a special file)\n if os.path.isfile(os.path.join(dir_path, file)):\n # Append the file name to the list\n file_names.append(file)\n\n # Return the list of file names\n return file_names\n \n def get_extract_dir(self):\n return (\n f\"{os.getcwd()}/sca/data/\"\n + self.customer\n + \"/extract/\"\n )\n \n def read_json_file(self, file_path):\n assert file_path is not None\n # Open the file for reading\n with open(file_path, 'r') as file:\n # Read the contents of the file as a string\n file_contents = file.read()\n return json.loads(file_contents)\n \n def create_json_dict(self, dir_path):\n assert dir_path is not None\n # Get the list of file names in the directory\n file_names = self.scan_dir_for_extract_files()\n # Create an empty dictionary to store the JSON contents\n json_dict = {}\n\n # Iterate through the file names\n for file_name in file_names:\n # Get the full path to the file\n file_path = os.path.join(dir_path, file_name)\n # Read the JSON contents of the file\n json_contents = self.read_json_file(file_path)\n # Add the JSON contents to the dictionary with the file name as the key\n json_dict[file_name] = json_contents\n\n # Return the dictionary\n return json_dict\n\n\n def get_extract_file_name(self, blockchain_file: BLOCKCHAIN_FILE, address: str) -> str:\n file_name = None\n if Storage.config.get_value(\"location\") == \"local\":\n file_name = self.blockchain_file_finder(blockchain_file)\n else:\n print(\"yet to code S3\")\n return (\n f\"{os.getcwd()}/sca/data/\"\n + self.customer\n + \"/extract/\"\n + address\n + file_name\n )\n\n def get_trans_file_name(self, blockchain_file: BLOCKCHAIN_FILE, address: str) -> str:\n file_name = None\n if Storage.config.get_value(\"location\") == \"local\":\n file_name = self.blockchain_file_finder(blockchain_file)\n else:\n print(\"yet to code S3\")\n return (\n f\"{os.getcwd()}/sca/data/\"\n + self.customer\n + \"/trans/\"\n + address\n + file_name\n )\n\n def blockchain_file_finder(self, blockchain_file: BLOCKCHAIN_FILE) -> str:\n \"\"\"\n Converts enum to file name text\n \"\"\"\n file_name = None\n if blockchain_file == BLOCKCHAIN_FILE.TXN.name:\n file_name = Storage.config.get_value(\"txn_file\")\n elif blockchain_file == BLOCKCHAIN_FILE.INT_TXN.name:\n file_name = Storage.config.get_value(\"internal_txn_file\")\n elif blockchain_file == BLOCKCHAIN_FILE.ABI.name:\n file_name = Storage.config.get_value(\"abi_file\")\n elif blockchain_file == BLOCKCHAIN_FILE.LOG.name:\n file_name = Storage.config.get_value(\"log_file\")\n elif blockchain_file == BLOCKCHAIN_FILE.ERC20_ADD.name:\n file_name = Storage.config.get_value(\"erc20_add_file\")\n elif blockchain_file == BLOCKCHAIN_FILE.ERC721_ADD.name:\n file_name = Storage.config.get_value(\"erc721_add_file\")\n elif blockchain_file == BLOCKCHAIN_FILE.EOA_BALANCE.name:\n file_name = Storage.config.get_value(\"eoa_balance_file\")\n elif blockchain_file == BLOCKCHAIN_FILE.EOA_TOK_BALANCE.name:\n file_name = Storage.config.get_value(\"eoa_tok_balance_file\")\n elif blockchain_file == BLOCKCHAIN_FILE.CONTRACT_TOK_BALANCE.name:\n file_name = Storage.config.get_value(\"contract_tok_bal_file\") \n elif blockchain_file == BLOCKCHAIN_FILE.ERC20_CONTRACT.name:\n file_name = Storage.config.get_value(\"erc20_contract_file\") \n elif blockchain_file == BLOCKCHAIN_FILE.ERC20_CONTRACT_EOA.name:\n file_name = Storage.config.get_value(\"erc20_contract_eoa_file\") \n elif blockchain_file == BLOCKCHAIN_FILE.ERC721_CONTRACT_EOA.name:\n file_name = Storage.config.get_value(\"erc721_contract_eoa_file\") \n elif blockchain_file == BLOCKCHAIN_FILE.ERC721_CONTRACT.name:\n file_name = Storage.config.get_value(\"erc721_contract_file\") \n elif blockchain_file == BLOCKCHAIN_FILE.FST_TXN.name:\n file_name = Storage.config.get_value(\"first_txn_file\") \n elif blockchain_file == BLOCKCHAIN_FILE.TOKEN.name:\n file_name = Storage.config.get_value(\"token_info_file\") \n return file_name\n\n def create_dir(self):\n \"\"\"\n Create dir structure for extract, trans, and analytic for the given customer\n \"\"\"\n # check if the dir with customer exist, if not create new\n if not os.path.exists(f\"{os.getcwd()}/sca/data/\" + self.customer):\n os.makedirs(f\"{os.getcwd()}/sca/data/\" + self.customer)\n if not os.path.exists(\n f\"{os.getcwd()}/sca/data/\" + self.customer + \"/extract/\"\n ):\n os.makedirs(((f\"{os.getcwd()}/sca/data/\" + self.customer) + \"/extract/\"))\n if not os.path.exists(\n f\"{os.getcwd()}/sca/data/\" + self.customer + \"/analytic/\"\n ):\n os.makedirs(((f\"{os.getcwd()}/sca/data/\" + self.customer) + \"/analytic/\"))\n if not os.path.exists(\n f\"{os.getcwd()}/sca/data/\" + self.customer + \"/trans/\"\n ):\n os.makedirs(((f\"{os.getcwd()}/sca/data/\" + self.customer) + \"/trans/\"))\n\n\nclass LocalStore(Storage):\n '''\n LocalStore class to store files in local drive\n '''\n\n def __init__(self, config, customer):\n super().__init__(config, customer)\n\n # overriding abstract method\n def store_extract_data(self, blockchain_file: BLOCKCHAIN_FILE, address: str, data):\n \"\"\"\n Store data extracted from Blockchain through package extractor. It creates the file\n in /extract dir\n Parameters\n ----------\n blockchain_file\n address\n data\n \"\"\"\n assert blockchain_file is not None\n assert address is not None\n assert data is not None\n path = (\n f\"{os.getcwd()}/sca/data/\"\n + self.customer\n + \"/extract/\"\n + address\n + self.blockchain_file_finder(blockchain_file)\n )\n with open(path, 'w') as fout:\n json.dump(data, fout)\n\n # overriding abstract method\n def store_trans_data(self, blockchain_file: BLOCKCHAIN_FILE, address: str, df: pd.DataFrame):\n \"\"\"\n Stores data extracted from extractor package and transformed for analyic work. It creates the file\n in /trans dir\n Parameters\n ----------\n blockchain_file\n address\n df\n \"\"\"\n assert blockchain_file is not None\n assert df is not None\n # store csv files\n file_name = self.blockchain_file_finder(\n blockchain_file).replace(\".json\", \".csv\")\n path = f\"{os.getcwd()}/sca/data/\" + self.customer + \"/trans/\" + address\n df.to_csv(path + file_name, index=False)\n\n # overriding abstract method\n def store_analytic_data(self, blockchain_file: BLOCKCHAIN_FILE, address: str, df: pd.DataFrame):\n \"\"\"\n Stores the analytic data. It creates the file in /analytic dir\n Parameters\n ----------\n blockchain_file\n address\n df\n \"\"\"\n assert blockchain_file is not None\n assert df is not None\n file_name = self.blockchain_file_finder(blockchain_file)\n path = (\n (f\"{os.getcwd()}/sca/data/\" + self.customer + \"/analytic/\") + address\n ) + file_name\n df.to_csv(path + file_name, index=False)\n\n # overriding abstract method\n def retrieve_extract_data(self, blockchain_file: BLOCKCHAIN_FILE, address: str):\n \"\"\"\n Retrieves data from the specified file in /extract folder\n Parameters\n ----------\n blockchain_file\n address\n\n Returns\n -------\n\n \"\"\"\n assert blockchain_file is not None\n assert address is not None\n file_name = self.get_extract_file_name(blockchain_file, address)\n return pathlib.Path(file_name).read_text()\n\n # overriding abstract method\n def retrieve_trans_data(self, blockchain_file: BLOCKCHAIN_FILE, address: str) -> pd.DataFrame:\n \"\"\"\n Retrieves data from the specified file in /trans folder\n Parameters\n ----------\n blockchain_file\n address\n\n Returns\n -------\n\n \"\"\"\n assert blockchain_file is not None\n assert address is not None\n file_name = self.get_trans_file_name(\n blockchain_file, address).replace(\".json\", \".csv\")\n # Read in the file contents as DataFrame\n return pd.read_csv(file_name)\n\n\nclass S3Store(Storage):\n def __init__(self, config, customer):\n super().__init__(config, customer)\n", "repo_name": "nehanegi-07/kitlogin-new", "sub_path": "sca/common/storage.py", "file_name": "storage.py", "file_ext": "py", "file_size_in_byte": 11675, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "enum.Enum", "line_number": 10, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 34, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 37, "usage_type": "attribute"}, {"api_name": "logging.DEBUG", "line_number": 39, "usage_type": "attribute"}, {"api_name": "abc.abstractmethod", "line_number": 50, "usage_type": "name"}, {"api_name": "abc.abstractmethod", "line_number": 54, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 59, "usage_type": "attribute"}, {"api_name": "abc.abstractmethod", "line_number": 58, "usage_type": "name"}, {"api_name": "abc.abstractmethod", "line_number": 62, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 67, "usage_type": "attribute"}, {"api_name": "abc.abstractmethod", "line_number": 66, "usage_type": "name"}, {"api_name": "os.path.listdir", "line_number": 78, "usage_type": "call"}, {"api_name": "os.path", "line_number": 78, "usage_type": "name"}, {"api_name": "os.path.path.isfile", "line_number": 80, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 80, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 80, "usage_type": "name"}, {"api_name": "os.path.path.join", "line_number": 80, "usage_type": "call"}, {"api_name": "os.path.getcwd", "line_number": 89, "usage_type": "call"}, {"api_name": "os.path", "line_number": 89, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 100, "usage_type": "call"}, {"api_name": "os.path.path.join", "line_number": 112, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 112, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 112, "usage_type": "name"}, {"api_name": "os.path.getcwd", "line_number": 129, "usage_type": "call"}, {"api_name": "os.path", "line_number": 129, "usage_type": "name"}, {"api_name": "os.path.getcwd", "line_number": 143, "usage_type": "call"}, {"api_name": "os.path", "line_number": 143, "usage_type": "name"}, {"api_name": "os.path.path.exists", "line_number": 192, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 192, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 192, "usage_type": "name"}, {"api_name": "os.path.getcwd", "line_number": 192, "usage_type": "call"}, {"api_name": "os.path.makedirs", "line_number": 193, "usage_type": "call"}, {"api_name": "os.path", "line_number": 193, "usage_type": "name"}, {"api_name": "os.path.getcwd", "line_number": 193, "usage_type": "call"}, {"api_name": "os.path.path.exists", "line_number": 194, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 194, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 194, "usage_type": "name"}, {"api_name": "os.path.getcwd", "line_number": 195, "usage_type": "call"}, {"api_name": "os.path", "line_number": 195, "usage_type": "name"}, {"api_name": "os.path.makedirs", "line_number": 197, "usage_type": "call"}, {"api_name": "os.path", "line_number": 197, "usage_type": "name"}, {"api_name": "os.path.getcwd", "line_number": 197, "usage_type": "call"}, {"api_name": "os.path.path.exists", "line_number": 198, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 198, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 198, "usage_type": "name"}, {"api_name": "os.path.getcwd", "line_number": 199, "usage_type": "call"}, {"api_name": "os.path", "line_number": 199, "usage_type": "name"}, {"api_name": "os.path.makedirs", "line_number": 201, "usage_type": "call"}, {"api_name": "os.path", "line_number": 201, "usage_type": "name"}, {"api_name": "os.path.getcwd", "line_number": 201, "usage_type": "call"}, {"api_name": "os.path.path.exists", "line_number": 202, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 202, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 202, "usage_type": "name"}, {"api_name": "os.path.getcwd", "line_number": 203, "usage_type": "call"}, {"api_name": "os.path", "line_number": 203, "usage_type": "name"}, {"api_name": "os.path.makedirs", "line_number": 205, "usage_type": "call"}, {"api_name": "os.path", "line_number": 205, "usage_type": "name"}, {"api_name": "os.path.getcwd", "line_number": 205, "usage_type": "call"}, {"api_name": "os.path.getcwd", "line_number": 231, "usage_type": "call"}, {"api_name": "os.path", "line_number": 231, "usage_type": "name"}, {"api_name": "json.dump", "line_number": 238, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 241, "usage_type": "attribute"}, {"api_name": "os.path.getcwd", "line_number": 256, "usage_type": "call"}, {"api_name": "os.path", "line_number": 256, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 260, "usage_type": "attribute"}, {"api_name": "os.path.getcwd", "line_number": 273, "usage_type": "call"}, {"api_name": "os.path", "line_number": 273, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 293, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 313, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 296, "usage_type": "attribute"}]} +{"seq_id": "16187955514", "text": "from flask import Flask, request, jsonify\nfrom textblob import TextBlob\nimport pickle\nimport numpy as np\nfrom flask_basicauth import BasicAuth\nimport os\n\nurl_arquivo = \"../../models/model_random_forest_novexus_churn.pkl\"\nurl_scaler = \"../../models/scaler_random_forest_novexus_churn.pkl\"\n\ncolunas = ['Idoso', 'Contrato_Ativo', 'Valor_Mensal', 'Valor_Total', 'Genero',\n 'Conjuge', 'Dependentes', 'Servico_Telefone', 'Mult_Linhas',\n 'Servico_Internet_Fibra Otica', 'Servico_Internet', 'Seguranca_Online',\n 'Backup_Online', 'Protecao_Disp', 'Suporte_Tecnico', 'Stream_Tv',\n 'Stream_Filmes', 'Contrato_Dois anos', 'Contrato_Mensal',\n 'Fatura_Online', 'Forma_Pagamento_Correio',\n 'Forma_Pagamento_Pag. Eletronico', 'Forma_Pagamento_Transf. Aut.']\n\nwith open(url_arquivo, 'rb') as file:\n classificador_random = pickle.load(file)\n\nwith open(url_scaler, 'rb') as file:\n scaler = pickle.load(file)\n\n \napp = Flask(__name__)\napp.config['BASIC_AUTH_USERNAME'] = os.environ.get('BASIC_AUTH_USERNAME')\napp.config['BASIC_AUTH_PASSWORD'] = os.environ.get('BASIC_AUTH_PASSWORD')\n\nBasicAuth = BasicAuth(app=app)\n\n@app.route('/')\ndef home():\n return \"Minha primeira API.\"\n\n@app.route('/sentimento/')\n@BasicAuth.required\ndef sentimento(frase):\n tb = TextBlob(frase)\n traducao = tb.translate(from_lang='pt_br', to='en')\n polaridade = traducao.sentiment.polarity\n return \"Polaridade {}\".format(polaridade)\n\n@app.route('/novexus_churn/', methods=['post'])\n@BasicAuth.required\ndef analisa_churn():\n dados = request.get_json()\n dados_input = [dados[col] for col in colunas]\n dados_input = np.array(dados_input).reshape(1,-1)\n\n dados_scaler = scaler.transform(dados_input)\n previsao = classificador_random.predict(dados_scaler)[0]\n # return jsonify(previsao=previsao[0])\n\n if previsao == 0:\n return 'Previsão de NÃO continuar como cliente!'\n else:\n return 'Previsão de CONTINUAR como cliente!' \n\napp.run(debug=True, host='0.0.0.0')\n\n\n", "repo_name": "Johnny-DF26/Churn_Novexus_ML", "sub_path": "src/app/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 2046, "program_lang": "python", "lang": "pt", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "pickle.load", "line_number": 20, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 23, "usage_type": "call"}, {"api_name": "flask.Flask", "line_number": 26, "usage_type": "call"}, {"api_name": "os.environ.get", "line_number": 27, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 27, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 28, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 28, "usage_type": "attribute"}, {"api_name": "flask_basicauth.BasicAuth", "line_number": 30, "usage_type": "name"}, {"api_name": "textblob.TextBlob", "line_number": 39, "usage_type": "call"}, {"api_name": "flask_basicauth.BasicAuth.required", "line_number": 37, "usage_type": "attribute"}, {"api_name": "flask_basicauth.BasicAuth", "line_number": 37, "usage_type": "name"}, {"api_name": "flask.request.get_json", "line_number": 47, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 47, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 49, "usage_type": "call"}, {"api_name": "flask_basicauth.BasicAuth.required", "line_number": 45, "usage_type": "attribute"}, {"api_name": "flask_basicauth.BasicAuth", "line_number": 45, "usage_type": "name"}]} +{"seq_id": "9373561527", "text": "# print the board in console\ndef printBoard(board):\n print(board[1] + '|' + board[2] + '|' + board[3])\n print('-+-+-')\n print(board[4] + '|' + board[5] + '|' + board[6])\n print('-+-+-')\n print(board[7] + '|' + board[8] + '|' + board[9])\n\n# checks if a specific position is empty\ndef isPositionFree(board,position):\n return board[position] == ' '\n\n# checks for draw\ndef isFull(board):\n for key in board:\n if isPositionFree(board,key):\n return False\n return True\n\n# checks for a winner\ndef isWinner(board):\n if (board[1] == board[2] and board[1] == board[3] and board[1] != ' ') :\n return True\n if (board[4] == board[5] and board[4] == board[6] and board[4] != ' ') :\n return True\n if (board[7] == board[8] and board[7] == board[9] and board[7] != ' ') :\n return True\n if (board[1] == board[4] and board[1] == board[7] and board[1] != ' ') :\n return True\n if (board[2] == board[5] and board[2] == board[8] and board[2] != ' ') :\n return True\n if (board[3] == board[6] and board[3] == board[9] and board[3] != ' ') :\n return True\n if (board[1] == board[5] and board[1] == board[9] and board[1] != ' ') :\n return True\n if (board[7] == board[5] and board[7] == board[3] and board[7] != ' ') :\n return True\n return False\n\ndef isWinnerMark(board,mark):\n if (board[1] == board[2] and board[1] == board[3] and board[1] == mark) :\n return True\n if (board[4] == board[5] and board[4] == board[6] and board[4] == mark) :\n return True\n if (board[7] == board[8] and board[7] == board[9] and board[7] == mark) :\n return True\n if (board[1] == board[4] and board[1] == board[7] and board[1] == mark) :\n return True\n if (board[2] == board[5] and board[2] == board[8] and board[2] == mark) :\n return True\n if (board[3] == board[6] and board[3] == board[9] and board[3] == mark) :\n return True\n if (board[1] == board[5] and board[1] == board[9] and board[1] == mark) :\n return True\n if (board[7] == board[5] and board[7] == board[3] and board[7] == mark) :\n return True\n return False\n\nfrom utils.minimax import minimax\n\n# insert a symbol in a specific position\ndef insertLetter(board,letter,position):\n if isPositionFree(board,position):\n board[position] = letter\n else:\n newPosition = int(input(\"That position is already taken. \\nPlease choose another position: \"))\n insertLetter(board,letter,newPosition)\n\ndef player1Turn(board):\n position = int(input(\"Player o, choose a position: \"))\n insertLetter(board,'o',position)\n\ndef player2Turn(board):\n position = int(input(\"Player x, choose a position: \"))\n insertLetter(board,'x',position)\n\ndef computerTurn(board,depth):\n bestScore = -1000\n bestPosition = 0\n for key in board:\n if isPositionFree(board,key):\n board[key] = 'x'\n score = minimax(board,depth,'o')\n board[key] = ' '\n if score > bestScore:\n bestScore = score\n bestPosition = key\n insertLetter(board,'x',bestPosition)\n return", "repo_name": "AdhamMagdyA/TicTacToe", "sub_path": "utils/helpers.py", "file_name": "helpers.py", "file_ext": "py", "file_size_in_byte": 3156, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "utils.minimax.minimax", "line_number": 83, "usage_type": "call"}]} +{"seq_id": "72714072746", "text": "from collections import defaultdict\nfrom celery import shared_task\nfrom django.core.exceptions import ObjectDoesNotExist\nfrom django.utils import timezone\nfrom reminder.utils import send_weather_email, fetch_weather_data\nfrom .models import Weather, Subscription\nfrom datetime import timedelta\n\n\n@shared_task()\ndef send_weather_notifications():\n current_time = timezone.now()\n fetch_weather_data()\n\n subscriptions = Subscription.objects.select_related('user', 'city')\n\n user_weather_data = defaultdict(list)\n\n for subscription in subscriptions:\n user = subscription.user\n city = subscription.city\n\n try:\n latest_weather = Weather.objects.filter(city=city).latest('timestamp')\n weather_info = f\"Temperature: {latest_weather.temperature}, \" \\\n f\"Conditions: {latest_weather.weather_conditions}, \" \\\n f\"Feels Like: {latest_weather.feels_like}\"\n\n last_notification_sent = subscription.last_notification_sent\n last_weather_request = city.last_weather_request\n\n if last_notification_sent is None or (\n current_time - last_notification_sent) >= timedelta(hours=subscription.period):\n if last_weather_request is None or (\n current_time - last_weather_request) >= timedelta(hours=subscription.period):\n user_weather_data[user].append((city, weather_info))\n subscription.last_notification_sent = current_time\n city.last_weather_request = current_time\n subscription.save()\n city.save()\n\n except ObjectDoesNotExist:\n print(f\"No weather data found for city: {city}\")\n\n for user, city_weather_data in user_weather_data.items():\n cities = set()\n filtered_city_weather_data = []\n\n for city, weather_info in city_weather_data:\n if city not in cities:\n filtered_city_weather_data.append((city, weather_info))\n cities.add(city)\n\n if filtered_city_weather_data:\n send_weather_email(user, filtered_city_weather_data)\n print('Email sent')\n", "repo_name": "vombat007/Django_Weather_Reminder", "sub_path": "weather_reminder/reminder/tasks.py", "file_name": "tasks.py", "file_ext": "py", "file_size_in_byte": 2224, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "django.utils.timezone.now", "line_number": 12, "usage_type": "call"}, {"api_name": "django.utils.timezone", "line_number": 12, "usage_type": "name"}, {"api_name": "reminder.utils.fetch_weather_data", "line_number": 13, "usage_type": "call"}, {"api_name": "models.Subscription.objects.select_related", "line_number": 15, "usage_type": "call"}, {"api_name": "models.Subscription.objects", "line_number": 15, "usage_type": "attribute"}, {"api_name": "models.Subscription", "line_number": 15, "usage_type": "name"}, {"api_name": "collections.defaultdict", "line_number": 17, "usage_type": "call"}, {"api_name": "models.Weather.objects.filter", "line_number": 24, "usage_type": "call"}, {"api_name": "models.Weather.objects", "line_number": 24, "usage_type": "attribute"}, {"api_name": "models.Weather", "line_number": 24, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 33, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 35, "usage_type": "call"}, {"api_name": "django.core.exceptions.ObjectDoesNotExist", "line_number": 42, "usage_type": "name"}, {"api_name": "reminder.utils.send_weather_email", "line_number": 55, "usage_type": "call"}, {"api_name": "celery.shared_task", "line_number": 10, "usage_type": "call"}]} +{"seq_id": "26644249131", "text": "'''Given two .txt files that have lists of numbers in them, find the numbers that are overlapping. \nOne .txt file has a list of all prime numbers under 1000, and the other .txt file has a list of happy numbers up to 1000.'''\n\nurl1 = \"https://www.practicepython.org/assets/primenumbers.txt\"\nurl2 = \"https://www.practicepython.org/assets/happynumbers.txt\"\n\nimport requests\n\nr1 = requests.get(url1).text\nr2 = requests.get(url2).text\n\nlist1 = r1.split()\nlist2 = r2.split()\n\noverlap = [i for i in list1 if i in list2]\n\nprint(overlap)", "repo_name": "juliashal/Python-Practice", "sub_path": "File Overlap.py", "file_name": "File Overlap.py", "file_ext": "py", "file_size_in_byte": 528, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "requests.get", "line_number": 9, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 10, "usage_type": "call"}]} +{"seq_id": "27854124989", "text": "import appdaemon.plugins.hass.hassapi as hass\nfrom datetime import datetime, time\nimport os\nimport wait\n\nclass EntranceWorld2(hass.Hass):\n\n def initialize(self):\n self.log(\"Starting Entrance Service 2\")\n wait.wait_available(self,[\"binary_sensor.dev54_button\",\"binary_sensor.dev17_motion\",\"person.kolja_2\",\"person.caro_2\",\"proximity.caro_home\",\"proximity.kolja_home\"],False)\n\n try:\n os.makedirs('/tmp/cams')\n except:\n pass\n\n self.ring_cnt = [0,0,0]\n self.listen_state(self.ring, \"binary_sensor.dev54_button\", new = \"on\") #klingel\n self.listen_state(self.backdoor, \"binary_sensor.dev17_motion\", new = \"on\") #klingel\n\n self.run_daily(self.six, time(6, 0, 0))\n self.run_daily(self.twentytwo, time(22, 0, 0))\n self.run_daily(self.twentythree, time(23, 0, 0))\n try:\n self.run_at_sunrise(self.sunrise, offset = 30 * 60)\n self.run_at_sunset(self.sunset, offset = 15 * 60)\n except:\n pass\n self.listen_state(self.caro_home, \"person.caro_2\", new = \"home\", duration = 10*60, arg1=\"Caro home\") # everyone is home for 10 min\n self.listen_state(self.kolja_home, \"person.kolja_2\", new = \"home\", duration = 10*60, arg1=\"Kolja home\") # everyone is home for 10 min\n self.listen_state(self.approaching, \"proximity.caro_home\")\n self.listen_state(self.approaching, \"proximity.kolja_home\")\n\n now = datetime.now()\n self.recording_start=[now,now]\n self.listen_state(self.rec_front_door, \"binary_sensor.mymotiondetectorrule_cell_motion_detection\")\n self.listen_state(self.rec_garden, \"binary_sensor.mymotiondetectorrule_cell_motion_detection_2\")\n\n ######################################################\n\n def six(self, entity=\"\", attribute=\"\", old=\"\", new=\"\", kwargs=\"\"):\n self.outside_on(\"6am\")\n def twentytwo(self, entity=\"\", attribute=\"\", old=\"\", new=\"\", kwargs=\"\"):\n self.outside(\"22pm\")\n def twentythree(self, entity=\"\", attribute=\"\", old=\"\", new=\"\", kwargs=\"\"):\n self.outside(\"23pm\")\n def sunrise(self, entity=\"\", attribute=\"\", old=\"\", new=\"\", kwargs=\"\"):\n self.outside(\"Sunrise\")\n def sunset(self, entity=\"\", attribute=\"\", old=\"\", new=\"\", kwargs=\"\"):\n self.outside(\"Senset\")\n def kolja_home(self, entity=\"\", attribute=\"\", old=\"\", new=\"\", kwargs=\"\"):\n self.outside(\"Kolja home\")\n def caro_home(self, entity=\"\", attribute=\"\", old=\"\", new=\"\", kwargs=\"\"):\n self.outside(\"Caro home\")\n\n ######################################################\n\n def rec_front_door(self, entity=\"\", attribute=\"\", old=\"\", new=\"\",kwargs=\"\"):\n if(new == \"on\"):\n now = datetime.now()\n if((now-self.recording_start[0]).total_seconds()>30):\n fn = \"/tmp/cams/front_door_\"+str(self.ring_cnt[0]).zfill(2) # save filename without filetype ending \n self.log(\"========= front door motion -> recording 20 sec to \"+fn+\" ========================\") # log output\n self.ring_cnt[0] = (self.ring_cnt[0] + 1) % 40 # inc\n self.call_service(\"camera/record\", entity_id=\"camera.cam_dome1_profile_000\", filename=fn+\".mp4\", duration=\"25\", lookback=\"5\") # save video\n self.recording_start[0] = now\n def rec_garden(self, entity=\"\", attribute=\"\", old=\"\", new=\"\",kwargs=\"\"):\n if(new == \"on\"):\n now = datetime.now()\n if((now-self.recording_start[1]).total_seconds()>30):\n fn = \"/tmp/cams/garden_\"+str(self.ring_cnt[2]).zfill(2) # save filename without filetype ending \n self.log(\"========= garden motion -> recording 20 sec to \"+fn+\" ========================\") # log output\n self.ring_cnt[2] = (self.ring_cnt[2] + 1) % 40 # inc\n self.call_service(\"camera/record\", entity_id=\"camera.cam_dome3_profile_000\", filename=fn+\".mp4\", duration=\"25\", lookback=\"5\") # save video\n self.recording_start[1] = now\n\n ######################################################\n\n def ring(self, entity, attribute, old, new,kwargs):\n self.log(\"========= ring ========================\") # log output\n self.outside_wish(\"on!\",kwargs) # turn light on\n\n # generate filename, even if we won't record \n fn = \"/tmp/cams/ring_\"+str(self.ring_cnt[0]).zfill(2) # save filename without filetype ending\n self.ring_cnt[0] = (self.ring_cnt[0] + 1) % 40 # inc\n\n # grab screenshot and send that via MSG\n self.call_service(\"camera/snapshot\", entity_id=\"camera.cam_dome1_profile_000\", filename=fn+\".jpg\") # save snapshot\n self.notify_vid(arg={\"arg\":{\"t\":\"Frontdoor\",\"m\":\"Ding Dong\",\"d\":{\"file\":\"\"}}}) # send info\n self.run_in(self.notify_vid,1, arg={\"t\":\"Frontdoor image\",\"m\":\"Sensor triggered\",\"d\":{\"file\": fn+\".jpg\"}}) # send image\n\n # check we we should start our own recording of it that already exists driven by motion\n now = datetime.now()\n if((now-self.recording_start[0]).total_seconds()>30):\n # camera is NOT recording, driven by motion\n self.call_service(\"camera/record\", entity_id=\"camera.cam_dome1_profile_000\", filename=fn+\".mp4\", duration=\"25\", lookback=\"5\") # save video\n else:\n # camera IS recording\n #self.log(\"last motion triggered recording started less then 30 sec ago, so I'll take that video. ring_cnt[0]=\"+str(self.ring_cnt[0])) # log output\n fn = \"/tmp/cams/front_door_\"+str((self.ring_cnt[0]-2+40)%40).zfill(2) # save filename without filetype ending \n #self.log(\"so our filename will be: \"+fn)\n\n # send link to video\n self.run_in(self.notify_vid,20,arg={\"t\":\"Frontdoor video\",\"m\":\"http://192.168.2.84:8081/\"+fn.replace(\"/tmp/\",\"\")+\".mp4\",\"d\":{\"file\":\"\"}}) # send link to video\n self.run_in(self.outside,5*60) # turn off after 5 min\n\n # open stream on screen\n self.set_state(\"binary_sensor.dev16_motion\", state = \"on\")\n self.call_service(\"browser_mod/more_info\", entity_id=\"camera.cam_dome1_profile_000\",deviceID=\"33f1c020-593396b4\", large=True)\n self.run_in(self.close_info,20)\n\n def close_info(self,arg):\n self.set_state(\"binary_sensor.dev16_motion\", state = \"off\")\n self.call_service(\"browser_mod/close_popup\",deviceID=\"33f1c020-593396b4\")\n\n def backdoor(self, entity=\"\", attribute=\"\", old=\"\", new=\"\",kwargs=\"\"):\n self.log(\"========= backdoor ========================\") # log output\n fn = \"/tmp/cams/backdoor_\"+str(self.ring_cnt[1]).zfill(2) # save filename without firetype ending\n self.ring_cnt[1] = (self.ring_cnt[1] + 1) % 40 # inc\n self.call_service(\"camera/record\", entity_id=\"camera.cam_dome2_profile_000\", filename=fn+\".mp4\", duration=\"20\", lookback=\"5\") # save video\n self.call_service(\"camera/snapshot\", entity_id=\"camera.cam_dome2_profile_000\", filename=fn+\".jpg\") # save snapshot\n if(self.get_state(\"input_boolean.alarm_system\") == \"on\" and self.get_state(\"binary_sensor.someone_is_home\") == \"off\"):\n self.notify_vid(arg={\"arg\":{\"t\":\"Backdoor\",\"m\":\"Triggered\",\"d\":{\"file\":\"\"}}}) # send info\n self.run_in(self.notify_vid,1, arg={\"t\":\"Backdoor image\",\"m\":\"Sensor triggered\",\"d\":{\"file\": fn+\".jpg\"}}) # send image\n self.run_in(self.notify_vid,20,arg={\"t\":\"Backdoor video\",\"m\":\"http://192.168.2.84:8081/\"+fn.replace(\"/tmp/\",\"\")+\".mp4\",\"d\":{\"file\":\"\"}}) # send link to video\n\n def notify_vid(self, arg):\n #self.log(arg)\n t=arg[\"arg\"][\"t\"]\n m=arg[\"arg\"][\"m\"]\n d=arg[\"arg\"][\"d\"]\n self.log(\"sending notification: \"+t)\n if(d[\"file\"]!=\"\"):\n self.call_service(\"notify/pb\", title=t, message=m, data=d)\n self.call_service(\"notify/pb_c\", title=t, message=m, data=d)\n else:\n self.call_service(\"notify/pb\", title=t, message=m)\n self.call_service(\"notify/pb_c\", title=t, message=m)\n\n def approaching(self, entity, attribute, old, new,kwargs):\n #self.log(\"proxy\")\n #self.log(dir)\n #self.log(dist)\n if(self.get_state(\"person.caro_2\") == \"home\" and self.get_state(\"person.kolja_2\") == \"home\"):\n self.log(\"ignoring approach, both home\")\n return 0\n if(attribute == \"state\"):\n dir = self.get_state(entity, attribute=\"dir_of_travel\")\n dist = self.get_state(entity)\n if(dir == \"towards\" and attribute == \"state\"):\n if(int(dist) < 3):\n self.log(\"========= approach ========================\")\n #self.log(repr(kwargs[\"arg1\"])\n self.log(entity+\" is approaching home\")\n self.outside_wish(\"on\",kwargs)\n\n def outside_on(self, title):\n self.log(\"============= outside on ====================\")\n self.log(repr(title))\n self.outside_wish(\"on\")\n\n def outside(self, title):\n self.log(\"============== outside ===================\")\n self.log(repr(title))\n self.outside_wish(\"auto\")\n\n def outside_wish(self,w,kwargs=\"\"):\n #self.log(repr(kwargs))\n\n now = datetime.now().time()\n ele = float(self.get_state(\"sun.sun\", attribute=\"elevation\"))\n rising = self.get_state(\"sun.sun\", attribute=\"rising\")\n self.log(\"current elevation \"+str(ele))\n\n if(w==\"on!\"):\n self.log(\"COMMAND! to turn on lights\")\n self.turn_on(\"light.joiner_outdoor\")\n self.log(\"=================================\")\n elif(w==\"on\"):\n self.log(\"request to turn on lights\")\n if(ele < 2):\n self.log(\"request granted, sun is low, turning on\")\n self.turn_on(\"light.joiner_outdoor\")\n self.log(\"=================================\")\n else:\n self.log(\"request rejected, sun is up, turning off\")\n self.turn_off(\"light.joiner_outdoor\")\n self.log(\"=================================\")\n else: #if(w==\"auto\"):\n self.log(\"request in auto mode\")\n if(now >= time(23,00,00)):\n self.log(\"after 11 turn off\")\n self.turn_off(\"light.joiner_outdoor\")\n elif(now >= time(22,00,00) and self.get_state(\"binary_sensor.everyone_is_home\") == \"on\"):\n self.log(\"after 10 and everyone is home, turn off\")\n self.turn_off(\"light.joiner_outdoor\")\n elif(ele < 2 and now >= time(13,0,0)):\n self.log(\"sun low and falling, must be evening, turn on\")\n self.turn_on(\"light.joiner_outdoor\")\n elif(ele >= 2):\n self.log(\"sun is up, turn off\")\n self.turn_off(\"light.joiner_outdoor\")\n elif(now >= time(6,0,0)):\n self.log(\">= six AM but still dark, turn on\")\n self.turn_on(\"light.joiner_outdoor\")\n else:\n self.log(\"before six in the nigth, turn off\")\n self.turn_off(\"light.joiner_outdoor\")\n self.log(\"=================================\")\n\n\n\n", "repo_name": "KoljaWindeler/ha_config", "sub_path": "appdaemon/apps/dev54_entrance2.py", "file_name": "dev54_entrance2.py", "file_ext": "py", "file_size_in_byte": 11108, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "37", "api": [{"api_name": "appdaemon.plugins.hass.hassapi.Hass", "line_number": 6, "usage_type": "attribute"}, {"api_name": "appdaemon.plugins.hass.hassapi", "line_number": 6, "usage_type": "name"}, {"api_name": "wait.wait_available", "line_number": 10, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 13, "usage_type": "call"}, {"api_name": "datetime.time", "line_number": 21, "usage_type": "call"}, {"api_name": "datetime.time", "line_number": 22, "usage_type": "call"}, {"api_name": "datetime.time", "line_number": 23, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 34, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 34, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 60, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 60, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 69, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 69, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 93, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 93, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 170, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 170, "usage_type": "name"}, {"api_name": "datetime.time", "line_number": 191, "usage_type": "call"}, {"api_name": "datetime.time", "line_number": 194, "usage_type": "call"}, {"api_name": "datetime.time", "line_number": 197, "usage_type": "call"}, {"api_name": "datetime.time", "line_number": 203, "usage_type": "call"}]} +{"seq_id": "39339792909", "text": "#!/usr/bin/env python\n\nimport subprocess\nimport logging\nimport uuid\n\nimport requests\nimport json\n\nfrom sklearn import svm, datasets\nfrom bentoml import BentoService, load, api, env, artifacts\nfrom bentoml.artifact import PickleArtifact\nfrom bentoml.handlers import DataframeHandler\n\n\nlogger = logging.getLogger('bentoml.test')\n\n\n@artifacts([PickleArtifact('clf')])\n@env(pip_dependencies=['scikit-learn==0.20.3'])\nclass IrisClassifier(BentoService):\n @api(DataframeHandler)\n def predict(self, df):\n return self.artifacts.clf.predict(df)\n\n\nif __name__ == '__main__':\n logger.info('Training iris classifier')\n clf = svm.SVC(gamma='scale')\n iris = datasets.load_iris()\n X, y = iris.data, iris.target\n clf.fit(X, y)\n\n logger.info('Bundling iris classifier with BentoML')\n iris_clf_service = IrisClassifier()\n iris_clf_service.pack('clf', clf)\n saved_path = iris_clf_service.save()\n\n loaded_service = load(saved_path)\n sample_data = X[0:1]\n\n logger.info('Result from sample data is: ', loaded_service.predict(sample_data))\n deployment_failed = False\n logger.info(\n 'Creating AWS Lambda test deployment for iris classifier with BentoML CLI'\n )\n bento_name = '{}:{}'.format(loaded_service.name, loaded_service.version)\n random_hash = uuid.uuid4().hex[:6]\n deployment_name = 'tests-lambda-e2e-{}'.format(random_hash)\n create_deployment_command = [\n 'bentoml',\n '--verbose',\n 'deploy',\n 'create',\n deployment_name,\n '--bento',\n bento_name,\n '--platform',\n 'aws-lambda',\n '--region',\n 'us-west-2',\n ]\n logger.info('Deploy command: {}'.format(create_deployment_command))\n with subprocess.Popen(\n create_deployment_command, stdout=subprocess.PIPE, stderr=subprocess.PIPE\n ) as proc:\n create_deployment_stdout = proc.stdout.read().decode('utf-8')\n logger.info('Finish deploying to AWS Lambda')\n logger.info(create_deployment_stdout)\n if create_deployment_stdout.startswith('Failed to create deployment'):\n deployment_failed = True\n create_deployment_output_list = create_deployment_stdout.split('\\n')\n deployment_endpoint = ''\n for index, message in enumerate(create_deployment_output_list):\n if '\"endpoints\": [' in message:\n deployment_endpoint = (\n create_deployment_output_list[index + 1].strip().replace('\"', '')\n )\n\n if not deployment_failed:\n logger.info('Test deployment with sample request')\n try:\n request_result = requests.post(\n deployment_endpoint,\n data=json.dumps(sample_data.tolist()),\n headers={'Content-Type': 'application/json'},\n )\n if request_result.status_code != 200:\n deployment_failed = True\n if request_result.content.decode('utf-8') != '[0]':\n logger.info(\n 'Test request failed. {}:{}'.format(\n request_result.status_code,\n request_result.content.decode('utf-8'),\n )\n )\n deployment_failed = True\n except Exception as e:\n logger.error(str(e))\n deployment_failed = True\n\n logger.info('Delete test deployment with BentoML CLI')\n delete_deployment_command = [\n 'bentoml',\n 'deploy',\n 'delete',\n deployment_name,\n '--force',\n ]\n logger.info('Delete command: {}'.format(delete_deployment_command))\n with subprocess.Popen(\n delete_deployment_command, stdout=subprocess.PIPE, stderr=subprocess.PIPE\n ) as proc:\n delete_deployment_stdout = proc.stdout.read().decode('utf-8')\n logger.info(delete_deployment_stdout)\n\n logger.info('Finished')\n if deployment_failed:\n logger.info('E2E deployment failed, fix the issues before releasing')\n else:\n logger.info('E2E Lambda deployment testing is successful')\n", "repo_name": "smutuvi/BentoML", "sub_path": "scripts/e2e_tests/aws_lambda/e2e_lambda_deployment.py", "file_name": "e2e_lambda_deployment.py", "file_ext": "py", "file_size_in_byte": 4026, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "github-code", "pt": "37", "api": [{"api_name": "logging.getLogger", "line_number": 16, "usage_type": "call"}, {"api_name": "bentoml.BentoService", "line_number": 21, "usage_type": "name"}, {"api_name": "bentoml.api", "line_number": 22, "usage_type": "call"}, {"api_name": "bentoml.handlers.DataframeHandler", "line_number": 22, "usage_type": "argument"}, {"api_name": "bentoml.artifacts", "line_number": 19, "usage_type": "call"}, {"api_name": "bentoml.artifact.PickleArtifact", "line_number": 19, "usage_type": "call"}, {"api_name": "bentoml.env", "line_number": 20, "usage_type": "call"}, {"api_name": "sklearn.svm.SVC", "line_number": 29, "usage_type": "call"}, {"api_name": "sklearn.svm", "line_number": 29, "usage_type": "name"}, {"api_name": "sklearn.datasets.load_iris", "line_number": 30, "usage_type": "call"}, {"api_name": "sklearn.datasets", "line_number": 30, "usage_type": "name"}, {"api_name": "bentoml.load", "line_number": 39, "usage_type": "call"}, {"api_name": "uuid.uuid4", "line_number": 48, "usage_type": "call"}, {"api_name": "subprocess.Popen", "line_number": 64, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 65, "usage_type": "attribute"}, {"api_name": "requests.post", "line_number": 83, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 85, "usage_type": "call"}, {"api_name": "subprocess.Popen", "line_number": 111, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 112, "usage_type": "attribute"}]} +{"seq_id": "691408899", "text": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nfrom hashids import Hashids\nimport settings\nfrom utils.data_utils import datetime_encapsulator, data_updater, isiterable, clist\n\n\ndef load_as_objs(cls, items):\n return (cls(**item) for item in items)\n\n\ndef load_as_dicts(result_set):\n return (item.to_dict() for item in result_set)\n\n\ndef id2hashid(id):\n hashids = Hashids(salt=settings.SALT, min_length=5)\n hashid = hashids.encode(id)\n return hashid\n\n\ndef decoded_hashid(func):\n hashids = Hashids(salt=settings.SALT, min_length=5)\n\n def wrapper(*args, **kargs):\n args = list(args)\n try:\n (args[1],) = hashids.decode(args[1])\n except ValueError:\n pass\n result = func(*args, **kargs)\n return result\n return wrapper\n\n\ndef encoded_hashid(func):\n hashids = Hashids(salt=settings.SALT, min_length=5)\n\n def wrapper(*args, **kargs):\n args = list(args)\n args[1] = hashids.encode(args[1])\n result = func(*args, **kargs)\n return result\n return wrapper\n\n\ndef encode_hashid_list(ids):\n hashids = Hashids(salt=settings.SALT, min_length=5)\n return (hashids.encode(id) for id in ids)\n\n\ndef sqlite_datetime_compatibility(keys):\n def _(func):\n def wrapper(*args, **kargs):\n import types\n nonlocal keys\n args = list(args)\n items = args[1]\n if not isiterable(keys):\n keys = clist(keys)\n if not isiterable(items):\n items = clist(items)\n for key in keys:\n items = data_updater(key, key, datetime_encapsulator, True, items)\n args[1] = items\n result = func(*args, **kargs)\n return result\n return wrapper\n return _\n\n\ndef list_as_str(keys):\n def _(func):\n def wrapper(*args, **kargs):\n nonlocal keys\n args = list(args)\n items = args[1]\n if not isiterable(keys):\n keys = clist(keys)\n if not isiterable(items):\n items = clist(items)\n for key in keys:\n if all(key in item for item in items):\n items = data_updater(key, key, list2str, True, items)\n args[1] = items\n result = func(*args, **kargs)\n return result\n return wrapper\n return _\n\n\ndef str2list(string, delimiter=','):\n if not string:\n return []\n return sorted(filter(None, string.split(delimiter)))\n\n\ndef list2str(lst, delimiter=','):\n if not lst:\n return None\n return delimiter.join(sorted(lst))\n\n\ndef reload_keyword(keyword):\n keywords = str2list(keyword, ',')\n if len(keywords) > 1:\n return (keywords, 'OR')\n keywords = str2list(keyword, ' ')\n if len(keywords) > 1:\n return (keywords, 'AND')\n keywords = str2list(keyword, '+')\n if len(keywords) > 1:\n return (keywords, 'AND')\n keywords = str2list(keyword, '|')\n if len(keywords) > 1:\n return (keywords, 'OR')\n else:\n return (keywords, 'OR')\n\n\ndef auto_vacuum():\n from db.database import pg_vacuum\n import settings\n db_url = settings.DATABASE_URL\n pg_vacuum(db_url.startswith('postgres://'))\n", "repo_name": "ilyalee/newstw", "sub_path": "db/utils/db_utils.py", "file_name": "db_utils.py", "file_ext": "py", "file_size_in_byte": 3250, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "37", "api": [{"api_name": "hashids.Hashids", "line_number": 18, "usage_type": "call"}, {"api_name": "settings.SALT", "line_number": 18, "usage_type": "attribute"}, {"api_name": "hashids.encode", "line_number": 19, "usage_type": "call"}, {"api_name": "hashids.Hashids", "line_number": 24, "usage_type": "call"}, {"api_name": "settings.SALT", "line_number": 24, "usage_type": "attribute"}, {"api_name": "hashids.decode", "line_number": 29, "usage_type": "call"}, {"api_name": "hashids.Hashids", "line_number": 38, "usage_type": "call"}, {"api_name": "settings.SALT", "line_number": 38, "usage_type": "attribute"}, {"api_name": "hashids.encode", "line_number": 42, "usage_type": "call"}, {"api_name": "hashids.Hashids", "line_number": 49, "usage_type": "call"}, {"api_name": "settings.SALT", "line_number": 49, "usage_type": "attribute"}, {"api_name": "hashids.encode", "line_number": 50, "usage_type": "call"}, {"api_name": "utils.data_utils.isiterable", "line_number": 60, "usage_type": "call"}, {"api_name": "utils.data_utils.clist", "line_number": 61, "usage_type": "call"}, {"api_name": "utils.data_utils.isiterable", "line_number": 62, "usage_type": "call"}, {"api_name": "utils.data_utils.clist", "line_number": 63, "usage_type": "call"}, {"api_name": "utils.data_utils.data_updater", "line_number": 65, "usage_type": "call"}, {"api_name": "utils.data_utils.datetime_encapsulator", "line_number": 65, "usage_type": "argument"}, {"api_name": "utils.data_utils.isiterable", "line_number": 79, "usage_type": "call"}, {"api_name": "utils.data_utils.clist", "line_number": 80, "usage_type": "call"}, {"api_name": "utils.data_utils.isiterable", "line_number": 81, "usage_type": "call"}, {"api_name": "utils.data_utils.clist", "line_number": 82, "usage_type": "call"}, {"api_name": "utils.data_utils.data_updater", "line_number": 85, "usage_type": "call"}, {"api_name": "settings.DATABASE_URL", "line_number": 125, "usage_type": "attribute"}, {"api_name": "db.database.pg_vacuum", "line_number": 126, "usage_type": "call"}]} +{"seq_id": "72353369068", "text": "\"\"\"\nN-sphere fit with center and radius.\n\nThis module is a little different from the others because it fits an\nn-dimensional surface and because it does not have a model function\nbecause of the non-functional nature of n-spheres.\n\"\"\"\n\nfrom numpy import empty, sqrt, square\nfrom scipy.linalg import lstsq\n\nfrom .util import preprocess\n\n__all__ = ['nsphere_fit']\n\n\ndef nsphere_fit(x, axis=-1, scaling=False):\n r\"\"\"\n Fit an n-sphere to ND data.\n\n The center and radius of the n-sphere are optimized using the Coope\n method. The sphere is described by\n\n .. math::\n\n \\left \\lVert \\vec{x} - \\vec{c} \\right \\rVert_2 = r\n\n Parameters\n ----------\n x : array-like\n The n-vectors describing the data. Usually this will be a nxm\n array containing m n-dimensional data points.\n axis : int\n The axis that determines the number of dimensions of the\n n-sphere. All other axes are effectively raveled to obtain an\n ``(m, n)`` array.\n scaling : bool\n If `True`, scale and offset the data to a bounding box of -1 to\n +1 during computations for numerical stability. Default is\n `False`.\n\n Return\n ------\n r : scalar\n The optimal radius of the best-fit n-sphere for `x`.\n c : array\n An array of size `x.shape[axis]` with the optimized center of\n the best-fit n-sphere.\n\n References\n ----------\n - [Coope]_ \"\\ :ref:`ref-cfblanls`\\ \"\n \"\"\"\n x = preprocess(x, float=True, axis=axis)\n n = x.shape[-1]\n x = x.reshape(-1, n)\n m = x.shape[0]\n\n B = empty((m, n + 1), dtype=x.dtype)\n X = B[:, :-1]\n X[:] = x\n B[:, -1] = 1\n\n if scaling:\n xmin = X.min()\n xmax = X.max()\n scale = 0.5 * (xmax - xmin)\n offset = 0.5 * (xmax + xmin)\n X -= offset\n X /= scale\n\n d = square(X).sum(axis=-1)\n\n y, *_ = lstsq(B, d, overwrite_a=True, overwrite_b=True)\n\n c = 0.5 * y[:-1]\n r = sqrt(y[-1] + square(c).sum())\n\n if scaling:\n r *= scale\n c *= scale\n c += offset\n\n return r, c\n\n", "repo_name": "madphysicist/scikit-guess", "sub_path": "src/skg/nsphere.py", "file_name": "nsphere.py", "file_ext": "py", "file_size_in_byte": 2073, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 10, "dataset": "github-code", "pt": "37", "api": [{"api_name": "util.preprocess", "line_number": 54, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 59, "usage_type": "call"}, {"api_name": "numpy.square", "line_number": 72, "usage_type": "call"}, {"api_name": "scipy.linalg.lstsq", "line_number": 74, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 77, "usage_type": "call"}, {"api_name": "numpy.square", "line_number": 77, "usage_type": "call"}]} +{"seq_id": "7895750756", "text": "__author__ = 'Christopher'\n\nimport requests\nimport yaml\nfrom smtplib import SMTP_SSL\nfrom email.mime.text import MIMEText\n\nconfig = yaml.load(open(\"config.yaml\", \"r\"))\n\ndef send_email(url):\n\temail = \"\"\"From: me\nTo: you\nSubject: Microcenter product is available!\n\n\"\"\"\n\n\temail += url\t\n\n\tsmtp = SMTP_SSL(\"smtp.gmail.com\", 465, timeout=20)\n\tsmtp.login(config[\"username\"], config[\"password\"])\n\n\tsmtp.sendmail(config[\"username\"], config[\"username\"], email)\n\tsmtp.quit()\n\n\ndef in_stock(url):\n r = requests.get(url)\n return \"'InStock', 'True'\" in r.text\n\n\nif in_stock(config[\"url\"]):\n print(\"item in stock :)\")\n print(\"sending email...\")\n send_email(config[\"url\"])\nelse:\n print(\"not in stock yet :(\")\n\nprint(\"done!\")\n", "repo_name": "despertargz/WebCheck", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 727, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "yaml.load", "line_number": 8, "usage_type": "call"}, {"api_name": "email.mime.text", "line_number": 11, "usage_type": "name"}, {"api_name": "email.mime.text", "line_number": 17, "usage_type": "name"}, {"api_name": "smtplib.SMTP_SSL", "line_number": 19, "usage_type": "call"}, {"api_name": "email.mime.text", "line_number": 22, "usage_type": "argument"}, {"api_name": "requests.get", "line_number": 27, "usage_type": "call"}]} +{"seq_id": "17543594467", "text": "import argparse\nfrom telethon import TelegramClient, events\nimport logging\n\nparser = argparse.ArgumentParser(description=\"Forward Group Messages Bot\")\nparser.add_argument(\"--max_messages\", type=int, default=100, help=\"Número máximo de mensagens para encaminhar\")\nargs = parser.parse_args()\n\napi_id = 1234\napi_hash = 'xxxxx'\n\nsource_group = -11111111111111\ndestination_group = -11111111111111\n\nlogging.basicConfig(level=logging.WARNING,\n format='%(asctime)s - Forward Group Messages Bot - %(levelname)s - %(message)s',\n datefmt='%d-%m-%Y %H:%M:%S')\n\nlogging.warning(\"Iniciando script.\")\n\nasync def forward_messages(client, source, destination, max_messages):\n @client.on(events.NewMessage(chats=[source]))\n async def message_group_handler(event):\n logging.warning(\"Mensagem recebida: {0}.\".format(event.message.text))\n\n with open(\"messages_saved.txt\", \"a+\") as file:\n file.write(event.message.text + '\\n')\n\n with open(\"messages_saved.txt\", \"r+\") as file:\n lines_in_file = file.readlines()\n if len(lines_in_file) >= max_messages:\n messages = ''.join(lines_in_file)\n logging.warning(\"Enviando {0} mensagens para o grupo.\".format(len(lines_in_file)))\n await client.send_message(destination, '=== INÍCIO MENSAGENS ===')\n await client.send_message(destination, messages, parse_mode='html')\n await client.send_message(destination, '=== FINAL MENSAGENS ===')\n file.truncate(0)\n\n await client.run_until_disconnected()\n\nwith TelegramClient('name', api_id, api_hash) as client:\n logging.warning(\"Script iniciado com sucesso, ouvindo group_id {0}.\".format(source_group))\n client.loop.run_until_complete(forward_messages(client, source_group, destination_group, args.max_messages))", "repo_name": "thevans/telegram_chat_pingbot", "sub_path": "forward_from_group_bot.py", "file_name": "forward_from_group_bot.py", "file_ext": "py", "file_size_in_byte": 1868, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 5, "usage_type": "call"}, {"api_name": "logging.basicConfig", "line_number": 15, "usage_type": "call"}, {"api_name": "logging.WARNING", "line_number": 15, "usage_type": "attribute"}, {"api_name": "logging.warning", "line_number": 19, "usage_type": "call"}, {"api_name": "logging.warning", "line_number": 24, "usage_type": "call"}, {"api_name": "logging.warning", "line_number": 33, "usage_type": "call"}, {"api_name": "telethon.events.NewMessage", "line_number": 22, "usage_type": "call"}, {"api_name": "telethon.events", "line_number": 22, "usage_type": "name"}, {"api_name": "telethon.TelegramClient", "line_number": 41, "usage_type": "call"}, {"api_name": "logging.warning", "line_number": 42, "usage_type": "call"}]} +{"seq_id": "19675809517", "text": "\"\"\"Creating User Table\n\nRevision ID: 177d816fd717\nRevises: 2e40b858f75a\nCreate Date: 2014-06-22 11:29:13.865581\n\n\"\"\"\n\n# revision identifiers, used by Alembic.\nrevision = '177d816fd717'\ndown_revision = '2e40b858f75a'\n\nfrom alembic import op\nimport sqlalchemy as sa\n\n\ndef upgrade():\n op.create_table(\n 'users',\n sa.Column('id', sa.Integer, primary_key=True),\n sa.Column('name', sa.String(2000), nullable=False),\n sa.Column('slug', sa.String(2000), nullable=False),\n sa.Column('created_at', sa.DateTime, nullable=True),\n )\n\n\ndef downgrade():\n op.drop_table('users')\n", "repo_name": "heynemann/gaas", "sub_path": "gaas/storage/sqlalchemy/migrations/versions/177d816fd717_creating_user_table.py", "file_name": "177d816fd717_creating_user_table.py", "file_ext": "py", "file_size_in_byte": 607, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "37", "api": [{"api_name": "alembic.op.create_table", "line_number": 18, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 18, "usage_type": "name"}, {"api_name": "sqlalchemy.Column", "line_number": 20, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 20, "usage_type": "attribute"}, {"api_name": "sqlalchemy.Column", "line_number": 21, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 21, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 22, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 22, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 23, "usage_type": "call"}, {"api_name": "sqlalchemy.DateTime", "line_number": 23, "usage_type": "attribute"}, {"api_name": "alembic.op.drop_table", "line_number": 28, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 28, "usage_type": "name"}]} +{"seq_id": "26287998986", "text": "import sys\n\nsys.path.append(\".\")\nimport webdataset as wds\nfrom data.base import filter_keys, log_and_continue, tarfile_to_samples_nothrow\n\nfrom data.policies import CenterCropSDTransform\nfrom utils.data_utils import filter_no_caption_or_no_image\nfrom torch.utils.data import default_collate\n\nfrom utils.logging import Path\nfrom torchvision.utils import make_grid\nimport torchvision.transforms as transforms\nimport math\nimport json\nimport tqdm\nimport os\n\nfrom utils.logging import tar_and_remove_dir\n\n\ndef main():\n url = \"/path/to/wds\"\n\n dataset, loader, num_batches = get_dataset_and_loader(\n url, train=True, num_examples_to_see=10000, num_workers=18\n )\n\n out_file = Path(\"../hr_metadata.jsonl\")\n outs = []\n\n for batch in loader:\n outs.extend(batch[\"metadata\"])\n \n out_file.write_text(\"\\n\".join([str(o, \"utf-8\") for o in outs]))\n\n\ndef filter_samples_by_fields(field_lists):\n # has_caption = ('txt' in sample)\n # has_image = ('png' in sample or 'jpg' in sample or 'jpeg' in sample or 'webp' in sample)\n\n def filter_fn(sample):\n select = True\n\n for fields in field_lists:\n sub_select = False\n for field in fields:\n sub_select = sub_select or (field in sample)\n\n select = select and sub_select\n\n return select\n\n return filter_fn\n\n\ndef get_dataset_and_loader(\n url, train, num_examples_to_see, per_worker_batch_size=32, num_workers=32\n):\n transform = CenterCropSDTransform(center_crop=True, size=512)\n\n pipeline = [wds.ResampledShards(url)]\n\n # TODO: Currently does not support validation sampling well\n # Don't split by worker and node since we're sampling with replacement\n # if train:\n # pipeline.append(wds.shuffle(2000))\n\n pipeline.extend(\n [\n tarfile_to_samples_nothrow,\n ]\n )\n\n if train:\n pipeline.append(wds.shuffle(2000))\n\n pipeline.extend(\n [\n wds.select(\n filter_samples_by_fields(\n [[\"txt\"], [\"png\", \"jpg\", \"jpeg\", \"webp\"], [\"json\"]]\n )\n ),\n wds.rename(metadata=\"json\"),\n wds.map(filter_keys(set([\"metadata\"]))),\n wds.batched(per_worker_batch_size, partial=not train, collation_fn=default_collate),\n ]\n )\n\n num_worker_batches = math.ceil(\n num_examples_to_see / (per_worker_batch_size * num_workers)\n )\n\n # Number of batches produced is _at least_ the requisite num_examples_to_see // effective_batch_size\n\n dataset = wds.DataPipeline(*pipeline).with_epoch(num_worker_batches)\n loader = wds.WebLoader(\n dataset,\n batch_size=None,\n shuffle=False, # Shuffling done in the webdataset\n num_workers=num_workers,\n persistent_workers=True,\n )\n\n return dataset, loader, num_worker_batches\n\n\nif __name__ == \"__main__\":\n main()\n", "repo_name": "mlfoundations/open-diffusion", "sub_path": "scripts/extract_wds_metadata.py", "file_name": "extract_wds_metadata.py", "file_ext": "py", "file_size_in_byte": 2903, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 99, "dataset": "github-code", "pt": "37", "api": [{"api_name": "sys.path.append", "line_number": 3, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 3, "usage_type": "attribute"}, {"api_name": "utils.logging.Path", "line_number": 29, "usage_type": "call"}, {"api_name": "data.policies.CenterCropSDTransform", "line_number": 60, "usage_type": "call"}, {"api_name": "webdataset.ResampledShards", "line_number": 62, "usage_type": "call"}, {"api_name": "data.base.tarfile_to_samples_nothrow", "line_number": 71, "usage_type": "name"}, {"api_name": "webdataset.shuffle", "line_number": 76, "usage_type": "call"}, {"api_name": "webdataset.select", "line_number": 80, "usage_type": "call"}, {"api_name": "webdataset.rename", "line_number": 85, "usage_type": "call"}, {"api_name": "webdataset.map", "line_number": 86, "usage_type": "call"}, {"api_name": "data.base.filter_keys", "line_number": 86, "usage_type": "call"}, {"api_name": "webdataset.batched", "line_number": 87, "usage_type": "call"}, {"api_name": "torch.utils.data.default_collate", "line_number": 87, "usage_type": "name"}, {"api_name": "math.ceil", "line_number": 91, "usage_type": "call"}, {"api_name": "webdataset.DataPipeline", "line_number": 97, "usage_type": "call"}, {"api_name": "webdataset.WebLoader", "line_number": 98, "usage_type": "call"}]} +{"seq_id": "14516456859", "text": "# -*- coding:utf-8 -*-\nimport time\nimport eventlet\n\nfrom sqlalchemy.orm import joinedload\nfrom sqlalchemy.sql import and_\n\nfrom simpleutil.common.exceptions import InvalidArgument\nfrom simpleutil.log import log as logging\nfrom simpleutil.utils import jsonutils\nfrom simpleutil.utils import uuidutils\nfrom simpleutil.config import cfg\n\nfrom simpleservice.ormdb.api import model_query\nfrom simpleservice.ormdb.api import model_count_with_key\n\nfrom goperation import threadpool\nfrom goperation.manager import common as manager_common\nfrom goperation.manager.api import get_client\nfrom goperation.manager.api import rpcfinishtime\nfrom goperation.manager.utils import resultutils\nfrom goperation.manager.utils import targetutils\nfrom goperation.manager.wsgi.port.controller import PortReuest\nfrom goperation.manager.wsgi.entity.controller import EntityReuest\nfrom goperation.manager.wsgi.exceptions import RpcResultError\n\nfrom gopdb.api.wsgi.controller import SchemaReuest\nfrom gopdb.api.wsgi.controller import DatabaseReuest\n\nfrom gopcdn.api.wsgi.resource import CdnQuoteRequest\nfrom gopcdn.api.wsgi.resource import CdnResourceReuest\n\nfrom gogamechen1 import common\nfrom gogamechen1.api import get_gamelock\nfrom gogamechen1.api import endpoint_session\nfrom gogamechen1.api import exceptions\n\nfrom gogamechen1.models import AppEntity\nfrom gogamechen1.models import GameArea\nfrom gogamechen1.models import MergeTask\nfrom gogamechen1.models import MergeEntity\n\nfrom .base import AppEntityReuestBase\n\nLOG = logging.getLogger(__name__)\n\nport_controller = PortReuest()\nentity_controller = EntityReuest()\nschema_controller = SchemaReuest()\ndatabase_controller = DatabaseReuest()\ncdnquote_controller = CdnQuoteRequest()\ncdnresource_controller = CdnResourceReuest()\n\nCONF = cfg.CONF\n\n\nclass AppEntityMergeReuest(AppEntityReuestBase):\n \"\"\"合服相关代码\"\"\"\n\n MERGEAPPENTITYS = {'type': 'object',\n 'required': [common.APPFILE, 'entitys', 'group_id'],\n 'properties': {\n 'entitys': {'type': 'array',\n 'items': {'type': 'integer', 'minimum': 2},\n 'description': '需要合并的实体列表'},\n common.APPFILE: {'type': 'string', 'format': 'md5',\n 'description': '程序文件md5'},\n 'agent_id': {'type': 'integer', 'minimum': 0,\n 'description': '合并后程序运行服务器,不填自动分配'},\n 'zone': {'type': 'string', 'description': '自动分配的安装区域,默认zone为all'},\n 'opentime': {'type': 'integer', 'minimum': 1514736000,\n 'description': '合并后的开服时间'},\n 'cross_id': {'type': 'integer', 'minimum': 1,\n 'description': '合并后对应跨服程序的实体id'},\n 'group_id': {'type': 'integer', 'minimum': 1,\n 'description': '区服所在的组的ID'},\n 'databases': {'type': 'object', 'description': '程序使用的数据库,不填自动分配'}}\n }\n\n def merge(self, req, body=None):\n \"\"\"合服接口,用于合服, 部分代码和create代码一直,未整合\"\"\"\n body = body or {}\n jsonutils.schema_validate(body, self.MERGEAPPENTITYS)\n\n group_id = body.pop('group_id')\n # 需要合并的实体\n entitys = list(set(body.pop('entitys')))\n entitys.sort()\n\n session = endpoint_session()\n\n # 安装文件信息\n appfile = body.pop(common.APPFILE)\n # 选择合并后实例运行服务器\n agent_id = body.get('agent_id') or self._agentselect(req, common.GAMESERVER, **body)\n # 选择合并后实体数据库\n databases = self._dbselect(req, common.GAMESERVER, **body)\n opentime = body.get('opentime')\n # 合服任务ID\n uuid = uuidutils.generate_uuid()\n\n # chiefs信息初始化\n query = model_query(session,\n AppEntity,\n filter=and_(AppEntity.group_id == group_id,\n AppEntity.objtype.in_([common.GMSERVER, common.CROSSSERVER])))\n # 找到同组的gm和战场服\n gm = None\n cross = None\n crosss = []\n # 默认平台识标\n platform = None\n # 锁组\n glock = get_gamelock()\n with glock.grouplock(group_id):\n if model_count_with_key(session, MergeEntity, filter=MergeEntity.entity.in_(entitys)):\n raise InvalidArgument('Target entity merged or in mergeing')\n for appentity in query:\n if appentity.status != common.OK:\n continue\n if appentity.objtype == common.GMSERVER:\n gm = appentity\n else:\n crosss.append(appentity)\n if not gm:\n raise InvalidArgument('Group not exist or gm not active/exist?')\n if not crosss:\n raise InvalidArgument('Group has no cross server?')\n if not body.get('cross_id'):\n cross = crosss[0]\n else:\n for appentity in crosss:\n if appentity.entity == body.get('cross_id'):\n cross = appentity\n break\n if not cross:\n raise InvalidArgument('cross server can not be found?')\n # 获取实体相关服务器信息(端口/ip)\n maps = entity_controller.shows(endpoint=common.NAME, entitys=[gm.entity, cross.entity])\n chiefs = dict()\n # 战场与GM服务器信息\n for chief in (cross, gm):\n chiefmetadata = maps.get(chief.entity).get('metadata')\n ports = maps.get(chief.entity).get('ports')\n if not chiefmetadata:\n raise InvalidArgument('%s.%d is offline' % (chief.objtype, chief.entity))\n need = common.POSTS_COUNT[chief.objtype]\n if need and len(ports) != need:\n raise InvalidArgument('%s.%d port count error, '\n 'find %d, need %d' % (chief.objtype, chief.entity,\n len(ports), need))\n chiefs.setdefault(chief.objtype,\n dict(entity=chief.entity,\n ports=ports,\n local_ip=chiefmetadata.get('local_ip')))\n\n # 需要合服的实体\n appentitys = []\n query = model_query(session, AppEntity,\n filter=and_(AppEntity.group_id == group_id, AppEntity.entity.in_(entitys)))\n query = query.options(joinedload(AppEntity.areas, innerjoin=False))\n with session.begin():\n for appentity in query:\n if appentity.objtype != common.GAMESERVER:\n raise InvalidArgument('Target entity %d is not %s' % (appentity.entity, common.GAMESERVER))\n if appentity.status != common.UNACTIVE:\n raise InvalidArgument('Target entity %d is not unactive' % appentity.entity)\n if not appentity.areas:\n raise InvalidArgument('Target entity %d has no area?' % appentity.entity)\n if appentity.versions:\n raise InvalidArgument('Traget entity %d version is not None' % appentity.entity)\n if platform is None:\n platform = appentity.platform\n else:\n # 区服平台不相同, 位操作合并platform\n platform = platform | appentity.platform\n appentitys.append(appentity)\n if not opentime:\n opentime = appentity.opentime\n if len(appentitys) != len(entitys):\n raise InvalidArgument('Can not match entitys count')\n # 完整的rpc数据包,准备发送合服命令到agent\n body = dict(appfile=appfile,\n databases=databases,\n opentime=opentime,\n chiefs=chiefs,\n uuid=uuid,\n entitys=entitys)\n body.setdefault('finishtime', rpcfinishtime()[0] + 5)\n try:\n create_result = entity_controller.create(req=req, agent_id=agent_id,\n endpoint=common.NAME, body=body,\n action='merge')['data'][0]\n except RpcResultError as e:\n LOG.error('Create entity rpc call fail: %s' % e.message)\n raise InvalidArgument(e.message)\n mergetd_entity = create_result.get('entity')\n rpc_result = create_result.get('notify')\n LOG.info('Merge to entity %d, agent %d' % (mergetd_entity, agent_id))\n LOG.debug('Entity controller merge rpc result %s' % str(rpc_result))\n # 插入实体信息\n appentity = AppEntity(entity=mergetd_entity,\n agent_id=agent_id,\n group_id=group_id, objtype=common.GAMESERVER,\n cross_id=cross.entity,\n opentime=opentime,\n platform=platform)\n session.add(appentity)\n session.flush()\n # 插入数据库绑定信息\n if rpc_result.get('databases'):\n self._bondto(session, mergetd_entity, rpc_result.get('databases'))\n else:\n LOG.error('New entity database miss')\n # 插入合服记录\n mtask = MergeTask(uuid=uuid, entity=mergetd_entity, mergetime=int(time.time()))\n session.add(mtask)\n session.flush()\n for _appentity in appentitys:\n session.add(MergeEntity(entity=_appentity.entity, uuid=uuid))\n session.flush()\n # 批量修改被合并服的状态\n query.update({'status': common.MERGEING},\n synchronize_session=False)\n session.flush()\n port_controller.unsafe_create(agent_id, common.NAME,\n mergetd_entity, rpc_result.get('ports'))\n # agent 后续通知\n threadpool.add_thread(entity_controller.post_create_entity,\n appentity.entity, common.NAME, objtype=common.GAMESERVER,\n status=common.UNACTIVE,\n opentime=opentime,\n group_id=group_id, areas=[])\n # 添加端口\n # threadpool.add_thread(port_controller.unsafe_create,\n # agent_id, common.NAME, mergetd_entity, rpc_result.get('ports'))\n return resultutils.results(result='entitys is mergeing',\n data=[dict(uuid=uuid, entitys=entitys, entity=mergetd_entity)])\n\n def continues(self, req, uuid, body=None):\n \"\"\"中途失败的合服任务再次运行\"\"\"\n session = endpoint_session()\n query = model_query(session, MergeTask, filter=MergeTask.uuid == uuid)\n query = query.options(joinedload(MergeTask.entitys, innerjoin=False))\n etask = query.one()\n if etask.status == common.MERGEFINISH:\n raise InvalidArgument('Merge task has all ready finished')\n _query = model_query(session, AppEntity, filter=AppEntity.entity == etask.entity)\n _query = _query.options(joinedload(AppEntity.databases, innerjoin=False))\n appentity = _query.one_or_none()\n if not appentity or not appentity.databases or appentity.objtype != common.GAMESERVER:\n LOG.error('Etask entity can not be found or type/database error')\n raise exceptions.MergeException('Etask entity can not be found or type/database error')\n databases = self._database_to_dict(appentity)\n rpc = get_client()\n metadata, ports = self._entityinfo(req=req, entity=appentity.entity)\n target = targetutils.target_agent_by_string(metadata.get('agent_type'), metadata.get('host'))\n target.namespace = common.NAME\n rpc_ret = rpc.call(target, ctxt={'agents': [appentity.agent_id, ]},\n msg={'method': 'continue_merge',\n 'args': dict(entity=etask.entity, uuid=uuid, databases=databases)})\n if not rpc_ret:\n raise RpcResultError('continue entity result is None')\n if rpc_ret.get('resultcode') != manager_common.RESULT_SUCCESS:\n raise RpcResultError('continue entity fail %s' % rpc_ret.get('result'))\n return resultutils.results(result='continue merge task command has been send',\n data=[dict(uuid=etask.uuid, entity=etask.entity)])\n\n def swallow(self, req, entity, body=None):\n\n \"\"\"合服内部接口,一般由agent调用\n 用于新实体吞噬旧实体的区服和数据库\"\"\"\n body = body or {}\n entity = int(entity)\n uuid = body.get('uuid')\n if not uuid:\n raise InvalidArgument('Merger uuid is None')\n session = endpoint_session()\n query = model_query(session, MergeTask, filter=MergeTask.uuid == uuid)\n query = query.options(joinedload(MergeTask.entitys, innerjoin=False))\n glock = get_gamelock()\n rpc = get_client()\n with session.begin():\n etask = query.one_or_none()\n if not etask:\n raise InvalidArgument('Not task exit with %s' % uuid)\n # 新实体不匹配\n if etask.entity != body.get('entity'):\n raise InvalidArgument('New entity not %d' % etask.entity)\n # 找到目标实体\n appentity = None\n for _entity in etask.entitys:\n if _entity.entity == entity:\n if _entity.status != common.MERGEING:\n if _entity.status != common.SWALLOWING:\n raise InvalidArgument('Swallow entity find status error')\n if not _entity.databases or not _entity.areas:\n raise InvalidArgument('Entity is swallowing but database or ares is None')\n LOG.warning('Entit is swallowing, return saved data')\n return resultutils.results(result='swallow entity is success',\n data=[dict(databases=jsonutils.loads_as_bytes(_entity.databases),\n areas=jsonutils.loads_as_bytes(_entity.areas))])\n _query = model_query(session, AppEntity, filter=AppEntity.entity == entity)\n _query = _query.options(joinedload(AppEntity.databases, innerjoin=False))\n appentity = _query.one_or_none()\n break\n if not appentity:\n raise InvalidArgument('Can not find app entity?')\n if appentity.objtype != common.GAMESERVER:\n raise InvalidArgument('objtype error, entity not %s' % common.GAMESERVER)\n if appentity.status != common.MERGEING:\n raise InvalidArgument('find status error, when swallowing')\n databases = self._database_to_dict(appentity)\n areas = [area.to_dict()\n for area in appentity.areas]\n if not databases or not areas:\n LOG.error('Entity no areas or databases record')\n return resultutils.results(result='swallow entity fail, '\n 'target entity can not found database or areas',\n resultcode=manager_common.RESULT_ERROR)\n with glock.grouplock(group=appentity.group_id):\n # 发送吞噬命令到目标区服agent\n metadata, ports = self._entityinfo(req=req, entity=entity)\n target = targetutils.target_agent_by_string(metadata.get('agent_type'), metadata.get('host'))\n target.namespace = common.NAME\n rpc_ret = rpc.call(target, ctxt={'agents': [appentity.agent_id, ]},\n msg={'method': 'swallow_entity',\n 'args': dict(entity=entity)})\n if not rpc_ret:\n raise RpcResultError('swallow entity result is None')\n if rpc_ret.get('resultcode') != manager_common.RESULT_SUCCESS:\n raise RpcResultError('swallow entity fail %s' % rpc_ret.get('result'))\n # 修改实体在合服任务中的状态,存储areas以及databases\n appentity.status = common.SWALLOWING\n _entity.status = common.SWALLOWING\n _entity.areas = jsonutils.dumps(areas)\n _entity.databases = jsonutils.dumps(databases)\n session.flush()\n return resultutils.results(result='swallow entity is success',\n data=[dict(databases=databases, areas=areas)])\n\n def swallowed(self, req, entity, body=None):\n \"\"\"\n 合服内部接口,一般由agent调用\n 用于新实体吞噬旧实体的区服完成后调用\n 调用后将设置appentity为deleted状态\n \"\"\"\n body = body or {}\n entity = int(entity)\n uuid = body.get('uuid')\n if not uuid:\n raise InvalidArgument('Merger uuid is None')\n session = endpoint_session()\n query = model_query(session, MergeTask, filter=MergeTask.uuid == uuid)\n query = query.options(joinedload(MergeTask.entitys, innerjoin=False))\n glock = get_gamelock()\n rpc = get_client()\n appentity = None\n with session.begin():\n etask = query.one_or_none()\n if not etask:\n raise InvalidArgument('Not task exit with %s' % uuid)\n # 新实体不匹配\n if etask.entity != body.get('entity'):\n raise InvalidArgument('New entity not %d' % etask.entity)\n for _entity in etask.entitys:\n if _entity.entity == entity:\n if _entity.status != common.SWALLOWING:\n raise InvalidArgument('Swallowed entity find status error')\n _query = model_query(session, AppEntity, filter=AppEntity.entity == entity)\n _query = _query.options(joinedload(AppEntity.databases, innerjoin=False))\n appentity = _query.one_or_none()\n break\n if not appentity:\n raise InvalidArgument('Can not find app entity?')\n if appentity.objtype != common.GAMESERVER:\n raise InvalidArgument('objtype error, entity not %s' % common.GAMESERVER)\n if appentity.status != common.SWALLOWING:\n raise InvalidArgument('find status error, when swallowed')\n\n with glock.grouplock(group=appentity.group_id):\n # 发送吞噬完成命令到目标区服agent\n metadata, ports = self._entityinfo(req=req, entity=entity)\n target = targetutils.target_agent_by_string(metadata.get('agent_type'), metadata.get('host'))\n target.namespace = common.NAME\n rpc_ret = rpc.call(target, ctxt={'agents': [appentity.agent_id, ]},\n msg={'method': 'swallowed_entity',\n 'args': dict(entity=entity)})\n if not rpc_ret:\n raise RpcResultError('swallowed entity result is None')\n if rpc_ret.get('resultcode') != manager_common.RESULT_SUCCESS:\n raise RpcResultError('swallowed entity fail %s' % rpc_ret.get('result'))\n # appentity状态修改为deleted\n appentity.status = common.DELETED\n # 修改实体在合服任务中的状态\n _entity.status = common.MERGEED\n session.flush()\n # area绑定新实体\n _query = model_query(session, GameArea, filter=GameArea.entity == entity)\n _query.update({'entity': etask.entity})\n session.flush()\n\n def _unquote():\n LOG.info('Swallowed %d finish, try unquote database' % appentity.entity)\n for database in appentity.databases:\n try:\n schema_controller.unquote(req, quote_id=database.quote_id)\n except Exception:\n LOG.error('Delete database quote fail')\n\n eventlet.spawn_n(_unquote)\n\n return resultutils.results(result='swallowed entity is success',\n data=[dict(databases=jsonutils.loads_as_bytes(_entity.databases),\n areas=jsonutils.loads_as_bytes(_entity.areas))])\n\n def finish(self, req, uuid, body=None):\n \"\"\"合服完毕接口\"\"\"\n session = endpoint_session()\n query = model_query(session, MergeTask, filter=MergeTask.uuid == uuid)\n query = query.options(joinedload(MergeTask.entitys, innerjoin=False))\n etask = query.one()\n if etask.status == common.MERGEFINISH:\n return resultutils.results(result='swallowed finished',\n data=[dict(uuid=etask.uuid,\n entity=etask.entity)])\n for _entity in etask.entitys:\n if _entity.status != common.MERGEED:\n raise InvalidArgument('Entity %d status is not mergeed' % _entity.entity)\n etask.status = common.MERGEFINISH\n session.flush()\n return resultutils.results(result='swallowed finished',\n data=[dict(uuid=etask.uuid,\n entity=etask.entity)])\n", "repo_name": "lolizeppelin/gogamechen1", "sub_path": "gogamechen1/api/wsgi/game/entity/merge.py", "file_name": "merge.py", "file_ext": "py", "file_size_in_byte": 22668, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "37", "api": [{"api_name": "simpleutil.log.log.getLogger", "line_number": 45, "usage_type": "call"}, {"api_name": "simpleutil.log.log", "line_number": 45, "usage_type": "name"}, {"api_name": "goperation.manager.wsgi.port.controller.PortReuest", "line_number": 47, "usage_type": "call"}, {"api_name": "goperation.manager.wsgi.entity.controller.EntityReuest", "line_number": 48, "usage_type": "call"}, {"api_name": "gopdb.api.wsgi.controller.SchemaReuest", "line_number": 49, "usage_type": "call"}, {"api_name": "gopdb.api.wsgi.controller.DatabaseReuest", "line_number": 50, "usage_type": "call"}, {"api_name": "gopcdn.api.wsgi.resource.CdnQuoteRequest", "line_number": 51, "usage_type": "call"}, {"api_name": "gopcdn.api.wsgi.resource.CdnResourceReuest", "line_number": 52, "usage_type": "call"}, {"api_name": "simpleutil.config.cfg.CONF", "line_number": 54, "usage_type": "attribute"}, {"api_name": "simpleutil.config.cfg", "line_number": 54, "usage_type": "name"}, {"api_name": "base.AppEntityReuestBase", "line_number": 57, "usage_type": "name"}, {"api_name": "gogamechen1.common.APPFILE", "line_number": 61, "usage_type": "attribute"}, {"api_name": "gogamechen1.common", "line_number": 61, "usage_type": "name"}, {"api_name": "gogamechen1.common.APPFILE", "line_number": 66, "usage_type": "attribute"}, {"api_name": "gogamechen1.common", "line_number": 66, "usage_type": "name"}, {"api_name": "simpleutil.utils.jsonutils.schema_validate", "line_number": 83, "usage_type": "call"}, {"api_name": "simpleutil.utils.jsonutils", "line_number": 83, "usage_type": "name"}, {"api_name": "gogamechen1.api.endpoint_session", "line_number": 90, "usage_type": "call"}, {"api_name": "gogamechen1.common.APPFILE", "line_number": 93, "usage_type": "attribute"}, {"api_name": "gogamechen1.common", "line_number": 93, "usage_type": "name"}, {"api_name": "gogamechen1.common.GAMESERVER", "line_number": 95, "usage_type": "attribute"}, {"api_name": "gogamechen1.common", "line_number": 95, "usage_type": "name"}, {"api_name": "gogamechen1.common.GAMESERVER", "line_number": 97, "usage_type": "attribute"}, {"api_name": "gogamechen1.common", "line_number": 97, "usage_type": "name"}, {"api_name": "simpleutil.utils.uuidutils.generate_uuid", "line_number": 100, "usage_type": "call"}, {"api_name": "simpleutil.utils.uuidutils", "line_number": 100, "usage_type": "name"}, {"api_name": "simpleservice.ormdb.api.model_query", "line_number": 103, "usage_type": "call"}, {"api_name": "gogamechen1.models.AppEntity", "line_number": 104, "usage_type": "argument"}, {"api_name": "sqlalchemy.sql.and_", "line_number": 105, "usage_type": "call"}, {"api_name": "gogamechen1.models.AppEntity.group_id", "line_number": 105, "usage_type": "attribute"}, {"api_name": "gogamechen1.models.AppEntity", "line_number": 105, "usage_type": "name"}, {"api_name": "gogamechen1.models.AppEntity.objtype.in_", "line_number": 106, "usage_type": "call"}, {"api_name": "gogamechen1.models.AppEntity.objtype", "line_number": 106, "usage_type": "attribute"}, {"api_name": "gogamechen1.models.AppEntity", "line_number": 106, "usage_type": "name"}, {"api_name": "gogamechen1.common.GMSERVER", "line_number": 106, "usage_type": "attribute"}, {"api_name": "gogamechen1.common", "line_number": 106, "usage_type": "name"}, {"api_name": "gogamechen1.common.CROSSSERVER", "line_number": 106, "usage_type": "attribute"}, {"api_name": "gogamechen1.api.get_gamelock", "line_number": 114, "usage_type": "call"}, {"api_name": "simpleservice.ormdb.api.model_count_with_key", "line_number": 116, "usage_type": "call"}, {"api_name": "gogamechen1.models.MergeEntity", "line_number": 116, "usage_type": "argument"}, {"api_name": "gogamechen1.models.MergeEntity.entity.in_", "line_number": 116, "usage_type": "call"}, {"api_name": "gogamechen1.models.MergeEntity.entity", "line_number": 116, "usage_type": "attribute"}, {"api_name": "simpleutil.common.exceptions.InvalidArgument", "line_number": 117, "usage_type": "call"}, {"api_name": "gogamechen1.common.OK", "line_number": 119, "usage_type": "attribute"}, {"api_name": "gogamechen1.common", "line_number": 119, "usage_type": "name"}, {"api_name": "gogamechen1.common.GMSERVER", "line_number": 121, "usage_type": "attribute"}, {"api_name": "gogamechen1.common", "line_number": 121, "usage_type": "name"}, {"api_name": "simpleutil.common.exceptions.InvalidArgument", "line_number": 126, "usage_type": "call"}, {"api_name": "simpleutil.common.exceptions.InvalidArgument", "line_number": 128, "usage_type": "call"}, {"api_name": "simpleutil.common.exceptions.InvalidArgument", "line_number": 137, "usage_type": "call"}, {"api_name": "gogamechen1.common.NAME", "line_number": 139, "usage_type": "attribute"}, {"api_name": "gogamechen1.common", "line_number": 139, "usage_type": "name"}, {"api_name": "simpleutil.common.exceptions.InvalidArgument", "line_number": 146, "usage_type": "call"}, {"api_name": "gogamechen1.common.POSTS_COUNT", "line_number": 147, "usage_type": "attribute"}, {"api_name": "gogamechen1.common", "line_number": 147, "usage_type": "name"}, {"api_name": "simpleutil.common.exceptions.InvalidArgument", "line_number": 149, "usage_type": "call"}, {"api_name": "simpleservice.ormdb.api.model_query", "line_number": 159, "usage_type": "call"}, {"api_name": "gogamechen1.models.AppEntity", "line_number": 159, "usage_type": "argument"}, {"api_name": "sqlalchemy.sql.and_", "line_number": 160, "usage_type": "call"}, {"api_name": "gogamechen1.models.AppEntity.group_id", "line_number": 160, "usage_type": "attribute"}, {"api_name": "gogamechen1.models.AppEntity", "line_number": 160, "usage_type": "name"}, {"api_name": "gogamechen1.models.AppEntity.entity.in_", "line_number": 160, "usage_type": "call"}, {"api_name": "gogamechen1.models.AppEntity.entity", "line_number": 160, "usage_type": "attribute"}, {"api_name": "sqlalchemy.orm.joinedload", "line_number": 161, "usage_type": "call"}, {"api_name": "gogamechen1.models.AppEntity.areas", "line_number": 161, "usage_type": "attribute"}, {"api_name": "gogamechen1.models.AppEntity", "line_number": 161, "usage_type": "name"}, {"api_name": "gogamechen1.common.GAMESERVER", "line_number": 164, "usage_type": "attribute"}, {"api_name": "gogamechen1.common", "line_number": 164, "usage_type": "name"}, {"api_name": "simpleutil.common.exceptions.InvalidArgument", "line_number": 165, "usage_type": "call"}, {"api_name": "gogamechen1.common.GAMESERVER", "line_number": 165, "usage_type": "attribute"}, {"api_name": "gogamechen1.common", "line_number": 165, "usage_type": "name"}, {"api_name": "gogamechen1.common.UNACTIVE", "line_number": 166, "usage_type": "attribute"}, {"api_name": "gogamechen1.common", "line_number": 166, "usage_type": "name"}, {"api_name": "simpleutil.common.exceptions.InvalidArgument", "line_number": 167, "usage_type": "call"}, {"api_name": "simpleutil.common.exceptions.InvalidArgument", "line_number": 169, "usage_type": "call"}, {"api_name": "simpleutil.common.exceptions.InvalidArgument", "line_number": 171, "usage_type": "call"}, {"api_name": "simpleutil.common.exceptions.InvalidArgument", "line_number": 181, "usage_type": "call"}, {"api_name": "goperation.manager.api.rpcfinishtime", "line_number": 189, "usage_type": "call"}, {"api_name": "gogamechen1.common.NAME", "line_number": 192, "usage_type": "attribute"}, {"api_name": "gogamechen1.common", "line_number": 192, "usage_type": "name"}, {"api_name": "goperation.manager.wsgi.exceptions.RpcResultError", "line_number": 194, "usage_type": "name"}, {"api_name": "simpleutil.common.exceptions.InvalidArgument", "line_number": 196, "usage_type": "call"}, {"api_name": "gogamechen1.models.AppEntity", "line_number": 202, "usage_type": "call"}, {"api_name": "gogamechen1.common.GAMESERVER", "line_number": 204, "usage_type": "attribute"}, {"api_name": "gogamechen1.common", "line_number": 204, "usage_type": "name"}, {"api_name": "gogamechen1.models.MergeTask", "line_number": 216, "usage_type": "call"}, {"api_name": "time.time", "line_number": 216, "usage_type": "call"}, {"api_name": "gogamechen1.models.MergeEntity", "line_number": 220, "usage_type": "call"}, {"api_name": "gogamechen1.common.MERGEING", "line_number": 223, "usage_type": "attribute"}, {"api_name": "gogamechen1.common", "line_number": 223, "usage_type": "name"}, {"api_name": "gogamechen1.common.NAME", "line_number": 226, "usage_type": "attribute"}, {"api_name": "gogamechen1.common", "line_number": 226, "usage_type": "name"}, {"api_name": "goperation.threadpool.add_thread", "line_number": 229, "usage_type": "call"}, {"api_name": "goperation.threadpool", "line_number": 229, "usage_type": "name"}, {"api_name": "gogamechen1.common.NAME", "line_number": 230, "usage_type": "attribute"}, {"api_name": "gogamechen1.common", "line_number": 230, "usage_type": "name"}, {"api_name": "gogamechen1.common.GAMESERVER", "line_number": 230, "usage_type": "attribute"}, {"api_name": "gogamechen1.common.UNACTIVE", "line_number": 231, "usage_type": "attribute"}, {"api_name": "gogamechen1.common", "line_number": 231, "usage_type": "name"}, {"api_name": "goperation.manager.utils.resultutils.results", "line_number": 237, "usage_type": "call"}, {"api_name": "goperation.manager.utils.resultutils", "line_number": 237, "usage_type": "name"}, {"api_name": "gogamechen1.api.endpoint_session", "line_number": 242, "usage_type": "call"}, {"api_name": "simpleservice.ormdb.api.model_query", "line_number": 243, "usage_type": "call"}, {"api_name": "gogamechen1.models.MergeTask", "line_number": 243, "usage_type": "argument"}, {"api_name": "gogamechen1.models.MergeTask.uuid", "line_number": 243, "usage_type": "attribute"}, {"api_name": "sqlalchemy.orm.joinedload", "line_number": 244, "usage_type": "call"}, {"api_name": "gogamechen1.models.MergeTask.entitys", "line_number": 244, "usage_type": "attribute"}, {"api_name": "gogamechen1.models.MergeTask", "line_number": 244, "usage_type": "name"}, {"api_name": "gogamechen1.common.MERGEFINISH", "line_number": 246, "usage_type": "attribute"}, {"api_name": "gogamechen1.common", "line_number": 246, "usage_type": "name"}, {"api_name": "simpleutil.common.exceptions.InvalidArgument", "line_number": 247, "usage_type": "call"}, {"api_name": "simpleservice.ormdb.api.model_query", "line_number": 248, "usage_type": "call"}, {"api_name": "gogamechen1.models.AppEntity", "line_number": 248, "usage_type": "argument"}, {"api_name": "gogamechen1.models.AppEntity.entity", "line_number": 248, "usage_type": "attribute"}, {"api_name": "sqlalchemy.orm.joinedload", "line_number": 249, "usage_type": "call"}, {"api_name": "gogamechen1.models.AppEntity.databases", "line_number": 249, "usage_type": "attribute"}, {"api_name": "gogamechen1.models.AppEntity", "line_number": 249, "usage_type": "name"}, {"api_name": "gogamechen1.common.GAMESERVER", "line_number": 251, "usage_type": "attribute"}, {"api_name": "gogamechen1.common", "line_number": 251, "usage_type": "name"}, {"api_name": "gogamechen1.api.exceptions.MergeException", "line_number": 253, "usage_type": "call"}, {"api_name": "gogamechen1.api.exceptions", "line_number": 253, "usage_type": "name"}, {"api_name": "goperation.manager.api.get_client", "line_number": 255, "usage_type": "call"}, {"api_name": "goperation.manager.utils.targetutils.target_agent_by_string", "line_number": 257, "usage_type": "call"}, {"api_name": "goperation.manager.utils.targetutils", "line_number": 257, "usage_type": "name"}, {"api_name": "gogamechen1.common.NAME", "line_number": 258, "usage_type": "attribute"}, {"api_name": "gogamechen1.common", "line_number": 258, "usage_type": "name"}, {"api_name": "goperation.manager.wsgi.exceptions.RpcResultError", "line_number": 263, "usage_type": "call"}, {"api_name": "goperation.manager.common.RESULT_SUCCESS", "line_number": 264, "usage_type": "attribute"}, {"api_name": "goperation.manager.common", "line_number": 264, "usage_type": "name"}, {"api_name": "goperation.manager.wsgi.exceptions.RpcResultError", "line_number": 265, "usage_type": "call"}, {"api_name": "goperation.manager.utils.resultutils.results", "line_number": 266, "usage_type": "call"}, {"api_name": "goperation.manager.utils.resultutils", "line_number": 266, "usage_type": "name"}, {"api_name": "simpleutil.common.exceptions.InvalidArgument", "line_number": 277, "usage_type": "call"}, {"api_name": "gogamechen1.api.endpoint_session", "line_number": 278, "usage_type": "call"}, {"api_name": "simpleservice.ormdb.api.model_query", "line_number": 279, "usage_type": "call"}, {"api_name": "gogamechen1.models.MergeTask", "line_number": 279, "usage_type": "argument"}, {"api_name": "gogamechen1.models.MergeTask.uuid", "line_number": 279, "usage_type": "attribute"}, {"api_name": "sqlalchemy.orm.joinedload", "line_number": 280, "usage_type": "call"}, {"api_name": "gogamechen1.models.MergeTask.entitys", "line_number": 280, "usage_type": "attribute"}, {"api_name": "gogamechen1.models.MergeTask", "line_number": 280, "usage_type": "name"}, {"api_name": "gogamechen1.api.get_gamelock", "line_number": 281, "usage_type": "call"}, {"api_name": "goperation.manager.api.get_client", "line_number": 282, "usage_type": "call"}, {"api_name": "simpleutil.common.exceptions.InvalidArgument", "line_number": 286, "usage_type": "call"}, {"api_name": "simpleutil.common.exceptions.InvalidArgument", "line_number": 289, "usage_type": "call"}, {"api_name": "gogamechen1.common.MERGEING", "line_number": 294, "usage_type": "attribute"}, {"api_name": "gogamechen1.common", "line_number": 294, "usage_type": "name"}, {"api_name": "gogamechen1.common.SWALLOWING", "line_number": 295, "usage_type": "attribute"}, {"api_name": "gogamechen1.common", "line_number": 295, "usage_type": "name"}, {"api_name": "simpleutil.common.exceptions.InvalidArgument", "line_number": 296, "usage_type": "call"}, {"api_name": "simpleutil.common.exceptions.InvalidArgument", "line_number": 298, "usage_type": "call"}, {"api_name": "goperation.manager.utils.resultutils.results", "line_number": 300, "usage_type": "call"}, {"api_name": "goperation.manager.utils.resultutils", "line_number": 300, "usage_type": "name"}, {"api_name": "simpleutil.utils.jsonutils.loads_as_bytes", "line_number": 301, "usage_type": "call"}, {"api_name": "simpleutil.utils.jsonutils", "line_number": 301, "usage_type": "name"}, {"api_name": "simpleutil.utils.jsonutils.loads_as_bytes", "line_number": 302, "usage_type": "call"}, {"api_name": "simpleutil.utils.jsonutils", "line_number": 302, "usage_type": "name"}, {"api_name": "simpleservice.ormdb.api.model_query", "line_number": 303, "usage_type": "call"}, {"api_name": "gogamechen1.models.AppEntity", "line_number": 303, "usage_type": "argument"}, {"api_name": "gogamechen1.models.AppEntity.entity", "line_number": 303, "usage_type": "attribute"}, {"api_name": "sqlalchemy.orm.joinedload", "line_number": 304, "usage_type": "call"}, {"api_name": "gogamechen1.models.AppEntity.databases", "line_number": 304, "usage_type": "attribute"}, {"api_name": "gogamechen1.models.AppEntity", "line_number": 304, "usage_type": "name"}, {"api_name": "simpleutil.common.exceptions.InvalidArgument", "line_number": 308, "usage_type": "call"}, {"api_name": "gogamechen1.common.GAMESERVER", "line_number": 309, "usage_type": "attribute"}, {"api_name": "gogamechen1.common", "line_number": 309, "usage_type": "name"}, {"api_name": "simpleutil.common.exceptions.InvalidArgument", "line_number": 310, "usage_type": "call"}, {"api_name": "gogamechen1.common.GAMESERVER", "line_number": 310, "usage_type": "attribute"}, {"api_name": "gogamechen1.common", "line_number": 310, "usage_type": "name"}, {"api_name": "gogamechen1.common.MERGEING", "line_number": 311, "usage_type": "attribute"}, {"api_name": "gogamechen1.common", "line_number": 311, "usage_type": "name"}, {"api_name": "simpleutil.common.exceptions.InvalidArgument", "line_number": 312, "usage_type": "call"}, {"api_name": "goperation.manager.utils.resultutils.results", "line_number": 318, "usage_type": "call"}, {"api_name": "goperation.manager.utils.resultutils", "line_number": 318, "usage_type": "name"}, {"api_name": "goperation.manager.common.RESULT_ERROR", "line_number": 320, "usage_type": "attribute"}, {"api_name": "goperation.manager.common", "line_number": 320, "usage_type": "name"}, {"api_name": "goperation.manager.utils.targetutils.target_agent_by_string", "line_number": 324, "usage_type": "call"}, {"api_name": "goperation.manager.utils.targetutils", "line_number": 324, "usage_type": "name"}, {"api_name": "gogamechen1.common.NAME", "line_number": 325, "usage_type": "attribute"}, {"api_name": "gogamechen1.common", "line_number": 325, "usage_type": "name"}, {"api_name": "goperation.manager.wsgi.exceptions.RpcResultError", "line_number": 330, "usage_type": "call"}, {"api_name": "goperation.manager.common.RESULT_SUCCESS", "line_number": 331, "usage_type": "attribute"}, {"api_name": "goperation.manager.common", "line_number": 331, "usage_type": "name"}, {"api_name": "goperation.manager.wsgi.exceptions.RpcResultError", "line_number": 332, "usage_type": "call"}, {"api_name": "gogamechen1.common.SWALLOWING", "line_number": 334, "usage_type": "attribute"}, {"api_name": "gogamechen1.common", "line_number": 334, "usage_type": "name"}, {"api_name": "gogamechen1.common.SWALLOWING", "line_number": 335, "usage_type": "attribute"}, {"api_name": "gogamechen1.common", "line_number": 335, "usage_type": "name"}, {"api_name": "simpleutil.utils.jsonutils.dumps", "line_number": 336, "usage_type": "call"}, {"api_name": "simpleutil.utils.jsonutils", "line_number": 336, "usage_type": "name"}, {"api_name": "simpleutil.utils.jsonutils.dumps", "line_number": 337, "usage_type": "call"}, {"api_name": "simpleutil.utils.jsonutils", "line_number": 337, "usage_type": "name"}, {"api_name": "goperation.manager.utils.resultutils.results", "line_number": 339, "usage_type": "call"}, {"api_name": "goperation.manager.utils.resultutils", "line_number": 339, "usage_type": "name"}, {"api_name": "simpleutil.common.exceptions.InvalidArgument", "line_number": 352, "usage_type": "call"}, {"api_name": "gogamechen1.api.endpoint_session", "line_number": 353, "usage_type": "call"}, {"api_name": "simpleservice.ormdb.api.model_query", "line_number": 354, "usage_type": "call"}, {"api_name": "gogamechen1.models.MergeTask", "line_number": 354, "usage_type": "argument"}, {"api_name": "gogamechen1.models.MergeTask.uuid", "line_number": 354, "usage_type": "attribute"}, {"api_name": "sqlalchemy.orm.joinedload", "line_number": 355, "usage_type": "call"}, {"api_name": "gogamechen1.models.MergeTask.entitys", "line_number": 355, "usage_type": "attribute"}, {"api_name": "gogamechen1.models.MergeTask", "line_number": 355, "usage_type": "name"}, {"api_name": "gogamechen1.api.get_gamelock", "line_number": 356, "usage_type": "call"}, {"api_name": "goperation.manager.api.get_client", "line_number": 357, "usage_type": "call"}, {"api_name": "simpleutil.common.exceptions.InvalidArgument", "line_number": 362, "usage_type": "call"}, {"api_name": "simpleutil.common.exceptions.InvalidArgument", "line_number": 365, "usage_type": "call"}, {"api_name": "gogamechen1.common.SWALLOWING", "line_number": 368, "usage_type": "attribute"}, {"api_name": "gogamechen1.common", "line_number": 368, "usage_type": "name"}, {"api_name": "simpleutil.common.exceptions.InvalidArgument", "line_number": 369, "usage_type": "call"}, {"api_name": "simpleservice.ormdb.api.model_query", "line_number": 370, "usage_type": "call"}, {"api_name": "gogamechen1.models.AppEntity", "line_number": 370, "usage_type": "argument"}, {"api_name": "gogamechen1.models.AppEntity.entity", "line_number": 370, "usage_type": "attribute"}, {"api_name": "sqlalchemy.orm.joinedload", "line_number": 371, "usage_type": "call"}, {"api_name": "gogamechen1.models.AppEntity.databases", "line_number": 371, "usage_type": "attribute"}, {"api_name": "gogamechen1.models.AppEntity", "line_number": 371, "usage_type": "name"}, {"api_name": "simpleutil.common.exceptions.InvalidArgument", "line_number": 375, "usage_type": "call"}, {"api_name": "gogamechen1.common.GAMESERVER", "line_number": 376, "usage_type": "attribute"}, {"api_name": "gogamechen1.common", "line_number": 376, "usage_type": "name"}, {"api_name": "simpleutil.common.exceptions.InvalidArgument", "line_number": 377, "usage_type": "call"}, {"api_name": "gogamechen1.common.GAMESERVER", "line_number": 377, "usage_type": "attribute"}, {"api_name": "gogamechen1.common", "line_number": 377, "usage_type": "name"}, {"api_name": "gogamechen1.common.SWALLOWING", "line_number": 378, "usage_type": "attribute"}, {"api_name": "gogamechen1.common", "line_number": 378, "usage_type": "name"}, {"api_name": "simpleutil.common.exceptions.InvalidArgument", "line_number": 379, "usage_type": "call"}, {"api_name": "goperation.manager.utils.targetutils.target_agent_by_string", "line_number": 384, "usage_type": "call"}, {"api_name": "goperation.manager.utils.targetutils", "line_number": 384, "usage_type": "name"}, {"api_name": "gogamechen1.common.NAME", "line_number": 385, "usage_type": "attribute"}, {"api_name": "gogamechen1.common", "line_number": 385, "usage_type": "name"}, {"api_name": "goperation.manager.wsgi.exceptions.RpcResultError", "line_number": 390, "usage_type": "call"}, {"api_name": "goperation.manager.common.RESULT_SUCCESS", "line_number": 391, "usage_type": "attribute"}, {"api_name": "goperation.manager.common", "line_number": 391, "usage_type": "name"}, {"api_name": "goperation.manager.wsgi.exceptions.RpcResultError", "line_number": 392, "usage_type": "call"}, {"api_name": "gogamechen1.common.DELETED", "line_number": 394, "usage_type": "attribute"}, {"api_name": "gogamechen1.common", "line_number": 394, "usage_type": "name"}, {"api_name": "gogamechen1.common.MERGEED", "line_number": 396, "usage_type": "attribute"}, {"api_name": "gogamechen1.common", "line_number": 396, "usage_type": "name"}, {"api_name": "simpleservice.ormdb.api.model_query", "line_number": 399, "usage_type": "call"}, {"api_name": "gogamechen1.models.GameArea", "line_number": 399, "usage_type": "argument"}, {"api_name": "gogamechen1.models.GameArea.entity", "line_number": 399, "usage_type": "attribute"}, {"api_name": "eventlet.spawn_n", "line_number": 411, "usage_type": "call"}, {"api_name": "goperation.manager.utils.resultutils.results", "line_number": 413, "usage_type": "call"}, {"api_name": "goperation.manager.utils.resultutils", "line_number": 413, "usage_type": "name"}, {"api_name": "simpleutil.utils.jsonutils.loads_as_bytes", "line_number": 414, "usage_type": "call"}, {"api_name": "simpleutil.utils.jsonutils", "line_number": 414, "usage_type": "name"}, {"api_name": "simpleutil.utils.jsonutils.loads_as_bytes", "line_number": 415, "usage_type": "call"}, {"api_name": "simpleutil.utils.jsonutils", "line_number": 415, "usage_type": "name"}, {"api_name": "gogamechen1.api.endpoint_session", "line_number": 419, "usage_type": "call"}, {"api_name": "simpleservice.ormdb.api.model_query", "line_number": 420, "usage_type": "call"}, {"api_name": "gogamechen1.models.MergeTask", "line_number": 420, "usage_type": "argument"}, {"api_name": "gogamechen1.models.MergeTask.uuid", "line_number": 420, "usage_type": "attribute"}, {"api_name": "sqlalchemy.orm.joinedload", "line_number": 421, "usage_type": "call"}, {"api_name": "gogamechen1.models.MergeTask.entitys", "line_number": 421, "usage_type": "attribute"}, {"api_name": "gogamechen1.models.MergeTask", "line_number": 421, "usage_type": "name"}, {"api_name": "gogamechen1.common.MERGEFINISH", "line_number": 423, "usage_type": "attribute"}, {"api_name": "gogamechen1.common", "line_number": 423, "usage_type": "name"}, {"api_name": "goperation.manager.utils.resultutils.results", "line_number": 424, "usage_type": "call"}, {"api_name": "goperation.manager.utils.resultutils", "line_number": 424, "usage_type": "name"}, {"api_name": "gogamechen1.common.MERGEED", "line_number": 428, "usage_type": "attribute"}, {"api_name": "gogamechen1.common", "line_number": 428, "usage_type": "name"}, {"api_name": "simpleutil.common.exceptions.InvalidArgument", "line_number": 429, "usage_type": "call"}, {"api_name": "gogamechen1.common.MERGEFINISH", "line_number": 430, "usage_type": "attribute"}, {"api_name": "gogamechen1.common", "line_number": 430, "usage_type": "name"}, {"api_name": "goperation.manager.utils.resultutils.results", "line_number": 432, "usage_type": "call"}, {"api_name": "goperation.manager.utils.resultutils", "line_number": 432, "usage_type": "name"}]} +{"seq_id": "43087369025", "text": "from typing import Tuple, Union\n\nimport torch\nfrom torch import Tensor\nfrom torch.nn.modules.pooling import (\n AdaptiveAvgPool1d,\n AdaptiveAvgPool2d,\n AdaptiveAvgPool3d,\n AdaptiveMaxPool1d,\n AdaptiveMaxPool2d,\n AdaptiveMaxPool3d,\n AvgPool2d,\n AvgPool3d,\n MaxPool2d,\n MaxPool3d,\n _triple,\n)\n\nfrom logging import getLogger\n\nfrom .utils import FillMode\n\nState = Tuple[Tensor, int]\nPool2D = Union[AvgPool2d, MaxPool2d, AdaptiveAvgPool2d, AdaptiveMaxPool2d]\n\n\nlogger = getLogger(__name__)\n\n__all__ = [\n \"AvgPoolCo3d\",\n \"MaxPoolCo3d\",\n \"AdaptiveAvgPoolCo3d\",\n \"AdaptiveMaxPoolCo3d\",\n \"convert_avgpool3d\",\n \"convert_maxpool3d\",\n \"convert_adaptiveavgpool3d\",\n \"convert_adaptivemaxpool3d\",\n]\n\n\ndef RecursivelyWindowPooled(cls: Pool2D) -> torch.nn.Module: # noqa: C901\n \"\"\"Wraps a pooling module to create a recursive version which pools across execusions\n\n Args:\n cls (Pool2D): A 2D pooling Module\n \"\"\"\n assert cls in {AdaptiveAvgPool2d, MaxPool2d, AvgPool2d, AdaptiveMaxPool2d}\n\n class RePooled(cls):\n def __init__(\n self,\n window_size: int,\n temporal_fill: FillMode = \"replicate\",\n temporal_dilation: int = 1,\n *args,\n **kwargs,\n ):\n assert window_size > 0\n assert temporal_fill in {\"zeros\", \"replicate\"}\n self.window_size = window_size\n self.temporal_dilation = temporal_dilation\n self.make_padding = {\"zeros\": torch.zeros_like, \"replicate\": torch.clone}[\n temporal_fill\n ]\n super(RePooled, self).__init__(*args, **kwargs)\n\n self.temporal_pool = (\n AdaptiveAvgPool1d\n if \"avg\" in str(cls.__name__).lower()\n else AdaptiveMaxPool1d\n )(1)\n\n if self.temporal_dilation > 1:\n self.frame_index_selection = torch.tensor(\n range(0, self.window_size, self.temporal_dilation)\n )\n\n # state is initialised in self.forward\n\n def init_state(self, first_output: Tensor,) -> State:\n padding = self.make_padding(first_output)\n state_buffer = torch.stack(\n [padding for _ in range(self.window_size)], dim=0\n )\n state_index = 0\n if not hasattr(self, \"state_buffer\"):\n self.register_buffer(\"state_buffer\", state_buffer, persistent=False)\n return state_buffer, state_index\n\n def clean_state(self):\n self.state_buffer = None\n self.state_index = None\n\n def get_state(self):\n if (\n hasattr(self, \"state_buffer\") and\n self.state_buffer is not None and\n hasattr(self, \"state_index\") and\n self.state_buffer is not None\n ):\n return (self.state_buffer, self.state_index)\n else:\n return None\n\n def forward(self, input: Tensor) -> Tensor:\n output, (self.state_buffer, self.state_index) = self._forward(\n input, self.get_state()\n )\n return output\n\n def _forward(self, input: Tensor, prev_state: State,) -> Tuple[Tensor, State]:\n assert (\n len(input.shape) == 4\n ), \"Only a single frame should be passed at a time.\"\n\n pooled_frame = super(RePooled, self).forward(input)\n\n if prev_state is None:\n buffer, index = self.init_state(pooled_frame)\n else:\n buffer, index = prev_state\n\n buffer[index] = pooled_frame\n\n if self.temporal_dilation == 1:\n frame_selection = buffer\n else:\n frame_selection = buffer.index_select(\n dim=0, index=self.frame_index_selection\n )\n\n # Pool along temporal dimension\n T, B, C, H, W = frame_selection.shape\n x = frame_selection.permute(1, 3, 4, 2, 0) # B, H, W, C, T\n x = x.reshape(B * H * W, C, T)\n x = self.temporal_pool(x)\n x = x.reshape(B, H, W, C)\n x = x.permute(0, 3, 1, 2) # B, C, H, W\n pooled_window = x\n\n new_index = (index + 1) % self.window_size\n new_buffer = buffer.clone() if self.training else buffer.detach()\n\n return pooled_window, (new_buffer, new_index)\n\n def forward3d(self, input: Tensor):\n \"\"\" If input.shape[2] == self.window_size, a global pooling along temporal dimension is performed\n Otherwise, the pooling is performed per frame\n \"\"\"\n assert (\n len(input.shape) == 5\n ), \"A tensor of size B,C,T,H,W should be passed as input.\"\n\n outs = []\n for t in range(input.shape[2]):\n o = self.forward(input[:, :, t])\n if self.window_size - 1 <= t:\n outs.append(o)\n\n if len(outs) == 0:\n return torch.tensor([])\n\n if input.shape[2] == self.window_size:\n # In order to be compatible with downstream forward3d, select only last frame\n # This corrsponds to the regular global pool\n return outs[-1].unsqueeze(2)\n\n else:\n return torch.stack(outs, dim=2)\n\n RePooled.__doc__ = f\"\"\"\n Recursive {cls.__name__}\n\n Pooling results are stored between `forward` exercutions and used to pool subsequent\n inputs along the temporal dimension with a spacified `window_size`.\n Example: For `window_size = 3`, the two previous results are stored and used for pooling.\n `temporal_fill` determines whether to initialize the state with a ``'replicate'`` of the\n output of the first execution or with with ``'zeros'``.\n\n Parent doc:\n {cls.__doc__}\n \"\"\"\n\n return RePooled\n\n\nAvgPoolCo3d = RecursivelyWindowPooled(AvgPool2d)\nMaxPoolCo3d = RecursivelyWindowPooled(MaxPool2d)\nAdaptiveAvgPoolCo3d = RecursivelyWindowPooled(AdaptiveAvgPool2d)\nAdaptiveMaxPoolCo3d = RecursivelyWindowPooled(AdaptiveMaxPool2d)\n\n\ndef convert_avgpool3d(\n instance: AvgPool3d,\n window_size: int = None, # Not used: only there to satisfy interface\n temporal_fill: FillMode = \"replicate\",\n):\n kernel_size = _triple(instance.kernel_size)\n padding = _triple(instance.padding)\n stride = _triple(instance.stride)\n assert padding[0] == 0, \"Cannot convert AvgPool3d with padding[0] != 0\"\n assert stride[0] == 1, \"Cannot convert AvgPool3d with stride[0] != 1\"\n return AvgPoolCo3d(\n window_size=kernel_size[0],\n temporal_fill=temporal_fill,\n kernel_size=kernel_size[1:],\n stride=stride[1:],\n padding=padding[1:],\n ceil_mode=instance.ceil_mode,\n count_include_pad=instance.count_include_pad,\n divisor_override=instance.divisor_override,\n )\n\n\ndef convert_maxpool3d(\n instance: MaxPool3d,\n window_size: int = None, # Not used: only there to satisfy interface\n temporal_fill: FillMode = \"replicate\",\n):\n kernel_size = _triple(instance.kernel_size)\n padding = _triple(instance.padding)\n stride = _triple(instance.stride)\n dilation = _triple(instance.dilation)\n assert padding[0] == 0, \"Cannot convert MaxPool3d with padding[0] != 0\"\n assert stride[0] == 1, \"Cannot convert MaxPool3d with stride[0] != 1\"\n assert dilation[0] == 1, \"Cannot convert MaxPool3d with dilation[0] != 1\"\n assert (\n instance.return_indices is False\n ), \"return_indices currently not supported for MaxPool3d\"\n return MaxPoolCo3d(\n window_size=kernel_size[0],\n temporal_fill=temporal_fill,\n kernel_size=kernel_size[1:],\n stride=stride[1:],\n padding=padding[1:],\n dilation=dilation[1:],\n return_indices=instance.return_indices,\n ceil_mode=instance.ceil_mode,\n )\n\n\ndef convert_adaptiveavgpool3d(\n instance: AdaptiveAvgPool3d,\n window_size: int,\n temporal_fill: FillMode = \"replicate\",\n):\n assert (\n instance.output_size[0] == 1\n ), \"Cannot convert AdaptiveAvgPool3d without output_size[0] != 1\"\n return AdaptiveAvgPoolCo3d(\n window_size=window_size,\n temporal_fill=temporal_fill,\n output_size=instance.output_size[1:],\n )\n\n\ndef convert_adaptivemaxpool3d(\n instance: AdaptiveMaxPool3d,\n window_size: int,\n temporal_fill: FillMode = \"replicate\",\n):\n assert (\n instance.output_size[0] == 1\n ), \"Cannot convert AdaptiveMaxPool3d without output_size[0] != 1\"\n assert (\n instance.return_indices is False\n ), \"return_indices currently not supported for AdaptiveMaxPool3d\"\n return AdaptiveAvgPoolCo3d(\n window_size=window_size,\n temporal_fill=temporal_fill,\n output_size=instance.output_size,\n )\n", "repo_name": "passalis/demos", "sub_path": "src/opendr/perception/activity_recognition/cox3d/algorithm/pooling.py", "file_name": "pooling.py", "file_ext": "py", "file_size_in_byte": 8920, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "typing.Tuple", "line_number": 23, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 23, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 24, "usage_type": "name"}, {"api_name": "torch.nn.modules.pooling.AvgPool2d", "line_number": 24, "usage_type": "name"}, {"api_name": "torch.nn.modules.pooling.MaxPool2d", "line_number": 24, "usage_type": "name"}, {"api_name": "torch.nn.modules.pooling.AdaptiveAvgPool2d", "line_number": 24, "usage_type": "name"}, {"api_name": "torch.nn.modules.pooling.AdaptiveMaxPool2d", "line_number": 24, "usage_type": "name"}, {"api_name": "logging.getLogger", "line_number": 27, "usage_type": "call"}, {"api_name": "torch.nn.modules.pooling.AdaptiveAvgPool2d", "line_number": 47, "usage_type": "name"}, {"api_name": "torch.nn.modules.pooling.MaxPool2d", "line_number": 47, "usage_type": "name"}, {"api_name": "torch.nn.modules.pooling.AvgPool2d", "line_number": 47, "usage_type": "name"}, {"api_name": "torch.nn.modules.pooling.AdaptiveMaxPool2d", "line_number": 47, "usage_type": "name"}, {"api_name": "utils.FillMode", "line_number": 53, "usage_type": "name"}, {"api_name": "torch.zeros_like", "line_number": 62, "usage_type": "attribute"}, {"api_name": "torch.clone", "line_number": 62, "usage_type": "attribute"}, {"api_name": "torch.nn.modules.pooling.AdaptiveAvgPool1d", "line_number": 68, "usage_type": "name"}, {"api_name": "torch.nn.modules.pooling.AdaptiveMaxPool1d", "line_number": 70, "usage_type": "name"}, {"api_name": "torch.tensor", "line_number": 74, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 80, "usage_type": "name"}, {"api_name": "torch.stack", "line_number": 82, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 105, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 111, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 111, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 146, "usage_type": "name"}, {"api_name": "torch.tensor", "line_number": 161, "usage_type": "call"}, {"api_name": "torch.stack", "line_number": 169, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 41, "usage_type": "attribute"}, {"api_name": "torch.nn.modules.pooling.AvgPool2d", "line_number": 187, "usage_type": "argument"}, {"api_name": "torch.nn.modules.pooling.MaxPool2d", "line_number": 188, "usage_type": "argument"}, {"api_name": "torch.nn.modules.pooling.AdaptiveAvgPool2d", "line_number": 189, "usage_type": "argument"}, {"api_name": "torch.nn.modules.pooling.AdaptiveMaxPool2d", "line_number": 190, "usage_type": "argument"}, {"api_name": "torch.nn.modules.pooling.AvgPool3d", "line_number": 194, "usage_type": "name"}, {"api_name": "utils.FillMode", "line_number": 196, "usage_type": "name"}, {"api_name": "torch.nn.modules.pooling._triple", "line_number": 198, "usage_type": "call"}, {"api_name": "torch.nn.modules.pooling._triple", "line_number": 199, "usage_type": "call"}, {"api_name": "torch.nn.modules.pooling._triple", "line_number": 200, "usage_type": "call"}, {"api_name": "torch.nn.modules.pooling.MaxPool3d", "line_number": 216, "usage_type": "name"}, {"api_name": "utils.FillMode", "line_number": 218, "usage_type": "name"}, {"api_name": "torch.nn.modules.pooling._triple", "line_number": 220, "usage_type": "call"}, {"api_name": "torch.nn.modules.pooling._triple", "line_number": 221, "usage_type": "call"}, {"api_name": "torch.nn.modules.pooling._triple", "line_number": 222, "usage_type": "call"}, {"api_name": "torch.nn.modules.pooling._triple", "line_number": 223, "usage_type": "call"}, {"api_name": "torch.nn.modules.pooling.AdaptiveAvgPool3d", "line_number": 243, "usage_type": "name"}, {"api_name": "utils.FillMode", "line_number": 245, "usage_type": "name"}, {"api_name": "torch.nn.modules.pooling.AdaptiveMaxPool3d", "line_number": 258, "usage_type": "name"}, {"api_name": "utils.FillMode", "line_number": 260, "usage_type": "name"}]} +{"seq_id": "6301083875", "text": "from django.core.exceptions import ImproperlyConfigured\nfrom django.db import models\nfrom django.http import Http404\nfrom django.utils.translation import gettext as _\nfrom django.views.generic.base import ContextMixin, TemplateResponseMixin, View\n\n\nclass SingleObjectMixin(ContextMixin):\n \"\"\"\n Provide the ability to retrieve a single object for further manipulation.\n \"\"\"\n\n model = None\n queryset = None\n slug_field = \"slug\"\n context_object_name = None\n slug_url_kwarg = \"slug\"\n pk_url_kwarg = \"pk\"\n query_pk_and_slug = False\n\n def get_object(self, queryset=None):\n \"\"\"\n Return the object the view is displaying.\n\n Require `self.queryset` and a `pk` or `slug` argument in the URLconf.\n Subclasses can override this to return any object.\n \"\"\"\n # Use a custom queryset if provided; this is required for subclasses\n # like DateDetailView\n if queryset is None:\n queryset = self.get_queryset()\n\n # Next, try looking up by primary key.\n pk = self.kwargs.get(self.pk_url_kwarg)\n slug = self.kwargs.get(self.slug_url_kwarg)\n if pk is not None:\n queryset = queryset.filter(pk=pk)\n\n # Next, try looking up by slug.\n if slug is not None and (pk is None or self.query_pk_and_slug):\n slug_field = self.get_slug_field()\n queryset = queryset.filter(**{slug_field: slug})\n\n # If none of those are defined, it's an error.\n if pk is None and slug is None:\n raise AttributeError(\n \"Generic detail view %s must be called with either an object \"\n \"pk or a slug in the URLconf.\" % self.__class__.__name__\n )\n\n try:\n # Get the single item from the filtered queryset\n obj = queryset.get()\n except queryset.model.DoesNotExist:\n raise Http404(\n _(\"No %(verbose_name)s found matching the query\")\n % {\"verbose_name\": queryset.model._meta.verbose_name}\n )\n return obj\n\n def get_queryset(self):\n \"\"\"\n Return the `QuerySet` that will be used to look up the object.\n\n This method is called by the default implementation of get_object() and\n may not be called if get_object() is overridden.\n \"\"\"\n if self.queryset is None:\n if self.model:\n return self.model._default_manager.all()\n else:\n raise ImproperlyConfigured(\n \"%(cls)s is missing a QuerySet. Define \"\n \"%(cls)s.model, %(cls)s.queryset, or override \"\n \"%(cls)s.get_queryset().\" % {\"cls\": self.__class__.__name__}\n )\n return self.queryset.all()\n\n def get_slug_field(self):\n \"\"\"Get the name of a slug field to be used to look up by slug.\"\"\"\n return self.slug_field\n\n def get_context_object_name(self, obj):\n \"\"\"Get the name to use for the object.\"\"\"\n if self.context_object_name:\n return self.context_object_name\n elif isinstance(obj, models.Model):\n return obj._meta.model_name\n else:\n return None\n\n def get_context_data(self, **kwargs):\n \"\"\"Insert the single object into the context dict.\"\"\"\n context = {}\n if self.object:\n context[\"object\"] = self.object\n context_object_name = self.get_context_object_name(self.object)\n if context_object_name:\n context[context_object_name] = self.object\n context.update(kwargs)\n return super().get_context_data(**context)\n\n\nclass BaseDetailView(SingleObjectMixin, View):\n \"\"\"A base view for displaying a single object.\"\"\"\n\n def get(self, request, *args, **kwargs):\n self.object = self.get_object()\n context = self.get_context_data(object=self.object)\n return self.render_to_response(context)\n\n\nclass SingleObjectTemplateResponseMixin(TemplateResponseMixin):\n template_name_field = None\n template_name_suffix = \"_detail\"\n\n def get_template_names(self):\n \"\"\"\n Return a list of template names to be used for the request. May not be\n called if render_to_response() is overridden. Return the following list:\n\n * the value of ``template_name`` on the view (if provided)\n * the contents of the ``template_name_field`` field on the\n object instance that the view is operating upon (if available)\n * ``/.html``\n \"\"\"\n try:\n names = super().get_template_names()\n except ImproperlyConfigured:\n # If template_name isn't specified, it's not a problem --\n # we just start with an empty list.\n names = []\n\n # If self.template_name_field is set, grab the value of the field\n # of that name from the object; this is the most specific template\n # name, if given.\n if self.object and self.template_name_field:\n name = getattr(self.object, self.template_name_field, None)\n if name:\n names.insert(0, name)\n\n # The least-specific option is the default /_detail.html;\n # only use this if the object in question is a model.\n if isinstance(self.object, models.Model):\n object_meta = self.object._meta\n names.append(\n \"%s/%s%s.html\"\n % (\n object_meta.app_label,\n object_meta.model_name,\n self.template_name_suffix,\n )\n )\n elif getattr(self, \"model\", None) is not None and issubclass(\n self.model, models.Model\n ):\n names.append(\n \"%s/%s%s.html\"\n % (\n self.model._meta.app_label,\n self.model._meta.model_name,\n self.template_name_suffix,\n )\n )\n\n # If we still haven't managed to find any template names, we should\n # re-raise the ImproperlyConfigured to alert the user.\n if not names:\n raise\n\n return names\n\n\nclass DetailView(SingleObjectTemplateResponseMixin, BaseDetailView):\n \"\"\"\n Render a \"detail\" view of an object.\n\n By default this is a model instance looked up from `self.queryset`, but the\n view will support display of *any* object by overriding `self.get_object()`.\n \"\"\"\n", "repo_name": "django/django", "sub_path": "django/views/generic/detail.py", "file_name": "detail.py", "file_ext": "py", "file_size_in_byte": 6663, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 74132, "dataset": "github-code", "pt": "37", "api": [{"api_name": "django.views.generic.base.ContextMixin", "line_number": 8, "usage_type": "name"}, {"api_name": "django.http.Http404", "line_number": 55, "usage_type": "call"}, {"api_name": "django.utils.translation.gettext", "line_number": 56, "usage_type": "call"}, {"api_name": "django.core.exceptions.ImproperlyConfigured", "line_number": 72, "usage_type": "call"}, {"api_name": "django.db.models.Model", "line_number": 87, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 87, "usage_type": "name"}, {"api_name": "django.views.generic.base.View", "line_number": 104, "usage_type": "name"}, {"api_name": "django.views.generic.base.TemplateResponseMixin", "line_number": 113, "usage_type": "name"}, {"api_name": "django.core.exceptions.ImproperlyConfigured", "line_number": 129, "usage_type": "name"}, {"api_name": "django.db.models.Model", "line_number": 144, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 144, "usage_type": "name"}, {"api_name": "django.db.models.Model", "line_number": 155, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 155, "usage_type": "name"}]} +{"seq_id": "70617658667", "text": "\"\"\"Stories module\"\"\"\n\nfrom . import stories\nfrom .. import db\nimport app.stories.forms\nimport app.models\n\n\nimport flask\nimport flask_login\n\n\n@stories.route('/story', methods=['GET', 'POST'])\n@flask_login.login_required\ndef make_story():\n \"\"\"Generate a view with a form to publish a story.\"\"\"\n\n form = app.stories.forms.StoryForm()\n if flask.request.method == 'POST' and form.validate_on_submit():\n story = app.models.Story()\n story.title = form.title.data\n story.post = form.post.data\n app.db.session.add(story)\n app.db.session.commit()\n return flask.redirect(flask.url_for('stories.stories'))\n return flask.render_template(\"form_template.html\", form=form, title=\"edit Story\")\n\n\n@stories.route('/story/edit/', methods=['GET', 'POST'])\n@flask_login.login_required\ndef edit_story(story_id):\n \"\"\"Get the story id as argument and return a view\n with a form to publish or update a story.\"\"\"\n\n story = app.models.Story.query.filter_by(\n _id=story_id).first_or_404()\n form = app.stories.forms.EditStoryForm(obj=story)\n form.populate_obj(story)\n if flask.request.method == 'POST' and form.validate_on_submit():\n db.session.add(story)\n db.session.commit()\n return flask.redirect(flask.url_for('stories.stories'))\n return flask.render_template(\"form_template.html\", form=form, title=\"edit Story\")\n\n\n@stories.route('/story/delete/', methods=['POST'])\n@flask_login.login_required\ndef delete_story(story_id):\n \"\"\"Delete story\"\"\"\n\n if flask.request.method == 'POST':\n story = app.models.Story.query.filter_by(\n _id=story_id).first_or_404()\n db.session.delete(story)\n db.session.commit()\n return flask.redirect(flask.url_for('stories.stories'))\n\n\n@stories.route('/stories')\n@flask_login.login_required\ndef stories():\n \"\"\"Get all published stories\"\"\"\n\n stories = app.models.Story.query.all()\n return flask.render_template(\"stories.html\", title=\"Stories\", stories=stories)\n", "repo_name": "mariocov/westernstories", "sub_path": "app/stories/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 2037, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "app.stories.forms.stories.forms.StoryForm", "line_number": 18, "usage_type": "call"}, {"api_name": "app.stories.forms.stories", "line_number": 18, "usage_type": "attribute"}, {"api_name": "app.stories.forms", "line_number": 18, "usage_type": "name"}, {"api_name": "flask.request", "line_number": 19, "usage_type": "attribute"}, {"api_name": "app.stories.forms.models.Story", "line_number": 20, "usage_type": "call"}, {"api_name": "app.stories.forms.models", "line_number": 20, "usage_type": "attribute"}, {"api_name": "app.stories.forms", "line_number": 20, "usage_type": "name"}, {"api_name": "app.stories.forms.db.session.add", "line_number": 23, "usage_type": "call"}, {"api_name": "app.stories.forms.db", "line_number": 23, "usage_type": "attribute"}, {"api_name": "app.stories.forms", "line_number": 23, "usage_type": "name"}, {"api_name": "app.stories.forms.db.session.commit", "line_number": 24, "usage_type": "call"}, {"api_name": "app.stories.forms.db", "line_number": 24, "usage_type": "attribute"}, {"api_name": "app.stories.forms", "line_number": 24, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 25, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 25, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 26, "usage_type": "call"}, {"api_name": "flask_login.login_required", "line_number": 14, "usage_type": "attribute"}, {"api_name": "app.stories.forms.models.Story.query.filter_by", "line_number": 35, "usage_type": "call"}, {"api_name": "app.stories.forms.models", "line_number": 35, "usage_type": "attribute"}, {"api_name": "app.stories.forms", "line_number": 35, "usage_type": "name"}, {"api_name": "app.stories.forms.stories.forms.EditStoryForm", "line_number": 37, "usage_type": "call"}, {"api_name": "app.stories.forms.stories", "line_number": 37, "usage_type": "attribute"}, {"api_name": "app.stories.forms", "line_number": 37, "usage_type": "name"}, {"api_name": "flask.request", "line_number": 39, "usage_type": "attribute"}, {"api_name": "flask.redirect", "line_number": 42, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 42, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 43, "usage_type": "call"}, {"api_name": "flask_login.login_required", "line_number": 30, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 51, "usage_type": "attribute"}, {"api_name": "app.stories.forms.models.Story.query.filter_by", "line_number": 52, "usage_type": "call"}, {"api_name": "app.stories.forms.models", "line_number": 52, "usage_type": "attribute"}, {"api_name": "app.stories.forms", "line_number": 52, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 56, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 56, "usage_type": "call"}, {"api_name": "flask_login.login_required", "line_number": 47, "usage_type": "attribute"}, {"api_name": "app.stories.forms.models.Story.query.all", "line_number": 64, "usage_type": "call"}, {"api_name": "app.stories.forms.models", "line_number": 64, "usage_type": "attribute"}, {"api_name": "app.stories.forms", "line_number": 64, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 65, "usage_type": "call"}, {"api_name": "flask_login.login_required", "line_number": 60, "usage_type": "attribute"}]} +{"seq_id": "2845762576", "text": "\nimport os.path\nimport boto3\nimport email\nfrom botocore.exceptions import ClientError\nfrom email.mime.multipart import MIMEMultipart\nfrom email.mime.text import MIMEText\nfrom email.mime.application import MIMEApplication\n\n\ns3 = boto3.client(\"s3\")\n\ndef lambda_handler(event, context):\n \n #list all the accounts\n client_org = boto3.client('organizations')\n response_org = client.list_accounts(\n NextToken='string',\n MaxResults=123\n )\n\n #acc_email = ['Accounts']['Email']\n\n email_list = []\n\n for item in response_org['Accounts']:\n email_list += [item['Email']]\n print(item['Email'])\n\n # pull the files we need from the S3 bucket into the email.\n # Get the records for the triggered event\n FILEOBJ = event[\"Records\"][0]\n \n BUCKET_NAME = str(FILEOBJ['s3']['bucket']['name'])\n\n KEY = str(FILEOBJ['s3']['object']['key'])\n\n s3_dir_path = os.path.dirname(KEY)\n\n account_folder = os.path.basename(s3_dir_path)\n\n #check if recommendation is for the account owner(id)\n if account_folder == item['Id']:\n\n SENDER = \"ddaksh@amazon.com\"\n\n #RECIPIENT = \"ddaksh+org1@amazon.com\"\n RECIPIENT = item['Email']\n\n AWS_REGION = \"us-east-1\"\n SUBJECT = \"Test : EC2 Optimization Recommendation with Attachment\"\n\n # extract the file name from the file.\n FILE_NAME = os.path.basename(KEY) \n\n # Using the file name, create a new file location for the lambda.\n TMP_FILE_NAME = '/tmp/' +FILE_NAME\n\n # Download the file/s from the event (extracted above) to the tmp location\n s3.download_file(BUCKET_NAME, KEY, TMP_FILE_NAME)\n\n ATTACHMENT = TMP_FILE_NAME\n\n # The email body for recipients with non-HTML email clients.\n BODY_TEXT = \"Hello,\\r\\nPlease see the attached file related to recent EC2 usage for your account.\"\n\n # The HTML body of the email.\n BODY_HTML = \"\"\"\\\n \n \n \n

Hello!

\n

Please see the attached file related to recent EC2 usage for your account.

\n \n \n \"\"\"\n\n # The character encoding for the email.\n CHARSET = \"utf-8\"\n\n # New SES resource\n client = boto3.client('ses',region_name=AWS_REGION)\n\n # Create a multipart/mixed parent container.\n msg = MIMEMultipart('mixed')\n\n msg['Subject'] = SUBJECT \n msg['From'] = SENDER \n msg['To'] = RECIPIENT\n\n # Create a multipart/alternative child container.\n msg_body = MIMEMultipart('alternative')\n\n # Encode the text and HTML content and set the character encoding\n textpart = MIMEText(BODY_TEXT.encode(CHARSET), 'plain', CHARSET)\n htmlpart = MIMEText(BODY_HTML.encode(CHARSET), 'html', CHARSET)\n\n # Add the text and HTML parts to the child container.\n msg_body.attach(textpart)\n msg_body.attach(htmlpart)\n\n # Define the attachment part and encode it using MIMEApplication.\n att = MIMEApplication(open(ATTACHMENT, 'rb').read())\n\n # Add a header to tell the email client to treat this part as an attachment,\n \n att.add_header('Content-Disposition','attachment',filename=os.path.basename(ATTACHMENT))\n\n msg.attach(msg_body)\n\n # Add the attachment to the parent container.\n msg.attach(att)\n print(msg)\n try:\n\n response = client.send_raw_email(\n Source=SENDER,\n Destinations=[\n RECIPIENT\n ],\n RawMessage={\n 'Data':msg.as_string(),\n },\n # ConfigurationSetName=CONFIGURATION_SET\n )\n # Display an error if something goes wrong. \n except ClientError as e:\n print(e.response['Error']['Message'])\n else:\n print(\"Email sent! Message ID:\"),\n print(response['MessageId'])\n\n\n\n \n", "repo_name": "ddaksh/rightSizing", "sub_path": "emailCompute.py", "file_name": "emailCompute.py", "file_ext": "py", "file_size_in_byte": 4284, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "boto3.client", "line_number": 11, "usage_type": "call"}, {"api_name": "boto3.client", "line_number": 16, "usage_type": "call"}, {"api_name": "os.path.path.dirname", "line_number": 38, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 38, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 38, "usage_type": "name"}, {"api_name": "os.path.path.basename", "line_number": 40, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 40, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 40, "usage_type": "name"}, {"api_name": "os.path.path.basename", "line_number": 54, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 54, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 54, "usage_type": "name"}, {"api_name": "boto3.client", "line_number": 82, "usage_type": "call"}, {"api_name": "email.mime.multipart.MIMEMultipart", "line_number": 85, "usage_type": "call"}, {"api_name": "email.mime.multipart.MIMEMultipart", "line_number": 92, "usage_type": "call"}, {"api_name": "email.mime.text.MIMEText", "line_number": 95, "usage_type": "call"}, {"api_name": "email.mime.text.MIMEText", "line_number": 96, "usage_type": "call"}, {"api_name": "email.mime.application.MIMEApplication", "line_number": 103, "usage_type": "call"}, {"api_name": "os.path.path.basename", "line_number": 107, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 107, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 107, "usage_type": "name"}, {"api_name": "botocore.exceptions.ClientError", "line_number": 127, "usage_type": "name"}]} +{"seq_id": "32631045885", "text": "import uuid\nimport datetime\nimport flask_bcrypt\nfrom sqlalchemy import or_, and_\n\nfrom app.main import db\nfrom app.main.model.lecturer import Lecturer\nfrom app.main.model.course import Course\n\n\ndef create_lecturer(data):\n lecturer = Lecturer.query.filter(or_(Lecturer.email_address==data['email_address'], Lecturer.lecturer_id==data['lecturer_id'])).first()\n if not lecturer:\n new_lecturer = Lecturer(\n public_id=str(uuid.uuid4()),\n lecturer_id = data['lecturer_id'],\n first_name = data['first_name'],\n last_name = data['last_name'],\n email_address = data['email_address'],\n faculty = data['faculty'],\n department = data['department'],\n level = data['level'],\n password = data['password']\n )\n save_changes(new_lecturer)\n return generate_token(new_lecturer)\n else:\n response_object = {\n 'status': 'fail',\n 'message': 'Lecturer already exists. Please Log in.',\n }\n return response_object, 409\n\n\ndef get_all_lecturers():\n lecturers = Lecturer.query.all()\n if lecturers:\n return lecturers\n else:\n response_object = {\n 'status': 'fail',\n 'message': 'No Lecturer exists.',\n }\n return response_object, 409\n\n\ndef get_lecturer(public_id):\n lecturer = Lecturer.query.filter_by(public_id=public_id).first()\n if lecturer:\n return lecturer\n else:\n response_object = {\n 'status': 'fail',\n 'message': 'Lecturer does not exist.',\n }\n return response_object, 409\n\n\ndef update_lecturer(public_id, data):\n lecturer = Lecturer.query.filter_by(public_id=public_id).first()\n if lecturer:\n lecturer.first_name = data['first_name']\n lecturer.last_name = data['last_name']\n lecturer.email_address = data['email_address']\n lecturer.faculty = data['faculty']\n lecturer.department = data['department']\n lecturer.level = data['level']\n lecturer.password_hash = flask_bcrypt.generate_password_hash(data['password']).decode('utf-8')\n save_changes(lecturer)\n return lecturer, 201\n else:\n response_object = {\n 'status': 'fail',\n 'message': 'Lecturer does not exist.',\n }\n return response_object, 409\n\n\ndef remove_lecturer(public_id):\n lecturer = Lecturer.query.filter_by(public_id=public_id).first()\n if lecturer:\n lecturer.courses.clear()\n lecturer.attendance_sessions.clear()\n db.session.add(lecturer)\n db.session.delete(lecturer)\n db.session.commit()\n response_object = {\n 'status': 'success',\n 'message': 'Successfully removed.'\n }\n return response_object, 201\n else:\n response_object = {\n 'status': 'fail',\n 'message': 'Lecturer does not exist.',\n }\n return response_object, 409\n\n\ndef get_lecturer_courses(public_id, registered):\n lecturer = Lecturer.query.filter_by(public_id=public_id).first()\n if lecturer:\n reg_courses = lecturer.courses\n unreg_courses = Course.query.filter(\n and_(\n or_(Course.department==lecturer.department, Course.strict==False),\n ~Course.public_id.in_([course.public_id for course in lecturer.courses])\n )\n ).all()\n if registered:\n return reg_courses\n else:\n return unreg_courses\n else:\n response_object = {\n 'status': 'fail',\n 'message': 'Lecturer does not exist.',\n }\n return response_object, 409\n\n\ndef update_lecturer_course(public_id, data):\n lecturer = Lecturer.query.filter_by(public_id=public_id).first()\n course = Course.query.filter_by(public_id=data['public_id']).first()\n if lecturer and course:\n lecturer.courses.append(course)\n save_changes(lecturer)\n response_object = {\n 'status': 'success',\n 'message': 'Successfully added.'\n }\n return response_object, 201\n else:\n response_object = {\n 'status': 'fail',\n 'message': 'Lecturer or Course does not exist.',\n }\n return response_object, 409\n\n\ndef remove_lecturer_course(public_id, data):\n lecturer = Lecturer.query.filter_by(public_id=public_id).first()\n course = Course.query.filter_by(public_id=data['public_id']).first()\n if lecturer and course:\n lecturer.courses.remove(course)\n save_changes(lecturer)\n response_object = {\n 'status': 'success',\n 'message': 'Successfully removed.'\n }\n return response_object, 201\n else:\n response_object = {\n 'status': 'fail',\n 'message': 'Lecturer or Course does not exist.',\n }\n return response_object, 409\n\n\ndef generate_token(lecturer):\n try:\n # generate the auth token\n auth_token = Lecturer.encode_auth_token(lecturer.lecturer_id)\n response_object = {\n 'status': 'success',\n 'message': 'Successfully created.',\n 'public_id': lecturer.public_id,\n 'Authorization': auth_token.decode()\n }\n return response_object, 201\n except Exception as e:\n response_object = {\n 'status': 'fail',\n 'message': 'Some error occurred. Please try again.'\n }\n return response_object, 401\n\n\ndef save_changes(data):\n db.session.add(data)\n db.session.commit()\n\n", "repo_name": "tobeyOguney/rfid_class_attendance", "sub_path": "class-attendance-api/app/main/service/lecturer_service.py", "file_name": "lecturer_service.py", "file_ext": "py", "file_size_in_byte": 5597, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "app.main.model.lecturer.Lecturer.query.filter", "line_number": 12, "usage_type": "call"}, {"api_name": "app.main.model.lecturer.Lecturer.query", "line_number": 12, "usage_type": "attribute"}, {"api_name": "app.main.model.lecturer.Lecturer", "line_number": 12, "usage_type": "name"}, {"api_name": "sqlalchemy.or_", "line_number": 12, "usage_type": "call"}, {"api_name": "app.main.model.lecturer.Lecturer.email_address", "line_number": 12, "usage_type": "attribute"}, {"api_name": "app.main.model.lecturer.Lecturer.lecturer_id", "line_number": 12, "usage_type": "attribute"}, {"api_name": "app.main.model.lecturer.Lecturer", "line_number": 14, "usage_type": "call"}, {"api_name": "uuid.uuid4", "line_number": 15, "usage_type": "call"}, {"api_name": "app.main.model.lecturer.Lecturer.query.all", "line_number": 36, "usage_type": "call"}, {"api_name": "app.main.model.lecturer.Lecturer.query", "line_number": 36, "usage_type": "attribute"}, {"api_name": "app.main.model.lecturer.Lecturer", "line_number": 36, "usage_type": "name"}, {"api_name": "app.main.model.lecturer.Lecturer.query.filter_by", "line_number": 48, "usage_type": "call"}, {"api_name": "app.main.model.lecturer.Lecturer.query", "line_number": 48, "usage_type": "attribute"}, {"api_name": "app.main.model.lecturer.Lecturer", "line_number": 48, "usage_type": "name"}, {"api_name": "app.main.model.lecturer.Lecturer.query.filter_by", "line_number": 60, "usage_type": "call"}, {"api_name": "app.main.model.lecturer.Lecturer.query", "line_number": 60, "usage_type": "attribute"}, {"api_name": "app.main.model.lecturer.Lecturer", "line_number": 60, "usage_type": "name"}, {"api_name": "flask_bcrypt.generate_password_hash", "line_number": 68, "usage_type": "call"}, {"api_name": "app.main.model.lecturer.Lecturer.query.filter_by", "line_number": 80, "usage_type": "call"}, {"api_name": "app.main.model.lecturer.Lecturer.query", "line_number": 80, "usage_type": "attribute"}, {"api_name": "app.main.model.lecturer.Lecturer", "line_number": 80, "usage_type": "name"}, {"api_name": "app.main.db.session.add", "line_number": 84, "usage_type": "call"}, {"api_name": "app.main.db.session", "line_number": 84, "usage_type": "attribute"}, {"api_name": "app.main.db", "line_number": 84, "usage_type": "name"}, {"api_name": "app.main.db.session.delete", "line_number": 85, "usage_type": "call"}, {"api_name": "app.main.db.session", "line_number": 85, "usage_type": "attribute"}, {"api_name": "app.main.db", "line_number": 85, "usage_type": "name"}, {"api_name": "app.main.db.session.commit", "line_number": 86, "usage_type": "call"}, {"api_name": "app.main.db.session", "line_number": 86, "usage_type": "attribute"}, {"api_name": "app.main.db", "line_number": 86, "usage_type": "name"}, {"api_name": "app.main.model.lecturer.Lecturer.query.filter_by", "line_number": 101, "usage_type": "call"}, {"api_name": "app.main.model.lecturer.Lecturer.query", "line_number": 101, "usage_type": "attribute"}, {"api_name": "app.main.model.lecturer.Lecturer", "line_number": 101, "usage_type": "name"}, {"api_name": "app.main.model.course.Course.query.filter", "line_number": 104, "usage_type": "call"}, {"api_name": "app.main.model.course.Course.query", "line_number": 104, "usage_type": "attribute"}, {"api_name": "app.main.model.course.Course", "line_number": 104, "usage_type": "name"}, {"api_name": "sqlalchemy.and_", "line_number": 105, "usage_type": "call"}, {"api_name": "sqlalchemy.or_", "line_number": 106, "usage_type": "call"}, {"api_name": "app.main.model.course.Course.department", "line_number": 106, "usage_type": "attribute"}, {"api_name": "app.main.model.course.Course", "line_number": 106, "usage_type": "name"}, {"api_name": "app.main.model.course.Course.strict", "line_number": 106, "usage_type": "attribute"}, {"api_name": "app.main.model.course.Course.public_id.in_", "line_number": 107, "usage_type": "call"}, {"api_name": "app.main.model.course.Course.public_id", "line_number": 107, "usage_type": "attribute"}, {"api_name": "app.main.model.course.Course", "line_number": 107, "usage_type": "name"}, {"api_name": "app.main.model.lecturer.Lecturer.query.filter_by", "line_number": 123, "usage_type": "call"}, {"api_name": "app.main.model.lecturer.Lecturer.query", "line_number": 123, "usage_type": "attribute"}, {"api_name": "app.main.model.lecturer.Lecturer", "line_number": 123, "usage_type": "name"}, {"api_name": "app.main.model.course.Course.query.filter_by", "line_number": 124, "usage_type": "call"}, {"api_name": "app.main.model.course.Course.query", "line_number": 124, "usage_type": "attribute"}, {"api_name": "app.main.model.course.Course", "line_number": 124, "usage_type": "name"}, {"api_name": "app.main.model.lecturer.Lecturer.query.filter_by", "line_number": 142, "usage_type": "call"}, {"api_name": "app.main.model.lecturer.Lecturer.query", "line_number": 142, "usage_type": "attribute"}, {"api_name": "app.main.model.lecturer.Lecturer", "line_number": 142, "usage_type": "name"}, {"api_name": "app.main.model.course.Course.query.filter_by", "line_number": 143, "usage_type": "call"}, {"api_name": "app.main.model.course.Course.query", "line_number": 143, "usage_type": "attribute"}, {"api_name": "app.main.model.course.Course", "line_number": 143, "usage_type": "name"}, {"api_name": "app.main.model.lecturer.Lecturer.encode_auth_token", "line_number": 163, "usage_type": "call"}, {"api_name": "app.main.model.lecturer.Lecturer", "line_number": 163, "usage_type": "name"}, {"api_name": "app.main.db.session.add", "line_number": 180, "usage_type": "call"}, {"api_name": "app.main.db.session", "line_number": 180, "usage_type": "attribute"}, {"api_name": "app.main.db", "line_number": 180, "usage_type": "name"}, {"api_name": "app.main.db.session.commit", "line_number": 181, "usage_type": "call"}, {"api_name": "app.main.db.session", "line_number": 181, "usage_type": "attribute"}, {"api_name": "app.main.db", "line_number": 181, "usage_type": "name"}]} +{"seq_id": "815840222", "text": "import os\nimport cv2\nimport matplotlib.pyplot as plt\nfrom keras.applications.mobilenetv2 import MobileNetV2\nfrom keras.layers import *\nfrom keras.models import Model\nfrom keras.models import model_from_json\nfrom keras.optimizers import Adam\n\n# output dims -> (1,x,x,1,5)\n\n# boxes = decode_to_boxes(output) output to boxes\n# corner_boxes = boxes_to_corners(boxes) boxes to corners\n# final_out = non_max_suppress(corner_boxes) \n# iou()\n\nOUTPUT_FOLDER_PATH = \"Results2\"\nMODEL_PATH = \"model\"\nDATA_PATH = \"Data\"\nIOU = 0.5\n\n# Variable Definition\nimg_w = 512\nimg_h = 512\nchannels = 3\nclasses = 1\ninfo = 5\ngrid_w = 16\ngrid_h = 16\n\n# optimizer\noptimizer = Adam(lr=0.0001, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=0.0)\n\n\ndef load_date(data_path='Data'):\n \"\"\"\n load data\n Parameters\n ----------\n data_path\n\n Returns\n -------\n\n \"\"\"\n X = np.load(os.path.join(data_path, 'X.npy'))\n Y = np.load(os.path.join(data_path, 'Y.npy'))\n return X, Y\n\n\ndef yolo_model(input_shape):\n \"\"\"\n define model\n # input : 512,512,3\n # output : 16,16,1,5\n\n Parameters\n ----------\n input_shape\n\n Returns\n -------\n\n \"\"\"\n inp = Input(input_shape)\n\n model = MobileNetV2(input_tensor=inp, include_top=False, weights='imagenet')\n last_layer = model.output\n\n conv = Conv2D(512, (3, 3), activation='relu', padding='same')(last_layer)\n conv = Dropout(0.4)(conv)\n bn = BatchNormalization()(conv)\n lr = LeakyReLU(alpha=0.1)(bn)\n\n conv = Conv2D(128, (3, 3), activation='relu', padding='same')(lr)\n conv = Dropout(0.4)(conv)\n bn = BatchNormalization()(conv)\n lr = LeakyReLU(alpha=0.1)(bn)\n\n conv = Conv2D(5, (3, 3), activation='relu', padding='same')(lr)\n\n final = Reshape((grid_h, grid_w, classes, info))(conv)\n\n model = Model(inp, final)\n\n return model\n\n\n# define loss function\ndef yolo_loss_func(y_true, y_pred):\n \"\"\"\n yolo loss functions\n\n Parameters\n ----------\n y_true\n y_pred\n\n Returns\n -------\n\n \"\"\"\n # y_true : 16,16,1,5\n # y_pred : 16,16,1,5\n l_coords = 5.0\n l_noob = 0.5\n coords = y_true[:, :, :, :, 0] * l_coords\n noobs = (-1 * (y_true[:, :, :, :, 0] - 1) * l_noob)\n p_pred = y_pred[:, :, :, :, 0]\n p_true = y_true[:, :, :, :, 0]\n x_true = y_true[:, :, :, :, 1]\n x_pred = y_pred[:, :, :, :, 1]\n yy_true = y_true[:, :, :, :, 2]\n yy_pred = y_pred[:, :, :, :, 2]\n w_true = y_true[:, :, :, :, 3]\n w_pred = y_pred[:, :, :, :, 3]\n h_true = y_true[:, :, :, :, 4]\n h_pred = y_pred[:, :, :, :, 4]\n\n p_loss_absent = K.sum(K.square(p_pred - p_true) * noobs)\n p_loss_present = K.sum(K.square(p_pred - p_true))\n x_loss = K.sum(K.square(x_pred - x_true) * coords)\n yy_loss = K.sum(K.square(yy_pred - yy_true) * coords)\n xy_loss = x_loss + yy_loss\n w_loss = K.sum(K.square(K.sqrt(w_pred) - K.sqrt(w_true)) * coords)\n h_loss = K.sum(K.square(K.sqrt(h_pred) - K.sqrt(h_true)) * coords)\n wh_loss = w_loss + h_loss\n\n loss = p_loss_absent + p_loss_present + xy_loss + wh_loss\n\n return loss\n\n\ndef save_model(model, model_path=\"model1\"):\n \"\"\"\n save model\n Parameters\n ----------\n model\n model_path\n\n Returns\n -------\n\n \"\"\"\n model_json = model.to_json()\n with open(os.path.join(model_path, \"text_detect_model.json\"), \"w\") as json_file:\n json_file.write(model_json)\n model.save_weights(os.path.join(model_path, 'text_detect.h5'))\n\n\ndef load_model(model_path):\n \"\"\"\n load path\n Parameters\n ----------\n model_path\n\n Returns\n -------\n return loaded_model\n \"\"\"\n json_file = open(os.path.join(model_path, 'text_detect_model.json'), 'r')\n loaded_model_json = json_file.read()\n json_file.close()\n loaded_model = model_from_json(loaded_model_json)\n loaded_model.load_weights(os.path.join(model_path, 'text_detect.h5'))\n return loaded_model\n\n\ndef decode_to_boxes(output, ht, wd):\n # output : (x,x,1,5)\n # x,y,h,w\n\n img_ht = ht\n img_wd = wd\n threshold = 0.5\n grid_h, grid_w = output.shape[:2]\n final_boxes = []\n scores = []\n\n for i in range(grid_h):\n for j in range(grid_w):\n if output[i, j, 0, 0] > threshold:\n temp = output[i, j, 0, 1:5]\n\n x_unit = ((j + (temp[0])) / grid_w) * img_wd\n y_unit = ((i + (temp[1])) / grid_h) * img_ht\n width = temp[2] * img_wd * 1.3\n height = temp[3] * img_ht * 1.3\n\n final_boxes.append([x_unit - width / 2, y_unit - height / 2, x_unit + width / 2, y_unit + height / 2])\n scores.append(output[i, j, 0, 0])\n\n return final_boxes, scores\n\n\ndef iou(box1, box2):\n x1 = max(box1[0], box2[0])\n x2 = min(box1[2], box2[2])\n y1 = max(box1[1], box2[1])\n y2 = min(box1[3], box2[3])\n\n inter = (x2 - x1) * (y2 - y1)\n area1 = (box1[2] - box1[0]) * (box1[3] - box1[1])\n area2 = (box2[2] - box2[0]) * (box2[3] - box2[1])\n fin_area = area1 + area2 - inter\n\n iou = inter / fin_area\n\n return iou\n\n\ndef non_max(boxes, scores, iou_num):\n scores_sort = scores.argsort().tolist()\n keep = []\n\n while len(scores_sort):\n\n index = scores_sort.pop()\n keep.append(index)\n\n if (len(scores_sort) == 0):\n break\n\n iou_res = []\n\n for i in scores_sort:\n iou_res.append(iou(boxes[index], boxes[i]))\n\n iou_res = np.array(iou_res)\n filtered_indexes = set((iou_res > iou_num).nonzero()[0])\n\n scores_sort = [v for (i, v) in enumerate(scores_sort) if i not in filtered_indexes]\n\n final = []\n\n for i in keep:\n final.append(boxes[i])\n\n return final\n\n\ndef decode(output, ht, wd, iou):\n boxes, scores = decode_to_boxes(output, ht, wd)\n boxes = non_max(boxes, np.array(scores), iou)\n return boxes\n\n\ndef predict(model, input):\n \"\"\"\n predict images\n Parameters\n ----------\n model\n input\n\n Returns\n -------\n\n \"\"\"\n ans = model.predict(input)\n img = ((input + 1) / 2)\n img = img[0]\n return ans, img\n\n\ndef draw_rectangle(img, rect):\n \"\"\"\n draw rectangle\n Parameters\n ----------\n img\n rect\n\n Returns\n -------\n\n \"\"\"\n return cv2.rectangle(img, (rect[0], rect[1]), (rect[2], rect[3]), color=(0, 255, 0), thickness=2)\n\n\ndef save_image(img, name):\n \"\"\"\n save image\n Parameters\n ----------\n name\n img\n\n Returns\n -------\n\n \"\"\"\n cv2.imwrite(os.path.join(OUTPUT_FOLDER_PATH, str(name) + '.jpg'), img * 255.0)\n\n\ndef predicted_box(predicted_output):\n \"\"\"\n convert predicted output to rectangle\n Parameters\n ----------\n predicted_output\n\n Returns\n -------\n\n \"\"\"\n boxes = decode(predicted_output[0], img_w, img_h, IOU)\n rectangles = []\n for r in boxes:\n rectangles.append([int(p) for p in r])\n return rectangles\n\n\ndef show_image(img):\n \"\"\"\n plot image\n Parameters\n ----------\n img\n\n Returns\n -------\n\n \"\"\"\n plt.imshow(img)\n plt.show()\n\n\ndef accuracy_checking(model, X, Y):\n data_checks = []\n for i in range(X.shape[0]):\n print(\"validating and checking: {}/{}\".format(i, X.shape[0]))\n pred_out, img = predict(model, X[i:i + 1])\n rects = predicted_box(pred_out)\n pred_rect_len = len(rects)\n act_rects = predicted_box(Y[i:i + 1])\n act_rects_len = len(act_rects)\n data_checks.append([act_rects_len, pred_rect_len, act_rects_len == pred_rect_len])\n\n accuracy = sum([1 for x in data_checks if x[2] == True]) / len(data_checks) * 100.00\n\n return accuracy\n\n\ndef predict_func(model, inp, name, image_save=False):\n \"\"\"\n predict image and show and save\n Parameters\n ----------\n model\n inp\n name\n image_save\n\n Returns\n -------\n\n \"\"\"\n r_img = None\n pred_out, img = predict(model, inp)\n\n rects = predicted_box(pred_out)\n for rect in rects:\n r_img = draw_rectangle(img, rect)\n print('draw rectangle image')\n show_image(r_img)\n if image_save:\n save_image(r_img, name)", "repo_name": "hsali/yolo-td", "sub_path": "Utils.py", "file_name": "Utils.py", "file_ext": "py", "file_size_in_byte": 8068, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "keras.optimizers.Adam", "line_number": 32, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 46, "usage_type": "call"}, {"api_name": "os.path", "line_number": 46, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 47, "usage_type": "call"}, {"api_name": "os.path", "line_number": 47, "usage_type": "attribute"}, {"api_name": "keras.applications.mobilenetv2.MobileNetV2", "line_number": 67, "usage_type": "call"}, {"api_name": "keras.models.Model", "line_number": 84, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 147, "usage_type": "call"}, {"api_name": "os.path", "line_number": 147, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 149, "usage_type": "call"}, {"api_name": "os.path", "line_number": 149, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 163, "usage_type": "call"}, {"api_name": "os.path", "line_number": 163, "usage_type": "attribute"}, {"api_name": "keras.models.model_from_json", "line_number": 166, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 167, "usage_type": "call"}, {"api_name": "os.path", "line_number": 167, "usage_type": "attribute"}, {"api_name": "cv2.rectangle", "line_number": 280, "usage_type": "call"}, {"api_name": "cv2.imwrite", "line_number": 295, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 295, "usage_type": "call"}, {"api_name": "os.path", "line_number": 295, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 327, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 327, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 328, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 328, "usage_type": "name"}]} +{"seq_id": "10074871273", "text": "# Licensed under a 3-clause BSD style license - see LICENSE.rst\nfrom astropy.tests.helper import pytest\nimport numpy as np\nimport warnings\nfrom ... import utils\nfrom ...tests import helpers\nfrom astropy import units as u\n\n\nclass TestKKrelation_supportfuncs:\n def test_transrefcoefficients(self):\n \"\"\"\n Make sure that the calculations for the transmission and\n reflection coefficients work as expected\n \"\"\"\n\n testspec = helpers.generate_cdespectrum()\n testm1 = testspec.m\n testm0 = 1.0+0.0j\n testm2 = 1.3+1.3j\n\n t01, t02, t12, r01, r02, r12 = \\\n utils.complex_transmission_reflection(testm0, testm1, testm2)\n\n # these are the functions which are supposed to work\n def complex_transmission(m1, m2):\n return (2.*m1.real)/(m1+m2)\n\n def complex_reflection(m1, m2):\n return (m1-m2)/(m1+m2)\n\n assert np.all(t01 == complex_transmission(testm0, testm1))\n assert np.all(t02 == complex_transmission(testm0, testm2))\n assert np.all(t12 == complex_transmission(testm1, testm2))\n\n assert np.all(r01 == complex_reflection(testm0, testm1))\n assert np.all(r02 == complex_reflection(testm0, testm2))\n assert np.all(r12 == complex_reflection(testm1, testm2))\n\n\nclass TestKKIter:\n def test_kkiternoconverge(self):\n \"\"\"\n Test basic functionality of KK iteration.\n Not going for a full iteration; just making sure it doesn't crash\n and that it returns an array of what looks like the right shape\n \"\"\"\n testspec = helpers.generate_absspectrum()\n assert testspec.x.unit == u.kayser\n assert testspec.y.unit == utils.unit_od\n testspec.subspectrum(2200., 3900.)\n freq = testspec.x\n transmittance = testspec.y.to(\n utils.unit_transmittance,\n equivalencies=utils.equivalencies_absorption)\n m_substrate = 1.74+0.0j # CsI window, like in the Hudgins paper\n d_ice = 2.0*u.micron\n m0 = 1.3 + 0.0j\n with u.set_enabled_equivalencies(u.equivalencies.spectral()):\n freq_m0 = (250.*u.micron).to(u.kayser).value\n with pytest.raises(utils.KKError):\n with warnings.catch_warnings():\n warnings.simplefilter(\"ignore\")\n utils.kramers_kronig(\n freq,\n transmittance,\n m_substrate,\n d_ice,\n m0,\n freq_m0,\n maxiter=1)\n\n def test_kkiternanfailure(self):\n \"\"\"\n Make sure that KK iteration stops instantly when it starts\n producing NaNs. Producing NaNs means that the iteration has\n been given input parameters which fail\n to converge to anything sane.\n \"\"\"\n testspec = helpers.generate_absspectrum()\n assert testspec.x.unit == u.kayser\n assert testspec.y.unit == utils.unit_od\n testspec.subspectrum(2200., 3900.)\n freq = testspec.x\n transmittance = testspec.y.to(\n utils.unit_transmittance,\n equivalencies=utils.equivalencies_absorption)\n m_substrate = 1.74+0.0j # CsI window, like in the Hudgins paper\n d_ice = 0.5*u.micron\n m0 = 1.3 + 0.0j\n with u.set_enabled_equivalencies(u.equivalencies.spectral()):\n freq_m0 = (250.*u.micron).to(u.kayser).value\n with pytest.raises(utils.KKError):\n with warnings.catch_warnings():\n warnings.simplefilter(\"ignore\")\n utils.kramers_kronig(\n freq,\n transmittance,\n m_substrate,\n d_ice,\n m0,\n freq_m0)\n", "repo_name": "RiceMunk/omnifit", "sub_path": "omnifit/utils/tests/test_kkrelation.py", "file_name": "test_kkrelation.py", "file_ext": "py", "file_size_in_byte": 3782, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 6, "dataset": "github-code", "pt": "37", "api": [{"api_name": "tests.helpers.generate_cdespectrum", "line_number": 17, "usage_type": "call"}, {"api_name": "tests.helpers", "line_number": 17, "usage_type": "name"}, {"api_name": "numpy.all", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.all", "line_number": 33, "usage_type": "call"}, {"api_name": "numpy.all", "line_number": 34, "usage_type": "call"}, {"api_name": "numpy.all", "line_number": 36, "usage_type": "call"}, {"api_name": "numpy.all", "line_number": 37, "usage_type": "call"}, {"api_name": "numpy.all", "line_number": 38, "usage_type": "call"}, {"api_name": "tests.helpers.generate_absspectrum", "line_number": 48, "usage_type": "call"}, {"api_name": "tests.helpers", "line_number": 48, "usage_type": "name"}, {"api_name": "astropy.units.kayser", "line_number": 49, "usage_type": "attribute"}, {"api_name": "astropy.units", "line_number": 49, "usage_type": "name"}, {"api_name": "astropy.units.micron", "line_number": 57, "usage_type": "attribute"}, {"api_name": "astropy.units", "line_number": 57, "usage_type": "name"}, {"api_name": "astropy.units.set_enabled_equivalencies", "line_number": 59, "usage_type": "call"}, {"api_name": "astropy.units", "line_number": 59, "usage_type": "name"}, {"api_name": "astropy.units.equivalencies.spectral", "line_number": 59, "usage_type": "call"}, {"api_name": "astropy.units.equivalencies", "line_number": 59, "usage_type": "attribute"}, {"api_name": "astropy.units.micron", "line_number": 60, "usage_type": "attribute"}, {"api_name": "astropy.units", "line_number": 60, "usage_type": "name"}, {"api_name": "astropy.units.kayser", "line_number": 60, "usage_type": "attribute"}, {"api_name": "astropy.tests.helper.pytest.raises", "line_number": 61, "usage_type": "call"}, {"api_name": "astropy.tests.helper.pytest", "line_number": 61, "usage_type": "name"}, {"api_name": "warnings.catch_warnings", "line_number": 62, "usage_type": "call"}, {"api_name": "warnings.simplefilter", "line_number": 63, "usage_type": "call"}, {"api_name": "tests.helpers.generate_absspectrum", "line_number": 80, "usage_type": "call"}, {"api_name": "tests.helpers", "line_number": 80, "usage_type": "name"}, {"api_name": "astropy.units.kayser", "line_number": 81, "usage_type": "attribute"}, {"api_name": "astropy.units", "line_number": 81, "usage_type": "name"}, {"api_name": "astropy.units.micron", "line_number": 89, "usage_type": "attribute"}, {"api_name": "astropy.units", "line_number": 89, "usage_type": "name"}, {"api_name": "astropy.units.set_enabled_equivalencies", "line_number": 91, "usage_type": "call"}, {"api_name": "astropy.units", "line_number": 91, "usage_type": "name"}, {"api_name": "astropy.units.equivalencies.spectral", "line_number": 91, "usage_type": "call"}, {"api_name": "astropy.units.equivalencies", "line_number": 91, "usage_type": "attribute"}, {"api_name": "astropy.units.micron", "line_number": 92, "usage_type": "attribute"}, {"api_name": "astropy.units", "line_number": 92, "usage_type": "name"}, {"api_name": "astropy.units.kayser", "line_number": 92, "usage_type": "attribute"}, {"api_name": "astropy.tests.helper.pytest.raises", "line_number": 93, "usage_type": "call"}, {"api_name": "astropy.tests.helper.pytest", "line_number": 93, "usage_type": "name"}, {"api_name": "warnings.catch_warnings", "line_number": 94, "usage_type": "call"}, {"api_name": "warnings.simplefilter", "line_number": 95, "usage_type": "call"}]} +{"seq_id": "18775679118", "text": "import os\nimport abc\nimport sys\nimport vtk\nimport math\nimport time\nimport PythonQt\nfrom PythonQt import QtCore, QtGui\nimport director.objectmodel as om\nfrom director import cameraview\nfrom director import drcargs\nfrom director import robotstate\nfrom director.timercallback import TimerCallback\nfrom director.utime import getUtime\nfrom director.simpletimer import MovingAverageComputer\nimport vtkDRCFiltersPython as drc\nfrom director.debugpolydata import DebugData\nimport director.visualization as vis\nfrom director import perceptionmeta\nimport functools\nfrom director import vtkNumpy as vnp\nimport numpy as np\nimport rospy\n\n\nclass CheckProvider(object):\n \"\"\"\n A decorator class to use to ensure that functions which require a provider for their data source are not called\n if that data source has not yet been initialised. Also counts how many times a function has been skipped.\n \"\"\"\n\n def __init__(self, func):\n functools.update_wrapper(self, func)\n self.func = func\n self.num_calls = 0\n\n def __get__(self, obj, objtype):\n \"\"\"Support instance methods\n https://stackoverflow.com/questions/5469956/python-decorator-self-is-mixed-up\n https://stackoverflow.com/questions/2365701/decorating-python-class-methods-how-do-i-pass-the-instance-to-the-decorator\n \"\"\"\n return functools.partial(self.__call__, obj)\n\n def __call__(self, *args, **kwargs):\n if args[0].provider:\n return self.func(*args, **kwargs)\n else:\n if self.num_calls % 50 == 0:\n print(\n \"Provider not yet intialised, skipping execution of {}.{}\"\n \" (skipped {} times)\".format(\n args[0].__class__.__name__, self.func.__name__, self.num_calls\n )\n )\n self.num_calls += 1\n return\n\n\nclass MultisenseItem(om.ObjectModelItem):\n def __init__(self, model):\n\n om.ObjectModelItem.__init__(self, \"Multisense\", om.Icons.Laser)\n\n self.model = model\n self.scalarBarWidget = None\n self.addProperty(\n \"Color By\",\n 0,\n attributes=om.PropertyAttributes(\n enumNames=[\n \"Solid Color\",\n \"Intensity\",\n \"Z Coordinate\",\n \"Range\",\n \"Spindle Angle\",\n \"Azimuth\",\n \"Camera RGB\",\n \"Scan Delta\",\n ]\n ),\n )\n self.addProperty(\"Show Scalar Bar\", False)\n self.addProperty(\"Updates Enabled\", True)\n self.addProperty(\n \"Min Range\",\n model.reader.GetDistanceRange()[0],\n attributes=om.PropertyAttributes(\n decimals=2, minimum=0.0, maximum=100.0, singleStep=0.25, hidden=False\n ),\n )\n self.addProperty(\n \"Max Range\",\n model.reader.GetDistanceRange()[1],\n attributes=om.PropertyAttributes(\n decimals=2, minimum=0.0, maximum=100.0, singleStep=0.25, hidden=False\n ),\n )\n self.addProperty(\n \"Edge Filter Angle\",\n model.reader.GetEdgeAngleThreshold(),\n attributes=om.PropertyAttributes(\n decimals=0, minimum=0.0, maximum=60.0, singleStep=1, hidden=False\n ),\n )\n self.addProperty(\n \"Number of Scan Lines\",\n model.numberOfScanLines,\n attributes=om.PropertyAttributes(\n decimals=0, minimum=0, maximum=100, singleStep=1, hidden=False\n ),\n )\n self.addProperty(\"Visible\", model.visible)\n self.addProperty(\n \"Point Size\",\n model.pointSize,\n attributes=om.PropertyAttributes(\n decimals=0, minimum=1, maximum=20, singleStep=1, hidden=False\n ),\n )\n self.addProperty(\n \"Alpha\",\n model.alpha,\n attributes=om.PropertyAttributes(\n decimals=2, minimum=0, maximum=1.0, singleStep=0.1, hidden=False\n ),\n )\n self.addProperty(\n \"Min Height\",\n model.reader.GetHeightRange()[0],\n attributes=om.PropertyAttributes(\n decimals=2, minimum=-80.0, maximum=80.0, singleStep=0.25, hidden=False\n ),\n )\n self.addProperty(\n \"Max Height\",\n model.reader.GetHeightRange()[1],\n attributes=om.PropertyAttributes(\n decimals=2, minimum=-80.0, maximum=80.0, singleStep=0.25, hidden=False\n ),\n )\n\n # self.addProperty('Color', QtGui.QColor(255,255,255))\n # self.addProperty('Scanline Color', QtGui.QColor(255,0,0))\n\n def _onPropertyChanged(self, propertySet, propertyName):\n om.ObjectModelItem._onPropertyChanged(self, propertySet, propertyName)\n\n if propertyName == \"Updates Enabled\":\n if self.getProperty(\"Updates Enabled\"):\n self.model.start()\n else:\n self.model.stop()\n\n elif propertyName == \"Edge Filter Angle\":\n self.model.reader.SetEdgeAngleThreshold(\n self.getProperty(\"Edge Filter Angle\")\n )\n self.model.showRevolution(self.model.displayedRevolution)\n\n elif propertyName == \"Alpha\":\n self.model.setAlpha(self.getProperty(propertyName))\n\n elif propertyName == \"Visible\":\n self.model.setVisible(self.getProperty(propertyName))\n\n elif propertyName == \"Point Size\":\n self.model.setPointSize(self.getProperty(propertyName))\n\n elif propertyName == \"Number of Scan Lines\":\n self.model.numberOfScanLines = self.getProperty(propertyName)\n self.model.initScanLines()\n\n elif propertyName in (\"Min Range\", \"Max Range\"):\n self.model.reader.SetDistanceRange(\n self.getProperty(\"Min Range\"), self.getProperty(\"Max Range\")\n )\n self.model.showRevolution(self.model.displayedRevolution)\n\n elif propertyName in (\"Min Height\", \"Max Height\"):\n self.model.reader.SetHeightRange(\n self.getProperty(\"Min Height\"), self.getProperty(\"Max Height\")\n )\n self.model.showRevolution(self.model.displayedRevolution)\n\n elif propertyName == \"Color By\":\n self._updateColorBy()\n\n elif propertyName == \"Show Scalar Bar\":\n self._updateScalarBar()\n\n self.model.polyDataObj._renderAllViews()\n\n def _updateColorBy(self):\n\n arrayMap = {\n 0: \"Solid Color\",\n 1: \"intensity\",\n 2: \"z\",\n 3: \"distance\",\n 4: \"spindle_angle\",\n 5: \"azimuth\",\n 6: \"rgb\",\n 7: \"scan_delta\",\n }\n\n colorBy = self.getProperty(\"Color By\")\n arrayName = arrayMap.get(colorBy)\n\n if (\n arrayName == \"rgb\"\n and arrayName not in self.model.polyDataObj.getArrayNames()\n ):\n self.model.colorizeCallback()\n self.model.polyDataObj._updateColorByProperty()\n self.model.polyDataObj.setProperty(\"Color By\", arrayName)\n self._updateScalarBar()\n\n def hasDataSet(self, dataSet):\n return self.model.polyDataObj.hasDataSet(dataSet)\n\n def _updateScalarBar(self):\n self.model.polyDataObj.setProperty(\n \"Show Scalar Bar\", self.getProperty(\"Show Scalar Bar\")\n )\n\n\nclass LidarItem(om.ObjectModelItem):\n def __init__(self, model):\n om.ObjectModelItem.__init__(self, model.sensorName, om.Icons.EyeOff)\n\n self.model = model\n self.scalarBarWidget = None\n self.addProperty(\n \"Color By\",\n 0,\n attributes=om.PropertyAttributes(\n enumNames=[\n \"Solid Color\",\n \"Intensity\",\n \"Z Coordinate\",\n \"Range\",\n \"Spindle Angle\",\n \"Azimuth\",\n \"Camera RGB\",\n \"Scan Delta\",\n ]\n ),\n )\n self.addProperty(\"Show Scalar Bar\", False)\n self.addProperty(\"Updates Enabled\", True)\n self.addProperty(\n \"Min Range\",\n model.reader.GetDistanceRange()[0],\n attributes=om.PropertyAttributes(\n decimals=2, minimum=0.0, maximum=100.0, singleStep=0.25, hidden=False\n ),\n )\n self.addProperty(\n \"Max Range\",\n model.reader.GetDistanceRange()[1],\n attributes=om.PropertyAttributes(\n decimals=2, minimum=0.0, maximum=100.0, singleStep=0.25, hidden=False\n ),\n )\n self.addProperty(\n \"Edge Filter Angle\",\n model.reader.GetEdgeAngleThreshold(),\n attributes=om.PropertyAttributes(\n decimals=0, minimum=0.0, maximum=60.0, singleStep=1, hidden=False\n ),\n )\n self.addProperty(\n \"Number of Scan Lines\",\n model.numberOfScanLines,\n attributes=om.PropertyAttributes(\n decimals=0, minimum=0, maximum=5000, singleStep=1, hidden=False\n ),\n )\n self.addProperty(\"Visible\", model.visible)\n self.addProperty(\n \"Point Size\",\n model.pointSize,\n attributes=om.PropertyAttributes(\n decimals=0, minimum=-1, maximum=20, singleStep=1, hidden=False\n ),\n )\n self.addProperty(\n \"Alpha\",\n model.alpha,\n attributes=om.PropertyAttributes(\n decimals=2, minimum=0, maximum=1.0, singleStep=0.1, hidden=False\n ),\n )\n self.addProperty(\n \"Min Height\",\n model.reader.GetHeightRange()[0],\n attributes=om.PropertyAttributes(\n decimals=2, minimum=-80.0, maximum=80.0, singleStep=0.25, hidden=False\n ),\n )\n self.addProperty(\n \"Max Height\",\n model.reader.GetHeightRange()[1],\n attributes=om.PropertyAttributes(\n decimals=2, minimum=-80.0, maximum=80.0, singleStep=0.25, hidden=False\n ),\n )\n\n # self.addProperty('Color', QtGui.QColor(255,255,255))\n # self.addProperty('Scanline Color', QtGui.QColor(255,0,0))\n\n def _onPropertyChanged(self, propertySet, propertyName):\n om.ObjectModelItem._onPropertyChanged(self, propertySet, propertyName)\n\n if propertyName == \"Updates Enabled\":\n if self.getProperty(\"Updates Enabled\"):\n self.model.start()\n else:\n self.model.stop()\n\n elif propertyName == \"Edge Filter Angle\":\n self.model.reader.SetEdgeAngleThreshold(\n self.getProperty(\"Edge Filter Angle\")\n )\n # self.model.showRevolution(self.model.displayedRevolution)\n\n elif propertyName == \"Alpha\":\n self.model.setAlpha(self.getProperty(propertyName))\n\n elif propertyName == \"Visible\":\n self.model.setVisible(self.getProperty(propertyName))\n\n elif propertyName == \"Point Size\":\n self.model.setPointSize(self.getProperty(propertyName))\n\n elif propertyName == \"Number of Scan Lines\":\n self.model.numberOfScanLines = self.getProperty(propertyName)\n self.model.initScanLines()\n\n elif propertyName in (\"Min Range\", \"Max Range\"):\n self.model.reader.SetDistanceRange(\n self.getProperty(\"Min Range\"), self.getProperty(\"Max Range\")\n )\n # self.model.showRevolution(self.model.displayedRevolution)\n\n elif propertyName in (\"Min Height\", \"Max Height\"):\n self.model.reader.SetHeightRange(\n self.getProperty(\"Min Height\"), self.getProperty(\"Max Height\")\n )\n # self.model.showRevolution(self.model.displayedRevolution)\n\n elif propertyName == \"Color By\":\n self._updateColorBy()\n\n elif propertyName == \"Show Scalar Bar\":\n self._updateScalarBar()\n\n self.model.polyDataObj._renderAllViews()\n\n def _updateColorBy(self):\n\n arrayMap = {\n 0: \"Solid Color\",\n 1: \"intensity\",\n 2: \"z\",\n 3: \"distance\",\n 4: \"spindle_angle\",\n 5: \"azimuth\",\n 6: \"rgb\",\n 7: \"scan_delta\",\n }\n\n colorBy = self.getProperty(\"Color By\")\n arrayName = arrayMap.get(colorBy)\n\n self.model.setColorBy(arrayName)\n self._updateScalarBar()\n\n def hasDataSet(self, dataSet):\n return self.model.polyDataObj.hasDataSet(dataSet)\n\n def _updateScalarBar(self):\n self.model.polyDataObj.setProperty(\n \"Show Scalar Bar\", self.getProperty(\"Show Scalar Bar\")\n )\n\n\nclass SpindleAxisDebug(vis.PolyDataItem):\n def __init__(self, frameProvider):\n vis.PolyDataItem.__init__(self, \"spindle axis\", vtk.vtkPolyData(), view=None)\n self.frameProvider = frameProvider\n self.timer = TimerCallback()\n self.timer.callback = self.update\n self.setProperty(\"Color\", QtGui.QColor(0, 255, 0))\n self.setProperty(\"Visible\", False)\n\n def _onPropertyChanged(self, propertySet, propertyName):\n vis.PolyDataItem._onPropertyChanged(self, propertySet, propertyName)\n\n if propertyName == \"Visible\":\n if self.getProperty(propertyName):\n self.timer.start()\n else:\n self.timer.stop()\n\n def onRemoveFromObjectModel(self):\n vis.PolyDataItem.onRemoveFromObjectModel(self)\n self.timer.stop()\n\n def update(self):\n\n t = self.frameProvider.getFrame(\"MULTISENSE_SCAN\")\n\n p1 = [0.0, 0.0, 0.0]\n p2 = [2.0, 0.0, 0.0]\n\n p1 = t.TransformPoint(p1)\n p2 = t.TransformPoint(p2)\n\n d = DebugData()\n d.addSphere(p1, radius=0.01, color=[0, 1, 0])\n d.addLine(p1, p2, color=[0, 1, 0])\n self.setPolyData(d.getPolyData())\n\n\nclass LidarSource(TimerCallback):\n def __init__(\n self, view, channelName, coordinateFrame, sensorName, intensityRange=(400, 4000)\n ):\n TimerCallback.__init__(self)\n self.view = view\n self.channelName = channelName\n self.reader = None\n self.displayedRevolution = -1\n self.lastScanLine = 0\n self.numberOfScanLines = 100\n self.nextScanLineId = 0\n self.scanLines = []\n self.pointSize = 1\n self.alpha = 0.5\n self.visible = True\n self.colorBy = \"Solid Color\"\n self.intensityRange = intensityRange\n self.initScanLines()\n self.sensorName = sensorName\n self.coordinateFrame = coordinateFrame\n\n self.revPolyData = vtk.vtkPolyData()\n self.polyDataObj = vis.PolyDataItem(\"Lidar Sweep\", self.revPolyData, view)\n self.polyDataObj.actor.SetPickable(1)\n\n self.polyDataObj.setRangeMap(\"intensity\", intensityRange)\n\n self.setPointSize(self.pointSize)\n self.setAlpha(self.alpha)\n self.targetFps = 60\n self.colorizeCallback = None\n\n def initScanLines(self):\n\n for scanLine in self.scanLines:\n scanLine.removeFromAllViews()\n\n self.scanLines = []\n self.nextScanLineId = 0\n self.lastScanLine = max(self.lastScanLine - self.numberOfScanLines, 0)\n\n for i in range(self.numberOfScanLines):\n polyData = vtk.vtkPolyData()\n scanLine = vis.PolyDataItem(\"scan line %d\" % i, polyData, self.view)\n scanLine.rangeMap[\"intensity\"] = self.intensityRange\n\n scanLine.actor.SetPickable(0)\n # scanLine.setSolidColor((0,1,0))\n self.scanLines.append(scanLine)\n\n def getScanToLocal(self):\n return None\n\n def setPointSize(self, pointSize):\n for scanLine in self.scanLines:\n scanLine.setProperty(\"Point Size\", pointSize + 2)\n self.polyDataObj.setProperty(\"Point Size\", pointSize)\n\n def setAlpha(self, alpha):\n self.alpha = alpha\n for scanLine in self.scanLines:\n scanLine.setProperty(\"Alpha\", alpha)\n self.polyDataObj.setProperty(\"Alpha\", alpha)\n\n def setVisible(self, visible):\n self.visible = visible\n for scanLine in self.scanLines:\n scanLine.setProperty(\"Visible\", visible)\n self.polyDataObj.setProperty(\"Visible\", visible)\n\n def setColorBy(self, colorBy):\n self.colorBy = colorBy\n for scanLine in self.scanLines:\n if colorBy and colorBy in scanLine.getArrayNames():\n scanLine.colorBy(self.colorBy)\n elif colorBy == \"Solid Color\":\n scanLine.setSolidColor((1, 1, 1))\n\n def start(self):\n if self.reader is None:\n self.reader = drc.vtkLidarSource()\n self.reader.subscribe(self.channelName)\n self.reader.setCoordinateFrame(self.coordinateFrame)\n self.reader.InitBotConfig(drcargs.args().config_file)\n self.reader.SetDistanceRange(0.25, 80.0)\n self.reader.SetHeightRange(-80.0, 80.0)\n self.reader.Start()\n\n TimerCallback.start(self)\n\n def updateScanLines(self):\n\n if not self.numberOfScanLines:\n return\n\n currentScanLine = self.reader.GetCurrentScanLine() - 1\n scanLinesToUpdate = currentScanLine - self.lastScanLine\n scanLinesToUpdate = min(scanLinesToUpdate, self.numberOfScanLines)\n\n if not scanLinesToUpdate:\n return\n\n # print 'current scanline:', currentScanLine\n # print 'scan lines to update:', scanLinesToUpdate\n # print 'updating actors:', self.nextScanLineId, (self.nextScanLineId + (scanLinesToUpdate-1)) % self.numberOfActors\n # print 'updating scan lines:', self.lastScanLine + 1, self.lastScanLine + 1 + (scanLinesToUpdate-1)\n\n for i in range(scanLinesToUpdate):\n scanLine = self.scanLines[\n (self.nextScanLineId + i) % self.numberOfScanLines\n ]\n self.reader.GetDataForScanLine(self.lastScanLine + i + 1, scanLine.polyData)\n if self.colorBy and self.colorBy in scanLine.getArrayNames():\n scanLine.colorBy(self.colorBy)\n\n self.lastScanLine = currentScanLine\n self.nextScanLineId = (\n self.nextScanLineId + scanLinesToUpdate\n ) % self.numberOfScanLines\n\n if self.scanLines[0].getProperty(\"Visible\"):\n self.view.render()\n\n def getPolyData(self):\n self.revPolyData = vtk.vtkPolyData()\n self.reader.GetDataForHistory(self.numberOfScanLines, self.revPolyData)\n vis.updatePolyData(self.revPolyData, \"point cloud\", colorByName=self.colorBy)\n\n def tick(self):\n self.updateScanLines()\n\n def setIntensityRange(self, lowerBound, upperBound):\n self.polyDataObj.setRangeMap(\"intensity\", [lowerBound, upperBound])\n\n\nclass SpindleMonitor(object):\n def __init__(self, getSpindleAngleFunction):\n self.lastSpindleAngle = 0\n self.lastStateTime = 0\n self.spindleSpinRateAverager = MovingAverageComputer()\n self.spindleSpinRateAverager.timeWindow = 0.5\n self._getSpindleAngleFunction = getSpindleAngleFunction\n\n def onRobotStateChanged(self, newState):\n t, newAngle = self._getSpindleAngleFunction()\n elapsed = t - self.lastStateTime\n if elapsed > 0.001 and elapsed < 100:\n # unwrap\n diff = newAngle - self.lastSpindleAngle\n if abs(diff - 2 * math.pi) < abs(diff):\n diff = diff - 2 * math.pi\n if abs(diff + 2 * math.pi) < abs(diff):\n diff = diff + 2 * math.pi\n velocity = diff / elapsed\n self.spindleSpinRateAverager.update(velocity)\n # if avg veloicty is bad panic\n self.lastStateTime = t\n self.lastSpindleAngle = newAngle\n\n def getAverageSpindleVelocity(self):\n return self.spindleSpinRateAverager.getAverage()\n\n\nclass RosGridMap(vis.PolyDataItem):\n\n _requiredProviderClass = perceptionmeta.RosGridMapMeta\n\n def __init__(\n self, robotStateJointController, name, callbackFunc=None, provider=None\n ):\n vis.PolyDataItem.__init__(self, name, vtk.vtkPolyData(), view=None)\n self.firstData = True\n self.robotStateJointController = robotStateJointController\n self.timer = TimerCallback()\n self.timer.callback = self.showMap\n self.timer.start()\n self.callbackFunc = callbackFunc\n\n if provider:\n self.setProvider(provider)\n else:\n self.provider = None\n\n def setProvider(self, provider):\n \"\"\"\n Set the provider for this grid map. This completes the initialisation of the object and displays the grid map\n by pulling data from the provider\n\n :param provider: An instantiation of the RosGridMapMeta abstract class\n :return:\n \"\"\"\n if not issubclass(provider.__class__, self._requiredProviderClass):\n raise TypeError(\n \"Attempted to set {} provider to {}, but it was not a\"\n \" subclass of {} as is required.\".format(\n self.__class__,\n provider.__class__,\n self._requiredProviderClass.__class__,\n )\n )\n\n self.provider = provider\n self.provider.set_consumer(self)\n if self.getProperty(\"Visible\"):\n self.provider.start()\n\n def _onPropertyChanged(self, propertySet, propertyName):\n vis.PolyDataItem._onPropertyChanged(self, propertySet, propertyName)\n if propertyName == \"Visible\":\n if self.getProperty(propertyName):\n self.timer.start()\n if self.provider:\n self.provider.start()\n else:\n self.timer.stop()\n if self.provider:\n self.provider.stop()\n elif propertyName == \"Color By\":\n color = self.getPropertyEnumValue(propertyName)\n self.provider.set_color_layer(color)\n # only_new_data = False because the poly_date need to be redraw with the new color layer\n self.showMap(only_new_data=False)\n self._updateColorBy()\n\n if self.provider:\n self.provider._on_property_changed(propertySet, propertyName)\n\n @CheckProvider\n def showMap(self, only_new_data=True):\n\n polyData = vtk.vtkPolyData()\n self.provider.get_mesh(polyData, only_new_data)\n if polyData.GetNumberOfPoints() == 0:\n return\n\n bodyHeight = self.robotStateJointController.q[2]\n self.setRangeMap(\"z\", [bodyHeight - 0.5, bodyHeight])\n\n if self.callbackFunc:\n self.callbackFunc()\n # update view\n self.setPolyData(polyData)\n\n if self.firstData:\n self.firstData = False\n colorList = self.properties.getPropertyAttribute(\"Color By\", \"enumNames\")\n zIndex = colorList.index(\"z\") if \"z\" in colorList else 0\n self.properties.setProperty(\"Color By\", zIndex)\n\n @CheckProvider\n def resetTime(self):\n self.provider.reset_time()\n\n @CheckProvider\n def getPointCloud(self):\n polyData = vtk.vtkPolyData()\n self.provider.get_point_cloud(polyData)\n if polyData.GetNumberOfPoints() == 0:\n return None\n else:\n return polyData\n\n\nclass MarkerSource(vis.PolyDataItem):\n\n _requiredProviderClass = perceptionmeta.MarkerSourceMeta\n\n def __init__(self, name, callbackFunc=None, provider=None):\n vis.PolyDataItem.__init__(self, name, vtk.vtkPolyData(), view=None)\n self.timer = TimerCallback()\n self.timer.callback = self.showData\n self.timer.start()\n\n self.callbackFunc = callbackFunc\n self.resetColor = True\n\n if provider:\n self.setProvider(provider)\n else:\n self.provider = None\n\n def setProvider(self, provider):\n \"\"\"\n Set the provider for this marker. This completes the initialisation of the object and displays the markers\n by pulling data from the provider\n\n :param provider: An instantiation of the MarkerSourceMeta abstract class\n :return:\n \"\"\"\n if not issubclass(provider.__class__, self._requiredProviderClass):\n raise TypeError(\n \"Attempted to set {} provider to {}, but it was not a\"\n \" subclass of {} as is required.\".format(\n self.__class__,\n provider.__class__,\n self._requiredProviderClass.__class__,\n )\n )\n\n self.provider = provider\n self.provider.set_consumer(self)\n if self.getProperty(\"Visible\"):\n self.provider.start()\n\n def _onPropertyChanged(self, propertySet, propertyName):\n vis.PolyDataItem._onPropertyChanged(self, propertySet, propertyName)\n if propertyName == \"Visible\" or propertyName == \"Subscribe\":\n if self.getProperty(propertyName):\n self.timer.start()\n if self.provider:\n self.provider.start()\n else:\n self.timer.stop()\n if self.provider:\n self.provider.stop()\n\n if self.provider:\n self.provider._on_property_changed(propertySet, propertyName)\n\n @CheckProvider\n def resetTime(self):\n self.provider.reset_time()\n\n @CheckProvider\n def showData(self):\n polyData = vtk.vtkPolyData()\n self.provider.get_mesh(polyData)\n\n if polyData.GetNumberOfPoints() == 0:\n # if an empty message is received, we will reset the default color when the next message is received\n self.resetColor = True\n\n if self.callbackFunc:\n self.callbackFunc()\n\n # update view\n self.setPolyData(polyData)\n\n if self.resetColor and polyData.GetNumberOfPoints() != 0:\n self.resetColor = False\n colorList = self.properties.getPropertyAttribute(\"Color By\", \"enumNames\")\n index = colorList.index(\"color\") if \"color\" in colorList else 0\n self.properties.setProperty(\"Color By\", index)\n\n\nclass MarkerArraySource(vis.PolyDataItemList):\n\n _requiredProviderClass = perceptionmeta.MarkerArraySourceMeta\n\n def __init__(self, name, singlePolyData=False, callbackFunc=None, provider=None):\n vis.PolyDataItemList.__init__(self, name, \"color\")\n # if singlePolyData is True, it means that all the markers received are merged into a single one\n self.singlePolyData = singlePolyData\n self.timer = TimerCallback()\n self.timer.callback = self.showData\n self.timer.start()\n self.callbackFunc = callbackFunc\n # self.topicName = topicName\n if provider:\n self.setProvider(provider)\n else:\n self.provider = None\n\n def setProvider(self, provider):\n \"\"\"\n Set the provider for this marker array. This completes the initialisation of the object and displays the marker\n array by pulling data from the provider\n\n :param provider: An instantiation of the MarkerArraySourceMeta abstract class\n :return:\n \"\"\"\n if not issubclass(provider.__class__, self._requiredProviderClass):\n raise TypeError(\n \"Attempted to set {} provider to {}, but it was not a\"\n \" subclass of {} as is required.\".format(\n self.__class__,\n provider.__class__,\n self._requiredProviderClass.__class__,\n )\n )\n\n self.provider = provider\n self.provider.set_consumer(self)\n if self.getProperty(\"Visible\"):\n self.provider.start()\n\n def _onPropertyChanged(self, propertySet, propertyName):\n vis.PolyDataItemList._onPropertyChanged(self, propertySet, propertyName)\n if propertyName == \"Visible\" or propertyName == \"Subscribe\":\n if self.getProperty(propertyName):\n self.timer.start()\n if self.provider:\n self.provider.start()\n else:\n self.timer.stop()\n if self.provider:\n self.provider.stop()\n\n if self.provider:\n self.provider._on_property_changed(propertySet, propertyName)\n\n def resetTime(self):\n self.provider.reset_time()\n\n @CheckProvider\n def showData(self):\n numPoly = self.provider.get_number_of_mesh()\n polyDataList = []\n if self.singlePolyData:\n polyData = vtk.vtkPolyData()\n self.provider.get_mesh(polyData)\n if polyData.GetNumberOfPoints() > 0:\n polyDataList.append(polyData)\n else:\n for i in range(0, numPoly):\n polyData = vtk.vtkPolyData()\n self.provider.get_mesh(polyData, i)\n polyDataList.append(polyData)\n\n if self.callbackFunc:\n self.callbackFunc()\n\n self.setPolyData(polyDataList)\n\n\nclass PointCloudSource(vis.PolyDataItem):\n\n _requiredProviderClass = perceptionmeta.PointCloudSourceMeta\n\n def __init__(\n self, name, robotStateJointController, callbackFunc=None, provider=None\n ):\n vis.PolyDataItem.__init__(self, name, vtk.vtkPolyData(), view=None)\n self.firstData = True\n self.robotStateJointController = robotStateJointController\n self.timer = TimerCallback()\n self.timer.callback = self.showPointCloud\n self.timer.start()\n self.callbackFunc = callbackFunc\n if provider:\n self.setProvider(provider)\n else:\n self.provider = None\n\n def setProvider(self, provider):\n \"\"\"\n Set the provider for this cloud. This completes the initialisation of the object and displays the cloud by\n pulling data from the provider\n\n :param provider: An instantiation of the PointCloudSourceMeta abstract class\n :return:\n \"\"\"\n if not issubclass(provider.__class__, self._requiredProviderClass):\n raise TypeError(\n \"Attempted to set {} provider to {}, but it was not a\"\n \" subclass of {} as is required.\".format(\n self.__class__,\n provider.__class__,\n self._requiredProviderClass.__class__,\n )\n )\n\n self.provider = provider\n self.provider.set_consumer(self)\n self.addProperty(\"Updates Enabled\", True)\n self.addProperty(\n \"Number of Point Clouds\",\n 10,\n attributes=om.PropertyAttributes(\n decimals=0, minimum=1, maximum=100, singleStep=1, hidden=False\n ),\n )\n if self.getProperty(\"Visible\"):\n self.provider.start()\n\n def _onPropertyChanged(self, propertySet, propertyName):\n vis.PolyDataItem._onPropertyChanged(self, propertySet, propertyName)\n if propertyName == \"Visible\" or propertyName == \"Updates Enabled\":\n if self.getProperty(propertyName):\n self.timer.start()\n if self.provider:\n self.provider.start()\n else:\n self.timer.stop()\n if self.provider:\n self.provider.stop()\n elif propertyName == \"Number of Point Clouds\":\n numberOfPointCloud = self.getProperty(propertyName)\n self.provider.set_num_pointclouds(numberOfPointCloud)\n\n if self.provider:\n self.provider._on_property_changed(propertySet, propertyName)\n\n @CheckProvider\n def getPointCloud(self):\n polyData = vtk.vtkPolyData()\n self.provider.get_point_cloud(polyData)\n if polyData.GetNumberOfPoints() == 0:\n return None\n else:\n return polyData\n\n @CheckProvider\n def resetTime(self):\n self.provider.reset_time()\n\n @CheckProvider\n def showPointCloud(self):\n polyData = vtk.vtkPolyData()\n self.provider.get_point_cloud(polyData, True)\n if polyData.GetNumberOfPoints() == 0:\n return\n\n bodyHeight = self.robotStateJointController.q[2]\n self.setRangeMap(\"z\", [bodyHeight - 0.5, bodyHeight + 0.5])\n\n if self.callbackFunc:\n self.callbackFunc()\n # update view\n self.setPolyData(polyData)\n\n if self.firstData:\n self.firstData = False\n colorList = self.properties.getPropertyAttribute(\"Color By\", \"enumNames\")\n zIndex = colorList.index(\"z\") if \"z\" in colorList else 0\n self.properties.setProperty(\"Color By\", zIndex)\n\n\nclass DepthImagePointCloudSource(vis.PolyDataItem):\n\n _requiredProviderClass = perceptionmeta.DepthImageSourceMeta\n\n def __init__(\n self, name, cameraName, imageManager, robotStateJointController, provider=None\n ):\n vis.PolyDataItem.__init__(self, name, vtk.vtkPolyData(), view=None)\n\n self.robotStateJointController = robotStateJointController\n self.addProperty(\"Camera name\", cameraName)\n\n self.addProperty(\n \"Decimation\",\n 1,\n attributes=om.PropertyAttributes(enumNames=[\"1\", \"2\", \"4\", \"8\", \"16\"]),\n )\n self.addProperty(\n \"Remove Size\",\n 1000,\n attributes=om.PropertyAttributes(\n decimals=0, minimum=0, maximum=100000.0, singleStep=1000\n ),\n )\n self.addProperty(\n \"Target FPS\",\n 5.0,\n attributes=om.PropertyAttributes(\n decimals=1, minimum=0.1, maximum=30.0, singleStep=0.1\n ),\n )\n self.addProperty(\n \"Max Range\",\n 5.0,\n attributes=om.PropertyAttributes(\n decimals=2, minimum=0.0, maximum=30.0, singleStep=0.25\n ),\n )\n\n self.imageManager = imageManager\n self.cameraName = cameraName\n self.firstData = True\n self.timer = TimerCallback()\n self.timer.callback = self.update\n self.lastUtime = 0\n self.lastDataReceivedTime = 0\n\n if provider:\n self.setProvider(provider)\n else:\n self.provider = None\n\n def setProvider(self, provider):\n \"\"\"\n Set the provider for this depth image cloud. This completes the initialisation of the object and displays the\n cloud by pulling data from the provider\n\n :param provider: An instantiation of the DepthImageSourceMeta abstract class\n :return:\n \"\"\"\n if not issubclass(provider.__class__, self._requiredProviderClass):\n raise TypeError(\n \"Attempted to set {} provider to {}, but it was not a\"\n \" subclass of {} as is required.\".format(\n self.__class__,\n provider.__class__,\n self._requiredProviderClass.__class__,\n )\n )\n\n self.provider = provider\n self.provider.set_consumer(self)\n\n decimation = int(self.properties.getPropertyEnumValue(\"Decimation\"))\n removeSize = int(self.properties.getProperty(\"Remove Size\"))\n rangeThreshold = float(self.properties.getProperty(\"Max Range\"))\n self.addProperty(\"Remove Stale Data\", False)\n self.addProperty(\n \"Stale Data Timeout\",\n 5.0,\n attributes=om.PropertyAttributes(\n decimals=1, minimum=0.1, maximum=30.0, singleStep=0.1\n ),\n )\n\n self.provider.set_decimate(int(decimation))\n self.provider.set_remove_size(removeSize)\n self.provider.set_range_threshold(rangeThreshold)\n self.setProperty(\"Visible\", True)\n self.lastDataReceivedTime = time.time()\n\n def _onPropertyChanged(self, propertySet, propertyName):\n vis.PolyDataItem._onPropertyChanged(self, propertySet, propertyName)\n\n if propertyName == \"Visible\":\n if self.getProperty(propertyName):\n self.timer.start()\n if self.provider:\n self.provider.start()\n else:\n self.timer.stop()\n if self.provider:\n self.provider.stop()\n\n if propertyName in (\"Decimation\", \"Remove outliers\", \"Max Range\"):\n self.lastUtime = 0\n if propertyName == \"Decimation\":\n decimate = self.getPropertyEnumValue(propertyName)\n self.provider.set_decimate(int(decimate))\n elif propertyName == \"Remove Size\":\n remove_size = self.getProperty(propertyName)\n self.provider.set_remove_size(remove_size)\n elif propertyName == \"Max Range\":\n max_range = self.getProperty(propertyName)\n self.provider.set_range_threshold(max_range)\n\n @CheckProvider\n def getPointCloud(self):\n polyData = vtk.vtkPolyData()\n self.provider.get_point_cloud(polyData)\n if polyData.GetNumberOfPoints() == 0:\n return None\n else:\n return polyData\n\n def onRemoveFromObjectModel(self):\n vis.PolyDataItem.onRemoveFromObjectModel(self)\n self.timer.stop()\n\n @CheckProvider\n def resetTime(self):\n self.provider.reset_time()\n\n @CheckProvider\n def update(self):\n # utime = self.imageManager.queue.getCurrentImageTime(self.cameraName)\n utime = self.provider.get_sec() * 1e6 + round(self.provider.get_nsec() * 1e-3)\n\n if utime == self.lastUtime:\n if self.getProperty(\"Remove Stale Data\") and (\n (time.time() - self.lastDataReceivedTime)\n > self.getProperty(\"Stale Data Timeout\")\n ):\n if self.polyData.GetNumberOfPoints() > 0:\n self.setPolyData(vtk.vtkPolyData())\n return\n\n if utime < self.lastUtime:\n temp = 0 # dummy\n elif utime - self.lastUtime < 1e6 / self.getProperty(\"Target FPS\"):\n return\n\n polyData = vtk.vtkPolyData()\n new_data = self.provider.get_point_cloud(polyData, True)\n if polyData.GetNumberOfPoints() == 0:\n return\n\n # currently disabled\n # bodyToLocal = vtk.vtkTransform()\n # self.imageManager.queue.getTransform('body', 'local', utime, bodyToLocal)\n # bodyHeight = bodyToLocal.GetPosition()[2]\n\n bodyHeight = self.robotStateJointController.q[2]\n self.setRangeMap(\"z\", [bodyHeight - 0.5, bodyHeight + 0.5])\n\n self.setPolyData(polyData)\n\n if self.firstData:\n self.firstData = False\n colorList = self.properties.getPropertyAttribute(\"Color By\", \"enumNames\")\n zIndex = colorList.index(\"z\") if \"z\" in colorList else 0\n self.properties.setProperty(\"Color By\", zIndex)\n\n self.lastDataReceivedTime = time.time()\n self.lastUtime = utime\n\n\ndef init(view, robotStateJointController):\n global _multisenseItem\n\n sensorsFolder = om.getOrCreateContainer(\n \"sensors\", om.getOrCreateContainer(robotStateJointController.robotName)\n )\n\n config = drcargs.getRobotConfig(robotStateJointController.robotName)[\n \"perceptionSources\"\n ]\n\n validSourceTypes = [\"gridMap\", \"depthImagePointCloud\", \"pointCloud\"]\n\n perceptionSources = {}\n if not config: # if config == None\n return perceptionSources\n for sourceType in config:\n if sourceType not in validSourceTypes:\n raise ValueError(\n \"Source type {} is not a recognised perception source. Valid types are {}. Check your\"\n \" director configuration.\".format(sourceType, validSourceTypes)\n )\n # TODO might be nice to avoid the if statement in the loop by having a function to create each source\n for sourceConfig in config[sourceType]:\n if sourceType == \"gridMap\":\n source = RosGridMap(\n robotStateJointController,\n sourceConfig[\"name\"],\n callbackFunc=view.render,\n )\n source.addToView(view)\n om.addToObjectModel(source, sensorsFolder)\n\n if sourceType == \"depthImagePointCloud\":\n source = DepthImagePointCloudSource(\n sourceConfig[\"name\"],\n sourceConfig[\"sensor\"],\n None,\n robotStateJointController,\n )\n source.addToView(view)\n om.addToObjectModel(source, sensorsFolder)\n if sourceType == \"pointCloud\":\n source = PointCloudSource(\n sourceConfig[\"name\"],\n robotStateJointController,\n callbackFunc=view.render,\n )\n source.addToView(view)\n om.addToObjectModel(source, sensorsFolder)\n\n if \"properties\" in sourceConfig:\n for prop, value in sourceConfig[\"properties\"].items():\n source.setProperty(prop, value)\n\n perceptionSources[sourceConfig[\"robotSystemKey\"]] = source\n\n return perceptionSources\n", "repo_name": "ori-drs/director", "sub_path": "src/python/director/perception.py", "file_name": "perception.py", "file_ext": "py", "file_size_in_byte": 41538, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 28, "dataset": "github-code", "pt": "37", "api": [{"api_name": "functools.update_wrapper", "line_number": 33, "usage_type": "call"}, {"api_name": "functools.partial", "line_number": 42, "usage_type": "call"}, {"api_name": "director.objectmodel.ObjectModelItem", "line_number": 59, "usage_type": "attribute"}, {"api_name": "director.objectmodel", "line_number": 59, "usage_type": "name"}, {"api_name": "director.objectmodel.ObjectModelItem.__init__", "line_number": 62, "usage_type": "call"}, {"api_name": "director.objectmodel.ObjectModelItem", "line_number": 62, "usage_type": "attribute"}, {"api_name": "director.objectmodel", "line_number": 62, "usage_type": "name"}, {"api_name": "director.objectmodel.Icons", "line_number": 62, "usage_type": "attribute"}, {"api_name": "director.objectmodel.PropertyAttributes", "line_number": 69, "usage_type": "call"}, {"api_name": "director.objectmodel", "line_number": 69, "usage_type": "name"}, {"api_name": "director.objectmodel.PropertyAttributes", "line_number": 87, "usage_type": "call"}, {"api_name": "director.objectmodel", "line_number": 87, "usage_type": "name"}, {"api_name": "director.objectmodel.PropertyAttributes", "line_number": 94, "usage_type": "call"}, {"api_name": "director.objectmodel", "line_number": 94, "usage_type": "name"}, {"api_name": "director.objectmodel.PropertyAttributes", "line_number": 101, "usage_type": "call"}, {"api_name": "director.objectmodel", "line_number": 101, "usage_type": "name"}, {"api_name": "director.objectmodel.PropertyAttributes", "line_number": 108, "usage_type": "call"}, {"api_name": "director.objectmodel", "line_number": 108, "usage_type": "name"}, {"api_name": "director.objectmodel.PropertyAttributes", "line_number": 116, "usage_type": "call"}, {"api_name": "director.objectmodel", "line_number": 116, "usage_type": "name"}, {"api_name": "director.objectmodel.PropertyAttributes", "line_number": 123, "usage_type": "call"}, {"api_name": "director.objectmodel", "line_number": 123, "usage_type": "name"}, {"api_name": "director.objectmodel.PropertyAttributes", "line_number": 130, "usage_type": "call"}, {"api_name": "director.objectmodel", "line_number": 130, "usage_type": "name"}, {"api_name": "director.objectmodel.PropertyAttributes", "line_number": 137, "usage_type": "call"}, {"api_name": "director.objectmodel", "line_number": 137, "usage_type": "name"}, {"api_name": "director.objectmodel.ObjectModelItem._onPropertyChanged", "line_number": 146, "usage_type": "call"}, {"api_name": "director.objectmodel.ObjectModelItem", "line_number": 146, "usage_type": "attribute"}, {"api_name": "director.objectmodel", "line_number": 146, "usage_type": "name"}, {"api_name": "director.objectmodel.ObjectModelItem", "line_number": 227, "usage_type": "attribute"}, {"api_name": "director.objectmodel", "line_number": 227, "usage_type": "name"}, {"api_name": "director.objectmodel.ObjectModelItem.__init__", "line_number": 229, "usage_type": "call"}, {"api_name": "director.objectmodel.ObjectModelItem", "line_number": 229, "usage_type": "attribute"}, {"api_name": "director.objectmodel", "line_number": 229, "usage_type": "name"}, {"api_name": "director.objectmodel.Icons", "line_number": 229, "usage_type": "attribute"}, {"api_name": "director.objectmodel.PropertyAttributes", "line_number": 236, "usage_type": "call"}, {"api_name": "director.objectmodel", "line_number": 236, "usage_type": "name"}, {"api_name": "director.objectmodel.PropertyAttributes", "line_number": 254, "usage_type": "call"}, {"api_name": "director.objectmodel", "line_number": 254, "usage_type": "name"}, {"api_name": "director.objectmodel.PropertyAttributes", "line_number": 261, "usage_type": "call"}, {"api_name": "director.objectmodel", "line_number": 261, "usage_type": "name"}, {"api_name": "director.objectmodel.PropertyAttributes", "line_number": 268, "usage_type": "call"}, {"api_name": "director.objectmodel", "line_number": 268, "usage_type": "name"}, {"api_name": "director.objectmodel.PropertyAttributes", "line_number": 275, "usage_type": "call"}, {"api_name": "director.objectmodel", "line_number": 275, "usage_type": "name"}, {"api_name": "director.objectmodel.PropertyAttributes", "line_number": 283, "usage_type": "call"}, {"api_name": "director.objectmodel", "line_number": 283, "usage_type": "name"}, {"api_name": "director.objectmodel.PropertyAttributes", "line_number": 290, "usage_type": "call"}, {"api_name": "director.objectmodel", "line_number": 290, "usage_type": "name"}, {"api_name": "director.objectmodel.PropertyAttributes", "line_number": 297, "usage_type": "call"}, {"api_name": "director.objectmodel", "line_number": 297, "usage_type": "name"}, {"api_name": "director.objectmodel.PropertyAttributes", "line_number": 304, "usage_type": "call"}, {"api_name": "director.objectmodel", "line_number": 304, "usage_type": "name"}, {"api_name": "director.objectmodel.ObjectModelItem._onPropertyChanged", "line_number": 313, "usage_type": "call"}, {"api_name": "director.objectmodel.ObjectModelItem", "line_number": 313, "usage_type": "attribute"}, {"api_name": "director.objectmodel", "line_number": 313, "usage_type": "name"}, {"api_name": "director.visualization.PolyDataItem", "line_number": 388, "usage_type": "attribute"}, {"api_name": "director.visualization", "line_number": 388, "usage_type": "name"}, {"api_name": "director.visualization.PolyDataItem.__init__", "line_number": 390, "usage_type": "call"}, {"api_name": "director.visualization.PolyDataItem", "line_number": 390, "usage_type": "attribute"}, {"api_name": "director.visualization", "line_number": 390, "usage_type": "name"}, {"api_name": "vtk.vtkPolyData", "line_number": 390, "usage_type": "call"}, {"api_name": "director.timercallback.TimerCallback", "line_number": 392, "usage_type": "call"}, {"api_name": "PythonQt.QtGui.QColor", "line_number": 394, "usage_type": "call"}, {"api_name": "PythonQt.QtGui", "line_number": 394, "usage_type": "name"}, {"api_name": "director.visualization.PolyDataItem._onPropertyChanged", "line_number": 398, "usage_type": "call"}, {"api_name": "director.visualization.PolyDataItem", "line_number": 398, "usage_type": "attribute"}, {"api_name": "director.visualization", "line_number": 398, "usage_type": "name"}, {"api_name": "director.visualization.PolyDataItem.onRemoveFromObjectModel", "line_number": 407, "usage_type": "call"}, {"api_name": "director.visualization.PolyDataItem", "line_number": 407, "usage_type": "attribute"}, {"api_name": "director.visualization", "line_number": 407, "usage_type": "name"}, {"api_name": "director.debugpolydata.DebugData", "line_number": 420, "usage_type": "call"}, {"api_name": "director.timercallback.TimerCallback", "line_number": 426, "usage_type": "name"}, {"api_name": "director.timercallback.TimerCallback.__init__", "line_number": 430, "usage_type": "call"}, {"api_name": "director.timercallback.TimerCallback", "line_number": 430, "usage_type": "name"}, {"api_name": "vtk.vtkPolyData", "line_number": 448, "usage_type": "call"}, {"api_name": "director.visualization.PolyDataItem", "line_number": 449, "usage_type": "call"}, {"api_name": "director.visualization", "line_number": 449, "usage_type": "name"}, {"api_name": "vtk.vtkPolyData", "line_number": 469, "usage_type": "call"}, {"api_name": "director.visualization.PolyDataItem", "line_number": 470, "usage_type": "call"}, {"api_name": "director.visualization", "line_number": 470, "usage_type": "name"}, {"api_name": "vtkDRCFiltersPython.vtkLidarSource", "line_number": 507, "usage_type": "call"}, {"api_name": "director.drcargs.args", "line_number": 510, "usage_type": "call"}, {"api_name": "director.drcargs", "line_number": 510, "usage_type": "name"}, {"api_name": "director.timercallback.TimerCallback.start", "line_number": 515, "usage_type": "call"}, {"api_name": "director.timercallback.TimerCallback", "line_number": 515, "usage_type": "name"}, {"api_name": "vtk.vtkPolyData", "line_number": 551, "usage_type": "call"}, {"api_name": "director.visualization.updatePolyData", "line_number": 553, "usage_type": "call"}, {"api_name": "director.visualization", "line_number": 553, "usage_type": "name"}, {"api_name": "director.simpletimer.MovingAverageComputer", "line_number": 566, "usage_type": "call"}, {"api_name": "math.pi", "line_number": 576, "usage_type": "attribute"}, {"api_name": "math.pi", "line_number": 577, "usage_type": "attribute"}, {"api_name": "math.pi", "line_number": 578, "usage_type": "attribute"}, {"api_name": "math.pi", "line_number": 579, "usage_type": "attribute"}, {"api_name": "director.visualization.PolyDataItem", "line_number": 590, "usage_type": "attribute"}, {"api_name": "director.visualization", "line_number": 590, "usage_type": "name"}, {"api_name": "director.perceptionmeta.RosGridMapMeta", "line_number": 592, "usage_type": "attribute"}, {"api_name": "director.perceptionmeta", "line_number": 592, "usage_type": "name"}, {"api_name": "director.visualization.PolyDataItem.__init__", "line_number": 597, "usage_type": "call"}, {"api_name": "director.visualization.PolyDataItem", "line_number": 597, "usage_type": "attribute"}, {"api_name": "director.visualization", "line_number": 597, "usage_type": "name"}, {"api_name": "vtk.vtkPolyData", "line_number": 597, "usage_type": "call"}, {"api_name": "director.timercallback.TimerCallback", "line_number": 600, "usage_type": "call"}, {"api_name": "director.visualization.PolyDataItem._onPropertyChanged", "line_number": 634, "usage_type": "call"}, {"api_name": "director.visualization.PolyDataItem", "line_number": 634, "usage_type": "attribute"}, {"api_name": "director.visualization", "line_number": 634, "usage_type": "name"}, {"api_name": "vtk.vtkPolyData", "line_number": 657, "usage_type": "call"}, {"api_name": "vtk.vtkPolyData", "line_number": 682, "usage_type": "call"}, {"api_name": "director.visualization.PolyDataItem", "line_number": 690, "usage_type": "attribute"}, {"api_name": "director.visualization", "line_number": 690, "usage_type": "name"}, {"api_name": "director.perceptionmeta.MarkerSourceMeta", "line_number": 692, "usage_type": "attribute"}, {"api_name": "director.perceptionmeta", "line_number": 692, "usage_type": "name"}, {"api_name": "director.visualization.PolyDataItem.__init__", "line_number": 695, "usage_type": "call"}, {"api_name": "director.visualization.PolyDataItem", "line_number": 695, "usage_type": "attribute"}, {"api_name": "director.visualization", "line_number": 695, "usage_type": "name"}, {"api_name": "vtk.vtkPolyData", "line_number": 695, "usage_type": "call"}, {"api_name": "director.timercallback.TimerCallback", "line_number": 696, "usage_type": "call"}, {"api_name": "director.visualization.PolyDataItem._onPropertyChanged", "line_number": 732, "usage_type": "call"}, {"api_name": "director.visualization.PolyDataItem", "line_number": 732, "usage_type": "attribute"}, {"api_name": "director.visualization", "line_number": 732, "usage_type": "name"}, {"api_name": "vtk.vtkPolyData", "line_number": 752, "usage_type": "call"}, {"api_name": "director.visualization.PolyDataItemList", "line_number": 772, "usage_type": "attribute"}, {"api_name": "director.visualization", "line_number": 772, "usage_type": "name"}, {"api_name": "director.perceptionmeta.MarkerArraySourceMeta", "line_number": 774, "usage_type": "attribute"}, {"api_name": "director.perceptionmeta", "line_number": 774, "usage_type": "name"}, {"api_name": "director.visualization.PolyDataItemList.__init__", "line_number": 777, "usage_type": "call"}, {"api_name": "director.visualization.PolyDataItemList", "line_number": 777, "usage_type": "attribute"}, {"api_name": "director.visualization", "line_number": 777, "usage_type": "name"}, {"api_name": "director.timercallback.TimerCallback", "line_number": 780, "usage_type": "call"}, {"api_name": "director.visualization.PolyDataItemList._onPropertyChanged", "line_number": 814, "usage_type": "call"}, {"api_name": "director.visualization.PolyDataItemList", "line_number": 814, "usage_type": "attribute"}, {"api_name": "director.visualization", "line_number": 814, "usage_type": "name"}, {"api_name": "vtk.vtkPolyData", "line_number": 836, "usage_type": "call"}, {"api_name": "vtk.vtkPolyData", "line_number": 842, "usage_type": "call"}, {"api_name": "director.visualization.PolyDataItem", "line_number": 852, "usage_type": "attribute"}, {"api_name": "director.visualization", "line_number": 852, "usage_type": "name"}, {"api_name": "director.perceptionmeta.PointCloudSourceMeta", "line_number": 854, "usage_type": "attribute"}, {"api_name": "director.perceptionmeta", "line_number": 854, "usage_type": "name"}, {"api_name": "director.visualization.PolyDataItem.__init__", "line_number": 859, "usage_type": "call"}, {"api_name": "director.visualization.PolyDataItem", "line_number": 859, "usage_type": "attribute"}, {"api_name": "director.visualization", "line_number": 859, "usage_type": "name"}, {"api_name": "vtk.vtkPolyData", "line_number": 859, "usage_type": "call"}, {"api_name": "director.timercallback.TimerCallback", "line_number": 862, "usage_type": "call"}, {"api_name": "director.objectmodel.PropertyAttributes", "line_number": 895, "usage_type": "call"}, {"api_name": "director.objectmodel", "line_number": 895, "usage_type": "name"}, {"api_name": "director.visualization.PolyDataItem._onPropertyChanged", "line_number": 903, "usage_type": "call"}, {"api_name": "director.visualization.PolyDataItem", "line_number": 903, "usage_type": "attribute"}, {"api_name": "director.visualization", "line_number": 903, "usage_type": "name"}, {"api_name": "vtk.vtkPolyData", "line_number": 922, "usage_type": "call"}, {"api_name": "vtk.vtkPolyData", "line_number": 935, "usage_type": "call"}, {"api_name": "director.visualization.PolyDataItem", "line_number": 955, "usage_type": "attribute"}, {"api_name": "director.visualization", "line_number": 955, "usage_type": "name"}, {"api_name": "director.perceptionmeta.DepthImageSourceMeta", "line_number": 957, "usage_type": "attribute"}, {"api_name": "director.perceptionmeta", "line_number": 957, "usage_type": "name"}, {"api_name": "director.visualization.PolyDataItem.__init__", "line_number": 962, "usage_type": "call"}, {"api_name": "director.visualization.PolyDataItem", "line_number": 962, "usage_type": "attribute"}, {"api_name": "director.visualization", "line_number": 962, "usage_type": "name"}, {"api_name": "vtk.vtkPolyData", "line_number": 962, "usage_type": "call"}, {"api_name": "director.objectmodel.PropertyAttributes", "line_number": 970, "usage_type": "call"}, {"api_name": "director.objectmodel", "line_number": 970, "usage_type": "name"}, {"api_name": "director.objectmodel.PropertyAttributes", "line_number": 975, "usage_type": "call"}, {"api_name": "director.objectmodel", "line_number": 975, "usage_type": "name"}, {"api_name": "director.objectmodel.PropertyAttributes", "line_number": 982, "usage_type": "call"}, {"api_name": "director.objectmodel", "line_number": 982, "usage_type": "name"}, {"api_name": "director.objectmodel.PropertyAttributes", "line_number": 989, "usage_type": "call"}, {"api_name": "director.objectmodel", "line_number": 989, "usage_type": "name"}, {"api_name": "director.timercallback.TimerCallback", "line_number": 997, "usage_type": "call"}, {"api_name": "director.objectmodel.PropertyAttributes", "line_number": 1035, "usage_type": "call"}, {"api_name": "director.objectmodel", "line_number": 1035, "usage_type": "name"}, {"api_name": "time.time", "line_number": 1044, "usage_type": "call"}, {"api_name": "director.visualization.PolyDataItem._onPropertyChanged", "line_number": 1047, "usage_type": "call"}, {"api_name": "director.visualization.PolyDataItem", "line_number": 1047, "usage_type": "attribute"}, {"api_name": "director.visualization", "line_number": 1047, "usage_type": "name"}, {"api_name": "vtk.vtkPolyData", "line_number": 1073, "usage_type": "call"}, {"api_name": "director.visualization.PolyDataItem.onRemoveFromObjectModel", "line_number": 1081, "usage_type": "call"}, {"api_name": "director.visualization.PolyDataItem", "line_number": 1081, "usage_type": "attribute"}, {"api_name": "director.visualization", "line_number": 1081, "usage_type": "name"}, {"api_name": "time.time", "line_number": 1095, "usage_type": "call"}, {"api_name": "vtk.vtkPolyData", "line_number": 1099, "usage_type": "call"}, {"api_name": "vtk.vtkPolyData", "line_number": 1107, "usage_type": "call"}, {"api_name": "time.time", "line_number": 1128, "usage_type": "call"}, {"api_name": "director.objectmodel.getOrCreateContainer", "line_number": 1135, "usage_type": "call"}, {"api_name": "director.objectmodel", "line_number": 1135, "usage_type": "name"}, {"api_name": "director.objectmodel.getOrCreateContainer", "line_number": 1136, "usage_type": "call"}, {"api_name": "director.objectmodel", "line_number": 1136, "usage_type": "name"}, {"api_name": "director.drcargs.getRobotConfig", "line_number": 1139, "usage_type": "call"}, {"api_name": "director.drcargs", "line_number": 1139, "usage_type": "name"}, {"api_name": "director.objectmodel.addToObjectModel", "line_number": 1163, "usage_type": "call"}, {"api_name": "director.objectmodel", "line_number": 1163, "usage_type": "name"}, {"api_name": "director.objectmodel.addToObjectModel", "line_number": 1173, "usage_type": "call"}, {"api_name": "director.objectmodel", "line_number": 1173, "usage_type": "name"}, {"api_name": "director.objectmodel.addToObjectModel", "line_number": 1181, "usage_type": "call"}, {"api_name": "director.objectmodel", "line_number": 1181, "usage_type": "name"}]} +{"seq_id": "25615805579", "text": "import discord\nfrom discord.ext import commands\nfrom discord.utils import get\n\nclass c79(commands.Cog, name=\"c79\"):\n\n def __init__(self, bot: commands.Bot):\n self.bot = bot\n @commands.command(name='Light_Spire', aliases=['c79'])\n async def example_embed(self, ctx):\n embed = discord.Embed(title='Light Spire',\n color=0x1D9E74)\n embed.set_thumbnail(url='https://www.duelingbook.com/images/custom-pics/2300000/2321508.jpg')\n\n embed.add_field(name='Status (Archetype)', value='Casual:3/Tournament:3', inline=True)\n embed.add_field(name='Type', value='Spell/Normal', inline=False)\n embed.add_field(name='Card Effect', value='Draw 1 card, then reveal it. Until the end of this turn, you cannot activate cards of the same name as the card you drew, also, you must keep that card revealed. You can banish this card from your GY, except the turn it was sent there; this turn, your opponent must reveal their hand. You can only activate 1 \"Light Spire\" per turn.', inline=False)\n embed.set_footer(text='Set Code: ANCF')\n\n await ctx.send(embed=embed)\n\ndef setup(bot: commands.Bot):\n bot.add_cog(c79(bot))", "repo_name": "ProfessorSean/Kasutamaiza", "sub_path": "upcfcardsearch/c79.py", "file_name": "c79.py", "file_ext": "py", "file_size_in_byte": 1191, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "discord.ext.commands.Cog", "line_number": 5, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 5, "usage_type": "name"}, {"api_name": "discord.ext.commands.Bot", "line_number": 7, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 7, "usage_type": "name"}, {"api_name": "discord.Embed", "line_number": 11, "usage_type": "call"}, {"api_name": "discord.ext.commands.command", "line_number": 9, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 9, "usage_type": "name"}, {"api_name": "discord.ext.commands.Bot", "line_number": 22, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 22, "usage_type": "name"}]} +{"seq_id": "21435887213", "text": "import os\nimport config\nimport numpy as np\n\n\ndef load_embedding(filename):\n\t# load embedding into memory\n\twith open(filename, 'r') as embedding_file:\n\t\t# create a map of words to vectors\n\t\tembedding = dict()\n\t\tdictionary = dict()\n\t\tfor (i, line) in enumerate(embedding_file):\n\t\t\tvalues = line.split(' ')\n\t\t\tword = values[0]\n\t\t\t# key is string word, value is numpy array for vector\n\t\t\tembedding[word] = np.asarray(values[1:], dtype='float32')\n\t\t\tdictionary[word] = i + 1\n\treturn embedding, dictionary\n\n\ndef get_embedding_matrix(embedding, vocab):\n\t# total vocabulary size plus 0 for unknown words\n\tvocab_size = len(vocab) + 1\n\t# define weight matrix dimensions with all 0\n\tweight_matrix = np.zeros((vocab_size, config.EMBEDDING_DIM))\n\t# step vocab, store vectors using the Tokenizer's integer mapping\n\t\"\"\"for word in vocab.items():\n\t\tvector = embedding.get(word)\n\t\tif vector is not None:\n\t\t\tweight_matrix[i] = vector\"\"\"\n\tfor i, word in enumerate(vocab):\n\t\tvector = embedding[word]\n\t\tweight_matrix[i] = vector\n\treturn weight_matrix\n\n\n# load embedding from file\nprint(\"Loading Embedding file...\")\nraw_embedding, dictionary = load_embedding(\"Embedding/glove/glove_custom_{}d_{}.txt\".format(config.EMBEDDING_DIM, config.dataset_name))\n# get vectors in the right order\nprint(\"Building Embedding Matrix...\")\nembedding_vectors = get_embedding_matrix(raw_embedding, dictionary.keys())\n\nprint(\"Saving file...\")\nembedding_matrix_filename = config.dataset_name + '_embedding_matrix_' + str(config.EMBEDDING_DIM) + '.npz'\nword_embedding_filename = config.dataset_name + '_word_embedding_' + str(config.EMBEDDING_DIM) + '.npz'\ndictionary_name = config.dataset_name + '_dictionary.npz'\nnp.savez_compressed(os.path.join(config.embedding_base_dir, embedding_matrix_filename), embedding_vectors)\nnp.savez_compressed(os.path.join(config.embedding_base_dir, word_embedding_filename), raw_embedding)\nnp.savez_compressed(os.path.join(config.embedding_base_dir, dictionary_name), dictionary)\nprint(\"Finished!!\")\n", "repo_name": "ciolo/Sentiment-Analisys", "sub_path": "embedding.py", "file_name": "embedding.py", "file_ext": "py", "file_size_in_byte": 1989, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "numpy.asarray", "line_number": 16, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 25, "usage_type": "call"}, {"api_name": "config.EMBEDDING_DIM", "line_number": 25, "usage_type": "attribute"}, {"api_name": "config.EMBEDDING_DIM", "line_number": 39, "usage_type": "attribute"}, {"api_name": "config.dataset_name", "line_number": 39, "usage_type": "attribute"}, {"api_name": "config.dataset_name", "line_number": 45, "usage_type": "attribute"}, {"api_name": "config.EMBEDDING_DIM", "line_number": 45, "usage_type": "attribute"}, {"api_name": "config.dataset_name", "line_number": 46, "usage_type": "attribute"}, {"api_name": "config.EMBEDDING_DIM", "line_number": 46, "usage_type": "attribute"}, {"api_name": "config.dataset_name", "line_number": 47, "usage_type": "attribute"}, {"api_name": "numpy.savez_compressed", "line_number": 48, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 48, "usage_type": "call"}, {"api_name": "os.path", "line_number": 48, "usage_type": "attribute"}, {"api_name": "config.embedding_base_dir", "line_number": 48, "usage_type": "attribute"}, {"api_name": "numpy.savez_compressed", "line_number": 49, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 49, "usage_type": "call"}, {"api_name": "os.path", "line_number": 49, "usage_type": "attribute"}, {"api_name": "config.embedding_base_dir", "line_number": 49, "usage_type": "attribute"}, {"api_name": "numpy.savez_compressed", "line_number": 50, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 50, "usage_type": "call"}, {"api_name": "os.path", "line_number": 50, "usage_type": "attribute"}, {"api_name": "config.embedding_base_dir", "line_number": 50, "usage_type": "attribute"}]} +{"seq_id": "2372952043", "text": "from datetime import datetime\nimport sqlite3\nfrom . import err\n\nasync def checkReg(vk_id):\n conn = sqlite3.connect(\"booking.db\")\n cursor = conn.cursor()\n\n name = cursor.execute(\n \"\"\"\n SELECT name FROM users WHERE vid = ?;\n \"\"\",\n (vk_id, )\n ).fetchone()\n\n if name == None:\n return True\n else:\n return False\n\nasync def get_name(vk_id):\n conn = sqlite3.connect(\"booking.db\")\n cursor = conn.cursor()\n\n name = cursor.execute(\n \"\"\"\n SELECT name FROM users WHERE vid = ?;\n \"\"\",\n (vk_id, )\n ).fetchone()[0]\n\n conn.commit()\n conn.close()\n\n return name\n\nasync def registration(vk_id, name):\n conn = sqlite3.connect(\"booking.db\")\n cursor = conn.cursor()\n\n cursor.execute(\n \"\"\"\n INSERT INTO users(vid, name)\n VALUES (?, ?);\n \"\"\",\n (vk_id, name)\n )\n\n conn.commit()\n conn.close()\n\nasync def timebuttons():\n if datetime.now().minute > 30:\n hour = int(datetime.now().hour) + 1\n else:\n hour = int(datetime.now().hour)\n\n # hour = 21\n\n btime = []\n\n conn = sqlite3.connect(\"booking.db\")\n cursor = conn.cursor()\n\n if hour in err and hour != 16 and hour != 17:\n return btime\n\n for i in range(3):\n av = cursor.execute(\n \"SELECT * FROM availability WHERE time = ? OR time = ? OR time = ?\",\n (hour + i - 1, hour + i, hour + i + 1),\n ).fetchall()\n\n if (av[0][1] + av[1][1] < 30) and (av[1][1] + av[2][1] < 40):\n btime.append(str(av[1][0]) + \":00\")\n\n if len(btime) == 0:\n return [\"Full\"]\n\n if hour == 16 or hour == 17:\n try:\n btime.remove(\"18:00\")\n btime.remove(\"19:00\")\n except:\n pass\n\n conn.commit()\n conn.close()\n\n return btime\n\n\nasync def bookingDB(txt, vk_id):\n conn = sqlite3.connect(\"booking.db\")\n cursor = conn.cursor()\n\n av = cursor.execute(\n \"SELECT * FROM availability WHERE time = ?\", (txt.split(\":\")[0],)\n ).fetchall()\n cursor.execute(\n \"UPDATE availability SET amount = ? WHERE time = ?\", (av[0][1] + 1, av[0][0])\n )\n\n cursor.execute(\"INSERT INTO bookings(time, vid) VALUES(?, ?);\", (txt, vk_id))\n\n conn.commit()\n conn.close()\n\n\nasync def bookingCheck(time, vk_id):\n conn = sqlite3.connect(\"booking.db\")\n cursor = conn.cursor()\n\n bk = cursor.execute(\"SELECT * FROM bookings WHERE vid = ? and time = ?;\", (vk_id, time)).fetchone()\n\n conn.commit()\n conn.close()\n\n print(time, vk_id)\n print(bk)\n\n if bk == None:\n return False\n else:\n return True\n \n \nasync def bookingDelete(vk_id, time):\n conn = conn = sqlite3.connect(\"booking.db\")\n cursor = conn.cursor()\n\n cursor.execute(\n \"\"\"\n DELETE FROM bookings WHERE vid = ? and time = ?\n \"\"\", \n (vk_id, time)\n )\n\n time = int(time.split(':')[0])\n\n bk = int(cursor.execute(\"SELECT amount FROM availability WHERE time = ?\", (time, )).fetchone()[0])\n cursor.execute(\"UPDATE availability SET amount = ? WHERE time = ?\", (bk-1, time))\n\n conn.commit()\n conn.close()\n", "repo_name": "TechDepSut/gutspacebot", "sub_path": "src/utils/bookingtime.py", "file_name": "bookingtime.py", "file_ext": "py", "file_size_in_byte": 3162, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "sqlite3.connect", "line_number": 6, "usage_type": "call"}, {"api_name": "sqlite3.connect", "line_number": 22, "usage_type": "call"}, {"api_name": "sqlite3.connect", "line_number": 38, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 53, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 53, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 54, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 54, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 56, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 56, "usage_type": "name"}, {"api_name": "sqlite3.connect", "line_number": 62, "usage_type": "call"}, {"api_name": "sqlite3.connect", "line_number": 94, "usage_type": "call"}, {"api_name": "sqlite3.connect", "line_number": 111, "usage_type": "call"}, {"api_name": "sqlite3.connect", "line_number": 129, "usage_type": "call"}]} +{"seq_id": "34265796545", "text": "import numpy as np\r\nimport scipy as sp\r\nimport matplotlib.pyplot as plt\r\n\r\ndef immuneRec(y,t,N,betaS,pid,gamma):\r\n S,I,R,D = y\r\n dSdt = float(-S*I*betaS/N)\r\n dIdt = float(S*I*betaS/N - I*gamma - pid*I)\r\n dRdt = float(I*gamma)\r\n dDdt = float(pid*I)\r\n return dSdt,dIdt,dRdt,dDdt\r\n\r\ndef immuneRecNot(y,t,N,betaS,betaR,pid,gamma):\r\n S,I,R,D = y\r\n dSdt = float(-S*I*betaS/N)\r\n dIdt = float(S*I*betaS/N + R*I*betaR/N - I*gamma - pid*I)\r\n dRdt = float(I*gamma - R*I*betaR/N)\r\n dDdt = float(pid*I)\r\n return dSdt,dIdt,dRdt,dDdt\r\n\r\nN = 1000\r\npsi = 0.2\r\nm = 5\r\nd = 7\r\nbetaS = psi*m\r\npri = 0.1\r\nbetaR = pri*m\r\ngamma = 1/d\r\npid = 0.05\r\n\r\ntest_type = \"RecImmune\"\r\nS0, I0 = 999, 1\r\nR0 = 0\r\nD0 = 0\r\n\r\ny0 = [S0,I0,R0,D0]\r\n\r\nt = np.linspace(0,50,50)\r\nif test_type == \"RecImmune\":\r\n sol = sp.integrate.odeint(immuneRec, y0, t, args=(N,betaS,pid,gamma))\r\nelif test_type == \"RecNotImmune\":\r\n sol = sp.integrate.odeint(immuneRecNot, y0, t, args=(N,betaS,betaR,pid,gamma))\r\n#print(sol)\r\n\r\nplt.plot(t,sol[:,0],'b',label='Susceptible')\r\nplt.plot(t,sol[:,1],'y',label='Infected')\r\nplt.plot(t,sol[:,2],'g',label='recovered')\r\nplt.plot(t,sol[:,3],'',label='dead')\r\nplt.legend()\r\nplt.xlabel('t')\r\nplt.grid()\r\nplt.show()\r\n", "repo_name": "namak-kun/SEIRD-Pandemic-Simulation-Model", "sub_path": "SIR.py", "file_name": "SIR.py", "file_ext": "py", "file_size_in_byte": 1235, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "numpy.linspace", "line_number": 38, "usage_type": "call"}, {"api_name": "scipy.integrate.odeint", "line_number": 40, "usage_type": "call"}, {"api_name": "scipy.integrate", "line_number": 40, "usage_type": "attribute"}, {"api_name": "scipy.integrate.odeint", "line_number": 42, "usage_type": "call"}, {"api_name": "scipy.integrate", "line_number": 42, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 45, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 45, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 46, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 46, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 47, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 47, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 48, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 48, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 49, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 49, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 50, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 50, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 51, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 51, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 52, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 52, "usage_type": "name"}]} +{"seq_id": "73910426987", "text": "from itertools import product\n\n# getting user input\nuser_input1 = input(\"Please enter items in the list A \")\nuser_input2 = input(\"Please enter items in the list B \")\n\nA = set(user_input1.split(\" \"))\nB = set(user_input2.split(\" \"))\n\n\n# checking if B is subset of A\nprint(\"B is a subset of A: \" + str(B.issubset(A)))\n\n# A-B\nprint(\"A-B: \" + str(A.difference(B)))\n\n# cartesian product of A and B.\ncartesian_product = product(list(A), list(B))\nprint(\"AXB: \" + str(set(cartesian_product)))\n", "repo_name": "emilekamana/DM_PW_W7", "sub_path": "task2.py", "file_name": "task2.py", "file_ext": "py", "file_size_in_byte": 485, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "itertools.product", "line_number": 18, "usage_type": "call"}]} +{"seq_id": "10060687056", "text": "import sys\nimport time\nfrom Utils import bad_return_code\nfrom selenium import webdriver\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.support.ui import WebDriverWait\nfrom selenium.webdriver.support import expected_conditions as EC\n\n\ndef test_scores_service(url):\n\n driver = webdriver.Chrome('C:/DevOps/webdrivers/cromedriver')\n driver.get(url)\n\n time.sleep(3)\n\n element = WebDriverWait(driver, 10).until(EC.presence_of_element_located((By.ID, 'score')))\n element2 = WebDriverWait(driver, 10).until(EC.presence_of_element_located((By.ID, 'username')))\n score = int(element.text)\n username = element2.text\n\n if 1 <= score <= 1000:\n return True, print(f'The last winning score was {score}, and it was \"{username}\" who had done it.')\n\n else:\n return False, print(bad_return_code)\n # finally:\n # driver.quit()\n\n\ndef main_function():\n\n url = 'http://127.0.0.1:5000'\n if test_scores_service(url):\n print('Test passed!')\n sys.exit(0)\n\n else:\n print('Test failed.')\n sys.exit(-1)\n\n\nmain_function()\n\n\n", "repo_name": "Bobas-Feet/World_Of_Games", "sub_path": "e2e.py", "file_name": "e2e.py", "file_ext": "py", "file_size_in_byte": 1097, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "37", "api": [{"api_name": "selenium.webdriver.Chrome", "line_number": 12, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 12, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 15, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.ui.WebDriverWait", "line_number": 17, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.expected_conditions.presence_of_element_located", "line_number": 17, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.expected_conditions", "line_number": 17, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.ID", "line_number": 17, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 17, "usage_type": "name"}, {"api_name": "selenium.webdriver.support.ui.WebDriverWait", "line_number": 18, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.expected_conditions.presence_of_element_located", "line_number": 18, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.expected_conditions", "line_number": 18, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.ID", "line_number": 18, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 18, "usage_type": "name"}, {"api_name": "Utils.bad_return_code", "line_number": 26, "usage_type": "argument"}, {"api_name": "sys.exit", "line_number": 36, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 40, "usage_type": "call"}]} +{"seq_id": "1900060961", "text": "import glob\nimport os\nimport shutil\n\nfrom conans import AutoToolsBuildEnvironment, ConanFile, tools\n\n\nclass PkgconfConan(ConanFile):\n name = \"pkgconf\"\n version = tools.get_env(\"GIT_TAG\", \"1.6.3\")\n settings = \"os\", \"compiler\", \"build_type\", \"arch\"\n license = \"custom\"\n description = \"Package compiler and linker metadata toolkit\"\n\n # def build_requirements(self):\n # self.build_requires(\"gcc/[>=7.4.0]@%s/stable\" % self.user)\n # self.build_requires(\"autoconf/[>=2.69]@%s/stable\" % self.user)\n # self.build_requires(\"automake/[>=1.16.1]@%s/stable\" % self.user)\n # self.build_requires(\"libtool/[>=2.4.6]@%s/stable\" % self.user)\n\n def source(self):\n tools.get(\"https://github.com/pkgconf/pkgconf/archive/pkgconf-%s.tar.gz\" % self.version)\n\n def build(self):\n args = [\"--disable-static\"]\n with tools.chdir(\"pkgconf-pkgconf-%s\" % self.version):\n self.run(\"sh autogen.sh\")\n autotools = AutoToolsBuildEnvironment(self)\n autotools.configure(args=args)\n autotools.make()\n autotools.install()\n os.symlink(\"pkgconf\", os.path.join(self.package_folder, \"bin\", \"pkg-config\"))\n\n def package_info(self):\n self.env_info.PKG_CONFIG = os.path.join(self.package_folder, \"bin\", \"pkgconf\")\n self.env_info.ACLOCAL_PATH.append(os.path.join(self.package_folder, \"share\", \"aclocal\"))\n # Support system pkgconfig files\n if self.settings.os == \"Linux\":\n self.env_info.PKG_CONFIG_SYSTEM_PATH.append(\"/usr/share/pkgconfig\")\n if self.settings.arch == \"x86_64\":\n self.env_info.PKG_CONFIG_SYSTEM_PATH.append(\"/usr/lib/x86_64-linux-gnu/pkgconfig\")\n if self.settings.arch == \"armv8\":\n self.env_info.PKG_CONFIG_SYSTEM_PATH.append(\"/usr/lib/aarch64-linux-gnu/pkgconfig\")\n", "repo_name": "TUM-CONAN/conan-pkgconf", "sub_path": "conanfile.py", "file_name": "conanfile.py", "file_ext": "py", "file_size_in_byte": 1862, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "conans.ConanFile", "line_number": 8, "usage_type": "name"}, {"api_name": "conans.tools.get_env", "line_number": 10, "usage_type": "call"}, {"api_name": "conans.tools", "line_number": 10, "usage_type": "name"}, {"api_name": "conans.tools.get", "line_number": 22, "usage_type": "call"}, {"api_name": "conans.tools", "line_number": 22, "usage_type": "name"}, {"api_name": "conans.tools.chdir", "line_number": 26, "usage_type": "call"}, {"api_name": "conans.tools", "line_number": 26, "usage_type": "name"}, {"api_name": "conans.AutoToolsBuildEnvironment", "line_number": 28, "usage_type": "call"}, {"api_name": "os.symlink", "line_number": 32, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 32, "usage_type": "call"}, {"api_name": "os.path", "line_number": 32, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 35, "usage_type": "call"}, {"api_name": "os.path", "line_number": 35, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 36, "usage_type": "call"}, {"api_name": "os.path", "line_number": 36, "usage_type": "attribute"}]} +{"seq_id": "71204147626", "text": "#!/usr/bin/python\n#\nimport logging\nimport traceback\n\nimport thread\n\n\ndef display_loop(display):\n logger = logging.getLogger('iot_displaythread')\n while True:\n try:\n display.display()\n except KeyboardInterrupt:\n logger.info(\"[iot_displaythread] Caught keyboard interrupt. Bye!\")\n exit()\n except Exception as e:\n logger.error(\"[iot_displaythread] Caught exception - %s\", e.message)\n traceback.print_exc()\n\n\ndef run(display):\n thread.start_new_thread(display_loop, (display,))\n", "repo_name": "bryanhughes/bbg-demo", "sub_path": "java/scripts/iot_displaythread.py", "file_name": "iot_displaythread.py", "file_ext": "py", "file_size_in_byte": 559, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "logging.getLogger", "line_number": 10, "usage_type": "call"}, {"api_name": "traceback.print_exc", "line_number": 19, "usage_type": "call"}, {"api_name": "thread.start_new_thread", "line_number": 23, "usage_type": "call"}]} +{"seq_id": "6491508981", "text": "import timm\nimport numpy as np\nimport torch.nn as nn\n\n\ndef _make_divisible(x, divisible_by=8):\n return int(np.ceil(x * 1.0 / divisible_by) * divisible_by)\n\n\nclass MobileNetV3(nn.Module):\n def __init__(self, model_name, width_mult, depth=5, n_channels=1,num_classes=2):\n super().__init__()\n if \"large\" not in model_name and \"small\" not in model_name:\n raise ValueError(\"MobileNetV3 wrong model name {}\".format(model_name))\n\n self.mode = \"small\" if \"small\" in model_name else \"large\"\n self.depth = depth\n self.out_channels = self._get_channels(self.mode, width_mult)\n self.n_channels = n_channels\n # minimal models replace hardswish with relu\n self.model = timm.create_model(\n model_name=model_name,\n scriptable=True, # torch.jit scriptable\n exportable=True, # onnx export\n features_only=True,\n in_chans=n_channels\n )\n\n def _get_channels(self, mode, width_mult):\n if mode == \"small\":\n channels = [16, 16, 24, 48, 576]\n else:\n channels = [16, 24, 40, 112, 960]\n channels = [_make_divisible(x * width_mult) for x in channels]\n return tuple(channels)\n\n def get_stages(self):\n if self.mode == \"small\":\n return [\n nn.Identity(),\n nn.Sequential(\n self.model.conv_stem,\n self.model.bn1,\n self.model.act1,\n ),\n self.model.blocks[0],\n self.model.blocks[1],\n self.model.blocks[2:4],\n self.model.blocks[4:],\n ]\n elif self.mode == \"large\":\n return [\n nn.Identity(),\n nn.Sequential(\n self.model.conv_stem,\n self.model.bn1,\n self.model.act1,\n self.model.blocks[0],\n ),\n self.model.blocks[1],\n self.model.blocks[2],\n self.model.blocks[3:5],\n self.model.blocks[5:],\n ]\n else:\n ValueError(\"MobileNetV3 mode should be small or large, got {}\".format(self._mode))\n\n def forward(self, x):\n stages = self.get_stages()\n\n features = []\n for i in range(self.depth + 1):\n x = stages[i](x)\n if i != 0:\n features.append(x)\n\n return features\n\n def load_state_dict(self, state_dict, **kwargs):\n state_dict.pop(\"conv_head.weight\", None)\n state_dict.pop(\"conv_head.bias\", None)\n state_dict.pop(\"classifier.weight\", None)\n state_dict.pop(\"classifier.bias\", None)\n self.model.load_state_dict(state_dict, **kwargs)\n\n\n\ndef mobilenetv3_large_075(**kwargs):\n \"\"\" MobileNet V3 \"\"\"\n model = MobileNetV3('tf_mobilenetv3_large_075', 0.75, **kwargs)\n return model\n\n\ndef mobilenetv3_large_100(**kwargs):\n \"\"\" MobileNet V3 \"\"\"\n model = MobileNetV3('tf_mobilenetv3_large_100', 1.0, **kwargs)\n return model\n\n\ndef mobilenetv3_large_minimal_100(**kwargs):\n \"\"\" MobileNet V3 \"\"\"\n model = MobileNetV3('tf_mobilenetv3_large_minimal_100', 1.0, **kwargs)\n return model\n\n\ndef mobilenetv3_small_075(**kwargs):\n \"\"\" MobileNet V3 \"\"\"\n model = MobileNetV3('tf_mobilenetv3_small_075', 0.75, **kwargs)\n return model\n\n\ndef mobilenetv3_small_100(**kwargs):\n \"\"\" MobileNet V3 \"\"\"\n model = MobileNetV3('tf_mobilenetv3_small_100', 1.0, **kwargs)\n return model\n\n\ndef mobilenetv3_small_minimal_100(**kwargs):\n \"\"\" MobileNet V3 \"\"\"\n model = MobileNetV3('tf_mobilenetv3_small_minimal_100', 1.0, **kwargs)\n return model\n", "repo_name": "shijun18/TMLI-PLAN", "sub_path": "model/encoder/mobilenetv3.py", "file_name": "mobilenetv3.py", "file_ext": "py", "file_size_in_byte": 3720, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "37", "api": [{"api_name": "numpy.ceil", "line_number": 7, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 10, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 10, "usage_type": "name"}, {"api_name": "timm.create_model", "line_number": 21, "usage_type": "call"}, {"api_name": "torch.nn.Identity", "line_number": 40, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 40, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 41, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 41, "usage_type": "name"}, {"api_name": "torch.nn.Identity", "line_number": 53, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 53, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 54, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 54, "usage_type": "name"}]} +{"seq_id": "39234947401", "text": "\"\"\"Encoding/decoding stuff.\"\"\"\n\nimport base64\nimport string\n\nimport errors\n\n\nCHARS = string.digits + string.ascii_letters + \"-_\"\nRADIX = len(CHARS)\n\n\ndef urlenc_int(i):\n if i < 0:\n raise errors.InternalError(\"bad int for urlenc %r\" % i)\n result = \"\"\n while True:\n i, r = divmod(i, RADIX)\n result = CHARS[r] + result\n if i == 0:\n return result\n\n\ndef urldec_int(s):\n result = 0\n while len(s):\n result *= RADIX\n c = s[0]\n result += CHARS.index(c)\n s = s[1:]\n return result\n\n\ndef b32enc(s):\n return base64.b32encode(s).rstrip(\"=\").lower()\n\n\ndef b32dec(s):\n s = s + \"=\" * (len(s) % 8)\n return base64.b32decode(s.upper())\n\n\ndef b64enc(s):\n return base64.urlsafe_b64encode(s).rstrip(\"=\")\n\n\ndef b64dec(s):\n s = s + \"=\" * (len(s) % 4)\n return base64.urlsafe_b64decode(s)\n", "repo_name": "fiesta/gitlists", "sub_path": "coding.py", "file_name": "coding.py", "file_ext": "py", "file_size_in_byte": 865, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 5, "dataset": "github-code", "pt": "37", "api": [{"api_name": "string.digits", "line_number": 9, "usage_type": "attribute"}, {"api_name": "string.ascii_letters", "line_number": 9, "usage_type": "attribute"}, {"api_name": "errors.InternalError", "line_number": 15, "usage_type": "call"}, {"api_name": "base64.b32encode", "line_number": 35, "usage_type": "call"}, {"api_name": "base64.b32decode", "line_number": 40, "usage_type": "call"}, {"api_name": "base64.urlsafe_b64encode", "line_number": 44, "usage_type": "call"}, {"api_name": "base64.urlsafe_b64decode", "line_number": 49, "usage_type": "call"}]} +{"seq_id": "73413448426", "text": "from builtins import str\nfrom builtins import range\nimport os\nimport os.path as opath\nimport numpy as np\nfrom scipy.io import loadmat\n\nfrom .misc import mkdir_p, wordhash\nfrom .trace2p import Trace2P\nfrom .classifier.train import train_classifier\nfrom . import config\n\nparams = config.params()\n\ndatad = params['paths'].get('data', '/data')\noutd = params['paths'].get('output', '/output')\ngraphd = params['paths'].get('graph', '/graphs')\n\n\ndef dataframe(name):\n \"\"\"Save a pandas datafarme to a directory in output\n\n :param name: name of file, to which h5 will be appended if not there.\n :return: path\n \"\"\"\n\n if name[-3:] != '.h5':\n name = name + '.h5'\n\n path = opath.join(outd, 'dataframes')\n if not opath.exists(path):\n os.mkdir(path)\n\n path = opath.join(path, name)\n return path\n\ndef gett2p(mouse, date, run):\n path = opath.join(datad, '%s/%s/%s_%s_%03i.simpcell' % (\n mouse, date, mouse, date, run))\n out = Trace2P(path)\n return out\n\ndef getc2p(mouse, date, run, pars, randomize='', nrand=10):\n \"\"\"\n Return a path to classifier or randomized classifier file.\n \"\"\"\n\n pars['mouse'] = mouse\n pars['comparison-date'] = str(date)\n pars['comparison-run'] = run\n\n title = ['classifier']\n if len(randomize) > 0:\n title += [randomize, str(nrand)]\n title = '-'.join(title) + '.mat'\n\n path = output(pars, use_new=True)\n return opath.join(path, title)\n\n# DO NOT DELETE ME UNTIL THE STUPID PAPER IS PUBLISHED\ndef classifier2p(run, pars, randomize=''):\n # NOTE: Remove when replay disappears.\n from .classify2p import Classify2P\n path = output(pars)\n fs = os.listdir(path)[::-1]\n paths = []\n\n # Change what you open whether real or random\n if len(randomize) == 0:\n for f in fs:\n if f[:4] == 'real':\n paths.append(opath.join(path, f))\n else:\n for f in fs:\n if f[:4] == 'rand' and f[5:5 + len(randomize)] == randomize:\n paths.append(opath.join(path, f))\n\n # If we can't find a classifier output, try re-running it.\n if not len(paths):\n train_classifier(run, **pars)\n return classifier2p(run, pars, randomize)\n\n out = Classify2P(paths, pars, randomize)\n\n return out\n\n# DO NOT DELETE ME UNTIL THE STUPID PAPER IS PUBLISHED\ndef getglm(mouse, date):\n path = opath.join(datad, '%s/%s/%s_%s.simpglm' % (mouse, date, mouse, date))\n if not opath.exists(path):\n return None\n else:\n data = loadmat(path, appendmat=False)\n cgs = [str(data['cellgroups'].flatten()[i][0]) for i in range(len(data['cellgroups'].flatten()))]\n devexp = data['deviance_explained']\n return [cgs, devexp, data]\n\ndef getonsets(mouse, date=None, run=None):\n \"\"\"\n Return the extra onsets file\n :param mouse: can be mouse name, str, or the complete filename if date and run are left none\n :param date: date, str, or None if filename\n :param run: run, int, or None if filename\n :return: loaded onsets file if possible\n \"\"\"\n\n if date is None or run is None:\n path = opath.join(datad, 'onsets/%s' % (mouse))\n else:\n path = opath.join(datad, 'onsets/%s_%s_%03i.onsets' % (mouse, date, run))\n\n if not opath.exists(path):\n return None\n else:\n out = loadmat(path)\n return out\n\n\ndef cosdists():\n return '%s/cosdists.mat' % outd\n\n\ndef glmpath(mouse, date, glm_type='simpglm'):\n \"\"\"\n Get the path to a .simpglm or other type file,\n set to None if it does not exist\n :param mouse: mouse name, str\n :param date: date, str\n :param glm_type: str {'simpglm', 'simpglm2', 'safeglm'}\n :return:\n \"\"\"\n\n path = opath.join(datad, '%s/%s/%s_%s.%s' % (mouse, date, mouse, date, glm_type))\n if not opath.exists(path):\n return None\n else:\n return path\n\ndef gettclassmarginals(pars):\n \"\"\"\n Return the path to the marginal probabilities measured from the time classifier.\n :return: path, str\n \"\"\"\n word = wordhash.word(pars)\n path = opath.join(outd, 'time-classifier-training')\n if not opath.exists(path): os.mkdir(path)\n return opath.join(path, '%s-time-classifier-marginals.mat'%word)\n\ndef exist(mouse, date, run):\n path = opath.join(datad, '%s/%s/%s_%s_%03i.simpcell' % (mouse, date, mouse, date, run))\n return opath.isfile(path)\n\ndef ids(mouse, date):\n \"\"\"\n Return the crossday cell IDs if they exist, otherwise return an\n empty path.\n \"\"\"\n\n path = opath.join(datad, '%s/%s/%s_%s_crossday-cell-ids.txt' % (mouse, date, mouse, date))\n if opath.isfile(path): return path\n else: return ''\n\ndef cell_scores(mouse, date):\n \"\"\"\n Return the crossday cell IDs if they exist, otherwise return an\n empty path.\n \"\"\"\n\n path = opath.join(datad, '%s/%s/%s_%s_crossday-cell-scores.txt' % (mouse, date, mouse, date))\n if opath.isfile(path): return path\n else: return ''\n\ndef pairids(mouse, day1, day2):\n \"\"\"\n Return the paired ids of cells\n \"\"\"\n\n mpath = opath.join(datad, '%s/crossday' % (mouse))\n if not opath.exists(mpath): return [], []\n\n day1, day2 = str(day1), str(day2)\n\n fs = os.listdir(mpath)\n for name in fs:\n cd = name.split('--')\n if len(cd) == 2:\n cd = cd[0].split('-')\n if len(cd) == 2:\n if day1 in cd[0] and day2 in cd[1]:\n x, y = np.loadtxt(opath.join(mpath, name), unpack=True)\n x = x.astype(np.int32) - 1\n y = y.astype(np.int32) - 1\n return x, y\n elif day2 in cd[0] and day1 in cd[1]:\n y, x = np.loadtxt(opath.join(mpath, name), unpack=True)\n x = x.astype(np.int32) - 1\n y = y.astype(np.int32) - 1\n return x, y\n\n return [], []\n\ndef db(mouse, old=False):\n \"\"\"\n Return the path to the analysis database per mouse.\n \"\"\"\n\n path = outd\n if old:\n path = opath.join(path, 'old-standard')\n\n path = opath.join(path, mouse)\n if not opath.exists(path): os.mkdir(path)\n\n path = opath.join(path, '%s.db'%mouse)\n #if opath.isfile(path): return path\n #else: return ''\n return path\n\ndef udb(mouse, old=False):\n \"\"\"\n Return the path to the analysis database per mouse.\n \"\"\"\n\n path = outd\n if old:\n path = opath.join(path, 'old-standard')\n\n path = opath.join(path, '%s/%s-updated.db' % (mouse, mouse))\n #if opath.isfile(path): return path\n #else: return ''\n return path\n\ndef graph(pars):\n word = wordhash.word(pars)\n mouse = pars['mouse']\n date = pars['training-date']\n crun = pars['comparison-run']\n\n # Base/mouse\n path = opath.join(graphd, mouse)\n if not opath.exists(path): os.mkdir(path)\n # Base/mouse/date\n path = opath.join(path, date)\n if not opath.exists(path): os.mkdir(path)\n # Base/mouse/date/run-parameterWord\n path = opath.join(path, '%03i-%s' % (crun, word))\n if not opath.exists(path): os.mkdir(path)\n\n return path\n\ndef graphcrossday(filename=''):\n # Base/crossday\n path = opath.join(graphd, 'crossday')\n if not opath.exists(path): os.mkdir(path)\n\n return opath.join(path, filename)\n\ndef graphgroup(pars={}, group='', classifier=True):\n # Base/plots/classifier-keyword/group/\n\n path = opath.join(graphd, 'plot')\n if not opath.exists(path): os.mkdir(path)\n\n if pars != {} and classifier:\n word = wordhash.word(pars)\n path = opath.join(path, word)\n if not opath.exists(path): os.mkdir(path)\n\n if len(group) > 0:\n path = opath.join(path, group)\n if not opath.exists(path): os.mkdir(path)\n\n return opath.join(path, '')\n\ndef graphgroup2(mouse, group, date=None, pars=None):\n # Base/group/mouse/date/classifier-word\n path = opath.join(graphd, group, mouse)\n if date is not None:\n path = opath.join(path, str(date))\n if pars is not None:\n word = wordhash.word(pars)\n path = opath.join(path, word)\n\n mkdir_p(path)\n return path\n\ndef graphmdr(pars):\n word = wordhash.word(pars)\n mouse = pars['mouse']\n date = pars['training-date']\n crun = pars['comparison-run']\n\n # Base/mouse\n path = opath.join(graphd, mouse)\n if not opath.exists(path): os.mkdir(path)\n # Base/mouse/date\n path = opath.join(path, date)\n if not opath.exists(path): os.mkdir(path)\n # Base/mouse/date/run-parameterWord\n path = opath.join(path, '%03i-%s' % (crun, word))\n if not opath.exists(path): os.mkdir(path)\n\n # Add mouse, date, and run to the beginning of the graph title\n path = opath.join(path, '%s-%s-%02i' % (mouse, date, crun))\n\n return path\n\ndef classifierword(pars):\n \"\"\"\n Return a random word generated from the hash of a classifier\n :param pars: parameters, from settings\n :return: word, str\n \"\"\"\n\n return wordhash.word(pars)\n\ndef output(pars, use_new=False):\n word = wordhash.word(pars, use_new=use_new)\n # print 'Classifier %s' % (word)\n mouse = pars['mouse']\n date = pars['training-date']\n crun = pars['comparison-run']\n\n # Base/mouse\n path = opath.join(outd, mouse)\n # if not opath.exists(path): os.mkdir(path)\n # Base/mouse/date\n path = opath.join(path, date)\n # if not opath.exists(path): os.mkdir(path)\n # Base/mouse/date/run-parameterWord\n path = opath.join(path, '%03i-%s' % (crun, word))\n # if not opath.exists(path): os.mkdir(path)\n\n return path\n\ndef neuralnet(mouse, date, netpars={}, mtype='full'):\n word = wordhash.word(netpars)\n # print 'Classifier %s' % (word)\n\n # Base/mouse\n path = opath.join(outd, mouse)\n if not opath.exists(path): os.mkdir(path)\n # Base/mouse/date\n path = opath.join(path, date)\n if not opath.exists(path): os.mkdir(path)\n # Base/mouse/date/nn-parameterWord.h5\n path = opath.join(path, 'nn-%s-%s.h5' % (word, mtype))\n\n return path\n\ndef training(pars):\n word = wordhash.word(pars)\n # print 'Classifier %s' % (word)\n mouse = pars['mouse']\n date = pars['training-date']\n\n # Base/mouse\n path = opath.join(outd, mouse)\n if not opath.exists(path): os.mkdir(path)\n # Base/mouse/date\n path = opath.join(path, date)\n if not opath.exists(path): os.mkdir(path)\n # Base/mouse/date/run-parameterWord\n path = opath.join(path, '%s-training.mat' % word)\n\n return path\n\ndef ctraindump(pars):\n word = wordhash.word(pars)\n # print 'Classifier %s' % (word)\n mouse = pars['mouse']\n date = pars['training-date']\n\n # Base/mouse\n path = opath.join(outd, mouse)\n if not opath.exists(path): os.mkdir(path)\n # Base/mouse/date\n path = opath.join(path, date)\n if not opath.exists(path): os.mkdir(path)\n # Base/mouse/date/parameterWord\n path = opath.join(path, '%s-ctrain.pickle'%word)\n\n return path\n\n\ndef psytrack(mouse, pars_word, runs_word):\n \"\"\"Path to psytrack file.\"\"\"\n\n path = opath.join(\n outd, 'psytrack', mouse, pars_word,\n '{}_{}_{}.psy'.format(mouse, pars_word, runs_word))\n\n return path\n\n\ndef pupilpos(mouse, date, run):\n \"\"\"\n Get the ancillary pupil position\n :param mouse: mouse name, str\n :param date: date, str (yymmdd)\n :param run: run, int\n :return:\n \"\"\"\n\n path = opath.join(datad, 'pupilpos')\n path = opath.join(path, '%s-%s-%03i-pupil.mat' % (mouse, date, run))\n if opath.exists(path):\n return loadmat(path)\n else:\n return None\n\ndef xlabel(mouse, date):\n \"\"\"\n Crossday labels\n :param mouse:\n :param date:\n :return:\n \"\"\"\n\n # Base/mouse\n path = opath.join(outd, mouse)\n if not opath.exists(path): os.mkdir(path)\n\n path = opath.join(path, 'xlabel')\n if not opath.exists(path): os.mkdir(path)\n\n # Base/mouse/xlabel/date.txt\n path = opath.join(path, '%s.txt' % (str(date)))\n return path", "repo_name": "asugden/flow", "sub_path": "flow/paths.py", "file_name": "paths.py", "file_ext": "py", "file_size_in_byte": 11848, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "37", "api": [{"api_name": "os.path.join", "line_number": 30, "usage_type": "call"}, {"api_name": "os.path", "line_number": 30, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 31, "usage_type": "call"}, {"api_name": "os.path", "line_number": 31, "usage_type": "name"}, {"api_name": "os.mkdir", "line_number": 32, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 34, "usage_type": "call"}, {"api_name": "os.path", "line_number": 34, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 38, "usage_type": "call"}, {"api_name": "os.path", "line_number": 38, "usage_type": "name"}, {"api_name": "trace2p.Trace2P", "line_number": 40, "usage_type": "call"}, {"api_name": "builtins.str", "line_number": 49, "usage_type": "call"}, {"api_name": "builtins.str", "line_number": 54, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 58, "usage_type": "call"}, {"api_name": "os.path", "line_number": 58, "usage_type": "name"}, {"api_name": "os.listdir", "line_number": 65, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 72, "usage_type": "call"}, {"api_name": "os.path", "line_number": 72, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 76, "usage_type": "call"}, {"api_name": "os.path", "line_number": 76, "usage_type": "name"}, {"api_name": "classifier.train.train_classifier", "line_number": 80, "usage_type": "call"}, {"api_name": "classify2p.Classify2P", "line_number": 83, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 89, "usage_type": "call"}, {"api_name": "os.path", "line_number": 89, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 90, "usage_type": "call"}, {"api_name": "os.path", "line_number": 90, "usage_type": "name"}, {"api_name": "scipy.io.loadmat", "line_number": 93, "usage_type": "call"}, {"api_name": "builtins.str", "line_number": 94, "usage_type": "call"}, {"api_name": "builtins.range", "line_number": 94, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 108, "usage_type": "call"}, {"api_name": "os.path", "line_number": 108, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 110, "usage_type": "call"}, {"api_name": "os.path", "line_number": 110, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 112, "usage_type": "call"}, {"api_name": "os.path", "line_number": 112, "usage_type": "name"}, {"api_name": "scipy.io.loadmat", "line_number": 115, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 133, "usage_type": "call"}, {"api_name": "os.path", "line_number": 133, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 134, "usage_type": "call"}, {"api_name": "os.path", "line_number": 134, "usage_type": "name"}, {"api_name": "misc.wordhash.word", "line_number": 144, "usage_type": "call"}, {"api_name": "misc.wordhash", "line_number": 144, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 145, "usage_type": "call"}, {"api_name": "os.path", "line_number": 145, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 146, "usage_type": "call"}, {"api_name": "os.path", "line_number": 146, "usage_type": "name"}, {"api_name": "os.mkdir", "line_number": 146, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 147, "usage_type": "call"}, {"api_name": "os.path", "line_number": 147, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 150, "usage_type": "call"}, {"api_name": "os.path", "line_number": 150, "usage_type": "name"}, {"api_name": "os.path.isfile", "line_number": 151, "usage_type": "call"}, {"api_name": "os.path", "line_number": 151, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 159, "usage_type": "call"}, {"api_name": "os.path", "line_number": 159, "usage_type": "name"}, {"api_name": "os.path.isfile", "line_number": 160, "usage_type": "call"}, {"api_name": "os.path", "line_number": 160, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 169, "usage_type": "call"}, {"api_name": "os.path", "line_number": 169, "usage_type": "name"}, {"api_name": "os.path.isfile", "line_number": 170, "usage_type": "call"}, {"api_name": "os.path", "line_number": 170, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 178, "usage_type": "call"}, {"api_name": "os.path", "line_number": 178, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 179, "usage_type": "call"}, {"api_name": "os.path", "line_number": 179, "usage_type": "name"}, {"api_name": "builtins.str", "line_number": 181, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 183, "usage_type": "call"}, {"api_name": "numpy.loadtxt", "line_number": 190, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 190, "usage_type": "call"}, {"api_name": "os.path", "line_number": 190, "usage_type": "name"}, {"api_name": "numpy.int32", "line_number": 191, "usage_type": "attribute"}, {"api_name": "numpy.int32", "line_number": 192, "usage_type": "attribute"}, {"api_name": "numpy.loadtxt", "line_number": 195, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 195, "usage_type": "call"}, {"api_name": "os.path", "line_number": 195, "usage_type": "name"}, {"api_name": "numpy.int32", "line_number": 196, "usage_type": "attribute"}, {"api_name": "numpy.int32", "line_number": 197, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 209, "usage_type": "call"}, {"api_name": "os.path", "line_number": 209, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 211, "usage_type": "call"}, {"api_name": "os.path", "line_number": 211, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 212, "usage_type": "call"}, {"api_name": "os.path", "line_number": 212, "usage_type": "name"}, {"api_name": "os.mkdir", "line_number": 212, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 214, "usage_type": "call"}, {"api_name": "os.path", "line_number": 214, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 226, "usage_type": "call"}, {"api_name": "os.path", "line_number": 226, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 228, "usage_type": "call"}, {"api_name": "os.path", "line_number": 228, "usage_type": "name"}, {"api_name": "misc.wordhash.word", "line_number": 234, "usage_type": "call"}, {"api_name": "misc.wordhash", "line_number": 234, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 240, "usage_type": "call"}, {"api_name": "os.path", "line_number": 240, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 241, "usage_type": "call"}, {"api_name": "os.path", "line_number": 241, "usage_type": "name"}, {"api_name": "os.mkdir", "line_number": 241, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 243, "usage_type": "call"}, {"api_name": "os.path", "line_number": 243, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 244, "usage_type": "call"}, {"api_name": "os.path", "line_number": 244, "usage_type": "name"}, {"api_name": "os.mkdir", "line_number": 244, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 246, "usage_type": "call"}, {"api_name": "os.path", "line_number": 246, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 247, "usage_type": "call"}, {"api_name": "os.path", "line_number": 247, "usage_type": "name"}, {"api_name": "os.mkdir", "line_number": 247, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 253, "usage_type": "call"}, {"api_name": "os.path", "line_number": 253, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 254, "usage_type": "call"}, {"api_name": "os.path", "line_number": 254, "usage_type": "name"}, {"api_name": "os.mkdir", "line_number": 254, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 256, "usage_type": "call"}, {"api_name": "os.path", "line_number": 256, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 261, "usage_type": "call"}, {"api_name": "os.path", "line_number": 261, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 262, "usage_type": "call"}, {"api_name": "os.path", "line_number": 262, "usage_type": "name"}, {"api_name": "os.mkdir", "line_number": 262, "usage_type": "call"}, {"api_name": "classifier.train", "line_number": 264, "usage_type": "name"}, {"api_name": "misc.wordhash.word", "line_number": 265, "usage_type": "call"}, {"api_name": "misc.wordhash", "line_number": 265, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 266, "usage_type": "call"}, {"api_name": "os.path", "line_number": 266, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 267, "usage_type": "call"}, {"api_name": "os.path", "line_number": 267, "usage_type": "name"}, {"api_name": "os.mkdir", "line_number": 267, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 270, "usage_type": "call"}, {"api_name": "os.path", "line_number": 270, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 271, "usage_type": "call"}, {"api_name": "os.path", "line_number": 271, "usage_type": "name"}, {"api_name": "os.mkdir", "line_number": 271, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 273, "usage_type": "call"}, {"api_name": "os.path", "line_number": 273, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 277, "usage_type": "call"}, {"api_name": "os.path", "line_number": 277, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 279, "usage_type": "call"}, {"api_name": "os.path", "line_number": 279, "usage_type": "name"}, {"api_name": "builtins.str", "line_number": 279, "usage_type": "call"}, {"api_name": "misc.wordhash.word", "line_number": 281, "usage_type": "call"}, {"api_name": "misc.wordhash", "line_number": 281, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 282, "usage_type": "call"}, {"api_name": "os.path", "line_number": 282, "usage_type": "name"}, {"api_name": "misc.mkdir_p", "line_number": 284, "usage_type": "call"}, {"api_name": "misc.wordhash.word", "line_number": 288, "usage_type": "call"}, {"api_name": "misc.wordhash", "line_number": 288, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 294, "usage_type": "call"}, {"api_name": "os.path", "line_number": 294, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 295, "usage_type": "call"}, {"api_name": "os.path", "line_number": 295, "usage_type": "name"}, {"api_name": "os.mkdir", "line_number": 295, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 297, "usage_type": "call"}, {"api_name": "os.path", "line_number": 297, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 298, "usage_type": "call"}, {"api_name": "os.path", "line_number": 298, "usage_type": "name"}, {"api_name": "os.mkdir", "line_number": 298, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 300, "usage_type": "call"}, {"api_name": "os.path", "line_number": 300, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 301, "usage_type": "call"}, {"api_name": "os.path", "line_number": 301, "usage_type": "name"}, {"api_name": "os.mkdir", "line_number": 301, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 304, "usage_type": "call"}, {"api_name": "os.path", "line_number": 304, "usage_type": "name"}, {"api_name": "misc.wordhash.word", "line_number": 315, "usage_type": "call"}, {"api_name": "misc.wordhash", "line_number": 315, "usage_type": "name"}, {"api_name": "misc.wordhash.word", "line_number": 318, "usage_type": "call"}, {"api_name": "misc.wordhash", "line_number": 318, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 325, "usage_type": "call"}, {"api_name": "os.path", "line_number": 325, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 328, "usage_type": "call"}, {"api_name": "os.path", "line_number": 328, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 331, "usage_type": "call"}, {"api_name": "os.path", "line_number": 331, "usage_type": "name"}, {"api_name": "misc.wordhash.word", "line_number": 337, "usage_type": "call"}, {"api_name": "misc.wordhash", "line_number": 337, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 341, "usage_type": "call"}, {"api_name": "os.path", "line_number": 341, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 342, "usage_type": "call"}, {"api_name": "os.path", "line_number": 342, "usage_type": "name"}, {"api_name": "os.mkdir", "line_number": 342, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 344, "usage_type": "call"}, {"api_name": "os.path", "line_number": 344, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 345, "usage_type": "call"}, {"api_name": "os.path", "line_number": 345, "usage_type": "name"}, {"api_name": "os.mkdir", "line_number": 345, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 347, "usage_type": "call"}, {"api_name": "os.path", "line_number": 347, "usage_type": "name"}, {"api_name": "misc.wordhash.word", "line_number": 352, "usage_type": "call"}, {"api_name": "misc.wordhash", "line_number": 352, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 358, "usage_type": "call"}, {"api_name": "os.path", "line_number": 358, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 359, "usage_type": "call"}, {"api_name": "os.path", "line_number": 359, "usage_type": "name"}, {"api_name": "os.mkdir", "line_number": 359, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 361, "usage_type": "call"}, {"api_name": "os.path", "line_number": 361, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 362, "usage_type": "call"}, {"api_name": "os.path", "line_number": 362, "usage_type": "name"}, {"api_name": "os.mkdir", "line_number": 362, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 364, "usage_type": "call"}, {"api_name": "os.path", "line_number": 364, "usage_type": "name"}, {"api_name": "misc.wordhash.word", "line_number": 369, "usage_type": "call"}, {"api_name": "misc.wordhash", "line_number": 369, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 375, "usage_type": "call"}, {"api_name": "os.path", "line_number": 375, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 376, "usage_type": "call"}, {"api_name": "os.path", "line_number": 376, "usage_type": "name"}, {"api_name": "os.mkdir", "line_number": 376, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 378, "usage_type": "call"}, {"api_name": "os.path", "line_number": 378, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 379, "usage_type": "call"}, {"api_name": "os.path", "line_number": 379, "usage_type": "name"}, {"api_name": "os.mkdir", "line_number": 379, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 381, "usage_type": "call"}, {"api_name": "os.path", "line_number": 381, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 389, "usage_type": "call"}, {"api_name": "os.path", "line_number": 389, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 405, "usage_type": "call"}, {"api_name": "os.path", "line_number": 405, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 406, "usage_type": "call"}, {"api_name": "os.path", "line_number": 406, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 407, "usage_type": "call"}, {"api_name": "os.path", "line_number": 407, "usage_type": "name"}, {"api_name": "scipy.io.loadmat", "line_number": 408, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 421, "usage_type": "call"}, {"api_name": "os.path", "line_number": 421, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 422, "usage_type": "call"}, {"api_name": "os.path", "line_number": 422, "usage_type": "name"}, {"api_name": "os.mkdir", "line_number": 422, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 424, "usage_type": "call"}, {"api_name": "os.path", "line_number": 424, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 425, "usage_type": "call"}, {"api_name": "os.path", "line_number": 425, "usage_type": "name"}, {"api_name": "os.mkdir", "line_number": 425, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 428, "usage_type": "call"}, {"api_name": "os.path", "line_number": 428, "usage_type": "name"}, {"api_name": "builtins.str", "line_number": 428, "usage_type": "call"}]} +{"seq_id": "72929849708", "text": "import pydle\nimport markovify\nimport re\nfrom .secrets import *\nfrom .utils import normalize_nick, normalize_lover\nfrom collections import defaultdict\nfrom hashlib import md5\nfrom random import choice\n\n\nclass CustomNewlineText(markovify.NewlineText):\n def __init__(self, input_text, *args, **kwargs):\n super(markovify.NewlineText, self).__init__(input_text, *args, **kwargs)\n self.size = len(input_text)\n\n\nclass Marchov(pydle.Client):\n def __init__(self, *args, **kwargs):\n self.models = {}\n self.tintin = []\n super(pydle.Client, self).__init__(*args, **kwargs)\n\n def recreate_models(self):\n messages = defaultdict(list)\n with open(MARKOV_INPUT, \"r\") as f:\n corpus = f.read().splitlines()\n for idx, line in enumerate(corpus):\n fields = line.split()\n if len(fields) >= 3:\n nick = fields[2]\n else:\n continue\n if nick in (\"*\", \"--\", \"<--\", \"-->\"):\n continue\n nick = normalize_nick(nick)\n message = \" \".join(fields[3:])\n messages[nick].append(message)\n for nick in sorted(messages.keys()):\n print(f\"Commence {nick}\")\n try:\n model = CustomNewlineText(\"\\n\".join(messages[nick]), state_size=3)\n self.models[nick] = model\n except KeyError:\n pass\n print(\"Modèles recréés\")\n\n async def on_connect(self):\n print(f\"Connecté sur {SERVER}\")\n self.recreate_models()\n await self.join(CHANNEL)\n print(f\"A rejoint {CHANNEL}\")\n with open(\"tintin5.txt\", \"r\") as f:\n self.tintin = f.read().splitlines()\n\n async def on_message(self, target, source, message):\n if not message or message[0] != \"?\" or source == self.nickname:\n return\n\n parsed = re.search(r\"^.([a-zA-Z]+)(?: +(.*))?$\", message)\n if not parsed:\n return\n\n command = parsed.group(1).lower()\n args = parsed.group(2)\n args = args.strip() if args else None\n\n if command in (\"markov\", \"marchov\"):\n if not args:\n return\n\n parsed = re.search(r\"^([^ ]+)(?: +(.*))?$\", args)\n if not parsed:\n return\n\n nick = parsed.group(1)\n if nick.strip() == \"?\":\n nicks = [m[0] for m in self.models.items() if m[1].size >= 10_000]\n normalized_nick = choice(nicks)\n nick = normalized_nick\n else:\n normalized_nick = normalize_nick(nick)\n prompt = parsed.group(2)\n prompt = prompt.strip() if prompt else None\n if normalized_nick not in self.models.keys():\n await self.message(target, f\"{source}: aucun modèle trouvé pour {nick}\")\n return\n\n model = self.models[normalized_nick]\n try:\n if prompt:\n sentence = model.make_sentence_with_start(\n beginning=prompt, tries=MARKOV_TRIES\n )\n else:\n sentence = model.make_sentence(tries=MARKOV_TRIES)\n except Exception:\n sentence = None\n\n if sentence:\n await self.message(target, f\"{nick} dit: {sentence}\")\n else:\n await self.message(\n target,\n f\"{source}: impossible d'imiter {nick}{' avec ce prompt ' if prompt else ' '}(pas assez de contenu ?)\",\n )\n return\n\n if command == \"love\":\n if not args:\n return\n\n parsed = re.search(r\"^([^,]+),([^,]+)$\", args)\n if not parsed:\n return\n lover_a = parsed.group(1).strip()\n lover_b = parsed.group(2).strip()\n normalized_lover_a = normalize_lover(lover_a)\n normalized_lover_b = normalize_lover(lover_b)\n normalized_lover_a, normalized_lover_b = sorted(\n (normalized_lover_a, normalized_lover_b)\n )\n query = f\"{normalized_lover_a} {normalized_lover_b}\"\n hash_ = md5(query.encode()).hexdigest()\n modulo = int(hash_, 16) % 101\n if normalized_lover_a == \"barul\" and normalized_lover_b == \"biganon\":\n modulo = 101\n await self.message(\n target,\n f\"Compatibilité amoureuse entre {lover_a} et {lover_b} : {modulo}%\",\n )\n\n if command == \"tintin\":\n if args:\n return\n sentence = choice(self.tintin)\n await self.message(target, f\"{source}: {sentence}\")\n", "repo_name": "Biganon/marchov", "sub_path": "marchov/marchov.py", "file_name": "marchov.py", "file_ext": "py", "file_size_in_byte": 4762, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "markovify.NewlineText", "line_number": 11, "usage_type": "attribute"}, {"api_name": "markovify.NewlineText", "line_number": 13, "usage_type": "attribute"}, {"api_name": "pydle.Client", "line_number": 17, "usage_type": "attribute"}, {"api_name": "pydle.Client", "line_number": 21, "usage_type": "attribute"}, {"api_name": "collections.defaultdict", "line_number": 24, "usage_type": "call"}, {"api_name": "utils.normalize_nick", "line_number": 35, "usage_type": "call"}, {"api_name": "re.search", "line_number": 59, "usage_type": "call"}, {"api_name": "re.search", "line_number": 71, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 78, "usage_type": "call"}, {"api_name": "utils.normalize_nick", "line_number": 81, "usage_type": "call"}, {"api_name": "re.search", "line_number": 112, "usage_type": "call"}, {"api_name": "utils.normalize_lover", "line_number": 117, "usage_type": "call"}, {"api_name": "utils.normalize_lover", "line_number": 118, "usage_type": "call"}, {"api_name": "hashlib.md5", "line_number": 123, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 135, "usage_type": "call"}]} +{"seq_id": "15229336355", "text": "## find 大 小 中\n## 312 sequence\n###leetcode 456 132 pattern 树状数组 枚举3的位置\nfrom collections import defaultdict\n\n\nclass Solution(object):\n def validateStackSequences(self, pushed, popped):\n \"\"\"\n :type pushed: List[int]\n :type popped: List[int]\n :rtype: bool\n \"\"\"\n\n g = defaultdict(int)\n n = len(pushed)\n for i in range(n):\n g[pushed[i]] = i + 1\n for i in range(n):\n popped[i] = g[popped[i]]\n", "repo_name": "Petershen-csworld/Leetcode-journey", "sub_path": "946.py", "file_name": "946.py", "file_ext": "py", "file_size_in_byte": 497, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "37", "api": [{"api_name": "collections.defaultdict", "line_number": 15, "usage_type": "call"}]} +{"seq_id": "18503762995", "text": "import argparse\nimport random\n\nimport pandas as pd\nimport numpy as np\nimport os\nimport sys\nsys.path.append(\"/Users/milos.ojdanic/phd_workspace/Mutants_CI/relevantMutant_Milos/study_I\")\n\nfrom scripts.mutation_comparision import map_mutants, calculate_minimal_mutants\n\n\ndef parse_args() -> argparse.ArgumentParser:\n parser = argparse.ArgumentParser(description=\"Comparing mutants\")\n parser.add_argument(\"-s\", \"--statistics_file\", action=\"store\", help=\"File where information about mutants is\")\n parser.add_argument(\"-p\", \"--path_to_mutants_data\", action=\"store\", help=\"Set path to mutants data\")\n parser.add_argument(\"-o\", \"--output_dir\", action=\"store\", help=\"Set path to output directory\")\n\n return parser\n\nif __name__ == '__main__':\n arguments = parse_args().parse_args()\n\n data_frame = pd.read_csv(arguments.statistics_file, index_col=\"commit_id\", thousands=\",\")\n\n reveiling_ratis = dict()\n\n for _index, row in data_frame.iterrows():\n\n mutationMatrixPath = arguments.path_to_mutants_data + \"/\" + row[\"project_name\"] + \"/\" + _index + \"/\" + \"mutationMatrix.csv\"\n mutantsInfoPath = arguments.path_to_mutants_data + \"/\" + row[\"project_name\"] + \"/\" + _index + \"/\" + \"mutants_info.csv\"\n\n assert os.path.exists(mutationMatrixPath), \"Does not exists: \" + mutationMatrixPath\n assert os.path.exists(mutantsInfoPath), \"Does not exists: \" + mutantsInfoPath\n\n print(row[\"tests\"])\n\n faulty_tests = row[\"tests\"]\n faulty_tests = faulty_tests.split(\";\")\n faulty_tests_names = [test.split(\"::\")[-1] for test in faulty_tests]\n\n print(_index)\n all_fom_mutants, all_granularity_level, relevant_mutants, not_relevant_mutants, on_change_mutants, minimal_relevant_mutants_no_change = map_mutants(\n mutants_info_path=mutantsInfoPath, mutation_matrix_path=mutationMatrixPath)\n\n relevant_mutants = relevant_mutants + on_change_mutants\n minimal_mutants, subsumed_killed_mutants, mutants_killed, equivalent = calculate_minimal_mutants(\n all_granularity_level)\n subsumed_killed_mutants.update(equivalent)\n\n minimal_relevant_mutants, minimal_subsumed_killed_mutants, minimal_mutants_killed, minimal_equivalent = calculate_minimal_mutants(\n relevant_mutants)\n\n print()\n\n print(\"All Mutants: {number}\".format(number=len(all_fom_mutants)))\n print(\"All Gran Mutants: {number}\".format(number=len(all_granularity_level)))\n print(\"Relevant: {number}\".format(number=len(relevant_mutants)))\n print(\"Modification: {number}\".format(number=len(on_change_mutants)))\n print(\"Minimal relevant: {number}\".format(number=len(minimal_relevant_mutants)))\n print(\"Minimal: {number}\".format(number=len(minimal_mutants)))\n\n # matched_all_m = set()\n # for mutant in all_granularity_level:\n # for test in mutant.killingTests:\n # if test.split(\".\")[-1] in faulty_tests_names:\n # matched_all_m.add(mutant)\n #\n # print(\"All Mutants fault triggering mutants: {number}\".format(number=len(matched_all_m)))\n #\n # matched_Relevant_m = set()\n # for mutant in relevant_mutants:\n # for test in mutant.killingTests:\n # if test.split(\".\")[-1] in faulty_tests_names:\n # matched_Relevant_m.add(mutant)\n #\n # print(\"Relevant fault triggering mutants: {number}\".format(number=len(matched_Relevant_m)))\n #\n # matched_mod_m = set()\n # for mutant in on_change_mutants:\n # for test in mutant.killingTests:\n # if test.split(\".\")[-1] in faulty_tests_names:\n # matched_mod_m.add(mutant)\n #\n # print(\"Mod fault triggering mutants: {number}\".format(number=len(matched_mod_m)))\n #\n # matched_rel_min_m = set()\n # for mutant in minimal_relevant_mutants:\n # for test in mutant.killingTests:\n # if test.split(\".\")[-1] in faulty_tests_names:\n # matched_rel_min_m.add(mutant)\n #\n # print(\"Min relevant fault triggering mutants: {number}\".format(number=len(matched_rel_min_m)))\n #\n # matched_min_m = set()\n # for mutant in minimal_mutants:\n # for test in mutant.killingTests:\n # if test.split(\".\")[-1] in faulty_tests_names:\n # matched_min_m.add(mutant)\n #\n # print(\"Min fault triggering mutants: {number}\".format(number=len(matched_min_m)))\n #\n # ratio_over_all = len(matched_all_m) / len(all_granularity_level)\n # ratio_over_relevant = len(matched_Relevant_m) / len(relevant_mutants)\n # ratio_over_mod = len(matched_mod_m) / len(on_change_mutants)\n # reveiling_ratis[_index] = [ratio_over_all, ratio_over_relevant, ratio_over_mod]\n # print(ratio_over_all)\n # print(ratio_over_relevant)\n # print(ratio_over_mod)\n #\n # print()\n #\n # all = 0\n # rel = 0\n # mod = 0\n # for key, value in reveiling_ratis.items():\n # print(key , \" -- \" ,value)\n # all += value[0]\n # rel += value[1]\n # mod += value[2]\n #\n # print(\"all -- \", all / len(reveiling_ratis.keys()))\n # print(\"rel -- \", rel / len(reveiling_ratis.keys()))\n # print(\"mod -- \", mod / len(reveiling_ratis.keys()))\n\n developer_simulation = arguments.output_dir + \"/fault_revelation_ms.csv\"\n with open(developer_simulation, \"a+\") as output_file:\n if os.stat(developer_simulation).st_size == 0:\n output_file.write(\n \"commit,mutant_pool,percentage,iteration,ms\\n\")\n\n for pool, name in [(all_fom_mutants, \"all\"), (relevant_mutants, \"relevant\"),\n (on_change_mutants, \"modification\")]:\n # for to_sample_n in np.arange(0.01, 1.01, 0.01):\n for to_sample_n in range(1, 100, 1):\n n = 0\n for iteration in range(1, 1001):\n # to_sample = len(pool) * to_sample_n\n # to_sample_n = int(round(to_sample))\n if to_sample_n >= len(pool):\n sampled_pool = pool\n else:\n sampled_pool = random.sample(pool, to_sample_n)\n\n killint_test_suite = set()\n [[killint_test_suite.add(test) for test in mutant.killingTests] for mutant in sampled_pool]\n\n sampled_reveiling = set()\n\n while len(sampled_pool) > 0:\n chosen_mutant = random.choice(sampled_pool)\n sampled_pool.remove(chosen_mutant)\n killing_tests_chosen = chosen_mutant.killingTests\n chosen_test = None\n if len(killing_tests_chosen) > 0:\n chosen_test = random.sample(killing_tests_chosen, 1)[0]\n if chosen_test.split(\".\")[-1] in faulty_tests_names:\n n += 1\n break\n if chosen_test is not None:\n sampled_pool = [mutant for mutant in sampled_pool\n if chosen_test not in mutant.killingTests]\n\n # for mutant in sampled_pool:\n # for test in mutant.killingTests:\n # if test.split(\".\")[-1] in faulty_tests_names:\n # sampled_reveiling.add(mutant)\n\n # ms = 0\n # if len(sampled_reveiling) != 0:\n # if len(sampled_reveiling) != 0:\n # ms = zlen(sampled_reveiling) / len(sampled_pool)\n # n = n + 1\n\n if n != 0:\n n = n / 1000\n output_file.write(\"{},{},{},{},{}\".format(_index, name, str(round(to_sample_n, 2)), iteration, str(round(n, 2))))\n output_file.write(\"\\n\")\n", "repo_name": "Ojda22/study_I", "sub_path": "scripts/fault_revelation.py", "file_name": "fault_revelation.py", "file_ext": "py", "file_size_in_byte": 8211, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "sys.path.append", "line_number": 8, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 8, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentParser", "line_number": 14, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 13, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 24, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 33, "usage_type": "call"}, {"api_name": "os.path", "line_number": 33, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 34, "usage_type": "call"}, {"api_name": "os.path", "line_number": 34, "usage_type": "attribute"}, {"api_name": "scripts.mutation_comparision.map_mutants", "line_number": 43, "usage_type": "call"}, {"api_name": "scripts.mutation_comparision.calculate_minimal_mutants", "line_number": 47, "usage_type": "call"}, {"api_name": "scripts.mutation_comparision.calculate_minimal_mutants", "line_number": 51, "usage_type": "call"}, {"api_name": "os.stat", "line_number": 128, "usage_type": "call"}, {"api_name": "random.sample", "line_number": 143, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 151, "usage_type": "call"}, {"api_name": "random.sample", "line_number": 156, "usage_type": "call"}]} +{"seq_id": "8098289020", "text": "from __future__ import print_function\n\nfrom keras.models import Model, load_model\nfrom keras.layers import Input, LSTM, Dense\nfrom keras.callbacks import EarlyStopping, ModelCheckpoint\nimport numpy as np\nimport pandas as pd\nimport os\nimport tqdm\n\n# os.environ['CUDA_VISIBLE_DEVICES'] = '3'\n\nbatch_size = 128 # Batch size for training.\nepochs = 100 # Number of epochs to train for.\nlatent_dim = 256 # Latent dimensionality of the encoding space.\n# Path to the data txt file on disk.\n\nvoc = {}\nwith open('data/vocabs', encoding='utf-8')as vocab:\n for index, line in enumerate(vocab.readlines()):\n voc[line.strip()] = index\n\n# print(voc)\n\ninput_token = []\ntarget_token = []\ninput_token_index = []\ntarget_token_index = []\n\nmax_encoder_seq_length = 7\nmax_decoder_seq_length = 9\n\nimport threading\n\n\nclass createBatchGenerator:\n\n def __init__(self, batch_size=32):\n self.batch_size = batch_size\n self.lock = threading.Lock()\n\n def __iter__(self):\n return self\n\n def __next__(self):\n with self.lock:\n data = pd.read_csv('data/data.csv')\n count = 0\n for line in data.values:\n X1 = []\n X2 = []\n y = []\n count += 1\n encoder_input_data = np.zeros((max_encoder_seq_length, len(voc)), dtype='float16')\n decoder_input_data = np.zeros((max_decoder_seq_length, len(voc)), dtype='float16')\n decoder_target_data = np.zeros((max_decoder_seq_length, len(voc)), dtype='float16')\n for t, char in enumerate(str(line[0]).split()):\n encoder_input_data[t, voc[char]] = 1\n for t, char in enumerate(str(' ' + line[1] + ' ').split()):\n decoder_input_data[t, voc[char]] = 1\n if t > 0:\n decoder_target_data[t - 1, voc[char]] = 1\n\n X1.append(encoder_input_data)\n X2.append(decoder_input_data)\n y.append(decoder_target_data)\n\n if count == self.batch_size:\n count = 0\n # yield (np.array(X1), np.array(X2),np.array(y))\n yield ({'input_1': np.array(X1), 'input_2': np.array(X2)}, {'dense_1': np.array(y)})\n\n\ndef get_data(batch_size=128):\n while True:\n data = pd.read_csv('data/data.csv')\n count = 0\n for line in data.values:\n X1 = []\n X2 = []\n y = []\n count += 1\n encoder_input_data = np.zeros((max_encoder_seq_length, len(voc)), dtype='float16')\n decoder_input_data = np.zeros((max_decoder_seq_length, len(voc)), dtype='float16')\n decoder_target_data = np.zeros((max_decoder_seq_length, len(voc)), dtype='float16')\n for t, char in enumerate(str(line[0]).split()):\n encoder_input_data[t, voc[char]] = 1\n for t, char in enumerate(str(' ' + line[1] + ' ').split()):\n decoder_input_data[t, voc[char]] = 1\n if t > 0:\n decoder_target_data[t - 1, voc[char]] = 1\n\n X1.append(encoder_input_data)\n X2.append(decoder_input_data)\n y.append(decoder_target_data)\n\n if count == batch_size:\n count = 0\n yield ({'input_1': np.array(X1), 'input_2': np.array(X2)}, {'dense_1': np.array(y)})\n\n\n# Define an input sequence and process it.\nencoder_inputs = Input(shape=(None, len(voc)))\nencoder_outputs, state_h, state_c = LSTM(latent_dim, return_state=True)(encoder_inputs)\n# We discard `encoder_outputs` and only keep the states.\nencoder_states = [state_h, state_c]\n\n# Set up the decoder, using `encoder_states` as initial state.\ndecoder_inputs = Input(shape=(None, len(voc)))\n# We set up our decoder to return full output sequences,\n# and to return internal states as well. We don't use the\n# return states in the training model, but we will use them in inference.\ndecoder_lstm = LSTM(latent_dim, return_sequences=True, return_state=True)\ndecoder_outputs, _, _ = decoder_lstm(decoder_inputs,\n initial_state=encoder_states)\ndecoder_dense = Dense(len(voc), activation='softmax')\ndecoder_outputs = decoder_dense(decoder_outputs)\n\n# Define the model that will turn\n# `encoder_input_data` & `decoder_input_data` into `decoder_target_data`\nmodel = Model([encoder_inputs, decoder_inputs], decoder_outputs)\n\n# Run training\n\nprint('model train')\n# filepath = \"weights-improvement-{epoch:02d}-{val_acc:.2f}.hdf5\"\n# 中途训练效果提升, 则将文件保存, 每提升一次, 保存一次\n# checkpoint = ModelCheckpoint(filepath, monitor='val_acc', verbose=5, save_best_only=True,\n# mode='max')\n# callbacks_list = [checkpoint]\nearly_stopping = EarlyStopping(monitor='val_loss', patience=5)\n# [encoder_input_data, decoder_input_data], decoder_target_data,\nmodel.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['acc'])\nmodel.fit_generator(get_data(128),\n steps_per_epoch=1024,\n epochs=500)\n\n# Save model\nmodel.save('s2s.h5')\n\n# Next: inference mode (sampling).\n# Here's the drill:\n# 1) encode input and retrieve initial decoder state\n# 2) run one step of decoder with this initial state\n# and a \"start of sequence\" token as target.\n# Output will be the next target token\n# 3) Repeat with the current target token and current states\n\n# Define sampling models\nencoder_model = Model(encoder_inputs, encoder_states)\n\ndecoder_state_input_h = Input(shape=(latent_dim,))\ndecoder_state_input_c = Input(shape=(latent_dim,))\ndecoder_states_inputs = [decoder_state_input_h, decoder_state_input_c]\ndecoder_outputs, state_h, state_c = decoder_lstm(decoder_inputs, initial_state=decoder_states_inputs)\ndecoder_states = [state_h, state_c]\ndecoder_outputs = decoder_dense(decoder_outputs)\ndecoder_model = Model(\n [decoder_inputs] + decoder_states_inputs,\n [decoder_outputs] + decoder_states)\n\nencoder_model.save('encoder_model.h5')\ndecoder_model.save('decoder_model.h5')\nprint('save model done')\n\n\n# encoder_model = load_model('encoder_model.h5')\n# decoder_model = load_model('decoder_model.h5')\n\n# Reverse-lookup token index to decode sequences back to\n# something readable.\n# reverse_input_char_index = dict(\n# (i, char) for char, i in voc.items())\n# reverse_target_char_index = dict(\n# (i, char) for char, i in voc.items())\n\n\ndef decode_sequence(input_seq, seq_len):\n # Encode the input as state vectors.\n states_value = encoder_model.predict(input_seq)\n\n # Generate empty target sequence of length 1.\n target_seq = np.zeros((1, 1, len(voc)))\n # Populate the first character of target sequence with the start character.\n target_seq[0, 0, voc['']] = 1.\n\n # Sampling loop for a batch of sequences\n # (to simplify, here we assume a batch of size 1).\n stop_condition = False\n decoded_sentence = ''\n while not stop_condition:\n output_tokens, h, c = decoder_model.predict(\n [target_seq] + states_value)\n\n # Sample a token\n sampled_token_index = np.argmax(output_tokens[0, -1, :])\n sampled_char = reverse_target_char_index[sampled_token_index]\n decoded_sentence += sampled_char\n\n # Exit condition: either hit max length\n # or find stop character.\n if len(decoded_sentence) >= seq_len:\n stop_condition = True\n\n # Update the target sequence (of length 1).\n target_seq = np.zeros((1, 1, len(voc)))\n target_seq[0, 0, sampled_token_index] = 1.\n\n # Update states\n states_value = [h, c]\n\n return decoded_sentence\n\n# if __name__ == '__main__':\n# while True:\n# sentence = input('sentence: ')\n#\n# encoder_input_data = np.zeros((1, 32, len(voc)), dtype='float32')\n# for index, word in enumerate(sentence):\n# encoder_input_data[:, index, voc[word]] = 1\n#\n# decoded_sentence = decode_sequence(encoder_input_data, seq_len=len(sentence))\n# print('Input sentence:', sentence)\n# print('Decoded sentence:', decoded_sentence)\n# #\n# for seq_index in range(100):\n# # Take one sequence (part of the training set)\n# # for trying out decoding.\n# input_seq = encoder_input_data[seq_index: seq_index + 1]\n# decoded_sentence = decode_sequence(input_seq)\n# print('-')\n# print('Input sentence:', input_token[seq_index])\n# print('Decoded sentence:', decoded_sentence)\n", "repo_name": "terrifyzhao/keras_couplet", "sub_path": "mode.py", "file_name": "mode.py", "file_ext": "py", "file_size_in_byte": 8527, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "threading.Lock", "line_number": 40, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 47, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 54, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 55, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 56, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 71, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 76, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 83, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 84, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 85, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 99, "usage_type": "call"}, {"api_name": "keras.layers.Input", "line_number": 103, "usage_type": "call"}, {"api_name": "keras.layers.LSTM", "line_number": 104, "usage_type": "call"}, {"api_name": "keras.layers.Input", "line_number": 109, "usage_type": "call"}, {"api_name": "keras.layers.LSTM", "line_number": 113, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 116, "usage_type": "call"}, {"api_name": "keras.models.Model", "line_number": 121, "usage_type": "call"}, {"api_name": "keras.callbacks.EarlyStopping", "line_number": 131, "usage_type": "call"}, {"api_name": "keras.models.Model", "line_number": 150, "usage_type": "call"}, {"api_name": "keras.layers.Input", "line_number": 152, "usage_type": "call"}, {"api_name": "keras.layers.Input", "line_number": 153, "usage_type": "call"}, {"api_name": "keras.models.Model", "line_number": 158, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 183, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 196, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 206, "usage_type": "call"}]} +{"seq_id": "8256832144", "text": "import sys\n\nsys.path.insert(0, \"./\")\nsys.path.insert(0, \"../\")\n\nfrom django.http import StreamingHttpResponse\nfrom config_helper import ReadConfigFile\nfrom vehicle_detection.frame_grabber import *\nfrom vehicle_detection.vehicle_detection import *\n\nfrom loguru import logger\n\nconfig = ReadConfigFile().read_config(\"config.json\")\nframe_grabber = RTSPframeGrabber(config.camera_url)\nyolo = YoloLpd()\ncounter = VehicleCounter()\n\n\ndef cam(camera):\n while True:\n try:\n frame_grabber.start()\n frame = camera.latest_frame()\n _, jpeg = cv2.imencode(\".jpg\", frame)\n byte_img = jpeg.tobytes()\n yield (\n b\"--frame\\r\\n\"\n b\"Content-Type: image/jpeg\\r\\n\\r\\n\" + byte_img + b\"\\r\\n\\r\\n\"\n )\n except Exception as error:\n logger.error(f\"[CAM] Frame not received [error={error}]\")\n\n\ndef video_feed():\n while True:\n frame_grabber.start()\n img = frame_grabber.latest_frame()\n boxes, confs, class_ids = yolo.predict_lpd(img)\n\n img_labeled = yolo.draw_boxes(img, boxes, confs, class_ids, yolo.get_labels())\n flag, img_enc = cv2.imencode(\".jpg\", img_labeled)\n\n if not flag:\n continue\n img_byte = img_enc.tobytes()\n yield (\n b\"--frame\\r\\n\" b\"Content-Type: image/jpeg\\r\\n\\r\\n\" + img_byte + b\"\\r\\n\\r\\n\"\n )\n\n\ndef vehicle_counter():\n previous_frame_detections = [\n {(0, 0): 0} for i in range(counter.FRAMES_BEFORE_CURRENT)\n ]\n\n vehicle_count = 0\n\n while True:\n boxes, confidences, classIDs = [], [], []\n vehicle_crossed_line_flag = config.counter_bbox\n\n frame_grabber.start()\n img = frame_grabber.latest_frame()\n if not frame_grabber.grabbed:\n break\n\n boxes, confidences, classIDs = yolo.predict_lpd(img)\n\n vehicle_count, current_detections = counter.count_vehicles(\n boxes, classIDs, vehicle_count, previous_frame_detections, img\n )\n\n counter.display_vehicle_count(img, vehicle_count)\n logger.info(f\"[COUNTER] the number of counted vehicles {vehicle_count}\")\n\n # Updating with the current frame detections\n # Removing the first frame from the list\n previous_frame_detections.pop(0)\n # previous_frame_detections.append(spatial.KDTree(current_detections))\n previous_frame_detections.append(current_detections)\n\n # Draw detection box\n # counter.draw_detection_boxes(boxes, classIDs, confidences, img)\n yolo.draw_boxes(img, boxes, confidences, classIDs, yolo.get_labels())\n\n flag, img_enc = cv2.imencode(\".jpg\", img)\n\n if not flag:\n continue\n\n img_byte = img_enc.tobytes()\n yield (\n b\"--frame\\r\\n\" b\"Content-Type: image/jpeg\\r\\n\\r\\n\" + img_byte + b\"\\r\\n\\r\\n\"\n )\n\n\ndef render_camera_stream(request):\n return StreamingHttpResponse(\n cam(frame_grabber), content_type=\"multipart/x-mixed-replace; boundary=frame\"\n )\n\n\ndef render_detection_video(request):\n return StreamingHttpResponse(\n video_feed(), content_type=\"multipart/x-mixed-replace; boundary=frame\"\n )\n\n\ndef render_vehicle_counter_video(request):\n return StreamingHttpResponse(\n vehicle_counter(), content_type=\"multipart/x-mixed-replace; boundary=frame\"\n )\n", "repo_name": "yaqoobi/Smart-Vehicle-Detection", "sub_path": "vehicle_detection/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 3352, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "sys.path.insert", "line_number": 3, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 3, "usage_type": "attribute"}, {"api_name": "sys.path.insert", "line_number": 4, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 4, "usage_type": "attribute"}, {"api_name": "config_helper.ReadConfigFile", "line_number": 13, "usage_type": "call"}, {"api_name": "loguru.logger.error", "line_number": 31, "usage_type": "call"}, {"api_name": "loguru.logger", "line_number": 31, "usage_type": "name"}, {"api_name": "loguru.logger.info", "line_number": 74, "usage_type": "call"}, {"api_name": "loguru.logger", "line_number": 74, "usage_type": "name"}, {"api_name": "django.http.StreamingHttpResponse", "line_number": 98, "usage_type": "call"}, {"api_name": "django.http.StreamingHttpResponse", "line_number": 104, "usage_type": "call"}, {"api_name": "django.http.StreamingHttpResponse", "line_number": 110, "usage_type": "call"}]} +{"seq_id": "73619068268", "text": "import numpy as np\r\nimport pandas as pd\r\nimport matplotlib.pyplot as plt\r\nimport matplotlib.patches as mpatches\r\n\r\npath = 'D:/Users/felip/Documents/07. FEA/Dissertacao/dados/BOVESPA/dataprep/'\r\n\r\n\r\ndef plot_exemplo_semana(n, tam, x, y):\r\n nome = 'class_example_sup_learning_week.txt'\r\n file = path + nome\r\n fechamento = pd.read_csv(file,sep = ',')\r\n sample = fechamento.tail(n)\r\n # Function to map the colors as a list from the input list of x variables\r\n def pltcolor(lst):\r\n cols=[]\r\n for l in lst:\r\n if l==-1:\r\n cols.append('red')\r\n elif l==1:\r\n cols.append('blue')\r\n else:\r\n cols.append('green')\r\n return cols\r\n # Create the colors list using the function above\r\n cols=pltcolor(sample.classe)\r\n plt.rcParams['figure.figsize'] = (12, 8)\r\n plt.rcParams.update({'font.size': 15})\r\n fig, ax = plt.subplots()\r\n ax.scatter(x=sample.ret_d_2,y=sample.ret_d_1,s = tam, c=cols, marker='o') #Pass on the list created by the function here\r\n pop_a = mpatches.Patch(color = 'red', label = 'resultado negativo')\r\n pop_b = mpatches.Patch(color = 'blue', label = 'resultado positivo')\r\n ax.legend(handles=[pop_a,pop_b], scatterpoints=1)\r\n fig.suptitle('Classificação do Resultado Semanal de ITUB4')\r\n plt.grid(False)\r\n plt.xlabel('retorno semanal r[t-2]')\r\n plt.ylabel('retorno semanal r[t-1]')\r\n ax.axhline(0, color='black', lw=1)\r\n ax.axvline(0, color='black', lw=1)\r\n ax.axhline(y[0], color='gray', lw=1, linestyle = '--')\r\n ax.axhline(y[1], color='gray', lw=1, linestyle = '--')\r\n ax.axvline(x[0], color='gray', lw=1, linestyle = '--')\r\n ax.axvline(x[1], color='gray', lw=1, linestyle = '--')\r\n fig.savefig('ret_semanal_itub4.jpg')\r\n plt.show()\r\n \r\n\r\nplot_exemplo_semana(n = 50, tam = 75, x = [0.01,0.075], y = [-0.0080,0.06])\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n", "repo_name": "felipetshr/lstm_ibov", "sub_path": "class_example.py", "file_name": "class_example.py", "file_ext": "py", "file_size_in_byte": 1930, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "37", "api": [{"api_name": "pandas.read_csv", "line_number": 12, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.rcParams", "line_number": 27, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 27, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.rcParams.update", "line_number": 28, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.rcParams", "line_number": 28, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 28, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 29, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 29, "usage_type": "name"}, {"api_name": "matplotlib.patches.Patch", "line_number": 31, "usage_type": "call"}, {"api_name": "matplotlib.patches", "line_number": 31, "usage_type": "name"}, {"api_name": "matplotlib.patches.Patch", "line_number": 32, "usage_type": "call"}, {"api_name": "matplotlib.patches", "line_number": 32, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 35, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 35, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 36, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 36, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 37, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 37, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 45, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 45, "usage_type": "name"}]} +{"seq_id": "73413436586", "text": "from __future__ import division, print_function\nfrom builtins import object, range\n\nfrom copy import deepcopy\nimport math\nimport numpy as np\n\nimport runclassifier\n\n\nclass AODE(object):\n \"\"\"Create a generative model based on binarized spiking data.\n\n Use an average one-dependency estimator (AODE) to calculate the P(cs|data).\n Relies on estimating the probability of replay.\n\n \"\"\"\n\n def __init__(self):\n \"\"\"Initialize the AODE.\"\"\"\n\n self._cond = {} # Used to be _gmpjoint, now conditional\n self._marg = {} # Used to be _gmpsingle, now marginal\n self._pseudocount = 0.1\n self._classnames = []\n\n self.ncells = None\n\n @property\n def classnames(self):\n \"\"\"Return names of classes that the model was trained on.\"\"\"\n return deepcopy(self._classnames)\n\n @property\n def conditional(self):\n \"\"\"Return the conditional probability of each trained class.\"\"\"\n return deepcopy(self._cond)\n\n @property\n def marginal(self):\n \"\"\"Return the marginal probability of each trained class.\"\"\"\n return deepcopy(self._marg)\n\n def train(self, tevents):\n \"\"\"Train a classifier.\n\n Trains by measuring the probabilities of single-cell\n and pairwise firing over a range of frames relative to the stimuli.\n\n Parameters\n ----------\n tevents : matrix\n Deconvolved values, ranged from 0 to 1 of the shape\n nonsets x ncells x nframes\n classifier : str {'aode', 'naivebayes'}\n Change the classifier from AODE to naive bayes\n\n \"\"\"\n # Save the classnames in an order because dicts can change.\n self._classnames = [key for key in tevents]\n self.ncells = np.shape(tevents[self._classnames[0]])[1]\n\n for condition in self._classnames:\n # Take the max across frames\n stims = np.max(tevents[condition], axis=2).T\n stiminv = 1.0 - stims\n nonsets = np.shape(stims)[1]\n\n # List of probabilities of doublet and singlet spiking of\n # size (matching cells, total cells, 6)\n self._cond[condition] = np.zeros((self.ncells, self.ncells, 4))\n self._marg[condition] = np.zeros((self.ncells, 2))\n\n # Calculate single probabilities\n self._marg[condition][:, 0] = np.sum(stims, axis=1)\n self._marg[condition][:, 1] = np.sum(stiminv, axis=1)\n\n # For every matching cell, calculate the probabilities\n for c in range(self.ncells):\n # Repeat the value for each cell to make a tiled array of cell c\n crep = np.tile(stims[c, :], self.ncells).reshape((self.ncells, nonsets))\n crepinv = np.tile(stiminv[c, :], self.ncells).reshape((self.ncells, nonsets))\n\n self._cond[condition][c, :, 0] = np.sum(crep*stims, 1) # TT\n self._cond[condition][c, :, 1] = np.sum(crep*stiminv, 1) # TF\n\n self._cond[condition][c, :, 2] = np.sum(crepinv*stims, 1) # FT\n self._cond[condition][c, :, 3] = np.sum(crepinv*stiminv, 1) # FF\n\n # Set the joint of the same cell equal to 0 so that it's not included\n self._cond[condition][c, c, :] = 0\n\n # Add pseudocounts\n self._cond[condition] += self._pseudocount\n self._marg[condition] += self._pseudocount*2\n\n # Divide by the number of onsets\n self._cond[condition] /= float(np.shape(stims)[1] + 4*self._pseudocount)\n self._marg[condition] /= float(np.shape(stims)[1] + 4*self._pseudocount)\n\n # Divide by the marginal\n for c in range(self.ncells):\n self._cond[condition][c, :, 0] /= self._marg[condition][c, 0]\n self._cond[condition][c, :, 1] /= self._marg[condition][c, 0]\n self._cond[condition][c, :, 2] /= self._marg[condition][c, 1]\n self._cond[condition][c, :, 3] /= self._marg[condition][c, 1]\n\n # 0, P(x_i == T | x_j == T) = P(TT)/P(x_j == T)\n # 1, P(x_i == T | x_j == F)\n # 2, P(x_i == F | x_j == T)\n # 4, P(x_j == T)\n # 5, P(x_j == F)\n\n return self\n\n def compare(self, data, integrate_frames, priors, naive_bayes=False):\n \"\"\"Run the comparison using a numpy extension written in C for speed.\n\n Parameters\n ----------\n data : matrix\n Data of type ncells x ntimes\n integrate_frames : int\n The number of frames to take the max over, usually 4\n priors : dict of vectors\n The prior probability of each class type. Note:\n PRIORS MUST SUM TO 1! Assumes that one is using assign_temporal_priors\n naive_bayes : bool\n If True, run comparison as Naive Bayes rather than AODE.\n\n Returns\n -------\n dict of vectors\n The probability of reactivation in each case.\n\n \"\"\"\n\n # Double-check that the data has the correct number of cells\n ncells, nframes = np.shape(data)\n if ncells != self.ncells:\n raise ValueError('Wrong number of cells in dataset')\n\n # Double-check that priors have been set\n if not set(self._classnames).issubset(set(priors.keys())):\n raise ValueError('Not all classes have priors set')\n\n # Set the correct sizes based on the frame integration\n if integrate_frames > 1:\n rollframes = nframes - (integrate_frames - 1)\n frame_range = (int(math.floor(integrate_frames/2.0)),\n rollframes + int(math.floor(integrate_frames/2.0)))\n data = rollingmax(data, integrate_frames)\n else:\n rollframes = nframes\n frame_range = (0, nframes)\n\n if not naive_bayes:\n # Convert dicts to arrays for the numpy extension\n sprobs, jprobs, likelihood, res = self._prob_dict_to_np(rollframes)\n cprobs = np.array([priors[key][frame_range[0]:frame_range[1]]\n for key in self._classnames])\n\n # Run AODE\n runclassifier.aode(sprobs, jprobs, cprobs, data, res, likelihood)\n else:\n sprobs, likelihood, res = self._prob_dict_to_np(rollframes, True)\n cprobs = np.array([priors[key][frame_range[0]:frame_range[1]]\n for key in self._classnames])\n\n # Run Naive Bayes\n runclassifier.naivebayes(sprobs, cprobs, data, res, likelihood)\n\n # Copy output into the appropriate style\n out = {}\n likely = {}\n for i, key in enumerate(self._classnames):\n out[key] = np.zeros(nframes)\n out[key][frame_range[0]:frame_range[1]] = res[i]\n likely[key] = likelihood[i]\n\n return out, data, likely\n\n def describe(self):\n \"\"\"List the key bits of information about each stimulus.\"\"\"\n\n # out = '%13s %5s %6s %5s %5s %5s %5s %5s %5s %5s %5s %6s\\n'%(\n # 'Stimulus', 'Mean', 'Median', 'SMax', 'JMax', 'SSum', 'JSumTT', 'JSumTF', 'JSumFT', 'JSumFF', 'Cells',\n # 'Onsets')\n out = '%13s %5s %6s %5s %5s %5s %5s %5s %5s %5s %5s\\n'%(\n 'Stimulus', 'Mean', 'Median', 'SMax', 'JMax', 'SSum', 'JSumTT', 'JSumTF', 'JSumFT', 'JSumFF', 'Cells')\n for cs in self.classnames:\n # out += '%13s %.3f %.3f %.3f %.3f %5.2f %6.0f %6.0f %6.0f %6.0f %3i %4i\\n'%(\n out += '%13s %.3f %.3f %.3f %.3f %5.2f %6.0f %6.0f %6.0f %6.0f %3i\\n'%(\n cs,\n np.mean(self._marg[cs][:, 0]),\n np.median(self._marg[cs][:, 0]),\n np.max(self._marg[cs][:, 0]),\n np.max(self._cond[cs][:, :, 0]),\n np.sum(self._marg[cs][:, 0]),\n np.sum(self._cond[cs][:, :, 0]),\n np.sum(self._cond[cs][:, :, 2]),\n np.sum(self._cond[cs][:, :, 1]),\n np.sum(self._cond[cs][:, :, 3]),\n len(self._marg[cs][:, 0]),\n # np.shape(self.d[cs])[0],\n )\n return out\n\n def _prob_dict_to_np(self, nframes, naive_bayes=False):\n \"\"\"Convert the dict of probabilities into a single numpy array.\n\n Used to pass the probabilities to the C numpy extension.\n \"\"\"\n\n # Get a list of classes for results\n clses = self._classnames\n k = clses[0]\n\n # Allocate and fill each array\n sprobs = np.zeros((len(clses), np.shape(self._marg[k])[0], 2), dtype=np.float64)\n if not naive_bayes:\n jprobs = np.zeros((len(clses), np.shape(self._cond[k])[0],\n np.shape(self._cond[k])[1], 4), dtype=np.float64)\n likelihood = np.zeros((len(clses), nframes), dtype=np.float64)\n res = np.zeros((len(clses), nframes), dtype=np.float64)\n\n for i, key in enumerate(clses):\n sprobs[i] = self._marg[key]\n if not naive_bayes:\n jprobs[i] = self._cond[key]\n\n if not naive_bayes:\n return sprobs, jprobs, likelihood, res\n else:\n return sprobs, likelihood, res\n\n\ndef rollingmax(arr, integrate_frames):\n \"\"\"Get the rolling maximum across the final axis for a 1d or 2d array.\n\n Array will be converted to double.\n\n :param arr: 1d or 2d array of doubles\n :param integrate_frames: number of frames to integrate across the last axis, int\n :return: arr with the final axis of length input - (integrate_frames - 1)\n \"\"\"\n\n if arr.dtype != np.float64:\n arr = arr.astype(np.float64)\n\n if arr.ndim == 1:\n out = np.zeros(len(arr) - (integrate_frames - 1))\n elif arr.ndim == 2:\n out = np.zeros((np.shape(arr)[0], np.shape(arr)[1] - (integrate_frames - 1)))\n else:\n raise ValueError('Function only handles 1d and 2d arrays')\n runclassifier.rollmax(arr, out, integrate_frames)\n return out\n\n\ndef rollingmean(arr, integrate_frames):\n \"\"\"Get the rolling mean across the final axis for 1d or 2d array.\n\n Array will be converted to double.\n\n :param arr: 1d or 2d array of doubles\n :param integrate_frames: number of frames to integrate across the last axis, int\n :return: arr with the final axis of length input - (integrate_frames - 1)\n \"\"\"\n\n if arr.dtype != np.float64:\n arr = arr.astype(np.float64)\n\n if arr.ndim == 1:\n out = np.zeros(len(arr) - (integrate_frames - 1))\n elif arr.ndim == 2:\n out = np.zeros((np.shape(arr)[0], np.shape(arr)[1] - (integrate_frames - 1)))\n else:\n raise ValueError('Function only handles 1d and 2d arrays')\n\n runclassifier.rollmean(arr, out, integrate_frames)\n return out\n\n\ndef temporal_prior(traces, actmn, actvar, fwhm, stim_mask=None):\n \"\"\"Generate temporal-dependent priors.\n\n Uses basis sets and mexican-hat functions.\n\n :param traces: matrix of traces, ncells by nframes\n :param actmn: mean activity\n :param actvar: variation above which we will consider it a guaranteed event\n :param fwhm: the full-width at half-maximum to use for the temporal prior\n\n :return: prior vector\n \"\"\"\n\n from scipy.stats import norm\n\n # Set the half-width of the convolution kernel\n xhalfwidth = 100\n\n # Determine a normal function sigma from the full-width at half-maximum\n def sigma(fwhm_):\n return fwhm_/(2*np.sqrt(2*np.log(2)))\n\n # Generate the basis functions and correct population activity for baseline and variation\n basis = np.power(fwhm, np.arange(4) + 1)\n popact = (np.nanmean(traces, axis=0) - actmn)/actvar\n if stim_mask is not None:\n # Linearly interpolate through stims\n mask_indices = stim_mask.nonzero()[0]\n nonmask_indices = np.invert(stim_mask).nonzero()[0]\n popact[mask_indices] = np.interp(\n mask_indices, nonmask_indices, popact[nonmask_indices])\n fits = np.zeros((len(basis) - 1, len(popact)))\n\n # Get the first basis normal function\n defrange = int(norm.interval(0.99999, loc=0, scale=sigma(basis[0]))[1]) + 3\n defrange = min(xhalfwidth, defrange)\n b0 = np.zeros(2*xhalfwidth + 1)\n b0[xhalfwidth - defrange:xhalfwidth + defrange + 1] = norm.pdf(\n range(-defrange, defrange + 1), loc=0, scale=sigma(basis[0]))\n\n # Generate the fits\n for b in range(1, len(basis)):\n defrange = int(norm.interval(0.99999, loc=0, scale=sigma(basis[b]))[1]) + 3\n defrange = min(xhalfwidth, defrange)\n bn = np.zeros(2*xhalfwidth + 1)\n bn[xhalfwidth - defrange:xhalfwidth + defrange + 1] = norm.pdf(\n range(-defrange, defrange + 1), loc=0, scale=sigma(basis[b]))\n fits[b-1, :] = np.convolve(popact, b0 - bn, 'same')\n\n # And return the fits to the narrowest basis function\n weights = np.clip(np.nanmin(fits, axis=0), 0, 1)\n\n if stim_mask is not None:\n weights[stim_mask] = 0.\n\n return weights\n\n\ndef assign_temporal_priors(priors, tprior, keyword='other'):\n \"\"\"Apply the temporal prior to a prior dictionary.\n\n Applies the temporal prior to any member of the dict without keyword.\n\n :param priors: temporally-independent priors which will be combined with, anything with 'other' in it will get\n the inverse of the temporal prior, while the remaining groups will get multiplied with the temporal prior\n :param tprior: the temporal prior, ouput from temporal_prior()\n :param keyword: keyword for which dict values to exclude from temporal prior\n :return: dict of priors\n \"\"\"\n\n # Normalize priors\n psum = 0\n for key in priors: psum += priors[key]\n\n # Iterate over priors, accounting for \"others\" and non-\"others\" differently\n rclses = [key for key in priors if keyword not in key]\n oclses = [key for key in priors if keyword in key]\n rpriors = np.zeros((len(rclses), len(tprior)))\n for i, key in enumerate(rclses): rpriors[i, :] = priors[key]/psum*tprior\n\n # Get the remaining probability and add to others\n opriorsum = np.sum([priors[key]/psum for key in oclses])\n divvy = 1.0 - opriorsum - np.sum(rpriors, axis=0)\n opriors = np.zeros((len(oclses), len(tprior)))\n for i, key in enumerate(oclses): opriors[i, :] = (priors[key]/psum)*(1.0 + 1.0/opriorsum*divvy)\n\n # Put into dict and return\n out = {}\n for i, key in enumerate(rclses): out[key] = rpriors[i, :]\n for i, key in enumerate(oclses): out[key] = opriors[i, :]\n return out\n\n\ndef classify():\n \"\"\"Return a classifier class ready to be trained.\n\n :param data: Deconvolved or binarized calcium data\n :param priors: dict of stimulus types and prior probabilities\n :param integrate_frames: number of frames to integrate for comparison data\n :return: classifier class\n \"\"\"\n\n out = AODE()\n return out\n\n\nif __name__ == '__main__':\n print(rollingmean(np.arange(20), 3))\n", "repo_name": "asugden/flow", "sub_path": "flow/classifier/aode.py", "file_name": "aode.py", "file_ext": "py", "file_size_in_byte": 14992, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "37", "api": [{"api_name": "builtins.object", "line_number": 11, "usage_type": "name"}, {"api_name": "copy.deepcopy", "line_number": 32, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 37, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 42, "usage_type": "call"}, {"api_name": "numpy.shape", "line_number": 61, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 65, "usage_type": "call"}, {"api_name": "numpy.shape", "line_number": 67, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 71, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 72, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 75, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 76, "usage_type": "call"}, {"api_name": "builtins.range", "line_number": 79, "usage_type": "call"}, {"api_name": "numpy.tile", "line_number": 81, "usage_type": "call"}, {"api_name": "numpy.tile", "line_number": 82, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 84, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 85, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 87, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 88, "usage_type": "call"}, {"api_name": "numpy.shape", "line_number": 98, "usage_type": "call"}, {"api_name": "numpy.shape", "line_number": 99, "usage_type": "call"}, {"api_name": "builtins.range", "line_number": 102, "usage_type": "call"}, {"api_name": "numpy.shape", "line_number": 139, "usage_type": "call"}, {"api_name": "math.floor", "line_number": 150, "usage_type": "call"}, {"api_name": "math.floor", "line_number": 151, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 160, "usage_type": "call"}, {"api_name": "runclassifier.aode", "line_number": 164, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 167, "usage_type": "call"}, {"api_name": "runclassifier.naivebayes", "line_number": 171, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 177, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 195, "usage_type": "call"}, {"api_name": "numpy.median", "line_number": 196, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 197, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 198, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 199, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 200, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 201, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 202, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 203, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 220, "usage_type": "call"}, {"api_name": "numpy.shape", "line_number": 220, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 220, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 222, "usage_type": "call"}, {"api_name": "numpy.shape", "line_number": 222, "usage_type": "call"}, {"api_name": "numpy.shape", "line_number": 223, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 223, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 224, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 224, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 225, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 225, "usage_type": "attribute"}, {"api_name": "numpy.float64", "line_number": 248, "usage_type": "attribute"}, {"api_name": "numpy.float64", "line_number": 249, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 252, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 254, "usage_type": "call"}, {"api_name": "numpy.shape", "line_number": 254, "usage_type": "call"}, {"api_name": "runclassifier.rollmax", "line_number": 257, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 271, "usage_type": "attribute"}, {"api_name": "numpy.float64", "line_number": 272, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 275, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 277, "usage_type": "call"}, {"api_name": "numpy.shape", "line_number": 277, "usage_type": "call"}, {"api_name": "runclassifier.rollmean", "line_number": 281, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 305, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 305, "usage_type": "call"}, {"api_name": "numpy.power", "line_number": 308, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 308, "usage_type": "call"}, {"api_name": "numpy.nanmean", "line_number": 309, "usage_type": "call"}, {"api_name": "numpy.invert", "line_number": 313, "usage_type": "call"}, {"api_name": "numpy.interp", "line_number": 314, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 316, "usage_type": "call"}, {"api_name": "scipy.stats.norm.interval", "line_number": 319, "usage_type": "call"}, {"api_name": "scipy.stats.norm", "line_number": 319, "usage_type": "name"}, {"api_name": "numpy.zeros", "line_number": 321, "usage_type": "call"}, {"api_name": "scipy.stats.norm.pdf", "line_number": 322, "usage_type": "call"}, {"api_name": "scipy.stats.norm", "line_number": 322, "usage_type": "name"}, {"api_name": "builtins.range", "line_number": 323, "usage_type": "call"}, {"api_name": "builtins.range", "line_number": 326, "usage_type": "call"}, {"api_name": "scipy.stats.norm.interval", "line_number": 327, "usage_type": "call"}, {"api_name": "scipy.stats.norm", "line_number": 327, "usage_type": "name"}, {"api_name": "numpy.zeros", "line_number": 329, "usage_type": "call"}, {"api_name": "scipy.stats.norm.pdf", "line_number": 330, "usage_type": "call"}, {"api_name": "scipy.stats.norm", "line_number": 330, "usage_type": "name"}, {"api_name": "builtins.range", "line_number": 331, "usage_type": "call"}, {"api_name": "numpy.convolve", "line_number": 332, "usage_type": "call"}, {"api_name": "numpy.clip", "line_number": 335, "usage_type": "call"}, {"api_name": "numpy.nanmin", "line_number": 335, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 362, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 366, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 367, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 368, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 392, "usage_type": "call"}]} +{"seq_id": "35459077951", "text": "from pwn import *\nfrom tqdm import trange, tqdm\nfrom math import ceil\n\np = None\nlast_IV_for_flag = None\n\n# user={name};flag={flag}\n# 5 + len(name) + 6\nblock = 16\nbase_name_len = block - len('user=') - len(';flag=')\nflag_len = 32\nalphabet = b'abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789_{}'\n\nflag = b'flag{'\n\n\n# nc berserker.challs.olicyber.it 10507\ndef get_process():\n p = remote('berserker.challs.olicyber.it', 10507)\n p.recvuntil(b'> ')\n return p\n\ndef get_cookie(name):\n p.sendline(b'1')\n p.sendline(name)\n p.recvuntil(b'cifrato: ')\n cookie = p.recvline().strip()\n\n return bytes.fromhex(cookie.decode())\n\ndef cipher(msg):\n p.sendline(b'2')\n p.sendline(msg)\n p.recvuntil(b'Messaggio cifrato: ')\n ciphertext = p.recvline().strip()\n\n return bytes.fromhex(ciphertext.decode())\n\ndef xor(a, b):\n return bytes([x ^ y for x, y in zip(a, b)])\n\ndef get_new_letter():\n known_len = len(flag)\n name_len = base_name_len + (block - known_len % block) + block - 1\n name = b'a' * name_len\n expected_str = f'user={name.decode()};flag=' + flag.decode()\n to_check = expected_str[-block + 1:].encode()\n\n cookie = get_cookie(name)\n wanted_iv = cookie[len(expected_str) + 1 - block - block: len(expected_str) + 1 - block]\n ciphered_to_check = cookie[len(expected_str) + 1 - block: len(expected_str) + 1]\n\n latest_iv = cookie[-block:]\n for letter in tqdm(alphabet, position=1, leave=False, desc= flag.decode()):\n to_send = to_check + bytes([letter])\n to_send = xor(to_send, latest_iv)\n to_send = xor(to_send, wanted_iv)\n ciphered = cipher(to_send.hex().encode())\n if ciphered[:block] == ciphered_to_check:\n return letter\n \n latest_iv = ciphered[-block:]\n\n raise Exception('No letter found')\n\np = get_process()\nfor _ in trange(flag_len - len(flag), position=0):\n tmp = get_new_letter()\n flag += tmp.to_bytes(1, 'big')\n\nprint(flag)", "repo_name": "SamueleFacenda/Python-Scripts", "sub_path": "olicyber/crypto/berserker.py", "file_name": "berserker.py", "file_ext": "py", "file_size_in_byte": 1967, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "37", "api": [{"api_name": "tqdm.tqdm", "line_number": 55, "usage_type": "call"}, {"api_name": "tqdm.trange", "line_number": 68, "usage_type": "call"}]} +{"seq_id": "72697532907", "text": "from ..models.base_autoencoder import ConvLayers, DenseLayers, BAE_BaseClass, Autoencoder, flatten_torch, flatten_np, Encoder\nimport torch\nfrom torch.nn import Parameter\nimport torch.nn.functional as F\nfrom torch.nn.modules.conv import Conv2d, ConvTranspose2d\nfrom torch.autograd import Variable\nfrom sklearn.decomposition import PCA\nimport numpy as np\n\n#VI\nclass VariationalLinear(torch.nn.Module):\n def __init__(self, input_size, output_size, prior_mu=0., prior_sigma_1=1.0, prior_sigma_2=0.1, prior_pi=0.5):\n super(VariationalLinear, self).__init__()\n self.weight_mu = Parameter(torch.Tensor(output_size, input_size))\n self.weight_sigma = Parameter(torch.Tensor(output_size, input_size))\n\n self.bias_mu = Parameter(torch.Tensor(output_size))\n self.bias_sigma = Parameter(torch.Tensor(output_size))\n\n self.prior_mu = Parameter(torch.FloatTensor([prior_mu]),requires_grad=False)\n self.prior_sigma = Parameter(torch.FloatTensor([prior_sigma_1]),requires_grad=False)\n self.prior_mu_ =prior_mu\n self.prior_sigma_ = prior_sigma_1\n\n #scale gaussian mixture\n self.prior_sigma_1 = prior_sigma_1\n self.prior_sigma_2 = prior_sigma_2\n self.prior_pi = prior_pi\n\n self.input_size = input_size\n self.output_size = output_size\n self.reset_parameters()\n\n\n def reset_parameters(self):\n if hasattr(self, \"weight_mu\"):\n self.weight_mu.data.normal_(self.prior_mu_,self.prior_sigma_*0.1)\n self.weight_sigma.data = torch.ones_like(self.weight_sigma) * -3\n self.bias_mu.data.normal_(self.prior_mu_,self.prior_sigma_*0.1)\n self.bias_sigma.data = torch.ones_like(self.bias_sigma) * -3\n\n def log_gaussian_loss_sigma_2_torch(self, y_pred, y_true, sigma_2):\n log_likelihood = (-((y_true - y_pred)**2)/(2*sigma_2))-(0.5*torch.log(sigma_2))\n return log_likelihood\n\n def gaussian_loss_sigma_torch(self, y_pred, y_true, sigma):\n likelihood = (1/(sigma*2.506))*torch.exp(-(y_true-y_pred)**2)/(2*sigma**2)\n return likelihood\n\n def kl_loss_prior_mixture(self,weight,weight_mu,weight_sigma):\n q_variational_log_prob = self.log_gaussian_loss_sigma_2_torch(weight,weight_mu,weight_sigma)\n prior_log_prob = torch.log(self.prior_pi*self.gaussian_loss_sigma_torch(weight,self.prior_mu,self.prior_sigma_1) +\\\n (1-self.prior_pi)*self.gaussian_loss_sigma_torch(weight,self.prior_mu,self.prior_sigma_2))\n kl_loss = (q_variational_log_prob-prior_log_prob).mean()\n\n return kl_loss\n\n def kl_loss_prior_gaussian(self,weight,weight_mu,weight_sigma):\n q_variational_log_prob = self.log_gaussian_loss_sigma_2_torch(weight,weight_mu,weight_sigma)\n prior_log_prob = self.log_gaussian_loss_sigma_2_torch(weight,self.prior_mu,self.prior_sigma)\n kl_loss = (q_variational_log_prob-prior_log_prob).mean()\n\n return kl_loss\n\n def weight_sample(self):\n weight = self.weight_mu + F.softplus(self.weight_sigma) * torch.randn_like(self.weight_mu)\n return weight\n\n def bias_sample(self):\n bias = self.bias_mu + F.softplus(self.bias_sigma) * torch.randn_like(self.bias_mu)\n return bias\n\n def forward(self,x):\n #draw samples for weight and bias\n weight = self.weight_sample()\n bias = self.bias_sample()\n kl_loss = self.kl_loss_prior_mixture(weight,self.weight_mu,F.softplus(self.weight_sigma))\n y = F.linear(x,weight,bias)\n return y,kl_loss\n\nclass VariationalBaseConv():\n def __init__(self, prior_mu=0., prior_sigma_1=1.0, prior_sigma_2=0.1, prior_pi=0.5):\n self.weight_mu = Parameter(torch.Tensor(*self.weight.shape))\n self.weight_sigma = Parameter(torch.Tensor(*self.weight.shape))\n\n self.bias_mu = Parameter(torch.Tensor(*self.bias.shape))\n self.bias_sigma = Parameter(torch.Tensor(*self.bias.shape))\n\n self.prior_mu = Parameter(torch.FloatTensor([prior_mu]),requires_grad=False)\n self.prior_sigma = Parameter(torch.FloatTensor([prior_sigma_1]),requires_grad=False)\n self.prior_mu_ =prior_mu\n self.prior_sigma_ = prior_sigma_1\n\n #scale gaussian mixture\n self.prior_sigma_1 = prior_sigma_1\n self.prior_sigma_2 = prior_sigma_2\n self.prior_pi = prior_pi\n\n def reset_parameters(self):\n if hasattr(self, \"weight_mu\"):\n self.weight_mu.data.normal_(self.prior_mu_,self.prior_sigma_*0.1)\n self.weight_sigma.data = torch.ones_like(self.weight_sigma) * -3\n self.bias_mu.data.normal_(self.prior_mu_,self.prior_sigma_*0.1)\n self.bias_sigma.data = torch.ones_like(self.bias_sigma) * -3\n\n def log_gaussian_loss_sigma_2_torch(self, y_pred, y_true, sigma_2):\n log_likelihood = (-((y_true - y_pred)**2)/(2*sigma_2))-(0.5*torch.log(sigma_2))\n return log_likelihood\n\n def gaussian_loss_sigma_torch(self, y_pred, y_true, sigma):\n likelihood = (1/(sigma*2.506))*torch.exp(-(y_true-y_pred)**2)/(2*sigma**2)\n return likelihood\n\n def kl_loss_prior_mixture(self,weight,weight_mu,weight_sigma):\n q_variational_log_prob = self.log_gaussian_loss_sigma_2_torch(weight,weight_mu,weight_sigma)\n prior_log_prob = torch.log(self.prior_pi*self.gaussian_loss_sigma_torch(weight,self.prior_mu,self.prior_sigma_1) +\\\n (1-self.prior_pi)*self.gaussian_loss_sigma_torch(weight,self.prior_mu,self.prior_sigma_2))\n kl_loss = (q_variational_log_prob-prior_log_prob).mean()\n\n return kl_loss\n\n def kl_loss_prior_gaussian(self,weight,weight_mu,weight_sigma):\n q_variational_log_prob = self.log_gaussian_loss_sigma_2_torch(weight,weight_mu,weight_sigma)\n prior_log_prob = self.log_gaussian_loss_sigma_2_torch(weight,self.prior_mu,self.prior_sigma)\n kl_loss = (q_variational_log_prob-prior_log_prob).mean()\n\n return kl_loss\n\n def weight_sample(self):\n weight = self.weight_mu + F.softplus(self.weight_sigma) * torch.randn_like(self.weight_mu)\n return weight\n\n def bias_sample(self):\n bias = self.bias_mu + F.softplus(self.bias_sigma) * torch.randn_like(self.bias_mu)\n return bias\n\nclass VariationalConv2D(Conv2d,VariationalBaseConv):\n def __init__(self, **kwargs):\n Conv2d.__init__(self,**kwargs)\n VariationalBaseConv.__init__(self)\n self.reset_parameters()\n\n def reset_parameters(self):\n Conv2d.reset_parameters(self)\n VariationalBaseConv.reset_parameters(self)\n\n def forward(self,x):\n #draw samples for weight and bias\n weight = self.weight_sample()\n bias = self.bias_sample()\n kl_loss = self.kl_loss_prior_mixture(weight,self.weight_mu,F.softplus(self.weight_sigma))\n y = F.conv2d(x, weight, bias, self.stride, self.padding, self.dilation, self.groups)\n return y,kl_loss\n\nclass VariationalConv2DTranspose(ConvTranspose2d,VariationalBaseConv):\n def __init__(self, **kwargs):\n ConvTranspose2d.__init__(self,**kwargs)\n VariationalBaseConv.__init__(self)\n self.reset_parameters()\n\n def reset_parameters(self):\n ConvTranspose2d.reset_parameters(self)\n VariationalBaseConv.reset_parameters(self)\n\n def forward(self,x):\n #draw samples for weight and bias\n weight = self.weight_sample()\n bias = self.bias_sample()\n kl_loss = self.kl_loss_prior_mixture(weight,self.weight_mu,F.softplus(self.weight_sigma))\n y = F.conv_transpose2d(x, weight, bias, self.stride, self.padding, self.output_padding, self.groups, self.dilation)\n\n return y,kl_loss\n\nclass VariationalConv2DLayers(ConvLayers):\n def __init__(self, **kwargs):\n super(VariationalConv2DLayers, self).__init__(**kwargs, layer_type= [VariationalConv2D, VariationalConv2DTranspose])\n # total_kl_loss = Variable(torch.tensor([0.]))\n\n def forward(self,x):\n if isinstance(x,tuple):\n x, total_kl_loss = x\n total_kl_loss = total_kl_loss[0]\n else:\n total_kl_loss = Variable(torch.tensor([0.]))\n if self.use_cuda:\n total_kl_loss = total_kl_loss.cuda()\n #apply relu\n for layer_index,layer in enumerate(self.layers):\n #first sub layer is VI\n x,kl_loss = layer[0](x)\n total_kl_loss=total_kl_loss+kl_loss\n\n #relu, and other sub layers\n if len(layer) > 1:\n for sub_layer in layer[1:]:\n x = sub_layer(x)\n return x,total_kl_loss\n\nclass VariationalDenseLayers(DenseLayers):\n def __init__(self, **kwargs):\n super(VariationalDenseLayers, self).__init__(**kwargs, layer_type = VariationalLinear)\n\n self.activation_ids = []\n for layer_id, layer in enumerate(self.layers):\n if isinstance(layer,VariationalLinear) == False:\n self.activation_ids.append(layer_id)\n self.last_layer_id = len(self.layers)-1\n\n def forward(self,x):\n #if its a tuple, we expect it to carry the total_kl_loss from previous layer\n if isinstance(x,tuple):\n x, total_kl_loss = x\n total_kl_loss = total_kl_loss[0]\n else:\n total_kl_loss = Variable(torch.tensor([0.]))\n if self.use_cuda:\n total_kl_loss = total_kl_loss.cuda()\n\n for layer_index,layer in enumerate(self.layers):\n #if activation layer, the return does not include kl_loss\n if layer_index in self.activation_ids:\n x = layer(x)\n #otherwise, the layer is variational layer, it returns kl_loss\n #we add the kl_loss to total\n else:\n x, kl_loss = layer(x)\n total_kl_loss=total_kl_loss+kl_loss\n\n return x,total_kl_loss\n\nclass BAE_VI(BAE_BaseClass):\n \"\"\"\n Note: The output of autoencoder networks (encoder, decoder) consists of the prediction and KL-Loss.\n\n For an autoencoder with decoder_sigma, the nested tuples becomes ((decoder_mu, decoder_sigma), (output,kl_loss))\n \"\"\"\n def __init__(self, *args, model_name=\"BAE_VI\", num_train_samples=1, **kwargs):\n if num_train_samples <=1:\n num_train_samples = 1\n self.num_train_samples = num_train_samples #for training averaging\n super(BAE_VI, self).__init__(*args, model_name=model_name, model_type=\"stochastic\", **kwargs)\n\n def nll_kl_loss(self, autoencoder, x, y=None, mode=\"sigma\"):\n #likelihood\n if y is None:\n y = x\n\n #forward sample of autoencoder\n if self.decoder_sigma_enabled:\n decoded_mu, decoded_sigma = autoencoder(x)\n y_pred_mu, kl_mu = decoded_mu\n y_pred_sig, kl_sig = decoded_sigma\n kl_loss = kl_mu+kl_sig\n else:\n y_pred_mu, kl_loss = autoencoder(x)\n\n #get actual nll according to selected likelihood and mode\n if mode==\"sigma\":\n nll = self._nll(flatten_torch(y_pred_mu), flatten_torch(y), flatten_torch(y_pred_sig))\n elif mode ==\"mu\":\n nll = self._nll(flatten_torch(y_pred_mu), flatten_torch(y), autoencoder.log_noise)\n\n return nll, kl_loss\n\n def criterion(self, autoencoder, x,y=None, mode=\"sigma\"):\n #likelihood + kl_loss\n for num_train_sample in range(self.num_train_samples):\n if num_train_sample == 0:\n nll,kl_loss = self.nll_kl_loss(autoencoder,x,y,mode)\n else:\n nll_temp,kl_loss_temp = self.nll_kl_loss(autoencoder,x,y,mode)\n nll = nll + nll_temp\n kl_loss = kl_loss + kl_loss_temp\n nll = nll/self.num_train_samples\n kl_loss = kl_loss/self.num_train_samples\n nll = nll.mean()\n\n #scale kl by a constant hyperparameter\n kl_loss *= self.weight_decay\n\n return nll + kl_loss\n\n def _forward_latent_single(self,model,x):\n return model.encoder(x)[0]\n\n def _get_mu_sigma_single(self, autoencoder, x):\n if self.decoder_sigma_enabled:\n decoded_mu, decoded_sigma = autoencoder(x)\n y_mu, kl_mu = decoded_mu\n y_sigma, kl_sig = decoded_sigma\n del kl_mu\n del kl_sig\n else:\n y_mu, kl_loss = autoencoder(x)\n del kl_loss\n y_sigma = torch.ones_like(y_mu)\n\n #convert to numpy\n y_mu = y_mu.detach().cpu().numpy()\n y_sigma = y_sigma.detach().cpu().numpy()\n log_noise = autoencoder.log_noise.detach().cpu().numpy()\n\n return flatten_np(y_mu), flatten_np(y_sigma), log_noise\n\n def convert_conv_vi(self,conv_layer):\n conv_params = {}\n for key, val in conv_layer.__dict__.items():\n exclude_params = [\"activation_layer\", \"model_kwargs\",\"training\",\"conv2d_layer_type\",\"conv2d_trans_layer_type\"]\n if key[0] != '_' and key not in exclude_params:\n conv_params.update({key:val})\n conv_vi = VariationalConv2DLayers(**conv_params, reverse_params=False)\n return conv_vi\n\n def convert_dense_vi(self,dense_layer):\n dense_params = {}\n for key, val in dense_layer.__dict__.items():\n exclude_params = [\"activation_layer\", \"model_kwargs\"]\n if key[0] != '_' and key not in exclude_params:\n dense_params.update({key:val})\n converted_dense= VariationalDenseLayers(**dense_params)\n return converted_dense\n\n def convert_layer(self, layer):\n if isinstance(layer,ConvLayers):\n return self.convert_conv_vi(layer)\n if isinstance(layer,DenseLayers):\n return self.convert_dense_vi(layer)\n else:\n return layer\n\n def convert_torch_sequential(self, torch_sequential):\n converted_branch = []\n for layer in torch_sequential.children():\n converted_branch.append(self.convert_layer(layer))\n return torch.nn.Sequential(*converted_branch)\n\n def convert_autoencoder(self, autoencoder=Autoencoder):\n encoder = self.convert_torch_sequential(autoencoder.encoder) if isinstance(autoencoder.encoder, torch.nn.Sequential) else self.convert_layer(autoencoder.encoder)\n decoder_mu = self.convert_torch_sequential(autoencoder.decoder_mu) if isinstance(autoencoder.decoder_mu, torch.nn.Sequential) else self.convert_layer(autoencoder.decoder_mu)\n\n if autoencoder.decoder_sig_enabled:\n decoder_sig = self.convert_torch_sequential(autoencoder.decoder_sig) if isinstance(autoencoder.decoder_sig, torch.nn.Sequential) else self.convert_layer(autoencoder.decoder_sig)\n else:\n decoder_sig = None\n return Autoencoder(encoder=encoder, decoder_mu=decoder_mu, decoder_sig=decoder_sig)\n\nclass VAELinear(VariationalLinear):\n def forward(self,weight_mu,weight_sigma):\n #draw samples for weight and bias\n self.weight_mu.data = weight_mu\n self.weight_sigma.data = weight_sigma\n\n weight = self.weight_sample()\n\n kl_loss = self.kl_loss_prior_gaussian(weight,self.weight_mu,F.softplus(self.weight_sigma))\n\n return weight,kl_loss\n\nclass VAE_Module(Autoencoder):\n def __init__(self, **kwargs):\n super(VAE_Module, self).__init__(**kwargs)\n self.encoder, self.latent_layer_mu, self.latent_layer_sigma, self.latent_layer_vi = self.infer_latent_layers(self.encoder)\n\n #set cuda\n self.set_cuda(self.use_cuda)\n\n def infer_latent_layers(self, encoder: Encoder):\n new_encoder =[]\n\n for layer in encoder:\n if isinstance(layer, DenseLayers):\n encoder_dense_layer = layer\n\n if len(encoder_dense_layer.architecture) == 1:\n latent_input_size = encoder_dense_layer.architecture[-1]\n encoder_dense = torch.nn.Sequential(torch.nn.Linear(encoder_dense_layer.input_size, latent_input_size),torch.nn.ReLU())\n elif len(encoder_dense_layer.architecture) == 0:\n latent_input_size = encoder_dense_layer.get_input_dimensions()\n encoder_dense = None\n else:\n encoder_architecture = encoder_dense_layer.architecture[:-1]\n latent_input_size = encoder_dense_layer.architecture[-1]\n encoder_dense= DenseLayers(architecture=encoder_architecture,\n input_size=encoder_dense_layer.input_size,\n output_size=latent_input_size)\n latent_output_size = encoder_dense_layer.output_size\n latent_layer_mu = torch.nn.Linear(latent_input_size, latent_output_size)\n latent_layer_sigma = torch.nn.Linear(latent_input_size, latent_output_size)\n latent_layer_vi = VAELinear(latent_input_size, latent_output_size)\n\n if encoder_dense is not None:\n new_encoder.append(encoder_dense)\n else:\n new_encoder.append(layer)\n return torch.nn.Sequential(*new_encoder), latent_layer_mu, latent_layer_sigma, latent_layer_vi\n\n def log_gaussian_loss_sigma_2_torch(self, y_pred, y_true, sigma_2):\n log_likelihood = (-((y_true - y_pred)**2)/(2*sigma_2))-(0.5*torch.log(sigma_2))\n return log_likelihood\n\n def kl_loss_prior_gaussian(self,weight,weight_mu,weight_sigma):\n q_variational_log_prob = self.log_gaussian_loss_sigma_2_torch(weight,weight_mu,weight_sigma)\n prior_log_prob = self.log_gaussian_loss_sigma_2_torch(weight,self.latent_layer_vi.prior_mu,self.latent_layer_vi.prior_sigma)\n kl_loss = (q_variational_log_prob-prior_log_prob).mean()\n\n return kl_loss\n\n def forward(self, x):\n encoded = self.encoder(x)\n latent_mu = self.latent_layer_mu(encoded)\n latent_sigma = self.latent_layer_sigma(encoded)\n\n #draw samples for weight and bias\n latent_sample = latent_mu + F.softplus(latent_sigma) * torch.randn_like(latent_mu)\n kl_loss = self.kl_loss_prior_gaussian(latent_sample,latent_mu,F.softplus(latent_sigma))\n\n decoded_mu = self.decoder_mu(latent_sample)\n\n if self.decoder_sig_enabled:\n decoded_sig = self.decoder_sig(latent_sample)\n return decoded_mu,decoded_sig,kl_loss\n else:\n return decoded_mu,kl_loss\n\nclass VAE(BAE_VI):\n \"\"\"\n For VAE, only the latent dimension layer is considered as probabilistic, and trained using VI\n \"\"\"\n def __init__(self, *args, model_name=\"VAE\", num_train_samples=5, beta=1.0, **kwargs):\n self.num_train_samples = num_train_samples #for training averaging\n BAE_VI.__init__(self,*args, model_name=model_name, **kwargs)\n\n #beta for KL weighting\n self.beta = beta\n\n def convert_autoencoder(self, autoencoder:Autoencoder):\n if autoencoder.decoder_sig_enabled:\n return VAE_Module(encoder=autoencoder.encoder, decoder_mu=autoencoder.decoder_mu, decoder_sig=autoencoder.decoder_sig)\n else:\n return VAE_Module(encoder=autoencoder.encoder, decoder_mu=autoencoder.decoder_mu)\n\n def nll_kl_loss(self, autoencoder, x, y=None, mode=\"sigma\"):\n #likelihood\n if y is None:\n y = x\n\n #forward sample of autoencoder\n if self.decoder_sigma_enabled:\n y_pred_mu, y_pred_sig, kl_loss = autoencoder(x)\n else:\n y_pred_mu, kl_loss = autoencoder(x)\n\n #get actual nll according to selected likelihood and mode\n if mode==\"sigma\":\n nll = self._nll(flatten_torch(y_pred_mu), flatten_torch(y), flatten_torch(y_pred_sig))\n elif mode ==\"mu\":\n nll = self._nll(flatten_torch(y_pred_mu), flatten_torch(y), autoencoder.log_noise)\n\n return nll, kl_loss\n\n def criterion(self, autoencoder, x,y=None, mode=\"sigma\"):\n \"\"\"\n Note that the kl_loss is for the probablistic latent layer,\n while prior_loss is for deterministic encoder and decoder(s)\n\n \"\"\"\n # pass the data forward for num_train_samples times and obtain average loss\n for num_train_sample in range(self.num_train_samples):\n if num_train_sample == 0:\n nll,kl_loss = self.nll_kl_loss(autoencoder,x,y,mode)\n else:\n nll_temp,kl_loss_temp = self.nll_kl_loss(autoencoder,x,y,mode)\n nll = nll + nll_temp\n kl_loss = kl_loss + kl_loss_temp\n nll /= self.num_train_samples\n kl_loss /= self.num_train_samples\n\n #obtain mean of likelihood cost\n nll = nll.mean()\n\n #prior loss of encoder/decoder\n #note this doesn't include the latent layers\n #for kl loss already includes complexity cost due to prior on latent layers\n prior_loss_encoder = self.log_prior_loss(model=autoencoder.encoder).mean()\n prior_loss_decoder = self.log_prior_loss(model=autoencoder.decoder_mu).mean()\n prior_loss = prior_loss_encoder+prior_loss_decoder\n if self.decoder_sigma_enabled:\n prior_loss_decoder_sig = self.log_prior_loss(model=autoencoder.decoder_sig).mean()\n prior_loss = prior_loss+prior_loss_decoder_sig\n\n #scale by beta\n kl_loss *= self.beta\n prior_loss *= self.weight_decay\n\n return nll+kl_loss+prior_loss\n\n def get_optimisers(self, autoencoder: Autoencoder, mode=\"mu\", sigma_train=\"joint\"):\n optimiser_list = self.get_optimisers_list(autoencoder, mode=mode, sigma_train=sigma_train)\n\n if sigma_train == \"joint\" or mode == \"mu\":\n optimiser_list.append({'params':autoencoder.latent_layer_vi.parameters()})\n optimiser_list.append({'params':autoencoder.latent_layer_mu.parameters()})\n optimiser_list.append({'params':autoencoder.latent_layer_sigma.parameters()})\n\n return torch.optim.Adam(optimiser_list, lr=self.learning_rate)\n\n def _get_mu_sigma_single(self, autoencoder, x):\n if self.decoder_sigma_enabled:\n y_mu, y_sigma, kl = autoencoder(x)\n del kl\n else:\n y_mu, kl_loss = autoencoder(x)\n del kl_loss\n y_sigma = torch.ones_like(y_mu)\n\n #convert to numpy\n y_mu = y_mu.detach().cpu().numpy()\n y_sigma = y_sigma.detach().cpu().numpy()\n log_noise = autoencoder.log_noise.detach().cpu().numpy()\n\n return flatten_np(y_mu), flatten_np(y_sigma), log_noise\n\n def predict_latent(self, x, transform_pca = True):\n x = self.convert_tensor(x)\n encoded = self.autoencoder.encoder(x)\n latent_mu = self.autoencoder.latent_layer_mu(encoded).detach().cpu().numpy()\n latent_sigma = F.softplus(self.autoencoder.latent_layer_sigma(encoded)).detach().cpu().numpy()\n\n #remove from gpu\n encoded = encoded.detach().cpu() #detach\n del encoded\n\n #transform pca\n if transform_pca:\n pca = PCA(n_components=2)\n latent_pca_mu = pca.fit_transform(latent_mu)\n latent_pca_sig = pca.fit_transform(latent_sigma)\n return latent_pca_mu,latent_pca_sig\n else:\n return latent_mu, latent_sigma\n", "repo_name": "bangxiangyong/baetorch", "sub_path": "baetorch/models/bae_vi.py", "file_name": "bae_vi.py", "file_ext": "py", "file_size_in_byte": 23209, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 13, "dataset": "github-code", "pt": "37", "api": [{"api_name": "torch.nn", "line_number": 11, "usage_type": "attribute"}, {"api_name": "torch.nn.Parameter", "line_number": 14, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 14, "usage_type": "call"}, {"api_name": "torch.nn.Parameter", "line_number": 15, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 15, "usage_type": "call"}, {"api_name": "torch.nn.Parameter", "line_number": 17, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 17, "usage_type": "call"}, {"api_name": "torch.nn.Parameter", "line_number": 18, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 18, "usage_type": "call"}, {"api_name": "torch.nn.Parameter", "line_number": 20, "usage_type": "call"}, {"api_name": "torch.FloatTensor", "line_number": 20, "usage_type": "call"}, {"api_name": "torch.nn.Parameter", "line_number": 21, "usage_type": "call"}, {"api_name": "torch.FloatTensor", "line_number": 21, "usage_type": "call"}, {"api_name": "torch.ones_like", "line_number": 38, "usage_type": "call"}, {"api_name": "torch.ones_like", "line_number": 40, "usage_type": "call"}, {"api_name": "torch.log", "line_number": 43, "usage_type": "call"}, {"api_name": "torch.exp", "line_number": 47, "usage_type": "call"}, {"api_name": "torch.log", "line_number": 52, "usage_type": "call"}, {"api_name": "torch.nn.functional.softplus", "line_number": 66, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 66, "usage_type": "name"}, {"api_name": "torch.randn_like", "line_number": 66, "usage_type": "call"}, {"api_name": "torch.nn.functional.softplus", "line_number": 70, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 70, "usage_type": "name"}, {"api_name": "torch.randn_like", "line_number": 70, "usage_type": "call"}, {"api_name": "torch.nn.functional.softplus", "line_number": 77, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 77, "usage_type": "name"}, {"api_name": "torch.nn.functional.linear", "line_number": 78, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 78, "usage_type": "name"}, {"api_name": "torch.nn.Parameter", "line_number": 83, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 83, "usage_type": "call"}, {"api_name": "torch.nn.Parameter", "line_number": 84, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 84, "usage_type": "call"}, {"api_name": "torch.nn.Parameter", "line_number": 86, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 86, "usage_type": "call"}, {"api_name": "torch.nn.Parameter", "line_number": 87, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 87, "usage_type": "call"}, {"api_name": "torch.nn.Parameter", "line_number": 89, "usage_type": "call"}, {"api_name": "torch.FloatTensor", "line_number": 89, "usage_type": "call"}, {"api_name": "torch.nn.Parameter", "line_number": 90, "usage_type": "call"}, {"api_name": "torch.FloatTensor", "line_number": 90, "usage_type": "call"}, {"api_name": "torch.ones_like", "line_number": 102, "usage_type": "call"}, {"api_name": "torch.ones_like", "line_number": 104, "usage_type": "call"}, {"api_name": "torch.log", "line_number": 107, "usage_type": "call"}, {"api_name": "torch.exp", "line_number": 111, "usage_type": "call"}, {"api_name": "torch.log", "line_number": 116, "usage_type": "call"}, {"api_name": "torch.nn.functional.softplus", "line_number": 130, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 130, "usage_type": "name"}, {"api_name": "torch.randn_like", "line_number": 130, "usage_type": "call"}, {"api_name": "torch.nn.functional.softplus", "line_number": 134, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 134, "usage_type": "name"}, {"api_name": "torch.randn_like", "line_number": 134, "usage_type": "call"}, {"api_name": "torch.nn.modules.conv.Conv2d", "line_number": 137, "usage_type": "name"}, {"api_name": "torch.nn.modules.conv.Conv2d.__init__", "line_number": 139, "usage_type": "call"}, {"api_name": "torch.nn.modules.conv.Conv2d", "line_number": 139, "usage_type": "name"}, {"api_name": "torch.nn.modules.conv.Conv2d.reset_parameters", "line_number": 144, "usage_type": "call"}, {"api_name": "torch.nn.modules.conv.Conv2d", "line_number": 144, "usage_type": "name"}, {"api_name": "torch.nn.functional.softplus", "line_number": 151, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 151, "usage_type": "name"}, {"api_name": "torch.nn.functional.conv2d", "line_number": 152, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 152, "usage_type": "name"}, {"api_name": "torch.nn.modules.conv.ConvTranspose2d", "line_number": 155, "usage_type": "name"}, {"api_name": "torch.nn.modules.conv.ConvTranspose2d.__init__", "line_number": 157, "usage_type": "call"}, {"api_name": "torch.nn.modules.conv.ConvTranspose2d", "line_number": 157, "usage_type": "name"}, {"api_name": "torch.nn.modules.conv.ConvTranspose2d.reset_parameters", "line_number": 162, "usage_type": "call"}, {"api_name": "torch.nn.modules.conv.ConvTranspose2d", "line_number": 162, "usage_type": "name"}, {"api_name": "torch.nn.functional.softplus", "line_number": 169, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 169, "usage_type": "name"}, {"api_name": "torch.nn.functional.conv_transpose2d", "line_number": 170, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 170, "usage_type": "name"}, {"api_name": "models.base_autoencoder.ConvLayers", "line_number": 174, "usage_type": "name"}, {"api_name": "torch.autograd.Variable", "line_number": 184, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 184, "usage_type": "call"}, {"api_name": "models.base_autoencoder.DenseLayers", "line_number": 199, "usage_type": "name"}, {"api_name": "torch.autograd.Variable", "line_number": 215, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 215, "usage_type": "call"}, {"api_name": "models.base_autoencoder.BAE_BaseClass", "line_number": 231, "usage_type": "name"}, {"api_name": "models.base_autoencoder.flatten_torch", "line_number": 259, "usage_type": "call"}, {"api_name": "models.base_autoencoder.flatten_torch", "line_number": 261, "usage_type": "call"}, {"api_name": "torch.ones_like", "line_number": 296, "usage_type": "call"}, {"api_name": "models.base_autoencoder.flatten_np", "line_number": 303, "usage_type": "call"}, {"api_name": "models.base_autoencoder.ConvLayers", "line_number": 324, "usage_type": "argument"}, {"api_name": "models.base_autoencoder.DenseLayers", "line_number": 326, "usage_type": "argument"}, {"api_name": "torch.nn.Sequential", "line_number": 335, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 335, "usage_type": "attribute"}, {"api_name": "models.base_autoencoder.Autoencoder", "line_number": 337, "usage_type": "name"}, {"api_name": "torch.nn", "line_number": 338, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 339, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 342, "usage_type": "attribute"}, {"api_name": "models.base_autoencoder.Autoencoder", "line_number": 345, "usage_type": "call"}, {"api_name": "torch.nn.functional.softplus", "line_number": 355, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 355, "usage_type": "name"}, {"api_name": "models.base_autoencoder.Autoencoder", "line_number": 359, "usage_type": "name"}, {"api_name": "models.base_autoencoder.Encoder", "line_number": 367, "usage_type": "name"}, {"api_name": "models.base_autoencoder.DenseLayers", "line_number": 371, "usage_type": "argument"}, {"api_name": "torch.nn.Sequential", "line_number": 376, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 376, "usage_type": "attribute"}, {"api_name": "torch.nn.Linear", "line_number": 376, "usage_type": "call"}, {"api_name": "torch.nn.ReLU", "line_number": 376, "usage_type": "call"}, {"api_name": "models.base_autoencoder.DenseLayers", "line_number": 383, "usage_type": "call"}, {"api_name": "torch.nn.Linear", "line_number": 387, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 387, "usage_type": "attribute"}, {"api_name": "torch.nn.Linear", "line_number": 388, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 388, "usage_type": "attribute"}, {"api_name": "torch.nn.Sequential", "line_number": 395, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 395, "usage_type": "attribute"}, {"api_name": "torch.log", "line_number": 398, "usage_type": "call"}, {"api_name": "torch.nn.functional.softplus", "line_number": 414, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 414, "usage_type": "name"}, {"api_name": "torch.randn_like", "line_number": 414, "usage_type": "call"}, {"api_name": "torch.nn.functional.softplus", "line_number": 415, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 415, "usage_type": "name"}, {"api_name": "models.base_autoencoder.Autoencoder", "line_number": 436, "usage_type": "name"}, {"api_name": "models.base_autoencoder.flatten_torch", "line_number": 455, "usage_type": "call"}, {"api_name": "models.base_autoencoder.flatten_torch", "line_number": 457, "usage_type": "call"}, {"api_name": "models.base_autoencoder.Autoencoder", "line_number": 497, "usage_type": "name"}, {"api_name": "torch.optim.Adam", "line_number": 505, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 505, "usage_type": "attribute"}, {"api_name": "torch.ones_like", "line_number": 514, "usage_type": "call"}, {"api_name": "models.base_autoencoder.flatten_np", "line_number": 521, "usage_type": "call"}, {"api_name": "torch.nn.functional.softplus", "line_number": 527, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 527, "usage_type": "name"}, {"api_name": "sklearn.decomposition.PCA", "line_number": 535, "usage_type": "call"}]} +{"seq_id": "13474985602", "text": "#!/usr/bin/python3\n# -*-coding: utf8 -*-\n\nimport pygame\nfrom macgiver_game.fonction_chemin import path_to_image\n\n\nclass Player(pygame.sprite.Sprite):\n \"\"\"Class for the labyrinth player.\"\"\"\n\n def __init__(self, x, y, screen):\n \"\"\"\n Constructor of this class.\n\n Parameters:\n :param x : the character's position x on the labyrinth's\n configuration.\n :type x : int\n :param y : the character's position y on the labyrinth's\n configuration.\n :type y : int\n :param screen : the game screen.\n :type screen : pygame.surface.Surface\n\n The constructor allows you to create a Player object with an image,\n a position and 3 objects.\n \"\"\"\n\n super(Player, self).__init__()\n self.screen = screen\n self.image = pygame.image.load(path_to_image('MacGyver.png'))\\\n .convert_alpha()\n self.image = pygame.transform.scale(self.image, (20, 20))\n self.rect = self.image.get_rect()\n self.pos = self.rect.move((x, y))\n self.x = x\n self.y = y\n self.obj1 = False\n self.obj2 = False\n self.obj3 = False\n\n def move_right(self):\n \"\"\"Move the player on the right.\"\"\"\n\n self.pos = self.pos.move([20, 0])\n if self.pos.right >= 280:\n self.pos.right = 280\n\n def move_left(self):\n \"\"\"Move the player on the left.\"\"\"\n\n self.pos = self.pos.move([-20, 0])\n if self.pos.left <= 0:\n self.pos.left = 20\n\n def move_up(self):\n \"\"\"Move the player on the top.\"\"\"\n\n self.pos = self.pos.move([0, -20])\n if self.pos.top <= 0:\n self.pos.top = 20\n\n def move_down(self):\n \"\"\"Move the player on the bottom.\"\"\"\n\n self.pos = self.pos.move([0, 20])\n if self.pos.bottom >= 280:\n self.pos.bottom = 280\n\n def draw_me(self):\n \"\"\"Display the player's image on the game screen.\"\"\"\n\n self.screen.blit(self.image, self.pos)\n", "repo_name": "micktymoon/P3_pelletier_celine", "sub_path": "src/macgiver_game/classes/class_player.py", "file_name": "class_player.py", "file_ext": "py", "file_size_in_byte": 2030, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "pygame.sprite", "line_number": 8, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 31, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 31, "usage_type": "attribute"}, {"api_name": "macgiver_game.fonction_chemin.path_to_image", "line_number": 31, "usage_type": "call"}, {"api_name": "pygame.transform.scale", "line_number": 33, "usage_type": "call"}, {"api_name": "pygame.transform", "line_number": 33, "usage_type": "attribute"}]} +{"seq_id": "33399242571", "text": "import sys\nimport json\nimport logging\nimport ConfigParser\n\ndef create_meta(meta_filename, meta_type, meta_content_dict):\n \"\"\"\n Create meta.\n Attribute:\n filename: meta file name.\n meta_type: json of ini\n meta_content_dict: a dict of meta content.\n Return: None\n \"\"\"\n if meta_type == 'ini':\n config = ConfigParser.ConfigParser()\n meta_info_list = meta_content_dict.get('meta_info')\n for list_value in iter(meta_info_list):\n for section, items_dict in list_value.items():\n config.add_section(section)\n for items_key, items_value in items_dict.items():\n config.set(section, items_key, items_value)\n config.write(open(meta_filename, 'w'))\n return 0\n elif meta_type == 'json':\n json.dump(meta_content_dict, open(meta_filename, 'w'), sort_keys = True)\n logging.info(\"Create meta successfully.\")\n return 0\n else:\n logging.error(\"Create meta failed, meta type should be json or ini\")\n return -1\n", "repo_name": "cedricxie/apollo-r3.0.0", "sub_path": "modules/data/tools/recorder/meta_manager.py", "file_name": "meta_manager.py", "file_ext": "py", "file_size_in_byte": 1062, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 8, "dataset": "github-code", "pt": "37", "api": [{"api_name": "ConfigParser.ConfigParser", "line_number": 16, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 26, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 27, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 30, "usage_type": "call"}]} +{"seq_id": "70526077226", "text": "import alpaca_trade_api as tradeapi\nimport talib\nimport time\n\n# Alpaca API credentials\nAPI_KEY = 'YOUR_API_KEY'\nAPI_SECRET = 'YOUR_API_SECRET'\nBASE_URL = 'https://paper-api.alpaca.markets' # Use 'https://api.alpaca.markets' for live trading\n\n# Initialize Alpaca API\napi = tradeapi.REST(API_KEY, API_SECRET, base_url=BASE_URL, api_version='v2')\n\n# EMA lengths\nema9_length = 9\nema22_length = 22\nema15_length = 15\n\n# Symbol to trade\nsymbol = 'AAPL' # Change to the desired stock symbol\n\ndef ema_crossover_1h(symbol):\n # Get 1-hour historical data\n bars_1h = api.get_barset(symbol, '1H', limit=100).df[symbol]\n\n # Calculate EMA values\n ema9_1h = talib.EMA(bars_1h['close'], timeperiod=ema9_length)\n ema22_1h = talib.EMA(bars_1h['close'], timeperiod=ema22_length)\n\n # Check for EMA crossover\n if ema9_1h.iloc[-1] > ema22_1h.iloc[-1] and ema9_1h.iloc[-2] <= ema22_1h.iloc[-2]:\n return True\n else:\n return False\n\ndef close_below_ema15_15m(symbol):\n # Get 15-minute historical data\n bars_15m = api.get_barset(symbol, '15Min', limit=100).df[symbol]\n\n # Calculate EMA values\n ema15_15m = talib.EMA(bars_15m['close'], timeperiod=ema15_length)\n\n # Check if the last closed candle's close is below EMA15\n if bars_15m['close'].iloc[-1] < ema15_15m.iloc[-1]:\n return True\n else:\n return False\n\nwhile True:\n if ema_crossover_1h(symbol):\n # Place a buy order\n api.submit_order(\n symbol=symbol,\n qty=1,\n side='buy',\n type='limit',\n time_in_force='gtc',\n limit_price=api.get_latest_trade(symbol).price\n )\n print(\"Buy order placed\")\n\n if close_below_ema15_15m(symbol):\n # Place a sell order\n api.submit_order(\n symbol=symbol,\n qty=1,\n side='sell',\n type='limit',\n time_in_force='gtc',\n limit_price=api.get_latest_trade(symbol).price\n )\n print(\"Sell order placed\")\n\n time.sleep(900) # Sleep for 15 minutes (900 seconds)", "repo_name": "Ritesh1422/python_data_analytics2", "sub_path": "bot.py", "file_name": "bot.py", "file_ext": "py", "file_size_in_byte": 2071, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "alpaca_trade_api.REST", "line_number": 11, "usage_type": "call"}, {"api_name": "talib.EMA", "line_number": 26, "usage_type": "call"}, {"api_name": "talib.EMA", "line_number": 27, "usage_type": "call"}, {"api_name": "talib.EMA", "line_number": 40, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 73, "usage_type": "call"}]} +{"seq_id": "5308670252", "text": "from manager.dataManager import DataHelper\nimport os,json\nfrom multiprocessing import Process\nfrom threading import Thread\nimport tkinter as tk\n\n\nclass TradeHelper(DataHelper):\n def __init__(self,name=\"TradeHelper\"):\n DataHelper.__init__(self,name)\n\nclass winAPP():\n def __init__(self,title=\"TradeHelper\",size=\"400x300\",getDataFromServer=False):\n self.window = tk.Tk()\n self.window.title(title)\n self.window.geometry(size)\n self.var = tk.StringVar()\n self.var.set(\"点击开始运行\"+title)\n label = tk.Label(self.window,textvariable=self.var,font=(\"Arial\",12),width=30,height=2)\n label.pack()\n self.start = tk.Button(self.window,text=\"开始\",font=(\"Arial\",12),width=10,height=1,command=self.click_start)\n self.TradeHelper = TradeHelper()\n self.TradeHelper.data.getDataFromDataServer=getDataFromServer\n self.start.pack()\n self.getDataFromServer = tk.Button(self.window,text=\"服务器数据\",font=(\"Arial\",12),width=10,height=1,command=self.setServerData)\n self.getDataFromServer.pack()\n # self.runTradeHelper()\n\n def runTradeHelper(self):\n t = Process(target=self.TradeHelper.run)\n t.start()\n\n def click_start(self):\n self.var.set(\"正在运行\")\n # self.runTradeHelper()\n def setServerData(self):\n self.TradeHelper.data.getDataFromDataServer=True\n\n def run(self):\n # self.runTradeHelper()\n self.window.mainloop()\n\ndef runOnTheOthreProcess():\n app = TradeHelper()\n t = Process(target=app.run)\n t.start()\n\n\ndef runTradeHelper():\n app = TradeHelper()\n if os.path.exists(\"./config.json\"):\n with open(\"./config.json\",\"r\") as f:\n rewrite = False\n try:\n config = json.loads(f.read())\n app.data.getDataFromDataServer = config[\"getDataFromDataServer\"]\n except:\n rewrite = True\n if rewrite:\n with open(\"./config.json\", \"w\") as f:\n app.data.getDataFromDataServer = False\n f.write(json.dumps({\"getDataFromDataServer\": False}))\n else:\n with open(\"./config.json\",\"w\") as f:\n app.data.getDataFromDataServer = False\n f.write(json.dumps({\"getDataFromDataServer\":False}))\n app.run()\n\ndef writegetdatafromserverconfig(a=True):\n with open(\"./config.json\", \"w\") as f:\n # app.data.getDataFromDataServer = True\n f.write(json.dumps({\"getDataFromDataServer\": a}))\n\nif __name__ == '__main__':\n writegetdatafromserverconfig(True)\n runTradeHelper()\n", "repo_name": "lxy1492/TradeHelper", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 2603, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "manager.dataManager.DataHelper", "line_number": 8, "usage_type": "name"}, {"api_name": "manager.dataManager.DataHelper.__init__", "line_number": 10, "usage_type": "call"}, {"api_name": "manager.dataManager.DataHelper", "line_number": 10, "usage_type": "name"}, {"api_name": "tkinter.Tk", "line_number": 14, "usage_type": "call"}, {"api_name": "tkinter.StringVar", "line_number": 17, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 19, "usage_type": "call"}, {"api_name": "tkinter.Button", "line_number": 21, "usage_type": "call"}, {"api_name": "tkinter.Button", "line_number": 25, "usage_type": "call"}, {"api_name": "multiprocessing.Process", "line_number": 30, "usage_type": "call"}, {"api_name": "multiprocessing.Process", "line_number": 45, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 51, "usage_type": "call"}, {"api_name": "os.path", "line_number": 51, "usage_type": "attribute"}, {"api_name": "json.loads", "line_number": 55, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 62, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 66, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 72, "usage_type": "call"}]} +{"seq_id": "42076133470", "text": "# fmt: off\nimport os\nimport glob\nimport pytest\n\nfrom rlo import costs\nfrom rlo.expression import Expression, EF\nfrom rlo.expression_util import NamedExprWithEnv, ExprWithEnv\nfrom rlo.expr_sets import ExpressionSetFromFile\nfrom rlo import rewrites\nfrom rlo import sparser\nfrom rlo.utils import read_file, get_func_body, single_elem\nfrom ksc.type import Type\nfrom testutils import make_toplevel as MT\n\nFOLDER = os.path.dirname(os.path.abspath(__file__))\n\ndef _parse_defs_no_symtab(ks_str):\n exprs, _ = sparser.parse_defs_with_symtab(ks_str)\n return exprs\n\ndef parse_defs_folder_file(filename, **kwargs):\n return _parse_defs_no_symtab(read_file(os.path.join(FOLDER, filename)), **kwargs)\n\ndef load_expression_set(filename):\n return ExpressionSetFromFile(os.path.join(FOLDER, filename))\n\ndef test_s_expression_parser():\n a = Expression.Variable(\"a\")\n b = Expression.Variable(\"b\")\n assert sparser.parse_expr(\"(sub (mul a b ) 3)\") == a * b - 3.0\n assert sparser.parse_expr(\"(let a (div b 2) (add a 4))\") == EF.Let(a, b / 2.0, a + 4.0)\n assert sparser.parse_expr(\"(lt a b)\") == (b > a)\n assert sparser.parse_expr(\"(get$3$3 (tuple a b a))\") == EF.Select(EF.Tuple(a,b,a), 2) # ksc-indices are 1-based, we use 0-based\n assert sparser.parse_expr(\"(neg a)\") == (Expression.Constant(0.0) - a)\n\ndef test_s_expression_parser_error():\n with pytest.raises(AssertionError):\n sparser.parse_expr(\"(get$0$2 t)\")\n with pytest.raises(AssertionError):\n sparser.parse_expr(\"(get$3$2 t)\")\n\ndef test_s_expression_lambda():\n x = Expression.Variable(\"x\", Type.Float)\n inc_fn = EF.Lam(x, x + 1.0)\n assert sparser.parse_expr(\"(lam (x : Float) (add x 1.0))\") == inc_fn\n assert sparser.parse_expr(\"(lam (x : (Vec Float)) x)\") != sparser.parse_expr(\"(lam (y : Float) y)\")\n\n # Lambdas must have one argument, possibly of tuple type\n with pytest.raises(ValueError):\n sparser.parse_expr(\"(lam (x : Float) (y: Float) body)\")\n with pytest.raises(ValueError):\n sparser.parse_expr(\"(lam ((x : Float) (y: Float)) body)\")\n\n assert sparser.parse_expr(\"(let f (lam (x : Float) (add x 1.0)) (f 2.0))\") == EF.Let(\n \"f\", inc_fn, EF.Apply(\"f\", 2.0))\n\n assert sparser.parse_expr(\"(let f (lam (t : (Tuple Integer Float)) (get$1$2 t)) (f a b))\") == EF.Let(\n \"f\",\n EF.Lam(Expression.Variable(\"x\", Type.Tuple(Type.Integer, Type.Float)), EF.Select(\"x\", 0)),\n EF.Apply(\"f\", EF.Tuple(\"a\", \"b\")))\n\ndef check_can_rewrite(start, finish, rules):\n rewritten = [rewrite.apply(start) for rewrite in rules.get_all_rewrites(start)]\n assert any([r == finish for r in rewritten])\n\ndef test_lets():\n a = Expression.Variable(\"a\")\n b = Expression.Variable(\"b\")\n\n step0 = sparser.parse_expr(\"(let ((a (mul 2 3)) (b (div 1 2))) (add a b))\") #variables are independent\n check0 = EF.Let(a, Expression.Constant(2) * 3, EF.Let(b, Expression.Constant(1) / 2, a + b))\n assert step0 == check0\n\n step1 = sparser.parse_expr(\"(let ((a (mul 2 3)) (b (div a 2))) (add a b))\") #variables are defined in increasing order of dependencies\n check1 = EF.Let(a, Expression.Constant(2) * 3, EF.Let(b, a / 2, a + b))\n assert step1 == check1\n\n step2 = sparser.parse_expr(\"(let (a (mul 2 3)) (add a 1))\")\n check2 = EF.Let(a, Expression.Constant(2) * 3, a + 1)\n assert step2 == check2\n\ndef test_def_single_line():\n\n a = Expression.Variable(\"a\")\n x = Expression.Variable(\"x\", Type.Float)\n\n assert _parse_defs_no_symtab(\"(def a Float ((x : Float)) (mul 3.0 x))\") == [\n (\"a\", EF.Let(a, EF.Lam(x, 3.0 * x), a))]\n\ndef test_def_simple_dependency():\n\n a = Expression.Variable(\"a\")\n b = Expression.Variable(\"b\")\n c = Expression.Variable(\"c\")\n d = Expression.Variable(\"d\")\n f = Expression.Variable(\"f\")\n\n x = Expression.Variable(\"x\", Type.Float)\n z = Expression.Variable(\"z\", Type.Float)\n w = Expression.Variable(\"w\", Type.Float)\n\n assert _parse_defs_no_symtab(\"(def f Float ((x : Float)) (x))\") == [\n (\"f\", EF.Let(f, EF.Lam(x, x), f))]\n\n f_a = EF.Lam(x, 3.0 * x)\n f_b = EF.Lam(x, 2.0 - EF.Apply(a, x))\n f_c = EF.Lam(z, 4.0 + EF.Apply(b, z))\n f_d = EF.Lam(w, EF.Apply(c, w))\n # check step by step parsing of simple depepndencies (dependency on one function)\n assert parse_defs_folder_file(\"test_sparser_files/simple_dependency.kso\") == [\n (\"a\", EF.Let(a, f_a, a)),\n (\"b\", EF.Let(a, f_a, EF.Let(b, f_b, b))),\n (\"c\", EF.Let(a, f_a, EF.Let(b, f_b, EF.Let(c, f_c, c)))),\n (\"d\", EF.Let(a, f_a, EF.Let(b, f_b, EF.Let(c, f_c, EF.Let(d, f_d, d)))))]\n\ndef test_def_harder_dependency():\n import harder_dependency as f\n\n assert parse_defs_folder_file(\"test_sparser_files/harder_dependency.kso\") == [\n (\"a\", EF.Let(f.a, f.f_a, f.a)),\n # note here that \"a\" isn't used, so hopefully (Alan says) RLO will easily remove it\n # and simplify to EF.Let(b, EF.Lam(x, 1.0 / (x * x)), b), or even just the EF.Lam.\n # Hence the below is acceptable from the parser; and similarly for other examples below\n (\"b\", EF.Let(f.a, f.f_a, EF.Let(f.b, f.f_b, f.b))),\n (\"c\", EF.Let(f.a, f.f_a, EF.Let(f.b, f.f_b, EF.Let(f.c, f.f_c, f.c)))),\n (\"d\", EF.Let(f.a, f.f_a, EF.Let(f.b, f.f_b, EF.Let(f.c, f.f_c, EF.Let(f.d, f.f_d, f.d))))),\n (\"e\", EF.Let(f.a, f.f_a, EF.Let(f.b, f.f_b, EF.Let(f.c, f.f_c, EF.Let(f.d, f.f_d, EF.Let(f.e, f.f_e, f.e)))))),\n # note here the obvious dead-code-elimination to EF.Let(f, EF.Lam(x, x / (x * x)), f)\n (\"f\", EF.Let(f.a, f.f_a, EF.Let(f.b, f.f_b, EF.Let(f.c, f.f_c, EF.Let(f.d, f.f_d, EF.Let(f.e, f.f_e, EF.Let(f.f, f.f_f, f.f)))))))]\n\ndef test_def_assertions():\n with pytest.raises(ValueError): #No standalone exp in mult lined files\n parse_defs_folder_file(\"test_sparser_files/assertions/assertion_alone_exp.kso\")\n\ndef test_def_simple_dependency_tuples():\n\n a = Expression.Variable(\"a\")\n b = Expression.Variable(\"b\")\n c = Expression.Variable(\"c\")\n\n x = Expression.Variable(\"x\", Type.Float)\n y = Expression.Variable(\"y\", Type.Float)\n t1 = Expression.Variable(\"t1\", Type.Tuple(Type.Float, Type.Float))\n\n f_a = EF.Lam(t1, EF.Let(y, EF.Select(t1, 1), EF.Let(x, EF.Select(t1, 0), (x * y))))\n f_b = EF.Lam(t1, EF.Let(y, EF.Select(t1, 1), EF.Let(x, EF.Select(t1, 0), (2.0 - EF.Apply(a, EF.Tuple(x, y))))))\n # check step by step parsing of simple dependencies (dependency on one function)\n assert parse_defs_folder_file(\"test_sparser_files/simple_dependency_tuples.kso\") == [\n (\"a\", EF.Let(a, f_a, a)),\n (\"b\", EF.Let(a, f_a, EF.Let(b, f_b, b)))]\n\n assert _parse_defs_no_symtab(\"(def c Float ((y : Float) (x : Float)) (add x x))\") == [\n (\"c\", EF.Let(c, EF.Lam(t1, EF.Let(x, EF.Select(t1, 1), EF.Let(y, EF.Select(t1, 0), (x + x)))), c))]\n\n with pytest.raises(ValueError): # Wrong tuple format\n sparser.parse_defs(\"(def f Float ((x : Float) (y)) (mul x y))\")\n\ndef test_def_harder_dependency_tuples():\n\n a = Expression.Variable(\"a\")\n b = Expression.Variable(\"b\")\n c = Expression.Variable(\"c\")\n d = Expression.Variable(\"d\")\n\n x = Expression.Variable(\"x\", Type.Float)\n y = Expression.Variable(\"y\", Type.Float)\n tff = Type.Tuple(Type.Float, Type.Float)\n t1 = Expression.Variable(\"t1\", tff)\n t2 = Expression.Variable(\"t2\", tff)\n t3 = Expression.Variable(\"t3\", tff)\n\n res = dict(parse_defs_folder_file(\"test_sparser_files/harder_dependency_tuples.kso\"))\n c_body = EF.Lam(t1, EF.Let(y, EF.Select(t1, 1), EF.Let(x, EF.Select(t1, 0), ((2.0 * EF.Apply(b, EF.Tuple(x, y))) - (EF.Apply(a, EF.Tuple(y, x)) + 3.0)))))\n b_body = EF.Lam(t2, EF.Let(y, EF.Select(t2, 1), EF.Let(x, EF.Select(t2, 0), (1.0 / (y * x)))))\n a_body = EF.Lam(t3, EF.Let(y, EF.Select(t3, 1), EF.Let(x, EF.Select(t3, 0), (x * y))))\n assert res[\"c\"] == EF.Let(a, a_body, EF.Let(b, b_body, EF.Let(c, c_body, c)))\n\n d_body = EF.Lam(t1, EF.Let(y, EF.Select(t1, 1), EF.Let(x, EF.Select(t1, 0), (4.0 + EF.Apply(b, EF.Tuple(x, (2.0 * EF.Apply(a, EF.Tuple(x, y)))))))))\n assert res[\"d\"] == EF.Let(a, a_body, EF.Let(b, b_body, EF.Let(c, c_body, EF.Let(d, d_body, d))))\n\ndef test_def_args_syntax():\n\n d = Expression.Variable(\"d\")\n x = Expression.Variable(\"x\", Type.Float)\n z = Expression.Variable(\"z\", Type.Tensor(1,Type.Float))\n t1 = Expression.Variable(\"t1\", Type.Tuple(x.type, z.type))\n\n step0_2 = _parse_defs_no_symtab(\"(def d (Vec Float) ((x : Float) (z : (Vec Float))) (div z x))\") # this is just to check parsing (not semantically correct)\n check0 = EF.Lam(t1, EF.Let(z, EF.Select(t1,1), EF.Let(x, EF.Select(t1,0), (z / x))))\n assert step0_2 == [(\"d\", EF.Let(d, check0, d))]\n\n short_type_brackets = _parse_defs_no_symtab(\"(def d (Vec Float) ((x : Float)) (build 10 (lam (i : Integer) (div x x))))\")\n short_type_no_brackets = _parse_defs_no_symtab(\"(def d (Vec Float) (x : Float) (build 10 (lam (i : Integer) (div x x))))\")\n expected_short_type = [(\"d\", EF.Let(d, EF.Lam(x, EF.Build(10, \"i\", x / x)), d))]\n assert short_type_brackets == expected_short_type\n assert short_type_no_brackets == expected_short_type\n\n long_type = _parse_defs_no_symtab(\"(def d (Vec Float) (x : Vec Float) (div x x))\")\n long_type_brackets = _parse_defs_no_symtab(\"(def d (Vec Float) (x : (Vec Float)) (div x x))\")\n brackets_long_type = _parse_defs_no_symtab(\"(def d (Vec Float) ((x : Vec Float)) (div x x))\")\n brackets_long_type_brackets = _parse_defs_no_symtab(\"(def d (Vec Float) ((x : (Vec Float))) (div x x))\")\n expected_type = Type.Lam(Type.Tensor(1,Type.Float), Type.Tensor(1,Type.Float))\n\n def get_type(defs):\n return get_func_body(\"d\", single_elem(defs)[1]).type\n\n assert get_type(long_type) == expected_type\n assert get_type(long_type_brackets) == expected_type\n assert get_type(brackets_long_type) == expected_type\n assert get_type(brackets_long_type_brackets) == expected_type\n\n\ndef test_def_body_syntax():\n\n d = Expression.Variable(\"d\")\n x = Expression.Variable(\"x\", Type.Tensor(1,Type.Float))\n\n const_brackets = _parse_defs_no_symtab(\"(def d (Float) (x : Vec Float) (0.0))\")\n const_no_brackets = _parse_defs_no_symtab(\"(def d (Float) (x : Vec Float) 0.0)\")\n expected_const = EF.Lam(x, 0.0)\n assert const_brackets == [(\"d\", EF.Let(d, expected_const, d))]\n assert const_no_brackets == [(\"d\", EF.Let(d, expected_const, d))]\n\n var_brackets = _parse_defs_no_symtab(\"(def d (Vec Float) (x : Vec Float) (x))\")\n var_no_brackets = _parse_defs_no_symtab(\"(def d (Vec Float) (x : Vec Float) x)\")\n expected_fn = EF.Lam(x, x)\n assert var_brackets == [(\"d\", EF.Let(d, expected_fn, d))]\n assert var_no_brackets == [(\"d\", EF.Let(d, expected_fn, d))]\n\n with pytest.raises(ValueError): # Wrong body format\n sparser.parse_defs(\"(def d (Vec Float) (x : Vec Float) * x x)\")\n\n with pytest.raises(ValueError): # Wrong body format\n sparser.parse_defs(\"(def d (Vec Float) (x : Vec Float) ())\")\n\ndef test_get_expressions():\n import harder_dependency as f\n\n func_list_1 = load_expression_set(\"../../test/rlo/test_sparser_files/harder_dependency.kso\").named_exprenvs()\n func_list_check_1 = [(\"a\", EF.Let(f.a, f.f_a, f.a)),\n (\"b\", EF.Let(f.b, f.f_b, f.b)),\n (\"c\", EF.Let(f.a, f.f_a, EF.Let(f.b, f.f_b, EF.Let(f.c, f.f_c, f.c)))),\n (\"d\", EF.Let(f.a, f.f_a, EF.Let(f.b, f.f_b, EF.Let(f.d, f.f_d, f.d)))),\n (\"e\", EF.Let(f.a, f.f_a, EF.Let(f.b, f.f_b, EF.Let(f.c, f.f_c, EF.Let(f.d, f.f_d, EF.Let(f.e, f.f_e, f.e)))))),\n (\"f\", EF.Let(f.f, f.f_f, f.f))]\n\n for func, (expected_name, expected_body) in zip(func_list_1, func_list_check_1):\n assert func == NamedExprWithEnv(expected_name, MT(expected_body))\n\n\ndef test_type_in_get_expressions():\n ksc_files_train = glob.glob(os.path.join(FOLDER, \"../../src/rlo/ksc/*/*_train.kso\"))\n ksc_files_test = glob.glob(os.path.join(FOLDER, \"../../src/rlo/ksc/*/*_test.kso\"))\n ksc_files = ksc_files_train + ksc_files_test\n assert len(ksc_files) > 0\n\ndef test_edef():\n name, e = single_elem(sparser.parse_defs(\"\"\"\n (edef foo Float ((Vec Float) Float))\n (def cost$foo Float ((a : Vec Float) (b : Float)) 8.0)\n (def bar Float (a : Vec Float) (foo a (index 0 a)))\"\"\"))\n assert name == \"bar\"\n assert e.expr.op == \"let\" and e.expr.first.name == \"bar\"\n a = Expression.Variable(\"a\", Type.Tensor(1,Type.Float))\n assert e.expr == EF.Let(\"bar\", EF.Lam(a, EF.Apply(\"foo\", EF.Tuple(a, EF.Index(0, a)))), \"bar\")\n assert e.expr.type == Type.Lam(Type.Tensor(1,Type.Float), Type.Float)\n assert e.cost() == 8.0 + costs.apply_cost + costs.let_cost + MT(EF.Tuple(a, EF.Index(0, a))).cost() # cost() requires that the Expression passes type-checking\n\ndef test_edef_in_symtab():\n _defs, symtab_and_defs = sparser.parse_defs_with_symtab(\n \"(edef foo Integer (Tuple Float Float))\\n\\n(def bar Integer (x : Float) (foo x x))\")\n entry = symtab_and_defs.symtab[\"foo\"]\n assert entry == Type.Lam(Type.Tuple(Type.Float, Type.Float), Type.Integer)\n\ndef test_illegal_variables():\n with pytest.raises(Exception):\n sparser.parse_expr(\"(x / x)\")\n with pytest.raises(Exception):\n sparser.parse_expr(\"x:\")\n with pytest.raises(Exception):\n sparser.parse_expr(\"(div build x)\")\n\n with pytest.raises(Exception):\n sparser.parse_expr(\"(f let)\")\n sparser.parse_expr(\"(f x)\") # ok\n\n with pytest.raises(Exception):\n sparser.parse_expr(\"(div 99x x)\")\n sparser.parse_expr(\"(div x99 x)\") #ok\n\ndef test_roundtrip_str():\n # Get a reasonable corpus\n from rlo import best_results\n exprenvs = [expr for seqs in best_results._best_results.values() for (seq, _) in seqs for expr in seq]\n # Now test roundtrip on each of those exprs\n for exprenv in exprenvs:\n # First that output-then-parse gives us back something that's Expression.== (alpha-conversion etc.)\n s = str(exprenv.expr)\n # Free variable types are lost in serialization but it is ok for this test\n e2 = sparser.parse_expr(s)\n assert e2 == exprenv.expr\n # Then that parse-then-output gives the same string\n # (This is a stronger test, e.g. that there has been no alpha-conversion)\n s2 = str(e2)\n assert s == s2\n\n@pytest.mark.notquick\ndef test_roundtrip_ksc_str():\n # This tests that sparser.parse_defs and Expression.ksc_str are inverses.\n # It makes only very minor checks that the output (string) point in that roundtrip is what we expect.\n # ksc_str isn't generally applicable, it works only on chain of lets+lambdas that can be output to a def.\n test_sparser_files = glob.glob(os.path.join(FOLDER, \"test_sparser_files/*.kso\"))\n assert len(test_sparser_files) > 0\n ksc_files_train = glob.glob(os.path.join(FOLDER, \"../../src/rlo/ksc/*/*_train.kso\"))\n ksc_files_test = glob.glob(os.path.join(FOLDER, \"../../src/rlo/ksc/*/*_test.kso\"))\n blas_combined_path = os.path.join(FOLDER, \"../../src/rlo/ksc/blas/blas_combined.kso\")\n ksc_files = ksc_files_train + ksc_files_test + [blas_combined_path]\n assert len(ksc_files) > 0\n\n for file_path in test_sparser_files + ksc_files:\n print(\"Processing {}\".format(file_path))\n symtab_and_defs = None\n def parse(s):\n nonlocal symtab_and_defs\n exprs, symtab_and_defs = sparser.parse_defs_with_symtab(s, symtab_and_defs)\n return [(n, rewrites.delete_unused_defs(ExprWithEnv(e, symtab_and_defs))) for n,e in exprs]\n\n for (func_name, exprenv) in parse(read_file(file_path)):\n cost = exprenv.cost()\n type = exprenv.expr.type\n assert cost > 0.1\n assert type.lam_return_type is not None\n s2 = exprenv.expr.ksc_str()\n # Test on the string. We don't expect s2 == s in this general case,\n # as s contains e.g. comments, formatting/indenting, etc. (and may need alpha-renaming).\n # Here we check only the absence of lambdas; it's quite hard to check there are no (let ... lam), what with variable names and possibly types,\n # instead, we expect one lam inside each build or sumbuild.\n assert s2.count(\"lam\") == s2.count(\"build\")\n\n # Finish the roundtrip test\n fn2, exprenv2 = parse(s2)[-1]\n assert fn2 == func_name\n assert exprenv == exprenv2\n\n # Our parsing (of defs) generates new variables (generally from inside out, i.e. we build\n # the innermost \"let f = ... in f\" first), which are then alpha-renamed as the expression\n # is put together.\n # When we remove these synthetic variables in .ksc_str(), they leave \"gaps\" in the variable naming scheme in the output,\n # meaning that variable name generation and alpha-renaming proceeds differently when we re-parse that output.\n # Thus, to reach string equality, we iterate until a fixpoint is reached.\n s3 = exprenv2.expr.ksc_str()\n while s2 != s3:\n print(\"ITER {}\".format(func_name))\n s2 = s3\n fn3, exprenv3 = parse(s3)[-1]\n assert fn3 == func_name\n assert exprenv3 == exprenv2\n s3 = exprenv3.expr.ksc_str()\n\ndef test_ksc_exact_roundtrip_normalization():\n # Multiple elements => all bar the last will become the last (which roundtrips)\n for testset in [\n [\"(def f Float ((x : Float)) (add x 1.0))\", \"(def f Float ((x : Float)) (add x 1.0))\\n\\n\"],\n [\"(def f Float ((x : Float)) (add x 1.0))\\n\\n(def g Float ((y : Float)) (add (f y) (f (mul y 2.0))))\\n\\n\"],\n [\"(def f Float (x : Float) (if (lt x 1.0) 1.0 x))\", \"(def f Float (x : Float) (if (lt x 1.0) 1.0 x))\", \"(def f Float (x : Float) (if (gt 1.0 x) 1.0 x))\", \"(def f Float ((x : Float)) (if (gt 1.0 x) 1.0 x))\\n\\n\"],\n [\"(def f Float ((x : Float)) (x))\\n\\n\"],\n [\"(def f Float ((x : Float)) (let y (add x 1.0) y))\", \"(def f Float ((x : Float)) (let ((y (add x 1.0))) y))\",\n \"(def f Float ((x : Float)) (let (y (add x 1.0)) y))\\n\\n\"],\n [\"(def f Float ((x : Float)) (let ((y (add x 1.0)) (z (mul y 2.0))) y))\", \"(def f Float ((x : Float)) (let (y (add x 1.0)) (let (z (mul y 2.0)) y)))\\n\\n\"],\n # This will not be converted to multi-argument form because the body is not of the correct shape.\n [\"(def sum$TupFF Float ((t : (Tuple Float Float))) (add (get$1$2 t) (get$2$2 t)))\\n\\n\"],\n # Multiple arguments - ok to convert tuple to multi-arg form:\n [\"(def f Float (t : Tuple Float Float) (let ((y (get$2$2 t)) (x (get$1$2 t))) (add x y)))\",\n \"(def f Float ((x : Float) (y : Float)) (add x y))\\n\\n\"],\n [\"(def add$F$TupFF (Tuple Float Float) ((x : Float) (t : (Tuple Float Float))) (tuple (add x (get$1$2 t)) (add x (get$2$2 t))))\\n\\n\"],\n # A vector of empty tuples - yes, note Tuple must be in brackets.\n [\"(def len Integer ((v : (Tensor 1 (Tuple)))) (size v))\\n\\n\"]\n ]:\n for testcase in testset:\n (_, exprenv) = sparser.parse_defs(testcase)[-1]\n assert exprenv.expr.ksc_str() == testset[-1]\n\n@pytest.mark.skip\ndef test_ksc_exact_roundtrip_normalization_edefs():\n # TODO should `edef`s roundtrip?\n for testset in [\n [\"(edef foo Integer (Tuple Float Float))\\n\\n(def bar Integer (x : Float) (foo x x))\",\n \"(edef foo Integer ((Tuple Float Float)))\\n\\n(def bar Integer (x : Float) (foo x x))\",\n \"(edef foo Integer (Float Float))\\n\\n(def bar Integer ((x : Float)) (foo x x))\",\n \"(edef foo Integer (Float Float))\\n\\n(def bar Integer ((x : Float)) (foo x x))\\n\\n\"]\n ]:\n for testcase in testset:\n # pylint: disable=unexpected-keyword-arg\n (_, exp) = sparser.parse_defs(testcase, include_edefs=True)[-1]\n assert exp.ksc_str() == testset[-1]\n\ndef test_get_func_body_roundtrip():\n import harder_dependency as f\n func_list = load_expression_set(\"../../test/rlo/test_sparser_files/harder_dependency.kso\")\n func_list_check = [EF.Let(f.a, f.f_a, f.a), EF.Let(f.b, f.f_b, f.b), EF.Let(f.c, f.f_c, f.c), EF.Let(f.d, f.f_d, f.d), EF.Let(f.e, f.f_e, f.e), EF.Let(f.f, f.f_f, f.f)]\n\n for (func_name, functab), func_check in zip(func_list.named_exprenvs(), func_list_check):\n expr_lam = get_func_body(func_name, functab.expr)\n\n expr_name = Expression.Variable(func_name)\n assert EF.Let(expr_name, expr_lam, expr_name) == func_check\n", "repo_name": "microsoft/knossos-ksc", "sub_path": "rlo/test/rlo/test_sparser.py", "file_name": "test_sparser.py", "file_ext": "py", "file_size_in_byte": 20589, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 45, "dataset": "github-code", "pt": "37", "api": [{"api_name": "os.path.dirname", "line_number": 16, "usage_type": "call"}, {"api_name": "os.path", "line_number": 16, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 16, "usage_type": "call"}, {"api_name": "rlo.sparser.parse_defs_with_symtab", "line_number": 19, "usage_type": "call"}, {"api_name": "rlo.sparser", "line_number": 19, "usage_type": "name"}, {"api_name": "rlo.utils.read_file", "line_number": 23, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 23, "usage_type": "call"}, {"api_name": "os.path", "line_number": 23, "usage_type": "attribute"}, {"api_name": "rlo.expr_sets.ExpressionSetFromFile", "line_number": 26, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 26, "usage_type": "call"}, {"api_name": "os.path", "line_number": 26, "usage_type": "attribute"}, {"api_name": "rlo.expression.Expression.Variable", "line_number": 29, "usage_type": "call"}, {"api_name": "rlo.expression.Expression", "line_number": 29, "usage_type": "name"}, {"api_name": "rlo.expression.Expression.Variable", "line_number": 30, "usage_type": "call"}, {"api_name": "rlo.expression.Expression", "line_number": 30, "usage_type": "name"}, {"api_name": "rlo.sparser.parse_expr", "line_number": 31, "usage_type": "call"}, {"api_name": "rlo.sparser", "line_number": 31, "usage_type": "name"}, {"api_name": "rlo.sparser.parse_expr", "line_number": 32, "usage_type": "call"}, {"api_name": "rlo.sparser", "line_number": 32, "usage_type": "name"}, {"api_name": "rlo.expression.EF.Let", "line_number": 32, "usage_type": "call"}, {"api_name": "rlo.expression.EF", "line_number": 32, "usage_type": "name"}, {"api_name": "rlo.sparser.parse_expr", "line_number": 33, "usage_type": "call"}, {"api_name": "rlo.sparser", "line_number": 33, "usage_type": "name"}, {"api_name": "rlo.sparser.parse_expr", "line_number": 34, "usage_type": "call"}, {"api_name": "rlo.sparser", "line_number": 34, "usage_type": "name"}, {"api_name": "rlo.expression.EF.Select", "line_number": 34, "usage_type": "call"}, {"api_name": "rlo.expression.EF", "line_number": 34, "usage_type": "name"}, {"api_name": "rlo.expression.EF.Tuple", "line_number": 34, "usage_type": "call"}, {"api_name": "rlo.sparser.parse_expr", "line_number": 35, "usage_type": "call"}, {"api_name": "rlo.sparser", "line_number": 35, "usage_type": "name"}, {"api_name": "rlo.expression.Expression.Constant", "line_number": 35, "usage_type": "call"}, {"api_name": "rlo.expression.Expression", "line_number": 35, "usage_type": "name"}, {"api_name": "pytest.raises", "line_number": 38, "usage_type": "call"}, {"api_name": "rlo.sparser.parse_expr", "line_number": 39, "usage_type": "call"}, {"api_name": "rlo.sparser", "line_number": 39, "usage_type": "name"}, {"api_name": "pytest.raises", "line_number": 40, "usage_type": "call"}, {"api_name": "rlo.sparser.parse_expr", "line_number": 41, "usage_type": "call"}, {"api_name": "rlo.sparser", "line_number": 41, "usage_type": "name"}, {"api_name": "rlo.expression.Expression.Variable", "line_number": 44, "usage_type": "call"}, {"api_name": "rlo.expression.Expression", "line_number": 44, "usage_type": "name"}, {"api_name": "ksc.type.Type.Float", "line_number": 44, "usage_type": "attribute"}, {"api_name": "ksc.type.Type", "line_number": 44, "usage_type": "name"}, {"api_name": "rlo.expression.EF.Lam", "line_number": 45, "usage_type": "call"}, {"api_name": "rlo.expression.EF", "line_number": 45, "usage_type": "name"}, {"api_name": "rlo.sparser.parse_expr", "line_number": 46, "usage_type": "call"}, {"api_name": "rlo.sparser", "line_number": 46, "usage_type": "name"}, {"api_name": "rlo.sparser.parse_expr", "line_number": 47, "usage_type": "call"}, {"api_name": "rlo.sparser", "line_number": 47, "usage_type": "name"}, {"api_name": "pytest.raises", "line_number": 50, "usage_type": "call"}, {"api_name": "rlo.sparser.parse_expr", "line_number": 51, "usage_type": "call"}, {"api_name": "rlo.sparser", "line_number": 51, "usage_type": "name"}, {"api_name": "pytest.raises", "line_number": 52, "usage_type": "call"}, {"api_name": "rlo.sparser.parse_expr", "line_number": 53, "usage_type": "call"}, {"api_name": "rlo.sparser", "line_number": 53, "usage_type": "name"}, {"api_name": "rlo.sparser.parse_expr", "line_number": 55, "usage_type": "call"}, {"api_name": "rlo.sparser", "line_number": 55, "usage_type": "name"}, {"api_name": "rlo.expression.EF.Let", "line_number": 55, "usage_type": "call"}, {"api_name": "rlo.expression.EF", "line_number": 55, "usage_type": "name"}, {"api_name": "rlo.expression.EF.Apply", "line_number": 56, "usage_type": "call"}, {"api_name": "rlo.expression.EF", "line_number": 56, "usage_type": "name"}, {"api_name": "rlo.sparser.parse_expr", "line_number": 58, "usage_type": "call"}, {"api_name": "rlo.sparser", "line_number": 58, "usage_type": "name"}, {"api_name": "rlo.expression.EF.Let", "line_number": 58, "usage_type": "call"}, {"api_name": "rlo.expression.EF", "line_number": 58, "usage_type": "name"}, {"api_name": "rlo.expression.EF.Lam", "line_number": 60, "usage_type": "call"}, {"api_name": "rlo.expression.EF", "line_number": 60, "usage_type": "name"}, {"api_name": "rlo.expression.Expression.Variable", "line_number": 60, "usage_type": "call"}, {"api_name": "rlo.expression.Expression", "line_number": 60, "usage_type": "name"}, {"api_name": "ksc.type.Type.Tuple", "line_number": 60, "usage_type": "call"}, {"api_name": "ksc.type.Type", "line_number": 60, "usage_type": "name"}, {"api_name": "ksc.type.Type.Integer", "line_number": 60, "usage_type": "attribute"}, {"api_name": "ksc.type.Type.Float", "line_number": 60, "usage_type": "attribute"}, {"api_name": "rlo.expression.EF.Select", "line_number": 60, "usage_type": "call"}, {"api_name": "rlo.expression.EF.Apply", "line_number": 61, "usage_type": "call"}, {"api_name": "rlo.expression.EF", "line_number": 61, "usage_type": "name"}, {"api_name": "rlo.expression.EF.Tuple", "line_number": 61, "usage_type": "call"}, {"api_name": "rlo.expression.Expression.Variable", "line_number": 68, "usage_type": "call"}, {"api_name": "rlo.expression.Expression", "line_number": 68, "usage_type": "name"}, {"api_name": "rlo.expression.Expression.Variable", "line_number": 69, "usage_type": "call"}, {"api_name": "rlo.expression.Expression", "line_number": 69, "usage_type": "name"}, {"api_name": "rlo.sparser.parse_expr", "line_number": 71, "usage_type": "call"}, {"api_name": "rlo.sparser", "line_number": 71, "usage_type": "name"}, {"api_name": "rlo.expression.EF.Let", "line_number": 72, "usage_type": "call"}, {"api_name": "rlo.expression.EF", "line_number": 72, "usage_type": "name"}, {"api_name": "rlo.expression.Expression.Constant", "line_number": 72, "usage_type": "call"}, {"api_name": "rlo.expression.Expression", "line_number": 72, "usage_type": "name"}, {"api_name": "rlo.sparser.parse_expr", "line_number": 75, "usage_type": "call"}, {"api_name": "rlo.sparser", "line_number": 75, "usage_type": "name"}, {"api_name": "rlo.expression.EF.Let", "line_number": 76, "usage_type": "call"}, {"api_name": "rlo.expression.EF", "line_number": 76, "usage_type": "name"}, {"api_name": "rlo.expression.Expression.Constant", "line_number": 76, "usage_type": "call"}, {"api_name": "rlo.expression.Expression", "line_number": 76, "usage_type": "name"}, {"api_name": "rlo.sparser.parse_expr", "line_number": 79, "usage_type": "call"}, {"api_name": "rlo.sparser", "line_number": 79, "usage_type": "name"}, {"api_name": "rlo.expression.EF.Let", "line_number": 80, "usage_type": "call"}, {"api_name": "rlo.expression.EF", "line_number": 80, "usage_type": "name"}, {"api_name": "rlo.expression.Expression.Constant", "line_number": 80, "usage_type": "call"}, {"api_name": "rlo.expression.Expression", "line_number": 80, "usage_type": "name"}, {"api_name": "rlo.expression.Expression.Variable", "line_number": 85, "usage_type": "call"}, {"api_name": "rlo.expression.Expression", "line_number": 85, "usage_type": "name"}, {"api_name": "rlo.expression.Expression.Variable", "line_number": 86, "usage_type": "call"}, {"api_name": "rlo.expression.Expression", "line_number": 86, "usage_type": "name"}, {"api_name": "ksc.type.Type.Float", "line_number": 86, "usage_type": "attribute"}, {"api_name": "ksc.type.Type", "line_number": 86, "usage_type": "name"}, {"api_name": "rlo.expression.EF.Let", "line_number": 89, "usage_type": "call"}, {"api_name": "rlo.expression.EF", "line_number": 89, "usage_type": "name"}, {"api_name": "rlo.expression.EF.Lam", "line_number": 89, "usage_type": "call"}, {"api_name": "rlo.expression.Expression.Variable", "line_number": 93, "usage_type": "call"}, {"api_name": "rlo.expression.Expression", "line_number": 93, "usage_type": "name"}, {"api_name": "rlo.expression.Expression.Variable", "line_number": 94, "usage_type": "call"}, {"api_name": "rlo.expression.Expression", "line_number": 94, "usage_type": "name"}, {"api_name": "rlo.expression.Expression.Variable", "line_number": 95, "usage_type": "call"}, {"api_name": "rlo.expression.Expression", "line_number": 95, "usage_type": "name"}, {"api_name": "rlo.expression.Expression.Variable", "line_number": 96, "usage_type": "call"}, {"api_name": "rlo.expression.Expression", "line_number": 96, "usage_type": "name"}, {"api_name": "rlo.expression.Expression.Variable", "line_number": 97, "usage_type": "call"}, {"api_name": "rlo.expression.Expression", "line_number": 97, "usage_type": "name"}, {"api_name": "rlo.expression.Expression.Variable", "line_number": 99, "usage_type": "call"}, {"api_name": "rlo.expression.Expression", "line_number": 99, "usage_type": "name"}, {"api_name": "ksc.type.Type.Float", "line_number": 99, "usage_type": "attribute"}, {"api_name": "ksc.type.Type", "line_number": 99, "usage_type": "name"}, {"api_name": "rlo.expression.Expression.Variable", "line_number": 100, "usage_type": "call"}, {"api_name": "rlo.expression.Expression", "line_number": 100, "usage_type": "name"}, {"api_name": "ksc.type.Type.Float", "line_number": 100, "usage_type": "attribute"}, {"api_name": "ksc.type.Type", "line_number": 100, "usage_type": "name"}, {"api_name": "rlo.expression.Expression.Variable", "line_number": 101, "usage_type": "call"}, {"api_name": "rlo.expression.Expression", "line_number": 101, "usage_type": "name"}, {"api_name": "ksc.type.Type.Float", "line_number": 101, "usage_type": "attribute"}, {"api_name": "ksc.type.Type", "line_number": 101, "usage_type": "name"}, {"api_name": "rlo.expression.EF.Let", "line_number": 104, "usage_type": "call"}, {"api_name": "rlo.expression.EF", "line_number": 104, "usage_type": "name"}, {"api_name": "rlo.expression.EF.Lam", "line_number": 104, "usage_type": "call"}, {"api_name": "rlo.expression.EF.Lam", "line_number": 106, "usage_type": "call"}, {"api_name": "rlo.expression.EF", "line_number": 106, "usage_type": "name"}, {"api_name": "rlo.expression.EF.Lam", "line_number": 107, "usage_type": "call"}, {"api_name": "rlo.expression.EF", "line_number": 107, "usage_type": "name"}, {"api_name": "rlo.expression.EF.Apply", "line_number": 107, "usage_type": "call"}, {"api_name": "rlo.expression.EF.Lam", "line_number": 108, "usage_type": "call"}, {"api_name": "rlo.expression.EF", "line_number": 108, "usage_type": "name"}, {"api_name": "rlo.expression.EF.Apply", "line_number": 108, "usage_type": "call"}, {"api_name": "rlo.expression.EF.Lam", "line_number": 109, "usage_type": "call"}, {"api_name": "rlo.expression.EF", "line_number": 109, "usage_type": "name"}, {"api_name": "rlo.expression.EF.Apply", "line_number": 109, "usage_type": "call"}, {"api_name": "rlo.expression.EF.Let", "line_number": 112, "usage_type": "call"}, {"api_name": "rlo.expression.EF", "line_number": 112, "usage_type": "name"}, {"api_name": "rlo.expression.EF.Let", "line_number": 113, "usage_type": "call"}, {"api_name": "rlo.expression.EF", "line_number": 113, "usage_type": "name"}, {"api_name": "rlo.expression.EF.Let", "line_number": 114, "usage_type": "call"}, {"api_name": "rlo.expression.EF", "line_number": 114, "usage_type": "name"}, {"api_name": "rlo.expression.EF.Let", "line_number": 115, "usage_type": "call"}, {"api_name": "rlo.expression.EF", "line_number": 115, "usage_type": "name"}, {"api_name": "rlo.expression.EF.Let", "line_number": 121, "usage_type": "call"}, {"api_name": "rlo.expression.EF", "line_number": 121, "usage_type": "name"}, {"api_name": "harder_dependency.a", "line_number": 121, "usage_type": "attribute"}, {"api_name": "harder_dependency.f_a", "line_number": 121, "usage_type": "attribute"}, {"api_name": "rlo.expression.EF.Let", "line_number": 125, "usage_type": "call"}, {"api_name": "rlo.expression.EF", "line_number": 125, "usage_type": "name"}, {"api_name": "harder_dependency.a", "line_number": 125, "usage_type": "attribute"}, {"api_name": "harder_dependency.f_a", "line_number": 125, "usage_type": "attribute"}, {"api_name": "harder_dependency.b", "line_number": 125, "usage_type": "attribute"}, {"api_name": "harder_dependency.f_b", "line_number": 125, "usage_type": "attribute"}, {"api_name": "rlo.expression.EF.Let", "line_number": 126, "usage_type": "call"}, {"api_name": "rlo.expression.EF", "line_number": 126, "usage_type": "name"}, {"api_name": "harder_dependency.a", "line_number": 126, "usage_type": "attribute"}, {"api_name": "harder_dependency.f_a", "line_number": 126, "usage_type": "attribute"}, {"api_name": "harder_dependency.b", "line_number": 126, "usage_type": "attribute"}, {"api_name": "harder_dependency.f_b", "line_number": 126, "usage_type": "attribute"}, {"api_name": "harder_dependency.c", "line_number": 126, "usage_type": "attribute"}, {"api_name": "harder_dependency.f_c", "line_number": 126, "usage_type": "attribute"}, {"api_name": "rlo.expression.EF.Let", "line_number": 127, "usage_type": "call"}, {"api_name": "rlo.expression.EF", "line_number": 127, "usage_type": "name"}, {"api_name": "harder_dependency.a", "line_number": 127, "usage_type": "attribute"}, {"api_name": "harder_dependency.f_a", "line_number": 127, "usage_type": "attribute"}, {"api_name": "harder_dependency.b", "line_number": 127, "usage_type": "attribute"}, {"api_name": "harder_dependency.f_b", "line_number": 127, "usage_type": "attribute"}, {"api_name": "harder_dependency.c", "line_number": 127, "usage_type": "attribute"}, {"api_name": "harder_dependency.f_c", "line_number": 127, "usage_type": "attribute"}, {"api_name": "harder_dependency.d", "line_number": 127, "usage_type": "attribute"}, {"api_name": "harder_dependency.f_d", "line_number": 127, "usage_type": "attribute"}, {"api_name": "rlo.expression.EF.Let", "line_number": 128, "usage_type": "call"}, {"api_name": "rlo.expression.EF", "line_number": 128, "usage_type": "name"}, {"api_name": "harder_dependency.a", "line_number": 128, "usage_type": "attribute"}, {"api_name": "harder_dependency.f_a", "line_number": 128, "usage_type": "attribute"}, {"api_name": "harder_dependency.b", "line_number": 128, "usage_type": "attribute"}, {"api_name": "harder_dependency.f_b", "line_number": 128, "usage_type": "attribute"}, {"api_name": "harder_dependency.c", "line_number": 128, "usage_type": "attribute"}, {"api_name": "harder_dependency.f_c", "line_number": 128, "usage_type": "attribute"}, {"api_name": "harder_dependency.d", "line_number": 128, "usage_type": "attribute"}, {"api_name": "harder_dependency.f_d", "line_number": 128, "usage_type": "attribute"}, {"api_name": "harder_dependency.e", "line_number": 128, "usage_type": "attribute"}, {"api_name": "harder_dependency.f_e", "line_number": 128, "usage_type": "attribute"}, {"api_name": "rlo.expression.EF.Let", "line_number": 130, "usage_type": "call"}, {"api_name": "rlo.expression.EF", "line_number": 130, "usage_type": "name"}, {"api_name": "harder_dependency.a", "line_number": 130, "usage_type": "attribute"}, {"api_name": "harder_dependency.f_a", "line_number": 130, "usage_type": "attribute"}, {"api_name": "harder_dependency.b", "line_number": 130, "usage_type": "attribute"}, {"api_name": "harder_dependency.f_b", "line_number": 130, "usage_type": "attribute"}, {"api_name": "harder_dependency.c", "line_number": 130, "usage_type": "attribute"}, {"api_name": "harder_dependency.f_c", "line_number": 130, "usage_type": "attribute"}, {"api_name": "harder_dependency.d", "line_number": 130, "usage_type": "attribute"}, {"api_name": "harder_dependency.f_d", "line_number": 130, "usage_type": "attribute"}, {"api_name": "harder_dependency.e", "line_number": 130, "usage_type": "attribute"}, {"api_name": "harder_dependency.f_e", "line_number": 130, "usage_type": "attribute"}, {"api_name": "harder_dependency.f", "line_number": 130, "usage_type": "attribute"}, {"api_name": "harder_dependency.f_f", "line_number": 130, "usage_type": "attribute"}, {"api_name": "pytest.raises", "line_number": 133, "usage_type": "call"}, {"api_name": "rlo.expression.Expression.Variable", "line_number": 138, "usage_type": "call"}, {"api_name": "rlo.expression.Expression", "line_number": 138, "usage_type": "name"}, {"api_name": "rlo.expression.Expression.Variable", "line_number": 139, "usage_type": "call"}, {"api_name": "rlo.expression.Expression", "line_number": 139, "usage_type": "name"}, {"api_name": "rlo.expression.Expression.Variable", "line_number": 140, "usage_type": "call"}, {"api_name": "rlo.expression.Expression", "line_number": 140, "usage_type": "name"}, {"api_name": "rlo.expression.Expression.Variable", "line_number": 142, "usage_type": "call"}, {"api_name": "rlo.expression.Expression", "line_number": 142, "usage_type": "name"}, {"api_name": "ksc.type.Type.Float", "line_number": 142, "usage_type": "attribute"}, {"api_name": "ksc.type.Type", "line_number": 142, "usage_type": "name"}, {"api_name": "rlo.expression.Expression.Variable", "line_number": 143, "usage_type": "call"}, {"api_name": "rlo.expression.Expression", "line_number": 143, "usage_type": "name"}, {"api_name": "ksc.type.Type.Float", "line_number": 143, "usage_type": "attribute"}, {"api_name": "ksc.type.Type", "line_number": 143, "usage_type": "name"}, {"api_name": "rlo.expression.Expression.Variable", "line_number": 144, "usage_type": "call"}, {"api_name": "rlo.expression.Expression", "line_number": 144, "usage_type": "name"}, {"api_name": "ksc.type.Type.Tuple", "line_number": 144, "usage_type": "call"}, {"api_name": "ksc.type.Type", "line_number": 144, "usage_type": "name"}, {"api_name": "ksc.type.Type.Float", "line_number": 144, "usage_type": "attribute"}, {"api_name": "rlo.expression.EF.Lam", "line_number": 146, "usage_type": "call"}, {"api_name": "rlo.expression.EF", "line_number": 146, "usage_type": "name"}, {"api_name": "rlo.expression.EF.Let", "line_number": 146, "usage_type": "call"}, {"api_name": "rlo.expression.EF.Select", "line_number": 146, "usage_type": "call"}, {"api_name": "rlo.expression.EF.Lam", "line_number": 147, "usage_type": "call"}, {"api_name": "rlo.expression.EF", "line_number": 147, "usage_type": "name"}, {"api_name": "rlo.expression.EF.Let", "line_number": 147, "usage_type": "call"}, {"api_name": "rlo.expression.EF.Select", "line_number": 147, "usage_type": "call"}, {"api_name": "rlo.expression.EF.Apply", "line_number": 147, "usage_type": "call"}, {"api_name": "rlo.expression.EF.Tuple", "line_number": 147, "usage_type": "call"}, {"api_name": "rlo.expression.EF.Let", "line_number": 150, "usage_type": "call"}, {"api_name": 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{"api_name": "os.path", "line_number": 313, "usage_type": "attribute"}, {"api_name": "glob.glob", "line_number": 315, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 315, "usage_type": "call"}, {"api_name": "os.path", "line_number": 315, "usage_type": "attribute"}, {"api_name": "glob.glob", "line_number": 316, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 316, "usage_type": "call"}, {"api_name": "os.path", "line_number": 316, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 317, "usage_type": "call"}, {"api_name": "os.path", "line_number": 317, "usage_type": "attribute"}, {"api_name": "rlo.sparser.parse_defs_with_symtab", "line_number": 326, "usage_type": "call"}, {"api_name": "rlo.sparser", "line_number": 326, "usage_type": "name"}, {"api_name": "rlo.rewrites.delete_unused_defs", "line_number": 327, "usage_type": "call"}, {"api_name": "rlo.rewrites", "line_number": 327, "usage_type": "name"}, {"api_name": "rlo.expression_util.ExprWithEnv", "line_number": 327, "usage_type": "call"}, {"api_name": "rlo.utils.read_file", "line_number": 329, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 308, "usage_type": "attribute"}, {"api_name": "rlo.sparser.parse_defs", "line_number": 381, "usage_type": "call"}, {"api_name": "rlo.sparser", "line_number": 381, "usage_type": "name"}, {"api_name": "rlo.sparser.parse_defs", "line_number": 395, "usage_type": "call"}, {"api_name": "rlo.sparser", "line_number": 395, "usage_type": "name"}, {"api_name": "pytest.mark", "line_number": 384, "usage_type": "attribute"}, {"api_name": "rlo.expression.EF.Let", "line_number": 401, "usage_type": "call"}, {"api_name": "rlo.expression.EF", "line_number": 401, "usage_type": "name"}, {"api_name": "harder_dependency.a", "line_number": 401, "usage_type": "attribute"}, {"api_name": "harder_dependency.f_a", "line_number": 401, "usage_type": "attribute"}, {"api_name": "harder_dependency.b", "line_number": 401, "usage_type": "attribute"}, {"api_name": "harder_dependency.f_b", "line_number": 401, "usage_type": "attribute"}, {"api_name": "harder_dependency.c", "line_number": 401, "usage_type": "attribute"}, {"api_name": "harder_dependency.f_c", "line_number": 401, "usage_type": "attribute"}, {"api_name": "harder_dependency.d", "line_number": 401, "usage_type": "attribute"}, {"api_name": "harder_dependency.f_d", "line_number": 401, "usage_type": "attribute"}, {"api_name": "harder_dependency.e", "line_number": 401, "usage_type": "attribute"}, {"api_name": "harder_dependency.f_e", "line_number": 401, "usage_type": "attribute"}, {"api_name": "harder_dependency.f", "line_number": 401, "usage_type": "attribute"}, {"api_name": "harder_dependency.f_f", "line_number": 401, "usage_type": "attribute"}, {"api_name": "rlo.utils.get_func_body", "line_number": 404, "usage_type": "call"}, {"api_name": "rlo.expression.Expression.Variable", "line_number": 406, "usage_type": "call"}, {"api_name": "rlo.expression.Expression", "line_number": 406, "usage_type": "name"}, {"api_name": "rlo.expression.EF.Let", "line_number": 407, "usage_type": "call"}, {"api_name": "rlo.expression.EF", "line_number": 407, "usage_type": "name"}]} +{"seq_id": "10522570729", "text": "import json\r\nfrom flask import Flask, render_template, redirect, url_for, request\r\n\r\nfrom flask_wtf import FlaskForm\r\nfrom wtforms import StringField, SubmitField, DateField\r\nfrom wtforms.validators import DataRequired, Email\r\nfrom datetime import datetime\r\n\r\n\r\napp = Flask(__name__)\r\napp.config[\"SECRET_KEY\"] = \"ThisIsHereCosINeedItNotBecauseIWantIt\"\r\n\r\n# https://flask.palletsprojects.com/en/1.0.x/api/#flask.Flask.open_resource\r\nwith app.open_resource(\"users.json\", 'r') as fin:\r\n\tusers = json.load(fin)\r\n\r\n@app.route(\"/users/\")\r\ndef raw(uid):\r\n\tuser = users.get(str(uid), \"none\")\r\n\t\r\n\tif(user == \"none\"):\r\n\t\treturn f\"That user does not exist\"\r\n\r\n\tfriends = {}\r\n\tfriendsJSON = user.get(\"friends\")\r\n\r\n\tfor i in friendsJSON:\r\n\t\t#friends[str(i)] = \"http://127.0.0.1:5000/users/\" + str(i)\r\n\t\tmyFriend = users.get(str(i))\r\n\t\tfriends[str(i)] = myFriend.get(\"fname\")\r\n\t\t#print(friends[str(i)])\r\n\r\n\treturn render_template(\"UserProfiles.html\", user = user, friends = friends)\r\n\r\n\r\n#class to update form\r\nclass UpdatedForm(FlaskForm): \r\n\tfname = StringField(\"First Name\", validators = [DataRequired()])\r\n\tlname = StringField(\"Last Name\", validators = [DataRequired()])\r\n\temail = StringField(\"Email Address\", validators = [DataRequired(), Email()])\r\n\tdob = DateField(\"Date of Birth\", format='%Y-%m-%d', validators = [DataRequired()])\r\n\tsubmit = SubmitField(\"Submit\")\r\n\r\n\r\n#route\r\n@app.route(\"/update/users/\", methods = ['GET', 'POST'])\r\ndef update(uid):\r\n\tform = UpdatedForm()\r\n\tuser = users.get(str(uid), \"none\")\r\n\t\r\n\tif(user == \"none\"):\r\n\t\treturn f\"That user does not exist\"\r\n\t\r\n\tif request.method == \"POST\":\r\n\t\tif form.validate():\r\n\t\t\tuser[\"fname\"] = form.fname.data\r\n\t\t\tuser[\"lname\"] = form.lname.data\r\n\t\t\tuser[\"email\"] = form.email.data\r\n\t\t\tuser[\"dob\"] = str(form.dob.data)\r\n\t\t\treturn redirect(url_for(\"raw\", uid=uid))\r\n\t\telse:\r\n\t\t\treturn \"Invalid Form\", 400\r\n\telse:\r\n\t\tprint(user.get(\"fname\"))\r\n\t\tform.fname.data = user.get(\"fname\") \r\n\t\tform.lname.data = user.get(\"lname\")\r\n\t\tform.email.data = user.get(\"email\")\r\n\t\t#form.dob.data = user.get(\"dob\")\r\n\t\tform.dob.data = datetime.date(datetime.strptime(user.get(\"dob\"), '%Y-%m-%d'))\r\n\t\treturn render_template(\"UserEdit.html\", form = form);\r\n\t\r\n\r\n\r\napp.run()", "repo_name": "MaryfrancesU/College-Assignments", "sub_path": "CSC210/HW06_UserProfileEdit/UserProfiles.py", "file_name": "UserProfiles.py", "file_ext": "py", "file_size_in_byte": 2219, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "flask.Flask", "line_number": 10, "usage_type": "call"}, {"api_name": "json.load", "line_number": 15, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 33, "usage_type": "call"}, {"api_name": "flask_wtf.FlaskForm", "line_number": 37, "usage_type": "name"}, {"api_name": "wtforms.StringField", "line_number": 38, "usage_type": "call"}, {"api_name": "wtforms.validators.DataRequired", "line_number": 38, "usage_type": "call"}, {"api_name": "wtforms.StringField", "line_number": 39, "usage_type": "call"}, {"api_name": "wtforms.validators.DataRequired", "line_number": 39, "usage_type": "call"}, {"api_name": "wtforms.StringField", "line_number": 40, "usage_type": "call"}, {"api_name": "wtforms.validators.DataRequired", "line_number": 40, "usage_type": "call"}, {"api_name": "wtforms.validators.Email", "line_number": 40, "usage_type": "call"}, {"api_name": "wtforms.DateField", "line_number": 41, "usage_type": "call"}, {"api_name": "wtforms.validators.DataRequired", "line_number": 41, "usage_type": "call"}, {"api_name": "wtforms.SubmitField", "line_number": 42, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 54, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 54, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 60, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 60, "usage_type": "call"}, {"api_name": "datetime.datetime.date", "line_number": 69, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 69, "usage_type": "name"}, {"api_name": "datetime.datetime.strptime", "line_number": 69, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 70, "usage_type": "call"}]} +{"seq_id": "73344996907", "text": "import json\nfrom asgiref.sync import async_to_sync\nfrom channels.generic.websocket import WebsocketConsumer\n\nfrom django.contrib.auth.models import User\nfrom .models import Chat, Mensaje\n\nclass ChatConsumer(WebsocketConsumer):\n def connect(self):\n self.room_name = self.scope['url_route']['kwargs']['room_name']\n self.room_group_name = 'chat_%s' % self.room_name\n\n # Join room group\n async_to_sync(self.channel_layer.group_add)(\n self.room_group_name,\n self.channel_name\n )\n\n self.accept()\n\n def disconnect(self, close_code):\n # Leave room group\n async_to_sync(self.channel_layer.group_discard)(\n self.room_group_name,\n self.channel_name\n )\n \n def mensaje_to_json(self, mensaje):\n return {\n 'id': mensaje.id,\n 'chatId': mensaje.chat_set.all()[0].id,\n 'autor': mensaje.autor.username,\n 'contenido': mensaje.contenido,\n 'fecha': str(mensaje.fecha),\n 'entregado': mensaje.entregado,\n 'visto': mensaje.visto,\n }\n \n def mensajes_to_json(self, mensajes):\n result = []\n for mensaje in mensajes:\n result.append(self.mensaje_to_json(mensaje))\n return result\n \n def mensajes(self, data):\n respuesta = {'estado': False}\n chat_id = data['id']\n if Chat.objects.filter(id=chat_id).exists():\n chat = Chat.objects.get(id=chat_id)\n mensajes = chat.mensajes.all() \n respuesta['accion'] = 'mensajes'\n respuesta['mensajes'] = self.mensajes_to_json(mensajes) \n respuesta['estado'] = True\n self.responder(respuesta)\n \n def mensaje_nuevo(self, data):\n respuesta = {'estado': False}\n chat_id = data['id']\n usuario = User.objects.get(username=data['usuario'])\n destino = User.objects.get(username=data['destino'])\n contenido = data['mensaje']\n mensaje = Mensaje(autor=usuario, contenido=contenido)\n if Chat.objects.filter(id=chat_id).exists():\n chat = Chat.objects.get(id=chat_id)\n mensaje.sync = True\n mensaje.save()\n mensaje.destinos.add(destino)\n mensaje.save()\n chat.mensajes.add(mensaje)\n respuesta['accion'] = 'mensaje_nuevo'\n respuesta['mensaje'] = self.mensaje_to_json(mensaje)\n respuesta['estado'] = True\n self.responder_grupo(respuesta)\n\n def mensajes_no_vistos(self, data):\n respuesta = {'estado': False}\n usuario = User.objects.get(username=data['usuario'])\n mensajes = usuario.destinos.all().filter(visto=False)\n respuesta['accion'] = 'mensajes_no_vistos'\n respuesta['mensajes'] = self.mensajes_to_json(mensajes) \n respuesta['estado'] = True\n self.responder(respuesta)\n \n def usuario_to_json(self, usuario): \n return {\n 'id': usuario.id,\n 'username': usuario.username,\n 'last_login': str(usuario.last_login),\n 'img_url': usuario.profile.imagen.url,\n }\n\n def usuarios_to_json(self, usuarios):\n result = []\n for usuario in usuarios:\n result.append(self.usuario_to_json(usuario))\n return result\n \n def usuarios(self, data):\n respuesta = {'estado': False}\n usuarios = User.objects.all().exclude(username=data['usuario'])\n respuesta['accion'] = 'usuarios'\n respuesta['usuarios'] = self.usuarios_to_json(usuarios) \n respuesta['estado'] = True\n self.responder(respuesta) \n\n def chats(self, data):\n respuesta = {'estado': False}\n usuario = User.objects.get(username=data['usuario'])\n chats = usuario.chat_set.all()\n if chats:\n chats_list = []\n cantidad = 0 \n for chat in chats:\n contacto = chat.participantes.all().exclude(username=usuario.username)\n contacto = contacto.values('username')[0]\n mensajes = chat.mensajes.all()\n ultimo_mensaje = 'sin mensajes :-('\n if mensajes:\n contacto_mensajes = chat.mensajes.all().exclude(autor=usuario.id).order_by('-fecha')\n ultimo_mensaje_try = contacto_mensajes[:1]\n if ultimo_mensaje_try:\n ultimo_mensaje = ultimo_mensaje_try[0].contenido\n mensajes_nuevos = contacto_mensajes.count() \n new_chat = {'numero': cantidad, 'id': chat.id, 'contacto': contacto['username'], 'ultimo_mensaje': ultimo_mensaje, 'mensajes_nuevos': mensajes_nuevos}\n cantidad = cantidad + 1\n chats_list.append(new_chat)\n respuesta['accion'] = 'chats'\n respuesta['chats_list'] = chats_list\n respuesta['estado'] = True\n self.responder(respuesta)\n else:\n respuesta['mensaje'] = 'ningún chat creado aún'\n self.responder(respuesta)\n\n def informe_entrega(self, data):\n respuesta = {'estado': False}\n if Mensaje.objects.filter(id=data['id']).exists():\n mensaje = Mensaje.objects.get(id=data['id'])\n mensaje.entregado = True\n mensaje.sync = False\n mensaje.save\n respuesta['accion'] = 'informe_entrega'\n respuesta['estado'] = True\n respuesta['id'] = data['id']\n self.responder_grupo(respuesta)\n else:\n respuesta['accion'] = 'informe_entrega'\n respuesta['id'] = data['id']\n self.responder_grupo(respuesta)\n \n def informe_visto(self, data):\n respuesta = {'estado': False}\n if Mensaje.objects.filter(id=data['id']).exists():\n mensaje = Mensaje.objects.get(id=data['id'])\n mensaje.visto = True\n mensaje.sync = False\n mensaje.save\n respuesta['accion'] = 'informe_visto'\n respuesta['estado'] = True\n respuesta['id'] = data['id']\n self.responder_grupo(respuesta)\n else:\n respuesta['accion'] = 'informe_visto'\n respuesta['id'] = data['id']\n self.responder_grupo(respuesta)\n\n acciones = {\n 'mensajes': mensajes,\n 'mensajes_no_vistos': mensajes_no_vistos,\n 'mensaje_nuevo': mensaje_nuevo,\n 'usuarios': usuarios,\n 'chats': chats,\n 'informe_entrega': informe_entrega,\n 'informe_visto': informe_visto,\n }\n\n # Receive message from WebSocket\n def receive(self, text_data):\n data = json.loads(text_data)\n accion = data['accion']\n data = data['data']\n self.acciones[accion](self, data)\n\n def responder(self, data):\n data = json.dumps(data)\n self.send(data)\n \n def chat_message(self, event):\n message = event['message'] \n # Send message to WebSocket\n self.send(text_data=json.dumps(message))\n \n def responder_grupo(self, data): \n # Send message to room group \n async_to_sync(self.channel_layer.group_send)(\n self.room_group_name,\n {\n 'type': 'chat_message',\n 'message': data, \n }\n )", "repo_name": "iVanGB93/weblocal", "sub_path": "chat/consumers.py", "file_name": "consumers.py", "file_ext": "py", "file_size_in_byte": 7400, "program_lang": "python", "lang": "es", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "37", "api": [{"api_name": "channels.generic.websocket.WebsocketConsumer", "line_number": 8, "usage_type": "name"}, {"api_name": "asgiref.sync.async_to_sync", "line_number": 14, "usage_type": "call"}, {"api_name": "asgiref.sync.async_to_sync", "line_number": 23, "usage_type": "call"}, {"api_name": "models.Chat.objects.filter", "line_number": 48, "usage_type": "call"}, {"api_name": "models.Chat.objects", "line_number": 48, "usage_type": "attribute"}, {"api_name": "models.Chat", "line_number": 48, "usage_type": "name"}, {"api_name": "models.Chat.objects.get", "line_number": 49, "usage_type": "call"}, {"api_name": "models.Chat.objects", "line_number": 49, "usage_type": "attribute"}, {"api_name": "models.Chat", "line_number": 49, "usage_type": "name"}, {"api_name": "django.contrib.auth.models.User.objects.get", "line_number": 59, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects", "line_number": 59, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 59, "usage_type": "name"}, {"api_name": "django.contrib.auth.models.User.objects.get", "line_number": 60, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects", "line_number": 60, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 60, "usage_type": "name"}, {"api_name": "models.Mensaje", "line_number": 62, "usage_type": "call"}, {"api_name": "models.Chat.objects.filter", "line_number": 63, "usage_type": "call"}, {"api_name": "models.Chat.objects", "line_number": 63, "usage_type": "attribute"}, {"api_name": "models.Chat", "line_number": 63, "usage_type": "name"}, {"api_name": "models.Chat.objects.get", "line_number": 64, "usage_type": "call"}, {"api_name": "models.Chat.objects", "line_number": 64, "usage_type": "attribute"}, {"api_name": "models.Chat", "line_number": 64, "usage_type": "name"}, {"api_name": "django.contrib.auth.models.User.objects.get", "line_number": 77, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects", "line_number": 77, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 77, "usage_type": "name"}, {"api_name": "django.contrib.auth.models.User.objects.all", "line_number": 100, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects", "line_number": 100, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 100, "usage_type": "name"}, {"api_name": "django.contrib.auth.models.User.objects.get", "line_number": 108, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects", "line_number": 108, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 108, "usage_type": "name"}, {"api_name": "models.Mensaje.objects.filter", "line_number": 137, "usage_type": "call"}, {"api_name": "models.Mensaje.objects", "line_number": 137, "usage_type": "attribute"}, {"api_name": "models.Mensaje", "line_number": 137, "usage_type": "name"}, {"api_name": "models.Mensaje.objects.get", "line_number": 138, "usage_type": "call"}, {"api_name": "models.Mensaje.objects", "line_number": 138, "usage_type": "attribute"}, {"api_name": "models.Mensaje", "line_number": 138, "usage_type": "name"}, {"api_name": "models.Mensaje.objects.filter", "line_number": 153, "usage_type": "call"}, {"api_name": "models.Mensaje.objects", "line_number": 153, "usage_type": "attribute"}, {"api_name": "models.Mensaje", "line_number": 153, "usage_type": "name"}, {"api_name": "models.Mensaje.objects.get", "line_number": 154, "usage_type": "call"}, {"api_name": "models.Mensaje.objects", "line_number": 154, "usage_type": "attribute"}, {"api_name": "models.Mensaje", "line_number": 154, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 179, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 185, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 191, "usage_type": "call"}, {"api_name": "asgiref.sync.async_to_sync", "line_number": 195, "usage_type": "call"}]} +{"seq_id": "4612584235", "text": "#!/usr/bin/env python3\n\n# TODO:\n# - add help/description\n\nimport logging\nimport os\nimport re\nimport sys\n\nfrom enum import Enum, auto\nfrom queue import Queue\nfrom threading import Thread, Lock\nfrom urllib.request import urlopen, Request\n\nADBLOCK_HOSTS_FILE = \"/etc/dnsmasq.d/adblock.hosts\"\n\nINVALID_HOSTNAMES = [\"localhost\", \"local\", \"ip6-localhost\", \"ip6-loopback\"]\nINVALID_DOMAINS = [\".local\", \".localdomain\"]\nVALID_HOSTNAME_REGEX = re.compile(\n '^[a-z0-9]([a-z0-9-]{0,61}[a-z0-9])?$', re.IGNORECASE)\n\nHOSTS_FILE_BLOCK_IPS = [\"127.0.0.1\", \"0.0.0.0\", \"::1\", \"::\"]\n\n\nclass BLOCKLIST_TYPE(Enum):\n ABP = auto(),\n HOSTS_FILE = auto(),\n SIMPLE = auto()\n\n\nlogLevel = logging.WARN\nif 'DEBUG' in os.environ:\n if re.match(os.environ['DEBUG'], 'true', re.IGNORECASE):\n logLevel = logging.DEBUG\nlogging.basicConfig(\n format='%(asctime)s %(levelname)s %(message)s', level=logLevel)\n\n\ndef thread_get_hosts_to_block(blocklist_queue, hosts_to_block, hosts_to_block_lock):\n blocklist = blocklist_queue.get()\n while blocklist is not None:\n hosts = get_hosts_to_block(blocklist)\n with hosts_to_block_lock:\n hosts_to_block.update(hosts)\n blocklist = blocklist_queue.get()\n\n\ndef get_hosts_to_block(blocklist):\n list_type, url = blocklist\n\n if list_type == BLOCKLIST_TYPE.ABP:\n return fetch_and_convert_abp_list(url)\n if list_type == BLOCKLIST_TYPE.HOSTS_FILE:\n return fetch_and_convert_hosts_file(url)\n if list_type == BLOCKLIST_TYPE.SIMPLE:\n return fetch_and_convert_simple_list(url)\n\n logging.error(\"Unkown list type %s for url %s.\", list_type, url)\n return []\n\n\ndef fetch_and_convert_abp_list(url):\n host_list = []\n\n abp_list = fetch_url_content(url)\n for line in abp_list.split(\"\\n\"):\n if line.startswith(\"||\") and line.endswith(\"^\"):\n line = line[2:-1]\n if is_valid_hostname(line):\n host_list.append(line)\n\n return host_list\n\n\ndef fetch_and_convert_hosts_file(url):\n host_list = []\n\n hosts_file = fetch_url_content(url)\n for line in hosts_file.split(\"\\n\"):\n for ip in HOSTS_FILE_BLOCK_IPS:\n if line.startswith(ip):\n hostname = line[len(ip):].strip()\n if is_valid_hostname(hostname):\n host_list.append(hostname)\n break\n\n return host_list\n\n\ndef fetch_and_convert_simple_list(url):\n host_list = []\n\n simple_list = fetch_url_content(url)\n for line in simple_list.split(\"\\n\"):\n if is_valid_hostname(line):\n host_list.append(line)\n\n return host_list\n\n\ndef fetch_url_content(url):\n logging.info('Fetching blocklist from url %s...', url)\n\n url_content = ''\n try:\n url = urlopen(Request(url, headers={'User-Agent': 'Mozilla/5.0'}))\n\n url_content_charset = url.headers.get_content_charset()\n if url_content_charset is None:\n url_content_charset = sys.getdefaultencoding()\n\n url_content = url.read().decode(url_content_charset)\n except Exception as e:\n logging.error(\"Couldn't fetch blocklist from url %s: %s\", url, e)\n return url_content\n\n\ndef is_valid_hostname(hostname):\n if hostname.endswith('.'):\n hostname = hostname[:-1]\n\n if len(hostname) < 1 or len(hostname) > 253:\n return False\n\n if hostname in INVALID_HOSTNAMES:\n return False\n\n for domain in INVALID_DOMAINS:\n if hostname.endswith(domain):\n return False\n\n return all(VALID_HOSTNAME_REGEX.match(hn_part) for hn_part in hostname.split('.'))\n\n\ndef get_env_list(env_var):\n if len(os.environ[env_var]) > 0:\n return os.environ[env_var].split(\",\")\n return []\n\n\ndef write_adblock_hosts_file(blocklist):\n try:\n with open(ADBLOCK_HOSTS_FILE, \"w\") as hosts_file:\n for host in blocklist:\n if os.environ['PIXELSERV_IP4']:\n hosts_file.write(\"{} {}\\n\".format(\n os.environ['PIXELSERV_IP4'], host))\n if os.environ['PIXELSERV_IP6']:\n hosts_file.write(\"{} {}\\n\".format(\n os.environ['PIXELSERV_IP6'], host))\n except Exception as e:\n logging.fatal(\"Couldn't write adblock hosts file to %s: %s\",\n ADBLOCK_HOSTS_FILE, e)\n return False\n return True\n\n\nif __name__ == \"__main__\":\n host_to_block_threads = []\n\n blocklist_queue = Queue()\n hosts_to_block = set()\n hosts_to_block_lock = Lock()\n\n for _ in range(os.cpu_count()):\n thread = Thread(target=thread_get_hosts_to_block, args=(\n blocklist_queue, hosts_to_block, hosts_to_block_lock))\n\n thread.start()\n host_to_block_threads.append(thread)\n\n blocklists_simple = get_env_list('BLOCKLISTS_SIMPLE')\n blocklists_abp = get_env_list('BLOCKLISTS_ABP')\n blocklists_hosts = get_env_list('BLOCKLISTS_HOSTS')\n\n domain_blacklist = get_env_list('DOMAIN_BLACKLIST')\n domain_whitelist = get_env_list('DOMAIN_WHITELIST')\n\n for url in blocklists_abp:\n blocklist_queue.put((BLOCKLIST_TYPE.ABP, url))\n for url in blocklists_hosts:\n blocklist_queue.put((BLOCKLIST_TYPE.HOSTS_FILE, url))\n for url in blocklists_simple:\n blocklist_queue.put((BLOCKLIST_TYPE.SIMPLE, url))\n for thread in host_to_block_threads:\n blocklist_queue.put(None)\n\n for thread in host_to_block_threads:\n thread.join()\n\n for domain in domain_blacklist:\n if is_valid_hostname(domain):\n hosts_to_block.add(domain)\n else:\n logging.warn(\n \"%s is not a valid domain name. Won't add it to block list!\", domain)\n\n for domain in domain_whitelist:\n if domain in hosts_to_block:\n hosts_to_block.remove(domain)\n\n if len(hosts_to_block) == 0:\n logging.fatal(\"Blocklist is empty\")\n sys.exit(1)\n\n if not write_adblock_hosts_file(sorted(hosts_to_block)):\n sys.exit(1)\n\n sys.exit(0)\n", "repo_name": "fmirkes/adblock-dns", "sub_path": "dnsmasq/create-adblock-hosts-file.py", "file_name": "create-adblock-hosts-file.py", "file_ext": "py", "file_size_in_byte": 5985, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "re.compile", "line_number": 20, "usage_type": "call"}, {"api_name": "re.IGNORECASE", "line_number": 21, "usage_type": "attribute"}, {"api_name": "enum.Enum", "line_number": 26, "usage_type": "name"}, {"api_name": "enum.auto", "line_number": 27, "usage_type": "call"}, {"api_name": "enum.auto", "line_number": 28, "usage_type": "call"}, {"api_name": "enum.auto", "line_number": 29, "usage_type": "call"}, {"api_name": "logging.WARN", "line_number": 32, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 33, "usage_type": "attribute"}, {"api_name": "re.match", "line_number": 34, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 34, "usage_type": "attribute"}, {"api_name": "re.IGNORECASE", "line_number": 34, "usage_type": "attribute"}, {"api_name": "logging.DEBUG", "line_number": 35, "usage_type": "attribute"}, {"api_name": "logging.basicConfig", "line_number": 36, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 59, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 103, "usage_type": "call"}, {"api_name": "urllib.request.urlopen", "line_number": 107, "usage_type": "call"}, {"api_name": "urllib.request.Request", "line_number": 107, "usage_type": "call"}, {"api_name": "sys.getdefaultencoding", "line_number": 111, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 115, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 137, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 138, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 146, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 148, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 149, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 151, "usage_type": "attribute"}, {"api_name": "logging.fatal", "line_number": 153, "usage_type": "call"}, {"api_name": "queue.Queue", "line_number": 162, "usage_type": "call"}, {"api_name": "threading.Lock", "line_number": 164, "usage_type": "call"}, {"api_name": "os.cpu_count", "line_number": 166, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 167, "usage_type": "call"}, {"api_name": "logging.warn", "line_number": 196, "usage_type": "call"}, {"api_name": "logging.fatal", "line_number": 204, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 205, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 208, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 210, "usage_type": "call"}]} +{"seq_id": "31219713113", "text": "#!/usr/bin/env python\n\n'''\n\tFile name: main_fig4.py\n\tAuthor: Guillaume Viejo\n\tDate created: 30/03/2017 \n\tPython Version: 2.7\n\n\n'''\n\nimport warnings\nimport pandas as pd\nimport scipy.io\nimport numpy as np\n# Should not import fonctions if already using tensorflow for something else\nimport sys, os\nimport itertools\nimport cPickle as pickle\nfrom sklearn.model_selection import KFold\nimport xgboost as xgb\n\n#######################################################################\n# FONCTIONS DEFINITIONS\n#######################################################################\n\n\n#####################################################################\n# DATA LOADING | ALL SESSIONS WAKE\n#####################################################################\n# thresholds dict from z620 in ../data/results_peer_fig3/\n# good pr2 with 100 trees in ../data/results_peer_fig3_good_100trees\n\n\n\n\nthr_directory = \"../data/results_peer_fig3\"\npr2_directory = \"../data/results_peer_fig3\"\ngain_directory = \"../data/results_peer_fig3\"\nloo_directory = \"../data/results_peer_fig3\"\nequal_directory = \"../data/results_peer_fig3\"\n# THRESHOLDS\ndata_thr = {}\nfor ep in ['wake', 'rem', 'sws']:\n\t# os.system(\"scp viejo@guillimin.hpc.mcgill.ca:~/results_peer_fig3/\"+ep+\"/peer_bsts* ../data/results_peer_fig3/\"+ep+\"/\")\n\tdata_thr[ep] = {}\n\tfor f in os.listdir(thr_directory+\"/\"+ep+\"/\"):\n\t\tif 'bsts' in f:\n\t\t\tdata_thr[ep][f] = pickle.load(open(thr_directory+\"/\"+ep+\"/\"+f, 'rb'))\n\n# PR2\ndata = {}\n# TO RECHANGE WHEN IT\"S DONE IN Z620\nfor ep in ['wake', 'rem', 'sws']:\n\tdata[ep] = {}\n\tfor f in os.listdir(pr2_directory+\"/\"+ep+\"/\"):\n\t\tif 'pr2' in f:\n\t\t\tdata[ep][f.split(\".\")[1]] = pickle.load(open(pr2_directory+\"/\"+ep+\"/\"+f, 'rb'))\n\ndata_pr2 = {}\nfor g in ['ADn', 'Pos']:\n\tdata_pr2[g] = {}\n\tfor w in ['peer', 'cros']:\n\t\tdata_pr2[g][w] = {}\n\t\tfor e in ['wake', 'rem', 'sws']:\n\t\t\tdata_pr2[g][w][e] = []\n\t\t\tfor s in data[e].iterkeys():\n\t\t\t\tdata_pr2[g][w][e].append(data[e][s][g][w]['PR2'])\n\t\t\tdata_pr2[g][w][e] = np.vstack(data_pr2[g][w][e])\n# pr2_sleep = pickle.load(open(\"../data/fig3_pr2_sleep.pickle\", 'rb'))\n\n# CORRELATION\n# os.system(\"scp -r viejo@guillimin.hpc.mcgill.ca:~/results_peer_fig3/wake/peer_corr* ../data/results_peer_fig3/wake/\")\ncorr = {}\nfor g in ['ADn', 'Pos']:\n\tcorr[g] = {}\n\tfor f in os.listdir(pr2_directory+\"/wake/\"):\n\t\tif 'corr' in f:\n\t\t\ttmp = pickle.load(open(pr2_directory+\"/wake/\"+f, 'rb'))\n\t\t\tfor k in tmp[g]['peer']['corr'].keys():\n\t\t\t\tcorr[g][k] = tmp[g]['peer']['corr'][k]\n\n# GAIN\n# os.system(\"scp viejo@guillimin.hpc.mcgill.ca:~/results_peer_fig3/wake/peer_gain* ../data/results_peer_fig3/wake/\")\ndatagain = {}\nfor f in os.listdir(gain_directory+\"/wake/\"):\n\tif 'gain' in f:\n\t\tdatagain[f] = pickle.load(open(gain_directory+\"/wake/\"+f, 'rb'))\t\t\t\n\n# LOO\n# os.system(\"scp viejo@guillimin.hpc.mcgill.ca:~/results_peer_fig3/wake/peer_loo* ../data/results_peer_fig3/wake/\")\ndataloo = {}\nfor f in os.listdir(loo_directory+\"/wake/\"):\n\tif 'loo' in f:\n\t\tdataloo[f] = pickle.load(open(gain_directory+\"/wake/\"+f, 'rb'))\t\t\t\n\n# EQUAL\n# os.system(\"scp viejo@guillimin.hpc.mcgill.ca:~/results_peer_fig3/wake/peer_equal* ../data/results_peer_fig3/wake/\")\ndataequal = {}\nfor f in os.listdir(equal_directory+\"/wake/\"):\n\tif 'equal' in f:\n\t\tdataequal[f] = pickle.load(open(equal_directory+\"/wake/\"+f, 'rb'))\n\n# #####################################################################\n# # TUNING CURVE\n# #####################################################################\ntuningc = {}\nfor f in os.listdir(\"../data/results_density/wake/\"):\n\ttmp = pickle.load(open(\"../data/results_density/wake/\"+f))\n\ttmp = tmp['tuni']\n\tfor k in tmp.iterkeys():\n\t\ttuningc[k] = tmp[k]\t\n\n#####################################################################\n# EXTRACT TREE STRUCTURE\n#####################################################################\nnames = pickle.load(open(\"../data/results_peer_fig3/fig3_names.pickle\", 'rb'))\n\nthresholds = {}\ngain = {}\nfor g in ['ADn', 'Pos']:\n\tthresholds[g] = {}\n\tgain[g] = {}\n\tfor w in ['peer', 'cros']:\n\t\tthresholds[g][w] = {}\n\t\tgain[g][w] = {}\n\t\tfor s in data_thr['wake'].iterkeys(): # sessions\t\t\n\t\t\tfor k in data_thr['wake'][s][g][w].iterkeys():\t\t\n\t\t\t\tthresholds[g][w][k] = data_thr['wake'][s][g][w][k]\n\t\tfor s in datagain.iterkeys():\n\t\t\tfor k in datagain[s][g][w].iterkeys():\t\t\n\t\t\t\tgain[g][w][k] = datagain[s][g][w][k]\n\n\n\n# need to sort the features by the number of splits\nsorted_features = {}\nsorted_gain = {}\nfor g in thresholds.iterkeys():\n\tsorted_features[g] = {}\n\tsorted_gain[g] = {}\n\tfor w in thresholds[g].iterkeys():\n\t\tsorted_features[g][w] = {}\n\t\tsorted_gain[g][w] = {}\n\t\tfor k in thresholds[g][w].iterkeys(): # PREDICTED NEURONS\t\t\t\n\t\t\tcount = np.array([len(thresholds[g][w][k][f]) for f in thresholds[g][w][k].iterkeys()])\n\t\t\tname = np.array([names[g][w][k][int(f[1:])] for f in thresholds[g][w][k].iterkeys()])\n\t\t\tsorted_features[g][w][k] = np.array([name[np.argsort(count)], np.sort(count)])\n\t\t\tgain_ = np.array([gain[g][w][k][f] for f in gain[g][w][k].iterkeys()])\n\t\t\tname = np.array([names[g][w][k][int(f[1:])] for f in gain[g][w][k].iterkeys()])\n\t\t\tsorted_gain[g][w][k] = np.array([name[np.argsort(gain_)], np.sort(gain_)])\n\n\n\n#####################################################################\n# number of splits versus mean firing rate\n#####################################################################\nsplitvar = {}\nplotsplitvar = {}\nfor g in thresholds.iterkeys():\n\tsplitvar[g] = {}\n\tplotsplitvar[g] = {}\n\tfor w in thresholds[g].iterkeys():\n\t\tsplitvar[g][w] = {}\n\t\tplotsplitvar[g][w] = {'nsplit':[], 'meanf':[], 'meanfdiff':[]}\n\t\tfor k in thresholds[g][w].iterkeys():\n\t\t\tmean_firing_rate = []\n\t\t\tmean_firing_rate_diff = []\n\t\t\tfor n in sorted_features[g][w][k][0]:\n\t\t\t\tmean_firing_rate.append(np.mean(tuningc[n][1]))\n\t\t\t\tmean_firing_rate_diff.append(np.mean(tuningc[k][1])-np.mean(tuningc[n][1]))\n\t\t\tmean_firing_rate = np.array(mean_firing_rate)\n\t\t\tmean_firing_rate_diff = np.array(mean_firing_rate_diff)\t\t\t\n\t\t\tsplitvar[g][w][k] = np.array([mean_firing_rate, sorted_features[g][w][k][1]])\n\t\t\tplotsplitvar[g][w]['meanf'].append(mean_firing_rate)\n\t\t\tplotsplitvar[g][w]['meanfdiff'].append(mean_firing_rate_diff)\n\t\t\tplotsplitvar[g][w]['nsplit'].append(sorted_features[g][w][k][1].astype('float'))\n\t\tplotsplitvar[g][w]['meanf'] = np.hstack(np.array(plotsplitvar[g][w]['meanf']))\n\t\tplotsplitvar[g][w]['nsplit'] = np.hstack(np.array(plotsplitvar[g][w]['nsplit']))\n\t\tplotsplitvar[g][w]['meanfdiff'] = np.hstack(np.array(plotsplitvar[g][w]['meanfdiff']))\n\t\t\n\n#####################################################################\n# DISTANCE TO CENTER OF FIELD\n#####################################################################\ndistance = {}\nplotdistance = {}\nfor g in thresholds.iterkeys():\n\tdistance[g] = {}\n\tplotdistance[g] = {}\n\tfor w in thresholds[g].iterkeys():\n\t\tdistance[g][w] = {}\n\t\tplotdistance[g][w] = {'nsplit':[], 'distance':[]}\n\t\tfor k in thresholds[g][w].iterkeys():\n\t\t\tcom_neuron = tuningc[k][0][np.argmax(tuningc[k][1])]\t\t\t\t\n\t\t\tcom = np.array([tuningc[n][0][np.argmax(tuningc[n][1])] for n in sorted_features[g][w][k][0]])\t\t\t\n\t\t\tdist = np.abs(com - com_neuron)\n\t\t\ttmp = 2*np.pi - dist[dist>np.pi]\n\t\t\tdist[dist>np.pi] = tmp\n\t\t\tplotdistance[g][w]['distance'].append(dist)\n\t\t\tplotdistance[g][w]['nsplit'].append(sorted_features[g][w][k][1].astype('float'))\n\t\tplotdistance[g][w]['distance'] = np.hstack(np.array(plotdistance[g][w]['distance']))\n\t\tplotdistance[g][w]['nsplit'] = np.hstack(np.array(plotdistance[g][w]['nsplit']))\n\n#####################################################################\n# PEER CORRELATION \n#####################################################################\npeercorr = {}\nfor g in corr.iterkeys():\n\tpeercorr[g] = []\n\tfor k in corr[g].iterkeys():\n\t\tfor n,i in zip(corr[g][k][0], xrange(len(corr[g][k][0]))):\n\t\t\tcomn = tuningc[n][0][np.argmax(tuningc[n][1])]\n\t\t\tcomk = tuningc[k][0][np.argmax(tuningc[k][1])]\n\t\t\tdist = np.abs(comn - comk) \n\t\t\tif dist > np.pi: dist = 2*np.pi - dist\n\t\t\tif comn < comk: dist *= -1.0\n\t\t\tpeercorr[g].append(np.array([dist, float(corr[g][k][1][i])]))\n\tpeercorr[g] = np.array(peercorr[g])\n\n#####################################################################\n# LEAVE ONE OUT\n#####################################################################\nloo = {}\nfor g in ['ADn', 'Pos']:\n\tloo[g] = {}\n\tfor s in dataloo.iterkeys():\n\t\tfor k in dataloo[s][g]['peer'].iterkeys():\n\t\t\tfor n in dataloo[s][g]['peer'][k].iterkeys():\n\t\t\t\tif n in loo[g].keys():\n\t\t\t\t\tloo[g][n].append(dataloo[s][g]['peer'][k][n].flatten())\n\t\t\t\telse:\n\t\t\t\t\tloo[g][n] = [dataloo[s][g]['peer'][k][n].flatten()]\n\nmeanloo = {}\nfor g in loo.keys():\n\tmeanloo[g] = []\n\tfor n in loo[g].iterkeys():\n\t\tloo[g][n] = np.hstack(loo[g][n])\n\t\tmeanloo[g].append([np.mean(loo[g][n]), scipy.stats.sem(loo[g][n])])\n\tmeanloo[g] = np.array(meanloo[g])\n\n#####################################################################\n# EQUAL\n#####################################################################\nequal = {}\nfor g in ['ADn', 'Pos']:\n\tequal[g] = {}\t\n\tfor w in ['peer', 'cros']:\n\t\tequal[g][w] = {}\n\t\tfor s in dataequal.iterkeys():\t\t\n\t\t\tse = s.split(\".\")[1]\n\t\t\tequal[g][w][se] = []\n\t\t\tif g in dataequal[s].keys():\t\t\t\n\t\t\t\tif w in dataequal[s][g].keys():\t\t\t\t\t\t\t\t\t\n\t\t\t\t\tfor k in dataequal[s][g][w].iterkeys():\n\t\t\t\t\t\tequal[g][w][se].append(dataequal[s][g][w][k])\n\t\t\tequal[g][w][se] = np.array(equal[g][w][se])\n\nmeanequal = {}\nfor w in ['peer', 'cros']:\n\tmeanequal[w] = {}\n\tfor s in dataequal.keys():\n\t\tse = s.split(\".\")[1]\n\t\tmeanequal[w][se] = []\n\t\tfor g in ['ADn', 'Pos']:\n\t\t\tmeanequal[w][se].append(np.mean(equal[g][w][se]))\n\n#####################################################################\n# TIME SPLIT LOADING\n#####################################################################\ntime_data = pickle.load(open(\"../data/fig3_timesplit.pickle\", 'rb'))\n\n#####################################################################\n# PLOTTING\n#####################################################################\ndef figsize(scale):\n\tfig_width_pt = 483.69687 # Get this from LaTeX using \\the\\textwidth\n\tinches_per_pt = 1.0/72.27 # Convert pt to inch\n\tgolden_mean = (np.sqrt(5.0)-1.0)/2.0 # Aesthetic ratio (you could change this)\n\tfig_width = fig_width_pt*inches_per_pt*scale # width in inches\n\tfig_height = fig_width*golden_mean *0.9 # height in inches\n\t# fig_height = 4.696\n\tfig_size = [fig_width,fig_height]\n\treturn fig_size\n\ndef simpleaxis(ax):\n\tax.spines['top'].set_visible(False)\n\tax.spines['right'].set_visible(False)\n\tax.get_xaxis().tick_bottom()\n\tax.get_yaxis().tick_left()\n\t# ax.xaxis.set_tick_params(size=6)\n\t# ax.yaxis.set_tick_params(size=6)\n\ndef myticks(x,pos):\n\tif x == 0: return \"$0$\"\n\texponent = int(np.log10(x))\n\tcoeff = x/10**exponent\n\treturn r\"${:2.0f} \\times 10^{{ {:2d} }}$\".format(coeff,exponent)\n\nimport matplotlib as mpl\n\nmpl.use(\"pdf\")\n\n\n\npdf_with_latex = { # setup matplotlib to use latex for output\n\t\"pgf.texsystem\": \"pdflatex\", # change this if using xetex or lautex\n\t\"text.usetex\": True, # use LaTeX to write all text\n\t\"font.family\": \"serif\",\n\t\"font.serif\": [], # blank entries should cause plots to inherit fonts from the document\n\t\"font.sans-serif\": [],\n\t\"font.monospace\": [],\n\t\"axes.labelsize\": 7, # LaTeX default is 10pt font.\n\t\"font.size\": 7,\n\t\"legend.fontsize\": 7, # Make the legend/label fonts a little smaller\n\t\"xtick.labelsize\": 6,\n\t\"ytick.labelsize\": 6,\n\t\"figure.figsize\": figsize(1), # default fig size of 0.9 textwidth\n\t\"pgf.preamble\": [\n\t\tr\"\\usepackage[utf8x]{inputenc}\", # use utf8 fonts becasue your computer can handle it :)\n\t\tr\"\\usepackage[T1]{fontenc}\", # plots will be generated using this preamble\n\t\t],\n\t\"lines.markeredgewidth\" : 0.2,\n\t\"axes.linewidth\" : 0.5,\n\t\"ytick.major.size\" : 1.5,\n\t\"xtick.major.size\" : 1.5\n\t} \nmpl.rcParams.update(pdf_with_latex)\nimport matplotlib.gridspec as gridspec\nfrom matplotlib.pyplot import *\n\nmethods = ['xgb_run']\n\nlabels = {'mb_10':'MB \\n 10 bins', \n\t\t\t'mb_60':'MB \\n 60 bins', \n\t\t\t'mb_360':'MB \\n 360 bins', \n\t\t\t'lin_comb':'Lin', \n\t\t\t'nn':'NN', \n\t\t\t'xgb_run':'XGB'}\n\ncolors_ = {'ADn':'#EE6C4D', 'Pos':'#3D5A80'}\n\n\nlabels_plot = [labels[m] for m in methods[0:-1]]\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nfigure(figsize = figsize(1))\nouterspace = gridspec.GridSpec(1,2, width_ratios =[1.2,0.9], wspace = 0.3)\n\n#################################################################\n# LEFT\n#################################################################\n# outer = gridspec.GridSpec(outerspace[0], height_ratios=[0.5,1.2])\n\n# SUBPLOT 1 ################################################################\n# outer = gridspec.GridSpecFromSubplotSpec(2,1,subplot_spec = outerspace[0], height_ratios=[1, 0.8], hspace = 0.3)\n\ngs = gridspec.GridSpecFromSubplotSpec(2,2,subplot_spec = outerspace[0], height_ratios=[1.1, 0.5], hspace = 0.4, wspace = 0.5)\n\nsubplot(gs[0,:])\nsimpleaxis(gca())\n\n\ny = []\nerr = []\nx = [0.0]\ncolor = []\nfor w in ['peer', 'cros']:\t\t\n\tfor g in ['ADn', 'Pos']: \t\n\t\tfor e in ['wake', 'rem', 'sws']:\n\t\t\tPR2_art = data_pr2[g][w][e]\n\t\t\tcolor.append(colors_[g])\n\t\t\ty.append(np.mean(PR2_art))\n\t\t\terr.append(np.std(PR2_art)/np.sqrt(np.size(PR2_art)))\n\t\t\tx.append(x[-1]+0.42)\n\t\t\n\t\t\n\t\tx[-1] += 0.3\t\n\tx[-1] += 0.4\n\t\t\nx = np.array(x)[0:-1]\ny = np.array(y)\nerr = np.array(err)\t\t\n\nind_adn = [0,1,2,6,7,8]\nind_pos = [3,4,5,9,10,11]\nx_adn = x[ind_adn]\ny_adn = y[ind_adn]\ne_adn = err[ind_adn]\nx_pos = x[ind_pos]\ny_pos = y[ind_pos]\ne_pos = err[ind_pos]\n\nind = [0,3]\nbar(x_adn[ind], y_adn[ind], 0.4, align='center',\n\t\t\tecolor='k', color = colors_['ADn'], alpha=1, ec='w', yerr=e_adn[ind], label = 'Antero Dorsal')\nbar(x_pos[ind], y_pos[ind], 0.4, align='center',\n\t\t\tecolor='k', color = colors_['Pos'], alpha=1, ec='w', yerr=e_pos[ind], label = 'Post Subiculum')\nind = [1,4]\nbar(x_adn[ind], y_adn[ind], 0.4, align='center', facecolor = 'white', edgecolor='black', alpha=1, hatch=\"//////\", linewidth = 0, label = 'REM sleep')\nbar(x_adn[ind], y_adn[ind], 0.4, align='center', facecolor = colors_['ADn'], edgecolor='black', alpha=1, hatch=\"//////\", linewidth = 0)\nbar(x_pos[ind], y_pos[ind], 0.4, align='center', facecolor = colors_['Pos'], edgecolor='black', alpha=1, hatch=\"//////\", linewidth = 0)\nbar(x_adn[ind], y_adn[ind], 0.4, align='center', facecolor = 'none', alpha=1, edgecolor='w', yerr=e_adn[ind], ecolor = 'black')\nbar(x_pos[ind], y_pos[ind], 0.4, align='center', facecolor = 'none', alpha=1, edgecolor='w', yerr=e_pos[ind], ecolor = 'black')\n\nind = [2,5]\nbar(x_adn[ind], y_adn[ind], 0.4, align='center', facecolor = 'white', edgecolor='black', alpha=1, hatch=\"xxxx\", linewidth = 0, label = 'Slow wave sleep')\nbar(x_adn[ind], y_adn[ind], 0.4, align='center', facecolor = colors_['ADn'], edgecolor='black', alpha=1, hatch=\"xxxx\", linewidth = 0)\nbar(x_pos[ind], y_pos[ind], 0.4, align='center', facecolor = colors_['Pos'], edgecolor='black', alpha=1, hatch=\"xxxx\", linewidth = 0)\nbar(x_adn[ind], y_adn[ind], 0.4, align='center', facecolor = 'none', alpha=1, edgecolor='w', yerr=e_adn[ind], ecolor = 'black')\nbar(x_pos[ind], y_pos[ind], 0.4, align='center', facecolor = 'none', alpha=1, edgecolor='w', yerr=e_pos[ind], ecolor = 'black')\n\n\nplot(x, y, 'k.', markersize=3) \nlocator_params(nbins=4)\t\t\t\t\nxlim(np.min(x)-0.3, np.max(x)+0.3)\nylabel('Pseudo-R2 (XGBoost)')\nxticks(x[[1,4,7,10]], \n\t[\"ADn$\\Rightarrow$ADn\", \"PoSub$\\Rightarrow$PoSub\", \"PoSub$\\Rightarrow$ADn\", \"ADn$\\Rightarrow$PoSub\"], \n\t# rotation = 30, \n\t# ha = 'right'\n\tfontsize = 5\n\t)\n\nlegend(bbox_to_anchor=(0.55, 1.15), loc='upper center', ncol=2, frameon = False, columnspacing = 0.6)\n\ntitle2 = ['WITHIN', 'BETWEEN']\ncount = 0\nlabels2 = {'peer':['ADn$\\Rightarrow$ADn', 'PoSub$\\Rightarrow$PoSub'],'cros':['ADn$\\Rightarrow$PoSub', 'PoSub$\\Rightarrow$ADn']}\nfor w in ['peer', 'cros']:\n\tsubplot(gs[1,count])\n\tsimpleaxis(gca())\t\n\tfor s in meanequal[w].iterkeys():\t\n\t\tplot([0], meanequal[w][s][0], 'o', color=colors_['ADn'], markersize = 4)\n\t\tplot([1], meanequal[w][s][1], 'o', color=colors_['Pos'], markersize = 4)\n\t\tplot([0,1], meanequal[w][s], '-', color = 'grey')\n\n\txticks(fontsize = 4)\n\tyticks(fontsize = 4)\t\t\n\t# xlabel(\"Number of neurons\", fontsize = 5, labelpad = 0.5)\n\tylabel(\"p-$R^2$\", fontsize = 6)\n\txticks([0, 1], labels2[w], fontsize = 5)\n\txlim(-0.4, 1.4)\n\ttitle(title2[count], fontsize = 6, y = 1.1)\n\tcount += 1\n\tylim(0, 0.8)\n\tlocator_params(axis='y', nbins = 5)\n\n# figtext(0.2, -0.2, \"ADn $\\Rightarrow$ ADn \\n Post-S $\\Rightarrow$ Post-S \\n \\scriptsize{(Features $\\Rightarrow$ Target)}\")\n# figtext(0.6, -0.14, \"ADn $\\Rightarrow$ Post-S \\n Post-S $\\Rightarrow$ ADn\")\n\n\n#################################################################\n# RIGHT\n#################################################################\nmatplotlib.rcParams.update({\"axes.labelsize\": \t7,\n\t\t\t\t\t\t\t\"font.size\": \t\t8,\n\t\t\t\t\t\t\t\"legend.fontsize\": \t8,\n\t\t\t\t\t\t\t\"xtick.labelsize\": \t5,\n\t\t\t\t\t\t\t\"ytick.labelsize\": \t5, \n\t\t\t\t\t\t\t}) # Make the legend/label fonts a little smaller\nouter = gridspec.GridSpecFromSubplotSpec(2,2,subplot_spec = outerspace[1], hspace = 0.3, wspace = 0.5)\n\ncount = 0\n\ntitle_ = [\"ADn $\\Rightarrow$ ADn \\n(wake)\", \"PoSub $\\Rightarrow$ PoSub \\n(wake)\"]\t\t\t\t\t\t\t\n\n\n\t\n\nfor g in plotsplitvar.keys():\n\tfor w in ['peer']:\n\t\tsubplot(outer[count])\n\t\tsimpleaxis(gca())\n\t\tplot(plotdistance[g][w]['distance'], plotdistance[g][w]['nsplit'], 'o', color = colors_[g], markersize = 1)\n\t\t# slope, intercept, r_value, p_value, std_err = scipy.stats.linregress(plotdistance[g][w]['distance'], plotdistance[g][w]['nsplit'])\n\t\t# print r_value, p_value\n\t\t# x = np.array([np.min(plotdistance[g][w]['distance']), np.max(plotdistance[g][w]['distance'])])\n\t\t# plot(x, x*slope + intercept, '-', color = 'black', linewidth = 0.7)\n\t\tx, y = (plotdistance[g][w]['distance'], plotsplitvar[g][w]['nsplit'])\n\t\tnb_bins=5\n\t\tbins = np.linspace(x.min(), x.max()+1e-8, nb_bins+1)\n\t\tindex = np.digitize(x, bins).flatten()\n\t\tcurve = np.array([np.mean(y[index == i]) for i in xrange(1, nb_bins+1)])\n\t\txx = bins[0:-1] + (bins[1]-bins[0])/2.\n\t\tplot(xx, curve, 'o-', color = 'black', linewidth = 0.8, markersize = 2.0) \n\t\t# ax2.set_yticks([], [])\t\t\t\n\t\tlocator_params(nbins=2)\t\t\t\t\n\t\t# ticklabel_format(style='sci', axis='x', scilimits=(0,0), fontsize = 4)\n\t\t# ticklabel_format(style='sci', axis='y', scilimits=(0,0), fontsize = 4)\n\t\txticks([0, np.pi], ['0', '$\\pi$'], fontsize = 4)\n\t\tyticks(fontsize = 4)\t\t\n\t\txlabel(\"Angular distance\", fontsize = 6, labelpad = 0.4)\t\t\t\t\n\t\tylabel(\"Number of splits\", fontsize = 7, labelpad = 0.6)\n\t\txlim(0, np.pi)\n\t\tylim(0,)\n\t\ttitle(title_[count-2], fontsize = 7)#, loc = 'left', y = 1.3)\t\t\n\n\n\t\tsubplot(outer[count+2])\n\t\tsimpleaxis(gca())\t\t\n\t\tplot(plotsplitvar[g][w]['meanf'], plotsplitvar[g][w]['nsplit'], 'o', color = colors_[g], markersize = 1)\n\t\t# slope, intercept, r_value, p_value, std_err = scipy.stats.linregress(plotsplitvar[g][w]['meanf'], plotsplitvar[g][w]['nsplit'])\n\t\t# x = np.array([np.min(plotsplitvar[g][w]['meanf']), np.max(plotsplitvar[g][w]['meanf'])])\n\t\t# plot(x, x*slope + intercept, '-', color = 'black', linewidth = 0.7)\n\t\t# print r_value, p_value\n\t\txticks(fontsize = 4)\n\t\tyticks(fontsize = 4)\t\t\n\t\txlabel(\"Firing rate (Hz)\", fontsize = 6, labelpad = 0.8)\n\t\tylabel(\"Number of splits\", fontsize = 7, labelpad = 0.6)\t\t\n\t\tx, y = (plotsplitvar[g][w]['meanf'], plotsplitvar[g][w]['nsplit'])\n\t\tnb_bins=5\n\t\tbins = np.linspace(x.min(), x.max()+1e-8, nb_bins+1)\n\t\tindex = np.digitize(x, bins).flatten()\n\t\tcurve = np.array([np.mean(y[index == i]) for i in xrange(1, nb_bins+1)])\n\t\t\n\t\txx = bins[0:-1] + (bins[1]-bins[0])/2.\n\n\t\tplot(xx, curve, 'o-', color = 'black', linewidth = 0.7, markersize = 2.0) \n\t\t\n\t\tlocator_params(axis='y', nbins = 5)\n\t\t\n\t\t\n\n\n\n\t\tcount += 1\n\n\n\n\nsavefig(\"../../figures/fig3.pdf\", dpi=900, bbox_inches = 'tight', facecolor = 'white')\nos.system(\"evince ../../figures/fig3.pdf &\")\n", "repo_name": "gviejo/Prediction_xgb_head_direction", "sub_path": "python/run/main_fig3.py", "file_name": "main_fig3.py", "file_ext": "py", "file_size_in_byte": 19847, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "os.listdir", "line_number": 47, "usage_type": "call"}, {"api_name": "cPickle.load", "line_number": 49, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 56, "usage_type": "call"}, {"api_name": "cPickle.load", "line_number": 58, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 69, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 77, "usage_type": "call"}, {"api_name": "cPickle.load", "line_number": 79, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 86, "usage_type": "call"}, {"api_name": "cPickle.load", "line_number": 88, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 93, "usage_type": "call"}, {"api_name": "cPickle.load", "line_number": 95, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 100, "usage_type": "call"}, {"api_name": "cPickle.load", "line_number": 102, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 108, "usage_type": "call"}, {"api_name": "cPickle.load", "line_number": 109, "usage_type": "call"}, {"api_name": "cPickle.load", "line_number": 117, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 146, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 147, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 148, "usage_type": "call"}, {"api_name": "numpy.argsort", "line_number": 148, "usage_type": "call"}, {"api_name": "numpy.sort", "line_number": 148, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 149, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 150, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 151, "usage_type": "call"}, {"api_name": "numpy.argsort", "line_number": 151, "usage_type": "call"}, {"api_name": "numpy.sort", "line_number": 151, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 170, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 171, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 172, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 173, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 174, "usage_type": "call"}, {"api_name": "numpy.hstack", "line_number": 178, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 178, "usage_type": "call"}, {"api_name": "numpy.hstack", "line_number": 179, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 179, "usage_type": "call"}, {"api_name": "numpy.hstack", "line_number": 180, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 180, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 195, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 196, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 196, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 197, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 198, "usage_type": "attribute"}, {"api_name": "numpy.pi", "line_number": 199, "usage_type": "attribute"}, {"api_name": "numpy.hstack", "line_number": 202, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 202, "usage_type": "call"}, {"api_name": "numpy.hstack", "line_number": 203, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 203, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 213, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 214, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 215, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 216, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 218, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 219, "usage_type": "call"}, {"api_name": "numpy.hstack", "line_number": 239, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 240, "usage_type": "call"}, {"api_name": "scipy.io.stats.sem", "line_number": 240, "usage_type": "call"}, {"api_name": "scipy.io.stats", "line_number": 240, "usage_type": "attribute"}, {"api_name": "scipy.io", "line_number": 240, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 241, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 258, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 267, "usage_type": "call"}, {"api_name": "cPickle.load", "line_number": 272, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 280, "usage_type": "call"}, {"api_name": "numpy.log10", "line_number": 297, "usage_type": "call"}, {"api_name": "matplotlib.use", "line_number": 303, "usage_type": "call"}, {"api_name": "matplotlib.rcParams.update", "line_number": 329, "usage_type": "call"}, {"api_name": "matplotlib.rcParams", "line_number": 329, "usage_type": "attribute"}, {"api_name": "matplotlib.gridspec.GridSpec", "line_number": 366, "usage_type": "call"}, {"api_name": "matplotlib.gridspec", "line_number": 366, "usage_type": "name"}, {"api_name": "matplotlib.gridspec.GridSpecFromSubplotSpec", "line_number": 376, "usage_type": "call"}, {"api_name": "matplotlib.gridspec", "line_number": 376, "usage_type": "name"}, {"api_name": "numpy.mean", "line_number": 391, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 392, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 392, "usage_type": "call"}, {"api_name": "numpy.size", "line_number": 392, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 399, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 400, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 401, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 434, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 434, "usage_type": "call"}, {"api_name": "matplotlib.rcParams.update", "line_number": 474, "usage_type": "call"}, {"api_name": "matplotlib.rcParams", "line_number": 474, "usage_type": "attribute"}, {"api_name": "matplotlib.gridspec.GridSpecFromSubplotSpec", "line_number": 480, "usage_type": "call"}, {"api_name": "matplotlib.gridspec", "line_number": 480, "usage_type": "name"}, {"api_name": "numpy.linspace", "line_number": 500, "usage_type": "call"}, {"api_name": "numpy.digitize", "line_number": 501, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 502, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 502, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 509, "usage_type": "attribute"}, {"api_name": "numpy.pi", "line_number": 513, "usage_type": "attribute"}, {"api_name": "numpy.linspace", "line_number": 531, "usage_type": "call"}, {"api_name": "numpy.digitize", "line_number": 532, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 533, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 533, "usage_type": "call"}, {"api_name": "os.system", "line_number": 551, "usage_type": "call"}]} +{"seq_id": "1100638529", "text": "import streamlit as st\nimport pandas as pd\nimport preprocessor,helper\nimport plotly.express as px\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nimport plotly.figure_factory as ff\n\ndf=pd.read_csv(r'https://drive.google.com/file/d/1k8zhTyDeY_O9R9GF7uPhsETTs6wRcFll/view?usp=drive_link')\nregion_df=pd.read_csv(r'https://drive.google.com/file/d/13epMolX7mrWfTqpwWNKAmUYrjYShaExN/view?usp=drive_link')\n\ndf = preprocessor.preprocess(df,region_df)\n\nst.sidebar.title(\"Summer Olympics Analysis\")\nst.sidebar.image(r'https://png.pngtree.com/png-vector/20220611/ourmid/pngtree-olympic-rings-colorful-rings-on-a-white-background-png-image_4825904.png')\nuser_menu = st.sidebar.radio(\n 'Select an Option',\n ('Medal Tally','Overall Analysis','Country-wise analysis','Athlete wise analysis')\n)\n\nif user_menu == 'Medal Tally':\n st.sidebar.header(\"Medal Tally\")\n years,country = helper.country_year_list(df)\n\n selected_year = st.sidebar.selectbox(\"Select Year\",years)\n selected_country = st.sidebar.selectbox(\"Select Country\", country)\n\n medal_tally = helper.fetch_medal_tally(df,selected_year,selected_country)\n\n if selected_year == 'Overall' and selected_country == 'All the Countries':\n st.title('Overall Tally')\n if selected_year != 'Overall' and selected_country == 'All the Countries':\n st.title('Medal Tally in ' + str(selected_year))\n if selected_year == 'Overall' and selected_country != 'All the Countries':\n st.title(selected_country+'\\'s Overall Performance')\n if selected_year != 'Overall' and selected_country != 'All the Countries':\n st.title(selected_country+'\\'s Performance in the Year '+ str(selected_year))\n st.table(medal_tally)\n\nif user_menu == 'Overall Analysis':\n editions = df['Year'].unique().shape[0] - 1\n cities = df['City'].unique().shape[0]\n sports = df['Sport'].unique().shape[0]\n events = df['Event'].unique().shape[0]\n athletes = df['Name'].unique().shape[0]\n nations = df['region'].unique().shape[0]\n\n st.title(\"Top Statistics\")\n\n col1,col2,col3 = st.columns(3)\n with col1:\n st.header(\"Editions\")\n st.title(editions)\n with col2:\n st.header(\"Hosts\")\n st.title(cities)\n with col3:\n st.header(\"Sports\")\n st.title(sports)\n\n col1, col2, col3 = st.columns(3)\n with col1:\n st.header(\"Events\")\n st.title(events)\n with col2:\n st.header(\"Nations\")\n st.title(nations)\n with col3:\n st.header(\"Athletes\")\n st.title(athletes)\n\n nations_over_time = helper.participating_nations_over_time(df)\n fig = px.line(nations_over_time, x='Edition', y='No. of Countries')\n st.title(\"Participating Nations over the Years\")\n st.plotly_chart(fig)\n\n events_over_time = helper.number_of_events_over_time(df)\n fig = px.line(events_over_time, x='Edition', y='Event')\n st.title(\"Events over the Years\")\n st.plotly_chart(fig)\n\n athletes_over_time = helper.number_of_athletes_over_time(df)\n fig = px.line(athletes_over_time, x='Edition', y='No. of Athletes')\n st.title(\"No. of Athletes over the Years\")\n st.plotly_chart(fig)\n\n st.title('No. of Events over time (Every Sport)')\n fig,ax = plt.subplots(figsize=(50,50))\n x = df.drop_duplicates(['Year', 'Sport', 'Event'])\n ax = sns.heatmap(x.pivot_table(index='Sport', columns='Year', values='Event', aggfunc='count').fillna(0).astype('int'),annot=True)\n st.pyplot(fig)\n\n\nif user_menu == 'Country-wise analysis':\n\n st.sidebar.title('Country-wise Analysis')\n\n country_list = df['region'].dropna().unique().tolist()\n country_list.sort()\n selected_country = st.sidebar.selectbox('Select a Country',country_list)\n\n country_df = helper.yearwise_medal_tally(df,selected_country)\n fig = px.line(country_df,x='Year',y='Medal')\n st.title(selected_country +'\\'s Medal Tally over the years')\n st.plotly_chart(fig)\n\n st.title(selected_country + '\\'s Sportwise Analaysis over the Years')\n pt = helper.country_event_heatmap(df,selected_country)\n fig,ax = plt.subplots(figsize=(20,20))\n if pt.size == 0:\n st.header(selected_country + ' has won no medals')\n else:\n ax = sns.heatmap(pt, annot=True)\n st.pyplot(fig)\n\nif user_menu == 'Athlete wise analysis':\n athlete_df = df.drop_duplicates(subset=['Name', 'region'])\n\n x1 = athlete_df['Age'].dropna()\n x2 = athlete_df[athlete_df['Medal'] == 'Gold']['Age'].dropna()\n x3 = athlete_df[athlete_df['Medal'] == 'Silver']['Age'].dropna()\n x4 = athlete_df[athlete_df['Medal'] == 'Bronze']['Age'].dropna()\n\n fig = ff.create_distplot([x1, x2, x3, x4], ['Overall Age', 'Gold Medalist', 'Silver Medalist', 'Bronze Medalist'],show_hist=False, show_rug=False)\n\n fig.update_layout(autosize=False,width=950,height=540)\n st.title(\"Distribution of Age\")\n st.plotly_chart(fig)\n\n x = []\n name = []\n famous_sports = ['Basketball','Judo','Football','Tug-of-war','Athletics','Swimming','Badminton','Sailing','Gymnastics',\n 'Art Competitions','Handball','Weightlifting','Wrestling','Water Polo','Hockey','Rowing','Fencing',\n 'Shooting','Boxing','Taekwondo','Cycling','Diving','Canoeing','Tennis','Golf','Softball','Archery',\n 'Rhythmic Gymnastics','Rugby Sevens','Beach Volleyball','Triathlon','Rugby','Polo','Ice Hockey','Cricket']\n\n for sport in famous_sports:\n temp_df = athlete_df[athlete_df['Sport'] == sport]\n gold_ages = temp_df[temp_df['Medal'] == 'Gold']['Age'].dropna()\n if not gold_ages.empty:\n x.append(gold_ages)\n name.append(sport)\n fig = ff.create_distplot(x, name, show_hist=False, show_rug=False)\n fig.update_layout(autosize=False, width=950, height=540)\n st.title(\"Distribution of Age wrt Sport (only for Gold Medal winners)\")\n st.plotly_chart(fig)\n\n st.title('Height vs Weight')\n sport_list = df['Sport'].unique().tolist()\n sport_list.sort()\n sport_list.insert(0, 'Overall')\n selected_sport = st.selectbox('Select a Sport', sport_list)\n temp_df = helper.weight_v_height(df,selected_sport)\n fig,ax = plt.subplots()\n ax = sns.scatterplot(x='Weight', y='Height', data=temp_df,hue=temp_df['Medal'],style=temp_df['Sex'],s=75)\n st.pyplot(fig)\n\n st.title('Men VS Women Participation over the Years')\n final = helper.men_vs_women(df)\n fig = px.line(final, x='Year', y=['Male', 'Female'])\n fig.update_layout(autosize=False, width=950, height=540)\n st.plotly_chart(fig)\n\n\n", "repo_name": "nirajpatil02/olympicsdata-niraj", "sub_path": "app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 6509, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "pandas.read_csv", "line_number": 9, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 10, "usage_type": "call"}, {"api_name": "preprocessor.preprocess", "line_number": 12, "usage_type": "call"}, {"api_name": "streamlit.sidebar.title", "line_number": 14, "usage_type": "call"}, {"api_name": "streamlit.sidebar", "line_number": 14, "usage_type": "attribute"}, {"api_name": "streamlit.sidebar.image", "line_number": 15, "usage_type": "call"}, {"api_name": "streamlit.sidebar", "line_number": 15, "usage_type": "attribute"}, {"api_name": "streamlit.sidebar.radio", "line_number": 16, "usage_type": "call"}, {"api_name": "streamlit.sidebar", "line_number": 16, "usage_type": "attribute"}, {"api_name": "streamlit.sidebar.header", "line_number": 22, "usage_type": "call"}, {"api_name": "streamlit.sidebar", "line_number": 22, "usage_type": "attribute"}, {"api_name": "helper.country_year_list", "line_number": 23, "usage_type": "call"}, {"api_name": "streamlit.sidebar.selectbox", "line_number": 25, "usage_type": "call"}, {"api_name": "streamlit.sidebar", "line_number": 25, "usage_type": "attribute"}, {"api_name": "streamlit.sidebar.selectbox", "line_number": 26, "usage_type": "call"}, {"api_name": "streamlit.sidebar", "line_number": 26, "usage_type": "attribute"}, {"api_name": "helper.fetch_medal_tally", "line_number": 28, "usage_type": "call"}, {"api_name": "streamlit.title", "line_number": 31, "usage_type": "call"}, {"api_name": "streamlit.title", "line_number": 33, "usage_type": "call"}, {"api_name": "streamlit.title", "line_number": 35, "usage_type": "call"}, {"api_name": "streamlit.title", "line_number": 37, "usage_type": "call"}, {"api_name": "streamlit.table", "line_number": 38, "usage_type": "call"}, {"api_name": "streamlit.title", "line_number": 48, "usage_type": "call"}, {"api_name": "streamlit.columns", "line_number": 50, "usage_type": "call"}, {"api_name": "streamlit.header", "line_number": 52, "usage_type": "call"}, {"api_name": "streamlit.title", "line_number": 53, "usage_type": "call"}, {"api_name": "streamlit.header", "line_number": 55, "usage_type": "call"}, {"api_name": "streamlit.title", "line_number": 56, "usage_type": "call"}, {"api_name": "streamlit.header", "line_number": 58, "usage_type": "call"}, {"api_name": "streamlit.title", "line_number": 59, "usage_type": "call"}, {"api_name": "streamlit.columns", "line_number": 61, "usage_type": "call"}, {"api_name": "streamlit.header", "line_number": 63, "usage_type": "call"}, {"api_name": "streamlit.title", "line_number": 64, "usage_type": "call"}, {"api_name": "streamlit.header", "line_number": 66, "usage_type": "call"}, {"api_name": "streamlit.title", "line_number": 67, "usage_type": "call"}, {"api_name": "streamlit.header", "line_number": 69, "usage_type": "call"}, {"api_name": "streamlit.title", "line_number": 70, "usage_type": "call"}, {"api_name": "helper.participating_nations_over_time", "line_number": 72, "usage_type": "call"}, {"api_name": "plotly.express.line", "line_number": 73, "usage_type": "call"}, {"api_name": "plotly.express", "line_number": 73, "usage_type": "name"}, {"api_name": "streamlit.title", "line_number": 74, "usage_type": "call"}, {"api_name": "streamlit.plotly_chart", "line_number": 75, "usage_type": "call"}, {"api_name": "helper.number_of_events_over_time", "line_number": 77, "usage_type": "call"}, {"api_name": "plotly.express.line", "line_number": 78, "usage_type": "call"}, {"api_name": "plotly.express", "line_number": 78, "usage_type": "name"}, {"api_name": "streamlit.title", "line_number": 79, "usage_type": "call"}, {"api_name": "streamlit.plotly_chart", "line_number": 80, "usage_type": "call"}, {"api_name": "helper.number_of_athletes_over_time", "line_number": 82, "usage_type": "call"}, {"api_name": "plotly.express.line", "line_number": 83, "usage_type": "call"}, {"api_name": "plotly.express", "line_number": 83, "usage_type": "name"}, {"api_name": "streamlit.title", "line_number": 84, "usage_type": "call"}, {"api_name": "streamlit.plotly_chart", "line_number": 85, "usage_type": "call"}, {"api_name": "streamlit.title", "line_number": 87, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 88, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 88, "usage_type": "name"}, {"api_name": "seaborn.heatmap", "line_number": 90, "usage_type": "call"}, {"api_name": "streamlit.pyplot", "line_number": 91, "usage_type": "call"}, {"api_name": "streamlit.sidebar.title", "line_number": 96, "usage_type": "call"}, {"api_name": "streamlit.sidebar", "line_number": 96, "usage_type": "attribute"}, {"api_name": "streamlit.sidebar.selectbox", "line_number": 100, "usage_type": "call"}, {"api_name": "streamlit.sidebar", "line_number": 100, "usage_type": "attribute"}, {"api_name": "helper.yearwise_medal_tally", "line_number": 102, "usage_type": "call"}, {"api_name": "plotly.express.line", "line_number": 103, "usage_type": "call"}, {"api_name": "plotly.express", "line_number": 103, "usage_type": "name"}, {"api_name": "streamlit.title", "line_number": 104, "usage_type": "call"}, {"api_name": "streamlit.plotly_chart", "line_number": 105, "usage_type": "call"}, {"api_name": "streamlit.title", "line_number": 107, "usage_type": "call"}, {"api_name": "helper.country_event_heatmap", "line_number": 108, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 109, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 109, "usage_type": "name"}, {"api_name": "streamlit.header", "line_number": 111, "usage_type": "call"}, {"api_name": "seaborn.heatmap", "line_number": 113, "usage_type": "call"}, {"api_name": "streamlit.pyplot", "line_number": 114, "usage_type": "call"}, {"api_name": "plotly.figure_factory.create_distplot", "line_number": 124, "usage_type": "call"}, {"api_name": "plotly.figure_factory", "line_number": 124, "usage_type": "name"}, {"api_name": "streamlit.title", "line_number": 127, "usage_type": "call"}, {"api_name": "streamlit.plotly_chart", "line_number": 128, "usage_type": "call"}, {"api_name": "plotly.figure_factory.create_distplot", "line_number": 143, "usage_type": "call"}, {"api_name": "plotly.figure_factory", "line_number": 143, "usage_type": "name"}, {"api_name": "streamlit.title", "line_number": 145, "usage_type": "call"}, {"api_name": "streamlit.plotly_chart", "line_number": 146, "usage_type": "call"}, {"api_name": "streamlit.title", "line_number": 148, "usage_type": "call"}, {"api_name": "streamlit.selectbox", "line_number": 152, "usage_type": "call"}, {"api_name": "helper.weight_v_height", "line_number": 153, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 154, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 154, "usage_type": "name"}, {"api_name": "seaborn.scatterplot", "line_number": 155, "usage_type": "call"}, {"api_name": "streamlit.pyplot", "line_number": 156, "usage_type": "call"}, {"api_name": "streamlit.title", "line_number": 158, "usage_type": "call"}, {"api_name": "helper.men_vs_women", "line_number": 159, "usage_type": "call"}, {"api_name": "plotly.express.line", "line_number": 160, "usage_type": "call"}, {"api_name": "plotly.express", "line_number": 160, "usage_type": "name"}, {"api_name": "streamlit.plotly_chart", "line_number": 162, "usage_type": "call"}]} +{"seq_id": "24175372446", "text": "# Built-in Imports\nfrom typing import Dict, Union, Callable, List\nimport time\nfrom os.path import join as pjoin\n\n# Libraries\nfrom jericho.util import clean\nimport wandb\nimport torch\n\n# Custom Imports\nfrom trainers import DrrnInvDynTrainer\n\nfrom agents import DrrnInvDynAgent\n\nfrom utils.vec_env import VecEnv\nfrom utils.memory import Transition\nimport utils.logger as logger\nfrom utils.env import JerichoEnv\nimport utils.drrn as Drrn\nimport utils.inv_dyn as InvDyn\nimport utils.ngram as Ngram\nfrom utils.util import process_action\n\n\nclass DrrnGraphInvDynTrainer(DrrnInvDynTrainer):\n def __init__(\n self,\n tb: logger.Logger,\n log: Callable[..., None],\n agent: DrrnInvDynAgent,\n envs: VecEnv,\n eval_env: JerichoEnv,\n args: Dict[str, Union[str, int, float]]\n ):\n super().__init__(tb, log, agent, envs, eval_env, args)\n\n self.graph_num_explore_steps = args.graph_num_explore_steps\n self.graph_rescore_freq = args.graph_rescore_freq\n self.graph_merge_freq = args.graph_merge_freq\n self.log_top_blue_acts_freq = args.log_top_blue_acts_freq\n\n self.use_il_graph_sampler = args.use_il_graph_sampler\n self.use_il_buffer_sampler = args.use_il_buffer_sampler\n self.use_il = args.use_il\n\n def train(self):\n start = time.time()\n max_score = 0\n\n obs, infos, states, valid_ids, transitions = Drrn.setup_env(\n self, self.envs)\n\n for step in range(1, self.max_steps + 1):\n self.steps = step\n self.log(\"Step {}\".format(step))\n action_ids, action_idxs, action_qvals = self.agent.act(states,\n valid_ids,\n [info['valid']\n for info in infos],\n sample=True)\n\n # Get the actual next action string for each env\n action_strs = [\n info['valid'][idx] for info, idx in zip(infos, action_idxs)\n ]\n\n # Log envs[0]\n s = ''\n for idx, (act, val) in enumerate(\n sorted(zip(infos[0]['valid'], action_qvals[0]),\n key=lambda x: x[1],\n reverse=True), 1):\n s += \"{}){:.2f} {} \".format(idx, val.item(), act)\n self.log('Q-Values: {}'.format(s))\n\n # Update all envs\n infos, next_states, next_valids, max_score, obs = self.update_envs(\n action_strs, action_ids, states, max_score, transitions, obs, infos, action_qvals)\n states, valid_ids = next_states, next_valids\n\n self.end_step(step, start, max_score, action_qvals)\n\n def update_envs(self, action_strs, action_ids, states, max_score: int,\n transitions, obs, infos, qvals):\n \"\"\"\n TODO\n \"\"\"\n next_obs, next_rewards, next_dones, next_infos = self.envs.step(\n action_strs)\n\n if self.use_il_graph_sampler:\n next_node_ids = [graph.state_hash(next_info, next_ob) for graph, next_ob, next_info in zip(\n self.agent.graphs, next_obs, next_infos)]\n\n # Add to environment trajectory\n trajs = self.envs.add_traj(\n list(map(lambda x: (process_action(x[0]), x[1]),\n zip(action_strs, next_node_ids))))\n\n # Update graph depending on state of environment\n self.log('Updating graph ...')\n for i, (graph, ob, info, qvals, next_ob, next_info, act) in enumerate(zip(self.agent.graphs, obs, infos, qvals, next_obs, next_infos, action_strs)):\n graph.maybe_update(ob, info, next_ob, next_info,\n qvals.cpu().detach().tolist(), i, process_action(act))\n if self.use_action_model:\n next_states = self.agent.build_states(\n next_obs, next_infos, action_strs, [state.acts for state in states])\n else:\n next_states = self.agent.build_states(next_obs, next_infos)\n\n # Update valid acts if next node is already in the tree\n next_valids = [self.agent.encode(next_info['valid'])\n for next_info in next_infos]\n\n if self.r_for > 0:\n reward_curiosity, _ = InvDyn.inv_loss_decode(self.agent.network,\n states, next_states, [[a] for a in action_ids], hat=True, reduction='none')\n next_rewards = next_rewards + reward_curiosity.detach().numpy() * self.r_for\n self.tb.logkv_mean('Curiosity', reward_curiosity.mean().item())\n\n for i, (next_ob, next_reward, next_done, next_info, state, next_state, next_action_str) in enumerate(zip(next_obs, next_rewards, next_dones, next_infos, states, next_states, action_strs)):\n # Log\n self.log('Action_{}: {}'.format(\n self.steps, next_action_str), condition=(i == 0))\n self.log(\"Reward{}: {}, Score {}, Done {}\".format(\n self.steps, next_reward, next_info['score'], next_done), condition=(i == 0))\n self.log('Obs{}: {} Inv: {} Desc: {}'.format(\n self.steps, clean(next_ob), clean(next_info['inv']),\n clean(next_info['look'])), condition=(i == 0))\n\n transition = Transition(\n state, action_ids[i], next_reward, next_state, next_valids[i], next_done)\n transitions[i].append(transition)\n self.agent.observe(transition)\n\n if next_done:\n # Add trajectory to graph\n if self.use_il_buffer_sampler:\n self.agent.il_buffer.add_traj(transitions[i])\n\n if next_info['score'] >= max_score: # put in alpha queue\n if next_info['score'] > max_score:\n self.agent.memory.clear_alpha()\n max_score = next_info['score']\n for transition in transitions[i]:\n self.agent.observe(transition, is_prior=True)\n transitions[i] = []\n\n if self.use_action_model:\n Ngram.log_recovery_metrics(self, i)\n\n # Add last node to graph\n if self.use_il_graph_sampler:\n if next_infos[i]['look'] != 'unknown' and next_infos[i]['inv'] != 'unknown':\n with torch.no_grad():\n _, qvals = self.agent.network.act(\n next_states, next_valids, [next_info['valid'] for next_info in next_infos])\n self.agent.graphs[i].maybe_update(\n next_ob, next_info, None, None, qvals[i].cpu().tolist(), i, None)\n \n\n next_infos = list(next_infos)\n\n next_obs[i], next_infos[i] = self.envs.reset_one(i)\n\n if self.use_action_model:\n next_states[i] = self.agent.build_skip_state(\n next_obs[i], next_infos[i], 'reset', [])\n else:\n next_states[i] = self.agent.build_state(\n next_obs[i], next_infos[i])\n\n next_valids[i] = self.agent.encode(next_infos[i]['valid'])\n\n return next_infos, next_states, next_valids, max_score, next_obs\n\n def end_step(self, step: int, start, max_score: int, action_qvals):\n \"\"\"\n TODO\n \"\"\"\n if step % self.q_update_freq == 0:\n self.update_agent()\n\n if step % self.target_update_freq == 0:\n self.agent.transfer_weights()\n\n if self.use_action_model:\n Ngram.end_step(self, step)\n\n if step % self.log_freq == 0:\n # rank_metrics = self.evaluate_optimal()\n rank_metrics = dict()\n self.write_to_logs(step, start, self.envs, max_score, action_qvals,\n rank_metrics)\n\n # Save model weights etc.\n if step % self.checkpoint_freq == 0:\n self.agent.save(int(step / self.checkpoint_freq))\n\n if self.use_il:\n # save locally\n torch.save(self.agent.action_models.state_dict(),\n pjoin(wandb.run.dir, 'il_weights_{}.pt'.format(step)))\n\n # upload to wandb\n wandb.save(\n pjoin(wandb.run.dir, 'il_weights_{}.pt'.format(step)))\n\n def write_to_logs(self, step, start, envs, max_score, qvals, rank_metrics,\n *args):\n \"\"\"\n Log any relevant metrics.\n \"\"\"\n self.tb.logkv('Step', step)\n for key, val in rank_metrics.items():\n self.tb.logkv(key, val)\n self.tb.logkv(\"FPS\", int(\n (step*self.envs.num_envs)/(time.time()-start)))\n self.tb.logkv(\"EpisodeScores100\", self.envs.get_end_scores().mean())\n self.tb.logkv('MaxScore', max_score)\n if self.use_il_graph_sampler:\n self.tb.logkv('#BlueActs', sum(\n [len(node['blue_acts']) for node in self.agent.graphs[0].graph.values()]))\n self.tb.dumpkvs()\n", "repo_name": "princeton-nlp/XTX", "sub_path": "trainers/drrn/drrn_graph_inv_dyn_trainer.py", "file_name": "drrn_graph_inv_dyn_trainer.py", "file_ext": "py", "file_size_in_byte": 9308, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 13, "dataset": "github-code", "pt": "37", "api": [{"api_name": "trainers.DrrnInvDynTrainer", "line_number": 26, "usage_type": "name"}, {"api_name": "utils.logger.Logger", "line_number": 29, "usage_type": "attribute"}, {"api_name": "utils.logger", "line_number": 29, "usage_type": "name"}, {"api_name": "typing.Callable", "line_number": 30, "usage_type": "name"}, {"api_name": "agents.DrrnInvDynAgent", "line_number": 31, "usage_type": "name"}, {"api_name": "utils.vec_env.VecEnv", "line_number": 32, "usage_type": "name"}, {"api_name": "utils.env.JerichoEnv", "line_number": 33, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 34, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 34, "usage_type": "name"}, {"api_name": "time.time", "line_number": 48, "usage_type": "call"}, {"api_name": "utils.drrn.setup_env", "line_number": 51, "usage_type": "call"}, {"api_name": "utils.drrn", "line_number": 51, "usage_type": "name"}, {"api_name": "utils.util.process_action", "line_number": 98, "usage_type": "call"}, {"api_name": "utils.util.process_action", "line_number": 105, "usage_type": "call"}, {"api_name": "utils.inv_dyn.inv_loss_decode", "line_number": 117, "usage_type": "call"}, {"api_name": "utils.inv_dyn", "line_number": 117, "usage_type": "name"}, {"api_name": "jericho.util.clean", "line_number": 129, "usage_type": "call"}, {"api_name": "jericho.util.clean", "line_number": 130, "usage_type": "call"}, {"api_name": "utils.memory.Transition", "line_number": 132, "usage_type": "call"}, {"api_name": "utils.ngram.log_recovery_metrics", "line_number": 151, "usage_type": "call"}, {"api_name": "utils.ngram", "line_number": 151, "usage_type": "name"}, {"api_name": "torch.no_grad", "line_number": 156, "usage_type": "call"}, {"api_name": "utils.ngram.end_step", "line_number": 189, "usage_type": "call"}, {"api_name": "utils.ngram", "line_number": 189, "usage_type": "name"}, {"api_name": "torch.save", "line_number": 203, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 204, "usage_type": "call"}, {"api_name": "wandb.run", "line_number": 204, "usage_type": "attribute"}, {"api_name": "wandb.save", "line_number": 207, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 208, "usage_type": "call"}, {"api_name": "wandb.run", "line_number": 208, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 219, "usage_type": "call"}]} +{"seq_id": "34762741855", "text": "from db import db\nfrom sqlalchemy import and_\n\nclass Author(db.Model):\n __tablename__ = 'authors'\n\n id = db.Column(db.Integer, primary_key=True)\n name = db.Column(db.String(50), unique=True, nullable=False)\n book = db.Column(db.String(20), unique=True, nullable=False)\n country = db.Column(db.String(50), nullable=False)\n booker_prize = db.Column(db.Boolean)\n user_id = db.Column(db.Integer, db.ForeignKey('users.id', ondelete='CASCADE'))\n\n @property\n def serialize(self):\n return {\n 'id': self.id,\n 'name': self.name,\n 'book': self.book,\n 'country': self.country,\n 'booker_prize': self.booker_prize,\n 'user_id': self.user_id\n }\n\n def save_to_db(self):\n db.session.add(self)\n try:\n db.session.commit()\n except Exception as e:\n db.session.rollback()\n db.session.flush()\n print(e)\n\n def delete_author(self):\n db.session.delete(self)\n try:\n db.session.commit()\n except Exception as e:\n db.session.rollback()\n db.session.flush()\n print(e)\n \n @classmethod\n def getAuthor(cls, name=None, user_id=None):\n return cls.query.filter(and_(name==name, user_id==user_id)).first()", "repo_name": "Solman28/api-jwt-demo", "sub_path": "models/Author.py", "file_name": "Author.py", "file_ext": "py", "file_size_in_byte": 1380, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "37", "api": [{"api_name": "db.db.Model", "line_number": 4, "usage_type": "attribute"}, {"api_name": "db.db", "line_number": 4, "usage_type": "name"}, {"api_name": "db.db.Column", "line_number": 7, "usage_type": "call"}, {"api_name": "db.db", "line_number": 7, "usage_type": "name"}, {"api_name": "db.db.Integer", "line_number": 7, "usage_type": "attribute"}, {"api_name": "db.db.Column", "line_number": 8, "usage_type": "call"}, {"api_name": "db.db", "line_number": 8, "usage_type": "name"}, {"api_name": "db.db.String", "line_number": 8, "usage_type": "call"}, {"api_name": "db.db.Column", "line_number": 9, "usage_type": "call"}, {"api_name": "db.db", "line_number": 9, "usage_type": "name"}, {"api_name": "db.db.String", "line_number": 9, "usage_type": "call"}, {"api_name": "db.db.Column", "line_number": 10, "usage_type": "call"}, {"api_name": "db.db", "line_number": 10, "usage_type": "name"}, {"api_name": "db.db.String", "line_number": 10, "usage_type": "call"}, {"api_name": "db.db.Column", "line_number": 11, "usage_type": "call"}, {"api_name": "db.db", "line_number": 11, "usage_type": "name"}, {"api_name": "db.db.Boolean", "line_number": 11, "usage_type": "attribute"}, {"api_name": "db.db.Column", "line_number": 12, "usage_type": "call"}, {"api_name": "db.db", "line_number": 12, "usage_type": "name"}, {"api_name": "db.db.Integer", "line_number": 12, "usage_type": "attribute"}, {"api_name": "db.db.ForeignKey", "line_number": 12, "usage_type": "call"}, {"api_name": "db.db.session.add", "line_number": 26, "usage_type": "call"}, {"api_name": "db.db.session", "line_number": 26, "usage_type": "attribute"}, {"api_name": "db.db", "line_number": 26, "usage_type": "name"}, {"api_name": "db.db.session.commit", "line_number": 28, "usage_type": "call"}, {"api_name": "db.db.session", "line_number": 28, "usage_type": "attribute"}, {"api_name": "db.db", "line_number": 28, "usage_type": "name"}, {"api_name": "db.db.session.rollback", "line_number": 30, "usage_type": "call"}, {"api_name": "db.db.session", "line_number": 30, "usage_type": "attribute"}, {"api_name": "db.db", "line_number": 30, "usage_type": "name"}, {"api_name": "db.db.session.flush", "line_number": 31, "usage_type": "call"}, {"api_name": "db.db.session", "line_number": 31, "usage_type": "attribute"}, {"api_name": "db.db", "line_number": 31, "usage_type": "name"}, {"api_name": "db.db.session.delete", "line_number": 35, "usage_type": "call"}, {"api_name": "db.db.session", "line_number": 35, "usage_type": "attribute"}, {"api_name": "db.db", "line_number": 35, "usage_type": "name"}, {"api_name": "db.db.session.commit", "line_number": 37, "usage_type": "call"}, {"api_name": "db.db.session", "line_number": 37, "usage_type": "attribute"}, {"api_name": "db.db", "line_number": 37, "usage_type": "name"}, {"api_name": "db.db.session.rollback", "line_number": 39, "usage_type": "call"}, {"api_name": "db.db.session", "line_number": 39, "usage_type": "attribute"}, {"api_name": "db.db", "line_number": 39, "usage_type": "name"}, {"api_name": "db.db.session.flush", "line_number": 40, "usage_type": "call"}, {"api_name": "db.db.session", "line_number": 40, "usage_type": "attribute"}, {"api_name": "db.db", "line_number": 40, "usage_type": "name"}, {"api_name": "sqlalchemy.and_", "line_number": 45, "usage_type": "call"}]} +{"seq_id": "2057565836", "text": "\"\"\"\n Simple View module example\n\"\"\"\n\nfrom flask import request, render_template\nfrom app import app\n\n\n@app.route('/', methods=['GET', 'POST'])\n@app.route('/index', methods=['GET', 'POST'])\ndef index():\n \"\"\" Render index page, if POST-ing data - payload also will be rendered \"\"\"\n\n if request.method == 'GET':\n return render_template('index.html')\n\n if request.method == 'POST':\n request_payload = request.data\n return render_template('index.html', request_payload=request_payload)\n", "repo_name": "nikonov1101/flask-template", "sub_path": "app/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 514, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "37", "api": [{"api_name": "flask.request.method", "line_number": 14, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 14, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 15, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 17, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 17, "usage_type": "name"}, {"api_name": "flask.request.data", "line_number": 18, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 18, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 19, "usage_type": "call"}, {"api_name": "app.app.route", "line_number": 9, "usage_type": "call"}, {"api_name": "app.app", "line_number": 9, "usage_type": "name"}, {"api_name": "app.app.route", "line_number": 10, "usage_type": "call"}, {"api_name": "app.app", "line_number": 10, "usage_type": "name"}]} +{"seq_id": "74634422506", "text": "from .resource import ResourceValidation\nfrom kubernetes.client.models import V1PodSpec\nfrom validator.config import check_list\nfrom . import messages\nfrom .base import PodResult\nfrom .container import validate_container\n\n\nclass PodValidation(ResourceValidation):\n\n def __init__(self, pod):\n super().__init__()\n self.pod: V1PodSpec = pod\n\n def validate_security(self):\n category = messages.CategorySecurity\n\n def validate_security_host_ipc_set(pv):\n name = \"hostIPCSet\"\n security_conf = check_list.get(\"security\", None)\n severity = security_conf.get(name, None)\n if pv.pod.host_ipc:\n pv.on_failure(messages.HostIPCFailure, severity, category, name)\n else:\n pv.on_success(messages.HostIPCSuccess, category, name)\n\n def validate_security_host_pid_set(pv):\n name = \"hostPIDSet\"\n security_conf = check_list.get(\"security\", None)\n severity = security_conf.get(name, None)\n if pv.pod.host_pid:\n pv.on_failure(messages.HostPIDFailure, severity, category, name)\n else:\n pv.on_success(messages.HostPIDSuccess, category, name)\n\n validate_security_host_ipc_set(self)\n ## validate_security_host_pid_set(self)\n\n def validate_networking(self):\n category = messages.CategoryNetworking\n name = \"HostNetworkSet\"\n networking_conf = check_list.get(\"networking\", None)\n severity = networking_conf.get(name, None)\n if self.pod.host_network:\n self.on_failure(messages.HostNetworkFailure, severity, category, name)\n else:\n self.on_success(messages.HostNetworkSuccess, category, name)\n\n def validate_containers(self):\n containers = self.pod.containers\n container_results = []\n for container in containers:\n res = validate_container(container, self.pod)\n container_results.append(res)\n return container_results\n\n\ndef validate_pod(pod: V1PodSpec):\n pv = PodValidation(pod)\n pv.validate_networking()\n pv.validate_security()\n containers_result = pv.validate_containers()\n return PodResult(messages=pv.messages, container_results=containers_result)\n", "repo_name": "KubeOperator/KubeGrade", "sub_path": "validator/pod.py", "file_name": "pod.py", "file_ext": "py", "file_size_in_byte": 2280, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "37", "api": [{"api_name": "resource.ResourceValidation", "line_number": 9, "usage_type": "name"}, {"api_name": "kubernetes.client.models.V1PodSpec", "line_number": 13, "usage_type": "name"}, {"api_name": "validator.config.check_list.get", "line_number": 20, "usage_type": "call"}, {"api_name": "validator.config.check_list", "line_number": 20, "usage_type": "name"}, {"api_name": "validator.config.check_list.get", "line_number": 29, "usage_type": "call"}, {"api_name": "validator.config.check_list", "line_number": 29, "usage_type": "name"}, {"api_name": "validator.config.check_list.get", "line_number": 42, "usage_type": "call"}, {"api_name": "validator.config.check_list", "line_number": 42, "usage_type": "name"}, {"api_name": "container.validate_container", "line_number": 53, "usage_type": "call"}, {"api_name": "kubernetes.client.models.V1PodSpec", "line_number": 58, "usage_type": "name"}, {"api_name": "base.PodResult", "line_number": 63, "usage_type": "call"}]} +{"seq_id": "29900623345", "text": "from django.conf.urls import url\nfrom .views import *\n\nurlpatterns = [\n url(r'^$', index, name='index'),\n url(r'^cadastrar_usuario', cadastrar_usuario, name='cadastrar_usuario'),\n url(r'^login', do_login, name='login'),\n url(r'^logout', do_logout, name='logout'),\n url(r'^cadastrar_carro', cadastrar_carro, name='cadastrar_carro'),\n url(r'^listar_carros', listar_carro, name='carro_list'),\n url(r'^editar_carro/(?P[0-9]+)', editar_carro, name='editar_carro'),\n url(r'^remover_carro/(?P[0-9]+)', remover_carro, name='remover_carro'),\n]\n", "repo_name": "lakagawa/django-crud-carro", "sub_path": "carro/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 567, "program_lang": "python", "lang": "pt", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "django.conf.urls.url", "line_number": 5, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 6, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 7, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 8, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 9, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 10, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 11, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 12, "usage_type": "call"}]} +{"seq_id": "4954649947", "text": "import unittest\nfrom datetime import datetime\n\nfrom pycontrolflow.FlowExecutor import FlowExecutor\nfrom pycontrolflow.nodes.gates.DLatch import DLatch\n\n\nclass Test(unittest.TestCase):\n def test1(self) -> None:\n executor = FlowExecutor()\n\n var_d = executor.memory(\"var_d\", bool, initial_value=False)\n var_e = executor.memory(\"var_e\", bool, initial_value=False)\n var_out = executor.var(\"out\", bool)\n\n executor.add([\n DLatch[bool](var_d, var_e, initial_state=False).to(var_out)\n ])\n\n def tick(value: bool, enable: bool, expected_value: bool) -> None:\n var_d.set(value)\n var_e.set(enable)\n executor.run(datetime.now())\n self.assertEqual(expected_value, var_out.get())\n\n tick(value=True, enable=False, expected_value=False)\n tick(value=True, enable=True, expected_value=True)\n tick(value=True, enable=False, expected_value=True)\n tick(value=False, enable=False, expected_value=True)\n tick(value=False, enable=True, expected_value=False)\n tick(value=False, enable=True, expected_value=False)\n tick(value=False, enable=False, expected_value=False)\n\n tick(value=False, enable=True, expected_value=False)\n tick(value=True, enable=True, expected_value=True)\n tick(value=False, enable=True, expected_value=False)\n", "repo_name": "KrystianD/pycontrolflow", "sub_path": "pycontrolflow/nodes/gates/DLatch_test.py", "file_name": "DLatch_test.py", "file_ext": "py", "file_size_in_byte": 1379, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "37", "api": [{"api_name": "unittest.TestCase", "line_number": 8, "usage_type": "attribute"}, {"api_name": "pycontrolflow.FlowExecutor.FlowExecutor", "line_number": 10, "usage_type": "call"}, {"api_name": "pycontrolflow.nodes.gates.DLatch.DLatch", "line_number": 17, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 23, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 23, "usage_type": "name"}]} +{"seq_id": "6670140960", "text": "import tensorflow as tf\nfrom tensorflow.keras import layers, Sequential\nimport numpy as np\nimport os\nfrom sklearn.cluster import KMeans\nfrom model_ulti.get_tfrecord import tf_serialize_example\n# 定义Basic Block\nclass BasicBlock(layers.Layer):\n def __init__(self, filter_num, stride=1):\n super(BasicBlock, self).__init__()\n # 第一小块\n self.conv1 = layers.Conv3D(filter_num, (3, 3, 3), strides=stride, padding='same')\n self.bn1 = layers.BatchNormalization()\n self.relu = layers.Activation('relu')\n # 第二小块\n self.conv2 = layers.Conv3D(filter_num, (3, 3, 3), strides=1, padding='same')\n self.bn2 = layers.BatchNormalization()\n if stride != 1:\n self.downsample = Sequential()\n self.downsample.add(layers.Conv3D(filter_num, (1, 1, 1), strides=stride, padding='same'))\n else:\n self.downsample = lambda x: x\n\n def call(self, inputs, training=None):\n identity = self.downsample(inputs)\n out = self.conv1(inputs)\n out = self.bn1(out, training=training)\n out = self.relu(out)\n out = self.conv2(out)\n out = self.bn2(out, training=training)\n out = layers.add([out, identity])\n out = tf.nn.relu(out)\n return out\n\n# 定义ResNet\nclass ResNet_dc(tf.keras.Model):\n\n def build_resblock(self, filter_num, blocks, stride=1):\n res_blocks = Sequential()\n res_blocks.add(BasicBlock(filter_num, stride))\n for _ in range(1, blocks):\n res_blocks.add(BasicBlock(filter_num, stride=1))\n return res_blocks\n\n def __init__(self, layer_dims, num_classes=2, feature=True): # mnist有10类,此时2类\n super(ResNet_dc, self).__init__()\n self.feature = feature\n self.stem = Sequential([layers.Input(shape=(30, 30, 30, 1)),\n layers.Conv3D(16, (3, 3, 3), strides=(2, 2, 2), padding='same'), # 15，15，15\n layers.BatchNormalization(),\n layers.Activation('relu'),\n layers.MaxPool3D(pool_size=(2, 2, 2), strides=(1, 1, 1), padding='same')\n ])\n self.layer1 = self.build_resblock(32, layer_dims[0], stride=2) # 8，8，8\n self.layer2 = Sequential([layers.Dropout(0.5),\n layers.Conv3D(64, (3, 3, 3), strides=(2, 2, 2), padding='same')]) # 4，4，4\n\n self.layer3 = Sequential([layers.Flatten(),\n layers.Dense(512, activation='relu'),\n layers.Dense(64, activation='relu')\n ], name='Dense_1')\n\n self.layer4 = Sequential([layers.Dense(16, activation='relu'),\n layers.Dense(num_classes, activation='softmax')], name='Dense_2')\n\n def call(self, inputs, training=None):\n x = self.stem(inputs, training=training)\n x = self.layer1(x, training=training)\n x = self.layer2(x, training=training)\n # x = self.layer3(x,training=training)\n extrect_feature = self.layer3(x, training=training)\n x = self.layer4(extrect_feature, training=training)\n if self.feature:\n return x, extrect_feature\n\n return x\n\n def get_loss(self, inputs):\n x = self.stem(inputs)\n x = self.layer1(x)\n x = self.layer2(x)\n extrect_feature = self.layer3(x)\n x = self.layer4(extrect_feature)\n extrect_feature = tf.convert_to_tensor(extrect_feature)\n\n # 计算loss\n kmeans = KMeans(n_clusters=2, random_state=0).fit(extrect_feature)\n Y_test_pred_hot = tf.keras.utils.to_categorical(1 - kmeans.labels_, num_classes=2)\n ans = tf.keras.losses.binary_crossentropy(Y_test_pred_hot, x)\n return ans\n\ndef load_dataset(data_set_path, batch_size=3000):\n temp = os.listdir(data_set_path)\n file_list1 = [os.path.join(data_set_path, item) for item in temp]\n file_label1 = np.array([item.split('_')[0] for item in temp], np.int32)\n features = tf.constant(file_list1, tf.string, shape=(len(file_list1), 1)) # ==> 3x2 tensor\n labels = tf.constant(file_label1, shape=(len(file_list1))) # ==> 3x1 tensor\n\n features_dataset = tf.data.Dataset.from_tensor_slices(features)\n labels_dataset = tf.data.Dataset.from_tensor_slices(labels)\n dataset = tf.data.Dataset.zip((features_dataset, labels_dataset))\n dataset = dataset.shuffle(buffer_size=len(file_list1))\n dataset = dataset.map(tf_serialize_example)\n dataset = dataset.batch(batch_size=batch_size)\n return dataset\n\n\ndef load_dataset_all(data_set_path_list, batch_size=3000):\n file_list_all = []\n file_label_all = []\n for data_set_path in data_set_path_list:\n temp = os.listdir(data_set_path)\n file_list1 = [os.path.join(data_set_path, item) for item in temp]\n file_label1 = [item.split('_')[0] for item in temp]\n\n file_list_all = file_list_all + file_list1\n file_label_all = file_label_all + file_label1\n\n samples_num = len(file_list_all)\n file_label_all = np.array(file_label_all, np.int32)\n features = tf.constant(file_list_all, tf.string, shape=(samples_num, 1)) # ==> 3x2 tensor\n labels = tf.constant(file_label_all, shape=samples_num) # ==> 3x1 tensor\n\n features_dataset = tf.data.Dataset.from_tensor_slices(features)\n labels_dataset = tf.data.Dataset.from_tensor_slices(labels)\n dataset = tf.data.Dataset.zip((features_dataset, labels_dataset))\n dataset = dataset.shuffle(buffer_size=samples_num)\n dataset = dataset.map(tf_serialize_example)\n dataset = dataset.batch(batch_size=batch_size)\n\n return dataset, samples_num\n\n\nif __name__ == '__main__':\n pass", "repo_name": "Luoxiaoyu828/SS-3D-Clump", "sub_path": "deep_clustering.py", "file_name": "deep_clustering.py", "file_ext": "py", "file_size_in_byte": 5777, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "37", "api": [{"api_name": "tensorflow.keras.layers.Layer", "line_number": 8, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.layers", "line_number": 8, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.Conv3D", "line_number": 12, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 12, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.BatchNormalization", "line_number": 13, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 13, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.Activation", "line_number": 14, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 14, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.Conv3D", "line_number": 16, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 16, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.BatchNormalization", "line_number": 17, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 17, "usage_type": "name"}, {"api_name": "tensorflow.keras.Sequential", "line_number": 19, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Conv3D", "line_number": 20, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 20, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.add", "line_number": 31, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 31, "usage_type": "name"}, {"api_name": "tensorflow.nn.relu", "line_number": 32, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 32, "usage_type": "attribute"}, {"api_name": "tensorflow.keras", "line_number": 36, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.Sequential", "line_number": 39, "usage_type": "call"}, {"api_name": "tensorflow.keras.Sequential", "line_number": 48, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Input", "line_number": 48, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 48, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.Conv3D", "line_number": 49, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 49, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.BatchNormalization", "line_number": 50, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 50, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.Activation", "line_number": 51, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 51, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.MaxPool3D", "line_number": 52, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 52, "usage_type": "name"}, {"api_name": "tensorflow.keras.Sequential", "line_number": 55, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Dropout", "line_number": 55, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 55, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.Conv3D", "line_number": 56, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 56, "usage_type": "name"}, {"api_name": "tensorflow.keras.Sequential", "line_number": 58, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Flatten", "line_number": 58, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 58, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.Dense", "line_number": 59, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 59, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.Dense", "line_number": 60, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 60, "usage_type": "name"}, {"api_name": "tensorflow.keras.Sequential", "line_number": 63, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Dense", "line_number": 63, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 63, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.Dense", "line_number": 64, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 64, "usage_type": "name"}, {"api_name": "tensorflow.convert_to_tensor", "line_number": 84, "usage_type": "call"}, {"api_name": "sklearn.cluster.KMeans", "line_number": 87, "usage_type": "call"}, {"api_name": "tensorflow.keras.utils.to_categorical", "line_number": 88, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 88, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.losses.binary_crossentropy", "line_number": 89, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 89, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 93, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 94, "usage_type": "call"}, {"api_name": "os.path", "line_number": 94, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 95, "usage_type": "call"}, {"api_name": "numpy.int32", "line_number": 95, "usage_type": "attribute"}, {"api_name": "tensorflow.constant", "line_number": 96, "usage_type": "call"}, {"api_name": "tensorflow.string", "line_number": 96, "usage_type": "attribute"}, {"api_name": "tensorflow.constant", "line_number": 97, "usage_type": "call"}, {"api_name": "tensorflow.data.Dataset.from_tensor_slices", "line_number": 99, "usage_type": "call"}, {"api_name": "tensorflow.data", "line_number": 99, "usage_type": "attribute"}, {"api_name": "tensorflow.data.Dataset.from_tensor_slices", "line_number": 100, "usage_type": "call"}, {"api_name": "tensorflow.data", "line_number": 100, "usage_type": "attribute"}, {"api_name": "tensorflow.data.Dataset.zip", "line_number": 101, "usage_type": "call"}, {"api_name": "tensorflow.data", "line_number": 101, "usage_type": "attribute"}, {"api_name": "model_ulti.get_tfrecord.tf_serialize_example", "line_number": 103, "usage_type": "argument"}, {"api_name": "os.listdir", "line_number": 112, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 113, "usage_type": "call"}, {"api_name": "os.path", "line_number": 113, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 120, "usage_type": "call"}, {"api_name": "numpy.int32", "line_number": 120, "usage_type": "attribute"}, {"api_name": "tensorflow.constant", "line_number": 121, "usage_type": "call"}, {"api_name": "tensorflow.string", "line_number": 121, "usage_type": "attribute"}, {"api_name": "tensorflow.constant", "line_number": 122, "usage_type": "call"}, {"api_name": "tensorflow.data.Dataset.from_tensor_slices", "line_number": 124, "usage_type": "call"}, {"api_name": "tensorflow.data", "line_number": 124, "usage_type": "attribute"}, {"api_name": "tensorflow.data.Dataset.from_tensor_slices", "line_number": 125, "usage_type": "call"}, {"api_name": "tensorflow.data", "line_number": 125, "usage_type": "attribute"}, {"api_name": "tensorflow.data.Dataset.zip", "line_number": 126, "usage_type": "call"}, {"api_name": "tensorflow.data", "line_number": 126, "usage_type": "attribute"}, {"api_name": "model_ulti.get_tfrecord.tf_serialize_example", "line_number": 128, "usage_type": "argument"}]} +{"seq_id": "18515679858", "text": "# -*- coding: utf-8 -*-\n# ------------------------------------------------------------\n# streamondemand.- XBMC Plugin\n# Canale per cineblog01 - anime\n# http://www.mimediacenter.info/foro/viewforum.php?f=36\n# ------------------------------------------------------------\nimport re\n\nfrom core import config, httptools\nfrom core import logger\nfrom core import scrapertools\nfrom core import servertools\nfrom core.item import Item\n\n__channel__ = \"cb01anime\"\n__category__ = \"A\"\n__type__ = \"generic\"\n__title__ = \"CineBlog01 Anime\"\n__language__ = \"IT\"\n\nhost = \"http://www.cineblog01.video/\"\n\nheaders = [['Upgrade-Insecure-Requests', '1'],\n ['User-Agent', 'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Ubuntu Chromium/53.0.2785.143 Chrome/53.0.2785.143 Safari/537.36'],\n ['Accept', 'text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,*/*;q=0.8'],\n ['Accept-Encoding', 'gzip, deflate, sdch'],\n ['Accept-Language', 'en-US,en;q=0.8']]\n\nDEBUG = config.get_setting(\"debug\")\n\n\ndef isGeneric():\n return True\n\n\n# -----------------------------------------------------------------\ndef mainlist(item):\n logger.info(\"[cb01anime.py] mainlist\")\n\n # Main options\n itemlist = [#Item(channel=__channel__,\n #action=\"novita\",\n #title=\"[COLOR azure]Anime - Novita'[/COLOR]\",\n #url=\"%s/anime/\" % host,\n #thumbnail=\"https://raw.githubusercontent.com/orione7/Pelis_images/master/channels_icon_pureita/anime_new_P.png\"),\n Item(channel=__channel__,\n action=\"genere\",\n title=\"[COLOR azure]Anime - Per Genere[/COLOR]\",\n url=\"%s/anime/\" % host,\n thumbnail=\"https://raw.githubusercontent.com/orione7/Pelis_images/master/channels_icon_pureita/anime_genre_P.png\"),\n Item(channel=__channel__,\n action=\"alfabetico\",\n title=\"[COLOR azure]Anime - Per Lettera A-Z[/COLOR]\",\n url=\"%s/anime/\" % host,\n thumbnail=\"https://raw.githubusercontent.com/orione7/Pelis_images/master/channels_icon_pureita/anime_az_P.png\"),\n Item(channel=__channel__,\n action=\"listacompleta\",\n title=\"[COLOR azure]Anime - Lista Completa[/COLOR]\",\n url=\"%s/anime/lista-completa-anime-cartoon/\" % host,\n thumbnail=\"https://raw.githubusercontent.com/orione7/Pelis_images/master/channels_icon_pureita/anime_lista_P.png\"),\n Item(channel=__channel__,\n action=\"search\",\n title=\"[COLOR yellow]Cerca Anime[/COLOR]\",\n extra=\"anime\",\n thumbnail=\"https://raw.githubusercontent.com/orione7/Pelis_images/master/channels_icon_pureita/search_P.png\")]\n\n return itemlist\n\n\n# =================================================================\n\n\n# -----------------------------------------------------------------\ndef novita(item):\n logger.info(\"[cb01anime.py] mainlist\")\n itemlist = []\n\n # Descarga la página\n data = scrapertools.anti_cloudflare(item.url, headers)\n\n # Extrae las entradas (carpetas)\n patronvideos = '
(.*?)
'\n bloque = scrapertools.get_match(data, patron)\n\n # The categories are the options for the combo \n patron = '
  • ([^<]+)
    • '\n matches = re.compile(patron, re.DOTALL).findall(bloque)\n scrapertools.printMatches(matches)\n\n for url, titulo in matches:\n scrapedtitle = titulo\n scrapedurl = url\n scrapedthumbnail = \"\"\n scrapedplot = \"\"\n if (DEBUG): logger.info(\"title=[\" + scrapedtitle + \"], url=[\" + scrapedurl + \"]\")\n itemlist.append(\n Item(channel=__channel__,\n action=\"episodios\",\n fulltitle=scrapedtitle,\n show=scrapedtitle,\n title=scrapedtitle,\n url=scrapedurl,\n thumbnail=\"https://raw.githubusercontent.com/orione7/Pelis_images/master/channels_icon_pureita/anime_lista_P.png\",\n plot=scrapedplot))\n\n return itemlist\n\n\n# =================================================================\n\n\n# -----------------------------------------------------------------\ndef search(item, texto):\n logger.info(\"[cb01anime.py] \" + item.url + \" search \" + texto)\n\n item.url = host + \"/anime/?s=\" + texto\n\n return novita(item)\n\n\n# =================================================================\n\n\n# -----------------------------------------------------------------\ndef episodios(item):\n logger.info(\"[cb01anime.py] episodios\")\n\n itemlist = []\n\n # Descarga la página\n data = scrapertools.anti_cloudflare(item.url, headers)\n data = scrapertools.decodeHtmlentities(data)\n\n patron1 = '(?:

    |)(.*?)(?:

    )?(?:\\s*)?\\s*'\n patron2 = ']*>([^<]+)'\n matches1 = re.compile(patron1, re.DOTALL).findall(data)\n if len(matches1) > 0:\n for match1 in re.split('
    |

    ', matches1[0]):\n if len(match1) > 0:\n # Extrae las entradas\n titulo = None\n scrapedurl = ''\n matches2 = re.compile(patron2, re.DOTALL).finditer(match1)\n for match2 in matches2:\n if titulo is None:\n titulo = match2.group(2)\n scrapedurl += match2.group(1) + '#' + match2.group(2) + '|'\n if titulo is not None:\n title = item.title + \" \" + titulo\n itemlist.append(\n Item(channel=__channel__,\n action=\"findvideos\",\n contentType=\"episode\",\n title=title,\n extra=scrapedurl,\n fulltitle=item.fulltitle,\n show=item.show))\n\n if config.get_library_support() and len(itemlist) != 0:\n itemlist.append(\n Item(channel=__channel__,\n title=\"Aggiungi alla libreria\",\n url=item.url,\n action=\"add_serie_to_library\",\n extra=\"episodios\",\n show=item.show))\n itemlist.append(\n Item(channel=__channel__,\n title=\"Scarica tutti gli episodi della serie\",\n url=item.url,\n action=\"download_all_episodes\",\n extra=\"episodios\",\n show=item.show))\n\n return itemlist\n\n\n# =================================================================\n\n\n# -----------------------------------------------------------------\ndef findvideos(item):\n logger.info(\"[cb01anime.py] findvideos\")\n\n itemlist = []\n\n for match in item.extra.split(r'|'):\n match_split = match.split(r'#')\n scrapedurl = match_split[0]\n if len(scrapedurl) > 0:\n scrapedtitle = match_split[1]\n title = item.title + \" [COLOR blue][\" + scrapedtitle + \"][/COLOR]\"\n itemlist.append(\n Item(channel=__channel__,\n action=\"play\",\n title=title,\n url=scrapedurl,\n fulltitle=item.fulltitle,\n show=item.show,\n folder=False))\n\n return itemlist\n\n\n# =================================================================\n\n\n# -----------------------------------------------------------------\ndef play(item):\n logger.info(\"[cb01anime.py] play\")\n\n if '/goto/' in item.url:\n item.url = item.url.split('/goto/')[-1].decode('base64')\n\n item.url = item.url.replace('http://cineblog01.pw', 'http://k4pp4.pw')\n\n logger.debug(\"##############################################################\")\n if \"go.php\" in item.url:\n data = scrapertools.anti_cloudflare(item.url, headers)\n try:\n data = scrapertools.get_match(data, 'window.location.href = \"([^\"]+)\";')\n except IndexError:\n try:\n # data = scrapertools.get_match(data, r'clicca qui')\n # In alternativa, dato che a volte compare \"Clicca qui per proseguire\":\n data = scrapertools.get_match(data, r'.*?licca.*?')\n except IndexError:\n data = scrapertools.get_header_from_response(item.url, headers=headers, header_to_get=\"Location\")\n while 'vcrypt' in data:\n data = scrapertools.get_header_from_response(data, headers=headers, header_to_get=\"Location\")\n logger.debug(\"##### play go.php data ##\\n%s\\n##\" % data)\n elif \"/link/\" in item.url:\n data = scrapertools.anti_cloudflare(item.url, headers)\n from lib import jsunpack\n\n try:\n data = scrapertools.get_match(data, \"(eval\\(function\\(p,a,c,k,e,d.*?)\")\n data = jsunpack.unpack(data)\n logger.debug(\"##### play /link/ unpack ##\\n%s\\n##\" % data)\n except IndexError:\n logger.debug(\"##### The content is yet unpacked ##\\n%s\\n##\" % data)\n\n data = scrapertools.find_single_match(data, 'var link(?:\\s)?=(?:\\s)?\"([^\"]+)\";')\n while 'vcrypt' in data:\n data = scrapertools.get_header_from_response(data, headers=headers, header_to_get=\"Location\")\n logger.debug(\"##### play /link/ data ##\\n%s\\n##\" % data)\n else:\n data = item.url\n logger.debug(\"##### play else data ##\\n%s\\n##\" % data)\n logger.debug(\"##############################################################\")\n\n itemlist = servertools.find_video_items(data=data)\n\n for videoitem in itemlist:\n videoitem.title = item.show\n videoitem.fulltitle = item.fulltitle\n videoitem.show = item.show\n videoitem.thumbnail = item.thumbnail\n videoitem.channel = __channel__\n\n return itemlist\n\n\ndef HomePage(item):\n import xbmc\n xbmc.executebuiltin(\"ReplaceWindow(10024,plugin://plugin.video.streamondemand-pureita-master)\")\n", "repo_name": "kodirepositoryluxy/KM17_15.01.18-2", "sub_path": "addons/plugin.video.streamondemand-pureita-master/channels/cb01anime.py", "file_name": "cb01anime.py", "file_ext": "py", "file_size_in_byte": 15363, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "37", "api": [{"api_name": "core.config.get_setting", "line_number": 29, "usage_type": "call"}, {"api_name": "core.config", "line_number": 29, "usage_type": "name"}, {"api_name": "core.logger.info", "line_number": 38, "usage_type": "call"}, {"api_name": "core.logger", "line_number": 38, "usage_type": "name"}, {"api_name": "core.item.Item", "line_number": 46, "usage_type": "call"}, {"api_name": "core.item.Item", "line_number": 51, "usage_type": "call"}, {"api_name": "core.item.Item", "line_number": 56, "usage_type": "call"}, {"api_name": "core.item.Item", "line_number": 61, "usage_type": "call"}, {"api_name": "core.logger.info", "line_number": 75, "usage_type": "call"}, {"api_name": "core.logger", "line_number": 75, "usage_type": "name"}, {"api_name": "core.scrapertools.anti_cloudflare", "line_number": 79, "usage_type": "call"}, {"api_name": "core.scrapertools", "line_number": 79, "usage_type": "name"}, {"api_name": "re.compile", "line_number": 85, "usage_type": "call"}, {"api_name": "re.DOTALL", "line_number": 85, "usage_type": "attribute"}, {"api_name": "core.scrapertools.unescape", "line_number": 90, "usage_type": "call"}, {"api_name": "core.scrapertools", "line_number": 90, "usage_type": "name"}, {"api_name": "core.scrapertools.unescape", "line_number": 91, "usage_type": "call"}, {"api_name": "core.scrapertools", "line_number": 91, "usage_type": "name"}, {"api_name": "core.scrapertools.decodeHtmlentities", "line_number": 92, "usage_type": "call"}, {"api_name": "core.scrapertools", "line_number": 92, "usage_type": "name"}, {"api_name": "core.logger.info", "line_number": 97, "usage_type": "call"}, {"api_name": "core.logger", "line_number": 97, "usage_type": "name"}, {"api_name": "core.httptools.get_url_headers", "line_number": 101, "usage_type": "call"}, {"api_name": "core.httptools", "line_number": 101, "usage_type": "name"}, {"api_name": "core.item.Item", "line_number": 106, "usage_type": "call"}, {"api_name": "core.scrapertools.get_match", "line_number": 118, "usage_type": "call"}, {"api_name": "core.scrapertools", "line_number": 118, "usage_type": "name"}, {"api_name": "core.item.Item", "line_number": 120, "usage_type": "call"}, {"api_name": "core.item.Item", "line_number": 126, "usage_type": "call"}, {"api_name": "core.logger.info", "line_number": 142, "usage_type": "call"}, {"api_name": "core.logger", "line_number": 142, "usage_type": "name"}, {"api_name": "core.scrapertools.anti_cloudflare", "line_number": 145, "usage_type": "call"}, {"api_name": "core.scrapertools", "line_number": 145, "usage_type": "name"}, {"api_name": "core.scrapertools.get_match", "line_number": 148, "usage_type": "call"}, {"api_name": "core.scrapertools", "line_number": 148, "usage_type": "name"}, {"api_name": "re.compile", "line_number": 152, "usage_type": "call"}, {"api_name": "re.DOTALL", "line_number": 152, "usage_type": "attribute"}, {"api_name": "core.scrapertools.printMatches", "line_number": 153, "usage_type": "call"}, {"api_name": "core.scrapertools", "line_number": 153, "usage_type": "name"}, {"api_name": "core.scrapertools.decodeHtmlentities", "line_number": 156, "usage_type": "call"}, {"api_name": "core.scrapertools", "line_number": 156, "usage_type": "name"}, {"api_name": "core.logger.info", "line_number": 157, "usage_type": "call"}, {"api_name": "core.logger", "line_number": 157, "usage_type": "name"}, {"api_name": "core.item.Item", "line_number": 160, "usage_type": "call"}, {"api_name": "core.logger.info", "line_number": 173, "usage_type": "call"}, {"api_name": "core.logger", "line_number": 173, "usage_type": "name"}, {"api_name": "core.scrapertools.anti_cloudflare", "line_number": 176, "usage_type": "call"}, {"api_name": "core.scrapertools", "line_number": 176, "usage_type": "name"}, {"api_name": "core.scrapertools.get_match", "line_number": 179, "usage_type": "call"}, {"api_name": "core.scrapertools", "line_number": 179, "usage_type": "name"}, {"api_name": "re.compile", "line_number": 183, "usage_type": "call"}, {"api_name": "re.DOTALL", "line_number": 183, "usage_type": "attribute"}, {"api_name": "core.scrapertools.printMatches", "line_number": 184, "usage_type": "call"}, {"api_name": "core.scrapertools", "line_number": 184, "usage_type": "name"}, {"api_name": "core.logger.info", "line_number": 191, "usage_type": "call"}, {"api_name": "core.logger", "line_number": 191, "usage_type": "name"}, {"api_name": "core.item.Item", "line_number": 193, "usage_type": "call"}, {"api_name": "core.logger.info", "line_number": 208, "usage_type": "call"}, {"api_name": "core.logger", "line_number": 208, "usage_type": "name"}, {"api_name": "core.scrapertools.anti_cloudflare", "line_number": 211, "usage_type": "call"}, {"api_name": "core.scrapertools", "line_number": 211, "usage_type": "name"}, {"api_name": "core.scrapertools.get_match", "line_number": 215, "usage_type": "call"}, {"api_name": "core.scrapertools", "line_number": 215, "usage_type": "name"}, {"api_name": "re.compile", "line_number": 219, "usage_type": "call"}, {"api_name": "re.DOTALL", "line_number": 219, "usage_type": "attribute"}, {"api_name": "core.scrapertools.printMatches", "line_number": 220, "usage_type": "call"}, {"api_name": "core.scrapertools", "line_number": 220, "usage_type": "name"}, {"api_name": "core.logger.info", "line_number": 227, "usage_type": "call"}, {"api_name": "core.logger", "line_number": 227, "usage_type": "name"}, {"api_name": "core.item.Item", "line_number": 229, "usage_type": "call"}, {"api_name": "core.logger.info", "line_number": 246, "usage_type": "call"}, {"api_name": "core.logger", "line_number": 246, "usage_type": "name"}, {"api_name": "core.logger.info", "line_number": 258, "usage_type": "call"}, {"api_name": "core.logger", "line_number": 258, "usage_type": "name"}, {"api_name": "core.scrapertools.anti_cloudflare", "line_number": 263, "usage_type": "call"}, {"api_name": "core.scrapertools", "line_number": 263, "usage_type": "name"}, {"api_name": "core.scrapertools.decodeHtmlentities", "line_number": 264, "usage_type": "call"}, {"api_name": "core.scrapertools", "line_number": 264, "usage_type": "name"}, {"api_name": "re.compile", "line_number": 268, "usage_type": "call"}, {"api_name": "re.DOTALL", "line_number": 268, "usage_type": "attribute"}, {"api_name": "re.split", "line_number": 270, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 275, "usage_type": "call"}, {"api_name": "re.DOTALL", "line_number": 275, "usage_type": "attribute"}, {"api_name": "core.item.Item", "line_number": 283, "usage_type": "call"}, {"api_name": "core.config.get_library_support", "line_number": 291, "usage_type": "call"}, {"api_name": "core.config", "line_number": 291, "usage_type": "name"}, {"api_name": "core.item.Item", "line_number": 293, "usage_type": "call"}, {"api_name": "core.item.Item", "line_number": 300, "usage_type": "call"}, {"api_name": "core.logger.info", "line_number": 315, "usage_type": "call"}, {"api_name": "core.logger", "line_number": 315, "usage_type": "name"}, {"api_name": "core.item.Item", "line_number": 326, "usage_type": "call"}, {"api_name": "core.logger.info", "line_number": 342, "usage_type": "call"}, {"api_name": "core.logger", "line_number": 342, "usage_type": "name"}, {"api_name": "core.logger.debug", "line_number": 349, "usage_type": "call"}, {"api_name": "core.logger", "line_number": 349, "usage_type": "name"}, {"api_name": "core.scrapertools.anti_cloudflare", "line_number": 351, "usage_type": "call"}, {"api_name": "core.scrapertools", "line_number": 351, "usage_type": "name"}, {"api_name": "core.scrapertools.get_match", "line_number": 353, "usage_type": "call"}, {"api_name": "core.scrapertools", "line_number": 353, "usage_type": "name"}, {"api_name": "core.scrapertools.get_match", "line_number": 358, "usage_type": "call"}, {"api_name": "core.scrapertools", "line_number": 358, "usage_type": "name"}, {"api_name": "core.scrapertools.get_header_from_response", "line_number": 360, "usage_type": "call"}, {"api_name": "core.scrapertools", "line_number": 360, "usage_type": "name"}, {"api_name": "core.scrapertools.get_header_from_response", "line_number": 362, "usage_type": "call"}, {"api_name": "core.scrapertools", "line_number": 362, "usage_type": "name"}, {"api_name": "core.logger.debug", "line_number": 363, "usage_type": "call"}, {"api_name": "core.logger", "line_number": 363, "usage_type": "name"}, {"api_name": "core.scrapertools.anti_cloudflare", "line_number": 365, "usage_type": "call"}, {"api_name": "core.scrapertools", "line_number": 365, "usage_type": "name"}, {"api_name": "core.scrapertools.get_match", "line_number": 369, "usage_type": "call"}, {"api_name": "core.scrapertools", "line_number": 369, "usage_type": "name"}, {"api_name": "lib.jsunpack.unpack", "line_number": 370, "usage_type": "call"}, {"api_name": "lib.jsunpack", "line_number": 370, "usage_type": "name"}, {"api_name": "core.logger.debug", "line_number": 371, "usage_type": "call"}, {"api_name": "core.logger", "line_number": 371, "usage_type": "name"}, {"api_name": "core.logger.debug", "line_number": 373, "usage_type": "call"}, {"api_name": "core.logger", "line_number": 373, "usage_type": "name"}, {"api_name": "core.scrapertools.find_single_match", "line_number": 375, "usage_type": "call"}, {"api_name": "core.scrapertools", "line_number": 375, "usage_type": "name"}, {"api_name": "core.scrapertools.get_header_from_response", "line_number": 377, "usage_type": "call"}, {"api_name": "core.scrapertools", "line_number": 377, "usage_type": "name"}, {"api_name": "core.logger.debug", "line_number": 378, "usage_type": "call"}, {"api_name": "core.logger", "line_number": 378, "usage_type": "name"}, {"api_name": "core.logger.debug", "line_number": 381, "usage_type": "call"}, {"api_name": "core.logger", "line_number": 381, "usage_type": "name"}, {"api_name": "core.logger.debug", "line_number": 382, "usage_type": "call"}, {"api_name": "core.logger", "line_number": 382, "usage_type": "name"}, {"api_name": "core.servertools.find_video_items", "line_number": 384, "usage_type": "call"}, {"api_name": "core.servertools", "line_number": 384, "usage_type": "name"}, {"api_name": "xbmc.executebuiltin", "line_number": 398, "usage_type": "call"}]} +{"seq_id": "26947024777", "text": "import sys\nimport functools\nfrom logging import getLogger\n\nfrom . import coroutine\nfrom .coroutine import Coroutine\nfrom .popen import Popen\nfrom .fd_pool import FDPool\n\nlogger = getLogger(__name__)\n\n\nclass Worker(object):\n\tdef __init__(self, entry_func):\n\t\tself.entry_func = entry_func\n\t\tself.wrapper = self._wrap(entry_func)\n\t\tself._fd_pool = FDPool.get_instance()\n\n\tdef __call__(self):\n\t\treturn self.wrapper()\n\n\tdef _wrap(self, func):\n\t\t@functools.wraps(func)\n\t\tdef wrapper():\n\t\t\tself._fd_pool.on_fork()\n\t\t\tfunc()\n\t\t\tsys.exit(0)\n\n\t\treturn wrapper\n\n\nclass WorkerManager(object):\n\tdef __init__(\n\t\tself, entry_func, replicas, graceful_timeout=30, heartbeat_timeout=30\n\t):\n\t\tself.entry_func = entry_func\n\t\tself.replicas = replicas\n\t\tself.graceful_timeout = graceful_timeout\n\t\tself.heartbeat_timeout = heartbeat_timeout\n\n\t\tself._popens = []\n\t\tself._running = False\n\n\t\tself.worker = Worker(entry_func)\n\n\tdef run(self):\n\t\tself._running = True\n\t\twhile self._running:\n\t\t\tCoroutine(target=self.maintain).start()\n\t\t\tcoroutine.sleep(1)\n\n\tdef stop(self):\n\t\tself._running = False\n\t\tself.purge(sterilize=True)\n\n\tdef purge(self, sterilize=False):\n\t\tlogger.info('[arbiter] kill all children')\n\n\t\tif sterilize:\n\t\t\tself.replicas = 0\n\n\t\tcoros = []\n\t\twhile self._popens:\n\t\t\tpopen = self._popens.pop()\n\t\t\tcoro = Coroutine(\n\t\t\t\ttarget=self._gracefully_terminate, args=(popen, self.graceful_timeout)\n\t\t\t)\n\t\t\tcoro.start()\n\t\t\tcoros.append(coro)\n\n\t\tfor coro in coros:\n\t\t\tcoro.join()\n\n\tdef _gracefully_terminate(self, popen, timeout):\n\t\tpopen.terminate()\n\t\texit_code = popen.wait(timeout)\n\t\tif exit_code is None:\n\t\t\tpopen.kill()\n\n\tdef maintain(self):\n\t\tlogger.debug('[arbiter] maintaining workers')\n\t\tself._reap()\n\t\tself._repopulate()\n\t\tself._depopulate()\n\n\tdef _reap(self):\n\t\tfor i, popen in enumerate(self._popens):\n\t\t\tif popen.poll() is not None:\n\t\t\t\tlogger.info('[arbiter] worker %s reaped' % popen.pid)\n\t\t\t\tdel self._popens[i]\n\n\tdef _depopulate(self):\n\t\tcoros = []\n\t\twhile len(self._popens) > self.replicas:\n\t\t\tpopen = self._popens.pop()\n\t\t\tcoro = Coroutine(\n\t\t\t\ttarget=self._gracefully_terminate, args=(\n\t\t\t\tpopen,\n\t\t\t\tself.graceful_timeout,\n\t\t\t\t)\n\t\t\t)\n\t\t\tcoro.start()\n\t\t\tcoros.append(coro)\n\n\t\tfor coro in coros:\n\t\t\tcoro.join()\n\n\tdef _repopulate(self):\n\t\tshortage = self.replicas - len(self._popens)\n\t\tfor _ in range(shortage):\n\t\t\tpopen = Popen(self.worker)\n\t\t\tself._popens.append(popen)\n\n\t\tif shortage > 0:\n\t\t\tlogger.info('[arbiter] %s worker(s) supplemented' % shortage)\n\n\tdef incr_worker(self):\n\t\tlogger.info('[arbiter] one worker increased')\n\t\tself.replicas += 1\n\t\tself.maintain()\n\n\tdef decr_worker(self):\n\t\tlogger.info('[arbiter] one worker decreased')\n\t\tself.replicas -= 1\n\t\tself.maintain()\n", "repo_name": "jschwinger233/arbiter", "sub_path": "arbiter/worker.py", "file_name": "worker.py", "file_ext": "py", "file_size_in_byte": 2678, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "logging.getLogger", "line_number": 10, "usage_type": "call"}, {"api_name": "fd_pool.FDPool.get_instance", "line_number": 17, "usage_type": "call"}, {"api_name": "fd_pool.FDPool", "line_number": 17, "usage_type": "name"}, {"api_name": "sys.exit", "line_number": 27, "usage_type": "call"}, {"api_name": "functools.wraps", "line_number": 23, "usage_type": "call"}, {"api_name": "coroutine.Coroutine", "line_number": 49, "usage_type": "call"}, {"api_name": "coroutine.sleep", "line_number": 50, "usage_type": "call"}, {"api_name": "coroutine.Coroutine", "line_number": 65, "usage_type": "call"}, {"api_name": "popen.terminate", "line_number": 75, "usage_type": "call"}, {"api_name": "popen.wait", "line_number": 76, "usage_type": "call"}, {"api_name": "popen.kill", "line_number": 78, "usage_type": "call"}, {"api_name": "popen.poll", "line_number": 88, "usage_type": "call"}, {"api_name": "popen.pid", "line_number": 89, "usage_type": "attribute"}, {"api_name": "coroutine.Coroutine", "line_number": 96, "usage_type": "call"}, {"api_name": "popen.Popen", "line_number": 111, "usage_type": "call"}]} +{"seq_id": "13836179912", "text": "import lime\nfrom lime import lime_image\nimport lime.lime_tabular\nfrom lime import lime_text\nfrom lime.lime_text import LimeTextExplainer\nfrom lime import submodular_pick\nimport numpy as np\nimport os\nimport keras\nfrom keras.preprocessing import image\nfrom keras.applications import inception_v3 as inc_net\nimport sklearn\nfrom skimage.segmentation import mark_boundaries\n\n\ndef impl(model_predict,test,train=None,feature_names=None,class_names=None,idx_test=None,num_features=6,top_labels=5,hide_color=0,num_samples=1000):\n \n \n if(type(test) == bytes):\n img = transforming_img(test)\n explainer = lime_image.LimeImageExplainer(verbose=False)\n explanation = explainer.explain_instance(image= img[0], classifier_fn=model_predict, top_labels=top_labels, hide_color=hide_color, num_samples=num_samples)\n listtop = explanation.top_labels\n result = []\n for n in range(top_labels):\n top_local = listtop[n]\n temp, mask = explanation.get_image_and_mask(top_local, positive_only=True, num_features=5, hide_rest=True)\n img = mark_boundaries(temp / 2 + 0.5, mask)\n result.append(img)\n return result\n \n elif(type(test) == np.ndarray):\n explainer = lime.lime_tabular.LimeTabularExplainer(training_data=train, feature_names=feature_names, class_names=class_names, discretize_continuous=True)\n exp = explainer.explain_instance(data_row=test[idx_test], predict_fn=model_predict, num_features=num_features)\n return exp.show_in_notebook(show_table=True, show_all=False) \n else:\n explainer = LimeTextExplainer(class_names=class_names)\n exp = explainer.explain_instance(text_instance=test[idx_test], classifier_fn=model_predict, num_features=num_features)\n sp_obj = submodular_pick.SubmodularPick(explainer=explainer, data=test, predict_fn=model_predict, sample_size=2, num_features=6,num_exps_desired=2,top_labels=3)\n return [exp.as_pyplot_figure(label=0) for exp in sp_obj.sp_explanations];\n\ndef transforming_img(exemple):\n with open(os.path.join('data','image.jpg'), 'wb') as handler:\n handler.write(exemple)\n images = transform_img_fn([os.path.join('data','image.jpg')])\n\n return images\n\n\ndef transform_img_fn(path_list):\n #Transform image so it can be processed by inception.\n out = []\n for img_path in path_list:\n img = image.load_img(img_path, target_size=(299, 299))\n x = image.img_to_array(img)\n x = np.expand_dims(x, axis=0)\n x = inc_net.preprocess_input(x)\n out.append(x)\n return np.vstack(out)\n\n\n", "repo_name": "viniaraujoo/IPML", "sub_path": "impl/impl.py", "file_name": "impl.py", "file_ext": "py", "file_size_in_byte": 2662, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "lime.lime_image.LimeImageExplainer", "line_number": 21, "usage_type": "call"}, {"api_name": "lime.lime_image", "line_number": 21, "usage_type": "name"}, {"api_name": "skimage.segmentation.mark_boundaries", "line_number": 28, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 32, "usage_type": "attribute"}, {"api_name": "lime.lime_tabular.LimeTabularExplainer", "line_number": 33, "usage_type": "call"}, {"api_name": "lime.lime_tabular", "line_number": 33, "usage_type": "attribute"}, {"api_name": "lime.lime_text.LimeTextExplainer", "line_number": 37, "usage_type": "call"}, {"api_name": "lime.submodular_pick.SubmodularPick", "line_number": 39, "usage_type": "call"}, {"api_name": "lime.submodular_pick", "line_number": 39, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 43, "usage_type": "call"}, {"api_name": "os.path", "line_number": 43, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 45, "usage_type": "call"}, {"api_name": "os.path", "line_number": 45, "usage_type": "attribute"}, {"api_name": "keras.preprocessing.image.load_img", "line_number": 54, "usage_type": "call"}, {"api_name": "keras.preprocessing.image", "line_number": 54, "usage_type": "name"}, {"api_name": "keras.preprocessing.image.img_to_array", "line_number": 55, "usage_type": "call"}, {"api_name": "keras.preprocessing.image", "line_number": 55, "usage_type": "name"}, {"api_name": "numpy.expand_dims", "line_number": 56, "usage_type": "call"}, {"api_name": "keras.applications.inception_v3.preprocess_input", "line_number": 57, "usage_type": "call"}, {"api_name": "keras.applications.inception_v3", "line_number": 57, "usage_type": "name"}, {"api_name": "numpy.vstack", "line_number": 59, "usage_type": "call"}]} +{"seq_id": "13704469435", "text": "import cv2\r\nimport numpy as np\r\nfrom bosdyn.api import image_pb2\r\nfrom bosdyn.client.image import ImageClient, build_image_request\r\n\r\nfrom SpotSDK.SpotCamera.GripperCameraParameter import GripperCameraParameter\r\n\r\ndef image_to_opencv(image, auto_rotate=True):\r\n \"\"\"Convert an image proto message to an openCV image.\"\"\"\r\n num_channels = 1 # Assume a default of 1 byte encodings.\r\n if image.shot.image.pixel_format == image_pb2.Image.PIXEL_FORMAT_DEPTH_U16:\r\n dtype = np.uint16\r\n extension = \".png\"\r\n else:\r\n dtype = np.uint8\r\n if image.shot.image.pixel_format == image_pb2.Image.PIXEL_FORMAT_RGB_U8:\r\n num_channels = 3\r\n elif image.shot.image.pixel_format == image_pb2.Image.PIXEL_FORMAT_RGBA_U8:\r\n num_channels = 4\r\n elif image.shot.image.pixel_format == image_pb2.Image.PIXEL_FORMAT_GREYSCALE_U8:\r\n num_channels = 1\r\n elif image.shot.image.pixel_format == image_pb2.Image.PIXEL_FORMAT_GREYSCALE_U16:\r\n num_channels = 1\r\n dtype = np.uint16\r\n extension = \".jpg\"\r\n\r\n img = np.frombuffer(image.shot.image.data, dtype=dtype)\r\n if image.shot.image.pixel_format == image_pb2.Image.PIXEL_FORMAT_DEPTH_U16:\r\n cv_depth = img.reshape(image.shot.image.rows,\r\n image.shot.image.cols)\r\n\r\n # Visual is a JPEG\r\n cv_visual = cv2.imdecode(np.frombuffer(image.shot.image.data, dtype=np.uint8), -1)\r\n\r\n # Convert the visual image from a single channel to RGB so we can add color\r\n # visual_rgb = cv_visual if len(cv_visual.shape) == 3 else cv2.cvtColor(cv_visual, cv2.COLOR_GRAY2RGB)\r\n\r\n # cv2.applyColorMap() only supports 8-bit; convert from 16-bit to 8-bit and do scaling\r\n min_val = np.min(cv_depth)\r\n max_val = np.max(cv_depth)\r\n depth_range = max_val - min_val\r\n try:\r\n depth8 = (255.0 / depth_range * (cv_depth - min_val)).astype('uint8')\r\n except RuntimeWarning:\r\n print(\"image 없음\")\r\n # os._exit(1)\r\n depth8_rgb = cv2.cvtColor(depth8, cv2.COLOR_GRAY2RGB)\r\n depth_color = cv2.applyColorMap(depth8_rgb, cv2.COLORMAP_JET)\r\n # Add the two images together.\r\n # out = cv2.addWeighted(visual_rgb, 0.5, depth_color, 0.5, 0)\r\n\r\n if auto_rotate:\r\n # out = ndimage.rotate(depth_color, ROTATION_ANGLE[image.source.name])\r\n if image.source.name[0:5] == \"front\":\r\n depth_color = cv2.rotate(depth_color, cv2.ROTATE_90_CLOCKWISE)\r\n\r\n elif image.source.name[0:5] == \"right\":\r\n depth_color = cv2.rotate(depth_color, cv2.ROTATE_180)\r\n # pixel_format = image.shot.image.pixel_format\r\n\r\n return depth_color, extension\r\n\r\n if image.shot.image.format == image_pb2.Image.FORMAT_RAW:\r\n try:\r\n # Attempt to reshape array into a RGB rows X cols shape.\r\n img = img.reshape((image.shot.image.rows, image.shot.image.cols, num_channels))\r\n except ValueError:\r\n # Unable to reshape the image data, trying a regular decode.\r\n img = cv2.imdecode(img, -1)\r\n else:\r\n img = cv2.imdecode(img, -1)\r\n\r\n if auto_rotate:\r\n # img = ndimage.rotate(img, ROTATION_ANGLE[image.source.name])\r\n if image.source.name[0:5] == \"front\":\r\n img = cv2.rotate(img, cv2.ROTATE_90_CLOCKWISE)\r\n\r\n elif image.source.name[0:5] == \"right\":\r\n img = cv2.rotate(img, cv2.ROTATE_180)\r\n # pixel_format = image.shot.image.pixel_format\r\n if len(img.shape) == 2:\r\n img = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB)\r\n else:\r\n print(\"exception\")\r\n print(img.shape)\r\n return img, extension\r\n\r\nclass Camera:\r\n\r\n def __init__(self, robot):\r\n self.image_client = robot.ensure_client(ImageClient.default_service_name)\r\n self.ParameterManager = GripperCameraParameter(robot)\r\n self.video_mode = False\r\n\r\n def take_image(self):\r\n source_name = 'hand_color_image'\r\n image = self.image_client.get_image_from_sources([source_name])\r\n image, _ = image_to_opencv(image[0], auto_rotate=True)\r\n return image\r\n\r\n def take_image_from_source(self, camera_name):\r\n image_client = self.image_client\r\n source_name = camera_name\r\n image_sources = image_client.list_image_sources()\r\n source = [source for source in image_sources if source.name == source_name]\r\n pixel_format = source[0].pixel_formats[0]\r\n image_request = [\r\n build_image_request(source_name, pixel_format=pixel_format)\r\n # for source in image_sources if source.name == source_name\r\n ]\r\n image_responses = image_client.get_image(image_request)\r\n image, _ = image_to_opencv(image_responses[0], auto_rotate=True)\r\n return image\r\n", "repo_name": "sain0722/test0802", "sub_path": "SpotSDK/SpotCamera/Camera.py", "file_name": "Camera.py", "file_ext": "py", "file_size_in_byte": 4867, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "bosdyn.api.image_pb2.Image", "line_number": 11, "usage_type": "attribute"}, {"api_name": "bosdyn.api.image_pb2", "line_number": 11, "usage_type": "name"}, {"api_name": "numpy.uint16", "line_number": 12, "usage_type": "attribute"}, {"api_name": "numpy.uint8", "line_number": 15, "usage_type": "attribute"}, {"api_name": "bosdyn.api.image_pb2.Image", "line_number": 16, "usage_type": "attribute"}, {"api_name": "bosdyn.api.image_pb2", "line_number": 16, "usage_type": "name"}, {"api_name": "bosdyn.api.image_pb2.Image", "line_number": 18, "usage_type": "attribute"}, {"api_name": "bosdyn.api.image_pb2", "line_number": 18, "usage_type": "name"}, {"api_name": "bosdyn.api.image_pb2.Image", "line_number": 20, "usage_type": "attribute"}, {"api_name": "bosdyn.api.image_pb2", "line_number": 20, "usage_type": "name"}, {"api_name": "bosdyn.api.image_pb2.Image", "line_number": 22, "usage_type": "attribute"}, {"api_name": "bosdyn.api.image_pb2", "line_number": 22, "usage_type": "name"}, {"api_name": "numpy.uint16", "line_number": 24, "usage_type": "attribute"}, {"api_name": "numpy.frombuffer", "line_number": 27, "usage_type": "call"}, {"api_name": "bosdyn.api.image_pb2.Image", "line_number": 28, "usage_type": "attribute"}, {"api_name": "bosdyn.api.image_pb2", "line_number": 28, "usage_type": "name"}, {"api_name": "cv2.imdecode", "line_number": 33, "usage_type": "call"}, {"api_name": "numpy.frombuffer", "line_number": 33, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 33, "usage_type": "attribute"}, {"api_name": "numpy.min", "line_number": 39, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 40, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 47, "usage_type": "call"}, {"api_name": "cv2.COLOR_GRAY2RGB", "line_number": 47, "usage_type": "attribute"}, {"api_name": "cv2.applyColorMap", "line_number": 48, "usage_type": "call"}, {"api_name": "cv2.COLORMAP_JET", "line_number": 48, "usage_type": "attribute"}, {"api_name": "cv2.rotate", "line_number": 55, "usage_type": "call"}, {"api_name": "cv2.ROTATE_90_CLOCKWISE", "line_number": 55, "usage_type": "attribute"}, {"api_name": "cv2.rotate", "line_number": 58, "usage_type": "call"}, {"api_name": "cv2.ROTATE_180", "line_number": 58, "usage_type": "attribute"}, {"api_name": "bosdyn.api.image_pb2.Image", "line_number": 63, "usage_type": "attribute"}, {"api_name": "bosdyn.api.image_pb2", "line_number": 63, "usage_type": "name"}, {"api_name": "cv2.imdecode", "line_number": 69, "usage_type": "call"}, {"api_name": "cv2.imdecode", "line_number": 71, "usage_type": "call"}, {"api_name": "cv2.rotate", "line_number": 76, "usage_type": "call"}, {"api_name": "cv2.ROTATE_90_CLOCKWISE", "line_number": 76, "usage_type": "attribute"}, {"api_name": "cv2.rotate", "line_number": 79, "usage_type": "call"}, {"api_name": "cv2.ROTATE_180", "line_number": 79, "usage_type": "attribute"}, {"api_name": "cv2.cvtColor", "line_number": 82, "usage_type": "call"}, {"api_name": "cv2.COLOR_GRAY2RGB", "line_number": 82, "usage_type": "attribute"}, {"api_name": "bosdyn.client.image.ImageClient.default_service_name", "line_number": 91, "usage_type": "attribute"}, {"api_name": "bosdyn.client.image.ImageClient", "line_number": 91, "usage_type": "name"}, {"api_name": "SpotSDK.SpotCamera.GripperCameraParameter.GripperCameraParameter", "line_number": 92, "usage_type": "call"}, {"api_name": "bosdyn.client.image.build_image_request", "line_number": 108, "usage_type": "call"}]} +{"seq_id": "13679097355", "text": "\"\"\"\r\nПолигон - 1\r\n\"\"\"\r\nimport datetime\r\n\r\nPATH = 'what-what'\r\n\r\n\r\ndef write_memory(text_memory, mode):\r\n with open(PATH, mode=mode, encoding='utf-8') as wf:\r\n wf.write(f'{datetime.datetime.now()}\\n'\r\n f'{text_memory}\\n')\r\n with open(PATH, mode='r', encoding='utf-8') as wf:\r\n print(*wf)\r\n\r\n\r\nif __name__ == '__main__':\r\n write_memory('Но вот как это сделать...?', 'a')\r\n", "repo_name": "FoxyWRTH/FXR_LH", "sub_path": "Poligon_1.py", "file_name": "Poligon_1.py", "file_ext": "py", "file_size_in_byte": 434, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "datetime.datetime.now", "line_number": 11, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 11, "usage_type": "attribute"}]} +{"seq_id": "10729184241", "text": "import pandas as pd\nimport matplotlib.pyplot as plt\n\n# Load the events data with state information\nevents_data = pd.read_csv('events_with_states.csv')\n\n# Group the data by state and count the number of events in each state\nevents_by_state = events_data.groupby('state_name').size().reset_index(name='count')\n\n# Create a bar chart showing the number of events in each state\nplt.bar(events_by_state['state_name'], events_by_state['count'])\nplt.xticks(rotation=90)\nplt.xlabel('State')\nplt.ylabel('Number of Events')\nplt.title('Distribution of Events Across States')\nplt.show()\n", "repo_name": "iliana-by/test_DirectlyApply", "sub_path": "task2_bar chart.py", "file_name": "task2_bar chart.py", "file_ext": "py", "file_size_in_byte": 574, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "pandas.read_csv", "line_number": 5, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.bar", "line_number": 11, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 11, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 12, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 12, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 13, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 13, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 14, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 14, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 15, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 15, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 16, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 16, "usage_type": "name"}]} +{"seq_id": "19946098116", "text": "# credit to my homework from Coursera:\r\n# Neural Networks and DeepLearning,\r\n# Hyperparameter tuning, Regulation and Optimization\r\n\r\nimport pandas as pd\r\nimport numpy as np\r\nimport math\r\nimport matplotlib.pyplot as plt\r\n\r\n\r\ndef sigmoid(x):\r\n \"\"\"\r\n Compute the sigmoid of x\r\n \"\"\"\r\n s = 1 / (1 + np.exp(-x))\r\n return s\r\n\r\n\r\ndef relu(x):\r\n \"\"\"\r\n Compute the relu of x\r\n \"\"\"\r\n s = np.maximum(0, x)\r\n\r\n return s\r\n\r\n\r\ndef initialize_parameters_deep(layer_dims):\r\n \"\"\"\r\n Arguments:\r\n layer_dims -- python array (list) containing the dimensions of each layer in our network\r\n\r\n Returns:\r\n parameters -- python dictionary containing your parameters \"W1\", \"b1\", ..., \"WL\", \"bL\":\r\n Wl -- weight matrix of shape (layer_dims[l], layer_dims[l-1])\r\n bl -- bias vector of shape (layer_dims[l], 1)\r\n \"\"\"\r\n\r\n np.random.seed(1)\r\n parameters = {}\r\n L = len(layer_dims) # number of layers in the network\r\n\r\n for l in range(1, L):\r\n parameters['W' + str(l)] = np.random.randn(layer_dims[l], layer_dims[l - 1]) / np.sqrt(\r\n layer_dims[l - 1]) # *0.01\r\n parameters['b' + str(l)] = np.zeros((layer_dims[l], 1))\r\n\r\n assert (parameters['W' + str(l)].shape == (layer_dims[l], layer_dims[l - 1]))\r\n assert (parameters['b' + str(l)].shape == (layer_dims[l], 1))\r\n\r\n return parameters\r\n\r\n\r\ndef initialize_adam(parameters):\r\n \"\"\"\r\n Initializes v and s as two python dictionaries with:\r\n - keys: \"dW1\", \"db1\", ..., \"dWL\", \"dbL\"\r\n - values: numpy arrays of zeros of the same shape as the corresponding gradients/parameters.\r\n\r\n Arguments:\r\n parameters -- python dictionary containing your parameters.\r\n parameters[\"W\" + str(l)] = Wl\r\n parameters[\"b\" + str(l)] = bl\r\n\r\n Returns:\r\n v -- python dictionary that will contain the exponentially weighted average of the gradient.\r\n v[\"dW\" + str(l)] = ...\r\n v[\"db\" + str(l)] = ...\r\n s -- python dictionary that will contain the exponentially weighted average of the squared gradient.\r\n s[\"dW\" + str(l)] = ...\r\n s[\"db\" + str(l)] = ...\r\n\r\n \"\"\"\r\n\r\n L = len(parameters) // 2 # number of layers in the neural networks\r\n v = {}\r\n s = {}\r\n\r\n # Initialize v, s. Input: \"parameters\". Outputs: \"v, s\".\r\n for l in range(L):\r\n v[\"dW\" + str(l + 1)] = np.zeros_like(parameters[\"W\" + str(l + 1)])\r\n v[\"db\" + str(l + 1)] = np.zeros_like(parameters[\"b\" + str(l + 1)])\r\n s[\"dW\" + str(l + 1)] = np.zeros_like(parameters[\"W\" + str(l + 1)])\r\n s[\"db\" + str(l + 1)] = np.zeros_like(parameters[\"b\" + str(l + 1)])\r\n\r\n return v, s\r\n\r\n\r\ndef random_mini_batches(X, Y, mini_batch_size=64, seed=0):\r\n \"\"\"\r\n Creates a list of random minibatches from (X, Y)\r\n\r\n Arguments:\r\n X -- input data, of shape (input size, number of examples)\r\n Y -- true \"label\" vector (1 for blue dot / 0 for red dot), of shape (1, number of examples)\r\n mini_batch_size -- size of the mini-batches, integer\r\n\r\n Returns:\r\n mini_batches -- list of synchronous (mini_batch_X, mini_batch_Y)\r\n \"\"\"\r\n\r\n np.random.seed(seed) # To make your \"random\" minibatches the same as ours\r\n m = X.shape[1] # number of training examples\r\n mini_batches = []\r\n\r\n # Step 1: Shuffle (X, Y)\r\n permutation = list(np.random.permutation(m))\r\n shuffled_X = X.values[:, permutation]\r\n shuffled_Y = Y[:, permutation].reshape((1, m))\r\n\r\n # Step 2: Partition (shuffled_X, shuffled_Y). Minus the end case.\r\n num_complete_minibatches = math.floor(\r\n m / mini_batch_size) # number of mini batches of size mini_batch_size in your partitionning\r\n for k in range(0, num_complete_minibatches):\r\n mini_batch_X = shuffled_X[:, k * mini_batch_size: (k + 1) * mini_batch_size]\r\n mini_batch_Y = shuffled_Y[:, k * mini_batch_size: (k + 1) * mini_batch_size]\r\n mini_batch = (mini_batch_X, mini_batch_Y)\r\n mini_batches.append(mini_batch)\r\n\r\n # Handling the end case (last mini-batch < mini_batch_size)\r\n if m % mini_batch_size != 0:\r\n mini_batch_X = shuffled_X[:, (mini_batch_size * num_complete_minibatches):]\r\n mini_batch_Y = shuffled_Y[:, (mini_batch_size * num_complete_minibatches):]\r\n mini_batch = (mini_batch_X, mini_batch_Y)\r\n mini_batches.append(mini_batch)\r\n\r\n return mini_batches\r\n\r\n\r\ndef predict(parameters, X, y=None):\r\n \"\"\"\r\n This function is used to predict the results of a n-layer neural network.\r\n\r\n Arguments:\r\n X -- data set of examples you would like to label\r\n\r\n Returns:\r\n p -- predictions for the given dataset X\r\n \"\"\"\r\n\r\n m = X.shape[1]\r\n p = np.zeros((1, m), dtype=np.int)\r\n\r\n # Forward propagation\r\n # a3, caches = forward_propagation(X, parameters)\r\n AL, parameters, Z, A = forward_propagation(X, parameters)\r\n\r\n # convert probas to 0/1 predictions\r\n for i in range(0, AL.shape[1]):\r\n if AL[0, i] > 0.5:\r\n p[0, i] = 1\r\n else:\r\n p[0, i] = 0\r\n\r\n # print results\r\n if y is not None:\r\n print(\"Accuracy: \" + str(np.mean((p[0, :] == y[0, :]))))\r\n\r\n return p\r\n\r\n\r\ndef compute_cost(AL, Y):\r\n \"\"\"\r\n Implement the cost function defined by equation (7).\r\n\r\n Arguments:\r\n AL -- probability vector corresponding to your label predictions, shape (1, number of examples)\r\n Y -- true \"label\" vector (for example: containing 0 if non-cat, 1 if cat), shape (1, number of examples)\r\n\r\n Returns:\r\n cost -- cross-entropy cost\r\n \"\"\"\r\n epsilon = 1e-7\r\n m = Y.shape[1]\r\n\r\n # Compute loss from aL and y.\r\n cost = (1. / m) * (-np.dot(Y, np.log(AL + epsilon).T) - np.dot(1 - Y, np.log(1 - AL + epsilon).T))\r\n\r\n cost = np.squeeze(cost) # To make sure your cost's shape is what we expect (e.g. this turns [[17]] into 17).\r\n assert (cost.shape == ())\r\n\r\n return cost\r\n\r\n\r\ndef compute_cost_with_regularization(AL, Y, parameters, lambd):\r\n \"\"\"\r\n Implement the cost function with L2 regularization. See formula (2) above.\r\n\r\n Arguments:\r\n AL -- post-activation, output of forward propagation, of shape (output size, number of examples)\r\n Y -- \"true\" labels vector, of shape (output size, number of examples)\r\n parameters -- python dictionary containing parameters of the model\r\n\r\n Returns:\r\n cost - value of the regularized loss function (formula (2))\r\n \"\"\"\r\n m = Y.shape[1]\r\n L = len(parameters) // 2\r\n\r\n cross_entropy_cost = compute_cost(AL, Y) # This gives you the cross-entropy part of the cost\r\n\r\n sum = 0\r\n for l in range(L):\r\n sum += np.sum(np.square(parameters[\"W\" + str(l + 1)]))\r\n\r\n L2_regularization_cost = lambd * sum / (2 * m)\r\n\r\n cost = cross_entropy_cost + L2_regularization_cost\r\n\r\n return cost\r\n\r\n\r\ndef forward_propagation(X, parameters):\r\n \"\"\"\r\n Implements the forward propagation (and computes the loss) presented in Figure 2.\r\n\r\n Arguments:\r\n X -- input dataset, of shape (input size, number of examples)\r\n parameters -- python dictionary containing your parameters \"W1\", \"b1\", \"W2\", \"b2\", ...:\r\n W1 -- weight matrix of shape ()\r\n b1 -- bias vector of shape ()\r\n ...\r\n\r\n Returns:\r\n loss -- the loss function (vanilla logistic loss)\r\n \"\"\"\r\n\r\n L = len(parameters) // 2\r\n Z = {}\r\n A = {}\r\n A[\"0\"] = X\r\n\r\n # LINEAR -> RELU -> LINEAR -> RELU -> ... -> LINEAR -> SIGMOID\r\n for l in range(L - 1):\r\n Z[str(l + 1)] = np.dot(parameters[\"W\" + str(l + 1)], A[str(l)]) + parameters[\"b\" + str(l + 1)]\r\n A[str(l + 1)] = relu(Z[str(l + 1)])\r\n Z[str(L)] = np.dot(parameters[\"W\" + str(L)], A[str(L - 1)]) + parameters[\"b\" + str(L)]\r\n A[str(L)] = sigmoid(Z[str(L)])\r\n\r\n return A[str(L)], parameters, Z, A\r\n\r\n\r\n# def backward_propagation_with_regularization(X, Y, cache, lambd):\r\ndef backward_propagation_with_regularization(X, Y, parameters, Z, A, lambd):\r\n \"\"\"\r\n Implements the backward propagation of our baseline model to which we added an L2 regularization.\r\n\r\n Arguments:\r\n X -- input dataset, of shape (input size, number of examples)\r\n Y -- \"true\" labels vector, of shape (output size, number of examples)\r\n cache -- cache output from forward_propagation()\r\n lambd -- regularization hyperparameter, scalar\r\n\r\n Returns:\r\n gradients -- A dictionary with the gradients with respect to each parameter, activation and pre-activation variables\r\n \"\"\"\r\n\r\n m = X.shape[1]\r\n L = len(parameters) // 2\r\n dZ = {}\r\n dW = {}\r\n db = {}\r\n dA = {}\r\n\r\n dZ[str(L)] = A[str(L)] - Y\r\n\r\n dW[str(L)] = 1. / m * np.dot(dZ[str(L)], A[str(L - 1)].T) + (lambd * parameters[\"W\" + str(L)]) / m\r\n db[str(L)] = 1. / m * np.sum(dZ[str(L)], axis=1, keepdims=True)\r\n\r\n for l in reversed(range(1, L)):\r\n dA[str(l)] = np.dot(parameters[\"W\" + str(l + 1)].T, dZ[str(l + 1)])\r\n dZ[str(l)] = np.multiply(dA[str(l)], np.int64(A[str(l)] > 0))\r\n dW[str(l)] = 1. / m * np.dot(dZ[str(l)], A[str(l - 1)].T) + (lambd * parameters[\"W\" + str(l)]) / m\r\n db[str(l)] = 1. / m * np.sum(dZ[str(l)], axis=1, keepdims=True)\r\n\r\n return dZ, dW, db, dA\r\n\r\n\r\ndef update_parameters_with_adam(parameters, dZ, dW, db, dA, v, s,\r\n t, learning_rate, beta1, beta2, epsilon):\r\n \"\"\"\r\n Update parameters using Adam\r\n\r\n Arguments:\r\n v -- Adam variable, moving average of the first gradient, python dictionary\r\n s -- Adam variable, moving average of the squared gradient, python dictionary\r\n learning_rate -- the learning rate, scalar.\r\n beta1 -- Exponential decay hyperparameter for the first moment estimates\r\n beta2 -- Exponential decay hyperparameter for the second moment estimates\r\n epsilon -- hyperparameter preventing division by zero in Adam updates\r\n\r\n Returns:\r\n parameters -- python dictionary containing your updated parameters\r\n v -- Adam variable, moving average of the first gradient, python dictionary\r\n s -- Adam variable, moving average of the squared gradient, python dictionary\r\n \"\"\"\r\n\r\n L = len(parameters) // 2 # number of layers in the neural networks\r\n v_corrected = {} # Initializing first moment estimate, python dictionary\r\n s_corrected = {} # Initializing second moment estimate, python dictionary\r\n\r\n # Perform Adam update on all parameters\r\n for l in range(L):\r\n # Moving average of the gradients. Inputs: \"v, grads, beta1\". Output: \"v\".\r\n v[\"dW\" + str(l + 1)] = beta1 * v[\"dW\" + str(l + 1)] + (1 - beta1) * dW[str(l + 1)]\r\n v[\"db\" + str(l + 1)] = beta1 * v[\"db\" + str(l + 1)] + (1 - beta1) * db[str(l + 1)]\r\n\r\n # Compute bias-corrected first moment estimate. Inputs: \"v, beta1, t\". Output: \"v_corrected\".\r\n v_corrected[\"dW\" + str(l + 1)] = v[\"dW\" + str(l + 1)] / (1 - np.power(beta1, t))\r\n v_corrected[\"db\" + str(l + 1)] = v[\"db\" + str(l + 1)] / (1 - np.power(beta1, t))\r\n\r\n # Moving average of the squared gradients. Inputs: \"s, grads, beta2\". Output: \"s\".\r\n s[\"dW\" + str(l + 1)] = beta2 * s[\"dW\" + str(l + 1)] + (1 - beta2) * np.power(dW[str(l + 1)], 2)\r\n s[\"db\" + str(l + 1)] = beta2 * s[\"db\" + str(l + 1)] + (1 - beta2) * np.power(db[str(l + 1)], 2)\r\n\r\n # Compute bias-corrected second raw moment estimate. Inputs: \"s, beta2, t\". Output: \"s_corrected\".\r\n s_corrected[\"dW\" + str(l + 1)] = s[\"dW\" + str(l + 1)] / (1 - np.power(beta2, t))\r\n s_corrected[\"db\" + str(l + 1)] = s[\"db\" + str(l + 1)] / (1 - np.power(beta2, t))\r\n\r\n # Update parameters.\r\n # Inputs: \"parameters, learning_rate, v_corrected, s_corrected, epsilon\". Output: \"parameters\".\r\n parameters[\"W\" + str(l + 1)] = parameters[\"W\" + str(l + 1)] - learning_rate * v_corrected[\r\n \"dW\" + str(l + 1)] / np.sqrt(s_corrected[\"dW\" + str(l + 1)] + epsilon)\r\n parameters[\"b\" + str(l + 1)] = parameters[\"b\" + str(l + 1)] - learning_rate * v_corrected[\r\n \"db\" + str(l + 1)] / np.sqrt(s_corrected[\"db\" + str(l + 1)] + epsilon)\r\n\r\n return parameters, v, s\r\n\r\n\r\ndef model(X, Y, layers_dims, optimizer, learning_rate=0.0007, mini_batch_size=64, beta=0.9,\r\n beta1=0.9, beta2=0.999, epsilon=1e-8, num_epochs=10000, print_cost=False, lambd=0):\r\n \"\"\"\r\n L-layer neural network model which can be run in different optimizer modes.\r\n\r\n Arguments:\r\n X -- input data, of shape (#attributes, number of examples)\r\n Y -- true \"label\" vector (1 for blue dot / 0 for red dot), of shape (1, number of examples)\r\n layers_dims -- python list, containing the size of each layer\r\n learning_rate -- the learning rate, scalar.\r\n mini_batch_size -- the size of a mini batch\r\n beta -- Momentum hyperparameter\r\n beta1 -- Exponential decay hyperparameter for the past gradients estimates\r\n beta2 -- Exponential decay hyperparameter for the past squared gradients estimates\r\n epsilon -- hyperparameter preventing division by zero in Adam updates\r\n num_epochs -- number of epochs\r\n print_cost -- True to print the cost every 1000 epochs\r\n\r\n Returns:\r\n parameters -- python dictionary containing your updated parameters\r\n \"\"\"\r\n\r\n L = len(layers_dims) # number of layers in the neural networks\r\n costs = [] # to keep track of the cost\r\n t = 0 # initializing the counter required for Adam update\r\n seed = 10\r\n\r\n # Initialize parameters\r\n parameters = initialize_parameters_deep(layers_dims)\r\n\r\n # Initialize the optimizer\r\n if optimizer == \"momentum\":\r\n pass\r\n # v = initialize_velocity(parameters)\r\n elif optimizer == \"adam\":\r\n v, s = initialize_adam(parameters)\r\n\r\n # Optimization loop\r\n for i in range(num_epochs):\r\n\r\n # Define the random minibatches. We increment the seed to reshuffle differently the dataset after each epoch\r\n seed = seed + 1\r\n minibatches = random_mini_batches(X, Y, mini_batch_size, seed)\r\n\r\n for minibatch in minibatches:\r\n\r\n # Select a minibatch\r\n (minibatch_X, minibatch_Y) = minibatch\r\n\r\n # Forward propagation\r\n AL, parameters, Z, A = forward_propagation(minibatch_X, parameters)\r\n\r\n # Compute cost\r\n cost = compute_cost_with_regularization(AL, minibatch_Y, parameters, lambd)\r\n\r\n # Backward propagation\r\n dZ, dW, db, dA = backward_propagation_with_regularization(minibatch_X, minibatch_Y, parameters, Z, A, lambd)\r\n\r\n # Update parameters\r\n if optimizer == \"gd\":\r\n pass\r\n # parameters = update_parameters_with_gd(parameters, grads, learning_rate)\r\n elif optimizer == \"momentum\":\r\n pass\r\n # parameters, v = update_parameters_with_momentum(parameters, grads, v, beta, learning_rate)\r\n elif optimizer == \"adam\":\r\n t = t + 1 # Adam counter\r\n parameters, v, s = update_parameters_with_adam(parameters, dZ, dW, db, dA, v, s,\r\n t, learning_rate, beta1, beta2, epsilon)\r\n\r\n # Print the cost every 1000 epoch\r\n if print_cost and i % 1000 == 0:\r\n print (\"Cost after epoch %i: %f\" % (i, cost))\r\n if print_cost and i % 100 == 0:\r\n costs.append(cost)\r\n\r\n # plot the cost\r\n plt.plot(costs)\r\n plt.ylabel('cost')\r\n plt.xlabel('epochs (per 100)')\r\n plt.title(\"Learning rate = \" + str(learning_rate))\r\n plt.show()\r\n\r\n return parameters\r\n", "repo_name": "csy99/Kaggle", "sub_path": "Binary_Classification_NN.py", "file_name": "Binary_Classification_NN.py", "file_ext": "py", "file_size_in_byte": 15686, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "numpy.exp", "line_number": 15, "usage_type": "call"}, {"api_name": "numpy.maximum", "line_number": 23, "usage_type": "call"}, {"api_name": "numpy.random.seed", "line_number": 39, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 39, "usage_type": "attribute"}, {"api_name": "numpy.random.randn", "line_number": 44, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 44, "usage_type": "attribute"}, {"api_name": "numpy.sqrt", "line_number": 44, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 46, "usage_type": "call"}, {"api_name": "numpy.zeros_like", "line_number": 81, "usage_type": "call"}, {"api_name": "numpy.zeros_like", "line_number": 82, "usage_type": "call"}, {"api_name": "numpy.zeros_like", "line_number": 83, "usage_type": "call"}, {"api_name": "numpy.zeros_like", "line_number": 84, "usage_type": "call"}, {"api_name": "numpy.random.seed", "line_number": 102, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 102, "usage_type": "attribute"}, {"api_name": "numpy.random.permutation", "line_number": 107, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 107, "usage_type": "attribute"}, {"api_name": "math.floor", "line_number": 112, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 142, "usage_type": "call"}, {"api_name": "numpy.int", "line_number": 142, "usage_type": "attribute"}, {"api_name": "numpy.mean", "line_number": 157, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 177, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 177, "usage_type": "call"}, {"api_name": "numpy.squeeze", "line_number": 179, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 204, "usage_type": "call"}, {"api_name": "numpy.square", "line_number": 204, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 235, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 237, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 267, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 268, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 271, "usage_type": "call"}, {"api_name": "numpy.multiply", "line_number": 272, "usage_type": "call"}, {"api_name": "numpy.int64", "line_number": 272, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 273, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 274, "usage_type": "call"}, {"api_name": "numpy.power", "line_number": 309, "usage_type": "call"}, {"api_name": "numpy.power", "line_number": 310, "usage_type": "call"}, {"api_name": "numpy.power", "line_number": 313, "usage_type": "call"}, {"api_name": "numpy.power", "line_number": 314, "usage_type": "call"}, {"api_name": "numpy.power", "line_number": 317, "usage_type": "call"}, {"api_name": "numpy.power", "line_number": 318, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 323, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 325, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 407, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 407, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 408, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 408, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 409, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 409, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 410, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 410, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 411, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 411, "usage_type": "name"}]} +{"seq_id": "28076698278", "text": "# coding=utf-8\nimport json\nfrom tqdm import tqdm\nfrom sklearn import metrics\nimport time\nimport torch\nfrom collections import defaultdict\n\n\n# 自定义数据集\nclass CustomDataset(torch.utils.data.Dataset):\n def __init__(self, file, tokenizer, max_len):\n self.tokenizer = tokenizer\n self.max_len = max_len\n\n self.ex_list = []\n with open('../dataset/' + file, \"r\", encoding='utf-8') as f:\n for line in f:\n sample = json.loads(line)\n query = sample[\"query\"]\n title = sample[\"title\"]\n relevant = int(sample[\"label\"])\n self.ex_list.append((query, title, relevant))\n\n def __len__(self):\n return len(self.ex_list)\n\n def __getitem__(self, index):\n query, title, relevant = self.ex_list[index]\n\n inputs = self.tokenizer.encode_plus(\n query, title,\n truncation=True,\n add_special_tokens=True,\n max_length=self.max_len,\n pad_to_max_length=True,\n return_token_type_ids=True\n )\n ids = inputs['input_ids']\n mask = inputs['attention_mask']\n token_type_ids = inputs[\"token_type_ids\"]\n return {\n 'ids': torch.tensor(ids, dtype=torch.long),\n 'mask': torch.tensor(mask, dtype=torch.long),\n 'token_type_ids': torch.tensor(token_type_ids, dtype=torch.long),\n 'targets': torch.tensor(relevant, dtype=torch.float)\n }\n\n\n# 各个类别性能度量的函数\ndef category_performance_measure(labels_right, labels_pred, num_label=3):\n text_labels = [i for i in range(num_label)]\n # text_labels = list(set(labels_right))\n\n TP = dict.fromkeys(text_labels, 0) # 预测正确的各个类的数目\n TP_FP = dict.fromkeys(text_labels, 0) # 测试数据集中各个类的数目\n TP_FN = dict.fromkeys(text_labels, 0) # 预测结果中各个类的数目\n\n label_dict = defaultdict(list)\n for num in range(num_label):\n label_dict[num].append(str(num))\n\n # 计算TP等数量\n for i in range(0, len(labels_right)):\n TP_FP[labels_right[i]] += 1\n TP_FN[labels_pred[i]] += 1\n if labels_right[i] == labels_pred[i]:\n TP[labels_right[i]] += 1\n # 计算准确率P，召回率R，F1值\n for key in TP_FP:\n P = float(TP[key]) / float(TP_FP[key] + 1e-9)\n R = float(TP[key]) / float(TP_FN[key] + 1e-9)\n F1 = P * R * 2 / (P + R) if (P + R) != 0 else 0\n print(\"%s:\\t P:%f\\t R:%f\\t F1:%f\" % (key, P, R, F1))\n\n\n# 模型评估\ndef evaluate_accuracy(args, data_iter, net, device=torch.device('cpu')):\n \"\"\"Evaluate accuracy of a model on the given data set.\"\"\"\n acc_sum, n = torch.tensor([0], dtype=torch.float32, device=device), 0\n y_pred_, y_true_ = [], []\n for data in tqdm(data_iter):\n # If device is the GPU, copy the data to the GPU.\n ids = data['ids'].to(device, dtype=torch.long)\n mask = data['mask'].to(device, dtype=torch.long)\n token_type_ids = data['token_type_ids'].to(device, dtype=torch.long)\n targets = data['targets'].to(device, dtype=torch.float)\n net.eval()\n y_hat_ = net(ids, mask, token_type_ids)\n with torch.no_grad():\n targets = targets.long()\n # [[0.2 ,0.4 ,0.5 ,0.6 ,0.8] ,[ 0.1,0.2 ,0.4 ,0.3 ,0.1]] => [ 4 , 2 ]\n acc_sum += torch.sum((torch.argmax(y_hat_, dim=1) == targets))\n y_pred_.extend(torch.argmax(y_hat_, dim=1).cpu().numpy().tolist())\n y_true_.extend(targets.cpu().numpy().tolist())\n n += targets.shape[0]\n valid_f1 = metrics.f1_score(y_true_, y_pred_, average='macro')\n if args.cate_performance:\n category_performance_measure(y_true_, y_pred_, args.num_labels)\n return acc_sum.item()/n, valid_f1\n\n\n# 模型训练\ndef train(net, train_iter, valid_iter, criterion, num_epochs, optimizer, device, args):\n print('training on', device)\n net.to(device)\n best_test_f1 = 0\n # 设置学习率下降策略\n scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=args.warmup_step, gamma=args.warmup_proportion)\n # scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=5, eta_min=2e-06) # 余弦退火\n for epoch in range(num_epochs):\n train_l_sum = torch.tensor([0.0], dtype=torch.float32, device=device)\n train_acc_sum = torch.tensor([0.0], dtype=torch.float32, device=device)\n n, start = 0, time.time()\n y_pred, y_true = [], []\n for data in tqdm(train_iter):\n net.train()\n optimizer.zero_grad()\n ids = data['ids'].to(device, dtype=torch.long)\n mask = data['mask'].to(device, dtype=torch.long)\n token_type_ids = data['token_type_ids'].to(device, dtype=torch.long)\n targets = data['targets'].to(device, dtype=torch.float)\n y_hat = net(ids, mask, token_type_ids)\n loss = criterion(y_hat, targets.long())\n loss.backward()\n optimizer.step()\n\n with torch.no_grad():\n targets = targets.long()\n train_l_sum += loss.float()\n train_acc_sum += (torch.sum((torch.argmax(y_hat, dim=1) == targets))).float()\n y_pred.extend(torch.argmax(y_hat, dim=1).cpu().numpy().tolist())\n y_true.extend(targets.cpu().numpy().tolist())\n n += targets.shape[0]\n valid_acc, valid_f1 = evaluate_accuracy(args, valid_iter, net, device)\n train_acc = train_acc_sum / n\n train_f1 = metrics.f1_score(y_true, y_pred, average='macro')\n print('epoch %d, loss %.4f, train acc %.3f, valid acc %.3f, '\n 'train f1 %.3f, valid f1 %.3f, time %.1f sec'\n % (epoch + 1, train_l_sum / n, train_acc, valid_acc,\n train_f1, valid_f1, time.time() - start))\n if valid_f1 > best_test_f1:\n print('find best! save at model/best.pth')\n best_test_f1 = valid_f1\n torch.save(net.state_dict(), '../model/best.pth')\n scheduler.step() # 更新学习率\n", "repo_name": "CLUEbenchmark/QBQTC", "sub_path": "baselines/clue/opt.py", "file_name": "opt.py", "file_ext": "py", "file_size_in_byte": 6122, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 33, "dataset": "github-code", "pt": "37", "api": [{"api_name": "torch.utils", "line_number": 11, "usage_type": "attribute"}, {"api_name": "json.loads", "line_number": 19, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 43, "usage_type": "call"}, {"api_name": "torch.long", "line_number": 43, "usage_type": "attribute"}, {"api_name": "torch.tensor", "line_number": 44, "usage_type": "call"}, {"api_name": "torch.long", "line_number": 44, "usage_type": "attribute"}, {"api_name": "torch.tensor", "line_number": 45, "usage_type": "call"}, {"api_name": "torch.long", "line_number": 45, "usage_type": "attribute"}, {"api_name": "torch.tensor", "line_number": 46, "usage_type": "call"}, {"api_name": "torch.float", "line_number": 46, "usage_type": "attribute"}, {"api_name": "collections.defaultdict", "line_number": 59, "usage_type": "call"}, {"api_name": "torch.device", "line_number": 78, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 80, "usage_type": "call"}, {"api_name": "torch.float32", "line_number": 80, "usage_type": "attribute"}, {"api_name": "tqdm.tqdm", "line_number": 82, "usage_type": "call"}, {"api_name": "torch.long", "line_number": 84, "usage_type": "attribute"}, {"api_name": "torch.long", "line_number": 85, "usage_type": "attribute"}, {"api_name": "torch.long", "line_number": 86, "usage_type": "attribute"}, {"api_name": "torch.float", "line_number": 87, "usage_type": "attribute"}, {"api_name": "torch.no_grad", "line_number": 90, "usage_type": "call"}, {"api_name": "torch.sum", "line_number": 93, "usage_type": "call"}, {"api_name": "torch.argmax", "line_number": 93, "usage_type": "call"}, {"api_name": "torch.argmax", "line_number": 94, "usage_type": "call"}, {"api_name": "sklearn.metrics.f1_score", "line_number": 97, "usage_type": "call"}, {"api_name": "sklearn.metrics", "line_number": 97, "usage_type": "name"}, {"api_name": "torch.optim.lr_scheduler.StepLR", "line_number": 109, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 109, "usage_type": "attribute"}, {"api_name": "torch.tensor", "line_number": 112, "usage_type": "call"}, {"api_name": "torch.float32", "line_number": 112, "usage_type": "attribute"}, {"api_name": "torch.tensor", "line_number": 113, "usage_type": "call"}, {"api_name": "torch.float32", "line_number": 113, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 114, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 116, "usage_type": "call"}, {"api_name": "torch.long", "line_number": 119, "usage_type": "attribute"}, {"api_name": "torch.long", "line_number": 120, "usage_type": "attribute"}, {"api_name": "torch.long", "line_number": 121, "usage_type": "attribute"}, {"api_name": "torch.float", "line_number": 122, "usage_type": "attribute"}, {"api_name": "torch.no_grad", "line_number": 128, "usage_type": "call"}, {"api_name": "torch.sum", "line_number": 131, "usage_type": "call"}, {"api_name": "torch.argmax", "line_number": 131, "usage_type": "call"}, {"api_name": "torch.argmax", "line_number": 132, "usage_type": "call"}, {"api_name": "sklearn.metrics.f1_score", "line_number": 137, "usage_type": "call"}, {"api_name": "sklearn.metrics", "line_number": 137, "usage_type": "name"}, {"api_name": "time.time", "line_number": 141, "usage_type": "call"}, {"api_name": "torch.save", "line_number": 145, "usage_type": "call"}]} +{"seq_id": "35010504153", "text": "from flask import Blueprint, render_template, url_for, redirect, abort\n\nfrom users.utils.generator.msg import Message\nfrom users.users.users import UserBlog\nfrom users.posts.form import PostForm\nfrom users.posts.views import get_updated_data\n\ndrafts_app = Blueprint(\"drafts_app\", __name__, url_prefix=\"/drafts\")\n\n\n@drafts_app.route(\"/drafts/\", methods=['GET', 'POST'])\ndef save_to_drafts(blog_id):\n \"\"\"\"\"\"\n\n form = PostForm()\n\n if form.validate_on_submit():\n child_blog = _get_blog(blog_id)\n child_blog.Post.Draft.save(form)\n\n Message.display_to_gui_screen(\"The newly created post has been saved to draft section\")\n return redirect(url_for('drafts_app.get_drafts', blog_id=blog_id))\n\n return render_template('posts/new_post.html', form=form, blog_id=blog_id)\n\n\n@drafts_app.route(\"//drafts/all\", methods=['GET', 'POST'])\ndef get_drafts(blog_id):\n \"\"\"\"\"\"\n\n child_blog = _get_blog(blog_id)\n drafts = child_blog.Post.Draft.get_all_draft_posts()\n return render_template(\"drafts/drafts.html\", blog_id=blog_id, drafts=drafts, num_of_drafts=len(drafts))\n\n\n@drafts_app.route('/view//', methods=['GET', 'POST'])\ndef view_draft(blog_id, draft_id):\n \"\"\"\"\"\"\n child_blog = _get_blog(blog_id)\n edit_draft = True\n\n assert child_blog or abort(404)\n\n draft = child_blog.Post.Draft.get_draft_post(draft_id, to_class=True)\n\n form = PostForm(obj=draft)\n\n if form.validate_on_submit():\n\n draft_data = get_updated_data(form, draft)\n if draft_data:\n draft.update_draft(draft_data)\n Message.display_to_gui_screen(\"You post has successfully been updated.\")\n return redirect(url_for(\"drafts_app.get_drafts\", blog_id=blog_id))\n return render_template('posts/new_post.html', form=form, blog_id=blog_id, draft_id=draft_id,\n edit_post=False, edit_draft=edit_draft)\n\n\n@drafts_app.route(\"///publish\", methods=['GET', 'POST'])\ndef publish(blog_id, draft_id):\n \"\"\"\"\"\"\n\n child_blog = _get_blog(blog_id)\n draft_form = child_blog.Post.Draft.get_draft_post(draft_id)\n child_blog.Post.create_new_post(draft_form.get('title'), draft_form.get('post'))\n child_blog.Post.Draft.delete_draft(draft_id)\n\n Message.display_to_gui_screen(\"The draft with the title '{}' has been published.\".format(draft_form.get('title')))\n return redirect(url_for('drafts_app.get_drafts', blog_id=blog_id))\n\n\n@drafts_app.route(\"/delete//\")\ndef delete_draft(blog_id, draft_id):\n \"\"\"\"\"\"\n\n child_blog = _get_blog(blog_id)\n child_blog.Post.Draft.delete_draft(draft_id)\n\n Message.display_to_gui_screen(\"The draft post has been deleted.\")\n return redirect(url_for('drafts_app.get_drafts', blog_id=blog_id))\n\n\ndef _get_blog(blog_id):\n \"\"\"\"\"\"\n blog = UserBlog()\n return blog.get_blog(blog_id)", "repo_name": "EgbieAndersonUku1/myBlog", "sub_path": "src/users/drafts/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 2887, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "flask.Blueprint", "line_number": 8, "usage_type": "call"}, {"api_name": "users.posts.form.PostForm", "line_number": 15, "usage_type": "call"}, {"api_name": "users.utils.generator.msg.Message.display_to_gui_screen", "line_number": 21, "usage_type": "call"}, {"api_name": "users.utils.generator.msg.Message", "line_number": 21, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 22, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 22, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 24, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 33, "usage_type": "call"}, {"api_name": "flask.abort", "line_number": 42, "usage_type": "call"}, {"api_name": "users.posts.form.PostForm", "line_number": 46, "usage_type": "call"}, {"api_name": "users.posts.views.get_updated_data", "line_number": 50, "usage_type": "call"}, {"api_name": "users.utils.generator.msg.Message.display_to_gui_screen", "line_number": 53, "usage_type": "call"}, {"api_name": "users.utils.generator.msg.Message", "line_number": 53, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 54, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 54, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 55, "usage_type": "call"}, {"api_name": "users.utils.generator.msg.Message.display_to_gui_screen", "line_number": 68, "usage_type": "call"}, {"api_name": "users.utils.generator.msg.Message", "line_number": 68, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 69, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 69, "usage_type": "call"}, {"api_name": "users.utils.generator.msg.Message.display_to_gui_screen", "line_number": 79, "usage_type": "call"}, {"api_name": "users.utils.generator.msg.Message", "line_number": 79, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 80, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 80, "usage_type": "call"}, {"api_name": "users.users.users.UserBlog", "line_number": 85, "usage_type": "call"}]} +{"seq_id": "37215545572", "text": "import numpy as np\nfrom numpy import linalg\nimport math\nfrom scipy import ndimage\nimport matplotlib.pyplot as plt\n\n\nclass Snake:\n def __init__(self, img, points, alpha=0.015, beta=10, gamma=0.001):\n self.img = img\n self.points = points\n self.contour = np.copy(points)\n self.tempContour = np.copy(self.contour)\n self.alpha = alpha\n self.betas = np.full(points.shape[0], beta)\n self.gamma = gamma\n self.Ecurvs = np.zeros(points.shape[0])\n self.Econts = np.zeros(points.shape[0])\n self.EImgs = np.zeros(points.shape[0])\n self.gradient = self.normalizeGradient(self.getGradient())\n self.distMean = 0\n self.Energy = 0\n self.energyCal()\n\n def getGradient(self):\n grad_x = ndimage.sobel(self.img, axis=0, mode='constant')\n grad_y = ndimage.sobel(self.img, axis=1, mode='constant')\n grad = np.hypot(grad_x, grad_y).astype(np.uint8)\n return grad\n\n def normalizeGradient(self, g):\n minimum = g.min()\n maximum = g.max()\n\n for i in range(g.shape[0]):\n for j in range(g.shape[1]):\n g[i][j] = (g[i][j]-minimum)/(minimum-maximum)\n\n return g\n\n def getNeighborsMatrix(self, point, nsize=1):\n neighbors = []\n borns = self.img.shape\n for i in range(point[0]-nsize, point[0]+nsize+1):\n for j in range(point[1]-nsize, point[1]+nsize+1):\n if i == point[0] and j == point[1]:\n continue\n if i >= 0 and i < borns[0]:\n x = i\n elif i >= borns[0]:\n continue\n elif i < 0:\n continue\n if j >= 0 and j < borns[1]:\n y = j\n elif j >= borns[0]:\n continue\n elif j < 0:\n continue\n neighbors.append((x, y))\n\n return neighbors\n\n def distance(self, p1, p2):\n return math.sqrt((p1[0]-p2[0])**2 + (p1[1]-p2[1])**2)\n\n def dMean(self):\n d = 0\n for i in range(len(self.tempContour)-1):\n d += self.distance(self.tempContour[i], self.tempContour[i+1])\n self.distMean = d/len(self.tempContour)\n\n def eContPerP(self, i):\n return (self.distMean-linalg.norm(self.tempContour[i]-self.tempContour[i-1]))**2\n\n def eCurvPerP(self, i):\n i_plus_1 = i+1 if i+1 < self.tempContour.shape[0] else 0\n return linalg.norm(self.tempContour[i-1] - 2*self.tempContour[i] + self.tempContour[i_plus_1])**2\n\n def energyCurv(self):\n for i in range(0, len(self.tempContour)):\n self.Ecurvs[i] = self.betas[i]*self.eCurvPerP(i)\n\n def energyImgPerP(self, p):\n return (1/(1+self.gradient[p[0]][p[1]]**2))\n\n def energyImg(self):\n for i in range(len(self.tempContour)):\n p = self.tempContour[i]\n self.EImgs[i] = self.energyImgPerP(p)\n\n def energyCont(self, updateFrom=0):\n for i in range(updateFrom, len(self.tempContour)):\n self.Econts[i] = self.eContPerP(i)\n\n def energyCal(self):\n self.calculEnergies()\n self.Energy = self.energySum()\n\n def energySum(self):\n E = 0\n E += self.alpha * np.sum(self.Econts)\n E += np.sum(self.Ecurvs)\n E -= self.gamma * np.sum(self.EImgs)\n return E\n\n def calculEnergies(self):\n self.dMean()\n self.energyCont()\n self.energyCurv()\n self.energyImg()\n\n def showDif(self):\n fig = plt.figure()\n plt.gray()\n ax1 = fig.add_subplot(121)\n ax2 = fig.add_subplot(122)\n cxs, cys = zip(*self.contour.tolist()+[self.contour[0]])\n x, y = zip(*(self.points.tolist()+[self.points[0]]))\n ax1.plot(cxs, cys)\n ax2.plot(x, y)\n ax1.imshow(self.img)\n ax2.imshow(self.img)\n plt.show()\n\n def run(self, iterations=100, nsize=1):\n it = 0\n while it < iterations:\n it += 1\n print(it)\n self.tempContour = np.copy(self.contour)\n stop = True\n for i in range(0, len(self.tempContour)):\n neighbors = self.getNeighborsMatrix(self.tempContour[i], nsize)\n minimumEnergies = (\n self.Ecurvs[i], self.EImgs[i], self.Econts[i])\n\n for n in neighbors:\n self.tempContour[i] = n\n self.Ecurvs[i] = self.betas[i] * self.eCurvPerP(i)\n self.Econts[i] = self.eContPerP(i)\n self.EImgs[i] = self.energyImgPerP(n)\n tempEnergy = self.energySum()\n if(self.Energy > tempEnergy):\n print(\"\\t\", i, \"from\",\n self.contour[i], \"to\", n, \"energy\", tempEnergy)\n\n # move to the new point with the lowest energy\n self.contour[i] = n\n self.Energy = tempEnergy\n\n minimumEnergies = (\n self.Ecurvs[i], self.EImgs[i], self.Econts[i])\n stop = False\n else:\n self.Ecurvs[i], self.EImgs[i], self.Econts[i] = minimumEnergies\n\n # update Energies and mean distance\n self.calculEnergies()\n\n # set betas to 0 for the maximum Ecurvs point\n self.betas[np.argmax(self.Ecurvs)] = 0\n\n # no new change to the snake minimum local\n if stop:\n break\n", "repo_name": "devamin/snake-active-contour", "sub_path": "Snake.py", "file_name": "Snake.py", "file_ext": "py", "file_size_in_byte": 5593, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "numpy.copy", "line_number": 12, "usage_type": "call"}, {"api_name": "numpy.copy", "line_number": 13, "usage_type": "call"}, {"api_name": "numpy.full", "line_number": 15, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 17, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 18, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 19, "usage_type": "call"}, {"api_name": "scipy.ndimage.sobel", "line_number": 26, "usage_type": "call"}, {"api_name": "scipy.ndimage", "line_number": 26, "usage_type": "name"}, {"api_name": "scipy.ndimage.sobel", "line_number": 27, "usage_type": "call"}, {"api_name": "scipy.ndimage", "line_number": 27, "usage_type": "name"}, {"api_name": "numpy.hypot", "line_number": 28, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 28, "usage_type": "attribute"}, {"api_name": "math.sqrt", "line_number": 65, "usage_type": "call"}, {"api_name": "numpy.linalg.norm", "line_number": 74, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 74, "usage_type": "name"}, {"api_name": "numpy.linalg.norm", "line_number": 78, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 78, "usage_type": "name"}, {"api_name": "numpy.sum", "line_number": 102, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 103, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 104, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 114, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 114, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.gray", "line_number": 115, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 115, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 124, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 124, "usage_type": "name"}, {"api_name": "numpy.copy", "line_number": 131, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 162, "usage_type": "call"}]} +{"seq_id": "20657780560", "text": "from typing import List\n\nfrom navec import Navec\nfrom slovnet import NER\n\nfrom purano.annotator.processors import Processor\nfrom purano.models import Document\nfrom purano.proto.info_pb2 import Info as InfoPb, EntitySpan as EntitySpanPb\n\n\n@Processor.register(\"ner_slovnet\")\nclass NerSlovnetProcessor(Processor):\n def __init__(self, model_path, vector_model_path):\n navec = Navec.load(vector_model_path)\n self.model = NER.load(model_path)\n self.model.navec(navec)\n\n def __call__(\n self,\n docs: List[Document],\n infos: List[InfoPb],\n input_fields: List[str],\n output_field: str\n ):\n for doc_num, (doc, info) in enumerate(zip(docs, infos)):\n sample = \" \".join([getattr(doc, input_field) for input_field in input_fields])\n markup = self.model(sample)\n spans = []\n for s in markup.spans:\n entity_span = EntitySpanPb()\n entity_span.begin = s.start\n entity_span.end = s.stop\n entity_span.tag = EntitySpanPb.Tag.Value(s.type)\n spans.append(entity_span)\n getattr(info, output_field).extend(spans)\n", "repo_name": "IlyaGusev/purano", "sub_path": "purano/annotator/processors/ner_slovnet.py", "file_name": "ner_slovnet.py", "file_ext": "py", "file_size_in_byte": 1189, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 5, "dataset": "github-code", "pt": "37", "api": [{"api_name": "purano.annotator.processors.Processor", "line_number": 12, "usage_type": "name"}, {"api_name": "navec.Navec.load", "line_number": 14, "usage_type": "call"}, {"api_name": "navec.Navec", "line_number": 14, "usage_type": "name"}, {"api_name": "slovnet.NER.load", "line_number": 15, "usage_type": "call"}, {"api_name": "slovnet.NER", "line_number": 15, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 20, "usage_type": "name"}, {"api_name": "purano.models.Document", "line_number": 20, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 21, "usage_type": "name"}, {"api_name": "purano.proto.info_pb2.Info", "line_number": 21, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 22, "usage_type": "name"}, {"api_name": "purano.proto.info_pb2.EntitySpan", "line_number": 30, "usage_type": "call"}, {"api_name": "purano.proto.info_pb2.EntitySpan.Tag.Value", "line_number": 33, "usage_type": "call"}, {"api_name": "purano.proto.info_pb2.EntitySpan.Tag", "line_number": 33, "usage_type": "attribute"}, {"api_name": "purano.proto.info_pb2.EntitySpan", "line_number": 33, "usage_type": "name"}, {"api_name": "purano.annotator.processors.Processor.register", "line_number": 11, "usage_type": "call"}, {"api_name": "purano.annotator.processors.Processor", "line_number": 11, "usage_type": "name"}]} +{"seq_id": "9120800900", "text": "from __future__ import annotations\n\n__all__ = [\n \"BaseMSBRegion\",\n \"BaseMSBRegionPoint\",\n \"BaseMSBRegionCircle\",\n \"BaseMSBRegionCylinder\",\n \"BaseMSBRegionSphere\",\n \"BaseMSBRegionRect\",\n \"BaseMSBRegionBox\",\n \"BaseMSBRegionList\",\n]\n\nimport abc\nimport logging\nimport struct\n\nfrom soulstruct.utilities.text import pad_chars\nfrom soulstruct.utilities.binary import BinaryStruct, BinaryReader\nfrom soulstruct.utilities.maths import Vector3\n\nfrom .enums import BaseMSBRegionSubtype\nfrom .msb_entry import MSBEntryEntityCoordinates\nfrom .msb_entry_list import BaseMSBEntryList\nfrom .utils import MapFieldInfo\n\n_LOGGER = logging.getLogger(__name__)\n\n\n# TODO: Migrate Regions and Models subtypes into games.\n# Regions can probably keep detailed base classes and just leave the enum assignment to game subclasses.\n\n\nclass BaseMSBRegion(MSBEntryEntityCoordinates, abc.ABC):\n\n ENTRY_SUBTYPE: BaseMSBRegionSubtype = None\n REGION_STRUCT: BinaryStruct = None\n REGION_TYPE_DATA_STRUCT: BinaryStruct = None\n NAME_ENCODING = \"\"\n UNKNOWN_DATA_SIZE = -1\n\n FIELD_INFO = MSBEntryEntityCoordinates.FIELD_INFO | {\n \"translate\": MapFieldInfo(\n \"Translate\",\n Vector3,\n Vector3.zero(),\n \"3D coordinates of the region's position. Note that this is the middle of the bottom face for box \"\n \"regions.\",\n ),\n \"rotate\": MapFieldInfo(\n \"Rotate\",\n Vector3,\n Vector3.zero(),\n \"Euler angles for region rotation around its local X, Y, and Z axes.\",\n ),\n }\n\n translate: Vector3\n rotate: Vector3\n\n def __init__(self, source=None, **kwargs):\n self._region_index = None # Final automatic assignment done on `MSB.pack()`.\n super().__init__(source=source, **kwargs)\n\n def unpack(self, msb_reader: BinaryReader):\n region_offset = msb_reader.position\n base_data = msb_reader.unpack_struct(self.REGION_STRUCT)\n self.name = msb_reader.unpack_string(\n offset=region_offset + base_data[\"name_offset\"], encoding=self.NAME_ENCODING,\n )\n self._region_index = base_data[\"__region_index\"]\n self.translate = Vector3(base_data[\"translate\"])\n self.rotate = Vector3(base_data[\"rotate\"])\n self.check_null_field(msb_reader, region_offset + base_data[\"unknown_offset_1\"])\n self.check_null_field(msb_reader, region_offset + base_data[\"unknown_offset_2\"])\n\n if base_data[\"type_data_offset\"] != 0:\n msb_reader.seek(region_offset + base_data[\"type_data_offset\"])\n self.unpack_type_data(msb_reader)\n\n msb_reader.seek(region_offset + base_data[\"entity_id_offset\"])\n self.entity_id = msb_reader.unpack_value(\"i\")\n\n return region_offset + base_data[\"entity_id_offset\"]\n\n def pack(self, region_index=0):\n name_offset = self.REGION_STRUCT.size\n packed_name = pad_chars(self.get_name_to_pack(), encoding=self.NAME_ENCODING, pad_to_multiple_of=4)\n unknown_offset_1 = name_offset + len(packed_name)\n unknown_offset_2 = unknown_offset_1 + 4\n packed_type_data = self.pack_type_data()\n if packed_type_data:\n type_data_offset = unknown_offset_2 + 4\n entity_id_offset = type_data_offset + len(packed_type_data)\n else:\n type_data_offset = 0\n entity_id_offset = unknown_offset_2 + 4\n packed_base_data = self.REGION_STRUCT.pack(\n name_offset=name_offset,\n __region_index=region_index,\n region_type=self.ENTRY_SUBTYPE,\n translate=list(self.translate),\n rotate=list(self.rotate),\n unknown_offset_1=unknown_offset_1,\n unknown_offset_2=unknown_offset_2,\n type_data_offset=type_data_offset,\n entity_id_offset=entity_id_offset,\n )\n packed_entity_id = struct.pack(\"i\", self.entity_id)\n return packed_base_data + packed_name + b\"\\0\\0\\0\\0\" * 2 + packed_type_data + packed_entity_id\n\n def unpack_type_data(self, msb_reader: BinaryReader):\n self.set(**msb_reader.unpack_struct(self.REGION_TYPE_DATA_STRUCT))\n\n def pack_type_data(self):\n return self.REGION_TYPE_DATA_STRUCT.pack(self)\n\n def set_indices(self, region_index):\n self._region_index = region_index\n\n @classmethod\n def check_null_field(cls, msb_reader: BinaryReader, offset_to_null):\n msb_reader.seek(offset_to_null)\n zero = msb_reader.read(cls.UNKNOWN_DATA_SIZE)\n if zero != b\"\\0\" * cls.UNKNOWN_DATA_SIZE:\n _LOGGER.warning(f\"Null data entry in `{cls.__name__}` was not zero: {zero}.\")\n\n\nclass BaseMSBRegionPoint(BaseMSBRegion, abc.ABC):\n \"\"\"No shape attributes. Note that the rotate attribute is still meaningful for many uses (e.g. what way will the\n player be facing when they spawn?).\"\"\"\n\n REGION_TYPE_DATA_STRUCT = None\n\n FIELD_ORDER = (\n \"entity_id\",\n \"translate\",\n \"rotate\",\n )\n\n def unpack_type_data(self, msb_reader: BinaryReader):\n pass\n\n def pack_type_data(self):\n return b\"\"\n\n\nclass BaseMSBRegionCircle(BaseMSBRegion, abc.ABC):\n \"\"\"Almost never used (no volume).\"\"\"\n\n REGION_TYPE_DATA_STRUCT = BinaryStruct(\n (\"radius\", \"f\"),\n )\n\n FIELD_INFO = BaseMSBRegion.FIELD_INFO | {\n \"radius\": MapFieldInfo(\n \"Radius\",\n float,\n 1.0,\n \"Radius (in xy-plane) of circular region.\",\n ),\n }\n\n FIELD_ORDER = (\n \"entity_id\",\n \"translate\",\n \"rotate\",\n \"radius\",\n )\n\n radius: float\n\n\nclass BaseMSBRegionSphere(BaseMSBRegion, abc.ABC):\n REGION_TYPE_DATA_STRUCT = BinaryStruct(\n (\"radius\", \"f\"),\n )\n\n FIELD_INFO = BaseMSBRegion.FIELD_INFO | {\n \"radius\": MapFieldInfo(\n \"Radius\",\n float,\n 1.0,\n \"Radius of sphere-shaped region.\",\n ),\n }\n\n FIELD_ORDER = (\n \"entity_id\",\n \"translate\",\n \"rotate\",\n \"radius\",\n )\n\n radius: float\n\n\nclass BaseMSBRegionCylinder(BaseMSBRegion, abc.ABC):\n REGION_TYPE_DATA_STRUCT = BinaryStruct(\n (\"radius\", \"f\"),\n (\"height\", \"f\"),\n )\n\n FIELD_INFO = BaseMSBRegion.FIELD_INFO | {\n \"radius\": MapFieldInfo(\n \"Radius\",\n float,\n 1.0,\n \"Radius (in xz-plane) of cylinder-shaped region.\",\n ),\n \"height\": MapFieldInfo(\n \"Height\",\n float,\n 1.0,\n \"Height (along y-axis) of cylinder-shaped region.\",\n ),\n }\n\n FIELD_ORDER = (\n \"entity_id\",\n \"translate\",\n \"rotate\",\n \"radius\",\n \"height\",\n )\n\n radius: float\n height: float\n\n\nclass BaseMSBRegionRect(BaseMSBRegion, abc.ABC):\n \"\"\"Almost never used (no volume).\"\"\"\n REGION_TYPE_DATA_STRUCT = BinaryStruct(\n (\"width\", \"f\"),\n (\"depth\", \"f\"),\n )\n\n FIELD_INFO = BaseMSBRegion.FIELD_INFO | {\n \"width\": MapFieldInfo(\n \"Width\",\n float,\n 1.0,\n \"Width (along x-axis) of rectangle-shaped region.\",\n ),\n \"height\": MapFieldInfo(\n \"Height\",\n float,\n 1.0,\n \"Height (along y-axis) of rectangle-shaped region.\",\n ),\n }\n\n FIELD_ORDER = (\n \"entity_id\",\n \"translate\",\n \"rotate\",\n \"width\",\n \"height\",\n )\n\n width: float\n height: float\n\n\nclass BaseMSBRegionBox(BaseMSBRegion, abc.ABC):\n REGION_TYPE_DATA_STRUCT = BinaryStruct(\n (\"width\", \"f\"),\n (\"depth\", \"f\"),\n (\"height\", \"f\"),\n )\n\n FIELD_INFO = BaseMSBRegion.FIELD_INFO | {\n \"width\": MapFieldInfo(\n \"Width\",\n float,\n 1.0,\n \"Width (along x-axis) of box-shaped region.\",\n ),\n \"depth\": MapFieldInfo(\n \"Depth\",\n float,\n 1.0,\n \"Depth (along z-axis) of box-shaped region.\",\n ),\n \"height\": MapFieldInfo(\n \"Height\",\n float,\n 1.0,\n \"Height (along y-axis) of box-shaped region.\",\n ),\n }\n\n FIELD_ORDER = (\n \"entity_id\",\n \"translate\",\n \"rotate\",\n \"width\",\n \"depth\",\n \"height\",\n )\n\n width: float\n depth: float\n height: float\n\n\nclass BaseMSBRegionList(BaseMSBEntryList, abc.ABC):\n\n @abc.abstractmethod\n def set_indices(self):\n pass\n", "repo_name": "Grimrukh/soulstruct", "sub_path": "soulstruct/base/maps/msb/regions.py", "file_name": "regions.py", "file_ext": "py", "file_size_in_byte": 8518, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 129, "dataset": "github-code", "pt": "37", "api": [{"api_name": "logging.getLogger", "line_number": 27, "usage_type": "call"}, {"api_name": "msb_entry.MSBEntryEntityCoordinates", "line_number": 34, "usage_type": "name"}, {"api_name": "abc.ABC", "line_number": 34, "usage_type": "attribute"}, {"api_name": "enums.BaseMSBRegionSubtype", "line_number": 36, "usage_type": "name"}, {"api_name": "soulstruct.utilities.binary.BinaryStruct", "line_number": 37, "usage_type": "name"}, {"api_name": "soulstruct.utilities.binary.BinaryStruct", "line_number": 38, "usage_type": "name"}, {"api_name": "msb_entry.MSBEntryEntityCoordinates.FIELD_INFO", "line_number": 42, "usage_type": "attribute"}, {"api_name": "msb_entry.MSBEntryEntityCoordinates", "line_number": 42, "usage_type": "name"}, {"api_name": "utils.MapFieldInfo", "line_number": 43, "usage_type": "call"}, {"api_name": "soulstruct.utilities.maths.Vector3", "line_number": 45, "usage_type": "argument"}, {"api_name": "soulstruct.utilities.maths.Vector3.zero", "line_number": 46, "usage_type": "call"}, {"api_name": "soulstruct.utilities.maths.Vector3", "line_number": 46, "usage_type": "name"}, {"api_name": "utils.MapFieldInfo", "line_number": 50, "usage_type": "call"}, {"api_name": "soulstruct.utilities.maths.Vector3", "line_number": 52, "usage_type": "argument"}, {"api_name": "soulstruct.utilities.maths.Vector3.zero", "line_number": 53, "usage_type": "call"}, {"api_name": "soulstruct.utilities.maths.Vector3", "line_number": 53, "usage_type": "name"}, {"api_name": "soulstruct.utilities.maths.Vector3", "line_number": 58, "usage_type": "name"}, {"api_name": "soulstruct.utilities.maths.Vector3", "line_number": 59, "usage_type": "name"}, {"api_name": "soulstruct.utilities.binary.BinaryReader", "line_number": 65, "usage_type": "name"}, {"api_name": "soulstruct.utilities.maths.Vector3", "line_number": 72, "usage_type": "call"}, {"api_name": "soulstruct.utilities.maths.Vector3", "line_number": 73, "usage_type": "call"}, {"api_name": "soulstruct.utilities.text.pad_chars", "line_number": 88, "usage_type": "call"}, {"api_name": "struct.pack", "line_number": 109, "usage_type": "call"}, {"api_name": "soulstruct.utilities.binary.BinaryReader", "line_number": 112, "usage_type": "name"}, {"api_name": "soulstruct.utilities.binary.BinaryReader", "line_number": 122, "usage_type": "name"}, {"api_name": "abc.ABC", "line_number": 129, "usage_type": "attribute"}, {"api_name": "soulstruct.utilities.binary.BinaryReader", "line_number": 141, "usage_type": "name"}, {"api_name": "abc.ABC", "line_number": 148, "usage_type": "attribute"}, {"api_name": "soulstruct.utilities.binary.BinaryStruct", "line_number": 151, "usage_type": "call"}, {"api_name": "utils.MapFieldInfo", "line_number": 156, "usage_type": "call"}, {"api_name": "abc.ABC", "line_number": 174, "usage_type": "attribute"}, {"api_name": "soulstruct.utilities.binary.BinaryStruct", "line_number": 175, "usage_type": "call"}, {"api_name": "utils.MapFieldInfo", "line_number": 180, "usage_type": "call"}, {"api_name": "abc.ABC", "line_number": 198, "usage_type": "attribute"}, {"api_name": "soulstruct.utilities.binary.BinaryStruct", "line_number": 199, "usage_type": "call"}, {"api_name": "utils.MapFieldInfo", "line_number": 205, "usage_type": "call"}, {"api_name": "utils.MapFieldInfo", "line_number": 211, "usage_type": "call"}, {"api_name": "abc.ABC", "line_number": 231, "usage_type": "attribute"}, {"api_name": "soulstruct.utilities.binary.BinaryStruct", "line_number": 233, "usage_type": "call"}, {"api_name": "utils.MapFieldInfo", "line_number": 239, "usage_type": "call"}, {"api_name": "utils.MapFieldInfo", "line_number": 245, "usage_type": "call"}, {"api_name": "abc.ABC", "line_number": 265, "usage_type": "attribute"}, {"api_name": "soulstruct.utilities.binary.BinaryStruct", "line_number": 266, "usage_type": "call"}, {"api_name": "utils.MapFieldInfo", "line_number": 273, "usage_type": "call"}, {"api_name": "utils.MapFieldInfo", "line_number": 279, "usage_type": "call"}, {"api_name": "utils.MapFieldInfo", "line_number": 285, "usage_type": "call"}, {"api_name": "msb_entry_list.BaseMSBEntryList", "line_number": 307, "usage_type": "name"}, {"api_name": "abc.ABC", "line_number": 307, "usage_type": "attribute"}, {"api_name": "abc.abstractmethod", "line_number": 309, "usage_type": "attribute"}]} +{"seq_id": "36360488720", "text": "__author__ = 'eyalf@google.com (Eyal Fink)'\n\nfrom google.appengine.ext import db\nimport datetime\nimport indexing\nimport logging\nimport model\nimport sys\nimport unittest\n\nfrom text_query import TextQuery\n\ndef create_person(given_name, family_name):\n return model.Person.create_original(\n 'test', given_name=given_name, family_name=family_name,\n full_name=('%s %s' % (given_name, family_name)),\n entry_date=datetime.datetime.utcnow())\n\n\nclass IndexingTests(unittest.TestCase):\n def setUp(self):\n db.delete(model.Person.all())\n\n def tearDown(self):\n db.delete(model.Person.all())\n\n def add_persons(self, *persons):\n for p in persons:\n indexing.update_index_properties(p)\n db.put(p)\n\n def get_matches(self, query, limit=100):\n results = indexing.search('test', TextQuery(query), limit)\n return [(p.given_name, p.family_name) for p in results]\n\n def get_ranked(self, results, query, limit=100):\n ranked = indexing.rank_and_order(results, TextQuery(query), limit)\n return [(p.given_name, p.family_name) for p in results]\n\n def test_rank_and_order(self):\n res= [create_person(given_name='Bryan', family_name='abc', ),\n create_person(given_name='Bryan', family_name='abcef'),\n create_person(given_name='abc', family_name='Bryan'),\n create_person(given_name='Bryan abc', family_name='efg')]\n\n sorted = indexing.rank_and_order(res, TextQuery('Bryan abc'), 100)\n assert ['%s %s'%(p.given_name, p.family_name) for p in sorted] == \\\n ['Bryan abc', 'abc Bryan', 'Bryan abc efg', 'Bryan abcef']\n\n sorted = indexing.rank_and_order(res, TextQuery('Bryan abc'), 2)\n assert ['%s %s'%(p.given_name, p.family_name) for p in sorted] == \\\n ['Bryan abc', 'abc Bryan']\n\n sorted = indexing.rank_and_order(res, TextQuery('abc Bryan'), 100)\n assert ['%s %s'%(p.given_name, p.family_name) for p in sorted] == \\\n ['abc Bryan', 'Bryan abc', 'Bryan abc efg', 'Bryan abcef']\n\n\n res= [create_person(given_name='abc', family_name='efg'),\n create_person(given_name='ABC', family_name='EFG'),\n create_person(given_name='ABC', family_name='efghij')]\n\n sorted = indexing.rank_and_order(res, TextQuery('abc'), 100)\n assert ['%s %s'%(p.given_name, p.family_name) for p in sorted] == \\\n ['abc efg', 'ABC EFG', 'ABC efghij']\n\n def test_cjk_ranking_1(self):\n # This is Jackie Chan's Chinese name. His family name is CHAN and given\n # name is KONG + SANG; the usual Chinese order is CHAN + KONG + SANG.\n CHAN, KONG, SANG = u'\\u9673', u'\\u6e2f', u'\\u751f'\n\n # This is I. M. Pei's Chinese name. His family name is BEI and his\n # given name is YU + MING; the usual Chinese order is BEI + YU + MING.\n BEI, YU, MING = u'\\u8c9d', u'\\u807f', u'\\u9298'\n persons = [\n create_person(given_name=CHAN + KONG + SANG, family_name='foo'),\n create_person(given_name=SANG, family_name=CHAN + KONG),\n create_person(given_name=CHAN, family_name=KONG + SANG),\n create_person(given_name=KONG + SANG, family_name=CHAN),\n create_person(given_name=KONG + CHAN, family_name=SANG),\n create_person(given_name=KONG, family_name=SANG),\n create_person(given_name=YU + MING, family_name=BEI),\n ]\n\n assert self.get_ranked(persons, CHAN + KONG + SANG) == [\n (KONG + SANG, CHAN), # surname + given name is best\n (SANG, CHAN + KONG), # then multi-char surname + given name\n (CHAN, KONG + SANG), # then surname/given switched\n (KONG + CHAN, SANG), # then out-of-order match\n (CHAN + KONG + SANG, 'foo'), # then exact given name match\n (KONG, SANG), # then partial match\n (YU + MING, BEI), # then nothing match\n ]\n\n assert self.get_ranked(persons, CHAN + ' ' + KONG + SANG) == [\n (KONG + SANG, CHAN), # surname + given name is best\n (CHAN, KONG + SANG), # then surname/given switched\n (KONG + CHAN, SANG), # then multi-char surname / given switched\n (SANG, CHAN + KONG), # then out-of-order match\n (CHAN + KONG + SANG, 'foo'), # then exact given name match\n (KONG, SANG), # then partial match\n (YU + MING, BEI), # then nothing match\n ]\n\n def test_cjk_ranking_2(self):\n # This is Steven Chu's Chinese name. His family name is ZHU and his\n # given name is DI + WEN; the usual Chinese order is ZHU + DI + WEN.\n ZHU, DI, WEN = u'\\u6731', u'\\u68e3', u'\\u6587'\n\n # A test database of 3 records with various permutations of the name.\n persons = [\n create_person(given_name=WEN, family_name=ZHU + DI),\n create_person(given_name=DI + WEN, family_name=ZHU),\n create_person(given_name=ZHU, family_name=DI + WEN),\n ]\n\n # When the search query is ZHU + DI + WEN:\n assert self.get_ranked(persons, ZHU + DI + WEN) == [\n (DI + WEN, ZHU), # best: treat query as 1-char surname + given name\n (WEN, ZHU + DI), # then: treat as multi-char surname + given name\n (ZHU, DI + WEN), # then: treat query as given name + surname\n ]\n\n # When the search query is ZHU + ' ' + DI + WEN (no multi-char surname):\n assert self.get_ranked(persons, ZHU + ' ' + DI + WEN) == [\n (DI + WEN, ZHU), # best: treat query as surname + ' ' + given name\n (ZHU, DI + WEN), # then: treat query as given name + ' ' + surname\n (WEN, ZHU + DI), # then: match query characters out of order\n ]\n\n def test_sort_query_words(self):\n # Sorted lexicographically.\n assert indexing.sort_query_words(\n ['CC', 'BB', 'AA']) == ['AA', 'BB', 'CC']\n # Sorted by lengths.\n assert indexing.sort_query_words(\n ['A', 'AA', 'AAA']) == ['AAA', 'AA', 'A']\n # Sorted by popularity.\n assert indexing.sort_query_words(\n [u'川', u'口', u'良']) == [u'口', u'良', u'川']\n # Test sort key precedence.\n assert indexing.sort_query_words(\n ['CCC', 'BB', 'AA', 'A']) == ['CCC', 'AA', 'BB', 'A']\n\n def test_search(self):\n persons = [create_person(given_name='Bryan', family_name='abc'),\n create_person(given_name='Bryan', family_name='abcef'),\n create_person(given_name='abc', family_name='Bryan'),\n create_person(given_name='Bryan abc', family_name='efg'),\n create_person(given_name='AAAA BBBB', family_name='CCC DDD')]\n for p in persons:\n indexing.update_index_properties(p)\n db.put(p)\n\n res = indexing.search('test', TextQuery('Bryan abc'), 1)\n assert [(p.given_name, p.family_name) for p in res] == [('Bryan', 'abc')]\n\n res = indexing.search('test', TextQuery('CC AAAA'), 100)\n assert [(p.given_name, p.family_name) for p in res] == \\\n [('AAAA BBBB', 'CCC DDD')]\n\n def test_cjk_first_only(self):\n self.add_persons(\n create_person(given_name=u'\\u4f59\\u5609\\u5e73', family_name='foo'),\n create_person(given_name=u'\\u80e1\\u6d9b\\u5e73', family_name='foo'),\n )\n\n # Any single character should give a hit.\n assert self.get_matches(u'\\u4f59') == [(u'\\u4f59\\u5609\\u5e73', 'foo')]\n assert self.get_matches(u'\\u5609') == [(u'\\u4f59\\u5609\\u5e73', 'foo')]\n assert self.get_matches(u'\\u5e73') == [\n (u'\\u4f59\\u5609\\u5e73', 'foo'),\n (u'\\u80e1\\u6d9b\\u5e73', 'foo')\n ]\n\n # Order of characters in the query should not matter.\n assert self.get_matches(u'\\u5609\\u5e73') == \\\n [(u'\\u4f59\\u5609\\u5e73', 'foo')]\n assert self.get_matches(u'\\u5e73\\u5609') == \\\n [(u'\\u4f59\\u5609\\u5e73', 'foo')]\n assert self.get_matches(u'\\u4f59\\u5609\\u5e73') == \\\n [(u'\\u4f59\\u5609\\u5e73', 'foo')]\n\n def test_cjk_last_only(self):\n self.add_persons(\n create_person(given_name='foo', family_name=u'\\u4f59\\u5609\\u5e73'),\n create_person(given_name='foo', family_name=u'\\u80e1\\u6d9b\\u5e73'),\n )\n\n # Any single character should give a hit.\n assert self.get_matches(u'\\u4f59') == \\\n [('foo', u'\\u4f59\\u5609\\u5e73')]\n assert self.get_matches(u'\\u5609') == \\\n [('foo', u'\\u4f59\\u5609\\u5e73')]\n assert self.get_matches(u'\\u5e73') == [\n ('foo', u'\\u4f59\\u5609\\u5e73'),\n ('foo', u'\\u80e1\\u6d9b\\u5e73')\n ]\n\n # Order of characters in the query should not matter.\n assert self.get_matches(u'\\u5609\\u5e73') == \\\n [('foo', u'\\u4f59\\u5609\\u5e73')]\n assert self.get_matches(u'\\u5e73\\u5609') == \\\n [('foo', u'\\u4f59\\u5609\\u5e73')]\n assert self.get_matches(u'\\u4f59\\u5609\\u5e73') == \\\n [('foo', u'\\u4f59\\u5609\\u5e73')]\n\n def test_cjk_first_last(self):\n self.add_persons(\n create_person(given_name=u'\\u5609\\u5e73', family_name=u'\\u4f59'),\n create_person(given_name=u'\\u6d9b\\u5e73', family_name=u'\\u80e1'),\n )\n\n # Any single character should give a hit.\n assert self.get_matches(u'\\u4f59') == \\\n [(u'\\u5609\\u5e73', u'\\u4f59')]\n assert self.get_matches(u'\\u5609') == \\\n [(u'\\u5609\\u5e73', u'\\u4f59')]\n assert self.get_matches(u'\\u5e73') == [\n (u'\\u5609\\u5e73', u'\\u4f59'),\n (u'\\u6d9b\\u5e73', u'\\u80e1')\n ]\n\n # Order of characters in the query should not matter.\n assert self.get_matches(u'\\u5609\\u5e73') == \\\n [(u'\\u5609\\u5e73', u'\\u4f59')]\n assert self.get_matches(u'\\u5e73\\u5609') == \\\n [(u'\\u5609\\u5e73', u'\\u4f59')]\n assert self.get_matches(u'\\u4f59\\u5609\\u5e73') == \\\n [(u'\\u5609\\u5e73', u'\\u4f59')]\n\n def test_no_query_terms(self):\n # Regression test (this used to throw an exception).\n assert indexing.search('test', TextQuery(''), 100) == []\n\n\nif __name__ == '__main__':\n logging.basicConfig( stream=sys.stderr )\n unittest.main()\n", "repo_name": "google/personfinder", "sub_path": "tests/test_indexing.py", "file_name": "test_indexing.py", "file_ext": "py", "file_size_in_byte": 10326, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 515, "dataset": "github-code", "pt": "37", "api": [{"api_name": "model.Person.create_original", "line_number": 14, "usage_type": "call"}, {"api_name": "model.Person", "line_number": 14, "usage_type": "attribute"}, {"api_name": "datetime.datetime.utcnow", "line_number": 17, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 17, "usage_type": "attribute"}, {"api_name": "unittest.TestCase", "line_number": 20, "usage_type": "attribute"}, {"api_name": "google.appengine.ext.db.delete", "line_number": 22, "usage_type": "call"}, {"api_name": "google.appengine.ext.db", "line_number": 22, "usage_type": "name"}, {"api_name": "model.Person.all", "line_number": 22, "usage_type": "call"}, {"api_name": "model.Person", "line_number": 22, "usage_type": "attribute"}, {"api_name": "google.appengine.ext.db.delete", "line_number": 25, "usage_type": "call"}, {"api_name": "google.appengine.ext.db", "line_number": 25, "usage_type": "name"}, {"api_name": "model.Person.all", "line_number": 25, "usage_type": "call"}, {"api_name": "model.Person", "line_number": 25, "usage_type": "attribute"}, {"api_name": "indexing.update_index_properties", "line_number": 29, "usage_type": "call"}, {"api_name": "google.appengine.ext.db.put", "line_number": 30, "usage_type": "call"}, {"api_name": "google.appengine.ext.db", "line_number": 30, "usage_type": "name"}, {"api_name": "indexing.search", "line_number": 33, "usage_type": "call"}, {"api_name": "text_query.TextQuery", "line_number": 33, "usage_type": "call"}, {"api_name": "indexing.rank_and_order", "line_number": 37, "usage_type": "call"}, {"api_name": "text_query.TextQuery", "line_number": 37, "usage_type": "call"}, {"api_name": "indexing.rank_and_order", "line_number": 46, "usage_type": "call"}, {"api_name": "text_query.TextQuery", "line_number": 46, "usage_type": "call"}, {"api_name": "indexing.rank_and_order", "line_number": 50, "usage_type": "call"}, {"api_name": "text_query.TextQuery", "line_number": 50, "usage_type": "call"}, {"api_name": "indexing.rank_and_order", "line_number": 54, "usage_type": "call"}, {"api_name": "text_query.TextQuery", "line_number": 54, "usage_type": "call"}, {"api_name": "indexing.rank_and_order", "line_number": 63, "usage_type": "call"}, {"api_name": "text_query.TextQuery", "line_number": 63, "usage_type": "call"}, {"api_name": "indexing.sort_query_words", "line_number": 133, "usage_type": "call"}, {"api_name": "indexing.sort_query_words", "line_number": 136, "usage_type": "call"}, {"api_name": "indexing.sort_query_words", "line_number": 139, "usage_type": "call"}, {"api_name": "indexing.sort_query_words", "line_number": 142, "usage_type": "call"}, {"api_name": "indexing.update_index_properties", "line_number": 152, "usage_type": "call"}, {"api_name": "google.appengine.ext.db.put", "line_number": 153, "usage_type": "call"}, {"api_name": "google.appengine.ext.db", "line_number": 153, "usage_type": "name"}, {"api_name": "indexing.search", "line_number": 155, "usage_type": "call"}, {"api_name": "text_query.TextQuery", "line_number": 155, "usage_type": "call"}, {"api_name": "indexing.search", "line_number": 158, "usage_type": "call"}, {"api_name": "text_query.TextQuery", "line_number": 158, "usage_type": "call"}, {"api_name": "indexing.search", "line_number": 234, "usage_type": "call"}, {"api_name": "text_query.TextQuery", "line_number": 234, "usage_type": "call"}, {"api_name": "logging.basicConfig", "line_number": 238, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 238, "usage_type": "attribute"}, {"api_name": "unittest.main", "line_number": 239, "usage_type": "call"}]} +{"seq_id": "5912620821", "text": "import json\nimport math\nfrom pathlib import Path\n\nimport numpy as np\n\n\ndef network_distance(network_0, network_1):\n # Frobenius Norm\n # Kullback–Leibler divergence\n # Jensen–Shannon divergence\n # Jordan normal form\n # Frobenius normal form\n # RV coefficient\n # Matrix similarity\n\n return\n\n\ndef relative(start_loc, end_loc):\n assert len(start_loc) == len(end_loc)\n\n dx = end_loc[0] - start_loc[0]\n dy = end_loc[1] - start_loc[1]\n angle = np.arctan2(dy, dx)\n angle = np.degrees(angle)\n angle = angle % 360\n\n dist = np.linalg.norm(np.asarray(end_loc) - np.asarray(start_loc))\n return angle, dist\n\n\ndef observed_agents(observing_set, observed_set):\n closest = {}\n for observed_agent in observed_set:\n observed_location = observed_agent.location\n closest_agent = None\n closest_dist = math.inf\n for observing_agent in observing_set:\n observing_location = observing_agent.location\n angle, dist = relative(observing_location, observed_location)\n if dist < closest_dist and dist <= observing_agent.observation_radius:\n closest_dist = dist\n closest_agent = observing_agent\n if closest_agent is not None:\n closest[observed_agent.name] = (closest_agent, closest_dist)\n return closest\n\n\ndef pol2cart(angle, radius):\n x = radius * np.cos(angle)\n y = radius * np.sin(angle)\n return x, y\n\n\ndef deterministic_ring(num_points, center, radius, start_proportion=0, seed=None):\n angles = np.linspace(start=start_proportion, stop=1, num=num_points, endpoint=False)\n angles += start_proportion\n angles *= 2 * np.pi\n\n # polar_coords = np.vstack((radius, angles))\n # polar_coords = np.transpose(polar_coords)\n\n # calculating coordinates\n vector_pol2cart = np.vectorize(pol2cart, )\n cart_coords = vector_pol2cart(angles, radius)\n cart_coords = np.transpose(cart_coords)\n center_arr = np.tile(center, (cart_coords.shape[0], 1))\n\n cart_coords = cart_coords + center_arr\n return cart_coords\n\n\ndef random_ring(num_points, center, min_rad, max_rad, seed=None):\n rng = np.random.default_rng(seed=seed)\n angles = rng.normal(size=num_points)\n angles *= 2 * np.pi\n\n radius = rng.uniform(low=min_rad, high=max_rad, size=num_points)\n\n # polar_coords = np.vstack((radius, angles))\n # polar_coords = np.transpose(polar_coords)\n\n # calculating coordinates\n vector_pol2cart = np.vectorize(pol2cart, )\n cart_coords = vector_pol2cart(angles, radius)\n cart_coords = np.transpose(cart_coords)\n center_arr = np.tile(center, (cart_coords.shape[0], 1))\n\n cart_coords = cart_coords + center_arr\n return cart_coords\n\n\ndef euclidean(positions_a: np.ndarray, positions_b: np.ndarray, axis=0):\n \"\"\"\n Calculate the distance between positions A and positions B\n\n :param positions_a:\n :param positions_b:\n :param axis:\n :return:\n \"\"\"\n return np.linalg.norm(positions_a - positions_b, axis=axis)\n\n\ndef save_config(config, save_dir, config_name='config', indent=2):\n if not save_dir.exists():\n save_dir.mkdir(exist_ok=True, parents=True)\n save_path = Path(save_dir, f'{config_name}.json')\n with open(save_path, 'w') as config_file:\n json.dump(config, config_file, indent=indent)\n return save_path\n\n\ndef load_config(experiment_dir, config_stem='config'):\n config_fname = Path(experiment_dir, f'{config_stem}.json')\n with open(config_fname, 'r') as config_file:\n ccea_config = json.load(config_file)\n return ccea_config", "repo_name": "Adrang/SmartWow", "sub_path": "smartWow/utils.py", "file_name": "utils.py", "file_ext": "py", "file_size_in_byte": 3579, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "numpy.arctan2", "line_number": 25, "usage_type": "call"}, {"api_name": "numpy.degrees", "line_number": 26, "usage_type": "call"}, {"api_name": "numpy.linalg.norm", "line_number": 29, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 29, "usage_type": "attribute"}, {"api_name": "numpy.asarray", "line_number": 29, "usage_type": "call"}, {"api_name": "math.inf", "line_number": 38, "usage_type": "attribute"}, {"api_name": "numpy.cos", "line_number": 51, "usage_type": "call"}, {"api_name": "numpy.sin", "line_number": 52, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 57, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 59, "usage_type": "attribute"}, {"api_name": "numpy.vectorize", "line_number": 65, "usage_type": "call"}, {"api_name": "numpy.transpose", "line_number": 67, "usage_type": "call"}, {"api_name": "numpy.tile", "line_number": 68, "usage_type": "call"}, {"api_name": "numpy.random.default_rng", "line_number": 75, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 75, "usage_type": "attribute"}, {"api_name": "numpy.pi", "line_number": 77, "usage_type": "attribute"}, {"api_name": "numpy.vectorize", "line_number": 85, "usage_type": "call"}, {"api_name": "numpy.transpose", "line_number": 87, "usage_type": "call"}, {"api_name": "numpy.tile", "line_number": 88, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 94, "usage_type": "attribute"}, {"api_name": "numpy.linalg.norm", "line_number": 103, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 103, "usage_type": "attribute"}, {"api_name": "pathlib.Path", "line_number": 109, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 111, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 116, "usage_type": "call"}, {"api_name": "json.load", "line_number": 118, "usage_type": "call"}]} +{"seq_id": "15505093639", "text": "import urllib\nimport requests\nimport pathlib\nimport yaml\nimport time\nfrom typing import Optional, Mapping\nimport numpy as np\nimport pandas as pd\nfrom requests.exceptions import InvalidJSONError, JSONDecodeError\nfrom urllib3.exceptions import ProtocolError\n\n\n# define the baseurl and set the fritz token to connect\nconfig_path = pathlib.Path(__file__).parent.parent.absolute() / \"config.yaml\"\nwith open(config_path) as config_yaml:\n config = yaml.load(config_yaml, Loader=yaml.FullLoader)\nBASE_URL = f\"{config['fritz']['protocol']}://{config['fritz']['host']}/\"\nMAX_ATTEMPTS = config['fritz']['max_attempts']\nSLEEP_TIME = config['fritz']['sleep_time']\nfritz_token = config['fritz']['token']\ndefault_catalog = config['kowalski']['collections'].get('sources')\n\n\ndef api(\n method: str,\n endpoint: str,\n data: Optional[Mapping] = None,\n token: str = fritz_token,\n base_url: str = BASE_URL,\n max_attempts: int = MAX_ATTEMPTS,\n sleep_time: int = SLEEP_TIME,\n):\n method = method.upper()\n headers = {\"Authorization\": f\"token {token}\"}\n kwargs = {\n \"method\": method,\n \"url\": urllib.parse.urljoin(base_url, endpoint),\n \"headers\": headers,\n }\n if method not in (\"GET\", \"HEAD\"):\n kwargs[\"json\"] = data\n elif method == \"GET\":\n kwargs[\"params\"] = data\n\n for attempt in range(max_attempts):\n try:\n response = requests.request(**kwargs)\n break\n except (\n InvalidJSONError,\n ConnectionError,\n ProtocolError,\n OSError,\n JSONDecodeError,\n ):\n print(f'Error - Retrying (attempt {attempt+1}).')\n time.sleep(sleep_time)\n continue\n\n return response\n\n\ndef radec_to_iau_name(ra: float, dec: float, prefix: str = \"ZTFJ\"):\n \"\"\"Transform R.A./Decl. in degrees to IAU-style hexadecimal designations.\"\"\"\n if not 0.0 <= ra < 360.0:\n raise ValueError(\"Bad RA value in degrees\")\n if not -90.0 <= dec <= 90.0:\n raise ValueError(\"Bad Dec value in degrees\")\n\n ra_h = np.floor(ra * 12.0 / 180.0)\n ra_m = np.floor((ra * 12.0 / 180.0 - ra_h) * 60.0)\n ra_s = ((ra * 12.0 / 180.0 - ra_h) * 60.0 - ra_m) * 60.0\n\n dec_d = np.floor(abs(dec)) * np.sign(dec)\n dec_m = np.floor(np.abs(dec - dec_d) * 60.0)\n dec_s = np.abs(np.abs(dec - dec_d) * 60.0 - dec_m) * 60.0\n\n hms = f\"{ra_h:02.0f}{ra_m:02.0f}{ra_s:05.2f}\"\n dms = f\"{dec_d:+03.0f}{dec_m:02.0f}{dec_s:04.1f}\"\n\n return prefix + hms + dms\n\n\ndef get_lightcurves_via_coords(\n kowalski_instances,\n ra,\n dec,\n radius=2.0,\n catalog=default_catalog,\n program_id_selector=list([1, 2, 3]),\n limit_per_query=1000,\n Ncore=1,\n get_basic_data=False,\n):\n\n if catalog is None:\n raise ValueError(\n 'No catalog specified. Please add one to config.yaml under kowalski: collectons: sources:'\n )\n\n light_curve_ids = []\n query = {\n \"query_type\": \"near\",\n \"query\": {\n \"max_distance\": radius,\n \"distance_units\": \"arcsec\",\n \"radec\": {\"query_coords\": [ra, dec]},\n \"catalogs\": {\n catalog: {\n \"filter\": {},\n \"projection\": {\"_id\": 1},\n }\n },\n },\n \"kwargs\": {\n \"max_time_ms\": 10000,\n \"limit\": 1000,\n },\n }\n\n responses = kowalski_instances.query(query=query)\n for name in responses.keys():\n if len(responses[name]) > 0:\n response = responses[name]\n if response.get(\"status\", \"error\") == \"success\":\n lc_ids = [\n item[\"_id\"]\n for item in response.get(\"data\")[catalog][\"query_coords\"]\n ]\n light_curve_ids += lc_ids\n\n if len(light_curve_ids) == 0:\n return None\n else:\n print(\"Found %d lightcurves\" % len(light_curve_ids))\n\n return get_lightcurves_via_ids(\n kowalski_instances,\n light_curve_ids,\n catalog,\n program_id_selector=program_id_selector,\n limit_per_query=limit_per_query,\n Ncore=Ncore,\n get_basic_data=get_basic_data,\n )\n\n\ndef get_lightcurves_via_ids(\n kowalski_instances,\n ids,\n catalog,\n program_id_selector=list([1, 2, 3]),\n limit_per_query=1000,\n Ncore=1,\n get_basic_data=False,\n):\n\n itr = 0\n lcs = []\n Nsources = len(ids)\n\n if get_basic_data:\n # Only retrive basic data (esp. for feature generation)\n projection = {\n \"_id\": 1,\n \"filter\": 1,\n \"data.hjd\": 1,\n \"data.mag\": 1,\n \"data.magerr\": 1,\n \"data.catflags\": 1,\n }\n else:\n projection = {\n \"_id\": 1,\n \"ra\": 1,\n \"dec\": 1,\n \"filter\": 1,\n \"meanmag\": 1,\n \"vonneumannratio\": 1,\n \"refchi\": 1,\n \"refmag\": 1,\n \"refmagerr\": 1,\n \"iqr\": 1,\n \"data\": 1,\n }\n\n while True:\n Nqueries = int(np.ceil(Nsources / limit_per_query))\n\n queries = [\n {\n \"query_type\": \"find\",\n \"query\": {\n \"catalog\": catalog,\n \"filter\": {\n \"_id\": {\n \"$in\": ids[i * limit_per_query : (i + 1) * limit_per_query]\n },\n \"data.programid\": {\n \"$in\": program_id_selector,\n },\n },\n \"projection\": projection,\n },\n }\n for i in range(itr, itr + min(Nqueries, Ncore))\n ]\n\n responses = kowalski_instances.query(\n queries=queries, use_batch_query=True, max_n_threads=Ncore\n )\n Nsources -= len(queries) * limit_per_query\n\n for name in responses.keys():\n if len(responses[name]) > 0:\n response_list = responses[name]\n for response in response_list:\n if response.get(\"status\", \"error\") == \"success\":\n light_curves = response.get(\"data\")\n lcs += light_curves\n\n if Nsources <= 0:\n print(f'{len(ids)} done')\n break\n itr += len(queries)\n if (itr * limit_per_query) % limit_per_query == 0:\n print(itr * limit_per_query, \"done\")\n\n return lcs\n\n\ndef make_photometry(light_curves: list, drop_flagged: bool = False):\n \"\"\"\n Make a pandas.DataFrame with photometry\n :param light_curves: list of photometric time series\n :param drop_flagged: drop data points with catflags!=0\n :return:\n \"\"\"\n dfs = []\n for light_curve in light_curves:\n if len(light_curve[\"data\"]):\n df = pd.DataFrame.from_records(light_curve[\"data\"])\n df[\"fid\"] = light_curve[\"filter\"]\n dfs.append(df)\n\n df_light_curve = pd.concat(dfs, ignore_index=True, sort=False)\n\n ztf_filters = {1: \"ztfg\", 2: \"ztfr\", 3: \"ztfi\"}\n df_light_curve[\"ztf_filter\"] = df_light_curve[\"fid\"].apply(lambda x: ztf_filters[x])\n df_light_curve[\"magsys\"] = \"ab\"\n df_light_curve[\"zp\"] = 23.9\n df_light_curve[\"mjd\"] = df_light_curve[\"hjd\"] - 2400000.5\n\n df_light_curve[\"mjd\"] = df_light_curve[\"mjd\"].apply(lambda x: np.float64(x))\n df_light_curve[\"mag\"] = df_light_curve[\"mag\"].apply(lambda x: np.float32(x))\n df_light_curve[\"magerr\"] = df_light_curve[\"magerr\"].apply(lambda x: np.float32(x))\n\n # filter out flagged data:\n if drop_flagged:\n mask_not_flagged = df_light_curve[\"catflags\"] == 0\n df_light_curve = df_light_curve.loc[mask_not_flagged]\n\n return df_light_curve\n\n\ndef save_newsource(\n kowalski_instances,\n group_ids,\n ra,\n dec,\n radius=2.0,\n obj_id=None,\n post_source=True,\n period=None,\n return_id=False,\n return_phot=False,\n skip_phot=False,\n instrument_id=1,\n):\n\n # get the lightcurves\n light_curves = get_lightcurves_via_coords(kowalski_instances, ra, dec, radius)\n\n # generate position-based name if obj_id not set\n newsource = False\n if obj_id is None:\n newsource = True\n if light_curves is not None:\n ra_mean = float(\n np.mean(\n [\n light_curve[\"ra\"]\n for light_curve in light_curves\n if light_curve.get(\"ra\") is not None\n ]\n )\n )\n dec_mean = float(\n np.mean(\n [\n light_curve[\"dec\"]\n for light_curve in light_curves\n if light_curve.get(\"dec\") is not None\n ]\n )\n )\n\n else:\n print(\"No lightcurves found. Skipping source.\")\n return None\n\n obj_id = radec_to_iau_name(ra_mean, dec_mean, prefix=\"ZTFJ\")\n\n else:\n ra_mean, dec_mean = ra, dec\n\n # get photometry; drop flagged/nan data\n df_photometry = make_photometry(light_curves, drop_flagged=True)\n df_photometry = (\n df_photometry.dropna().drop_duplicates('uexpid').reset_index(drop=True)\n )\n\n photometry = {\n \"obj_id\": obj_id,\n \"instrument_id\": instrument_id,\n \"mjd\": df_photometry[\"mjd\"].tolist(),\n \"mag\": df_photometry[\"mag\"].tolist(),\n \"magerr\": df_photometry[\"magerr\"].tolist(),\n \"limiting_mag\": df_photometry[\"zp\"].tolist(),\n \"magsys\": df_photometry[\"magsys\"].tolist(),\n \"filter\": df_photometry[\"ztf_filter\"].tolist(),\n \"ra\": df_photometry[\"ra\"].tolist(),\n \"dec\": df_photometry[\"dec\"].tolist(),\n \"group_ids\": group_ids,\n }\n\n if len(photometry.get(\"mag\", ())) == 0:\n print('No unflagged photometry available. Skipping source.')\n return None\n\n # post new source to Fritz\n if newsource or post_source:\n post_source_data = {\n \"id\": obj_id,\n \"ra\": ra_mean,\n \"dec\": dec_mean,\n \"group_ids\": group_ids,\n \"origin\": \"Fritz\",\n }\n\n response = api(\n \"POST\",\n \"/api/sources\",\n post_source_data,\n max_attempts=MAX_ATTEMPTS,\n )\n\n if response.json()[\"status\"] == \"error\":\n print(f\"Failed to save {obj_id} as a Source\")\n return None\n\n # post photometry\n if not skip_phot:\n print(\"Uploading photometry for %s\" % obj_id)\n response = api(\"PUT\", \"/api/photometry\", photometry, max_attempts=MAX_ATTEMPTS)\n if response.json()[\"status\"] == \"error\":\n print('Failed to post photometry to Fritz')\n print(response.json())\n return None\n\n if period is not None:\n response_anotations = api(\n 'GET', 'api/sources/%s/annotations' % obj_id, max_attempts=MAX_ATTEMPTS\n )\n\n annotations_data = response_anotations.json().get('data')\n\n has_period_annotation = False\n for annotation in annotations_data:\n if annotation['origin'] == 'uploadedperiod':\n has_period_annotation = True\n\n if not has_period_annotation:\n # upload the period if it is provided and there is not already a period annotation\n data = {\n \"origin\": \"uploadedperiod\",\n \"group_ids\": group_ids,\n \"data\": {'period': period},\n }\n response = api(\n \"POST\",\n \"api/sources/%s/annotations\" % obj_id,\n data=data,\n max_attempts=MAX_ATTEMPTS,\n )\n\n if return_id & return_phot:\n return obj_id, photometry\n elif return_id:\n return obj_id\n elif return_phot:\n return photometry\n else:\n return None\n\n\ndef get_highscoring_objects(\n G,\n otype='vnv',\n condition=\"$or\",\n limit=0.9,\n limit_dnn=None,\n limit_xgb=None,\n):\n\n if limit_dnn is None:\n limit_dnn = limit\n if limit_xgb is None:\n limit_xgb = limit\n\n # example\n q = {\n \"query_type\": \"find\",\n \"query\": {\n \"catalog\": \"ZTF_source_classifications_DR16\",\n \"filter\": {\n condition: [\n {'%s_xgb' % otype: {'$gt': limit_xgb}},\n {'%s_dnn' % otype: {'$gt': limit_dnn}},\n ],\n },\n \"projection\": {},\n },\n \"kwargs\": {},\n }\n\n r = G.query(q)\n\n return pd.DataFrame(r['data'])\n\n\ndef get_stats(G, ids):\n qs = [\n {\n \"query_type\": \"find\",\n \"query\": {\n \"catalog\": \"ZTF_source_features_DR16\",\n \"filter\": {'_id': i},\n \"projection\": {},\n },\n \"kwargs\": {},\n }\n for i in ids\n ]\n rs = G.batch_query(qs, n_treads=32)\n\n return pd.DataFrame([s['data'][0] for s in rs])\n", "repo_name": "ZwickyTransientFacility/scope", "sub_path": "scope/fritz.py", "file_name": "fritz.py", "file_ext": "py", "file_size_in_byte": 13031, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 6, "dataset": "github-code", "pt": "37", "api": [{"api_name": "pathlib.Path", "line_number": 14, "usage_type": "call"}, {"api_name": "yaml.load", "line_number": 16, "usage_type": "call"}, {"api_name": "yaml.FullLoader", "line_number": 16, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 27, "usage_type": "name"}, {"api_name": "typing.Mapping", "line_number": 27, "usage_type": "name"}, {"api_name": "urllib.parse.urljoin", "line_number": 37, "usage_type": "call"}, {"api_name": "urllib.parse", "line_number": 37, "usage_type": "attribute"}, {"api_name": "requests.request", "line_number": 47, "usage_type": "call"}, {"api_name": "requests.exceptions.InvalidJSONError", "line_number": 50, "usage_type": "name"}, {"api_name": "urllib3.exceptions.ProtocolError", "line_number": 52, "usage_type": "name"}, {"api_name": "requests.exceptions.JSONDecodeError", "line_number": 54, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 57, "usage_type": "call"}, {"api_name": "numpy.floor", "line_number": 70, "usage_type": "call"}, {"api_name": "numpy.floor", "line_number": 71, "usage_type": "call"}, {"api_name": "numpy.floor", "line_number": 74, "usage_type": "call"}, {"api_name": "numpy.sign", "line_number": 74, "usage_type": "call"}, {"api_name": "numpy.floor", "line_number": 75, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 75, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 76, "usage_type": "call"}, {"api_name": "numpy.ceil", "line_number": 188, "usage_type": "call"}, {"api_name": "pandas.DataFrame.from_records", "line_number": 242, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 242, "usage_type": "attribute"}, {"api_name": "pandas.concat", "line_number": 246, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 254, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 255, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 256, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 290, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 299, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 439, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 457, "usage_type": "call"}]} +{"seq_id": "34500044431", "text": "import pandas as pd\nimport dtale\nfrom pycoingecko import CoinGeckoAPI\nfrom datetime import datetime\nfrom convert_unix import ConvertUnixToDatetime as co\n\ncg = CoinGeckoAPI()\ndef coin_market_chart_range(id = \"bitcoin\", vs_currency = \"usd\", from_timestamp = \"1555459200\", to_timestamp = \"1587081600\"):\n price = cg.get_coin_market_chart_range_by_id(id = id, vs_currency=vs_currency, to_timestamp=to_timestamp, from_timestamp=from_timestamp)\n df = pd.DataFrame(price)\n df2 = pd.DataFrame(df['prices'].values.tolist(), columns = ['time', f'{id}_price'])\n df3 = pd.DataFrame(df['prices'], columns = ['time', f'{id}_price'])\n df2['time'] = df2['time'].apply(lambda x: co(x).convert_unix())\n df2 = df2.set_index('time')\n\n total_volumes = []\n market_caps = []\n for keys, values in df['market_caps']:\n market_caps.append(values)\n print(df.columns)\n for key, values in df['total_volumes']:\n total_volumes.append(values)\n\n df2[f'{id}_market_caps'] = market_caps\n df2[f'{id}_total_volumes'] = total_volumes\n return df2\n\nbitcoin = coin_market_chart_range()\n\n\n# etherium = coin_market_chart_range(id = 'ethereum')", "repo_name": "rohilzalke1995/dash-plotly", "sub_path": "D-tale/dtale.py", "file_name": "dtale.py", "file_ext": "py", "file_size_in_byte": 1154, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "pycoingecko.CoinGeckoAPI", "line_number": 7, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 10, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 11, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 12, "usage_type": "call"}, {"api_name": "convert_unix.ConvertUnixToDatetime", "line_number": 13, "usage_type": "call"}]} +{"seq_id": "38087918109", "text": "\"\"\"\n--- Part Two ---\n\nAs you finish the last group's customs declaration, you notice that you misread one word in the instructions:\n\nYou don't need to identify the questions to which anyone answered \"yes\"; you need to identify the questions to which everyone answered \"yes\"!\n\nUsing the same example as above:\n\nabc\n\na\nb\nc\n\nab\nac\n\na\na\na\na\n\nb\nThis list represents answers from five groups:\n\nIn the first group, everyone (all 1 person) answered \"yes\" to 3 questions: a, b, and c.\nIn the second group, there is no question to which everyone answered \"yes\".\nIn the third group, everyone answered yes to only 1 question, a. Since some people did not answer \"yes\" to b or c, they don't count.\nIn the fourth group, everyone answered yes to only 1 question, a.\nIn the fifth group, everyone (all 1 person) answered \"yes\" to 1 question, b.\nIn this example, the sum of these counts is 3 + 0 + 1 + 1 + 1 = 6.\n\nFor each group, count the number of questions to which everyone answered \"yes\". What is the sum of those counts?\n\"\"\"\nimport sys\nfrom collections import Counter\nfrom functools import reduce\n\nDEBUG = False\ndata = [\n [\n \"\"\"abcx\nabcy\nabcz\"\"\",\n 3,\n ],\n [\n \"\"\"abc\n\na\nb\nc\n\nab\nac\n\na\na\na\na\n\nb\"\"\",\n 6,\n ],\n]\n\n\ndef main(groups_data):\n to_group_uniq = lambda acc, person_choice: set(person_choice) if acc is None else set(person_choice) & acc\n calc_group_uniq_count = lambda group: len(reduce(to_group_uniq, group.split(\"\\n\"), None))\n\n return sum(map(calc_group_uniq_count, groups_data))\n\n\ndef test():\n errors = False\n for input, test_result in data:\n result = main(input.split(\"\\n\\n\"))\n\n print(input, \", expected:\", test_result, \", actual:\", result, \"\\n\")\n try:\n assert test_result == result\n except AssertionError as exc:\n print(\"ERROR\", exc)\n errors = True\n\n if errors:\n print(\"\\n\\ngot errors!\")\n\n\nif __name__ == \"__main__\":\n if len(sys.argv) > 1 and sys.argv[1] == \"--debug\":\n DEBUG = True\n\n if DEBUG:\n test()\n\n else:\n with open(\"task6_1.input\") as f:\n print(main(f.read().split(\"\\n\\n\")))\n", "repo_name": "q210/AdventOfCode", "sub_path": "2020/task6_2.py", "file_name": "task6_2.py", "file_ext": "py", "file_size_in_byte": 2144, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "37", "api": [{"api_name": "functools.reduce", "line_number": 71, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 93, "usage_type": "attribute"}]} +{"seq_id": "21599452655", "text": "import logging\nimport random\nimport string\nfrom typing import Optional, TYPE_CHECKING, Union\n\nimport discord.ext.commands\n\nfrom . import PokestarBotCog\nfrom ..utils import Embed, StopCommand, partition, send_embeds_fields\n\nif TYPE_CHECKING:\n from ..bot import PokestarBot\n\nlogger = logging.getLogger(__name__)\n\n\nclass Private(PokestarBotCog):\n\n def __init__(self, bot: \"PokestarBot\"):\n super().__init__(bot)\n self.bot.add_check_recursive(self.private, discord.ext.commands.bot_has_guild_permissions(manage_channels=True))\n\n async def pre_create(self):\n async with self.bot.conn.execute(\"\"\"CREATE TABLE IF NOT EXISTS PRIVATE_CHANNELS(CHANNEL_ID BIGINT PRIMARY KEY, OWNER BIGINT)\"\"\"):\n pass\n async with self.bot.conn.execute(\n \"\"\"CREATE TABLE IF NOT EXISTS BLOCKED_PRIVATE_CHANNELS(ID INTEGER PRIMARY KEY, CHANNEL_ID BIGINT, USER_ID BIGINT, UNIQUE (CHANNEL_ID, \n USER_ID))\"\"\"):\n pass\n\n @property\n def random_code(self):\n return \"\".join(random.choice(string.ascii_letters + string.digits) for _ in range(10))\n\n @discord.ext.commands.group(brief=\"Work with private channels\", invoke_without_command=True)\n async def private(self, ctx: discord.ext.commands.Context):\n await self.bot.generic_help(ctx)\n\n @private.command(brief=\"Make a private channel\", usage=\"[name]\")\n @discord.ext.commands.cooldown(1, 60, discord.ext.commands.BucketType.member)\n async def create(self, ctx: discord.ext.commands.Context, *, name: Optional[str] = None):\n await self.pre_create()\n name = name or (\"private-channel-\" + self.random_code)\n category = self.bot.get_channel_data(ctx.guild.id, \"private-channel\")\n if category and type(category) is not discord.CategoryChannel:\n category = None\n overwrites = {\n ctx.guild.default_role: discord.PermissionOverwrite(read_messages=False), ctx.author: discord.PermissionOverwrite(read_messages=True)\n }\n channel: discord.TextChannel = await ctx.guild.create_text_channel(name, category=category,\n reason=f\"Creating private channel requested by {ctx.author}\",\n overwrites=overwrites)\n async with self.bot.conn.execute(\"\"\"INSERT INTO PRIVATE_CHANNELS(CHANNEL_ID, OWNER) VALUES (?, ?)\"\"\", [channel.id, ctx.author.id]):\n pass\n await channel.send(embed=Embed(ctx, title=\"Channel Created\",\n description=f\"Your private channel has been created! Add some members using `\"\n f\"{self.bot.command_prefix}private add`!\",\n color=discord.Color.green()))\n\n @private.command(name=\"import\", brief=\"Import an existing private channel that is not under the bot\", usage=\"[channel] [owner]\",\n not_channel_locked=True)\n @discord.ext.commands.has_guild_permissions(manage_channels=True)\n async def import_channel(self, ctx: discord.ext.commands.Context, channel: Optional[discord.TextChannel], user: Optional[discord.Member]):\n channel = channel or ctx.channel\n user = user or ctx.author\n in_bot = await self.check_in_bot(channel)\n if in_bot:\n embed = Embed(ctx, title=\"Channel Already In The Bot\",\n description=\"The channel is already in the bot's database, and cannot be imported.\", color=discord.Color.red())\n embed.add_field(name=\"Channel\", value=channel.mention)\n return await ctx.send(embed=embed)\n else:\n perms: discord.PermissionOverwrite = channel.overwrites_for(channel.guild.default_role)\n if perms.read_messages is not False:\n embed = Embed(ctx, title=\"Not Private Channel\", description=\"The channel is not private.\", color=discord.Color.red())\n embed.add_field(name=\"Channel\", value=channel.mention)\n return await ctx.send(embed=embed)\n else:\n overwrites = channel.overwrites\n overwrites[user] = discord.PermissionOverwrite(read_messages=True)\n await channel.edit(overwrites=overwrites, reason=\"Adding user to the channel as owner\")\n async with self.bot.conn.execute(\"\"\"INSERT INTO PRIVATE_CHANNELS(CHANNEL_ID, OWNER) VALUES (?, ?)\"\"\", [channel.id, user.id]):\n pass\n embed = Embed(ctx, title=\"Imported\", description=\"The channel has been imported.\")\n embed.add_field(name=\"Channel\", value=channel.mention)\n embed.add_field(name=\"Channel Owner\", value=user.id)\n return await ctx.send(embed=embed)\n\n async def check_in_bot(self, channel: discord.TextChannel):\n await self.pre_create()\n async with self.bot.conn.execute(\"\"\"SELECT OWNER FROM PRIVATE_CHANNELS WHERE CHANNEL_ID==?\"\"\", [channel.id]) as cursor:\n data = await cursor.fetchone()\n return bool(data is None)\n\n async def verify_owner(self, ctx: discord.ext.commands.Context, user: discord.Member, channel: discord.TextChannel,\n verifying_existence_only: bool = False):\n await self.pre_create()\n async with self.bot.conn.execute(\"\"\"SELECT OWNER FROM PRIVATE_CHANNELS WHERE CHANNEL_ID==?\"\"\", [channel.id]) as cursor:\n data = await cursor.fetchone()\n if data is None:\n embed = Embed(ctx, title=\"Channel not a Bot Private Channel\", description=\"The channel is not a private channel created by the bot.\",\n color=discord.Color.red())\n embed.add_field(name=\"Channel\", value=channel.mention)\n await ctx.send(embed=embed)\n raise StopCommand\n if verifying_existence_only:\n return True\n owner_id, = data\n if user.id != owner_id and not user.guild_permissions.administrator:\n embed = Embed(ctx, title=\"Not Owner\", description=\"You are not the owner of the Private Channel.\", color=discord.Color.red())\n embed.add_field(name=\"Channel\", value=channel.mention)\n embed.add_field(name=\"Owner\", value=self.bot.get_user(ctx.guild, owner_id).mention)\n await ctx.send(embed=embed)\n raise StopCommand\n else:\n return ctx.guild.get_member(owner_id)\n\n @private.command(brief=\"Add users/roles to the channel\", usage=\"[channel] user_or_role [user_or_role] [...]\", not_channel_locked=True)\n async def add(self, ctx: discord.ext.commands.Context, channel: Optional[discord.TextChannel] = None,\n *member_or_roles: Union[discord.Member, discord.Role]):\n channel = channel or ctx.channel\n await self.verify_owner(ctx, ctx.author, channel)\n if len(member_or_roles) == 0:\n self.bot.missing_argument(\"member_or_role\")\n existing_overwrites = channel.overwrites\n async with self.bot.conn.execute(\"\"\"SELECT USER_ID FROM BLOCKED_PRIVATE_CHANNELS WHERE CHANNEL_ID==?\"\"\", [channel.id]) as cursor:\n data = [user_id async for user_id in cursor]\n blocked = []\n for member_or_role in member_or_roles:\n if isinstance(member_or_role, discord.Role):\n for member in member_or_role.members:\n if member.id in data:\n if not ctx.author.permissions_in(channel).manage_channels:\n existing_overwrites[member] = discord.PermissionOverwrite(read_messages=False)\n blocked.append(member)\n existing_overwrites[member_or_role] = discord.PermissionOverwrite(read_messages=True)\n else:\n if member_or_role.id in data:\n if not ctx.author.permissions_in(channel).manage_channels:\n existing_overwrites[member_or_role] = discord.PermissionOverwrite(read_messages=False)\n blocked.append(member_or_role)\n else:\n existing_overwrites[member_or_role] = discord.PermissionOverwrite(read_messages=True)\n await channel.edit(overwrites=existing_overwrites, reason=f\"Users added by {ctx.author}\")\n users, roles = partition(member_or_roles, lambda item: isinstance(item, discord.Member))\n embed = Embed(ctx, title=\"Users/Roles Added\", description=\"The provided users/roles have been added to the channel.\",\n color=discord.Color.green())\n embed.add_field(name=\"Channel\", value=channel.mention)\n fields = [(\"Users Added\", \"\\n\".join(user.mention for user in users) or \"None\"),\n (\"Roles Added\", \"\\n\".join(role.mention for role in roles) or \"None\"),\n (\"Blocked Users Not Added\", \"\\n\".join(blocked_user.mention for blocked_user in blocked) or \"None\")]\n await send_embeds_fields(ctx, embed, fields)\n\n @private.command(brief=\"Remove users/roles from the channel\", usage=\"[channel] user_or_role [user_or_role] [...]\", not_channel_locked=True)\n async def remove(self, ctx: discord.ext.commands.Context, channel: Optional[discord.TextChannel] = None,\n *member_or_roles: Union[discord.Member, discord.Role]):\n channel = channel or ctx.channel\n await self.verify_owner(ctx, ctx.author, channel)\n if len(member_or_roles) == 0:\n self.bot.missing_argument(\"member_or_role\")\n existing_overwrites: dict = channel.overwrites\n for member_or_role in member_or_roles:\n existing_overwrites[member_or_role] = discord.PermissionOverwrite(read_messages=False)\n await channel.edit(overwrites=existing_overwrites, reason=f\"Users removed by {ctx.author}\")\n users, roles = partition(member_or_roles, lambda item: isinstance(item, discord.Member))\n embed = Embed(ctx, title=\"Users/Roles Removed\", description=\"The provided users/roles have been removed from the channel.\",\n color=discord.Color.green())\n embed.add_field(name=\"Channel\", value=channel.mention)\n fields = [(\"Users Removed\", \"\\n\".join(user.mention for user in users) or \"None\"),\n (\"Roles Removed\", \"\\n\".join(role.mention for role in roles) or \"None\")]\n await send_embeds_fields(ctx, embed, fields)\n\n @private.command(brief=\"Leave this channel so you cannot see it\", usage=\"[channel]\", not_channel_locked=True)\n async def leave(self, ctx: discord.ext.commands.Context, channel: Optional[discord.TextChannel] = None):\n channel = channel or ctx.channel\n await self.verify_owner(ctx, ctx.author, channel, verifying_existence_only=True)\n existing_overwrites: dict = channel.overwrites\n existing_overwrites[ctx.author] = discord.PermissionOverwrite(read_messages=False)\n await channel.edit(overwrites=existing_overwrites, reason=f\"{ctx.author} left Private Channel\")\n embed = Embed(ctx, title=\"Left Channel\", description=\"You have left the private channel.\", color=discord.Color.green())\n embed.add_field(name=\"Channel\", value=channel.mention)\n return await ctx.send(embed=embed)\n\n @private.command(brief=\"Block yourself from being added to the channel. Also leaves the channel.\", usage=\"[channel]\")\n async def block(self, ctx: discord.ext.commands.Context, channel: Optional[discord.TextChannel] = None):\n channel = channel or ctx.channel\n await self.leave.fully_run_command(ctx, channel)\n async with self.bot.conn.execute(\"\"\"INSERT INTO BLOCKED_PRIVATE_CHANNELS(CHANNEL_ID, USER_ID) VALUES (?, ?)\"\"\", [channel.id, ctx.author.id]):\n pass\n embed = Embed(ctx, title=\"Blocked\",\n description=\"You have been blocked from being able to see the channel. If the user adding can manage the channel, \"\n \"you can be added regardless.\",\n color=discord.Color.green())\n embed.add_field(name=\"Channel\", value=channel.mention)\n return await ctx.send(embed=embed)\n\n @private.group(brief=\"Edit parts of the channel.\", invoke_without_command=True)\n async def modify(self, ctx: discord.ext.commands.Context):\n await self.bot.generic_help(ctx)\n\n @modify.command(name=\"name\", brief=\"Edit the channel name\", usage=\"[channel] [name]\", not_channel_locked=True)\n async def modify_name(self, ctx: discord.ext.commands.Context, channel: Optional[discord.TextChannel] = None, *, name: Optional[str] = None):\n channel = channel or ctx.channel\n old_name = channel.name\n await self.verify_owner(ctx, ctx.author, channel)\n if name is None:\n embed = Embed(ctx, title=\"Name\", description=\"Enter the channel's new name. Channel names can be up to 100 characters.\",\n color=discord.Color.green())\n await ctx.send(embed=embed)\n msg = await self.bot.wait_for(\"message\", check=lambda\n message: message.author == ctx.author and message.channel == ctx.channel and message.content and len(message.content) <= 100,\n timeout=120)\n name = msg.content\n if len(name) > 100:\n embed = Embed(ctx, title=\"Name Too Long\", description=\"The channel name can only be up to 100 characters.\", color=discord.Color.red())\n embed.add_field(name=\"Channel\", value=channel.mention)\n embed.add_field(name=\"Name\", value=name)\n embed.add_field(name=\"Length\", value=str(len(name)))\n return await ctx.send(embed=embed)\n else:\n await channel.edit(reason=f\"Name Modification by {ctx.author}\", name=name)\n embed = Embed(ctx, title=\"Channel Name Changed\", description=\"The channel name has been successfully changed.\",\n color=discord.Color.green())\n embed.add_field(name=\"Channel\", value=channel.mention)\n embed.add_field(name=\"Old Name\", value=old_name)\n embed.add_field(name=\"New Name\", value=name)\n return await ctx.send(embed=embed)\n\n @modify.command(name=\"description\", brief=\"Edit the channel description\", usage=\"[channel] [description]\", not_channel_locked=True)\n async def modify_description(self, ctx: discord.ext.commands.Context, channel: Optional[discord.TextChannel] = None, *,\n description: Optional[str] = None):\n channel = channel or ctx.channel\n old_description = channel.topic\n await self.verify_owner(ctx, ctx.author, channel)\n if description is None:\n embed = Embed(ctx, title=\"Description\",\n description=\"Enter the channel's new description. Channel description can be up to 1024 characters.\",\n color=discord.Color.green())\n await ctx.send(embed=embed)\n msg = await self.bot.wait_for(\"message\", check=lambda\n message: message.author == ctx.author and message.channel == ctx.channel and message.content and len(message.content) <= 1024,\n timeout=120)\n description = msg.content\n if len(description) > 1024:\n embed = Embed(ctx, title=\"Description Too Long\", description=\"The channel description can only be up to 1024 characters.\",\n color=discord.Color.red())\n fields = [(\"Description\", description), (\"Length\", str(len(description)))]\n await send_embeds_fields(ctx, embed, fields)\n else:\n await channel.edit(reason=f\"Description Modification by {ctx.author}\", topic=description)\n embed = Embed(ctx, title=\"Channel Description Changed\", description=\"The channel description has been successfully changed.\",\n color=discord.Color.green())\n embed.add_field(name=\"Channel\", value=channel.mention)\n embed.add_field(name=\"Old Description\", value=old_description)\n embed.add_field(name=\"New Description\", value=description)\n return await ctx.send(embed=embed)\n\n @modify.command(name=\"owner\", brief=\"Edit the channel's owner\", usage=\"[channel] owner\", not_channel_locked=True)\n async def modify_owner(self, ctx: discord.ext.commands.Context, channel: Optional[discord.TextChannel], owner: Optional[discord.Member]):\n channel = channel or ctx.channel\n new_owner = owner or ctx.author\n old_owner = await self.verify_owner(ctx, ctx.author, channel)\n if old_owner == new_owner:\n embed = Embed(ctx, title=\"Owners are identical\", description=\"The new owner and old owner is identical.\", color=discord.Color.red())\n embed.add_field(name=\"Owner\", value=new_owner.id)\n return await ctx.send(embed=embed)\n else:\n async with self.bot.conn.execute(\"\"\"UPDATE PRIVATE_CHANNELS SET OWNER=? WHERE CHANNEL_ID==?\"\"\", [new_owner.id, channel.id]):\n pass\n embed = Embed(ctx, title=\"Owner Changed\", description=\"The owner has been changed.\", color=discord.Color.green())\n embed.add_field(name=\"Channel\", value=channel.mention)\n embed.add_field(name=\"Old Owner\", value=old_owner.mention if old_owner else \"None\")\n embed.add_field(name=\"New Owner\", value=new_owner.mention)\n return await ctx.send(embed=embed)\n\n @discord.ext.commands.Cog.listener()\n async def on_member_join(self, member: discord.Member):\n async with self.bot.conn.execute(\"\"\"SELECT CHANNEL_ID FROM PRIVATE_CHANNELS WHERE OWNER==?\"\"\", [member.id]) as cursor:\n data = [channel_id async for channel_id, in cursor]\n for channel_id in data:\n guild: discord.Guild = member.guild\n channel: Optional[discord.TextChannel] = guild.get_channel(channel_id)\n if channel is not None:\n await channel.set_permissions(member, read_messages=True, reason=\"Restoring access to private channels owned by user\")\n\n\ndef setup(bot: \"PokestarBot\"):\n bot.add_cog(Private(bot))\n logger.info(\"Loaded the Private extension.\")\n\n\ndef teardown(_bot: \"PokestarBot\"):\n logger.warning(\"Unloading the Private extension.\")\n", "repo_name": "PythonCoderAS/PokestarBot-v1", "sub_path": "bot_data/extensions/private.py", "file_name": "private.py", "file_ext": "py", "file_size_in_byte": 18343, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "typing.TYPE_CHECKING", "line_number": 11, "usage_type": "name"}, {"api_name": "logging.getLogger", "line_number": 14, "usage_type": "call"}, {"api_name": "discord.ext.commands.ext.commands.bot_has_guild_permissions", "line_number": 21, "usage_type": "call"}, {"api_name": "discord.ext.commands.ext", "line_number": 21, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 21, "usage_type": "name"}, {"api_name": "random.choice", "line_number": 33, "usage_type": "call"}, {"api_name": "string.ascii_letters", "line_number": 33, "usage_type": "attribute"}, {"api_name": "string.digits", "line_number": 33, "usage_type": "attribute"}, {"api_name": "discord.ext.commands.ext", "line_number": 36, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 36, "usage_type": "name"}, {"api_name": "discord.ext.commands.ext.commands.group", "line_number": 35, "usage_type": "call"}, {"api_name": "discord.ext.commands.ext", "line_number": 35, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 35, "usage_type": "name"}, {"api_name": "discord.ext.commands.ext", "line_number": 41, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 41, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 41, "usage_type": "name"}, {"api_name": "discord.ext.commands.CategoryChannel", "line_number": 45, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 45, "usage_type": "name"}, {"api_name": "discord.ext.commands.PermissionOverwrite", "line_number": 48, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 48, "usage_type": "name"}, {"api_name": "discord.ext.commands.TextChannel", "line_number": 50, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 50, "usage_type": "name"}, {"api_name": "utils.Embed", "line_number": 55, "usage_type": "call"}, {"api_name": "discord.ext.commands.Color.green", "line_number": 58, "usage_type": "call"}, {"api_name": "discord.ext.commands.Color", "line_number": 58, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 58, "usage_type": "name"}, {"api_name": "discord.ext.commands.ext.commands.cooldown", "line_number": 40, "usage_type": "call"}, {"api_name": "discord.ext.commands.ext", "line_number": 40, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 40, "usage_type": "name"}, {"api_name": "discord.ext.commands.ext", "line_number": 63, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 63, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 63, "usage_type": "name"}, {"api_name": "discord.ext.commands.TextChannel", "line_number": 63, "usage_type": "attribute"}, {"api_name": "discord.ext.commands.Member", "line_number": 63, "usage_type": "attribute"}, {"api_name": "utils.Embed", "line_number": 68, "usage_type": "call"}, {"api_name": "discord.ext.commands.Color.red", "line_number": 69, "usage_type": "call"}, {"api_name": "discord.ext.commands.Color", "line_number": 69, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 69, "usage_type": "name"}, {"api_name": "discord.ext.commands.PermissionOverwrite", "line_number": 73, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 73, "usage_type": "name"}, {"api_name": "utils.Embed", "line_number": 75, "usage_type": "call"}, {"api_name": "discord.ext.commands.Color.red", "line_number": 75, "usage_type": "call"}, {"api_name": "discord.ext.commands.Color", "line_number": 75, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 75, "usage_type": "name"}, {"api_name": "discord.ext.commands.PermissionOverwrite", "line_number": 80, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 80, "usage_type": "name"}, {"api_name": "utils.Embed", "line_number": 84, "usage_type": "call"}, {"api_name": "discord.ext.commands.ext.commands.has_guild_permissions", "line_number": 62, "usage_type": "call"}, {"api_name": "discord.ext.commands.ext", "line_number": 62, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 62, "usage_type": "name"}, {"api_name": "discord.ext.commands.TextChannel", "line_number": 89, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 89, "usage_type": "name"}, {"api_name": "discord.ext.commands.ext", "line_number": 95, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 95, "usage_type": "name"}, {"api_name": "discord.ext.commands.Member", "line_number": 95, "usage_type": "attribute"}, {"api_name": "discord.ext.commands.TextChannel", "line_number": 95, "usage_type": "attribute"}, {"api_name": "utils.Embed", "line_number": 101, "usage_type": "call"}, {"api_name": "discord.ext.commands.Color.red", "line_number": 102, "usage_type": "call"}, {"api_name": "discord.ext.commands.Color", "line_number": 102, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 102, "usage_type": "name"}, {"api_name": "utils.StopCommand", "line_number": 105, "usage_type": "name"}, {"api_name": "utils.Embed", "line_number": 110, "usage_type": "call"}, {"api_name": "discord.ext.commands.Color.red", "line_number": 110, "usage_type": "call"}, {"api_name": "discord.ext.commands.Color", "line_number": 110, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 110, "usage_type": "name"}, {"api_name": "utils.StopCommand", "line_number": 114, "usage_type": "name"}, {"api_name": "discord.ext.commands.ext", "line_number": 119, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 119, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 119, "usage_type": "name"}, {"api_name": "discord.ext.commands.TextChannel", "line_number": 119, "usage_type": "attribute"}, {"api_name": "typing.Union", "line_number": 120, "usage_type": "name"}, {"api_name": "discord.ext.commands.Member", "line_number": 120, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 120, "usage_type": "name"}, {"api_name": "discord.ext.commands.Role", "line_number": 120, "usage_type": "attribute"}, {"api_name": "discord.ext.commands.Role", "line_number": 130, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 130, "usage_type": "name"}, {"api_name": "discord.ext.commands.PermissionOverwrite", "line_number": 134, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 134, "usage_type": "name"}, {"api_name": "discord.ext.commands.PermissionOverwrite", "line_number": 136, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 136, "usage_type": "name"}, {"api_name": "discord.ext.commands.PermissionOverwrite", "line_number": 140, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 140, "usage_type": "name"}, {"api_name": "discord.ext.commands.PermissionOverwrite", "line_number": 143, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 143, "usage_type": "name"}, {"api_name": "utils.partition", "line_number": 145, "usage_type": "call"}, {"api_name": "discord.ext.commands.Member", "line_number": 145, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 145, "usage_type": "name"}, {"api_name": "utils.Embed", "line_number": 146, "usage_type": "call"}, {"api_name": "discord.ext.commands.Color.green", "line_number": 147, "usage_type": "call"}, {"api_name": "discord.ext.commands.Color", "line_number": 147, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 147, "usage_type": "name"}, {"api_name": "utils.send_embeds_fields", "line_number": 152, "usage_type": "call"}, {"api_name": "discord.ext.commands.ext", "line_number": 155, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 155, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 155, "usage_type": "name"}, {"api_name": "discord.ext.commands.TextChannel", "line_number": 155, "usage_type": "attribute"}, {"api_name": "typing.Union", "line_number": 156, "usage_type": "name"}, {"api_name": "discord.ext.commands.Member", "line_number": 156, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 156, "usage_type": "name"}, {"api_name": "discord.ext.commands.Role", "line_number": 156, "usage_type": "attribute"}, {"api_name": "discord.ext.commands.PermissionOverwrite", "line_number": 163, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 163, "usage_type": "name"}, {"api_name": "utils.partition", "line_number": 165, "usage_type": "call"}, {"api_name": "discord.ext.commands.Member", "line_number": 165, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 165, "usage_type": "name"}, {"api_name": "utils.Embed", "line_number": 166, "usage_type": "call"}, {"api_name": "discord.ext.commands.Color.green", "line_number": 167, "usage_type": "call"}, {"api_name": "discord.ext.commands.Color", "line_number": 167, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 167, "usage_type": "name"}, {"api_name": "utils.send_embeds_fields", "line_number": 171, "usage_type": "call"}, {"api_name": "discord.ext.commands.ext", "line_number": 174, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 174, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 174, "usage_type": "name"}, {"api_name": "discord.ext.commands.TextChannel", "line_number": 174, "usage_type": "attribute"}, {"api_name": "discord.ext.commands.PermissionOverwrite", "line_number": 178, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 178, "usage_type": "name"}, {"api_name": "utils.Embed", "line_number": 180, "usage_type": "call"}, {"api_name": "discord.ext.commands.Color.green", "line_number": 180, "usage_type": "call"}, {"api_name": "discord.ext.commands.Color", "line_number": 180, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 180, "usage_type": "name"}, {"api_name": "discord.ext.commands.ext", "line_number": 185, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 185, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 185, "usage_type": "name"}, {"api_name": "discord.ext.commands.TextChannel", "line_number": 185, "usage_type": "attribute"}, {"api_name": "utils.Embed", "line_number": 190, "usage_type": "call"}, {"api_name": "discord.ext.commands.Color.green", "line_number": 193, "usage_type": "call"}, {"api_name": "discord.ext.commands.Color", "line_number": 193, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 193, "usage_type": "name"}, {"api_name": "discord.ext.commands.ext", "line_number": 198, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 198, "usage_type": "name"}, {"api_name": "discord.ext.commands.ext", "line_number": 202, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 202, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 202, "usage_type": "name"}, {"api_name": "discord.ext.commands.TextChannel", "line_number": 202, "usage_type": "attribute"}, {"api_name": "utils.Embed", "line_number": 207, "usage_type": "call"}, {"api_name": "discord.ext.commands.Color.green", "line_number": 208, "usage_type": "call"}, {"api_name": "discord.ext.commands.Color", "line_number": 208, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 208, "usage_type": "name"}, {"api_name": "utils.Embed", "line_number": 215, "usage_type": "call"}, {"api_name": "discord.ext.commands.Color.red", "line_number": 215, "usage_type": "call"}, {"api_name": "discord.ext.commands.Color", "line_number": 215, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 215, "usage_type": "name"}, {"api_name": "utils.Embed", "line_number": 222, "usage_type": "call"}, {"api_name": "discord.ext.commands.Color.green", "line_number": 223, "usage_type": "call"}, {"api_name": "discord.ext.commands.Color", "line_number": 223, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 223, "usage_type": "name"}, {"api_name": "discord.ext.commands.ext", "line_number": 230, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 230, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 230, "usage_type": "name"}, {"api_name": "discord.ext.commands.TextChannel", "line_number": 230, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 231, "usage_type": "name"}, {"api_name": "utils.Embed", "line_number": 236, "usage_type": "call"}, {"api_name": "discord.ext.commands.Color.green", "line_number": 238, "usage_type": "call"}, {"api_name": "discord.ext.commands.Color", "line_number": 238, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 238, "usage_type": "name"}, {"api_name": "utils.Embed", "line_number": 245, "usage_type": "call"}, {"api_name": "discord.ext.commands.Color.red", "line_number": 246, "usage_type": "call"}, {"api_name": "discord.ext.commands.Color", "line_number": 246, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 246, "usage_type": "name"}, {"api_name": "utils.send_embeds_fields", "line_number": 248, "usage_type": "call"}, {"api_name": "utils.Embed", "line_number": 251, "usage_type": "call"}, {"api_name": "discord.ext.commands.Color.green", "line_number": 252, "usage_type": "call"}, {"api_name": "discord.ext.commands.Color", "line_number": 252, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 252, "usage_type": "name"}, {"api_name": "discord.ext.commands.ext", "line_number": 259, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 259, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 259, "usage_type": "name"}, {"api_name": "discord.ext.commands.TextChannel", "line_number": 259, "usage_type": "attribute"}, {"api_name": "discord.ext.commands.Member", "line_number": 259, "usage_type": "attribute"}, {"api_name": "utils.Embed", "line_number": 264, "usage_type": "call"}, {"api_name": "discord.ext.commands.Color.red", "line_number": 264, "usage_type": "call"}, {"api_name": "discord.ext.commands.Color", "line_number": 264, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 264, "usage_type": "name"}, {"api_name": "utils.Embed", "line_number": 270, "usage_type": "call"}, {"api_name": "discord.ext.commands.Color.green", "line_number": 270, "usage_type": "call"}, {"api_name": "discord.ext.commands.Color", "line_number": 270, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 270, "usage_type": "name"}, {"api_name": "discord.ext.commands.Member", "line_number": 277, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 277, "usage_type": "name"}, {"api_name": "discord.ext.commands.Guild", "line_number": 281, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 281, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 282, "usage_type": "name"}, {"api_name": "discord.ext.commands.TextChannel", "line_number": 282, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 282, "usage_type": "name"}, {"api_name": "discord.ext.commands.ext.commands.Cog.listener", "line_number": 276, "usage_type": "call"}, {"api_name": "discord.ext.commands.ext", "line_number": 276, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 276, "usage_type": "name"}, {"api_name": "bot.add_cog", "line_number": 288, "usage_type": "call"}]} +{"seq_id": "37117226071", "text": "#import os, request, threading, urllib.request, urllib.error, urllib.parse, dan time\nimport os\nimport requests\nimport threading\nimport urllib.request, urllib.error, urllib.parse\nimport time\n\n#url awal\nurl = \"https://apod.nasa.gov/apod/image/1901/LOmbradellaTerraFinazzi.jpg\"\n\n# Fungsi ini digunakan untuk membuat hitungan range\ndef buildRange(value, numsplits):\n lst = []\n #Lakukan perulangan sebanyak nilai dari parameter numsplits\n for i in range(numsplits):\n if i == 0:\n lst.append('%s-%s' % (i, int(round(1 + i * value/(numsplits*1.0) + value/(numsplits*1.0)-1, 0))))\n else:\n lst.append('%s-%s' % (int(round(1 + i * value/(numsplits*1.0),0)), int(round(1 + i * value/(numsplits*1.0) + value/(numsplits*1.0)-1, 0))))\n return lst\n\n#Buat class dengan nama SplitBufferThreads\nclass SplitBufferThreads(threading.Thread):\n \"\"\" Splits the buffer to ny number of threads\n thereby, concurrently downloading through\n ny number of threads.\n \"\"\"\n # digunakan untuk init url dan hasil response\n def __init__(self, url, byteRange):\n super(SplitBufferThreads, self).__init__()\n self.__url = url\n self.__byteRange = byteRange\n self.req = None\n \n #fungsi utama yang akan dieksekusi ketika thread berjalan\n def run(self):\n # file yang didownload dalam ukuran byte\n self.req = urllib.request.Request(self.__url, headers={'Range': 'bytes=%s' % self.__byteRange})\n \n #fungsi untuk membuka dan membaca file yang didownload\n def getFileData(self):\n #untuk membaca file\n return urllib.request.urlopen(self.req).read()\n\n#main program, \ndef main(url=None, splitBy=3):\n start_time = time.time()\n #untuk cek url valid apa tidak\n if not url:\n print(\"Please Enter some url to begin download.\")\n return\n #variable berdasarkan data split dari hasil url\n fileName = url.split('/')[-1]\n #variable yg berisi besar data \n sizeInBytes = requests.head(url, headers={'Accept-Encoding': 'identity'}).headers.get('content-length', None)\n #memberi keterangan jika terdapat download dan besaran data\n print(\"%s bytes to download.\" % sizeInBytes)\n #kondisi jika sizeinbytes false artinya tidak valid\n if not sizeInBytes:\n print(\"Size cannot be determined.\")\n return\n #untuk menampung value\n dataLst = []\n # looping sebanyak nilai dari splitBY\n for idx in range(splitBy):\n #pecah data untuk looping dan dimasukan ke variable dataLst\n byteRange = buildRange(int(sizeInBytes), splitBy)[idx]\n bufTh = SplitBufferThreads(url, byteRange)\n bufTh.start()\n bufTh.join()\n #masukan data ke variable dataLst\n dataLst.append(bufTh.getFileData())\n # menggabungkan b dengan array dataList\n content = b''.join(dataLst)\n if dataLst:\n #hapus cache dari hasil download\n if os.path.exists(fileName):\n os.remove(fileName)\n print(\"--- %s seconds ---\" % str(time.time() - start_time))\n #menulis data foto yg didownload \n with open(fileName, 'wb') as fh:\n fh.write(content)\n print(\"Finished Writing file %s\" % fileName)\n\t\t\n#menjalankan fungsi main program\nif __name__ == '__main__':\n main(url)\n", "repo_name": "vikrikii/SISPARTER-L4L5-IF4108-Kel7", "sub_path": "L5_03.download_file.py", "file_name": "L5_03.download_file.py", "file_ext": "py", "file_size_in_byte": 3283, "program_lang": "python", "lang": "id", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "threading.Thread", "line_number": 23, "usage_type": "attribute"}, {"api_name": "urllib.request.request.Request", "line_number": 38, "usage_type": "call"}, {"api_name": "urllib.request.request", "line_number": 38, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 38, "usage_type": "name"}, {"api_name": "urllib.request.request.urlopen", "line_number": 43, "usage_type": "call"}, {"api_name": "urllib.request.request", "line_number": 43, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 43, "usage_type": "name"}, {"api_name": "time.time", "line_number": 47, "usage_type": "call"}, {"api_name": "requests.head", "line_number": 55, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 77, "usage_type": "call"}, {"api_name": "os.path", "line_number": 77, "usage_type": "attribute"}, {"api_name": "os.remove", "line_number": 78, "usage_type": "call"}, {"api_name": "time.time", "line_number": 79, "usage_type": "call"}]} +{"seq_id": "29765276000", "text": "import cv2\n\nsrc = cv2.imread(\"clark2.jpg\")\nscale_percent = 50\n\n#calculate the 50 percent of original dimensions\nwidth = int(src.shape[1] * scale_percent / 100)\nheight = int(src.shape[0] * scale_percent / 100)\n\n# dsize\ndsize = (width, height)\n\n# resize image\noutput = cv2.resize(src, dsize)\n\ncv2.imwrite('clark22.jpg',output) ", "repo_name": "AhmedAdel21/Computer-Vision", "sub_path": "Face Detection & Recognition/test.py", "file_name": "test.py", "file_ext": "py", "file_size_in_byte": 325, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "37", "api": [{"api_name": "cv2.imread", "line_number": 3, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 14, "usage_type": "call"}, {"api_name": "cv2.imwrite", "line_number": 16, "usage_type": "call"}]} +{"seq_id": "71942468908", "text": "'''\nGiven a collection of integers that might contain duplicates, nums, return all possible subsets (the power set).\n\nNote: The solution set must not contain duplicate subsets.\n\nExample:\n\nInput: [1,2,2]\nOutput:\n[\n [2],\n [1],\n [1,2,2],\n [2,2],\n [1,2],\n []\n]\n'''\n\n\nfrom typing import List\nclass Solution:\n def subsetsWithDup(self, nums: List[int]) -> List[List[int]]:\n res = [[]]\n nums.sort()\n preL = 0\n for i in range(len(nums)):\n if i == 0 or nums[i] != nums[i-1]:\n preL = len(res)\n for j in range(len(res) - preL, len(res)):\n res.append(res[j] + [nums[i]])\n return res\n\ns = Solution()\nprint(s.subsetsWithDup([1,2,2]))", "repo_name": "darrencheng0817/AlgorithmLearning", "sub_path": "Python/leetcode2/90. Subsets II.py", "file_name": "90. Subsets II.py", "file_ext": "py", "file_size_in_byte": 718, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "37", "api": [{"api_name": "typing.List", "line_number": 23, "usage_type": "name"}]} +{"seq_id": "39710394134", "text": "\"\"\"Function to load MPS files.\"\"\"\n\nimport gzip\n\nimport numpy as np\n\nfrom scipy import sparse\n\n\ndef mps_parser(f, fsol=None):\n \"\"\"\n Parse Linear programs in the MPS format.\n This file format is described here\n https://en.wikipedia.org/wiki/MPS_(format)\n This has been coded in a rush and might not handle all cases\n could have a look at mps2mat.f that is a part of the package LIPSOL by Yin Zhang\n or https://github.com/YimingYAN/cppipm/blob/master/src/mpsReader.cpp\n \"\"\"\n nb_ineq = 0\n nb_eq = 0\n nb_var = 0\n b_lower = dict()\n b_upper = dict()\n b_eq = dict()\n rows = dict()\n variables = dict()\n a_ineq_list = []\n a_eq_list = []\n v_id_to_var = dict()\n part_parsing = None\n while True:\n\n line = f.readline()\n line = line.rstrip(\"\\n\")\n line += \" \" * (len(line) - 61)\n t = []\n t.append(line[1:3].strip())\n t.append(line[4:12].ljust(8))\n t.append(line[14:22])\n t.append(line[25:36].strip())\n t.append(line[39:47])\n t.append(line[49:61].strip())\n # t=line.split()\n # t[0]=\n # 2-3 5-12 15-22 25-36 40-47 50-61\n if len(t) == 0:\n continue\n if line.startswith(\"ENDATA\"):\n break\n if line.startswith(\"*\"): # this is a comment\n continue\n if len(t) == 0:\n continue\n if line.startswith(\"NAME\"):\n problem_name = t[1]\n continue\n if line.startswith(\"ROWS\"):\n part_parsing = \"ROWS\"\n continue\n if line.startswith(\"COLUMNS\"):\n part_parsing = \"COLUMNS\"\n continue\n if line.startswith(\"RHS\"):\n part_parsing = \"RHS\"\n continue\n if line.startswith(\"BOUNDS\"):\n part_parsing = \"BOUNDS\"\n continue\n\n if line.startswith(\"RANGES\"):\n print(\"not coded yet\")\n raise\n\n if part_parsing == \"ROWS\":\n if t[0] == \"N\":\n costname = t[1]\n\n if t[1] in rows:\n raise\n r = dict()\n rows[t[1]] = r\n r[\"type\"] = t[0]\n if t[0] == \"G\":\n r[\"id\"] = nb_ineq\n b_lower[nb_ineq] = 0\n b_upper[nb_ineq] = np.inf\n nb_ineq += 1\n\n if t[0] == \"L\":\n r[\"id\"] = nb_ineq\n b_lower[nb_ineq] = -np.inf\n b_upper[nb_ineq] = 0\n nb_ineq += 1\n elif t[0] == \"E\":\n r[\"id\"] = nb_eq\n b_eq[nb_eq] = 0 # set default value\n nb_eq += 1\n\n continue\n\n if part_parsing == \"COLUMNS\":\n\n if t[1] in variables:\n\n var = variables[t[1]]\n else:\n var = dict()\n variables[t[1]] = var\n var[\"id\"] = nb_var\n var[\"UP\"] = np.inf\n var[\n \"LO\"\n ] = 0 # Variables not mentioned in a given BOUNDS set are taken to be non-negative (lower bound zero, no upper bound)\n var[\"cost\"] = 0\n v_id_to_var[nb_var] = var\n nb_var += 1\n\n j = var[\"id\"]\n for k in range(int((len(t) - 2) / 2)):\n if t[2 * k + 2] == \"\":\n break\n r = rows[t[2 * k + 2]]\n v = float(t[2 * k + 3])\n if r[\"type\"] == \"N\":\n var[\"cost\"] = v\n continue\n\n i = r[\"id\"]\n\n if r[\"type\"] == \"L\":\n a_ineq_list.append((i, j, v))\n elif r[\"type\"] == \"G\":\n a_ineq_list.append((i, j, v))\n elif r[\"type\"] == \"E\":\n a_eq_list.append((i, j, v))\n continue\n\n if part_parsing == \"RHS\":\n\n for k in range(int((len(t) - 2) / 2)):\n if t[2 * k + 2] == \"\":\n break\n r = rows[t[2 * k + 2]]\n i = r[\"id\"]\n v = float(t[2 * k + 3])\n if r[\"type\"] == \"N\":\n raise\n elif r[\"type\"] == \"L\":\n\n b_upper[i] = v\n elif r[\"type\"] == \"G\":\n b_lower[i] = v\n\n elif r[\"type\"] == \"E\":\n b_eq[i] = v\n continue\n\n if part_parsing == \"BOUNDS\":\n var = variables[t[2]]\n var[\"name\"] = t[2]\n if t[0] == \"UP\" or t[0] == \"LO\":\n var[t[0]] = float(t[3])\n elif t[0] == \"FR\":\n var[\"UP\"] = np.inf\n var[\"LO\"] = -np.inf\n elif t[0] == \"FX\":\n var[\"UP\"] = float(t[3])\n var[\"LO\"] = float(t[3])\n elif t[0] == \"MI\":\n var[\"LO\"] = -np.inf\n elif t[0] == \"PL\":\n var[\"UP\"] = np.inf\n elif t[0] == \"BV\" or t[0] == \"LI\" or t[0] == \"UI\":\n print(\"integer constraints ignored\")\n raise\n\n cost_vector = np.array([v_id_to_var[i][\"cost\"] for i in range(nb_var)])\n upper_bounds = np.array([v_id_to_var[i][\"UP\"] for i in range(nb_var)])\n lower_bounds = np.array([v_id_to_var[i][\"LO\"] for i in range(nb_var)])\n\n a_ineq = sparse.dok_matrix((nb_ineq, nb_var))\n for i, j, v in a_ineq_list:\n a_ineq[i, j] = v\n\n a_eq = sparse.dok_matrix((nb_eq, nb_var))\n for i, j, v in a_eq_list:\n a_eq[i, j] = v\n\n b_eq = np.array([b_eq[i] for i in range(nb_eq)])\n b_lower = np.array([b_lower[i] for i in range(nb_ineq)])\n b_upper = np.array([b_upper[i] for i in range(nb_ineq)])\n\n # print a_eq\n r = {\n \"cost_vector\": cost_vector,\n \"upper_bounds\": upper_bounds,\n \"lower_bounds\": lower_bounds,\n \"a_eq\": a_eq,\n \"b_eq\": b_eq,\n \"a_ineq\": a_ineq,\n \"b_lower\": b_lower,\n \"b_upper\": b_upper,\n \"problem_name\": problem_name,\n \"costname\": costname,\n }\n\n # parses Linear Program solution file generated by perPlex Version 1.00\n # examples of such file in http://www.zib.de/koch/perplex/data/netlib/txt/\n # paper here https://opus4.kobv.de/opus4-zib/files/727/ZR-03-05.pdf\n r[\"solution\"] = None\n if fsol is not None:\n\n while True:\n\n line = fsol.readline()\n line = line.rstrip(\"\\n\")\n if line == \"\":\n continue\n if len(t) == 0:\n continue\n if line.startswith(\"- EOF\"):\n break\n\n if line.startswith(\"* Objvalue\"):\n # objvalue = 4\n continue\n if line.startswith(\"- Variables\"):\n part_parsing = \"Variables\"\n continue\n\n if line.startswith(\"- Constraints\"):\n part_parsing = \"Constraints\"\n continue\n\n if part_parsing == \"Variables\":\n if line.startswith(\"V Name\"):\n name = line.split(\": \")[1].ljust(8)\n var = variables[name]\n continue\n\n if line.startswith(\"V Value\"):\n val1 = float(line.split(\":\")[1].split(\"=\")[0])\n frac = line.split(\":\")[1].split(\"=\")[1].split(\"/\")\n if len(frac) == 1:\n val = float(frac[0])\n else:\n val = float(frac[0]) / float(frac[1])\n if np.isnan(val): # happends with PEROLD\n var[\"sol\"] = val1\n else:\n var[\"sol\"] = val\n continue\n\n if line.startswith(\"V State : on lower\"):\n var[\"sol\"] = var[\"LO\"]\n continue\n\n if line.startswith(\"V State : on upper\"):\n var[\"sol\"] = var[\"UP\"]\n continue\n\n if line.startswith(\"V State : on both\"):\n assert var[\"UP\"] == var[\"LO\"]\n var[\"sol\"] = var[\"UP\"]\n continue\n\n solution = np.array([v_id_to_var[i][\"sol\"] for i in range(nb_var)])\n\n r[\"solution\"] = solution\n\n return r\n\n\nif __name__ == \"__main__\":\n\n filename_lp = \"./data/netlib/AFIRO.SIF\"\n filename_sol = \"./data/perPlex/afiro.txt.gz\"\n file_lp = open(filename_lp, \"r\")\n fsol = gzip.open(filename_sol, \"r\")\n LP = mps_parser(file_lp, fsol)\n", "repo_name": "martinResearch/PySparseLP", "sub_path": "pysparselp/MPSparser.py", "file_name": "MPSparser.py", "file_ext": "py", "file_size_in_byte": 8528, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 24, "dataset": "github-code", "pt": "37", "api": [{"api_name": "numpy.inf", "line_number": 86, "usage_type": "attribute"}, {"api_name": "numpy.inf", "line_number": 91, "usage_type": "attribute"}, {"api_name": "numpy.inf", "line_number": 110, "usage_type": "attribute"}, {"api_name": "numpy.inf", "line_number": 164, "usage_type": "attribute"}, {"api_name": "numpy.inf", "line_number": 165, "usage_type": "attribute"}, {"api_name": "numpy.inf", "line_number": 170, "usage_type": "attribute"}, {"api_name": "numpy.inf", "line_number": 172, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 177, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 178, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 179, "usage_type": "call"}, {"api_name": "scipy.sparse.dok_matrix", "line_number": 181, "usage_type": "call"}, {"api_name": "scipy.sparse", "line_number": 181, "usage_type": "name"}, {"api_name": "scipy.sparse.dok_matrix", "line_number": 185, "usage_type": "call"}, {"api_name": "scipy.sparse", "line_number": 185, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 189, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 190, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 191, "usage_type": "call"}, {"api_name": "numpy.isnan", "line_number": 248, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 267, "usage_type": "call"}, {"api_name": "gzip.open", "line_number": 279, "usage_type": "call"}]} +{"seq_id": "3493229441", "text": "from flask import Flask,render_template,request,url_for,session,redirect,jsonify\nimport json,sqlite3\nfrom datetime import date\nimport datetime\nfrom time import time\nfrom hashlib import sha256\nimport datetime,time,pymongo\nfrom passlib.context import CryptContext\nimport requests, ipfshttpclient\nimport os,webbrowser\nfrom werkzeug.utils import secure_filename\n\n\n\n\nstates=[\"Andhra Pradesh\",\"Arunachal Pradesh \",\"Assam\",\"Bihar\",\"Chhattisgarh\",\"Goa\",\"Gujarat\",\"Haryana\",\"Himachal Pradesh\",\"Jammu and Kashmir\",\"Jharkhand\",\"Karnataka\",\"Kerala\",\"Madhya Pradesh\",\"Maharashtra\",\"Manipur\",\"Meghalaya\",\"Mizoram\",\"Nagaland\",\"Odisha\",\"Punjab\",\"Rajasthan\",\"Sikkim\",\"Tamil Nadu\",\"Telangana\",\"Tripura\",\"Uttar Pradesh\",\"Uttarakhand\",\"West Bengal\",\"Andaman and Nicobar Islands\",\"Chandigarh\",\"Dadra and Nagar Haveli\",\"Daman and Diu\",\"Lakshadweep\",\"NCT\",\"Puducherry\"]\n#session['user']='Genesis'\n'''Blockchain=[{\n 'index':'0',zzz\n 'patientid':'00000',\n 'first': '',\n 'last': '',\n 'doctor id': '',\n 'Dor': '12-13-2019',\n 'Age': '',\n 'haemo':'',\n 'blood':'',\n\n\n }]'''\n\nlogin_status=0\napp= Flask(__name__)\napp.secret_key = 'PATREC Authentication'\n\n\n\n#Password encryption scheme\npwd_context = CryptContext(\n schemes=[\"pbkdf2_sha256\"],\n default=\"pbkdf2_sha256\",\n pbkdf2_sha256__default_rounds=30000\n)\n\n#Sessio variables\n'''class sessionlog:\n def __init__(self):\n self.username=''\n self.id=''\n\nsess=sessionlog()'''\n\n\ndef encrypt_password(password):\n return pwd_context.hash(password)\n\ndef check_encrypted_password(password, hashed):\n return pwd_context.verify(password, hashed)\n\n#Mongodb setup\nclient = pymongo.MongoClient(\"mongodb+srv://Antony:A8939469555p@blockchainehr-kpbxk.mongodb.net/test?retryWrites=true&w=majority\")\n#client = pymongo.MongoClient(\"mongodb+srv://Antony:A8939469555p@blockchainehr.kpbxk.mongodb.net/?retryWrites=true&w=majority\")\nmydb=client[\"newDB\"]\n\nmycol=mydb[\"Blockhead\"]\n\n\n'''hashset=[]\nind=-1\npatdoc= mycol.find()\nfor x in patdoc:\n hashset.append(x['hash'])\n ind=ind+1\n\nclass index:\n index=ind\n def getindex(self):\n self.index=int(self.index+1)\n return self.index\n\nidv=index()'''\n\n\n\n@app.route('/')\n@app.route('/home')\ndef home():\n return render_template('index.html')\n\n@app.route('/learnmore')\ndef learnmore():\n return render_template('generic.html')\n\n\n\n'''def oldhome():\n return render_template('welcome.html')'''\n\n@app.route('/addguard')\ndef addguard():\n return render_template('addguard.html')\n\n@app.route('/addguardian',methods=['post'])\ndef addguardian():\n con=mydb['Guardian_contract']\n contract={\n 'guardian':request.form['guardian'],\n 'owner':request.form['owner']\n }\n if(session['user']==contract['guardian']):\n return 'Contract invalid'\n contract['status']='ACTIVE'\n contract['level']=request.form['level']\n con.insert_one(contract)\n return redirect(url_for('linkedacc'))\n\n\n\n@app.route('/linkedacc')\ndef linkedacc():\n acc=session['user']\n con=mydb['Guardian_contract']\n contract={\n 'guardian':acc\n }\n myval=con.find(contract)\n mycontro=[]\n for i in myval:\n mycontro.append(i)\n contract={\n 'owner':acc\n }\n myval=con.find(contract)\n outcontro=[]\n for i in myval:\n outcontro.append(i)\n return render_template('linkedacc.html',mycontrol=mycontro,outcontrol=outcontro)\n\n\n@app.route('/deleteguard',methods=['post'])\ndef deleteguard():\n val=dict(request.form)\n myval=mydb['Guardian_contract']\n myval.delete_one(val)\n return redirect(url_for('linkedacc'))\n\n#Organ Donor Card\n@app.route('/organ_donor')\ndef donor():\n return render_template('organ.html')\n\n\n#Insurance\n@app.route('/viewmore')\ndef insure():\n return render_template('insurance.html')\n\n#Apply insurance\n@app.route('/apply')\ndef apply():\n return render_template('apply.html')\n\n\n\n'''\n#MEDREC form creation\n@app.route('/create', methods=['POST'])\ndef createblock():\n pid=request.form['pid']\n doc= request.form['doc']\n blood= request.form['blood']\n pp= request.form['pp']\n fast= request.form['fast']\n serum= request.form['serum']\n tot=request.form['tot']\n thdl=request.form['thdl']\n ldl=request.form['ldl']\n rbc=request.form['rbc']\n pulse=request.form['pulse']\n myrow=mydb[pid]\n patdoc= myrow.find()\n ind=-1\n for x in patdoc:\n prev=x['hash']\n ind=ind+1\n ts=time.time()\n st = datetime.datetime.fromtimestamp(ts).strftime('%Y-%m-%d %H:%M:%S')\n block={\n '_id':pid+'REC'+str(ind+1),\n 'owner':pid,\n 'doc':doc,\n 'gluc':pp,\n 'glucf':fast,\n 'serum':serum,\n 'blood':blood,\n 'chol': tot,\n 'thdl':thdl,\n 'ldl':ldl,\n 'rbc':rbc,\n 'pulse':pulse,\n 'prev': prev,\n 'timestamp':st\n\n\n }\n\n block_string = json.dumps(block, sort_keys=True)\n hashval=sha256(block_string.encode()).hexdigest()\n block={\n '_id': pid+'REC'+str(ind+1),\n 'owner':pid,\n 'doc':doc,\n 'gluc':pp,\n 'glucf':fast,\n 'serum':serum,\n 'blood':blood,\n 'chol': tot,\n 'thdl':thdl,\n 'ldl':ldl,\n 'rbc':rbc,\n 'pulse':pulse,\n 'prev': prev,\n 'hash':hashval,\n 'timestamp':st\n\n }\n myrow.insert_one(block)\n return render_template('newrec.html',post=block)\n\n\n#Options to create record\n@app.route('/medrecord')\ndef medrecord():\n return render_template('medrecord.html')\n'''\n\n\n#Different medical records\n@app.route('/medrecord')\n#@app.route('/main')\ndef medmain():\n return render_template('main.html')\n\n\n\n#General medical record\n@app.route('/gen')\ndef gen():\n return render_template('gen.html')\n\n@app.route('/genadd',methods=['post'])\ndef genadd():\n pid=request.form['pid']\n myrow=mydb[pid]\n patdoc= myrow.find()\n ind=-1\n prevs=0\n for x in patdoc:\n prevs=x['hash']\n ind=ind+1\n\n #ts=time.time()\n #st = datetime.datetime.fromtimestamp(ts).strftime('%Y-%m-%d %H:%M:%S')\n now = datetime.datetime.now()\n st=now.strftime(\"%Y-%m-%d %H:%M:%S\")\n block={\n '_id':pid+'REC'+str(ind+1),\n 'owner':pid,\n 'type':'General information',\n 'creator':session['user'],\n 'gender': request.form['gen'],\n 'Age':request.form['age'],\n 'Weight':request.form['wt'],\n 'height':request.form['ht'],\n 'BMI':request.form['bmival'],\n 'Blood_grp':request.form['blood'],\n 'BP':request.form['bp'],\n 'Diabetes':request.form['si'],\n 'Food_allergies':request.form['nah'],\n 'prev': prevs,\n 'timestamp':st\n }\n block_string = json.dumps(block, sort_keys=True)\n hashval=sha256(block_string.encode()).hexdigest()\n block={\n '_id':pid+'REC'+str(ind+1),\n 'owner':pid,\n 'type':'General information',\n 'creator':session['user'],\n 'gender': request.form['gen'],\n 'Age':request.form['age'],\n 'Weight':request.form['wt'],\n 'height':request.form['ht'],\n 'BMI':request.form['bmival'],\n 'Blood_grp':request.form['blood'],\n #'Blood_type':request.form['pos'],\n 'BP':request.form['bp'],\n 'Diabetes':request.form['si'],\n 'Food_allergies':request.form['nah'],\n 'hash':hashval,\n 'prev': prevs,\n 'timestamp':st\n }\n type='genadder'\n #myrow.insert_one(block)\n return render_template('disp.html',posts=block,direct=type)\n\n\n@app.route('/genadder',methods=['post'])\ndef genadder():\n pid=request.form['owner']\n now = datetime.datetime.now()\n st=now.strftime(\"%Y-%m-%d %H:%M:%S\")\n block={\n '_id':request.form['_id'],\n 'owner':pid,\n 'type':'General information',\n 'creator':session['user'],\n 'gender': request.form['gender'],\n 'Age':request.form['Age'],\n 'Weight':request.form['Weight'],\n 'height':request.form['height'],\n 'BMI':request.form['BMI'],\n 'Blood_grp':request.form['Blood_grp'],\n #'Blood_type':request.form['Blood_type'],\n 'BP':request.form['BP'],\n 'Diabetes':request.form['Diabetes'],\n 'Food_allergies':request.form['Food_allergies'],\n 'hash':request.form['hash'],\n 'prev': request.form['prev'],\n 'timestamp':st }\n myrow=mydb[pid]\n myrow.insert_one(block)\n return redirect(url_for('back'))\n\n\n\n\n#Basic clinical details\n@app.route('/bioc')\ndef bioc():\n return render_template('bioc.html')\n\n@app.route('/biocadd',methods=['post'])\ndef biocadd():\n pid=request.form['pid']\n myrow=mydb[pid]\n patdoc= myrow.find()\n ind=-1\n prevs=0\n for x in patdoc:\n prevs=x['hash']\n ind=ind+1\n\n\n #ts=time.time()\n #st = datetime.datetime.fromtimestamp(ts).strftime(\"%Y-%m-%d %H:%M:%S\")\n now = datetime.datetime.now()\n st=now.strftime(\"%Y-%m-%d %H:%M:%S\")\n block={\n '_id':pid+'REC'+str(ind+1),\n 'owner':pid,\n 'type':'Clinical Laboratory information',\n 'creator':session['user'],\n 'Haemoglobin (g/dL)': request.form['hdl'],\n 'Glucose (random PP)':request.form['glr'],\n 'Glucose (fasting)':request.form['glf'],\n #'HbA1c (EDTA Blood)':request.form['hba1c'],\n 'SERUM Appearance':request.form['seum'],\n 'Total Cholestrol':request.form['tch'],\n 'Triglycerides':request.form['try'],\n 'HDL Cholestrol':request.form['hch'],\n 'LDL Cholestrol':request.form['lch'],\n 'VLDL':request.form['vldl'],\n 'CHOL / HDL Ratio':request.form['chol'],\n 'Colour':request.form['colo'],\n 'Apperance':request.form['coloo'],\n 'PH':request.form['ph'],\n 'Protein':request.form['pro'],\n 'Sugar':request.form['sug'],\n 'Bile Salt':request.form['bsal'],\n 'Bile Pigment':request.form['bpig'],\n 'prev': prevs,\n 'timestamp':st\n }\n block_string = json.dumps(block, sort_keys=True)\n hashval=sha256(block_string.encode()).hexdigest()\n block={\n '_id':pid+'REC'+str(ind+1),\n 'owner':pid,\n 'type':'Clinical Laboratory information',\n 'creator':session['user'],\n 'Haemoglobin': request.form['hdl'],\n 'Random_PP':request.form['glr'],\n 'Fasting':request.form['glf'],\n #'HbA1c (EDTA Blood)':request.form['hbalc'],\n 'SERUM Appearance':request.form['seum'],\n 'Cholestrol':request.form['tch'],\n 'Triglycerides':request.form['try'],\n 'HDLCholestrol':request.form['hch'],\n 'LDLCholestrol':request.form['lch'],\n 'VLDL':request.form['vldl'],\n #'CHOL / HDL Ratio':request.form['chol'],\n 'Colour':request.form['colo'],\n 'Apperance':request.form['coloo'],\n 'PH':request.form['ph'],\n 'Protein':request.form['pro'],\n 'Sugar':request.form['sug'],\n 'BileSalt':request.form['bsal'],\n 'BilePigment':request.form['bpig'],\n 'hash':hashval,\n 'prev': prevs,\n 'timestamp':st}\n type='biocadder'\n return render_template('disp.html',posts=block,direct=type)\n\n\n@app.route('/biocadder',methods=['post'])\ndef biocadder():\n pid=request.form['owner']\n block={\n '_id':request.form['_id'],\n 'owner':pid,\n 'type':'Clinical Laboratory information',\n 'creator':session['user'],\n 'Haemoglobin': request.form['Haemoglobin'],\n #'Random_PP':request.form['Random_PP'],\n #'Fasting':request.form['Fasting'],\n #'HbA1c (EDTA Blood)':request.form['HbA1c (EDTA Blood)'],\n #'SERUM_Appearance':request.form['SERUM_Appearance'],\n #'Cholestrol':request.form['Cholestrol'],\n 'Triglycerides':request.form['Triglycerides'],\n 'HDLCholestrol':request.form['HDLCholestrol'],\n 'LDLCholestrol':request.form['LDLCholestrol'],\n 'VLDL':request.form['VLDL'],\n #'CHOL/HDL Ratio':request.form['CHOL / HDL Ratio'],\n 'Colour':request.form['Colour'],\n 'Apperance':request.form['Apperance'],\n 'PH':request.form['PH'],\n 'Protein':request.form['Protein'],\n 'Sugar':request.form['Sugar'],\n 'BileSalt':request.form['BileSalt'],\n 'BilePigment':request.form['BilePigment'],\n 'hash':request.form['hash'],\n 'prev': request.form['prev'],\n 'timestamp':request.form['timestamp']}\n myrow=mydb[pid]\n myrow.insert_one(block)\n return redirect(url_for('back'))\n\n\n\n#Cardiac details\n@app.route('/cardiac')\ndef cardiac():\n return render_template('cardiac.html')\n\n@app.route('/cardiacadd',methods=['post'])\ndef cardiacadd():\n pid=request.form['pid']\n myrow=mydb[pid]\n patdoc= myrow.find()\n ind=-1\n prevs=0\n f = request.files['ECG']\n g= request.files['EST']\n h= request.files['ECHOCARDIO']\n an=request.files['ANG']\n '''\n if f.filename!='':\n f.save(secure_filename(f.filename))\n try:\n api = ipfshttpclient.connect('/ip4/127.0.0.1/tcp/5001/http')\n new_file = api.add(f.filename)\n except ipfshttpclient.exceptions.ConnectionError as ce:\n new_file['hash']=''\n else:\n new_file['hash']=''\n #new_file['hash']=''\n '''\n for x in patdoc:\n prevs=x['hash']\n ind=ind+1\n ts=time.time()\n st = datetime.datetime.fromtimestamp(ts).strftime('%Y-%m-%d %H:%M:%S')\n block={\n '_id':pid+'REC'+str(ind+1),\n 'owner':pid,\n 'type':'Cardiac Report',\n 'creator':session['user'],\n 'CHOLESTROL': request.form['cho'],\n 'TRIGYCERIDES':request.form['tri'],\n 'ECG': f.filename,\n 'EST':g.filename,\n 'ECHOCARDIO':h.filename,\n 'ANGIOGRAM':an.filename,\n 'prev': prevs,\n 'timestamp':st}\n block_string = json.dumps(block, sort_keys=True)\n hashval=sha256(block_string.encode()).hexdigest()\n if f.filename!='':\n f.save(secure_filename(f.filename))\n if g.filename!='':\n g.save(secure_filename(g.filename))\n if h.filename!='':\n h.save(secure_filename(h.filename))\n if an.filename!='':\n an.save(secure_filename(an.filename))\n try:\n api = ipfshttpclient.connect('/ip4/127.0.0.1/tcp/5001/http')\n if block['ECG']!='':\n new_file = api.add(block['ECG'])\n block['ECGSCAN']=str(new_file['Hash'])\n if block['EST']!='':\n new_file = api.add(block['EST'])\n block['ESTSCAN']=str(new_file['Hash'])\n if block['ANGIOGRAM']!='':\n new_file = api.add(block['ANGIOGRAM'])\n block['ANGSCAN']=str(new_file['Hash'])\n if block['ECHOCARDIO']!='':\n new_file = api.add(block['ECHOCARDIO'])\n block['ECGSCAN']=str(new_file['Hash'])\n #link='http://localhost:8080/ipfs/'+str(new_file['Hash'])\n #webbrowser.open(link)\n except ipfshttpclient.exceptions.ConnectionError as ce:\n error='Could not add files'\n\n block['hash']=hashval\n type='cardiacadder'\n return render_template('disp.html',posts=block,direct=type)\n\n@app.route('/cardiacadder',methods=['post'])\ndef cardiacadder():\n '''{\n '_id':request.form['_id'],\n 'owner':request.form['owner'],\n 'type':'Cardiac Report',\n 'creator':session['user'],\n 'CHOLESTROL': request.form['CHOLESTROL'],\n 'TRIGYCERIDES':request.form['TRIGYCERIDES'],\n 'ECG': request.form['ECG'],\n 'hash':request.form['hash'],\n 'prev': request.form['prev'],\n 'timestamp':request.form['timestamp']}'''\n\n block=dict(request.form)\n myrow=mydb[request.form['owner']]\n myrow.insert_one(block)\n return redirect(url_for('back'))\n\n@app.route('/qrcode')\ndef qrcode():\n return render_template('qrcode.html')\n\n#Dermatology details\n@app.route('/derm')\ndef derm():\n return render_template('derm.html')\n\n\n#Login options page\n@app.route('/domain')\ndef domain():\n return render_template('domain.html')\n\n\n\n#Signup options page\n@app.route('/signup')\ndef signup():\n return render_template('signup.html')\n\n\n#PATIENT\n\n#Patient signup\n@app.route('/patient')\ndef patient():\n return render_template('patient.html')\n\n#Patient Login\n@app.route('/patientlog')\ndef patientlog():\n if 'user' in session and str(session['user']).find('PAT')!=-1:\n return render_template('patdash.html')\n else:\n return render_template('patientlog.html')\n\n#Patient Credential verification\n@app.route('/patientver',methods=['POST'])\ndef patientverify():\n userid=request.form['PID']\n pwd=request.form['pwd']\n patquery = { \"_id\": userid }\n\n patdoc= mycol.find(patquery)\n for x in patdoc:\n if check_encrypted_password(pwd,x['passwd']):\n #sess.username = userid\n session['user']=userid\n return render_template('patdash.html')\n\n return render_template('patientlog.html')\n\n#PAtient acc creation with credentials\n@app.route('/patcreate',methods=['POST'])\ndef patcreate():\n block_data = request.form['usr']\n first=request.form['usr']\n second=request.form['lsn']\n passwd= request.form['pwd']\n passwd=encrypt_password(passwd)\n addres=request.form['addres']\n age=request.form['age']\n city=request.form['city']\n state=request.form['state']\n '''\n try:\n statecode='0'+str(state.index(state))\n except:\n statecode='040\n '''\n aadhar=request.form['Aadhar']\n #ts=time.time()\n now = datetime.datetime.now()\n #st = datetime.datetime.fromtimestamp(ts).strftime('%Y-%m-%d %H:%M:%S')\n st=now.strftime(\"%Y-%m-%d %H:%M:%S\")\n myrow=mydb['Blockhead']\n patdoc= myrow.find()\n ind=-1\n for x in patdoc:\n prev=x['hash']\n ind=ind+1\n\n i='PAT00'+str(ind+1)\n block={\n '_id':i,\n 'timestamp':st,\n 'first': first,\n 'second':second,\n 'passwd': passwd,\n 'address':addres,\n 'city':city,\n 'state':state,\n 'aadhar':aadhar,\n 'prevhash':prev\n}\n block_string = json.dumps(block, sort_keys=True)\n hashval=sha256(block_string.encode()).hexdigest()\n session['user']=i\n block={\n '_id':i,\n 'timestamp':st,\n 'first': first,\n 'second':second,\n 'passwd': passwd,\n 'address':addres,\n 'record':i+'REC',\n 'city':city,\n 'state':state,\n 'aadhar':aadhar,\n 'prevhash':prev,\n 'hash': hashval\n}\n '''\n ima=open(file, \"rb\")\n f = ima.read()\n b = bytearray(f)'''\n myrow=mydb[i]\n rec={\n '_id':i+'REC'+'00',\n 'doc':'',\n 'gluc':0,\n 'glucf':0,\n 'serum':0,\n 'blood':0,\n 'chol': 0,\n 'thdl':0,\n 'ldl':0,\n 'rbc':0,\n 'pulse':'',\n 'prev': '0',\n\n }\n block_s = json.dumps(rec, sort_keys=True)\n hashrec=sha256(block_s.encode()).hexdigest()\n rec={\n '_id':i+'REC'+'00',\n 'doc':'',\n 'type':'none',\n 'gluc':0,\n 'glucf':0,\n 'serum':0,\n 'blood':0,\n 'chol': 0,\n 'thdl':0,\n 'ldl':0,\n 'rbc':0,\n 'pulse':'',\n 'timestamp':'',\n 'prev': '0',\n 'hash':hashrec\n }\n\n myrow.insert_one(rec)\n mycol.insert_one(block)\n\n Blockc=[]\n Bloc=mycol.find()\n for i in Bloc:\n Blockc.append(i)\n return render_template('patcreate.html',posts=Blockc)\n\n\n#Patient dashboard welcome page\n@app.route('/patdash')\ndef patdash():\n if 'user' in session:\n return render_template('patdash.html')\n else:\n #Login unsuccessful\n return render_template('domain.html')\n\n\n\n#Display patient user info\n@app.route('/patacc')\ndef views():\n patquery = { \"_id\": session['user'] }#sess.username\n\n patdoc= mycol.find_one(patquery)\n\n return render_template('patmyacc.html',post=patdoc)\n\n#View current patient's record\n@app.route('/viewrec',methods=['POST'])\ndef viewrec():\n s=request.form['owner']\n myrow=mydb[s]#change\n recs=myrow.find()\n records=[]\n for x in recs:\n records.append(x)\n return render_template('records.html',posts=records)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n#Doctor\n\n#Doctor signup\n@app.route('/doctor')\ndef doctor():\n return render_template('doctor.html')\n\n#Doctor Login\n@app.route('/doclog')\ndef doclog():\n if 'user' in session and session['user'].find('DOC')!=-1 :\n return render_template('docdash.html')\n else:\n return render_template('doctorlog.html')\n\n#Doctor credential verification\n@app.route('/doclogover',methods=['POST'])\ndef doclogver():\n userid=request.form['DID']\n pwd=request.form['pwd']\n patquery = { \"_id\": userid }\n myrow=mydb['Nodes']\n patdoc= myrow.find(patquery)\n for x in patdoc:\n if check_encrypted_password(pwd,x['password']):\n session['user']=userid\n #sess.username = userid\n return render_template('docdash.html')\n return render_template('doctorlog.html')\n\n#Doctor account creation\n@app.route('/docver',methods=['POST'])\ndef docverify():\n\n name=request.form['doc']\n specialization=request.form['special']\n address=request.form['add']\n qualification=request.form['qualific']\n study=request.form['grad']\n workcontact=request.form['num']\n personal=request.form['n']\n about=request.form['more']\n ts=time.time()\n st=datetime.datetime.fromtimestamp(ts).strftime('%Y-%m-%d %H:%M:%S')\n passwd= request.form['pwd']\n passwd=encrypt_password(passwd)\n myrow=mydb['Nodes']\n patdoc= myrow.find()\n ind=0\n for x in patdoc:\n if(x['_id'].find('DOC'))!=-1:\n ind=ind+1\n doc={\n '_id':'DOC'+str(ind+1),\n 'docname' : name ,\n 'specialization' : specialization,\n 'address' : address,\n 'qualification': qualification,\n 'edufrom' : study,\n 'appointment': workcontact,\n 'number': personal,\n 'moreabout' : about,\n 'timestamp':st,\n 'password':passwd,\n }\n session['user']=doc['_id']\n #sess.username=doc['_id']\n myrow.insert_one(doc)\n return render_template('docdash.html')\n\n\n#Doctor dashboard\n@app.route('/docdash')\ndef docdash():\n if 'user' in session and str(session['user']).find('DOC')!=-1:\n return render_template('docdash.html')\n else:\n #Unsuccessful login\n return render_template('domain.html')\n\n#Doctor Access request page\n@app.route('/docview')\ndef docview():\n if 'user' in session:\n return render_template('docview.html')\n else:\n #Unsuccessful login\n return render_template('domain.html')\n\n\n#Account info\n@app.route('/myacc')\ndef myacc():\n if 'user' not in session:\n return render_template('domain.html')\n patquery = { \"_id\": session['user'] }\n myrow=mydb['Nodes']\n patdoc= myrow.find_one(patquery)\n del patdoc['password']\n return render_template('myacc.html',post=patdoc)\n\n\n@app.route('/access',methods=['POST'])\ndef access():\n if 'user' not in session:\n return render_template('domain.html')\n owner=request.form['PID']\n accessor=session['user']\n '''url = \"https://www.fast2sms.com/dev/bulk\"\n\n querystring = {\"authorization\":\"4FzGm7K6haHIMiAJfuNsSwv50rT8cROE2UBCkP9yp3bZdXDlQqC0jLU1HVkQYE3sNdph24AIztabBcTO\",\"sender_id\":\"PATREC\",\"language\":\"english\",\"route\":\"qt\",\"numbers\":\"9789862702\",\"message\":\"Doctor has requested for your record.\"}\n\n headers = {\n 'cache-control': \"no-cache\"\n }\n\n response=requests.request(\"GET\", url, headers=headers, params=querystring)'''\n #myval=mydb['SMART_CONTRACT']\n #ts=time.time()\n #st = datetime.datetime.fromtimestamp(ts).strftime('%Y-%m-%d %H:%M:%S')\n con={\n 'accessor':accessor,\n 'owner': owner,\n #'timestamp':st,\n #'record':owner+'REC',\n #'status':0\n\n }\n lists=[]\n myval=mydb[owner]\n myvalue=myval.find()\n for i in myvalue:\n val={\n 'own':i['_id'],\n 'time': i['timestamp']}\n lists.append(val)\n #myval.insert_one(con)\n return render_template('recordchoice.html',post=con,posts=lists)\n\n\n@app.route('/createcon',methods=['POST'])\ndef createcon():\n if 'user' not in session:\n return render_template('domain.html')\n\n\n ts=time.time()\n st = datetime.datetime.fromtimestamp(ts).strftime('%Y-%m-%d %H:%M:%S')\n accessor=request.form['accessor']\n owner=request.form['owner']\n record=request.form['record']\n creation=request.form['time']\n con={\n 'accessor':accessor,\n 'owner': owner,\n 'timestamp':st,\n 'record': record,\n 'recordcreation':creation,\n 'status':0\n\n }\n #myval.insert_one(con)\n return render_template('contractred.html',posts=con)\n\n\n@app.route('/back')\ndef back():\n time.sleep(2)\n if 'user' in session:\n if str(session['user']).find('PAT')!=-1:\n return render_template('patdash.html')\n if str(session['user']).find('DOC')!=-1:\n return render_template('docdash.html')\n if str(session['user']).find('ADM')!=-1:\n return render_template('admindash.html')\n return render_template('domain.html')\n\n\n@app.route('/display',methods=['post'])\ndef display():\n if 'user' not in session:\n return render_template('domain.html')\n block={\n '_id':request.form['_id']\n }\n mycol=mydb[request.form['owner']]\n myview=mycol.find_one(block)\n if myview:\n return render_template('individualrec.html',post=myview)\n return redirect(url_for('back'))\n\n\n@app.route('/share',methods=['post'])\ndef sharee():\n if 'user' not in session:\n return render_template('domain.html')\n block=dict(request.form)\n return render_template('patientview.html',post=block)\n\n@app.route('/sharerec',methods=['post'])\ndef sharerec():\n if 'user' not in session:\n return render_template('domain.html')\n\n myval=mydb['SMART_CONTRACT']\n ts=time.time()\n st = datetime.datetime.fromtimestamp(ts).strftime('%Y-%m-%d %H:%M:%S')\n accessor=request.form['accessor']\n owner=request.form['owner']\n record=request.form['_id']\n #creation=request.form['time']\n con={\n 'accessor':accessor,\n 'owner': owner,\n 'timestamp':st,\n 'record': record,\n #'recordcreation':creation,\n 'status':1\n\n }\n #myval.insert_one(con)\n return render_template('contractred.html',posts=con)\n\n\n\n@app.route('/conadd',methods=['POST'])\ndef conadd():\n block=dict(request.form)\n myval=mydb['SMART_CONTRACT']\n query={'accessor':block['accessor'],\n 'owner':block['owner'],\n 'record':block['record']}\n che=myval.find_one(query)\n if str(che)=='None':\n myval.insert_one(block)\n return redirect(url_for('back'))\n\n@app.route(\"/cancel\",methods=['POST'])\ndef cancel():\n mycol=mydb['SMART_CONTRACT']\n temp = {\n 'owner':request.form['owner'],\n 'record':request.form['_id'],\n 'accessor':request.form['accessor']\n }\n mycol.delete_one(temp)\n #return temp\n return redirect(url_for('available'))\n\n\n@app.route('/bookapp')\ndef index():\n return render_template(\"bookapp.html\")\n\n@app.route('/formdis')\ndef form():\n return render_template(\"result.html\")\n\n\n@app.route('/search',methods=['GET','POST'])\ndef search():\n if request.method =='POST':\n area= request.form['special']\n mycol=mydb['Nodes']\n temp = {'specialization' : area}\n found = mycol.find(temp)\n arr=[]\n for i in found:\n arr.append(i)\n return render_template(\"show.html\" , posts=arr ,special=area)\n\n@app.route('/knowndoctor')\ndef known():\n return render_template('docinfo.html')\n\n\n@app.route('/schedule',methods=['POST'])\ndef schedule():\n docid = request.form['docid']\n mycol=mydb['Nodes']\n nm = request.form['name']\n temp = {'docname' : nm}\n f = mycol.find(temp)\n arr=[]\n for i in f:\n arr.append(i)\n return render_template(\"test.html\" , posts=arr)\n\n@app.route('/appoint')\ndef appoint():\n #msg=\"you have an appoinment with from.doc\"\n #clientn.send_message({'from' : 'nexmo' , 'to' : '+91 6383230641', 'text' : 'msg'})\n return render_template(\"booked.html\")\n\n\n\n\n\n#ADMIN -radiologists,lab technicians, hospital staff\n\n#Admin acc creation\n@app.route('/hospital')\ndef hospital():\n return render_template('hospital.html')\n\n#Admin login page\n@app.route('/adminlog')\ndef adminlog():\n if 'user' in session and str(session['user']).find('ADM')!=-1:\n return render_template('admindash.html')\n return render_template('hospitallog.html')\n\n#Admin account creation\n@app.route('/admver',methods=['POST'])\ndef admverify():\n nam=request.form['n']\n ts=time.time()\n st = datetime.datetime.fromtimestamp(ts).strftime('%Y-%m-%d %H:%M:%S')\n passwd= request.form['pwd']\n passwd=encrypt_password(passwd)\n myrow=mydb['Nodes']\n patdoc= myrow.find()\n ind=0\n for x in patdoc:\n if(x['_id'].find('ADM'))!=-1:\n ind=ind+1\n doc={\n '_id':'ADM'+str(ind+1),\n 'timestamp':st,\n 'password':passwd,\n 'name':nam\n }\n #sess.username=doc['_id']\n session['user']=doc['_id']\n myrow.insert_one(doc)\n return render_template('admindash.html')\n\n#Admin account verification\n@app.route('/admlogover',methods=['POST'])\ndef admlogver():\n userid=request.form['AID']\n pwd=request.form['pwd']\n patquery = { \"_id\": userid }\n myrow=mydb['Nodes']\n patdoc= myrow.find(patquery)\n for x in patdoc:\n if check_encrypted_password(pwd,x['password']):\n session['user']=userid\n #sess.username = userid\n return render_template('admindash.html')\n return render_template('hospitallog.html')\n\n\n#Admin dash board\n@app.route('/admindash')\ndef admindash():\n if 'user' in session:\n return render_template('admindash.html')\n return render_template('domain.html')\n\n\n\n@app.route('/accesslog')\ndef accesslog():\n if 'user' not in session:\n return render_template('domain.html')\n mycli=mydb['SMART_CONTRACT']\n query={\"owner\":session['user']}\n mydata=mycli.find(query)\n block=[]\n for x in mydata:\n block.append(x)\n block.reverse()\n #sess.id=x['accessor']\n return render_template('accesslog.html',posts=block)\n\n\n@app.route('/available')\ndef available():\n if 'user' not in session:\n return render_template('domain.html')\n myquery=patquery = { \"accessor\": session['user'] }\n myview=mydb['SMART_CONTRACT']\n Blockc=[]\n Block=[]\n ''' rec={\n 'doc':'Pending',\n 'gluc':0,\n 'glucf':0,\n 'serum':0,\n 'blood':0,\n 'chol': 0,\n 'thdl':0,\n 'ldl':0,\n 'rbc':0,\n 'pulse':'',\n 'timestamp':'',\n 'prev': '0',\n 'hash':'Pending'\n }'''\n mydat=myview.find(myquery)#Smart contract\n for x in mydat:\n mydata=mydb[x['owner']]#record of patient\n mycl=mydata.find()\n for y in mycl:\n if x['record']==y['_id']:\n if x['status']==1:\n Blockc.append(y)\n else:\n Block.append(y)\n\n\n return render_template('available.html',posts=Blockc,wait=Block)\n#bAPP_ROOT = os.path.dirname(os.path.abspath(__file__))\n\n\n'''@app.route(\"/upload\", methods=['POST'])\ndef upload():\n target = os.path.join(APP_ROOT, 'images/')\n\n if not os.path.isdir(target):\n os.mkdir(target)\n\n for file in request.files.getlist(\"file\"):\n print(file)\n filename = file.filename\n destination = \"/\".join([target, filename])\n print(destination)\n file.save(destination)\n\n return render_template(\"complete.html\")'''\n\n\n@app.route('/authorize',methods=['post'])\ndef authorize():\n if request.form['status']==1:\n return redirect(url_for('accesslog'))\n myview=mydb['SMART_CONTRACT']\n myquery = {'record':request.form['record'],\n 'accessor':request.form['accessor'],\n 'owner': session['user']\n }\n newvalues = { \"$set\": { \"status\": 1 } }\n myview.update_one(myquery, newvalues)\n return redirect(url_for('accesslog'))\n\n@app.route('/deny',methods=['post'])\ndef decline():\n if request.form['status']==0:\n return redirect(url_for('accesslog'))\n myview=mydb['SMART_CONTRACT']\n myquery = { 'record': request.form['record'] ,\n 'accessor':request.form['accessor'],\n 'owner': session['user'] }\n newvalues = { \"$set\": { \"status\": 0 } }\n myview.update_one(myquery, newvalues)\n return redirect(url_for('accesslog'))\n\n\n\n@app.route('/medrec')\ndef medrec():\n if 'user' in session:\n idv={ 'doc': session['user']}\n return render_template('medrec.html',posts=idv)\n else:\n #Unsuccessful login\n return render_template('domain.html')\n@app.route('/logout')\ndef logout():\n if 'user' in session:\n session.pop('user',None)\n return render_template('domain.html')\n\n\n\n\n\n\n\nclienth = pymongo.MongoClient(\"mongodb+srv://hemapriya:hema1512@medicare-w9kad.gcp.mongodb.net/test?retryWrites=true&w=majority\")\n\nmydbh=clienth[\"blogdetails\"]\n\nmycolh=mydbh[\"medblog\"]\n\n\n@app.route('/medblog')\ndef medblog():\n return render_template(\"medblog.html\")\n\n@app.route('/new')\ndef new():\n return render_template(\"new.html\")\n\n\n@app.route('/result',methods=['POST'])\ndef result():\n i=0\n title = request.form['title']\n imgurl =request.form['pic']\n content = request.form['comment']\n today = date.today()\n d = today.strftime(\"%B %d, %Y\")\n onepost = mycolh.find()\n\n for x in onepost:\n i=i+1\n block ={\n '_id':'POST'+str(i),\n 'title': title,\n 'url' :imgurl,\n 'content' : content,\n 'time' :d\n }\n mycolh.insert_one(block)\n\n arr=[]\n temp={'title': title}\n f = mycolh.find(temp)\n for i in f:\n arr.append(i)\n\n return render_template(\"output.html\" , posts=arr)\n\n@app.route('/view')\ndef view():\n\n mycolh = mydbh[\"medblog\"]\n temp =[]\n for x in mycolh.find():\n temp.append(x)\n\n return render_template(\"allposts.html\" , posts=temp)\n\n\n@app.route(\"/delete\")\ndef delete():\n return render_template(\"delete.html\")\n\n@app.route(\"/deletepost\",methods=['POST'])\ndef deletepost():\n pid = request.form['pid']\n tit = request.form['title']\n arr=[]\n temp = {'_id' : pid}\n found = mycolh.find(temp)\n\n for k in found:\n arr.append(k)\n\n mycolh.delete_one(temp)\n\n return render_template(\"deleted.html\" , posts=arr)\n\n@app.route('/update')\ndef update():\n return render_template(\"update.html\")\n\n@app.route('/updatepost' , methods=['POST'])\ndef updatepost():\n pid = request.form['pid']\n content = request.form['comment']\n temp ={'_id' :pid}\n change = {\"$set\" : { 'content' : content }}\n\n mycolh.update_one(temp,change)\n\n arr=[]\n temp = {'_id' : pid}\n found = mycolh.find(temp)\n\n for k in found:\n arr.append(k)\n\n\n return render_template(\"updatedpost.html\",posts=arr)\n\n\nif __name__=='__main__':\n app.run(port = int(os.environ.get('PORT', 5000)))\n", "repo_name": "antoprince001/Blockchain_for_EHR", "sub_path": "app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 35021, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 9, "dataset": "github-code", "pt": "37", "api": [{"api_name": "flask.Flask", "line_number": 33, "usage_type": "call"}, {"api_name": "passlib.context.CryptContext", "line_number": 39, "usage_type": "call"}, {"api_name": "pymongo.MongoClient", "line_number": 61, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 88, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 92, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 101, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 107, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 107, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 108, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 108, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 110, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 113, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 113, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 115, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 115, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 121, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 137, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 142, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 142, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 145, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 145, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 150, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 156, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 161, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 242, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 249, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 253, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 253, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 264, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 264, "usage_type": "attribute"}, {"api_name": "flask.session", "line_number": 270, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 271, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 271, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 272, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 272, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 273, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 273, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 274, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 274, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 275, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 275, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 276, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 276, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 277, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 277, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 278, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 278, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 279, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 279, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 283, "usage_type": "call"}, {"api_name": "hashlib.sha256", "line_number": 284, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 289, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 290, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 290, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 291, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 291, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 292, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 292, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 293, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 293, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 294, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 294, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 295, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 295, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 297, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 297, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 298, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 298, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 299, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 299, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 306, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 311, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 311, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 312, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 312, "usage_type": "attribute"}, {"api_name": "flask.request.form", "line_number": 315, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 315, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 318, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 319, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 319, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 320, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 320, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 321, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 321, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 322, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 322, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 323, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 323, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 324, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 324, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 326, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 326, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 327, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 327, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 328, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 328, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 329, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 329, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 330, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 330, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 334, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 334, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 342, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 346, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 346, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 358, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 358, "usage_type": "attribute"}, {"api_name": "flask.session", "line_number": 364, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 365, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 365, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 366, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 366, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 367, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 367, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 369, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 369, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 370, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 370, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 371, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 371, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 372, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 372, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 373, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 373, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 374, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 374, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 375, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 375, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 376, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 376, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 377, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 377, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 378, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 378, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 379, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 379, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 380, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 380, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 381, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 381, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 382, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 382, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 386, "usage_type": "call"}, {"api_name": "hashlib.sha256", "line_number": 387, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 392, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 393, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 393, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 394, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 394, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 395, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 395, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 397, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 397, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 398, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 398, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 399, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 399, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 400, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 400, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 401, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 401, 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"flask.request.form", "line_number": 1076, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 1076, "usage_type": "name"}, {"api_name": "time.time", "line_number": 1077, "usage_type": "call"}, {"api_name": "datetime.datetime.fromtimestamp", "line_number": 1078, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 1078, "usage_type": "attribute"}, {"api_name": "flask.request.form", "line_number": 1079, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 1079, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 1094, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 1096, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 1101, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 1101, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 1102, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 1102, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 1108, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 1110, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 1111, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 1117, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 1118, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 1119, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 1125, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 1126, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 1128, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 1135, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 1140, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 1141, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 1142, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 1173, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 1196, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 1196, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 1197, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 1197, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 1199, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 1199, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 1200, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 1200, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 1201, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 1205, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 1205, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 1209, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 1209, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 1210, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 1210, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 1212, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 1212, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 1213, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 1213, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 1214, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 1217, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 1217, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 1223, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 1224, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 1225, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 1228, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 1231, "usage_type": "name"}, {"api_name": "flask.session.pop", "line_number": 1232, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 1232, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 1233, "usage_type": "call"}, {"api_name": "pymongo.MongoClient", "line_number": 1241, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 1250, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 1254, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 1260, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 1260, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 1261, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 1261, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 1262, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 1262, "usage_type": "name"}, {"api_name": "datetime.date.today", "line_number": 1263, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 1263, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 1284, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 1294, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 1299, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 1303, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 1303, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 1304, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 1304, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 1314, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 1318, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 1322, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 1322, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 1323, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 1323, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 1337, "usage_type": "call"}, {"api_name": "os.environ.get", "line_number": 1341, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 1341, "usage_type": "attribute"}]} +{"seq_id": "16846015688", "text": "\nimport string\nfrom django.conf import settings\n\nif not settings.configured:\n\tsettings.configure(\n\t\t# DEBUG=True,\n\t\t# ROOT_URLCONF=name,\n\t)\n\n\ndef encode(val):\n\tif type(val) == int:\n\t\treturn b'i' + bytes(str(val), 'utf-8') + b'e'\n\n\telif type(val) == str:\n\t\treturn bytes(str(len(val.encode('utf-8'))) + ':' + val, 'utf-8')\n\n\telif type(val) == bytes:\n\t\treturn bytes(str(len(val)), 'utf-8') + b':' + val\n\n\telif type(val) == list:\n\t\tend = b'l'\n\t\tfor i in val:\n\t\t\tend += encode(i)\n\t\tend += b'e'\n\t\treturn end\n\n\telif type(val) == dict:\n\t\tkeys = []\n\t\tfor k, v in val.items():\n\t\t\tkeys.append(k)\n\t\t\tkeys.append(v)\n\t\tend = b'd'\n\t\tfor i in keys:\n\t\t\tend += encode(i)\n\t\tend += b'e'\n\t\treturn end\n\n\n\n\ndef decode(val):\n\n\tdef decode_b_str(val):\n\n\t\tif isinstance(val, str):\n\t\t\tval = val.encode()\n\n\t\t# int decoder\n\t\tif val.startswith(b\"i\"):\n\t\t\t'''\n\t\t\tGets string. if the zero index \"i\" - \n\t\t\tthe result will be integer in slice from\n\t\t\tletter \"i\" to letter \"e\".\n\t\t\tReturns int result and everything pasts letter \"e\" \n\t\t\t'''\n\t\t\tresult = int(val[1:val.find(b'e')])\n\t\t\treturn result, val[val.find(b'e') + 1:]\n\n\t\t# string decoder\n\t\telif ''.join(map(chr, val))[0] in string.digits:\n\t\t\t'''\n\t\t\tGets string. if the zero index is digit - \n\t\t\tthe result will be the string.\n\t\t\tCounter - counts the length of the first digit \n\t\t\tInteger - the first number (according to the counter) \n\t\t\tis the length of the result string.\n\t\t\tReturns - string slice equal to the integer(length)(from \n\t\t\tindex of count digits to the length of string) and \n\t\t\teverything pasts the end of the decoded string \n\t\t\t'''\n\t\t\tdig = True\n\t\t\tcounter = 0\n\t\t\twhile dig:\n\t\t\t\tif ''.join(map(chr, val))[counter] in string.digits:\n\t\t\t\t\tcounter += 1\n\t\t\t\telse:\n\t\t\t\t\tdig = False\n\t\t\tinteger = int(val[0:counter])\n\t\t\tif counter > 1:\n\t\t\t\treturn val[counter+1:integer+counter+1], val[(integer+counter+1):]\n\t\t\telse:\n\t\t\t\treturn val[2:integer+counter+1], val[integer+counter+1:]\n\n\t\t# list decoder\n\t\telif val.startswith(b\"l\"):\n\t\t\t'''\n\t\t\tGets string. if the zero index \"l\" - \n\t\t\tthe result will be the list.\n\t\t\tScript slices the string, finds patterns for \n\t\t\tdecoding ints, strings or dicts and runs suitable script.\n\t\t\tnew_b_str - updated input(string) after previous slice.\n\t\t\treturned_str - the data that returns after running the \n\t\t\tsuitable script according to the pattern from the 1st index\n\t\t\tof new_b_str.\n\t\t\tThe script goes from index to index until new_b_str = \"e\"\n\t\t\treturns list and everything that contains new_b_str\n\t\t\t'''\n\t\t\tdecoded_list = []\n\t\t\tnew_b_str = val[1:]\n\t\t\twhile not new_b_str.startswith(b\"e\"):\n\t\t\t\treturned_str = decode_b_str(new_b_str)[0]\n\t\t\t\tnew_b_str = decode_b_str(new_b_str)[1]\n\t\t\t\tdecoded_list.append(returned_str)\n\t\t\tnew_b_str = new_b_str[1:]\n\t\t\treturn decoded_list, new_b_str\n\n\t\t# dict decoder\n\t\telif val.startswith(b\"d\"):\n\t\t\t'''\n\t\t\tGets string. if the zero index \"l\" - \n\t\t\tthe result will be the list.\n\t\t\tScript slices the string, finds patterns for \n\t\t\tdecoding ints, strings or dicts and runs suitable script.\n\t\t\tnew_b_str - updated input(string) after previous slice.\n\t\t\treturned_str - the data that returns after running the \n\t\t\tsuitable script according to the pattern from the 1st index\n\t\t\tof new_b_str.\n\t\t\tThe script goes from index to index until new_b_str = \"e\",\n\t\t\tmakes list, and than zips even&odds indexes to dictionary.\n\t\t\tReturns dictionary and everything that contains new_b_str\n\t\t\t'''\n\t\t\tdecoded_list = []\n\t\t\tnew_b_str = val[1:]\n\t\t\twhile not new_b_str.startswith(b\"e\"):\n\t\t\t\treturned_str, new_b_str = decode_b_str(new_b_str)\n\t\t\t\tdecoded_list.append(returned_str)\n\t\t\tnew_b_str = new_b_str[1:]\n\t\t\tresult_dict = dict(zip(decoded_list[::2], decoded_list[1::2]))\n\t\t\treturn result_dict, new_b_str\n\n\t\telse:\n\t\t\t'''\n\t\t\tIf the script finds no patterns to decode the string \n\t\t\tit raises error\n\t\t\t'''\n\t\t\traise ValueError(\"Incorect value\")\n\n\treturned_data = decode_b_str(val)[0]\n\tnew_b_str = decode_b_str(val)[1]\n\n\tif new_b_str:\n\t\t'''\n\t\tIf there are last in the new_b_str after script done,\n\t\traises error, cause it should be empty\n\t\t'''\n\t\traise ValueError(\"Incorect value\")\n\treturn returned_data\n\n\nprint(decode(b'10:sdvssdg vs'))\nprint(decode(b'i-0653e'))\nprint(decode(b'1:\\x80'))\nprint(decode(encode('\\x80')))\n\nprint(decode(b'l4:spam4:eggsd4:wool3:cowee'))\nprint(decode(b'l4:spami3345e4:eggs53:papandopusvdvsewwwwwwwwwwwwwwwwwwwscscvsvseeeeeeeelosli33ei-6ei0eee'))\nprint(decode(b'd3:cow3:moo4:spam4:eggse'))\nprint(decode(b'd4:spaml1:a1:bee'))\nprint(decode(b'd4:spaml4:eggs5:applee3:vool4:lark5:argsee4:wool3:cow2:xo4:tezee'))\nprint(decode(encode({b'spam': [b'eggs', b'apple'], b'voo': [b'lark', b'argse'], b'wool': b'cow', b'xo': b'teze'})))\nprint()\nprint()\nprint(encode(b'sdvsdg vs'))\nprint(encode(b'sdvsvvsdvdg sdvsdvvs'))\nprint(encode(-653))\nprint(encode('\\x80'))\nprint(encode(b'\\x80'))\nprint(encode([b'spam', b'eggs', {b'wool': b'cow'}]))\nprint(encode([b'spam', 3345, b'eggs', b'papandopusvdvsewwwwwwwwwwwwwwwwwwwscscvsvseeeeeeeelos', [33, -6, 0]]))\nprint(encode({b'cow': b'moo', b'spam': b'eggs'}))\nprint(encode({b'spam': [b'a', b'b']}))\nprint(encode({b'spam': [b'eggs', b'apple'], b'voo': [b'lark', b'argse'], b'wool': b'cow', b'xo': b'teze'}))\n\n", "repo_name": "tuipik/ITEA", "sub_path": "Lesson_07/homework03.py", "file_name": "homework03.py", "file_ext": "py", "file_size_in_byte": 5119, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "django.conf.settings.configured", "line_number": 5, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 5, "usage_type": "name"}, {"api_name": "django.conf.settings.configure", "line_number": 6, "usage_type": "call"}, {"api_name": "django.conf.settings", "line_number": 6, "usage_type": "name"}, {"api_name": "string.digits", "line_number": 62, "usage_type": "attribute"}, {"api_name": "string.digits", "line_number": 76, "usage_type": "attribute"}]} +{"seq_id": "22284461161", "text": "import time\nimport random\nimport requests\n\nfrom dags.dbo import Terminal_Session\nfrom terminal.dto import OptionType\nimport datetime\nimport json\n\nfrom dags.spiders.setting.spider_setting import SpiderSetting\nfrom dags.spiders.utils.spider_utils import SpiderUtils\nfrom terminal import Logging\n\nlogger = Logging.getLogger(__name__)\n\n\nclass YinHeSpider:\n\n @staticmethod\n def process_detail_data(expire_date_dict, contract_type_dict, response_detail, underlier_dict):\n \"\"\"\n 格式化响应数据，\n :param response_detail: 响应数据\n :param underlier_dict: 标的信息字典\n :return: 看涨看跌字典列表\n \"\"\"\n\n data = json.loads(response_detail.text)['data']\n\n # 看跌\n put_list = []\n # 看涨\n call_list = []\n\n # 组合特定格式标的\n underlierType = underlier_dict['underlierType']\n expireDate = underlier_dict['expireDate']\n underlier = ''.join([underlierType, expireDate])\n\n # 例underlier -> underlier.DEC\n exc_underlier = SpiderUtils.exchanged_underlier(contract_type_dict, underlier)\n\n # 现价\n spot_price = data['result']['spot']\n\n # 标的列表数据\n underlier_list = data['result']['quotingUnits']\n for underlier in underlier_list:\n put_dict = {}\n call_dict = {}\n\n # 标的物\n put_dict['underlier'] = exc_underlier.upper()\n # 平台\n put_dict['company'] = 'YinHe'\n # 看涨看跌\n put_dict['option_type'] = OptionType.PUT.name\n # 欧式美式\n put_dict['product_type'] = data['result']['optionType']\n # 执行价\n put_dict['exercise_price'] = underlier['strike']\n # 看跌买价\n put_dict['ask_price'] = round(underlier['buyPut'], 2)\n # 看跌卖价\n put_dict['bid_price'] = round(underlier['sellPut'], 2)\n # 现价\n put_dict['spot_price'] = spot_price\n # 观察日\n put_dict['observe_date'] = datetime.datetime.today().date()\n # term\n put_dict['term'] = expire_date_dict[underlier_dict['expire_date']]\n # 到日期\n put_dict['expire_date'] = underlier_dict['expire_date']\n put_list.append(put_dict)\n\n # 标的物\n call_dict['underlier'] = exc_underlier.upper()\n # 平台\n call_dict['company'] = 'YinHe'\n # 看涨看跌\n call_dict['option_type'] = OptionType.CALL.name\n # 欧式美式\n call_dict['product_type'] = data['result']['optionType']\n # 执行价\n call_dict['exercise_price'] = underlier['strike']\n # 看涨买价\n call_dict['ask_price'] = round(underlier['buyCall'], 2)\n # 看涨卖价\n call_dict['bid_price'] = round(underlier['sellCall'], 2)\n # 现价\n call_dict['spot_price'] = spot_price\n # 观察日\n call_dict['observe_date'] = datetime.datetime.today().date()\n # term\n call_dict['term'] = expire_date_dict[underlier_dict['expire_date']]\n # 到日期\n call_dict['expire_date'] = underlier_dict['expire_date']\n call_list.append(call_dict)\n\n logger.info('put list: %s; call list: %s' % (put_list, call_list))\n return put_list, call_list\n\n @staticmethod\n def process_list_data(response_list):\n \"\"\"\n 格式化列表页响应数据\n :param response_list: 响应数据\n :return: 标的信息字典列表\n \"\"\"\n result = json.loads(response_list.text)['data']['result']\n underlier_list = []\n for i in result:\n underlier_dict = {}\n # 到期日\n underlier_dict['expire_date'] = i['option']['expireDate']\n # 看涨看跌\n underlier_dict['option_type'] = i['option']['optionType']\n # 欧式美式\n underlier_dict['product_type'] = i['option']['productType']\n # 现价\n underlier_dict['spot_price'] = i['option']['spot'].split('.')[0]\n # 行权价\n underlier_dict['exercise_price'] = i['option']['strike'].split('.')[0]\n # 标的到期日\n underlier_dict['expireDate'] = i['option']['underlier']['expireDate']\n # 标的类型\n underlier_dict['underlierType'] = i['option']['underlier']['underlierType']\n\n underlier_list.append(underlier_dict)\n\n return underlier_list\n\n @staticmethod\n def yinhe_spider():\n # 实例化数据库连接对象\n db_session = Terminal_Session()\n\n expire_date_dict = SpiderUtils.expire_date_fun(SpiderSetting.term_list, db_session)\n # 到instrument里取instrument_type=FUTURN的数据\n contract_type_dict = SpiderUtils.get_contract_type(db_session)\n\n # 列表页\n for i in expire_date_dict.keys():\n # 银河列表页post请求url\n post_list_url = SpiderSetting.yinhe_list_url\n\n # 银河列表页post请求表单数据\n post_list_data = {'json': '{\"method\":\"quotingATM\",\"params\":{\"underlierList\":null,\"expireDate\":\"%s\"}}' % i}\n time.sleep(random.randint(1, 9))\n\n # 发送列表页post请求\n response_list = []\n flag = 0\n while flag < 3:\n try:\n response_list = requests.post(url=post_list_url, data=post_list_data, headers={'Connection': 'close'})\n break\n except Exception as e:\n flag += 1\n if i == 3:\n logger.info('列表页发送3次post请求失败，错误: %s' % e)\n raise Exception('列表页发送3次post请求失败，错误: %s' % e)\n\n # 处理列表页数据\n underlier_list = YinHeSpider.process_list_data(response_list)\n\n # 详情页\n for underlier_dict in underlier_list:\n # 银河详情页post请求url\n post_detail_url = SpiderSetting.yinhe_detail_url\n\n # 银河详情页post请求表单数据\n post_datail_data = {\n 'json': '{\"method\":\"tQuoting\",\"params\":{\"underlier\":{\"underlierType\":\"%s\",\"expireDate\":\"%s\"},'\n '\"expireDate\":\"%s\",\"productType\":\"%s\"}}' % (underlier_dict['underlierType'],\n underlier_dict['expireDate'],\n underlier_dict['expire_date'],\n underlier_dict['product_type'])}\n time.sleep(random.randint(1, 9))\n\n # 发送详情页 post 请求\n response_detail = None\n flag1 = 0\n while flag1 < 3:\n try:\n response_detail = requests.post(url=post_detail_url, data=post_datail_data,\n headers={'Connection': 'close'})\n break\n except Exception as e:\n flag += 1\n if i == 3:\n logger.info('详情页发送3次post请求失败，错误: %s' % e)\n raise Exception('详情页发送3次post请求失败，错误: %s' % e)\n\n # 处理详情页数据\n put_list, call_list = YinHeSpider.process_detail_data(expire_date_dict, contract_type_dict,\n response_detail, underlier_dict)\n\n # 保存数据\n SpiderUtils.save_data(put_list, call_list, db_session)\n\n\nif __name__ == '__main__':\n YinHeSpider.yinhe_spider()\n", "repo_name": "zhanrendong/jkzx1", "sub_path": "scripts/airflow/dags/spiders/spiders/yinhe_spider.py", "file_name": "yinhe_spider.py", "file_ext": "py", "file_size_in_byte": 8034, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "terminal.Logging.getLogger", "line_number": 14, "usage_type": "call"}, {"api_name": "terminal.Logging", "line_number": 14, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 28, "usage_type": "call"}, {"api_name": "dags.spiders.utils.spider_utils.SpiderUtils.exchanged_underlier", "line_number": 41, "usage_type": "call"}, {"api_name": "dags.spiders.utils.spider_utils.SpiderUtils", "line_number": 41, "usage_type": "name"}, {"api_name": "terminal.dto.OptionType.PUT", "line_number": 57, "usage_type": "attribute"}, {"api_name": "terminal.dto.OptionType", "line_number": 57, "usage_type": "name"}, {"api_name": "datetime.datetime.today", "line_number": 69, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 69, "usage_type": "attribute"}, {"api_name": "terminal.dto.OptionType.CALL", "line_number": 81, "usage_type": "attribute"}, {"api_name": "terminal.dto.OptionType", "line_number": 81, "usage_type": "name"}, {"api_name": "datetime.datetime.today", "line_number": 93, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 93, "usage_type": "attribute"}, {"api_name": "json.loads", "line_number": 110, "usage_type": "call"}, {"api_name": "dags.dbo.Terminal_Session", "line_number": 136, "usage_type": "call"}, {"api_name": "dags.spiders.utils.spider_utils.SpiderUtils.expire_date_fun", "line_number": 138, "usage_type": "call"}, {"api_name": "dags.spiders.utils.spider_utils.SpiderUtils", "line_number": 138, "usage_type": "name"}, {"api_name": "dags.spiders.setting.spider_setting.SpiderSetting.term_list", "line_number": 138, "usage_type": "attribute"}, {"api_name": "dags.spiders.setting.spider_setting.SpiderSetting", "line_number": 138, "usage_type": "name"}, {"api_name": "dags.spiders.utils.spider_utils.SpiderUtils.get_contract_type", "line_number": 140, "usage_type": "call"}, {"api_name": "dags.spiders.utils.spider_utils.SpiderUtils", "line_number": 140, "usage_type": "name"}, {"api_name": "dags.spiders.setting.spider_setting.SpiderSetting.yinhe_list_url", "line_number": 145, "usage_type": "attribute"}, {"api_name": "dags.spiders.setting.spider_setting.SpiderSetting", "line_number": 145, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 149, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 149, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 156, "usage_type": "call"}, {"api_name": "dags.spiders.setting.spider_setting.SpiderSetting.yinhe_detail_url", "line_number": 170, "usage_type": "attribute"}, {"api_name": "dags.spiders.setting.spider_setting.SpiderSetting", "line_number": 170, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 179, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 179, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 186, "usage_type": "call"}, {"api_name": "dags.spiders.utils.spider_utils.SpiderUtils.save_data", "line_number": 200, "usage_type": "call"}, {"api_name": "dags.spiders.utils.spider_utils.SpiderUtils", "line_number": 200, "usage_type": "name"}]} +{"seq_id": "39724441321", "text": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\n\nimport urllib.request\nimport bs4\nimport pandas as pd\n\nfh = open('icao.txt', '+w')\n\nsource ='https://en.wikipedia.org/wiki/List_of_airports_in_the_United_Kingdom_and_the_British_Crown_Dependencies'\n\nsauce = urllib.request.urlopen(source).read()\n\nsoup = bs4.BeautifulSoup(sauce, 'html.parser')\n\ntable = soup.table\n#print(table)\n\ntable_row = table.find_all('tr')\n\nfor tr in table_row:\n td = tr.find_all('td')\n td = [i.text.replace(\"\\n\",\"\") for i in td]\n for i in td:\n if not i.isprintable() or i =='':\n td.remove(i)\n for i in td:\n if not i.isprintable() or i == '':\n td.remove(i)\n for i in td:\n if not i.isprintable() or i == '':\n td.remove(i)\n\n print(td)\n\n fh.writelines(str(td).replace('[','').replace(']',''))\n fh.writelines('\\n')\nfh.close()\n\n\n# for tr in table_row:\n# print(tr)\n# print('\\n')\n\n# table_data = table.find_all('td')\n# for td in table_data:\n# print(td)\n# print('\\n')\n\n\n#### Pandas\n\n# dfs = pd.read_html(source, index_col=0)\n# # print(dfs)\n# eng = dfs[0] # dataframe\n\n# # ## how to pull data:\n\n# print(eng.loc['Waddington']['ICAO'])\n# print('\\n')\n# print(eng.loc['Waddington']['ICAO'][0]) # pulls out string\n\n# dfs = pd.read_html(source)\n# print(dfs)\n# eng = dfs[0] # dataframe\n\n# icao ={}\n# icao[eng.iloc[11]['Airport name'][0]] = [eng.iloc[11]['ICAO'][0]][0]\n# print(icao['Wickenby Aerodrome'])\n", "repo_name": "nicksclater/web_scrapper", "sub_path": "icao_scrap.py", "file_name": "icao_scrap.py", "file_ext": "py", "file_size_in_byte": 1398, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "urllib.request.request.urlopen", "line_number": 13, "usage_type": "call"}, {"api_name": "urllib.request.request", "line_number": 13, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 13, "usage_type": "name"}, {"api_name": "bs4.BeautifulSoup", "line_number": 15, "usage_type": "call"}]} +{"seq_id": "40947619447", "text": "# -*- coding: utf-8 -*-\n\"\"\"\nЗадание 1.3\n\nНаписать тесты для функции send_show. Тесты должны проверять:\n\n* тип возвращаемых данных - словарь или None, если было исключение\n* при возникновении исключения, опционально можно сделать проверку на то правильное ли\n выводится сообщение на stdout, как минимум, что в stdout был вывод IP-адреса\n* что функция возвращает правильный результат при передаче команды строки\n и при передаче списка команд. И в том и в том случае должен возвращаться\n словарь в котором ключ команда, а значение вывод команды\n\n\nДля проверки разных ситуаций - доступное устройство, недоступное и так далее\nв файле devices.yaml создано несколько групп устройств:\n* reachable_ssh_telnet - это устройства на которых доступен Telnet и SSH, прописаны\n правильные логин и пароли\n* reachable_ssh_telnet_wrong_auth_password - это доступное устройство на котором разрешены\n SSH/Telnet, но настроен неправильный пароль auth_password\n* reachable_telnet_only - это доступное устройство на котором разрешен только Telnet\n и прописаны правильные логин и пароли\n* unreachable - это недоступное устройство\n\nДля корректной работы тестов надо написать в файле devices.yaml параметры ваших устройств\nили создать аналогичный файл с другим именем.\nПлюс надо соответственно настроить устройства так чтобы где нужно был только\nTelnet или неправильный пароль соответственно.\n\nВ целом тут свобода творчества и один из нюансов задания как раз в том чтобы\nпридумать что именно и как тестировать. В задании даны несколько идей для старта,\nно остальное надо продумать самостоятельно.\n\nТест(ы) написать в файле заданий.\n\nОграничение: функцию менять нельзя.\nДля заданий этого раздела нет тестов для проверки тестов :)\n\"\"\"\nimport socket\nfrom pprint import pprint\n\nimport yaml\nfrom scrapli import Scrapli\nfrom scrapli.exceptions import ScrapliException\nfrom paramiko.ssh_exception import SSHException\n\n\ndef send_show(device, show_commands):\n transport = device.get(\"transport\") or \"system\"\n host = device.get(\"host\")\n if type(show_commands) == str:\n show_commands = [show_commands]\n cmd_dict = {}\n print(f\">>> Connecting to {host}\")\n try:\n with Scrapli(**device) as ssh:\n for cmd in show_commands:\n reply = ssh.send_command(cmd)\n cmd_dict[cmd] = reply.result\n print(f\"<<< Received output from {host}\")\n return cmd_dict\n except (ScrapliException, SSHException, socket.timeout, OSError) as error:\n print(f\"Device {host}, Transport {transport}, Error {error}\")\n\n\nif __name__ == \"__main__\":\n with open(\"devices.yaml\") as f:\n devices = yaml.safe_load(f)\n for dev_type, device_list in devices.items():\n print(dev_type.upper())\n for dev in device_list:\n output = send_show(dev, \"sh clock\")\n pprint(output, width=120)\n", "repo_name": "natenka/advpyneng-examples-exercises", "sub_path": "exercises/01_pytest_basics/task_1_3.py", "file_name": "task_1_3.py", "file_ext": "py", "file_size_in_byte": 4039, "program_lang": "python", "lang": "ru", "doc_type": "code", "stars": 28, "dataset": "github-code", "pt": "37", "api": [{"api_name": "scrapli.Scrapli", "line_number": 56, "usage_type": "call"}, {"api_name": "scrapli.exceptions.ScrapliException", "line_number": 62, "usage_type": "name"}, {"api_name": "paramiko.ssh_exception.SSHException", "line_number": 62, "usage_type": "name"}, {"api_name": "socket.timeout", "line_number": 62, "usage_type": "attribute"}, {"api_name": "yaml.safe_load", "line_number": 68, "usage_type": "call"}, {"api_name": "pprint.pprint", "line_number": 73, "usage_type": "call"}]} +{"seq_id": "38984944247", "text": "# -*- coding: utf-8 -*-\n# @Time : 2021/8/1 18:33\n# @Author : lemon_zhenzhen\n# @Email :544578369@qq.com\n# @file : handle_phone.py\nfrom faker import Faker\nfrom common.my_mysql import MyMysql\n\ndef get_new_phone():\n \"\"\"\n 得到没有注册过的手机号码\n 1、使用faker生成手机号码\n 2、调用mysql数据库操作，去判断是否在数据中存在，如果不在，表示没有注册。\n :return:\n \"\"\"\n while True:\n phone = Faker(\"zh_CN\").phone_number()\n sql = \"SELECT * FROM `city_user`.`user` where user_id='{}'\".format(phone)\n res = MyMysql().get_count(sql)\n if res == 0:\n return phone\n\nprint(get_new_phone())\n\n\n", "repo_name": "fumingkun/requests_login", "sub_path": "common/handle_phone.py", "file_name": "handle_phone.py", "file_ext": "py", "file_size_in_byte": 697, "program_lang": "python", "lang": "zh", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "faker.Faker", "line_number": 17, "usage_type": "call"}, {"api_name": "common.my_mysql.MyMysql", "line_number": 19, "usage_type": "call"}]} +{"seq_id": "36017451844", "text": "import pandas as pd\n\n\n\n# path to data\nmain_file_path = 'train.csv' \n\n# Reading data\ndata = pd.read_csv(main_file_path)\n\n\n# Summary of data \nprint(data.describe())\n\n# *********************************\n# Selecting and Filtering Data in Pandas\n# *********************************\n\n\n# List of all columns/features\nprint(data.columns)\n\n\n# Select a Single column\ndata_yearSold = data.YrSold\nprint(data_yearSold)\n\n# Selecting Multiple columns\ncolumns_of_interest = ['YrSold','SalePrice']\ntwo_columns_of_data = data[columns_of_interest]\n# Show shortened result\nprint(two_columns_of_data.head())\n\n#describing data\ntwo_columns_summary = two_columns_of_data.describe()\nprint(two_columns_summary)\n\n# ********************************************\n#\t\tSckikit-Learn Model\n# ********************************************\n\n# Building model #\n\n# Prediction target/ column we want to predict\ny = data.SalePrice\n\n# loading predictors\n#fireplace, fullbath, yearbuilt\n\ndata_predictors = ['YearBuilt','FullBath','LotArea']\n\nX = data[data_predictors]\n\n\n# Define: What type of model will it be? A decision tree? Some other type of model? Some other parameters of the model type are specified too.\n# Fit: Capture patterns from provided data. This is the heart of modeling.\n# Predict: Just what it sounds like\n# Evaluate: Determine how accurate the model's predictions are.\n\n#***************************************************\n#\t\t\t\tModel Validation\n#***************************************************\n\n\nfrom sklearn.tree import DecisionTreeRegressor \n\n# Define model\ndata_model = DecisionTreeRegressor()\n\n# Fit model\ndata_model.fit(X,y)\n\n# parameters aboyt the type of model built\n# print(data_model.fit(X,y))\nprint('\\n')\nprint(\"Making predictions for the first 5 houses\")\nprint(X.head(),'\\n')\nprint(\"The predictions are\")\nprint(data_model.predict(X.head()))\n\n\n\n# Calculating mean absolute error\nfrom sklearn.metrics import mean_absolute_error\n\npredicted_sale_prices = data_model.predict(X)\n# in-sample score\navrg_error = mean_absolute_error(y, predicted_sale_prices)\nprint(\"The Mean Absolute Error for Decision Tree none-validation:\",avrg_error)\n# issue with DecisionTree none-validation is the model will fail on new data sets\n\n\n# Validation data (train/make predictions on new data)\nfrom sklearn.model_selection import train_test_split\n# split data into training and validation data, for both predictors and target\n# The split is based on a random number generator. Supplying a numeric value to\n# the random_state argument guarantees we get the same split every time we\n# run this script.\ntrain_X, val_X, train_y, val_y = train_test_split(X, y, random_state = 0)\n\n# Define model\ndata_model = DecisionTreeRegressor()\n# Fit model\ndata_model.fit(train_X, train_y)\n\n# Get predicited price on validation data\nval_predictions = data_model.predict(val_X)\navrg_error = mean_absolute_error(val_y, val_predictions)\nprint(\"The Mean Absolute Error for Decision Tree Validation:\",avrg_error)\nprint('\\n')\n\n# ********************************************\n#\n# Underfitting, Overfitting and Model Optimization\n#\n# ********************************************\nfrom sklearn.metrics import mean_absolute_error\nfrom sklearn.tree import DecisionTreeRegressor\n\ndef get_mae(max_leaf_nodes, predictors_train, predictors_val, targ_train, targ_val):\n\tmodel = DecisionTreeRegressor(max_leaf_nodes=max_leaf_nodes, random_state = 0)\n\tmodel.fit(predictors_train, targ_train)\n\tpreds_val = model.predict(predictors_val)\n\tmae = mean_absolute_error(targ_val, preds_val)\n\treturn mae\n\n# Finding the most node with the least ammount of errors (cost function)\nfor max_leaf_nodes in [5, 50, 500 , 5000]:\n\tmy_mae = get_mae(max_leaf_nodes, train_X, val_X, train_y, val_y)\n\tprint(\"Max leaf nodes: %d \\t\\t Mean Absolute Error: %d\" %(max_leaf_nodes,my_mae))\n\n# ********************************************\n#\t\t\t\tRANDOM FOREST TREE\n#\t\tMore powerful than Decision Tree\n# ********************************************\n\nfrom sklearn.ensemble import RandomForestRegressor\nfrom sklearn.metrics import mean_absolute_error\n\nforest_model = RandomForestRegressor()\nforest_model.fit(train_X, train_y)\nmelb_preds = forest_model.predict(val_X)\navrg_error = mean_absolute_error(val_y,melb_preds)\nprint(\"Mean Absolute error for Random Forest Regressor: \",avrg_error)\n\n#*************************************************\n#\n#\t\t\t\tTest Data and Kaggle Submission\n#\n#*************************************************\n\n# See first_model_submission.py", "repo_name": "Memhir-Yasue/Kaggle-Tutorial-", "sub_path": "first_model.py", "file_name": "first_model.py", "file_ext": "py", "file_size_in_byte": 4459, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "pandas.read_csv", "line_number": 9, "usage_type": "call"}, {"api_name": "sklearn.tree.DecisionTreeRegressor", "line_number": 68, "usage_type": "call"}, {"api_name": "sklearn.metrics.mean_absolute_error", "line_number": 88, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 99, "usage_type": "call"}, {"api_name": "sklearn.tree.DecisionTreeRegressor", "line_number": 102, "usage_type": "call"}, {"api_name": "sklearn.metrics.mean_absolute_error", "line_number": 108, "usage_type": "call"}, {"api_name": "sklearn.tree.DecisionTreeRegressor", "line_number": 121, "usage_type": "call"}, {"api_name": "sklearn.metrics.mean_absolute_error", "line_number": 124, "usage_type": "call"}, {"api_name": "sklearn.ensemble.RandomForestRegressor", "line_number": 140, "usage_type": "call"}, {"api_name": "sklearn.metrics.mean_absolute_error", "line_number": 143, "usage_type": "call"}]} +{"seq_id": "72429367146", "text": "import time\nfrom pathlib import Path\nfrom watchdog.observers import Observer\nfrom watchdog.events import FileSystemEventHandler\nfrom amari_logger import Amari_logger\n\n\nclass MyDirEventHandler(FileSystemEventHandler):\n\n def on_created(self, event):\n file = Path(event.src_path)\n if file.is_dir() or file.name == '.DS_Store':\n return\n\n print(f'\\n==> new file detected: {file}')\n parser = Amari_logger()\n parser.parse_and_send(file)\n print(f'\\n==> keep watching ...')\n\n\nif __name__ == \"__main__\":\n watching_folder_path = Path(\n input('==> Please type in the folder name to watch: '))\n\n if not watching_folder_path.exists():\n watching_folder_path.mkdir()\n\n event_handler = MyDirEventHandler()\n observer = Observer()\n\n observer.schedule(event_handler, watching_folder_path, recursive=True)\n observer.start()\n\n print('==> Start watching ...')\n try:\n while True:\n time.sleep(1)\n finally:\n observer.stop()\n observer.join()\n", "repo_name": "balao1312/amarisoft_test_logger", "sub_path": "watch_dog_parsing.py", "file_name": "watch_dog_parsing.py", "file_ext": "py", "file_size_in_byte": 1044, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "37", "api": [{"api_name": "watchdog.events.FileSystemEventHandler", "line_number": 8, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 11, "usage_type": "call"}, {"api_name": "amari_logger.Amari_logger", "line_number": 16, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 22, "usage_type": "call"}, {"api_name": "watchdog.observers.Observer", "line_number": 29, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 37, "usage_type": "call"}]} +{"seq_id": "41279323257", "text": "from typing import Any, Dict, Iterable, List, NamedTuple, Optional, Tuple\n\nfrom docutils import nodes\nfrom sphinx.addnodes import pending_xref\nfrom sphinx.builders import Builder\nfrom sphinx.domains import Domain, ObjType\nfrom sphinx.environment import BuildEnvironment\nfrom sphinx.locale import _, __\nfrom sphinx.roles import XRefRole\nfrom sphinx.util import logging\nfrom sphinx.util.nodes import make_refnode\n\nfrom sphinxcontrib.vyperlang.domain.directives import (\n VyContract,\n VyCurrentContract,\n VyEnum,\n VyEvent,\n VyFunction,\n VyStruct,\n VyVariable,\n)\nfrom sphinxcontrib.vyperlang.domain.indices import VyperContractIndex\n\nlogger = logging.getLogger(__name__)\n\n\nclass ObjectEntry(NamedTuple):\n docname: str\n node_id: str\n metadata: Dict[str, Any]\n\n\nclass VyperDomain(Domain):\n \"\"\"Vyper language domain.\"\"\"\n\n name = \"vy\"\n label = \"Vyper\"\n object_types = {\n \"contract\": ObjType(_(\"contract\"), \"contract\"),\n \"event\": ObjType(_(\"event\"), \"event\"),\n \"enum\": ObjType(_(\"enum\"), \"enum\"),\n \"struct\": ObjType(_(\"struct\"), \"struct\"),\n \"variable\": ObjType(_(\"variable\"), \"var\"),\n \"function\": ObjType(_(\"function\"), \"func\"),\n }\n directives = {\n \"contract\": VyContract,\n \"currentcontract\": VyCurrentContract,\n \"event\": VyEvent,\n \"enum\": VyEnum,\n \"struct\": VyStruct,\n \"variable\": VyVariable,\n \"function\": VyFunction,\n }\n roles = {\n \"contract\": XRefRole(),\n \"event\": XRefRole(),\n \"enum\": XRefRole(),\n \"struct\": XRefRole(),\n \"var\": XRefRole(),\n \"func\": XRefRole(),\n }\n initial_data: Dict[str, Dict[str, ObjectEntry]] = {\"objects\": {}}\n indices = [VyperContractIndex]\n\n @property\n def objects(self) -> Dict:\n return self.data.setdefault(\"objects\", {})\n\n def add_object(\n self, name: str, node_id: str, objtype: str, **metadata: Any\n ) -> None:\n \"\"\"Add an object to the domain data.\"\"\"\n objects = self.objects.setdefault(objtype, {})\n if name in objects:\n logger.warning(__(f\"duplicate description of {name!r}\"))\n objects[name] = ObjectEntry(self.env.docname, node_id, metadata)\n\n def clear_doc(self, docname: str) -> None:\n \"\"\"Purge object entries from the domain data which were in a document.\"\"\"\n for objtype, objects in self.objects.items():\n for name, entry in objects.copy().items():\n if entry.docname == docname:\n del self.objects[objtype][name]\n\n def merge_domaindata(self, docnames: List[str], otherdata: Dict) -> None:\n \"\"\"Merge domain data from a parallel process.\"\"\"\n for objtype, objects in otherdata.items():\n for name, entry in objects.items():\n if entry.docname not in docnames:\n continue\n elif name in self.objects.setdefault(objtype, {}):\n logger.warning(__(f\"duplicate description of {name!r}\"))\n self.objects[objtype][name] = entry\n\n def get_objects(self) -> Iterable[Tuple[str, str, str, str, str, int]]:\n for objtype, objects in self.objects.items():\n for name, entry in objects.items():\n yield (name, name, objtype, entry.docname, entry.node_id, 0)\n\n def resolve_xref(\n self,\n env: BuildEnvironment,\n fromdocname: str,\n builder: Builder,\n typ: str,\n target: str,\n node: pending_xref,\n contnode: nodes.Element,\n ) -> Optional[nodes.Element]:\n objects = self.objects.setdefault(typ, {})\n if target not in objects:\n return None\n\n entry = objects[target]\n return make_refnode(\n builder, fromdocname, entry.docname, entry.node_id, contnode, target\n )\n", "repo_name": "skellet0r/sphinxcontrib-vyperlang", "sub_path": "src/sphinxcontrib/vyperlang/domain/__init__.py", "file_name": "__init__.py", "file_ext": "py", "file_size_in_byte": 3847, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "37", "api": [{"api_name": "sphinx.util.logging.getLogger", "line_number": 24, "usage_type": "call"}, {"api_name": "sphinx.util.logging", "line_number": 24, "usage_type": "name"}, {"api_name": "typing.NamedTuple", "line_number": 27, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 30, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 30, "usage_type": "name"}, {"api_name": "sphinx.domains.Domain", "line_number": 33, "usage_type": "name"}, {"api_name": "sphinx.domains.ObjType", "line_number": 39, "usage_type": "call"}, {"api_name": "sphinx.locale._", "line_number": 39, "usage_type": "call"}, {"api_name": "sphinx.domains.ObjType", "line_number": 40, "usage_type": "call"}, {"api_name": "sphinx.locale._", "line_number": 40, "usage_type": "call"}, {"api_name": "sphinx.domains.ObjType", "line_number": 41, "usage_type": "call"}, {"api_name": "sphinx.locale._", "line_number": 41, "usage_type": "call"}, {"api_name": "sphinx.domains.ObjType", "line_number": 42, "usage_type": "call"}, {"api_name": "sphinx.locale._", "line_number": 42, "usage_type": "call"}, {"api_name": "sphinx.domains.ObjType", "line_number": 43, "usage_type": "call"}, {"api_name": "sphinx.locale._", "line_number": 43, "usage_type": "call"}, {"api_name": "sphinx.domains.ObjType", "line_number": 44, "usage_type": "call"}, {"api_name": "sphinx.locale._", "line_number": 44, "usage_type": "call"}, {"api_name": "sphinxcontrib.vyperlang.domain.directives.VyContract", "line_number": 47, "usage_type": "name"}, {"api_name": "sphinxcontrib.vyperlang.domain.directives.VyCurrentContract", "line_number": 48, "usage_type": "name"}, {"api_name": "sphinxcontrib.vyperlang.domain.directives.VyEvent", "line_number": 49, "usage_type": "name"}, {"api_name": "sphinxcontrib.vyperlang.domain.directives.VyEnum", "line_number": 50, "usage_type": "name"}, {"api_name": "sphinxcontrib.vyperlang.domain.directives.VyStruct", "line_number": 51, "usage_type": "name"}, {"api_name": "sphinxcontrib.vyperlang.domain.directives.VyVariable", "line_number": 52, "usage_type": "name"}, {"api_name": "sphinxcontrib.vyperlang.domain.directives.VyFunction", "line_number": 53, "usage_type": "name"}, {"api_name": "sphinx.roles.XRefRole", "line_number": 56, "usage_type": "call"}, {"api_name": "sphinx.roles.XRefRole", "line_number": 57, "usage_type": "call"}, {"api_name": "sphinx.roles.XRefRole", "line_number": 58, "usage_type": "call"}, {"api_name": "sphinx.roles.XRefRole", "line_number": 59, "usage_type": "call"}, {"api_name": "sphinx.roles.XRefRole", "line_number": 60, "usage_type": "call"}, {"api_name": "sphinx.roles.XRefRole", "line_number": 61, "usage_type": "call"}, {"api_name": "typing.Dict", "line_number": 63, "usage_type": "name"}, {"api_name": "sphinxcontrib.vyperlang.domain.indices.VyperContractIndex", "line_number": 64, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 67, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 71, "usage_type": "name"}, {"api_name": "sphinx.locale.__", "line_number": 76, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 86, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 86, "usage_type": "name"}, {"api_name": "sphinx.locale.__", "line_number": 93, "usage_type": "call"}, {"api_name": "typing.Iterable", "line_number": 96, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 96, "usage_type": "name"}, {"api_name": "sphinx.environment.BuildEnvironment", "line_number": 103, "usage_type": "name"}, {"api_name": "sphinx.builders.Builder", "line_number": 105, "usage_type": "name"}, {"api_name": "sphinx.addnodes.pending_xref", "line_number": 108, "usage_type": "name"}, {"api_name": "docutils.nodes.Element", "line_number": 109, "usage_type": "attribute"}, {"api_name": "docutils.nodes", "line_number": 109, "usage_type": "name"}, {"api_name": "sphinx.util.nodes.make_refnode", "line_number": 116, "usage_type": "call"}, {"api_name": "typing.Optional", "line_number": 110, "usage_type": "name"}, {"api_name": "docutils.nodes.Element", "line_number": 110, "usage_type": "attribute"}, {"api_name": "docutils.nodes", "line_number": 110, "usage_type": "name"}]} +{"seq_id": "43245605653", "text": "from django.contrib.auth.models import User\nfrom django.contrib.auth.decorators import login_required\nfrom django.shortcuts import render,redirect\nfrom instagram.models import *\nfrom instagram.forms import *\nfrom django.core.files.storage import FileSystemStorage\n\ndef index(request):\n if request.method == 'POST':\n form = UserForm(request.POST)\n if form.is_valid():\n curr_email = form.cleaned_data['email']\n curr_name = form.cleaned_data['name']\n curr_username = form.cleaned_data['username']\n curr_password = form.cleaned_data['password']\n user_object = User.objects.create_user(first_name=curr_name, username = curr_username, password = curr_password, email = curr_email)\n myuser = MyUser( user = user_object )\n myuser.save()\n return redirect('login')\n else:\n context = {'form':form}\n return render(request, 'index.html', context)\n else:\n if request.user.is_authenticated:\n return redirect('home')\n form = UserForm()\n context = {'form':form}\n return render(request, 'index.html', context)\n\n\n@login_required\ndef home(request):\n curr_user = request.user\n photo_list = []\n for user_aux in Follow.objects.filter( from_user = curr_user.myuser ):\n search_user = MyUser.objects.get( pk = user_aux.to_user.id )\n post_aux = Post.objects.filter(owner_user=search_user.user.id)\n if post_aux:\n for photo_aux in post_aux:\n print (\"Photo con id:\" + str(photo_aux.pk) )\n print (photo_aux.like_set.count())\n photo_list.append( photo_aux )\n for post_aux in Post.objects.filter(owner_user = curr_user):\n photo_list.append(post_aux)\n likes = []\n for like in Like.objects.filter(user = curr_user):\n likes.append(like.post.pk)\n context = { 'curr_user' : curr_user, 'photo_list' : photo_list, 'likes' : likes }\n return render(request, 'home.html', context)\n\n@login_required\ndef profile(request, _username):\n try:\n curr_user = User.objects.get(username=_username)\n except User.DoesNotExist:\n return render(request, 'error_user.html')\n media_user = Post.objects.filter( owner_user = curr_user )\n follow_number = Follow.objects.filter( from_user = curr_user.myuser ).count()\n followers_number = Follow.objects.filter( to_user = curr_user.myuser ).count()\n if _username != request.user:\n if Follow.objects.filter(from_user = request.user.myuser, to_user = curr_user.myuser).count() > 0:\n already_follow = True\n else:\n already_follow = False\n context = { 'register_user' : request.user,\n 'curr_user' : curr_user,\n 'media_user' : media_user,\n 'follow_number' : follow_number,\n 'followers_number' : followers_number,\n 'already_follow' : already_follow\n }\n return render(request, 'profile.html', context)\n\n@login_required\ndef uploadFile(request):\n curr_user = request.user\n if request.method == 'POST':\n form = PostForm(request.POST, request.FILES)\n if form.is_valid():\n post_user = Post.objects.filter(owner_user=curr_user.id).count();\n mediaFile = form.cleaned_data[ 'photo' ];\n newNameFile = curr_user.username + \"-\" + str(curr_user.id) + \"-\" + str(post_user);\n fs = FileSystemStorage()\n filename = fs.save(newNameFile, mediaFile)\n uploaded_file_url = fs.url(filename)\n photo = uploaded_file_url;\n description = form.cleaned_data[ 'description' ];\n newPost = Post( photo = photo, description = description, owner_user = curr_user );\n newPost.save();\n return redirect('profile',curr_user.username)\n else:\n context = { 'curr_user' : curr_user, 'form' : form }\n return render(request, 'uploadPhoto.html', context)\n else:\n form = PostForm()\n context = { 'curr_user' : curr_user, 'form' : form }\n return render(request, 'uploadPhoto.html', context)\n\n@login_required\ndef search( request ):\n curr_user = request.user\n if request.method == 'POST':\n query = request.POST['search']\n try:\n user_list = User.objects.filter(username__icontains=query)\n except User.DoesNotExist:\n user_list = None\n context = { 'curr_user' : curr_user, 'user_list' : user_list }\n return render(request, 'search.html', context)\n else:\n context = { 'curr_user' : curr_user }\n return render(request, 'search.html', context)\n\ndef follow( request, _username ):\n to = User.objects.get(username=_username)\n fo = Follow.objects.create( from_user = request.user.myuser, to_user = to.myuser )\n request.user.myuser.follow = fo\n return redirect('profile',to.username)\n\ndef unfollow(request, _username):\n to = User.objects.get(username=_username)\n row = Follow.objects.filter( from_user = request.user.myuser, to_user = to.myuser )\n row.delete()\n return redirect('profile', to.username)\n\ndef doLike(request, id_photo):\n curr_user = request.user\n post = Post.objects.get(pk = id_photo)\n if not Like.objects.filter(user = curr_user, post = post).exists():\n new_like = Like(user = curr_user, post = post)\n new_like.save()\n return redirect('home')\n\ndef removeLike(request, id_photo):\n curr_user = request.user\n post = Post.objects.get(pk = id_photo)\n if Like.objects.filter(user = curr_user, post = post).exists():\n Like.objects.filter(user = curr_user, post = post).delete()\n return redirect('home')\n\ndef doComment(request, id_photo):\n comment = request.POST['comment']\n curr_user = request.user\n post = Post.objects.get(pk = id_photo)\n new_comment = Comment(text = comment, user = curr_user, post = post)\n new_comment.save()\n return redirect('home')\n", "repo_name": "CapituloJaverianoACM/InstagramPython", "sub_path": "instagram/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 5995, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "django.contrib.auth.models.User.objects.create_user", "line_number": 16, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects", "line_number": 16, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 16, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 19, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 22, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 25, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 28, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 49, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 31, "usage_type": "name"}, {"api_name": "django.contrib.auth.models.User.objects.get", "line_number": 54, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects", "line_number": 54, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 54, "usage_type": "name"}, {"api_name": "django.contrib.auth.models.User.DoesNotExist", "line_number": 55, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 55, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 56, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 72, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 51, "usage_type": "name"}, {"api_name": "django.core.files.storage.FileSystemStorage", "line_number": 83, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 90, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 93, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 97, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 74, "usage_type": "name"}, {"api_name": "django.contrib.auth.models.User.objects.filter", "line_number": 105, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects", "line_number": 105, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 105, "usage_type": "name"}, {"api_name": "django.contrib.auth.models.User.DoesNotExist", "line_number": 106, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 106, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 109, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 112, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 99, "usage_type": "name"}, {"api_name": "django.contrib.auth.models.User.objects.get", "line_number": 115, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects", "line_number": 115, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 115, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 118, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects.get", "line_number": 121, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects", "line_number": 121, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 121, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 124, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 132, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 139, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 147, "usage_type": "call"}]} +{"seq_id": "73484300268", "text": "from cameras.base_camera import BaseCamera\nfrom cv2 import VideoCapture, cvtColor\nfrom cv2 import CAP_V4L, COLOR_BGR2RGB, CAP_PROP_BUFFERSIZE, CAP_PROP_FRAME_WIDTH, CAP_PROP_FRAME_HEIGHT\nfrom PIL import Image\nfrom io import BytesIO\n\n\nclass OpenCVCamera(BaseCamera):\n def __init__(self, videoSource, dims=(640, 480)):\n w, h = dims\n self.w = w\n self.h = h\n self.midX = int(w / 2)\n self.midY = int(h / 2)\n self.cropDim2 = int(min(w, h) / 2)\n\n self.videoSource = videoSource\n self.vcap = VideoCapture()\n\n def open(self):\n if not self.vcap.isOpened() and not self.vcap.open(self.videoSource, CAP_V4L):\n raise RuntimeError('Could not open camera')\n\n # Minimize the frame buffer to always receive frames in realtime.\n self.vcap.set(CAP_PROP_BUFFERSIZE, 1)\n self.vcap.set(CAP_PROP_FRAME_WIDTH, self.w)\n self.vcap.set(CAP_PROP_FRAME_HEIGHT, self.h)\n\n if (self.vcap.get(CAP_PROP_FRAME_WIDTH) != self.w or self.vcap.get(CAP_PROP_FRAME_HEIGHT) != self.h):\n raise RuntimeError('Resolution not supported')\n \n def close(self):\n self.vcap.release()\n\n @property\n def resolution(self):\n return {\n 'original': {\n 'width': self.w,\n 'height': self.h\n },\n 'crop': {\n 'width': self.cropDim2 * 2,\n 'height': self.cropDim2 * 2\n }\n }\n\n def read(self):\n (success, frame) = self.vcap.read()\n if (success == False):\n raise IOError('Frame could not be read from source')\n\n crop = frame[self.midY-self.cropDim2: self.midY+self.cropDim2,\n self.midX-self.cropDim2: self.midX+self.cropDim2]\n # cv2 uses BGR instead of RGB by default.\n return Image.fromarray(cvtColor(crop, COLOR_BGR2RGB))\n\n def encodeJPG(self, pilImage):\n jpg = BytesIO()\n pilImage.save(jpg, format='JPEG')\n return jpg.getvalue()\n", "repo_name": "jhoogstraat/EdgeAI", "sub_path": "cameras/opencv_camera.py", "file_name": "opencv_camera.py", "file_ext": "py", "file_size_in_byte": 2019, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "37", "api": [{"api_name": "cameras.base_camera.BaseCamera", "line_number": 8, "usage_type": "name"}, {"api_name": "cv2.VideoCapture", "line_number": 18, "usage_type": "call"}, {"api_name": "cv2.CAP_V4L", "line_number": 21, "usage_type": "argument"}, {"api_name": "cv2.CAP_PROP_BUFFERSIZE", "line_number": 25, "usage_type": "argument"}, {"api_name": "cv2.CAP_PROP_FRAME_WIDTH", "line_number": 26, "usage_type": "argument"}, {"api_name": "cv2.CAP_PROP_FRAME_HEIGHT", "line_number": 27, "usage_type": "argument"}, {"api_name": "cv2.CAP_PROP_FRAME_WIDTH", "line_number": 29, "usage_type": "argument"}, {"api_name": "cv2.CAP_PROP_FRAME_HEIGHT", "line_number": 29, "usage_type": "argument"}, {"api_name": "PIL.Image.fromarray", "line_number": 56, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 56, "usage_type": "name"}, {"api_name": "cv2.cvtColor", "line_number": 56, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2RGB", "line_number": 56, "usage_type": "argument"}, {"api_name": "io.BytesIO", "line_number": 59, "usage_type": "call"}]} +{"seq_id": "26904772945", "text": "# Day 11 is a milestone project\n# Your goal is to develop blackjack game in CLI\n\n############### Blackjack Project #####################\n\n#Difficulty Normal 😎: Use all Hints below to complete the project.\n#Difficulty Hard 🤔: Use only Hints 1, 2, 3 to complete the project.\n#Difficulty Extra Hard 😭: Only use Hints 1 & 2 to complete the project.\n#Difficulty Expert 🤯: Only use Hint 1 to complete the project.\n\n############### Our Blackjack House Rules #####################\n\n## The deck is unlimited in size. \n## There are no jokers. \n## The Jack/Queen/King all count as 10.\n## The the Ace can count as 11 or 1.\n## Use the following list as the deck of cards:\n## cards = [11, 2, 3, 4, 5, 6, 7, 8, 9, 10, 10, 10, 10]\n## The cards in the list have equal probability of being drawn.\n## Cards are not removed from the deck as they are drawn.\n## The computer is the dealer.\n\n##################### Hints #####################\n\n#Hint 1: Go to this website and try out the Blackjack game: \n# https://games.washingtonpost.com/games/blackjack/\n#Then try out the completed Blackjack project here: \n# http://blackjack-final.appbrewery.repl.run\n\nfrom art import logo\nimport random\n\nprint(logo)\n\ncards = [11, 2, 3, 4, 5, 6, 7, 8, 9, 10, 10, 10, 10]\nplayer_wins = 0\ndealer_wins = 0\ndealer_moves = ['draw', 'halt']\nrepeat = True\n\n\ndef drawCard(hand):\n hand.append(random.choice(cards))\n \ndef dealCards():\n drawCard(player_hand)\n drawCard(player_hand)\n\n drawCard(dealer_hand)\n drawCard(dealer_hand)\n\n\n\ndef showScoreAndHand():\n print(\"\\nPlayer's hand: \", player_hand)\n print(\"Dealer's hand : \", dealer_hand)\n print(\"\\nPlayer's score:\",calculateScore(array=player_hand))\n print(\"Dealer's score:\",calculateScore(array=dealer_hand))\n\ndef calculateScore(array):\n score = 0\n for card in array:\n if card == 11 and (score + 11) > 21:\n score += 1\n else:\n score += card\n return score\n\ndef make_move(move, hand, player):\n global didPlayerHalt\n global didDealerHalt\n\n if move == 'draw':\n drawCard(hand)\n elif move == 'halt':\n if player == 'player':\n didPlayerHalt = True\n elif player == 'dealer':\n didDealerHalt = True\n else:\n print(\"Incorrect player value\")\n else:\n print(\"Incorrect move value\")\n\ndef continueGame(): \n player_choice = input(\"Do you want to play another game? 'yes' / 'no': \")\n if player_choice == 'no':\n exit()\n elif player_choice != 'yes':\n continueGame()\n\nwhile repeat:\n player_score = 0\n dealer_score = 0\n\n player_hand = []\n dealer_hand = []\n\n didPlayerHalt = False\n didDealerHalt = False\n\n player_bust = False\n dealer_bust = False\n\n dealCards()\n showScoreAndHand()\n\n while not didPlayerHalt or not didDealerHalt:\n if not didPlayerHalt:\n player_choice = input(\"Do you want to draw another card or halt? type: 'draw' or 'halt'\")\n make_move(move=player_choice, hand=player_hand, player='player')\n if calculateScore(player_hand) > 21:\n dealer_wins += 1\n didPlayerHalt = True\n player_bust = True\n showScoreAndHand()\n print(\"\\nPlayer bust!\")\n print(\"***** Dealer wins! *****\")\n continueGame()\n break\n \n if not didDealerHalt:\n dealer_choice = random.choice(dealer_moves)\n print(\"dealer choice \",dealer_choice)\n make_move(move=dealer_choice, hand=dealer_hand, player='dealer')\n if calculateScore(dealer_hand) > 21:\n player_wins += 1\n didDealerHalt = True\n dealer_bust = True\n showScoreAndHand()\n print(\"\\nDealer bust!\")\n print(\"***** You win! *****\")\n continueGame()\n break\n\n if didPlayerHalt and didDealerHalt:\n if calculateScore(player_hand) == calculateScore(dealer_hand):\n showScoreAndHand()\n print(\"***** DRAW! *****\")\n continueGame()\n break\n elif calculateScore(player_hand) > calculateScore(dealer_hand):\n player_wins += 1\n showScoreAndHand()\n print(\"***** You win! *****\")\n continueGame()\n break\n else:\n dealer_wins += 1\n showScoreAndHand()\n print(\"***** Dealer wins! *****\")\n continueGame()\n break\n else: \n showScoreAndHand()\n\n \n\n\n\n\n\n", "repo_name": "greenMakaroni/100-days-of-python-challenge", "sub_path": "day 011/blackjack.py", "file_name": "blackjack.py", "file_ext": "py", "file_size_in_byte": 4676, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "art.logo", "line_number": 33, "usage_type": "argument"}, {"api_name": "random.choice", "line_number": 43, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 123, "usage_type": "call"}]} +{"seq_id": "2934228768", "text": "'''\n 2018/10/24 13:12\n 通过指定id值向小满对应URL发起get请求\n 获取相对应id值下商品类别的商品属性json列表并存入单独json文件\n 注：文件命名格式：proAttr + id值\n\n 2018/10/25 17:19\n 更新：\n 通过读取文件中所有商品类别id值定时向小满发起get请求\n 获取对应商品类别下的json属性列表并存入单独文件\n 请求时间间隔：60s 【 要不实现一个间隔大一点的随机数（嘻嘻嘻）】\n \n 2018/10/25遗留:4级目录id提取\n\n 2018/10/26 9:15\n 更新：\n 获取所有末级商品分类id\n idSet与请求需要一次完成(是否拆分)\n 实现定时读取所有末级商品分类id并向小满发送get请求，获取其对应的属性json列表\n 存入指定的文件中【请求时间间隔是否随机化？随机请求时间区间：30-299s】\n\n 2018/10/29 9:21\n 更新：去掉属性文件proAttr_id中时间及id标注写入\n 增加try...except...finally...模块捕捉异常\n 增加recordFile记录.txt文件\n 采用随机数作为sleep函数的参数，请求时间间隔随机化\n\n 2018/10/30 11:24\n 更新：为了方便下一步操作，单独存放的文件以.json格式存放\n \n'''\nimport requests\nimport os\nimport json\n\nimport time\nimport random\nimport traceback\n\nimport sys\nimport importlib\nimportlib.reload(sys)\n\nproId=''\nreadFilename = \"D:/LTest/Crawler/complete/getXiaomanLastCate&AttrList/simpleTest.json\"\n#readFilename = \"D:/LTest/Crawler/getXiaomanLastCate&AttrList/product_category.json\"\n\nheaders = {\"Cookie\":\"gr_user_id=8709560f-2f99-4d68-91c3-b7f0afb91a45; grwng_uid=7fbdf696-0d21-4099-9622-c152d00b095f; fingerprint=cb69e34450ea31f45f244c9605dfed1e; pskey=3a5aa6afb35b975361289cbdd3f0fac127828ab358e7617d5aaa841583951798; account=joe180%40qq.com; clientId=9134; userId=54908287; pskey_exist=1; set_id=670; Hm_lvt_925e072f764b8f193431ee7c9099e6f5=1540295446,1540344103; _t_language=zh-CN; Hm_lpvt_925e072f764b8f193431ee7c9099e6f5=1540347229; gr_session_id_ab214d89d8d4215b=a58ccc53-bd99-46e9-aea5-842864359a19; gr_cs1_a58ccc53-bd99-46e9-aea5-842864359a19=user_id%3A54908287; gr_session_id_ab214d89d8d4215b_a58ccc53-bd99-46e9-aea5-842864359a19=true\"}\n\nidFileName = \"D:/LTest/Crawler/complete/getXiaomanLastCate&AttrList/idSet.txt\"\nrecordFileName = \"D:/LTest/Crawler/complete/getXiaomanLastCate&AttrList/recordFile.txt\"\n\nwith open(readFilename,'r',encoding = \"UTF-8\") as file:\n data=json.load(file)\n\nfor level1 in data.get('product_category'):\n for level2 in level1.get('nodes'):\n level2_id=level2.get('id')\n if level2.get('nodes') is None:\n with open(idFileName,'a',encoding = 'utf-8') as file:\n file.write(level2_id + '\\n')\n continue\n for level3 in level2.get('nodes'):\n level3_id=level3.get('id')\n if level3.get('nodes') is None:\n with open(idFileName,'a',encoding = 'utf-8') as file:\n file.write(level3_id + '\\n')\n continue\n for level4 in level3.get('nodes'):\n level4_id=level4.get('id')\n if level4.get('nodes') is None:\n with open(idFileName,'a',encoding = 'utf-8') as file:\n file.write(level4_id + '\\n')\n\n#读取每行id值，定时发送get请求获取json属性列表并存入指定文件\nfor line in open(idFileName,encoding = \"utf-8\"):\n proId = line.strip('\\n')\n writeFileName = \"D:/LTest/Crawler/complete/getXiaomanLastCate&AttrList/dataFile/proAttr_\" + proId + \".json\"\n url = \"https://sales.xiaoman.cn/api/productRead/attrTpl?category_id=\"+proId\n try:\n get = requests.get(url,headers = headers)\n except:\n with open(recordFileName,'a',encoding = 'utf-8') as file:\n file.write(\"id:\" + proId+ \" errorTraceBack: \" + traceback.print_exc() + \"\\n\")\n finally:\n with open(recordFileName,'a',encoding = 'utf-8') as file:\n nowTimes = time.strftime('%Y-%m-%d %H:%M:%S',time.localtime(time.time())) #获取当前时间\n file.write(\"id:\" + proId + \" nowTimes: \" + nowTimes + \"\\n\")\n with open(writeFileName,'a',encoding = 'utf-8') as file:\n file.write(get.text)\n sleepTime = random.randint(0,2)*100+random.randint(3,9)*10+random.randint(0,9)\n time.sleep(sleepTime)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "repo_name": "1677462221LYT/MyWorkingCopy", "sub_path": "XiaoMan/Crawler/complete/getXiaomanLastCate&AttrList/getPara.py", "file_name": "getPara.py", "file_ext": "py", "file_size_in_byte": 4759, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "importlib.reload", "line_number": 42, "usage_type": "call"}, {"api_name": "json.load", "line_number": 54, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 81, "usage_type": "call"}, {"api_name": "traceback.print_exc", "line_number": 84, "usage_type": "call"}, {"api_name": "time.strftime", "line_number": 87, "usage_type": "call"}, {"api_name": "time.localtime", "line_number": 87, "usage_type": "call"}, {"api_name": "time.time", "line_number": 87, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 91, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 92, "usage_type": "call"}]} +{"seq_id": "5109615466", "text": "import numpy as np\nimport cv2\nimport matplotlib.pyplot as plt\n\ndef calibrate_camera(image_1, focal_length, cx, cy):\n\n points_2d = np.load(\"./data/vr2d.npy\")\n points_3d = np.load(\"./data/vr3d.npy\")\n\n points_2d = np.squeeze(points_2d)\n points_3d = np.squeeze(points_3d)\n\n camera_matrix = np.array([[focal_length, 0, cx],\n [0, focal_length, cy],\n [0, 0, 1]])\n\n _, camera_matrix, _, _, _ = cv2.calibrateCamera(\n [points_3d], [points_2d], image_1.shape[::-1], camera_matrix, None, flags=cv2.CALIB_USE_INTRINSIC_GUESS +\n cv2.CALIB_FIX_PRINCIPAL_POINT +\n cv2.CALIB_FIX_ASPECT_RATIO)\n\n return camera_matrix\n\n\ndef find_matches(image_1, image_2):\n\n #Flann/ORB etc. can be used here as well, just found the sift method accurate and fast enough for this case\n sift = cv2.SIFT_create()\n\n keypoints_1, descriptors_1 = sift.detectAndCompute(image_1, None)\n keypoints_2, descriptors_2 = sift.detectAndCompute(image_2, None)\n\n bf = cv2.BFMatcher()\n matches = bf.knnMatch(descriptors_1, descriptors_2, k=2)\n\n best_matches = []\n\n for (m, n) in matches:\n if m.distance < 0.9 * n.distance:\n best_matches.append(m)\n\n return keypoints_1, keypoints_2, best_matches\n\n\ndef find_translation_and_rotation(keypoints_1, keypoints_2, best_matches, camera_matrix):\n\n points_1 = np.float32([keypoints_1[m.queryIdx].pt for m in best_matches])\n points_2 = np.float32([keypoints_2[m.trainIdx].pt for m in best_matches])\n\n E, _ = cv2.findEssentialMat(\n points_1, points_2, camera_matrix, method=cv2.RANSAC, prob=0.999)\n\n _, rotation_estimation, translation_estimation, _ = cv2.recoverPose(\n E, points_1, points_2, camera_matrix)\n\n return translation_estimation, rotation_estimation\n\n\ndef camera_pose_estimator(image_1, image_2, focal_length, cx, cy):\n\n\n # calibrate the camera with f=100, cx=960, cy=540\n camera_matrix = calibrate_camera(image_1, focal_length, cx, cy)\n\n keypoints_1, keypoints_2, best_matches = find_matches(image_1, image_2)\n\n return find_translation_and_rotation(keypoints_1, keypoints_2, best_matches, camera_matrix)\n\nif __name__ == '__main__':\n\n # First on images 1 and 2\n img1 = cv2.imread(\"./res/img1.png\")\n img2 = cv2.imread(\"./res/img2.png\")\n img3 = cv2.imread(\"./res/img3.png\")\n\n img1 = cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY)\n img2 = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY)\n img3 = cv2.cvtColor(img3, cv2.COLOR_BGR2GRAY)\n\n focal_length, cx, cy = 100, 960, 540\n translation_12, rotation_12 = camera_pose_estimator(img1, img2, focal_length, cx, cy)\n translation_13, rotation_13 = camera_pose_estimator(img1, img3, focal_length, cx, cy)\n print(\"Translation vector img1 and img2: \\n\", translation_12)\n print(\"Rotation matrix img1 and img2: \\n\", rotation_12)\n print(\"Translation vector img1 and img3: \\n\", translation_13)\n print(\"Rotation matrix img1 and img3: \\n\", rotation_13)\n rotation_12 = rotation_12.transpose()\n pos_1 = np.matmul(-rotation_12,translation_12) \n rotation_13 = rotation_13.transpose()\n pos_2 = np.matmul(-rotation_13,translation_13)\n \n pos_0 = np.zeros_like(pos_1)\n plt.figure()\n plt.xlabel('X')\n plt.ylabel('Y')\n positions = np.array([pos_0, pos_1, pos_2])\n plt.plot(positions[:,0],positions[:,2])\n\n plt.savefig('./trajectory_plot.png')\n plt.show()\n\n\n", "repo_name": "mervansavga/CameraPoseEstimation", "sub_path": "pose_estimator.py", "file_name": "pose_estimator.py", "file_ext": "py", "file_size_in_byte": 3571, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "numpy.load", "line_number": 7, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 8, "usage_type": "call"}, {"api_name": "numpy.squeeze", "line_number": 10, "usage_type": "call"}, {"api_name": "numpy.squeeze", "line_number": 11, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 13, "usage_type": "call"}, {"api_name": "cv2.calibrateCamera", "line_number": 17, "usage_type": "call"}, {"api_name": "cv2.CALIB_USE_INTRINSIC_GUESS", "line_number": 18, "usage_type": "attribute"}, {"api_name": "cv2.CALIB_FIX_PRINCIPAL_POINT", "line_number": 19, "usage_type": "attribute"}, {"api_name": "cv2.CALIB_FIX_ASPECT_RATIO", "line_number": 20, "usage_type": "attribute"}, {"api_name": "cv2.SIFT_create", "line_number": 28, "usage_type": "call"}, {"api_name": "cv2.BFMatcher", "line_number": 33, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 47, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 48, "usage_type": "call"}, {"api_name": "cv2.findEssentialMat", "line_number": 50, "usage_type": "call"}, {"api_name": "cv2.RANSAC", "line_number": 51, "usage_type": "attribute"}, {"api_name": "cv2.recoverPose", "line_number": 53, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 72, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 73, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 74, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 76, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 76, "usage_type": "attribute"}, {"api_name": "cv2.cvtColor", "line_number": 77, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 77, "usage_type": "attribute"}, {"api_name": "cv2.cvtColor", "line_number": 78, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 78, "usage_type": "attribute"}, {"api_name": "numpy.matmul", "line_number": 88, "usage_type": "call"}, {"api_name": "numpy.matmul", "line_number": 90, "usage_type": "call"}, {"api_name": "numpy.zeros_like", "line_number": 92, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 93, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 93, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 94, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 94, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 95, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 95, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 96, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 97, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 97, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 99, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 99, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 100, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 100, "usage_type": "name"}]} +{"seq_id": "8278516236", "text": "import argparse\nimport os, time\nfrom collections import OrderedDict\nimport torch\nfrom torch.utils.data import Dataset, DataLoader, Subset\nimport numpy as np\nimport imutils\nimport operator\nimport os\nimport csv\nimport nibabel as nib\n\nclass img_dataset(Dataset):\n # Begin the initialization of the datasets. Creates dataset iterativey for each subject and\n # concatenates them together for both training and testing datasets (implements img_dataset class).\n def __init__(self, root_dir, view, key, data = 'healthy', size: int = 158, horizontal_flip: bool = False, \n vertical_flip: bool = False, rotation_angle: int = None):\n self.root_dir = root_dir\n self.view = view\n self.horizontal = horizontal_flip\n self.vertical = vertical_flip\n self.angle = rotation_angle\n self.size = size\n self.key = key\n self.data = data\n\n def __len__(self):\n if self.view == 'L':\n size = 110\n elif self.view == 'A':\n size = 158\n else:\n size = 126\n return size\n \n def extract_age(self):\n csv_path = '/neuro/labs/grantlab/research/MRI_processing/carlos.amador/anomaly_detection/extract_data.csv'\n id = 'Study ID'\n if self.data == 'vm':\n csv_path = '/neuro/labs/grantlab/research/MRI_processing/carlos.amador/anomaly_detection/ventriculomegaly-data.csv'\n id = 'subject'\n with open(csv_path, 'r') as csvfile:\n csvreader = csv.DictReader(csvfile)\n for row in csvreader:\n if row[id] == self.key:\n ga = float(row['GA'])\n ga = np.expand_dims(ga, axis = 0)\n ga = torch.tensor(ga).type(torch.float)\n return ga\n \n def rotation(self, x, alpha):\n y = x.astype(np.uint8)\n y_rot = imutils.rotate(y, angle = alpha)\n return y_rot.astype(np.float64)\n \n def resizing(self, img, n):\n target = (n, n)\n if (img.shape > np.array(target)).any():\n target_shape2 = np.min([target, img.shape],axis=0)\n start = tuple(map(lambda a, da: a//2-da//2, img.shape, target_shape2))\n end = tuple(map(operator.add, start, target_shape2))\n slices = tuple(map(slice, start, end))\n img = img[tuple(slices)]\n offset = tuple(map(lambda a, da: a//2-da//2, target, img.shape))\n slices = [slice(offset[dim], offset[dim] + img.shape[dim]) for dim in range(img.ndim)]\n result = np.zeros(target)\n result[tuple(slices)] = img\n return result\n\n def normalize_95(self, x):\n p98 = np.percentile(x, 98)\n num = x-np.min(x)\n den = p98-np.min(x)\n out = np.zeros((x.shape[0], x.shape[1]))\n\n x = np.divide(num, den, out=out, where=den!=0)\n return x.clip(0, 1)\n\n def __getitem__(self, idx):\n raw = nib.load(self.root_dir).get_fdata()\n ga = self.extract_age()\n\n if self.view == 'L':\n n_img = self.resizing(raw[idx,:,:], self.size) \n elif self.view == 'A':\n n_img = self.resizing(raw[:,idx,:], self.size)\n else:\n n_img = self.resizing(raw[:,:,idx], self.size)\n \n n_img = self.normalize_95(n_img)\n\n if self.horizontal == True:\n n_img = np.flip(n_img,axis=0)\n\n if self.vertical == True:\n n_img = np.flip(n_img, axis=1)\n\n if self.angle is not None:\n n_img = self.rotation(n_img, self.angle)\n\n n_img = np.expand_dims(n_img,axis=0)\n img_torch = torch.from_numpy(n_img.copy()).type(torch.float)\n\n dict = {'image': img_torch, 'ga': ga}\n\n return dict\n\ndef center_slices(view):\n if view == 'L':\n ids = np.arange(start=40,stop=70)\n elif view == 'A':\n ids = np.arange(start=64,stop=94)\n else:\n ids = np.arange(start=48,stop=78)\n return ids\n\ndef data_augmentation(base_set, path, view, key, h, ids):\n transformations = {1: (True, None),\n 2: (False, -10), 3: (True, -10),\n 4: (False, -5), 5: (True, -5),\n 6: (False, 5), 7: (True, 5),\n 8: (False, 10), 9: (True, 10)}\n \n for x, specs in transformations.items():\n aug = img_dataset(path, view, key, size = h, horizontal_flip = specs[0], rotation_angle = specs[1])\n aug = Subset(aug,ids)\n base_set = torch.utils.data.ConcatDataset([base_set, aug])\n return base_set\n\ndef loader(source_path, view, batch_size, h):\n train_id = os.listdir(source_path+'train/')\n test_id = os.listdir(source_path+'test/')\n\n ids = center_slices(view)\n\n train_set = img_dataset(source_path+'train/'+train_id[0], view, train_id[0][:-4], size = h)\n train_set = Subset(train_set,ids)\n # train_set = data_augmentation(train_set, source_path+'train/'+train_id[0], view, \n # train_id[0][:-4], h, ids)\n\n test_set = img_dataset(source_path+'test/'+test_id[0],view, test_id[0][:-4], size = h)\n test_set = Subset(test_set,ids)\n\n for idx,image in enumerate(train_id):\n if idx != 0:\n train_path = source_path + 'train/' + image\n tr_set = img_dataset(train_path, view, image[:-4], size = h)\n tr_set = Subset(tr_set,ids)\n # tr_set = data_augmentation(tr_set, train_path, view, image[:-4], h, ids)\n train_set = torch.utils.data.ConcatDataset([train_set, tr_set])\n\n for idx,image in enumerate(test_id):\n if idx != 0:\n test_path = source_path + 'test/' + image\n ts_set = img_dataset(test_path,view, image[:-4], size = h)\n ts_set = Subset(ts_set,ids)\n test_set = torch.utils.data.ConcatDataset([test_set, ts_set])\n\n# Dataloaders generated from datasets \n train_final = DataLoader(train_set, shuffle=True, batch_size=batch_size,num_workers=12)\n val_final = DataLoader(test_set, shuffle=True, batch_size=batch_size,num_workers=12)\n return train_final, val_final\n\ndef val_loader(val_path, view, key, data='healthy'):\n\n ids = int(np.mean(center_slices(view)))\n val_set = img_dataset(val_path, view, key, data=data)\n\n val_set = Subset(val_set,ids)\n\n loader = DataLoader(val_set, batch_size=1)\n\n return loader\n\ndef load_model(model_path, base, ga_method, w, h, z_dim, model='default'):\n\n if base == 'ga_VAE':\n from models.ga_vae import Encoder, Decoder\n encoder = Encoder(w,h,z_dim*2, method = ga_method, model = model)\n else:\n from models.vae import Encoder, Decoder\n encoder = Encoder(w,h,z_dim*2, model=model)\n decoder = Decoder(w,h,z_dim)\n\n cpe = torch.load(model_path+'encoder_best.pth', map_location=torch.device('cpu'))\n cpd = torch.load(model_path+'decoder_best.pth', map_location=torch.device('cpu'))\n\n cpe_new = OrderedDict()\n cpd_new = OrderedDict()\n\n for k, v in cpe['encoder'].items():\n name = k[7:]\n cpe_new[name] = v\n\n for k, v in cpd['decoder'].items():\n name = k[7:]\n cpd_new[name] = v\n\n encoder.load_state_dict(cpe_new)\n decoder.load_state_dict(cpd_new)\n return encoder, decoder\n\ndef path_generator(args):\n # Define paths for obtaining dataset and saving models and results.\n source_path = args.path + 'healthy_dataset/'\n\n date = time.strftime('%Y%m%d', time.localtime(time.time()))\n \n folder_name = \"/{0}_{1}_AE_{2}_b{3}_{4}\".format(\n args.view, args.type, args.loss, args.batch,date)\n folder_pretrained = \"/{0}_{1}_AE_{2}_b64_{4}\".format(\n args.view, args.type, args.loss, args.batch,args.date)\n\n if args.model == 'ga_VAE':\n folder_name += 'ga_VAE'\n folder_pretrained += 'ga_VAE'\n\n tensor_path = args.path + 'Results' + folder_name + '/history.txt'\n model_path = args.path + 'Results' + folder_name + '/Saved_models/'\n image_path = args.path + 'Results' + folder_name + '/Progress/'\n pre_path = args.path + 'Results' + folder_pretrained + '/Saved_models/'\n\n if not os.path.exists(args.path + 'Results' + folder_name):\n os.mkdir(args.path + 'Results' + folder_name)\n os.mkdir(model_path)\n os.mkdir(image_path)\n\n if (args.pre) and (not os.path.exists(pre_path)):\n raise NameError(\"model_path for pretraining is not correct.\")\n\n print('Directories and paths are correctly initialized.')\n print('-'*25)\n return source_path, model_path, tensor_path, image_path, pre_path\n\ndef settings_parser():\n parser = argparse.ArgumentParser()\n \n parser.add_argument('--task',\n dest='type',\n choices=['Train', 'Validate'],\n required=False,\n default='Train',\n help='''\n Task to be performed.''') \n parser.add_argument('--model',\n dest='model',\n choices=['default', 'ga_VAE'],\n default = 'default',\n required=False,\n help='''\n Type of model to train. Available options:\n \"defalut\" Default VAE using convolution blocks\n \"ga_VAE: VAE which includes GA as input''') \n parser.add_argument('--model_type',\n dest='type',\n choices=['default', 'bVAE'],\n required=True,\n help='''\n Type of model to train. Available options:\n \"defalut\" Default VAE using convolution blocks\n \"bVAE: VAE with disentanglement''') \n parser.add_argument('--model_view',\n dest='view',\n choices=['L', 'A', 'S'],\n required=True,\n help='''\n The view of the image input for the model. Options:\n \"L\" Left view\n \"A\" Axial view\n \"S\" Sagittal view''') \n parser.add_argument('--ga_method',\n dest='ga_method',\n choices=['multiplication', 'concat'],\n default = 'concat',\n required=False,\n help='''\n Method to implement GA. Available options:\n \"multiplication\", \"concat\"''') \n parser.add_argument('--gpu',\n dest='gpu',\n choices=['0', '1', '2'],\n default='0',\n required=False,\n help='''\n The GPU that will be used for training. Terminals have the following options:\n Hanyang: 0, 1\n Busan: 0, 1, 2\n Sejong 0, 1, 2\n Songpa 0, 1\n Gangnam 0, 1\n ''')\n parser.add_argument('--epochs',\n dest='epochs',\n type=int,\n default=50,\n choices=range(1, 15000),\n required=False,\n help='''\n Number of epochs for training.\n ''') \n parser.add_argument('--loss',\n dest='loss',\n default='SSIM',\n choices=['L2', 'L1', 'SSIM', 'MS_SSIM', \n 'Mixed1', 'Mixed2', 'Mixed3', 'Mixed4',\n 'Perceptual'],\n required=False,\n help='''\n Loss function for VAE:\n L2 = Mean square error.\n SSIM = Structural similarity index.\n ''')\n parser.add_argument('--batch',\n dest='batch',\n type=int,\n default=1,\n choices=range(1, 512),\n required=False,\n help='''\n Number of batch size.\n ''') \n parser.add_argument('--beta',\n dest='beta',\n type=float,\n default=None,\n required=False,\n help='''\n The value of the beta parameter.\n ''')\n parser.add_argument('--model_date',\n dest='date',\n default='20231211',\n required=False,\n help='''\n Date of model training.\n ''')\n parser.add_argument('--anomaly',\n dest='anomaly',\n default='healthy',\n choices = ['healthy', 'vm'],\n required=False,\n help='''\n Extra model name info.\n ''')\n parser.add_argument('--extra',\n dest='extra',\n default=False,\n required=False,\n help='''\n Extra model name info.\n ''')\n parser.add_argument('--z_dim',\n dest='z',\n type=int,\n default=512,\n required=False,\n help='''\n z dimension.\n ''')\n parser.add_argument('--pretrained',\n dest='pre',\n type=bool,\n default=True,\n required=False,\n help='''\n If VAE model is pre-trained.\n ''')\n parser.add_argument('--n',\n dest='n',\n type=int,\n default=158,\n required=False,\n help='''\n size of images from pre-processing.\n ''')\n parser.add_argument('--path',\n dest = 'path',\n type = str,\n default = '/neuro/labs/grantlab/research/MRI_processing/carlos.amador/anomaly_detection/',\n required = False,\n help='''\n Path to the project directory\n ''')\n\n return parser", "repo_name": "simonamador/Anomaly-Detection", "sub_path": "utils/config.py", "file_name": "config.py", "file_ext": "py", "file_size_in_byte": 12605, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "37", "api": [{"api_name": "torch.utils.data.Dataset", "line_number": 13, "usage_type": "name"}, {"api_name": "csv.DictReader", "line_number": 43, "usage_type": "call"}, {"api_name": "numpy.expand_dims", "line_number": 47, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 48, "usage_type": "call"}, {"api_name": "torch.float", "line_number": 48, "usage_type": "attribute"}, {"api_name": "numpy.uint8", "line_number": 52, "usage_type": "attribute"}, {"api_name": "imutils.rotate", "line_number": 53, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 54, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 58, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 59, "usage_type": "call"}, {"api_name": "operator.add", "line_number": 61, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 66, "usage_type": "call"}, {"api_name": "numpy.percentile", "line_number": 71, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 72, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 73, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 74, "usage_type": "call"}, {"api_name": "numpy.divide", "line_number": 76, "usage_type": "call"}, {"api_name": "nibabel.load", "line_number": 80, "usage_type": "call"}, {"api_name": "numpy.flip", "line_number": 93, "usage_type": "call"}, {"api_name": "numpy.flip", "line_number": 96, "usage_type": "call"}, {"api_name": "numpy.expand_dims", "line_number": 101, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 102, "usage_type": "call"}, {"api_name": "torch.float", "line_number": 102, "usage_type": "attribute"}, {"api_name": "numpy.arange", "line_number": 110, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 112, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 114, "usage_type": "call"}, {"api_name": "torch.utils.data.Subset", "line_number": 126, "usage_type": "call"}, {"api_name": "torch.utils.data.ConcatDataset", "line_number": 127, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 127, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 131, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 132, "usage_type": "call"}, {"api_name": "torch.utils.data.Subset", "line_number": 137, "usage_type": "call"}, {"api_name": "torch.utils.data.Subset", "line_number": 142, "usage_type": "call"}, {"api_name": "torch.utils.data.Subset", "line_number": 148, "usage_type": "call"}, {"api_name": "torch.utils.data.ConcatDataset", "line_number": 150, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 150, "usage_type": "attribute"}, {"api_name": "torch.utils.data.Subset", "line_number": 156, "usage_type": "call"}, {"api_name": "torch.utils.data.ConcatDataset", "line_number": 157, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 157, "usage_type": "attribute"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 160, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 161, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 166, "usage_type": "call"}, {"api_name": "torch.utils.data.Subset", "line_number": 169, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 171, "usage_type": "call"}, {"api_name": "models.ga_vae.Encoder", "line_number": 179, "usage_type": "call"}, {"api_name": "models.vae.Encoder", "line_number": 182, "usage_type": "call"}, {"api_name": "models.vae.Decoder", "line_number": 183, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 185, "usage_type": "call"}, {"api_name": "torch.device", "line_number": 185, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 186, "usage_type": "call"}, {"api_name": "torch.device", "line_number": 186, "usage_type": "call"}, {"api_name": "collections.OrderedDict", "line_number": 188, "usage_type": "call"}, {"api_name": "collections.OrderedDict", "line_number": 189, "usage_type": "call"}, {"api_name": "time.strftime", "line_number": 207, "usage_type": "call"}, {"api_name": "time.localtime", "line_number": 207, "usage_type": "call"}, {"api_name": "time.time", "line_number": 207, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 223, "usage_type": "call"}, {"api_name": "os.path", "line_number": 223, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 224, "usage_type": "call"}, {"api_name": "os.mkdir", "line_number": 225, "usage_type": "call"}, {"api_name": "os.mkdir", "line_number": 226, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 228, "usage_type": "call"}, {"api_name": "os.path", "line_number": 228, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentParser", "line_number": 236, "usage_type": "call"}]} +{"seq_id": "24544634190", "text": "\"\"\"\nFetches the geographical base for demographical analyses.\nAll data comes from IBGE\n\"\"\"\n\nimport lzma\nimport os\nimport tarfile\nimport tempfile\nfrom multiprocessing import Pool\n\nimport colorcet\nimport dask\nimport datashader as ds\nimport geobr\nimport geopandas as gpd\nimport georasters as gr\nimport numpy as np\nimport wget\nfrom shapely import geometry\n\nLEVELS = {\n \"Country\": geobr.read_country,\n \"Region\": geobr.read_region,\n \"State\": geobr.read_state,\n \"Meso region\": geobr.read_meso_region,\n \"Micro region\": geobr.read_micro_region,\n \"Immediate region\": geobr.read_immediate_region,\n \"Census weighting area\": geobr.read_weighting_area,\n \"Census tract\": geobr.read_census_tract,\n \"Municipality\": geobr.read_municipality,\n \"Municipality seats\": geobr.read_municipal_seat,\n \"Metropolitan areas\": geobr.read_metro_area,\n \"Urban footprints\": geobr.read_urban_area,\n \"Brazil's Legal Amazon\": geobr.read_amazon,\n \"Biomes\": geobr.read_biomes,\n \"Environmental Conservation Units\": geobr.read_conservation_units,\n \"Disaster risk areas\": geobr.read_disaster_risk_area,\n \"Indigenous lands\": geobr.read_indigenous_land,\n \"Semi Arid region\": geobr.read_semiarid,\n \"Health facilities\": geobr.read_health_facilities,\n \"Health regions\": geobr.read_health_region,\n \"Neighborhood limits\": geobr.read_neighborhood,\n}\n\n\nclass GeoBase:\n \"\"\"\n Parameterized geographical base\n \"\"\"\n\n def __init__(self, level, reset=False):\n \"\"\"\n Initialize geographical base at the specified level\n :param level: One of the valid levels specified in `pysus.demography.geobase.LEVELS`\n :param reset: reset the local directory cache. Set to True if you you are changing the parameters of the map.\n default: False\n \"\"\"\n if reset:\n if os.path.exists(f\"{level}_map.parquet\"):\n os.remove(f\"{level}_map.parquet\")\n if os.path.exists(f\"{level}_pop.parquet\"):\n os.remove(f\"{level}_pop.parquet\")\n if os.path.exists(f\"{level}_raster.parquet\"):\n os.remove(f\"{level}_raster.parquet\")\n else:\n if os.path.exists(f\"{level}_map.parquet\"):\n print(\n \"You have cached data for this level. Please set `reset=True` if you want to download fresh data\"\n )\n try:\n assert level in LEVELS\n except AssertionError:\n print(f\"Please select one of these levels: {', '.join(LEVELS.keys())}\")\n self.level = level\n self.mapdf = None\n self.pop = None\n self.pop_raster = None\n\n def __str__(self):\n return f\"{self.level} level Geobase\"\n\n def _persist(self, what):\n if what == \"map\":\n self.mapdf.to_parquet(f\"{self.level}_map.parquet\")\n elif what == \"pop\":\n self.pop.to_parquet(f\"{self.level}_pop.parquet\")\n elif what == \"raster\":\n self.raster.to_parquet(f\"{self.level}_raster.parquet\")\n\n def help_fetch_map(self):\n return help(LEVELS[self.level])\n\n def map(self, *args, **kwargs):\n \"\"\"\n Fetches map of `self.level` given parameters\n :param args: positional parameters for geobr map reading function\n :param kwargs: keyword parameters for geobr map reading function\n :return: GeoDataFrame\n \"\"\"\n if os.path.exists(f\"{self.level}_map.parquet\"):\n self.mapdf = gpd.read_parquet(f\"{self.level}_map.parquet\")\n return self.mapdf\n print(\"Dowloading the Map...\")\n if self.mapdf is None:\n self.mapdf = LEVELS[self.level](*args, **kwargs)\n self._persist(\"map\")\n return self.mapdf\n\n def plot_pop(self, **kwargs):\n \"\"\"\n Plots a chropletic representation of the population\n :param kwargs: Additional parameters passed to geopandas plot command\n \"\"\"\n self.mapdf.plot(column=\"population\", **kwargs)\n\n def demographics(self):\n \"\"\"\n Adds population data to geoographical base\n :return:\n \"\"\"\n if \"population\" in self.mapdf.columns:\n return\n print(\"Fetching population data...\")\n bbox = self.mapdf.to_crs(\"EPSG:4326\").total_bounds\n # raster = fetch_gpw4_raster(bbox)\n self.pop_raster = raster = get_full_pop_raster()\n self.mapdf[\"population\"] = [\n raster.clip(geom)[0].sum() for geom in self.mapdf.geometry\n ]\n self._persist(\"map\")\n\n def generate_populations(self, scale=0.05):\n \"\"\"\n Generate a synthetic population of size scale*population size for each polygon in self.mapdf\n :param scale:\n \"\"\"\n if os.path.exists(f\"{self.level}_pop.parquet\"):\n self.pop = gpd.read_parquet(f\"{self.level}_pop.parquet\")\n return\n if \"population\" not in self.mapdf.columns:\n self.demographics()\n for row in self.mapdf.itertuples():\n people = sample_random_people(int(row.population * scale), row.geometry)\n sex = np.random.randint(0, 2, size=len(people))\n age = np.random.randint(0, 100, size=len(people))\n print(len(people), people[0])\n self.pop = gpd.GeoDataFrame({\"sex\": sex, \"age\": age, \"geometry\": people})\n self.pop[\"longitude\"] = [pt.x for pt in self.pop.geometry]\n self.pop[\"latitude\"] = [pt.y for pt in self.pop.geometry]\n self._persist(\"pop\")\n\n def plot_synthetic_pop(self):\n canvas = ds.Canvas(plot_width=800, plot_height=600)\n agg = canvas.points(self.pop, x=\"longitude\", y=\"latitude\")\n return ds.tf.shade(agg, cmap=colorcet.fire, how=\"log\")\n\n\ndef contains(args):\n polygon, point = args\n pt = geometry.Point(point)\n return pt, polygon.contains(pt)\n\n\ndef sample_random_people(n, polygon, overestimate=1.5):\n print(f\"Synthesizing {n} individuals\")\n min_x, min_y, max_x, max_y = polygon.bounds\n ratio = polygon.area / polygon.envelope.area\n samples = np.random.uniform(\n (min_x, min_y), (max_x, max_y), (int((n / ratio) * overestimate), 2)\n )[:n, :]\n # multipoint = geometry.MultiPoint(samples)\n # multipoint = multipoint.intersection(polygon)\n po = Pool()\n res = po.map(contains, ((polygon, p) for p in samples))\n pts = [p for p, c in res if c] # List of inscribed points\n po.terminate()\n po.join()\n\n return pts\n\n\ndef get_population(geometry, raster):\n return raster.clip(geometry)[0].sum()\n\n\ndef get_full_pop_raster(path=\".\"):\n url = \"https://www.dropbox.com/s/l9iphmawfjzt4lf/brazil_pop.tif.tar.xz?dl=1\"\n fn = os.path.join(path, \"brazil_pop.tif.tar.xz\")\n wget.download(url=url, out=path)\n fn = os.path.join(path, \"brazil_pop.tif.tar.xz\")\n with lzma.open(\"brazil_pop.tif.tar.xz\") as f:\n with tarfile.open(fileobj=f) as tar:\n tar.extractall()\n # with open('brazil_pop.tif', 'wb') as brr:\n # brr.write(tar.extractall(path=path))\n os.unlink(\"brazil_pop.tif.tar.xz\")\n raster = gr.from_file(\"brazil_pop.tif.tif\")\n os.unlink(\"brazil_pop.tif.tif\")\n\n return raster\n", "repo_name": "Marcos358/Montagem", "sub_path": "pysus/demography/geobase.py", "file_name": "geobase.py", "file_ext": "py", "file_size_in_byte": 7133, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "geobr.read_country", "line_number": 23, "usage_type": "attribute"}, {"api_name": "geobr.read_region", "line_number": 24, "usage_type": "attribute"}, {"api_name": "geobr.read_state", "line_number": 25, "usage_type": "attribute"}, {"api_name": "geobr.read_meso_region", "line_number": 26, "usage_type": "attribute"}, {"api_name": "geobr.read_micro_region", "line_number": 27, "usage_type": "attribute"}, {"api_name": "geobr.read_immediate_region", "line_number": 28, "usage_type": "attribute"}, {"api_name": "geobr.read_weighting_area", "line_number": 29, "usage_type": "attribute"}, {"api_name": "geobr.read_census_tract", "line_number": 30, "usage_type": "attribute"}, {"api_name": "geobr.read_municipality", "line_number": 31, "usage_type": "attribute"}, {"api_name": "geobr.read_municipal_seat", "line_number": 32, "usage_type": "attribute"}, {"api_name": "geobr.read_metro_area", "line_number": 33, "usage_type": "attribute"}, {"api_name": "geobr.read_urban_area", "line_number": 34, "usage_type": "attribute"}, {"api_name": "geobr.read_amazon", "line_number": 35, "usage_type": "attribute"}, {"api_name": "geobr.read_biomes", "line_number": 36, "usage_type": "attribute"}, {"api_name": "geobr.read_conservation_units", "line_number": 37, "usage_type": "attribute"}, {"api_name": "geobr.read_disaster_risk_area", "line_number": 38, "usage_type": "attribute"}, {"api_name": "geobr.read_indigenous_land", "line_number": 39, "usage_type": "attribute"}, {"api_name": "geobr.read_semiarid", "line_number": 40, "usage_type": "attribute"}, {"api_name": "geobr.read_health_facilities", "line_number": 41, "usage_type": "attribute"}, {"api_name": "geobr.read_health_region", "line_number": 42, "usage_type": "attribute"}, {"api_name": "geobr.read_neighborhood", "line_number": 43, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 60, "usage_type": "call"}, {"api_name": "os.path", "line_number": 60, "usage_type": "attribute"}, {"api_name": "os.remove", "line_number": 61, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 62, "usage_type": "call"}, {"api_name": "os.path", "line_number": 62, "usage_type": "attribute"}, {"api_name": "os.remove", "line_number": 63, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 64, "usage_type": "call"}, {"api_name": "os.path", "line_number": 64, "usage_type": "attribute"}, {"api_name": "os.remove", "line_number": 65, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 67, "usage_type": "call"}, {"api_name": "os.path", "line_number": 67, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 101, "usage_type": "call"}, {"api_name": "os.path", "line_number": 101, "usage_type": "attribute"}, {"api_name": "geopandas.read_parquet", "line_number": 102, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 138, "usage_type": "call"}, {"api_name": "os.path", "line_number": 138, "usage_type": "attribute"}, {"api_name": "geopandas.read_parquet", "line_number": 139, "usage_type": "call"}, {"api_name": "numpy.random.randint", "line_number": 145, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 145, "usage_type": "attribute"}, {"api_name": "numpy.random.randint", "line_number": 146, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 146, "usage_type": "attribute"}, {"api_name": "geopandas.GeoDataFrame", "line_number": 148, "usage_type": "call"}, {"api_name": "datashader.Canvas", "line_number": 154, "usage_type": "call"}, {"api_name": "datashader.tf.shade", "line_number": 156, "usage_type": "call"}, {"api_name": "datashader.tf", "line_number": 156, "usage_type": "attribute"}, {"api_name": "colorcet.fire", "line_number": 156, "usage_type": "attribute"}, {"api_name": "shapely.geometry.Point", "line_number": 161, "usage_type": "call"}, {"api_name": "shapely.geometry", "line_number": 161, "usage_type": "name"}, {"api_name": "numpy.random.uniform", "line_number": 169, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 169, "usage_type": "attribute"}, {"api_name": "multiprocessing.Pool", "line_number": 174, "usage_type": "call"}, {"api_name": "shapely.geometry", "line_number": 184, "usage_type": "argument"}, {"api_name": "os.path.join", "line_number": 189, "usage_type": "call"}, {"api_name": "os.path", "line_number": 189, "usage_type": "attribute"}, {"api_name": "wget.download", "line_number": 190, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 191, "usage_type": "call"}, {"api_name": "os.path", "line_number": 191, "usage_type": "attribute"}, {"api_name": "lzma.open", "line_number": 192, "usage_type": "call"}, {"api_name": "tarfile.open", "line_number": 193, "usage_type": "call"}, {"api_name": "os.unlink", "line_number": 197, "usage_type": "call"}, {"api_name": "georasters.from_file", "line_number": 198, "usage_type": "call"}, {"api_name": "os.unlink", "line_number": 199, "usage_type": "call"}]} +{"seq_id": "27215435598", "text": "\"\"\"login_proj URL Configuration\n\nThe `urlpatterns` list routes URLs to views. For more information please see:\n https://docs.djangoproject.com/en/2.2/topics/http/urls/\nExamples:\nFunction views\n 1. Add an import: from my_app import views\n 2. Add a URL to urlpatterns: path('', views.home, name='home')\nClass-based views\n 1. Add an import: from other_app.views import Home\n 2. Add a URL to urlpatterns: path('', Home.as_view(), name='home')\nIncluding another URLconf\n 1. Import the include() function: from django.urls import include, path\n 2. Add a URL to urlpatterns: path('blog/', include('blog.urls'))\n\"\"\"\nfrom django.contrib import admin\nfrom django.urls import path, include\nfrom . import views\n\nurlpatterns = [\n path('', views.default_view, name='login_main'),\n path('register_user', views.register_user),\n path('login_user', views.login_user),\n path('logout_user', views.logout_user),\n path('success', views.success_view),\n path('books', views.books_reviews_view, name='books_main'),\n path('books/', views.books_reviews_view, name='books_main'),\n path('books/all', views.all_books_view, name='books_all'),\n path('books/', views.book_by_id, name='book_by_id'),\n path('books/add_review', views.add_review, name='add_review'),\n path('books/delete_review', views.delete_review, name='delete_review'),\n path('books/get_reviews', views.get_reviews, name='get_reviews'),\n]\n", "repo_name": "twtseng/Dojo_Assignments", "sub_path": "Python/django/django_full_stack/dojoreads_proj/dojoreads_app/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 1447, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "django.urls.path", "line_number": 21, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 22, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 23, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 24, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 25, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 26, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 27, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 28, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 29, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 30, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 31, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 32, "usage_type": "call"}]} +{"seq_id": "6372920173", "text": "\n# coding: utf-8\n\n# ### Libraries Dependency\n\n# In[1]:\n\n\nfrom pyspark import SparkContext, SparkConf\nfrom pyspark.sql import SparkSession, HiveContext\n\n\n# ### Creating Spark context\n\n# In[2]:\n\n\nSparkContext.setSystemProperty(\"spark.executor.memory\", \"4g\")\nsc = SparkContext('local[1]')\nhc = HiveContext(sc)\n\n\n# In[3]:\n\n\nsc._conf.getAll()\n\n\n# ### Read a table from Hive\n\n# In[4]:\n\n\nhc.sql('use project')\ndf = hc.sql('select * from tweet_orc where line_number is not null')\ndf.show(10)\n\n\n# In[5]:\n\n\ndf.printSchema()\n\n\n# In[6]:\n\n\ndf.select(\"line_number\").show(10)\n\n\n# In[7]:\n\n\ntype(df)\n\n\n# ### drop nan\n\n# In[8]:\n\n\n#df = df.dropna()\ndf.count()\n\n\n# ### split dataset\n\n# In[9]:\n\n\n(train_set, val_set, test_set) = df.randomSplit([0.98, 0.01, 0.01], seed = 2000)\n\n\n# ### Logistic Regression with TFIDF\n\n# In[10]:\n\n\nfrom pyspark.ml import Pipeline\nfrom pyspark.ml.feature import HashingTF, IDF, Tokenizer, CountVectorizer\nfrom pyspark.ml.feature import StringIndexer\nfrom pyspark.ml.classification import LogisticRegression\nfrom pyspark.ml.evaluation import BinaryClassificationEvaluator\n\n\n# In[13]:\n\n\ntokenizer = Tokenizer(inputCol=\"text\", outputCol=\"words\")\nhashtf = HashingTF(numFeatures=2**16, inputCol=\"words\", outputCol='tf')\n#minDocFreq: remove sparse terms\nidf = IDF(inputCol='tf', outputCol=\"features\", minDocFreq=5) \nlabel_stringIdx = StringIndexer(inputCol = \"label\", outputCol = \"class\")\npipeline = Pipeline(stages=[tokenizer, hashtf, idf, label_stringIdx])\n\npipelineFit = pipeline.fit(train_set)\ntrain_df = pipelineFit.transform(train_set)\nval_df = pipelineFit.transform(val_set)\ntest_df = pipelineFit.transform(test_set)\ntrain_df.show(5)\n\n\n# In[15]:\n\n\nlr = LogisticRegression(maxIter=100)\nlrModel = lr.fit(train_df)\n\npredictions = lrModel.transform(val_df)\n# evaluator = BinaryClassificationEvaluator(rawPredictionCol=\"rawPrediction\")\n# evaluator.evaluate(predictions)\naccuracy = predictions.filter(predictions.label == predictions.prediction).count() / float(val_set.count())\nprint(\"valication accuracy: \", accuracy)\npredictions_test = lrModel.transform(test_df)\naccuracy_test = predictions_test.filter(predictions_test.label == predictions_test.prediction).count() / float(test_set.count())\nprint(\"test accuracy: \", accuracy_test)\n\n\n# In[17]:\n\n\n#evaluator.getMetricName()\n\n\n# ### Logistic Regression with CountVectorizer and IDF\n\n# In[11]:\n\n\nfrom pyspark.ml.feature import CountVectorizer\n\n\n# In[12]:\n\n\ntokenizer = Tokenizer(inputCol=\"text\", outputCol=\"words\")\ncv = CountVectorizer(vocabSize=2**16, inputCol=\"words\", outputCol='cv')\n#minDocFreq: remove sparse terms\nidf = IDF(inputCol='cv', outputCol=\"features\", minDocFreq=5) \nlabel_stringIdx = StringIndexer(inputCol = \"label\", outputCol = \"class\")\nlr = LogisticRegression(maxIter=100)\npipeline = Pipeline(stages=[tokenizer, cv, idf, label_stringIdx, lr])\npipelineFit = pipeline.fit(train_set)\n\n#train_df = pipelineFit.transform(train_set)\n#val_df = pipelineFit.transform(val_set)\n#test_df = pipelineFit.transform(test_set)\n#train_df.show(5)\n\n\n# In[16]:\n\n\npredictions_val = pipelineFit.transform(val_set)\naccuracy = predictions_val.filter(predictions_val.label == predictions_val.prediction).count() / float(val_set.count())\nprint(\"Validation Accuracy Score: {0:.4f}\".format(accuracy))\n#roc_auc = evaluator.evaluate(predictions)\n#print \"ROC-AUC: {0:.4f}\".format(roc_auc)\n\npredictions_t = pipelineFit.transform(test_set)\naccuracy_test = predictions_t.filter(predictions_t.label == predictions_t.prediction).count() / float(test_set.count())\nprint(\"Test Accuracy Score: {0:.4f}\".format(accuracy_test))\n\n\n# ### Logisitic Regression with N-gram\n\n# In[18]:\n\n\nfrom pyspark.ml.feature import NGram\n\n\n# In[31]:\n\n\ntokenizer = Tokenizer(inputCol=\"text\", outputCol=\"words\")\nngram = NGram(n=1, inputCol=\"words\", outputCol=\"n_gram\")\nhashtf = HashingTF(numFeatures=2**16,inputCol=\"n_gram\", outputCol=\"tf\")\nidf = IDF(inputCol='tf', outputCol=\"features\", minDocFreq=5) \nlabel_stringIdx = StringIndexer(inputCol = \"label\", outputCol = \"class\")\nlr = LogisticRegression(maxIter=100)\npipeline = Pipeline(stages=[tokenizer, ngram, hashtf, idf, label_stringIdx, lr])\npipelineFit = pipeline.fit(train_set)\n\n\n# In[34]:\n\n\npredictions_val = pipelineFit.transform(val_set)\naccuracy = predictions_val.filter(predictions_val.label == predictions_val.prediction).count() / float(val_set.count())\nprint(\"Validation Accuracy Score: {0:.4f}\".format(accuracy))\n\npredictions_t = pipelineFit.transform(test_set)\naccuracy_test = predictions_t.filter(predictions_t.label == predictions_t.prediction).count() / float(test_set.count())\nprint(\"Test Accuracy Score: {0:.4f}\".format(accuracy_test))\n\n", "repo_name": "steven-cheng-com/twitter_sentiment_analysis_for_us_airlines", "sub_path": "Twitter-Spark/code/pyspark-sentiment-analysis-with-hive.py", "file_name": "pyspark-sentiment-analysis-with-hive.py", "file_ext": "py", "file_size_in_byte": 4617, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "pyspark.SparkContext.setSystemProperty", "line_number": 18, "usage_type": "call"}, {"api_name": "pyspark.SparkContext", "line_number": 18, "usage_type": "name"}, {"api_name": "pyspark.SparkContext", "line_number": 19, "usage_type": "call"}, {"api_name": "pyspark.sql.HiveContext", "line_number": 20, "usage_type": "call"}, {"api_name": "pyspark.ml.feature.Tokenizer", "line_number": 89, "usage_type": "call"}, {"api_name": "pyspark.ml.feature.HashingTF", "line_number": 90, "usage_type": "call"}, {"api_name": "pyspark.ml.feature.IDF", "line_number": 92, "usage_type": "call"}, {"api_name": "pyspark.ml.feature.StringIndexer", "line_number": 93, "usage_type": "call"}, {"api_name": "pyspark.ml.Pipeline", "line_number": 94, "usage_type": "call"}, {"api_name": "pyspark.ml.classification.LogisticRegression", "line_number": 106, "usage_type": "call"}, {"api_name": "pyspark.ml.feature.Tokenizer", "line_number": 136, "usage_type": "call"}, {"api_name": "pyspark.ml.feature.CountVectorizer", "line_number": 137, "usage_type": "call"}, {"api_name": "pyspark.ml.feature.IDF", "line_number": 139, "usage_type": "call"}, {"api_name": "pyspark.ml.feature.StringIndexer", "line_number": 140, "usage_type": "call"}, {"api_name": "pyspark.ml.classification.LogisticRegression", "line_number": 141, "usage_type": "call"}, {"api_name": "pyspark.ml.Pipeline", "line_number": 142, "usage_type": "call"}, {"api_name": "pyspark.ml.feature.Tokenizer", "line_number": 176, "usage_type": "call"}, {"api_name": "pyspark.ml.feature.NGram", "line_number": 177, "usage_type": "call"}, {"api_name": "pyspark.ml.feature.HashingTF", "line_number": 178, "usage_type": "call"}, {"api_name": "pyspark.ml.feature.IDF", "line_number": 179, "usage_type": "call"}, {"api_name": "pyspark.ml.feature.StringIndexer", "line_number": 180, "usage_type": "call"}, {"api_name": "pyspark.ml.classification.LogisticRegression", "line_number": 181, "usage_type": "call"}, {"api_name": "pyspark.ml.Pipeline", "line_number": 182, "usage_type": "call"}]} +{"seq_id": "16521456336", "text": "from typing import Any\nimport os\nfrom pathlib import Path\nfrom fastapi import FastAPI, HTTPException\nfrom llama_cpp import Llama\nfrom pydantic import BaseModel, Field\n\nmodel_file_path = os.getenv('MODEL_FILE_PATH')\n\nllama2_model = Llama(model_path=model_file_path, seed=42)\n\napp = FastAPI()\n\n\nclass TextInput(BaseModel):\n inputs: str = Field(..., example=\"Translate the following to Spanish: Hello, how are you?\")\n parameters: dict[str, Any] = Field(..., example={\"max_tokens\": 4096, \"temperature\": 0.0})\n\n\nSYSTEM_PROMPT = \"\"\"\nYou are a helpful assistant.\n\"\"\"\n\n\n@app.post(\"/generate/\")\nasync def generate_text(data: TextInput) -> dict[str, str]:\n try:\n params = data.parameters or {}\n response = llama2_model(prompt=data.inputs, **params)\n model_out = response['choices'][0]['text']\n return {\"generated_text\": model_out}\n except Exception as e:\n raise HTTPException(status_code=500, detail=str(e))\n", "repo_name": "alex000kim/llama_cpp_webserver_example", "sub_path": "web_server.py", "file_name": "web_server.py", "file_ext": "py", "file_size_in_byte": 945, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "os.getenv", "line_number": 8, "usage_type": "call"}, {"api_name": "llama_cpp.Llama", "line_number": 10, "usage_type": "call"}, {"api_name": "fastapi.FastAPI", "line_number": 12, "usage_type": "call"}, {"api_name": "pydantic.BaseModel", "line_number": 15, "usage_type": "name"}, {"api_name": "pydantic.Field", "line_number": 16, "usage_type": "call"}, {"api_name": "typing.Any", "line_number": 17, "usage_type": "name"}, {"api_name": "pydantic.Field", "line_number": 17, "usage_type": "call"}, {"api_name": "fastapi.HTTPException", "line_number": 33, "usage_type": "call"}]} +{"seq_id": "9120889670", "text": "\"\"\"\nTODO:\n - Search for all existing entities and color them, and hyperlink to Entities tab or Maps tab with right click?\n\"\"\"\nfrom __future__ import annotations\n\n__all__ = [\"EventEditor\"]\n\nimport logging\nimport re\nimport typing as tp\nfrom pathlib import Path\nfrom tkinter import TclError\n\nfrom soulstruct.base.events.emevd.evs import EVSError\nfrom soulstruct.base.project.utilities import TagData, TextEditor\nfrom soulstruct.utilities.window import SmartFrame\n\nif tp.TYPE_CHECKING:\n from soulstruct.base.events.emevd_directory import EMEVDDirectory\n\n_LOGGER = logging.getLogger(__name__)\n\n\nclass EvsTextEditor(TextEditor):\n\n TAGS = {\n \"restart_type\": TagData(\"#FFFFAA\", r\"^@[\\w_]+\", (0, 0)),\n \"python_word\": TagData(\n \"#FF7F50\", r\"(^| )(class|def|if|and|or|not|elif|else|return|import|from|for|True|False|await)(\\n| |:)\", (0, 1)\n ),\n \"true_false\": TagData(\"#FF7F50\", r\"[ =](True|False)(,|\\n| |:|\\))\", (1, 1)),\n \"event_def\": TagData(\"#FF6980\", r\"^def [\\w\\d_]+\", (4, 0)),\n \"import\": TagData(\"#FFAAAA\", r\"^(from|import) [\\w\\d_ .*]+\", (0, 0)),\n \"instruction_or_high_level_test\": TagData(\"#E6C975\", r\"[ \\(][\\w\\d_]+(?=\\()\", (1, 0)),\n \"low_level_test\": TagData(\"#AAAAFF\", r\"^ +(If|Skip|Goto)[\\w\\d_]+\", (0, 0)),\n \"if_main_condition\": TagData(\"#FF3355\", r\"^ +If[\\w\\d_]+(?=[(]0 *,)\", (0, 0)),\n \"main_condition\": TagData(\"#FF3355\", r\"^ +MAIN\\.Await\\(\", (0, 1)),\n \"await_statement\": TagData(\"#FF3355\", r\" await \", (0, 0)),\n \"named_arg\": TagData(\"#AAFFFF\", r\"[(,=\\|][ \\n]*(?!False)(?!True)\\w[\\w\\d.]* *[,)\\|]\", (1, 1)),\n \"func_arg_name\": TagData(\"#FFCCAA\", r\"[\\w\\d_]+ *(?=\\=)\", (0, 0)),\n \"event_arg_name\": TagData(\"#FFAAFF\", r\"^def [\\w\\d_]+\\(([\\w\\d_:, \\n]+)\\)\", None),\n \"number_literal\": TagData(\"#AADDFF\", r\"[ ,=({\\[-][\\d.]+(?=($|[ ,:)}\\]]))\", (1, 0)),\n \"and_or_condition\": TagData(\"#AAAAFF\", r\"[ \\(] *(AND|OR)_[\\d]+(\\.Add)?\", (1, 0)),\n \"comment\": TagData(\"#777777\", r\"#.*$\", (0, 0)),\n \"docstring\": TagData(\"#00ABA9\", r\"^ +\\\"\\\"\\\"[\\w\\d\\n :.]+\\\"\\\"\\\"\", (0, 0)),\n \"module_docstring\": TagData(\"#00ABA9\", r'^\"\"\"(.|\\n)*\"\"\"', (0, 0)),\n }\n\n def color_syntax(self, start=\"1.0\", end=\"end\"):\n super().color_syntax(start, end)\n self._apply_event_arg_name_tags()\n\n def _apply_event_arg_name_tags(self):\n \"\"\"Get all event arg names (e.g. \"arg_0_3\") and color them.\"\"\"\n self.tag_remove(\"event_arg_name\", \"1.0\", \"end\")\n start_index = \"1.0\"\n while True:\n def_index = self.search(r\"^def [\\w\\d_]+\\(\", start_index, regexp=True)\n if not def_index:\n break\n next_def_index = self.search(r\"^def [\\w\\d_]+\\(\", f\"{def_index} lineend\", regexp=True)\n if int(next_def_index.split(\".\")[0]) <= int(def_index.split(\".\")[0]):\n break # finished searching\n event_text = self.get(def_index, next_def_index)\n event_args_match = re.match(self.TAGS[\"event_arg_name\"].pattern, event_text, flags=re.MULTILINE)\n if event_args_match:\n event_args = event_args_match.group(1).replace(\"\\n\", \"\").replace(\" \", \"\")\n for event_arg in event_args.split(\",\"):\n if event_arg == \"_\":\n continue # don't recolor `slot` underscore argument\n parts = event_arg.split(\":\")\n if len(parts) == 2:\n arg_name, arg_type = parts\n else:\n arg_name, arg_type = parts[0], None\n self.highlight_pattern(\n arg_name, tag=\"event_arg_name\", start=def_index, end=next_def_index, clear=False\n )\n start_index = next_def_index\n\n\nclass EventEditor(SmartFrame):\n DATA_NAME = \"Events\"\n TAB_NAME = \"events\"\n TEXT_BG = \"#232323\"\n TEXT_BOX_WIDTH = 300\n\n events: EMEVDDirectory\n\n def __init__(\n self,\n project,\n evs_directory,\n game_root,\n global_map_choice_func,\n text_font_size=10,\n master=None,\n toplevel=False,\n ):\n super().__init__(master=master, toplevel=toplevel, window_title=\"Soulstruct EMEVD Manager\")\n self._project = project\n self.evs_directory = Path(evs_directory)\n self.game_root = Path(game_root)\n self.global_map_choice_func = global_map_choice_func\n self.text_font_size = text_font_size\n self.evs_file_paths = {}\n self.evs_text = {}\n self.selected_map_id = None\n\n self.map_choice = None\n self.line_number = None\n self.go_to_line = None\n self.string_to_find = None\n self.evs_editor_canvas = None\n self.text_editor = None\n self.compile_button = None\n self.reload_button = None\n\n self.scan_evs_files()\n\n with self.set_master(sticky=\"nsew\", row_weights=[0, 1, 0, 0], column_weights=[1], auto_rows=0):\n self.build()\n\n self.bind_to_all_children(\"\", lambda _: self._compile_selected(mimic_click=True))\n self.bind_to_all_children(\"\", lambda _: self.reload_selected(mimic_click=True))\n\n self.refresh()\n\n @property\n def events(self) -> EMEVDDirectory:\n return self._project.events\n\n def build(self):\n\n with self.set_master(sticky=\"nsew\", row_weights=[1], column_weights=[1, 1, 1, 1], auto_columns=0):\n self.map_choice = self.Combobox(\n values=(),\n initial_value=\"\",\n width=35,\n on_select_function=self.on_map_choice,\n sticky=\"w\",\n label=\"Map:\",\n label_font_size=12,\n label_position=\"left\",\n font=(\"Segoe UI\", 12),\n padx=10,\n pady=10,\n )\n self.line_number = self.Label(text=\"Line: None\", padx=10, width=10, fg=\"#CCF\", anchor=\"w\", sticky=\"w\").var\n self.go_to_line = self.Entry(label=\"Go to Line:\", padx=5, width=6, sticky=\"w\")\n self.go_to_line.bind(\"\", self._go_to_line)\n self.string_to_find = self.Entry(label=\"Find Text:\", padx=5, width=20, sticky=\"w\")\n self.string_to_find.bind(\"\", self._find_string)\n\n with self.set_master(sticky=\"nsew\", row_weights=[1], column_weights=[1, 0], padx=50, pady=10):\n self.evs_editor_canvas = self.Canvas(\n horizontal_scrollbar=True,\n sticky=\"nsew\",\n bg=\"#232323\",\n borderwidth=0,\n highlightthickness=0,\n column=0,\n row_weights=[1],\n column_weights=[1],\n )\n editor_i_frame = self.Frame(self.evs_editor_canvas, sticky=\"nsew\", row_weights=[1], column_weights=[1])\n self.evs_editor_canvas.create_window(0, 0, window=editor_i_frame, anchor=\"nw\")\n editor_i_frame.bind(\"\", lambda e, c=self.evs_editor_canvas: self.reset_canvas_scroll_region(c))\n\n self.text_editor = self.CustomWidget(\n editor_i_frame,\n custom_widget_class=EvsTextEditor,\n set_style_defaults=(\"text\", \"cursor\"),\n width=300,\n height=50,\n wrap=\"word\",\n bg=\"#232323\",\n font=(\"Consolas\", self.text_font_size),\n )\n vertical_scrollbar_w = self.Scrollbar(\n orient=\"vertical\", command=self.text_editor.yview, column=1, sticky=\"ns\"\n )\n self.text_editor.config(bd=0, yscrollcommand=vertical_scrollbar_w.set)\n self.link_to_scrollable(self.text_editor, editor_i_frame)\n\n def _update_textbox_height(e):\n font_size = int(self.text_editor[\"font\"].split()[1])\n self.text_editor[\"height\"] = e.height // (font_size * 1.5) # 1.5 line spacing\n\n self.evs_editor_canvas.bind(\"\", lambda e: _update_textbox_height(e))\n\n self.text_editor.bind(\"<>\", self._update_line_number)\n self.text_editor.bind(\"\", self._control_f_search)\n\n with self.set_master(auto_columns=0, pady=10, column_weights=[1, 1, 1], sticky=\"n\"):\n self.compile_button = self.Button(\n text=\"Save & Compile\",\n font_size=10,\n width=15,\n padx=5,\n command=self._compile_selected,\n tooltip_text=\"Save script, then compile it to test syntax. Text will flash blue if test is successful. \"\n \"(Ctrl + Shift + C)\",\n )\n self.reload_button = self.Button(\n text=\"Reload Script\",\n font_size=10,\n width=15,\n padx=5,\n command=self.reload_selected,\n tooltip_text=\"Reload script from project. Unsaved changes to current script will be lost. (Ctrl + R)\",\n )\n self.Button(\n text=\"Reload & Export\",\n font_size=10,\n width=15,\n padx=5,\n bg=\"#822\",\n command=self.reload_and_export,\n tooltip_text=\"Reload script from project, then immediately export it to game.\",\n )\n\n def scan_evs_files(self):\n \"\"\"Detect all EVS files in project event directory.\"\"\"\n\n # Check for `common_func.py` first, which does not use EVS extension (for importing purposes).\n common_func_path = self.evs_directory / \"common_func.py\"\n if common_func_path.is_file():\n self.evs_file_paths[\"common_func\"] = common_func_path\n with common_func_path.open(\"r\", encoding=\"utf-8\") as f:\n self.evs_text[\"common_func\"] = f.read()\n\n # Search for all `evs.py` files.\n # TODO: Extension should be modifiable, surely?\n for evs_file_path in self.evs_directory.glob(\"*.evs.py\"):\n if evs_file_path.name.startswith(\"_\"):\n # Ignore files starting with an underscore.\n continue\n evs_name = evs_file_path.name.split(\".\")[0]\n self.evs_file_paths[evs_name] = evs_file_path\n with evs_file_path.open(\"r\", encoding=\"utf-8\") as f:\n self.evs_text[evs_name] = f.read()\n\n def refresh(self):\n game_maps = [self.events.GET_MAP(m) for m in self.evs_file_paths]\n map_options = [f\"{game_map.emevd_file_stem} [{game_map.verbose_name}]\" for game_map in game_maps]\n self.map_choice[\"values\"] = map_options\n if map_options:\n self.map_choice.var.set(map_options[0])\n self.selected_map_id = self.map_choice.get().split(\" [\")[0]\n self.text_editor.delete(1.0, \"end\")\n self.text_editor.insert(1.0, self.evs_text[self.selected_map_id])\n self.text_editor.mark_set(\"insert\", \"1.0\")\n self.text_editor.color_syntax()\n\n def _update_line_number(self, _):\n current_line = self.text_editor.index(\"insert\").split(\".\")[0]\n self.line_number.set(f\"Line: {current_line}\")\n\n def _control_f_search(self, _):\n if self.selected_map_id:\n try:\n highlighted = self.text_editor.selection_get()\n except TclError: # just focus on search box\n self.string_to_find.focus_force()\n else:\n self.string_to_find.var.set(highlighted)\n self.string_to_find.select_range(0, \"end\")\n self.string_to_find.icursor(\"end\")\n self.string_to_find.focus_force()\n\n def _go_to_line(self, _):\n number = self.go_to_line.var.get()\n if not number:\n return\n number = int(number)\n if not self.selected_map_id or number < 1 or int(self.text_editor.index(\"end-1c\").split(\".\")[0]) < number:\n self.flash_bg(self.go_to_line)\n return\n self.text_editor.mark_set(\"insert\", f\"{number}.0\")\n self.text_editor.see(f\"{number}.0\")\n self.text_editor.highlight_line(number, \"found\")\n\n def _find_string(self, _):\n string = self.string_to_find.var.get()\n if not string or not self.selected_map_id:\n return\n start_line, start_char = self.text_editor.index(\"insert\").split(\".\")\n index = self.text_editor.search(string, index=f\"{start_line}.{int(start_char) + 1}\")\n\n if index:\n self.clear_bg_tags()\n self.text_editor.mark_set(\"insert\", index)\n self.text_editor.see(index)\n index_line, index_char = index.split(\".\")\n self.text_editor.tag_add(\"found\", index, f\"{index_line}.{int(index_char) + len(string)}\")\n else:\n self.flash_bg(self.string_to_find)\n\n def clear_bg_tags(self):\n for tag in {\"found\", \"error\"}:\n self.text_editor.tag_remove(tag, \"1.0\", \"end\")\n\n def _ignored_unsaved(self):\n if self._get_current_text() != self.evs_text[self.selected_map_id]:\n if (\n self.CustomDialog(\n title=\"Lose Unsaved Changes?\",\n message=\"Current text has changed but not been saved. Lose changes?\",\n button_names=(\"Yes, lose changes\", \"No, stay here\"),\n button_kwargs=(\"YES\", \"NO\"),\n cancel_output=1,\n default_output=1,\n )\n == 1\n ):\n return False\n return True\n\n def on_map_choice(self, event=None):\n \"\"\"Check if current text has changed (and warn), then switch to other text.\"\"\"\n if not self._ignored_unsaved():\n game_map = self.events.GET_MAP(self.selected_map_id)\n self.map_choice.var.set(f\"{game_map.emevd_file_stem} [{game_map.verbose_name}]\")\n return\n self.selected_map_id = self.map_choice.var.get().split(\" [\")[0]\n if self.global_map_choice_func and event is not None:\n self.global_map_choice_func(self.selected_map_id, ignore_tabs=(\"events\",))\n self.text_editor.delete(1.0, \"end\")\n self.text_editor.insert(1.0, self.evs_text[self.selected_map_id])\n self.text_editor.mark_set(\"insert\", \"1.0\")\n self.text_editor.color_syntax()\n\n def save_selected_evs(self):\n if self.selected_map_id:\n self.text_editor.color_syntax()\n current_text = self._get_current_text()\n self.evs_text[self.selected_map_id] = current_text\n with self.evs_file_paths[self.selected_map_id].open(\"w\", encoding=\"utf-8\") as f:\n f.write(current_text)\n\n def save_all_evs(self):\n \"\"\"Updates the current script, then saves all EVS scripts to 'events' project subdirectory.\"\"\"\n # TODO: Use `self.events` directory class.\n if self.selected_map_id:\n current_text = self._get_current_text()\n self.evs_text[self.selected_map_id] = current_text\n for evs_name, text in self.evs_text.items():\n evs_file_path = self.evs_file_paths[evs_name]\n with evs_file_path.open(\"w\", encoding=\"utf-8\") as f:\n f.write(text)\n\n def _raise_error(self, lineno=None, message=None):\n if lineno:\n self.text_editor.mark_set(\"insert\", f\"{lineno}.0\")\n self.text_editor.see(f\"{lineno}.0\")\n self.text_editor.highlight_line(lineno, \"error\")\n if message:\n self.error_dialog(\n \"EVS Error\",\n f\"Error encountered when trying to parse EVS script (see console for full traceback):\\n\\n\" f\"{message}\",\n )\n\n def _compile_selected(self, mimic_click=False, flash_bg=True):\n if not self.selected_map_id:\n return\n self.save_selected_evs()\n if mimic_click:\n self.mimic_click(self.compile_button)\n try:\n self.events.EMEVD_CLASS(\n self._get_current_text(),\n script_directory=str(self.evs_file_paths[self.selected_map_id].parent)\n )\n except EVSError as e:\n self._raise_error(e.lineno, str(e))\n except Exception as e:\n lineno_match = re.search(r\"line (\\d+)\", str(e))\n if lineno_match:\n self._raise_error(lineno_match.group(1), str(e))\n else:\n self._raise_error(message=str(e))\n else:\n self.text_editor.tag_remove(\"error\", \"1.0\", \"end\")\n if flash_bg:\n self.flash_bg(self.text_editor, \"#224\")\n\n def export_selected_evs(self, export_directory=None):\n \"\"\"Convert project EVS file to game EMEVD file. Does not check any loaded text.\"\"\"\n # TODO: Update `self.events`?\n if not self.selected_map_id:\n return\n if export_directory is None:\n export_directory = self.FileDialog.askdirectory(initialdir=str(self.evs_directory))\n if export_directory is None:\n return\n export_directory = Path(export_directory)\n try:\n emevd = self.events.EMEVD_CLASS(\n self.evs_file_paths[self.selected_map_id],\n script_directory=str(self.evs_file_paths[self.selected_map_id].parent),\n dcx_type=self.events.EMEVD_CLASS.DCX_TYPE if self.events.IS_DCX else None,\n )\n except Exception as e:\n return self.error_dialog(\n \"EVS Error\",\n f\"Could not interpret current EVS file in project.\\n\"\n f\"Fix this error and try again (see console for full traceback):\\n\\n{str(e)}\",\n )\n emevd.write(export_directory / f\"event/{self.selected_map_id}.emevd{'.dcx' if self.events.IS_DCX else ''}\")\n\n def reload_selected(self, mimic_click=False, flash_bg=True):\n if not self.selected_map_id:\n return\n if mimic_click:\n self.mimic_click(self.reload_button)\n if self._ignored_unsaved():\n evs_path = self.evs_file_paths[self.selected_map_id]\n with evs_path.open(\"r\", encoding=\"utf-8\") as f:\n self.evs_text[self.selected_map_id] = f.read()\n self.text_editor.delete(1.0, \"end\")\n self.text_editor.insert(1.0, self.evs_text[self.selected_map_id])\n self.text_editor.mark_set(\"insert\", \"1.0\")\n self.text_editor.color_syntax()\n if flash_bg:\n self.flash_bg(self.text_editor, \"#242\")\n\n def save_and_export(self):\n self.save_selected_evs()\n self.export_selected_evs(self.game_root)\n\n def reload_and_export(self):\n self.reload_selected()\n self.export_selected_evs(self.game_root)\n\n def _get_current_text(self):\n \"\"\"Get all current text from TextBox, minus final newline (added by Tk).\"\"\"\n return self.text_editor.get(1.0, \"end\")[:-1]\n", "repo_name": "Grimrukh/soulstruct", "sub_path": "soulstruct/base/project/editors/events.py", "file_name": "events.py", "file_ext": "py", "file_size_in_byte": 18921, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 129, "dataset": "github-code", "pt": "37", "api": [{"api_name": "typing.TYPE_CHECKING", "line_number": 19, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 22, "usage_type": "call"}, {"api_name": "soulstruct.base.project.utilities.TextEditor", "line_number": 25, "usage_type": "name"}, {"api_name": "soulstruct.base.project.utilities.TagData", "line_number": 28, "usage_type": "call"}, {"api_name": "soulstruct.base.project.utilities.TagData", "line_number": 29, "usage_type": "call"}, {"api_name": "soulstruct.base.project.utilities.TagData", "line_number": 32, "usage_type": "call"}, {"api_name": "soulstruct.base.project.utilities.TagData", "line_number": 33, "usage_type": "call"}, {"api_name": "soulstruct.base.project.utilities.TagData", "line_number": 34, "usage_type": "call"}, {"api_name": "soulstruct.base.project.utilities.TagData", "line_number": 35, "usage_type": "call"}, {"api_name": "soulstruct.base.project.utilities.TagData", "line_number": 36, "usage_type": "call"}, {"api_name": "soulstruct.base.project.utilities.TagData", "line_number": 37, "usage_type": "call"}, {"api_name": "soulstruct.base.project.utilities.TagData", "line_number": 38, "usage_type": "call"}, {"api_name": "soulstruct.base.project.utilities.TagData", "line_number": 39, "usage_type": "call"}, {"api_name": "soulstruct.base.project.utilities.TagData", "line_number": 40, "usage_type": "call"}, {"api_name": "soulstruct.base.project.utilities.TagData", "line_number": 41, "usage_type": "call"}, {"api_name": "soulstruct.base.project.utilities.TagData", "line_number": 42, "usage_type": "call"}, {"api_name": "soulstruct.base.project.utilities.TagData", "line_number": 43, "usage_type": "call"}, {"api_name": "soulstruct.base.project.utilities.TagData", "line_number": 44, "usage_type": "call"}, {"api_name": "soulstruct.base.project.utilities.TagData", "line_number": 45, "usage_type": "call"}, {"api_name": "soulstruct.base.project.utilities.TagData", "line_number": 46, "usage_type": "call"}, {"api_name": "soulstruct.base.project.utilities.TagData", "line_number": 47, "usage_type": "call"}, {"api_name": "re.match", "line_number": 66, "usage_type": "call"}, {"api_name": "re.MULTILINE", "line_number": 66, "usage_type": "attribute"}, {"api_name": "soulstruct.utilities.window.SmartFrame", "line_number": 83, "usage_type": "name"}, {"api_name": "soulstruct.base.events.emevd_directory.EMEVDDirectory", "line_number": 89, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 103, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 104, "usage_type": "call"}, {"api_name": "soulstruct.base.events.emevd_directory.EMEVDDirectory", "line_number": 131, "usage_type": "name"}, {"api_name": "tkinter.TclError", "line_number": 265, "usage_type": "name"}, {"api_name": "soulstruct.base.events.emevd.evs.EVSError", "line_number": 376, "usage_type": "name"}, {"api_name": "re.search", "line_number": 379, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 398, "usage_type": "call"}]} +{"seq_id": "31472105112", "text": "import pickle\nfrom random import shuffle \nfrom PIL import Image\nfrom model import vgg11_bn \nfrom torch import nn \nimport torch \nfrom torchvision import datasets, transforms \nfrom torch.utils.data import Dataset \nimport skimage.io as io\nfrom PIL import Image\n\nfrom net_parameter import vgg11_predict_layers, resnet50_predict_layers \nfrom torchvision.io import read_image \nimport os \n\n\ndef image_preprocess_transform():\n pretrained_size = 224\n pretrained_means = [0.485, 0.456, 0.406]\n pretrained_stds = [0.229, 0.224, 0.225]\n\n train_transform = transforms.Compose([\n transforms.Resize(pretrained_size),\n transforms.RandomRotation(5),\n transforms.RandomHorizontalFlip(0.5),\n transforms.RandomCrop(pretrained_size, padding=10),\n transforms.ToTensor(),\n transforms.Normalize(mean=pretrained_means,\n std=pretrained_stds)\n ])\n\n test_transform = transforms.Compose([\n transforms.Resize(pretrained_size),\n transforms.ToTensor(),\n transforms.Normalize(mean=pretrained_means,\n std=pretrained_stds)\n ]) \n return train_transform, test_transform \n\n\n\ndef cifa10_data_load(data_path='data/cifar', batch_size=8, distribution=False):\n # image transform \n train_transform, test_transform = image_preprocess_transform() \n\n classes = ('plane', 'car', 'bird', 'cat',\n 'deer', 'dog', 'frog', 'horse', 'ship', 'truck') \n\n train_set = datasets.CIFAR10(data_path, train=True, download=False, transform=train_transform)\n \n test_set = datasets.CIFAR10(data_path, train=False, download=False, transform=test_transform)\n\n if distribution == False:\n train_loader = torch.utils.data.DataLoader(train_set, batch_size=batch_size,\n shuffle=True) \n test_loader = torch.utils.data.DataLoader(test_set, batch_size=batch_size,\n shuffle=False) # num_workers=2 \n else: \n train_sampler = torch.utils.data.distributed.DistributedSampler(\n train_set)\n val_sampler = torch.utils.data.sampler.SequentialSampler(test_set) \n train_loader = torch.utils.data.DataLoader(train_set, batch_size=batch_size, num_workers=8,\n sampler=train_sampler) \n test_loader = torch.utils.data.DataLoader(test_set, batch_size=batch_size, num_workers=8,\n sampler=val_sampler) \n\n return train_loader, test_loader\n\n\ndef cifa100_data_load(data_path='data/cifar', batch_size=8, distribution=False):\n # image transform \n train_transform, test_transform = image_preprocess_transform() \n\n\n train_set = datasets.CIFAR100(data_path, train=True, download=False, transform=train_transform)\n \n test_set = datasets.CIFAR100(data_path, train=False, download=False, transform=test_transform)\n\n if distribution == False:\n train_loader = torch.utils.data.DataLoader(train_set, batch_size=batch_size,\n shuffle=True) \n test_loader = torch.utils.data.DataLoader(test_set, batch_size=batch_size,\n shuffle=False) # num_workers=2 \n else: \n train_sampler = torch.utils.data.distributed.DistributedSampler(\n train_set)\n val_sampler = torch.utils.data.sampler.SequentialSampler(test_set) \n train_loader = torch.utils.data.DataLoader(train_set, batch_size=batch_size, num_workers=8,\n sampler=train_sampler) \n test_loader = torch.utils.data.DataLoader(test_set, batch_size=batch_size, num_workers=8,\n sampler=val_sampler) \n\n return train_loader, test_loader\n\n\n\n\nclass ImageNetDataset(Dataset): \n\n def __init__(self, annotation_path, image_dir, transform=None): \n with open(annotation_path, 'r') as f:\n lines = f.readlines() \n self.image_label_list = [] \n for line in lines: \n line = line.strip().split() \n self.image_label_list.append([line[0], int(line[1])]) \n self.image_dir = image_dir \n self.transform = transform \n \n def __len__(self):\n return len(self.image_label_list) \n \n def __getitem__(self, index):\n image_path = os.path.join(self.image_dir, self.image_label_list[index][0]) \n #image = io.imread(image_path) \n #image = Image.fromarray(image).convert(\"RGB\")\n image = Image.open(image_path).convert(\"RGB\")\n if self.transform:\n image = self.transform(image) \n label = torch.tensor(self.image_label_list[index][1]).long() \n return image, label \n\n\n\n\ndef imagenet_data_load(data_path='data/imagenet', batch_size=8): \n train_transform, test_transform = image_preprocess_transform() \n\n train_set = datasets.ImageNet(data_path, split='train', transform=train_transform, download=False) \n train_loader = torch.utils.data.DataLoader(train_set, batch_size=batch_size, shuffle=True) \n\n val_set = datasets.ImageNet(data_path, split='val', transform=test_transform, download=False) \n val_loader = torch.utils.data.DataLoader(val_set, batch_size=batch_size, shuffle=False) \n\n return train_loader, val_loader \n\n\n\n\ndef data_plot():\n cifar10_path = 'data/cifar-10/data_batch_1' \n with open(cifar10_path, 'rb') as f: \n dict = pickle.load(f, encoding='bytes') \n\n img = dict[b'data'][1].reshape(3, 32, 32).transpose(1,2,0)\n im = Image.fromarray(img) \n im.show() \n\n\ndef ckpt_load(ckpt_path): \n OUTPUT_DIM = 10\n\n state_dict = torch.load(ckpt_path, map_location=None) \n model = vgg11_bn() \n model.load_state_dict(state_dict) \n IN_FEATURES = model.classifier[-1].in_features\n final_fc = nn.Linear(IN_FEATURES, OUTPUT_DIM) \n model.classifier[-1] = final_fc \n\n #print(model) \n #print(model.state_dict().keys())\n #for key in model.state_dict().keys():\n # print(key, model.state_dict()[key].size()) \n return model \n\n\ndef weight_dict_print(weight_dict): \n for key in weight_dict.keys(): \n print(key, weight_dict[key].size())\n\n\ndef weight_size_dict_generate(weight_dict): \n weight_size_dict = {} \n for key in weight_dict.keys(): \n weight_size_dict[key] = weight_dict[key].size() \n return weight_size_dict \n\n\n# combine the model weights in one dict network={'vgg11', 'resnet50'}\ndef parameter_dict_combine(weight_dict_list, device, mode='linear', network='vgg11'): \n combine_weight_dict = {} \n weight_size_dict = {}\n\n if len(weight_dict_list) < 1: \n return combine_weight_dict, weight_size_dict \n\n for key in weight_dict_list[0].keys(): \n if network == 'vgg11' and key not in vgg11_predict_layers:\n continue \n if network == 'resnet50' and key not in resnet50_predict_layers:\n continue \n weight_matrix = weight_dict_list[0][key] \n if len(weight_matrix.size()) < 1:\n weight_matrix = weight_matrix.unsqueeze(0)\n out_dim = weight_matrix.size()[0] \n weight_size_dict[key] = weight_matrix.size()\n weight_matrix = weight_matrix.view(out_dim, -1).to(device)\n \n for i in range(1, len(weight_dict_list)): \n t_weight_matrix = weight_dict_list[i][key]\n t_weight_matrix = t_weight_matrix.view(out_dim, -1).to(device)\n if mode == 'transformer': \n weight_matrix = torch.cat((weight_matrix, t_weight_matrix), dim=0).to(device)\n else:\n weight_matrix = torch.cat((weight_matrix, t_weight_matrix), dim=-1).to(device)\n \n combine_weight_dict[key] = weight_matrix \n return combine_weight_dict, weight_size_dict \n \n\n # resize the weight dict for model loading \ndef weight_resize_for_model_load(weight_dict, original_weight_dict, device): \n for key in original_weight_dict.keys(): \n if key in weight_dict.keys():\n weight_dict[key] = weight_dict[key].view(original_weight_dict[key].size()).to(device)\n else:\n weight_dict[key] = original_weight_dict[key].to(device)\n return weight_dict\n\n# weight detach \ndef weight_detach(weight_dict): \n new_weight_dict = {} \n for key in weight_dict.keys(): \n new_weight_dict[key] = weight_dict[key].clone().detach() \n return new_weight_dict\n\n\n\nif __name__ == '__main__': \n ckpt_path = 'ckpt/vgg11_bn.pth' \n ckpt_load(ckpt_path)", "repo_name": "feizc/Meta-Ensemble", "sub_path": "utils.py", "file_name": "utils.py", "file_ext": "py", "file_size_in_byte": 8835, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 7, "dataset": "github-code", "pt": "37", "api": [{"api_name": "torchvision.transforms.Compose", "line_number": 22, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 22, "usage_type": "name"}, {"api_name": "torchvision.transforms.Resize", "line_number": 23, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 23, "usage_type": "name"}, {"api_name": "torchvision.transforms.RandomRotation", "line_number": 24, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 24, "usage_type": "name"}, {"api_name": "torchvision.transforms.RandomHorizontalFlip", "line_number": 25, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 25, "usage_type": "name"}, {"api_name": "torchvision.transforms.RandomCrop", "line_number": 26, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 26, "usage_type": "name"}, {"api_name": "torchvision.transforms.ToTensor", "line_number": 27, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 27, "usage_type": "name"}, {"api_name": "torchvision.transforms.Normalize", "line_number": 28, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 28, "usage_type": "name"}, {"api_name": "torchvision.transforms.Compose", "line_number": 32, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 32, "usage_type": "name"}, {"api_name": "torchvision.transforms.Resize", "line_number": 33, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 33, "usage_type": "name"}, {"api_name": "torchvision.transforms.ToTensor", "line_number": 34, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 34, "usage_type": "name"}, {"api_name": "torchvision.transforms.Normalize", "line_number": 35, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 35, "usage_type": "name"}, {"api_name": "torchvision.datasets.CIFAR10", "line_number": 49, "usage_type": "call"}, {"api_name": "torchvision.datasets", "line_number": 49, "usage_type": "name"}, {"api_name": "torchvision.datasets.CIFAR10", "line_number": 51, "usage_type": "call"}, {"api_name": "torchvision.datasets", "line_number": 51, "usage_type": "name"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 54, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 54, "usage_type": "attribute"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 56, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 56, "usage_type": "attribute"}, {"api_name": "torch.utils.data.distributed.DistributedSampler", "line_number": 59, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 59, "usage_type": "attribute"}, {"api_name": "torch.utils.data.sampler.SequentialSampler", "line_number": 61, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 61, "usage_type": "attribute"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 62, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 62, "usage_type": "attribute"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 64, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 64, "usage_type": "attribute"}, {"api_name": "torchvision.datasets.CIFAR100", "line_number": 75, "usage_type": "call"}, {"api_name": "torchvision.datasets", "line_number": 75, "usage_type": "name"}, {"api_name": "torchvision.datasets.CIFAR100", "line_number": 77, "usage_type": "call"}, {"api_name": "torchvision.datasets", "line_number": 77, "usage_type": "name"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 80, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 80, "usage_type": "attribute"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 82, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 82, "usage_type": "attribute"}, {"api_name": "torch.utils.data.distributed.DistributedSampler", "line_number": 85, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 85, "usage_type": "attribute"}, {"api_name": "torch.utils.data.sampler.SequentialSampler", "line_number": 87, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 87, "usage_type": "attribute"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 88, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 88, "usage_type": "attribute"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 90, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 90, "usage_type": "attribute"}, {"api_name": "torch.utils.data.Dataset", "line_number": 98, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 114, "usage_type": "call"}, {"api_name": "os.path", "line_number": 114, "usage_type": "attribute"}, {"api_name": "PIL.Image.open", "line_number": 117, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 117, "usage_type": "name"}, {"api_name": "torch.tensor", "line_number": 120, "usage_type": "call"}, {"api_name": "torchvision.datasets.ImageNet", "line_number": 129, "usage_type": "call"}, {"api_name": "torchvision.datasets", "line_number": 129, "usage_type": "name"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 130, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 130, "usage_type": "attribute"}, {"api_name": "torchvision.datasets.ImageNet", "line_number": 132, "usage_type": "call"}, {"api_name": "torchvision.datasets", "line_number": 132, "usage_type": "name"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 133, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 133, "usage_type": "attribute"}, {"api_name": "pickle.load", "line_number": 143, "usage_type": "call"}, {"api_name": "PIL.Image.fromarray", "line_number": 146, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 146, "usage_type": "name"}, {"api_name": "torch.load", "line_number": 153, "usage_type": "call"}, {"api_name": "model.vgg11_bn", "line_number": 154, "usage_type": "call"}, {"api_name": "model.load_state_dict", "line_number": 155, "usage_type": "call"}, {"api_name": "model.classifier", "line_number": 156, "usage_type": "attribute"}, {"api_name": "torch.nn.Linear", "line_number": 157, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 157, "usage_type": "name"}, {"api_name": "model.classifier", "line_number": 158, "usage_type": "attribute"}, {"api_name": "net_parameter.vgg11_predict_layers", "line_number": 188, "usage_type": "name"}, {"api_name": "net_parameter.resnet50_predict_layers", "line_number": 190, "usage_type": "name"}, {"api_name": "torch.cat", "line_number": 203, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 205, "usage_type": "call"}]} +{"seq_id": "21015174059", "text": "import cv2\n\nface_haar_cascade = cv2.CascadeClassifier(\n 'haarcascade_frontalface_default.xml')\n\nimage = cv2.imread('./images/face1.jpg')\n\ngray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n\n\nfaces = face_haar_cascade.detectMultiScale(gray, 1.1, 4)\n\nfor (x, y, w, h) in faces:\n cv2.rectangle(image, (x, y), (x+w, y+h), (0, 255, 0), 5)\n\ncv2.imshow(\"Faces\", image)\n\ncv2.waitKey()\n", "repo_name": "Daniel-16/image-recognition", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 379, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "cv2.CascadeClassifier", "line_number": 3, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 6, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 8, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 8, "usage_type": "attribute"}, {"api_name": "cv2.rectangle", "line_number": 14, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 16, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 18, "usage_type": "call"}]} +{"seq_id": "40122940770", "text": "from typing import List\nfrom collections import Counter\n\n\nclass Solution:\n def relativeSortArray(self, arr1: List[int], arr2: List[int]) -> List[int]:\n res = []\n for i in arr2:\n if i in arr1:\n res.extend([i] * arr1.count(i))\n not_exists = sorted(list(set(arr1) - set(arr2)))\n for i in not_exists:\n res.extend([i] * arr1.count(i))\n return res\n\n\nif __name__ == \"__main__\":\n sol = Solution()\n func = sol.relativeSortArray\n arr1 = [2, 3, 1, 3, 2, 4, 6, 7, 9, 2, 19]\n arr2 = [2, 1, 4, 3, 9, 6]\n print(func(arr1, arr2))\n", "repo_name": "pangyouzhen/data-structure", "sub_path": "array/1122 relativeSortArray.py", "file_name": "1122 relativeSortArray.py", "file_ext": "py", "file_size_in_byte": 604, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "typing.List", "line_number": 6, "usage_type": "name"}]} +{"seq_id": "15298289124", "text": "from django.urls import path\nfrom django.contrib.auth import views as auth_views\nfrom . import views\nfrom django.conf.urls.static import static\nfrom django.conf import settings\n\nurlpatterns = [\n path('index', views.index, name='index'),\n path('', views.mypost, name='home'),\n \n path('upload/', views.upload, name = 'upload-post'),\n path('update/', views.update_post ),\n path('delete/', views.delete_post),\n path('login/', auth_views.LoginView.as_view(), name='login'),\n \n path('logout/', auth_views.LogoutView.as_view(), name='logout'),\n path('register/', views.register, name='register'),\n]\nif settings.DEBUG:\n urlpatterns += static(settings.MEDIA_URL, document_root=settings.MEDIA_ROOT)\n", "repo_name": "jerumanu/Blog", "sub_path": "post/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 749, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "django.urls.path", "line_number": 8, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 9, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 11, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 12, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 13, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 14, "usage_type": "call"}, {"api_name": "django.contrib.auth.views.LoginView.as_view", "line_number": 14, "usage_type": "call"}, {"api_name": "django.contrib.auth.views.LoginView", "line_number": 14, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.views", "line_number": 14, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 16, "usage_type": "call"}, {"api_name": "django.contrib.auth.views.LogoutView.as_view", "line_number": 16, "usage_type": "call"}, {"api_name": "django.contrib.auth.views.LogoutView", "line_number": 16, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.views", "line_number": 16, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 17, "usage_type": "call"}, {"api_name": "django.conf.settings.DEBUG", "line_number": 19, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 19, "usage_type": "name"}, {"api_name": "django.conf.urls.static.static", "line_number": 20, "usage_type": "call"}, {"api_name": "django.conf.settings.MEDIA_URL", "line_number": 20, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 20, "usage_type": "name"}, {"api_name": "django.conf.settings.MEDIA_ROOT", "line_number": 20, "usage_type": "attribute"}]} +{"seq_id": "75092382506", "text": "import torch\nimport numpy as np\nimport pandas as pd\nimport pickle\nimport os\nimport argparse\n\n\nparser = argparse.ArgumentParser()\nparser.add_argument(\"--dir\", type=str, default='none', help=\"CSV directory path\" )\nargs = parser.parse_args()\n\ndframes = []\npath = './'+args.dir+'/'\nwith os.scandir( path ) as dir:\n for entry in dir:\n if entry.name.endswith(\".csv\") and entry.is_file():\n\n print( entry.name )\n dframes.append( pd.read_csv( path+entry.name ) )\n\n\ndframes_cat = pd.concat(dframes)\n\n\nprint(\"dframes_cat = \")\nprint( dframes_cat )\n\ndframes_cat.to_csv(args.dir+'.csv')\n#dframes_cat.to_csv('js_csv1019cp.csv')\n", "repo_name": "pnnl/LOPO", "sub_path": "Experiments_Dev/CSVmerge.py", "file_name": "CSVmerge.py", "file_ext": "py", "file_size_in_byte": 653, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "37", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 9, "usage_type": "call"}, {"api_name": "os.scandir", "line_number": 15, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 20, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 23, "usage_type": "call"}]} +{"seq_id": "17404139416", "text": "#encoding=utf-8\nimport requests,re,bs4,time,sys,hashlib,uuid,time,json,base64,rsa,platform,datetime,os,urllib\nimport execjs\nimport frida\nprocess = frida.get_usb_device().attach('com.example.seccon2015.rock_paper_scissors')\nUserAgent = 'Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/66.0.3359.139 Safari/537.36'\n\nheaders = {'User-Agent': UserAgent}\nres = requests.get('http://www.mps.gov.cn/n2253534/n2253535/index.html', headers=headers)\n#res.raise_for_status()\n__jsluid = res.headers[\"Set-Cookie\"].split(';')[0]\ncookie1 = __jsluid# 解密\nget_js = re.findall(r'', res.text)[0].replace('eval', 'return')\nresHtml = \"function getClearance(){\" + get_js + \"};\"\nctx = execjs.compile(resHtml)\n# 一级解密结果\ntemp1 = ctx.call('getClearance')\n\ns = 'var a' + temp1.split('document.cookie')[1].split(\"Path=/;'\")[0]+\"Path=/;';return a;\"\ns = re.sub(r'document.create.*?firstChild.href', '\"{}\"'.format('http://www.mps.gov.cn/n2253534/n2253535/index.html'), s)\n# print s\nresHtml = \"function getClearance(){\" + s + \"};\"\nctx = execjs.compile(resHtml)\n# 二级解密结果\njsl_clearance = ctx.call('getClearance')\njsl_clearance = jsl_clearance.split(';')[0]\nCookie = 'maxPageNum5097045=258; __jsluid={}; zh_choose=n; {}'.format(__jsluid,jsl_clearance)\nheaders = {'User-Agent': UserAgent,\n 'Cookie':Cookie}\nres = requests.get('http://www.mps.gov.cn/n2253534/n2253535/index.html', headers=headers)\nreg_content = res.content.decode('utf-8', 'ignore')\nhtml_page = bs4.BeautifulSoup(reg_content, 'lxml')\ninfos = html_page.find(class_='sec_list').findAll('a')\nfor url in infos:\n url = 'http://www.court.gov.cn'+url['href']\n res = requests.get(url, headers=headers)\n res.raise_for_status()\n reg_content = res.content.decode('UTF-8')\n html_page = bs4.BeautifulSoup(reg_content, 'lxml')\n date = html_page.find(class_='txt_txt').text\n date1 = re.search(r\"(\\d{4}-\\d{2})\", date)\n date2 = re.search(r\"(\\d{2})\", date)\n date = date1[0]+'-'+date2[0]\n infos = html_page.find(class_='text_content').findAll('p')\n print(infos)", "repo_name": "jiongjiong-zzzzzz/spider", "sub_path": "爬虫模板/Spider/js加密cookie.py", "file_name": "js加密cookie.py", "file_ext": "py", "file_size_in_byte": 2098, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 8, "dataset": "github-code", "pt": "37", "api": [{"api_name": "frida.get_usb_device", "line_number": 5, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 9, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 13, "usage_type": "call"}, {"api_name": "execjs.compile", "line_number": 15, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 20, "usage_type": "call"}, {"api_name": "execjs.compile", "line_number": 23, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 30, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 32, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 36, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 39, "usage_type": "call"}, {"api_name": "re.search", "line_number": 41, "usage_type": "call"}, {"api_name": "re.search", "line_number": 42, "usage_type": "call"}]} +{"seq_id": "29709345358", "text": "import os\nimport torch\nimport random\nimport numpy as np\n\nfrom skeleton_sequence import SkeletonSequence\n\nclass DanceDataset(torch.utils.data.Dataset):\n def __init__(self, holder, data_in='raw', bez_degree=None, window=10, for_animation=False):\n self.holder = holder # the object created in dataset_holder.py\n self.data_in = data_in\n self.bez_degree = bez_degree\n self.window = window\n self.for_animation = for_animation\n\n def __len__(self):\n return self.holder.n_samples\n\n def get_music_skel_seq(self, item):\n return None, SkeletonSequence(None) # to be overridden\n\n def __getitem__(self, item):\n # music, skel_seq = self.get_music_skel_seq(item)\n skel_seq = self.get_music_skel_seq(item)\n metadata = skel_seq.metadata\n label = metadata['label']\n dance = self.get_dance_data(skel_seq)\n\n if self.for_animation:\n # DX: Modified _data member here just for visualization\n skel_seq._data = dance\n return dance, label, metadata, skel_seq\n \n return dance, label, metadata\n \n def get_dance_data(self, skel_sequence):\n if self.data_in == 'raw':\n return skel_sequence.get_raw_data(as_is=True)\n \n elif self.data_in == 'raw+bcurve':\n return skel_sequence.get_raw_plus_bcurve_data(self.bez_degree, padding_size=self.holder.seq_length)\n \n elif self.data_in.split('+',1)[0] == 'bcurve':\n # frames_list_path = skel_sequence.metadata['filename'].replace('.json', '.npy')\n frames_list_path = skel_sequence.metadata['filename'].split('.',1)[0]+'.npy'\n frames_list_path = self.holder.data_path.rsplit('/', 1)[0] + '/frames_list/' + frames_list_path\n frames_list = np.load(frames_list_path).tolist()\n \n target_length = 1800 if self.holder.source == 'dancerevolution' else 2878\n \n b, _, outliers= skel_sequence.get_bezier_skeleton(order=self.bez_degree, body=0, window=self.window, overlap=4, target_length=None,\n frames_list=frames_list, bounds=(0, target_length-1))\n return b.astype(' List[str]:\n flag0, flag1 = 0, 1\n ans0, ans1 = [], []\n for i, (w, g) in enumerate(zip(words, groups)):\n if flag0 == g:\n ans0.append(w)\n flag0 = 1-flag0\n if flag1 == g:\n ans1.append(w)\n flag1 = 1-flag1\n return ans0 if len(ans0) > len(ans1) else ans1\n", "repo_name": "Keval78/Programming_Solutions", "sub_path": "LeetCode/Daily/2900 Longest Unequal Adjacent Groups Subsequence I.py", "file_name": "2900 Longest Unequal Adjacent Groups Subsequence I.py", "file_ext": "py", "file_size_in_byte": 663, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "typing.List", "line_number": 11, "usage_type": "name"}]} +{"seq_id": "29471990580", "text": "#ici on verifie qu'on a pris les bonnes classes et qu'on arrive bien à ressortir l'auteur et le \"content\"\n\nimport streamlit as st\nimport requests\nfrom bs4 import BeautifulSoup\n\n\ntags = st.selectbox('choisissez votre thème : ', ('love', 'humor', 'life', 'books', 'inspirational', 'friendship'))\n\n\nurl = f\"https://quotes.toscrape.com/tag/{tags}/\"\n\nres = requests.get(url)\n\ncontent = BeautifulSoup(res.content, 'html.parser')\n\nquotes = content.find_all('div', class_='quote')\nquote_file = []\n\nfor quote in quotes:\n text = quote.find('span', class_='text').text\n author = quote.find('small', class_='author').text\n link = quote.find('a')\n st.success(text)\n st.write(author)\n", "repo_name": "lealevii/app_datasciencetool", "sub_path": "test_unitaire/test4.py", "file_name": "test4.py", "file_ext": "py", "file_size_in_byte": 687, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "streamlit.selectbox", "line_number": 8, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 13, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 15, "usage_type": "call"}, {"api_name": "streamlit.success", "line_number": 24, "usage_type": "call"}, {"api_name": "streamlit.write", "line_number": 25, "usage_type": "call"}]} +{"seq_id": "27053282403", "text": "import json\nimport re\nimport math\nimport datetime\n\n\ndate_fmt = re.compile(r\"\\d\\d\\d\\d-\\d\\d-\\d\\d\")\n\ndef get_new_uuid4_string():\n from uuid import uuid4 as U4\n return str(U4())\n\n\ndef exclude_fields(dct, fieldnames=[]):\n # this is used to simplify sending a dict with only desired fields\n new_dict = {}\n\n for k, v in dct:\n if k not in fieldnames:\n new_dict[k] = v\n\n return new_dict\n\nclass DateEncoder(json.JSONEncoder):\n def default(self, obj):\n if isinstance(obj, datetime.date):\n return obj.isoformat\n \n return json.JSONEncoder.default(self, obj)\n\ndef DateDecoder(o):\n for k, v in o.items():\n if isinstance(v, str) and re.search(date_fmt, v):\n o[k] = datetime.date.fromisoformat(v)\n\n return o\n\n\ndef get_longest_word_from_list(list_of_words):\n # this will return the longest word within a list of strings\n longest_word = \"\"\n for word in list_of_words:\n if len(word) > len(longest_word):\n longest_word = word\n\n return longest_word\n\n\ndef display_list_in_console(data, align=\"left\", columns=4, width=8, indent=0):\n # use this to primarily display lists of strings\n # width can be set by the user, but if it is not a larger amount\n # then the longest word in the list, then it would be ignored\n # indent represents the indent depth (example: indent of 2 would be 8 spaces, or two tabs)\n rows = math.ceil(len(data) / columns)\n start = 0\n end = columns + 1\n indent = indent * 4\n\n if align == \"left\":\n row_format = \" \" * indent + \"{:<{width}}\" * (columns - 1) + \"{:<}\"\n elif align == \"center\":\n row_format = \" \" * indent + \"{:^{width}}\" * (columns - 1) + \"{:^}\"\n elif align == \"right\":\n row_format = \" \" * indent + \"{:>{width}}\" * (columns - 1) + \"{:>}\"\n\n # this is used to determine the longest word in the list\n # and dynamically change the spacing to fit the words neatly, with \n # enough padding in between the columns\n longest_word_length = len(get_longest_word_from_list(data))\n\n if longest_word_length > width:\n # the width will always be divisible by 4 (the length of a tab \n # character, or four spaces)\n # if the modulo operator returns 0, it will add padding anyways\n width = longest_word_length + (4 - (longest_word_length % 4))\n\n for x in range(rows):\n if len(data[start:]) < columns:\n row = data[start:]\n else:\n row = data[start:end]\n\n # this will fill in any rows that do not reach the\n # same length as the desired columns\n if len(row) < columns:\n for y in range(columns - len(row)):\n row.append(\"\")\n \n print(row_format.format(*row, width=width))\n\n start = end - 1\n end += columns\n\n\ndef validate_user_input(user_input, valid_input):\n # for now, this will just be a list of valid options that \n # a user can type in\n \n if user_input in valid_input:\n return True\n\n return False\n\n\ndef get_user_input(input_type=\"SINGLE\", message=\"\", validate_input=False, valid_input=[]):\n # this will handle user input, and also validate that input, if any valid input is\n # required. There will be a sepcial format for validation\n\n if input_type == \"SINGLE\":\n if message != \"\":\n message = f\"{message}\\n>>> \"\n else:\n message = \">>> \"\n user_input = input(message).strip()\n\n elif input_type == \"MULTI\":\n user_input = []\n if message != \"\":\n print(message)\n while True:\n line = input(\"(press enter twice to quit)>>> \").strip()\n\n if line != \"\":\n user_input.append(line)\n\n else:\n break\n\n if validate_input:\n if validate_user_input(user_input, valid_input):\n return (user_input, True)\n\n else:\n return (user_input, False) \n\n return user_input", "repo_name": "PretzelTheGreat/pretzel_utils", "sub_path": "extra_utils.py", "file_name": "extra_utils.py", "file_ext": "py", "file_size_in_byte": 3981, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "re.compile", "line_number": 7, "usage_type": "call"}, {"api_name": "uuid.uuid4", "line_number": 11, "usage_type": "call"}, {"api_name": "json.JSONEncoder", "line_number": 24, "usage_type": "attribute"}, {"api_name": "datetime.date", "line_number": 26, "usage_type": "attribute"}, {"api_name": "json.JSONEncoder.default", "line_number": 29, "usage_type": "call"}, {"api_name": "json.JSONEncoder", "line_number": 29, "usage_type": "attribute"}, {"api_name": "re.search", "line_number": 33, "usage_type": "call"}, {"api_name": "datetime.date.fromisoformat", "line_number": 34, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 34, "usage_type": "attribute"}, {"api_name": "math.ceil", "line_number": 54, "usage_type": "call"}]} +{"seq_id": "20865841307", "text": "#! /bin/env python\n\nimport matplotlib.pyplot as plt\nimport csv\nimport sys\n\ndef makeGraph(CSV):\n f = open(CSV, 'rt')\n try:\n reader = csv.reader(f)\n Vb = []\n Ve = []\n for row in reader:\n Vb.append(row[0])\n Ve.append(row[1])\n finally:\n f.close()\n return Vb[1:], Ve[1:]\n\nif __name__==\"__main__\":\n\n Vb, Ve = makeGraph(sys.argv[1])\n plt.plot(Vb, Ve, 'ro', linewidth=2)\n plt.xlabel(\"Voltage\", fontsize=14)\n plt.ylabel(\"Current\", fontsize=14)\n plt.title(\"VV curve data for 280 Ohm resistor\", fontsize=20)\n plt.show()\n", "repo_name": "bringsyrup/CircuitsLabs", "sub_path": "3/Experiment 3/graphIV.py", "file_name": "graphIV.py", "file_ext": "py", "file_size_in_byte": 608, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "37", "api": [{"api_name": "csv.reader", "line_number": 10, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 22, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 23, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 23, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 24, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 24, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 25, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 25, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 26, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 26, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 27, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 27, "usage_type": "name"}]} +{"seq_id": "8051902561", "text": "import os, sys, glob\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\nfrom mytools.plot_tools import print_all, plot_error_bands\nfrom do3se_tools import *\n\n# Clean up\nplt.close('all')\n\n# Source\nsrc = os.environ['DATA'] + '/DO3SE_results/'\nspecies = (\"Birch\", \"Norway spruce\", \"Perennial grass\")\n\n\n\n# Read the data only once\ntry:\n date_list\nexcept NameError:\n data_list = {}\n\n for spec in species:\n data_list.update({\"%s\" % spec:read_data(glob.glob(src+spec+'*')[0])})\n \n# Plot it\nstop_day = 300\nstart_day = 90\nfig1 = plt.figure(1)\nfig1.canvas.set_window_title(\"DO3SE_results\")\n\nax11 = plt.subplot(211)\nax11.set_title(\"2018\")\nax12 = plt.subplot(212)\nax12.set_title('2019')\n\nfor spec in species:\n data = data_list[spec]\n for ax, sheet, color in zip((ax11, ax12), data.sheet_names[1::2][:3], ('violet', 'purple')):\n date = pd.read_excel(data, sheet, header=2)\n date.index = date.index+(date['Day'].iloc[0]-1)*24\n date = date.reindex(np.arange(1,365*24))\n date['PODY (mmol/m^2 PLA)'].plot(ax=ax, linewidth=3, label=spec, color=color, use_index=False)\n\nax12.set_xlabel(\"Time (doy)\")\nax12.set_ylabel('$POD_y$ ($mmol\\,m^{-2}$ PLA)', y=1)\nfor ax in fig1.axes:\n ax.set_xlim(start_day*24,stop_day*24)\n ax.set_xticks(np.arange(start_day*24,stop_day*24,30*24))\n ax.set_xticklabels(np.arange(start_day*24,stop_day*24,30*24)/24)\n ax.legend()\n ax.set_ylim(0,15)\n\nfig2 = plt.figure(2, figsize=(12,12))\nb_delta = False\nif b_delta:\n fig2.canvas.set_window_title(\"DO3SE_results_rel_clim\")\nelse:\n fig2.canvas.set_window_title(\"DO3SE_results_rel\")\nax21 = plt.subplot(311)\nax22 = plt.subplot(312)\nax23 = plt.subplot(313)\nax21.set_title(\"(a)\", x=0.025, y=0.85)\nax22.set_title(\"(b)\", x=0.025, y=0.85)\nax23.set_title(\"(c)\", x=0.025, y=0.85)\n\n\n#key_list = ('Gsto (mmol/m^2/s)', 'Vd (m/s)', 'f_temp', 'VPD (kPa)', 'Ts_C (C)', 'PAR (umol/m^2/s)', 'f_light', 'LAI', 'f_phen', 'O3_zR (ppb)', 'precip (mm)', 'AOT40 (ppm)', 'f_VPD', 'LWP (MPa)', 'Rsur (s/m)')\nkey_list = ('Gsto (mmol/m^2/s)', 'f_temp', 'VPD (kPa)', 'Ts_C (C)', 'PAR (umol/m^2/s)', 'f_light', 'f_phen', 'O3_zR (ppb)', 'precip (mm)', 'AOT40 (ppm)', 'f_VPD', 'Vd (m/s)')\n\ncorrelation_pody_list = []\ncorrelation_gsto_list = []\n\nfor ax, spec in zip((ax21, ax22, ax23), species):\n # Check species\n print(spec)\n # Load excel file for the species\n data = data_list[spec]\n # Extract climatology (disregard fSWP simulations)\n date_clim = pd.read_excel(data, data.sheet_names[1::2][2], header=2)\n # Max PODY value\n pody_max = date_clim['PODY (mmol/m^2 PLA)'].max()\n # Max PODY value for fSWP simulation\n pody_fswp_max = pd.read_excel(data, data.sheet_names[2::2][2], header=2)['PODY (mmol/m^2 PLA)'].max()\n # Check on systematic uncertainty\n print(\"Relative syst. uncertainty from fSWP: %1.3f\" % ((pody_max-pody_fswp_max)/pody_max))\n # De-accumulate PODY and generate a DateFrame\n uncum_date_clim = uncumsum(date_clim,'PODY (mmol/m^2 PLA)')\n uncum_date_clim = pd.DataFrame({'PODY':uncum_date_clim, 'Month':date_clim['Month'], 'Doy':date_clim['Day']})\n if ~b_delta:\n #uncum_date_clim.groupby(['Doy']).agg([np.mean, np.std])['PODY'].plot(ax=ax, y = \"mean\", linewidth=3, label='_', color='black',ls='-')\n plot_error_bands(ax, uncum_date_clim.groupby(['Doy']).mean().index, uncum_date_clim.groupby(['Doy']).mean()['PODY'], uncum_date_clim.groupby(['Doy']).std()['PODY'], alpha=0.25, color='black')\n \n # Compute correlation coefficient for all variables (for daily means) for climatology\n # Sort dictionary: sorted(correlations(date_clim, \"PODY (mmol/m^2 PLA)\", uncum=True, keys=key_list).items(), key=lambda x: x[1], reverse=True)\n correlation_pody_list.append((correlations(date_clim.where((date_clim['Day']>100)&(date_clim['Day']<270)), \"PODY (mmol/m^2 PLA)\", uncum=True, keys=key_list, daily=True)))\n correlation_gsto_list.append((correlations(date_clim.where((date_clim['Day']>100)&(date_clim['Day']<270)), \"Gsto_l (mmol/m^2/s)\", keys=key_list, daily=True)))\n \n for sheet, color, year, marker in zip(data.sheet_names[1::2][:2], ('violet', 'purple'), (\"2018\", \"2019\"), ('^','v')):\n # Check year\n print(year)\n # Load excel sheet for each year\n date = pd.read_excel(data, sheet, header=2)\n # Max PODY value\n pody_max = date['PODY (mmol/m^2 PLA)'].max()\n # Max PODY value for fSWP simulation\n pody_fswp_max = pd.read_excel(data, sheet+'_SWP', header=2)['PODY (mmol/m^2 PLA)'].max()\n # Check on systematic uncertainty\n print(\"Relative syst. uncertainty from fSWP: %1.3f\" % ((pody_max-pody_fswp_max)/pody_max))\n \n # Reindex to match climatology\n date.index = date.index+(date['Day'].iloc[0]-1)*24\n date = date.reindex(np.arange(1,365*24))\n\n # Compute correlation coefficience for all variables (2018->2019)\n correlation_pody_list.append((correlations(date.where((date['Day']>100)&(date['Day']<270)), \"PODY (mmol/m^2 PLA)\", uncum=True, keys=key_list, daily=True)))\n correlation_gsto_list.append((correlations(date.where((date['Day']>100)&(date['Day']<270)), \"Gsto_l (mmol/m^2/s)\", keys=key_list, daily=True)))\n \n # De-accumulate and create DataFrame\n uncum_date = pd.DataFrame({'PODY':uncumsum(date, 'PODY (mmol/m^2 PLA)'), 'Month':date['Month'], 'Doy':date['Day']})\n\n # Compute difference with climatology\n delta_uncum_date = (uncum_date-uncum_date_clim)\n #\n # Compute deviation from monthly standart deviation for each hour \n #delta_siguncum_date = delta_uncum_date[['PODY']].div(uncum_date_clim.groupby('Month').transform('std')).join(date_clim['Month']) \n \n if b_delta:\n delta_uncum_date['PODY'].plot(ax=ax, linewidth=3, label=year, color=color, use_index=False)\n else:\n # Add mean and std to table using aggiate function and plot with errorbars\n uncum_date.groupby(['Doy']).agg([np.mean, np.std])['PODY'].plot(ax=ax, y = \"mean\", yerr = \"std\", label=year, color=color,ls='None', marker=marker, fillstyle='none')\n\nax23.set_xlabel(\"Time (doy)\")\nax22.set_ylabel('$\\Delta POD_y / \\Delta t$ ($mmol\\,m^{-2}\\,s^{-1}$)')\nfor ax in fig2.axes[:-1]:\n ax.set_xlabel('')\n ax.set_xticklabels(())\n \nfor ax in fig2.axes:\n if b_delta:\n ax.set_xlim(start_day*24,stop_day*24)\n ax.set_xticks(np.arange(start_day*24,stop_day*24,30*24))\n ax.set_xticklabels(np.arange(start_day*24,stop_day*24,30*24)/24)\n ax.set_ylim(-0.02,0.02)\n else:\n ax.set_xlim(start_day, stop_day)\n ax.set_xticks(np.arange(start_day, stop_day, 30))\n ax.set_ylim(0,0.02)\n ax.legend()\n\n\nfor spec, figi in zip(species, np.arange(3,6)):\n fig = plt.figure(figi, figsize=(16,6))\n fig.canvas.set_window_title(\"DO3SE_results_pody_gsto_o3_%s\" % spec.replace(' ', '_'))\n\n data = data_list[spec]\n for iax, sheet, color, ititle in zip(np.arange(1,10), data.sheet_names[1::2][:3], ('violet', 'purple', 'blueviolet'), char_range('a', 'c')):\n ax = plt.subplot(1,3,iax)\n ax.set_title(\"(%s)\" % ititle)\n\n date = pd.read_excel(data, sheet, header=2)\n date.index = date.index+(date['Day'].iloc[0]-1)*24\n date = date.reindex(np.arange(1,365*24))\n # Plot data\n plot_pody_gsto_o3(ax, date, o3color=color)\n\n for ax in fig.axes[1:-2]:\n ax.set_ylabel(\"\")\n ax.set_yticklabels(\"\")\n\nfig10 = plt.figure(10, figsize=(10,14))\nfig10.canvas.set_window_title(\"DO3SE_results_pody_gsto_o3\")\ntitle_letter = [tl for tl in char_range('a', 'f')]\niax = 1\nfor spec in species:\n data = data_list[spec]\n for sheet, color in zip(data.sheet_names[1::2][:2], ('violet', 'purple')):\n ax = plt.subplot(3,2,iax)\n ax.set_title(\"(%s)\" % title_letter[iax-1])\n iax += 1\n\n date = pd.read_excel(data, sheet, header=2)\n date.index = date.index+(date['Day'].iloc[0]-1)*24\n date = date.reindex(np.arange(1,365*24))\n # Plot data\n plot_pody_gsto_o3(ax, date, o3color=color)\n\n for i_off in np.arange(1,4):\n for ax in fig10.axes[i_off::6]:\n ax.set_ylabel(\"\")\n ax.set_yticklabels(\"\")\nplt.subplots_adjust(wspace=0.1, right=0.92)\n\n# Plot correlation coefficients\ncorr_birch = pd.DataFrame(correlation_pody_list[:3], index=('clim', '2018', '2019'))\ncorr_spruce = pd.DataFrame(correlation_pody_list[3:6], index=('clim', '2018', '2019'))\ncorr_grass = pd.DataFrame(correlation_pody_list[6:], index=('clim', '2018', '2019'))\n\nfig6 = plt.figure(6, figsize=(12,12))\nfig6.canvas.set_window_title(\"DO3SE_results_pody_corr\")\nax61 = plt.subplot(311)\nax62 = plt.subplot(312)\nax63 = plt.subplot(313)\n\ncorr_birch.transpose().plot.bar(ax=ax61, color=('blueviolet','violet','purple'))\ncorr_spruce.transpose().plot.bar(ax=ax62, color=('blueviolet','violet','purple'))\ncorr_grass.transpose().plot.bar(ax=ax63, color=('blueviolet','violet','purple'))\n\nfor ax, ititle in zip(fig6.axes, char_range('a','c')):\n ax.set_ylim(-0.7,1)\n ax.set_title(\"(%s)\" % ititle, x=0.025, y=0.875)\n ax.legend(loc='lower right', ncol=3)\n \nax61.set_xticklabels(())\nax62.set_xticklabels(())\n\nax63.set_xticklabels([label.get_text()[:label.get_text().find(' ')] for label in ax63.get_xticklabels()])\n \nax62.set_ylabel(\"$\\\\rho$\")\n\n# Show it\nplt.show(block=False)\n\n", "repo_name": "ziu1986/python_scripts", "sub_path": "DO3SE_results/plot_results.py", "file_name": "plot_results.py", "file_ext": "py", "file_size_in_byte": 9390, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "matplotlib.pyplot.close", "line_number": 10, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 10, "usage_type": "name"}, {"api_name": "os.environ", "line_number": 13, "usage_type": "attribute"}, {"api_name": "glob.glob", "line_number": 25, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 30, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 30, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 33, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 33, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 35, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 35, "usage_type": "name"}, {"api_name": "pandas.read_excel", "line_number": 41, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 43, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 50, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 51, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 55, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 55, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 61, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 61, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 62, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 62, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 63, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 63, "usage_type": "name"}, {"api_name": "pandas.read_excel", "line_number": 81, "usage_type": "call"}, {"api_name": "pandas.read_excel", "line_number": 85, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 90, "usage_type": "call"}, {"api_name": "mytools.plot_tools.plot_error_bands", "line_number": 93, "usage_type": "call"}, {"api_name": "pandas.read_excel", "line_number": 104, "usage_type": "call"}, {"api_name": "pandas.read_excel", "line_number": 108, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 114, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 121, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 133, "usage_type": "attribute"}, {"api_name": "numpy.std", "line_number": 133, "usage_type": "attribute"}, {"api_name": "numpy.arange", "line_number": 144, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 145, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 149, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 154, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 155, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 155, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 159, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 160, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 160, "usage_type": "name"}, {"api_name": "pandas.read_excel", "line_number": 163, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 165, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 173, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 173, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 180, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 180, "usage_type": "name"}, {"api_name": "pandas.read_excel", "line_number": 184, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 186, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 190, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots_adjust", "line_number": 194, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 194, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 197, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 198, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 199, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 201, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 201, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 203, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 203, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 204, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 204, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 205, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 205, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 224, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 224, "usage_type": "name"}]} +{"seq_id": "23826264408", "text": "import re\nfrom typing import List\nfrom typing import Optional\nfrom typing import TypeVar\n\n\nENCODING = \"UTF-8\"\nHTML_ENCODING = ENCODING\n\n\nA = TypeVar(\"A\", str, bytes)\n\n\nclass HTMLClipboard:\n \"\"\"Windows HTML Clipboard\"\"\"\n\n version = 0.9\n template = \"\"\"\n Version:{version}\n StartHTML:{start_html_byte}\n EndHTML:{end_html_byte}\n StartFragment:{start_fragment_byte}\n EndFragment:{end_fragment_byte}\n SourceURL:{source_url}\n \n \n \n {fragment}\n \n \n \n \"\"\"\n template = \"\\n\".join(\n [i for i in map(str.strip, template.splitlines()) if i]\n )\n\n def __init__(self, content: str = \"\"):\n self.fragments: List[str] = []\n\n self.start_html: int = -1\n self.end_html: int = -1\n\n self.start_fragment: int = -1\n self.end_fragment: int = -1\n\n # Optional\n self.start_selection: Optional[str] = None\n self.end_selection: Optional[str] = None\n\n # WIP\n self.content: str = content\n self.raw: bytes = content.encode(encoding=HTML_ENCODING)\n\n def generate_template(self) -> str:\n fragments: List[str] = (\n self.fragments if self.fragments else [self.content]\n )\n\n # Generate Fragments\n result: str = self.generate_fragments(fragments)\n # Generate HTML\n result = self.generate_html(result)\n # Add Header\n result = self.generate_header(result)\n # Get Byte Counts\n result = self.add_byte_counts(result)\n\n return result\n\n def generate_fragments(self, fragments: List) -> str:\n results: List[str] = []\n for fragment in fragments:\n results.append(\"\")\n results.append(f\"{fragment}\")\n results.append(\"\")\n\n # Clean\n result: str = \"\\n\".join(results)\n\n return result\n\n def generate_html(self, string: str) -> str:\n lines = string.splitlines()\n body = [\"\"] + lines + [\"\"]\n html = [\"\"] + body + [\"\"]\n\n return \"\\n\".join(html)\n\n def generate_header(self, string: str) -> str:\n lines = string.splitlines()\n\n version = self.version\n start_html_byte = self.start_html\n end_html_byte = self.end_html\n start_fragment_byte = self.start_fragment\n end_fragment_byte = self.end_fragment\n source_url = None\n\n if source_url is not None:\n lines.insert(0, f\"SourceURL:{source_url}\")\n lines.insert(0, f\"EndFragment:{end_fragment_byte}\")\n lines.insert(0, f\"StartFragment:{start_fragment_byte}\")\n lines.insert(0, f\"EndHTML:{end_html_byte}\")\n lines.insert(0, f\"StartHTML:{start_html_byte}\")\n lines.insert(0, f\"Version:{version}\")\n\n return \"\\n\".join(lines)\n\n def add_byte_counts(self, content: str) -> str:\n # Check\n current_values = self.get_byte_values(content)\n if all((i is not None and i != -1) for i in current_values.values()):\n content = self.update_byte_counts(content)\n return content\n\n # Setup\n content_bytes: bytes = content.encode(encoding=HTML_ENCODING)\n\n # Blocks to find\n html_start = \"\".encode(encoding=HTML_ENCODING)\n html_end = \"\".encode(encoding=HTML_ENCODING)\n fragment_start = \"\".encode(encoding=HTML_ENCODING)\n fragment_end = \"\".encode(encoding=HTML_ENCODING)\n\n # Find Values\n found_html_start = content_bytes.find(html_start)\n found_html_end = content_bytes.find(html_end)\n found_fragment_start = content_bytes.find(fragment_start)\n found_fragment_end = content_bytes.find(fragment_end)\n\n # Fix Values\n if HTML_ENCODING == \"UTF-8\":\n found_html_end += len(html_end)\n found_fragment_start += len(fragment_start)\n\n # Set Values\n self.start_html = found_html_start\n self.end_html = found_html_end\n self.start_fragment = found_fragment_start\n self.end_fragment = found_fragment_end\n\n # Update\n content_bytes = self.update_byte_counts(content_bytes)\n\n # Clean Up\n result = content_bytes.decode(encoding=HTML_ENCODING)\n\n return self.add_byte_counts(result)\n\n def get_byte_values(self, content: str) -> dict:\n re_StartHTML = re.compile(r\"StartHTML:(\\d+)\", flags=re.MULTILINE)\n StartHTML = int(\n re_StartHTML.findall(content)[0]\n if re_StartHTML.findall(content)\n else -1\n )\n\n re_EndHTML = re.compile(r\"EndHTML:(\\d+)\", flags=re.MULTILINE)\n EndHTML = int(\n re_EndHTML.findall(content)[0]\n if re_EndHTML.findall(content)\n else -1\n )\n\n re_StartFragment = re.compile(\n r\"StartFragment:(\\d+)\", flags=re.MULTILINE\n )\n StartFragment = int(\n re_StartFragment.findall(content)[0]\n if re_StartFragment.findall(content)\n else -1\n )\n\n re_EndFragment = re.compile(r\"EndFragment:(\\d+)\", flags=re.MULTILINE)\n EndFragment = int(\n re_EndFragment.findall(content)[0]\n if re_EndFragment.findall(content)\n else -1\n )\n\n return {\n \"StartHTML\": StartHTML,\n \"EndHTML\": EndHTML,\n \"StartFragment\": StartFragment,\n \"EndFragment\": EndFragment,\n }\n\n def update_byte_counts(self, content: A) -> A:\n data: str\n if isinstance(content, bytes):\n data = content.decode(encoding=HTML_ENCODING)\n elif isinstance(content, str):\n data = content\n else:\n raise TypeError(f\"{type(content)} is not a valid type\")\n\n re_value = r\"(None|-?\\d+)\"\n\n re_StartHTML = re.compile(rf\"StartHTML:{re_value}\", flags=re.MULTILINE)\n re_EndHTML = re.compile(rf\"EndHTML:{re_value}\", flags=re.MULTILINE)\n re_StartFragment = re.compile(\n rf\"StartFragment:{re_value}\", flags=re.MULTILINE\n )\n re_EndFragment = re.compile(\n rf\"EndFragment:{re_value}\", flags=re.MULTILINE\n )\n\n data = re.sub(re_StartHTML, rf\"StartHTML:{self.start_html}\", data)\n data = re.sub(re_EndHTML, rf\"EndHTML:{self.end_html}\", data)\n data = re.sub(\n re_StartFragment, rf\"StartFragment:{self.start_fragment}\", data\n )\n data = re.sub(\n re_EndFragment, rf\"EndFragment:{self.end_fragment}\", data\n )\n\n if isinstance(content, bytes):\n return data.encode(encoding=HTML_ENCODING)\n elif isinstance(content, str):\n return data\n else:\n raise TypeError(f\"{type(content)} is not a valid type\")\n", "repo_name": "AceofSpades5757/clip-util", "sub_path": "src/clipboard/html_clipboard.py", "file_name": "html_clipboard.py", "file_ext": "py", "file_size_in_byte": 6851, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "typing.TypeVar", "line_number": 11, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 38, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 47, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 48, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 55, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 70, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 71, "usage_type": "name"}, {"api_name": "re.compile", "line_number": 151, "usage_type": "call"}, {"api_name": "re.MULTILINE", "line_number": 151, "usage_type": "attribute"}, {"api_name": "re.compile", "line_number": 158, "usage_type": "call"}, {"api_name": "re.MULTILINE", "line_number": 158, "usage_type": "attribute"}, {"api_name": "re.compile", "line_number": 165, "usage_type": "call"}, {"api_name": "re.MULTILINE", "line_number": 166, "usage_type": "attribute"}, {"api_name": "re.compile", "line_number": 174, "usage_type": "call"}, {"api_name": "re.MULTILINE", "line_number": 174, "usage_type": "attribute"}, {"api_name": "re.compile", "line_number": 199, "usage_type": "call"}, {"api_name": "re.MULTILINE", "line_number": 199, "usage_type": "attribute"}, {"api_name": "re.compile", "line_number": 200, "usage_type": "call"}, {"api_name": "re.MULTILINE", "line_number": 200, "usage_type": "attribute"}, {"api_name": "re.compile", "line_number": 201, "usage_type": "call"}, {"api_name": "re.MULTILINE", "line_number": 202, "usage_type": "attribute"}, {"api_name": "re.compile", "line_number": 204, "usage_type": "call"}, {"api_name": "re.MULTILINE", "line_number": 205, "usage_type": "attribute"}, {"api_name": "re.sub", "line_number": 208, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 209, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 210, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 213, "usage_type": "call"}]} +{"seq_id": "72280815467", "text": "import rospy as rp\nimport jupyros as jr\nimport numpy as np\n\nfrom geometry_msgs.msg import Pose, Twist\nimport math\nfrom sensor_msgs.msg import Image,Illuminance,LaserScan\n\nfrom functools import partial \n\nimport time\n\nclass PID:\n def __init__(self,Kp,Ki,Kd):\n self.Kp = Kp # Initialise the proportional gain.\n self.Ki = Ki # Initialise the integral gain.\n self.Kd = Kd # Initialise the derivative gain.\n self.lastError = 0 # Set the previous error to zero.\n self.I = 0 # Initialise the integral to zero.\n \n def value(self,e):\n P = e\n self.I = self.I + e\n D = e - self.lastError\n \n self.lastError = e\n \n return P*self.Kp+self.I*self.Ki+D*self.Kd # Return the controller output.\n \ntrack_treshold = 80 # The sensor reading.\n\ndef drive(light_data): \n if min(light_data) > track_treshold:\n speed = -0.1\n turn = 0\n else:\n error = (8-np.argmin(light_data))\n turn = lfPID.value(error)\n \n speed = max(0.3-abs(turn)*0.15,0)\n \n return speed,turn\n\nd = True\n\ndef handle_scan(ranges,ls,a):\n global d\n\n left = ranges[60] # The ranges where the left light sensor can read.\n right = ranges[-60] # The ranges where the right light sensor can read.\n\n val = np.min(ranges[:30]+ranges[-30:]) # The ranges where the light sensor overall can read.\n\n if a != 1 and val < 0.35:\n if left < right:\n if a == 0:\n d = False # Not update the desired angular position.\n return 1,0.0,0.0\n else:\n if a == 0:\n d = True # Update the desired angular position.\n return 1,0.0,0.0\n elif a == 1:\n val = np.min(ranges[:30]+ranges[-30:])\n # The robot will move backwards if it detects an obstacle in front and goes within a minimum distance of 0.35.\n if val < 0.35:\n return a,-0.1,0.0\n else:\n return a+1,0,0\n # At stage two the robot starts turning left in front of the obstacle.\n elif a == 2:\n mi = np.argmin(ranges)\n\n # The robot's turning angle is set to be either around 90 degrees or 270 degrees.\n if mi > 80 and mi < 100 or mi > 260 and mi < 280:\n return a+1,0.2,0\n elif not d:\n return a,0.0,-0.5\n else:\n return a,0.0,0.5\n # The robot is circumnavigating the obstacle at the following two stages.\n elif a == 3:\n if np.min(ls) < 50:\n return a,0.2,0\n else:\n return a+1,0,0\n elif a == 4:\n if np.min(ls) < 30:\n return a+1,0,0\n\n idx = 90 if not d else 270\n\n r = np.array([v if v < 1 else 10 for v in ranges[idx:]+ranges[:idx]])\n\n error = (180-np.argmin(r-0.2))+(np.min(r)-0.2)*1500*(-1 if d else 1)\n turn = oPID.value(error)\n\n if turn > 1:\n turn = 1\n elif turn < -1:\n turn = -1\n\n speed = max(0.3-abs(turn)*1.2,0.1) # Update the linear/angular velocity of the robot by firstly multiplying the turning angle and then subtracting this part from our minimum velocity.\n\n return a,speed,turn\n # At stage five the robot will move back onto the track.\n elif a == 5:\n mi = np.argmin(ranges)\n\n if mi > 160 and mi < 200 or min(ls) > 50:\n return 0,0,0\n elif not d:\n return a,0.05,-0.5\n else:\n return a,0.05,0.5\n\n return 0,0,0\n\ndef getTwist(speed,turn):\n twist = Twist()\n # x, y, z corresponds to roll, pitch and yaw axis.\n twist.linear.x = speed; twist.linear.y = 0; twist.linear.z = 0\n twist.angular.x = 0; twist.angular.y = 0; twist.angular.z = turn\n \n return twist\n\nlfPID = PID(0.3,0,-0.08) # Kp = 0.3, Ki = 0, Kd = -0.08. \noPID = PID(1e-2,1e-5,1e-4) # Kp = 1e-2, Ki = 1e-5, Kd = 1e-4.\n\ndef main():\n global light_sensors\n global lidar_distances # LIDAR sensor is used to compute the distance between the light sensor and the obstacle.\n rp.init_node('runner')\n \n stop = False\n\n n_sensors = 16 # The number of light sensors for this robot.\n\n pub = rp.Publisher('/cmd_vel', Twist,queue_size=10)\n\n light_sensors = [0]*n_sensors\n\n def setLightSensor(x,i):\n global light_sensors\n light_sensors[i] = x.illuminance\n \n lidar_distances = None\n\n def setLidar(x):\n global lidar_distances\n lidar_distances = x.ranges\n \n cams = [rp.Subscriber(\"/camera_{}/rgb/image_raw\".format(i),Image) for i in range(n_sensors)]\n subs = [rp.Subscriber(\"/light_sensor_plugin/lightSensor/camera_{}\".format(i),Illuminance,partial(lambda x,k:setLightSensor(x,k),k=i)) for i in range(n_sensors)]\n scans = rp.Subscriber(\"/scan\",LaserScan,setLidar)\n\n while lidar_distances == None:\n print(\"\\r\",\"waiting for sensor data\",end=\"\")\n time.sleep(0.1)\n\n mode = 0\n\n print(\"starting\")\n\n while not stop:\n mode,control_speed,control_turn = handle_scan(lidar_distances,light_sensors,mode)\n\n if mode == 0:\n (control_speed,control_turn) = drive(light_sensors)\n\n\n pub.publish(getTwist(control_speed,control_turn))\n\n time.sleep(0.1) # The delay of light sensors.\n\n pub.publish(getTwist(0,0))\n\n print(\"unregistering\")\n for cam in cams:\n cam.unregister()\n\n for sub in subs:\n sub.unregister()\n pub.unregister()\n scans.unregister()\n print(\"done\")\n\nif __name__ == \"__main__\":\n main()\n", "repo_name": "Anderson-You/2nd-Year-Robotics-Group-Project", "sub_path": "bot.py", "file_name": "bot.py", "file_ext": "py", "file_size_in_byte": 5478, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "numpy.argmin", "line_number": 37, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 52, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 64, "usage_type": "call"}, {"api_name": "numpy.argmin", "line_number": 72, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 83, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 88, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 93, "usage_type": "call"}, {"api_name": "numpy.argmin", "line_number": 95, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 95, "usage_type": "call"}, {"api_name": "numpy.argmin", "line_number": 108, "usage_type": "call"}, {"api_name": "geometry_msgs.msg.Twist", "line_number": 120, "usage_type": "call"}, {"api_name": "rospy.init_node", "line_number": 133, "usage_type": "call"}, {"api_name": "rospy.Publisher", "line_number": 139, "usage_type": "call"}, {"api_name": "geometry_msgs.msg.Twist", "line_number": 139, "usage_type": "argument"}, {"api_name": "rospy.Subscriber", "line_number": 153, "usage_type": "call"}, {"api_name": "sensor_msgs.msg.Image", "line_number": 153, "usage_type": "argument"}, {"api_name": "rospy.Subscriber", "line_number": 154, "usage_type": "call"}, {"api_name": "sensor_msgs.msg.Illuminance", "line_number": 154, "usage_type": "argument"}, {"api_name": "functools.partial", "line_number": 154, "usage_type": "call"}, {"api_name": "rospy.Subscriber", "line_number": 155, "usage_type": "call"}, {"api_name": "sensor_msgs.msg.LaserScan", "line_number": 155, "usage_type": "argument"}, {"api_name": "time.sleep", "line_number": 159, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 174, "usage_type": "call"}]} +{"seq_id": "33254523322", "text": "import numpy as np\nnp.random.seed(42)\nimport pandas as pd\n\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import roc_auc_score\n\nfrom keras.models import Model\nfrom keras.layers import Input, Dense, Embedding, SpatialDropout1D, concatenate\nfrom keras.layers import GRU, Bidirectional, GlobalAveragePooling1D, GlobalMaxPooling1D\nfrom keras.preprocessing import text, sequence\nfrom keras.callbacks import Callback\nfrom keras.utils import to_categorical\nfrom keras.utils import multi_gpu_model\nfrom keras.callbacks import *\nfrom sklearn.utils import class_weight\n\n\nEMBEDDING_FILE = 'crawl-300d-2M.vec'\n\ntrain = pd.read_csv('train.csv')\ntest = pd.read_csv('test_1.csv')\n\nX_train = train[\"title\"].fillna(\"fillna\").values\ny_train = train[\"Category\"].values\nclassweight = class_weight.compute_class_weight('balanced', np.unique(y_train), y_train)\n\ny_train = to_categorical(y_train)\nX_test = test[\"title\"].fillna(\"fillna\").values\ny_test = test['Category'].values\ny_test = to_categorical(y_test)\n\nmax_features = 70000\nmaxlen = 72\nembed_size = 300\n\ntokenizer = text.Tokenizer(num_words=max_features)\ntokenizer.fit_on_texts(list(X_train) + list(X_test))\nX_train = tokenizer.texts_to_sequences(X_train)\nX_test = tokenizer.texts_to_sequences(X_test)\nx_train = sequence.pad_sequences(X_train, maxlen=maxlen)\nx_test = sequence.pad_sequences(X_test, maxlen=maxlen)\n\n\ndef get_coefs(word, *arr): return word, np.asarray(arr, dtype='float32')\nembeddings_index = dict(get_coefs(*o.rstrip().rsplit(' ')) for o in open(EMBEDDING_FILE))\n\nword_index = tokenizer.word_index\nnb_words = min(max_features, len(word_index))\nembedding_matrix = np.zeros((nb_words, embed_size))\nfor word, i in word_index.items():\n if i >= max_features: continue\n embedding_vector = embeddings_index.get(word)\n if embedding_vector is not None: embedding_matrix[i] = embedding_vector\n\n\nclass RocAucEvaluation(Callback):\n def __init__(self, validation_data=(), interval=1):\n super(Callback, self).__init__()\n\n self.interval = interval\n self.X_val, self.y_val = validation_data\n\n def on_epoch_end(self, epoch, logs={}):\n if epoch % self.interval == 0:\n y_pred = self.model.predict(self.X_val, verbose=0)\n score = roc_auc_score(self.y_val, y_pred)\n print(\"\\n ROC-AUC - epoch: %d - score: %.6f \\n\" % (epoch+1, score))\n\n\ndef get_model():\n inp = Input(shape=(maxlen, ))\n x = Embedding(max_features, embed_size, weights=[embedding_matrix])(inp)\n x = SpatialDropout1D(0.2)(x)\n x = Bidirectional(GRU(80, return_sequences=True))(x)\n avg_pool = GlobalAveragePooling1D()(x)\n max_pool = GlobalMaxPooling1D()(x)\n conc = concatenate([avg_pool, max_pool])\n outp = Dense(58, activation=\"sigmoid\")(conc)\n \n model = Model(inputs=inp, outputs=outp)\n model = multi_gpu_model(model, gpus=4)\n model.compile(loss='categorical_crossentropy',\n optimizer='adam',\n metrics=['accuracy'])\n\n return model\n\nmodel = get_model()\n\n\nbatch_size = 128\nepochs = 8\n\nX_tra, X_val, y_tra, y_val = train_test_split(x_train, y_train, train_size=0.95, random_state=233)\nfilepath=\"weights_best_gru_with_ft.h5\"\nRocAuc = RocAucEvaluation(validation_data=(X_val, y_val), interval=1)\ncheckpoint = ModelCheckpoint(filepath, monitor='val_loss', verbose=2, save_best_only=True, mode='min')\nreduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.5, patience=1, min_lr=0.0001, verbose=2)\nhist = model.fit(X_tra, y_tra, batch_size=batch_size, epochs=epochs, class_weight = classweight,validation_data=(X_val, y_val),\n callbacks=[RocAuc,checkpoint,reduce_lr], verbose=1)\n\nscore = model.evaluate(x_test, y_test,\n batch_size=128, verbose=1)\nprint('Test loss:', score[0])\nprint('Test accuracy:', score[1])\n\ntest_df = pd.read_csv(\"test.csv\")\n\ntest_x = test_df['title']\ntest_x = tokenizer.texts_to_sequences(test_x)\nX_te = sequence.pad_sequences(test_x,maxlen = maxlen)\nall_preds = model.predict(X_te)\ny_te = [np.argmax(pred) for pred in all_preds]\nsubmit_df = pd.DataFrame({\"itemid\": test_df[\"itemid\"], \"Category\": y_te})\nsubmit_df.to_csv(\"submission_gruwithft.csv\", index=False)\n", "repo_name": "llllxt/Product-Classification", "sub_path": "text/gru_with_ft.py", "file_name": "gru_with_ft.py", "file_ext": "py", "file_size_in_byte": 4186, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "37", "api": [{"api_name": "numpy.random.seed", "line_number": 2, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 2, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 21, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 22, "usage_type": "call"}, {"api_name": "sklearn.utils.class_weight.compute_class_weight", "line_number": 26, "usage_type": "call"}, {"api_name": "sklearn.utils.class_weight", "line_number": 26, "usage_type": "name"}, {"api_name": "numpy.unique", "line_number": 26, "usage_type": "call"}, {"api_name": "keras.utils.to_categorical", "line_number": 28, "usage_type": "call"}, {"api_name": "keras.utils.to_categorical", "line_number": 31, "usage_type": "call"}, {"api_name": "keras.preprocessing.text.Tokenizer", "line_number": 37, "usage_type": "call"}, {"api_name": "keras.preprocessing.text", "line_number": 37, "usage_type": "name"}, {"api_name": "keras.preprocessing.sequence.pad_sequences", "line_number": 41, "usage_type": "call"}, {"api_name": "keras.preprocessing.sequence", "line_number": 41, "usage_type": "name"}, {"api_name": "keras.preprocessing.sequence.pad_sequences", "line_number": 42, "usage_type": "call"}, {"api_name": "keras.preprocessing.sequence", "line_number": 42, "usage_type": "name"}, {"api_name": "numpy.asarray", "line_number": 45, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 50, "usage_type": "call"}, {"api_name": "keras.callbacks.Callback", "line_number": 57, "usage_type": "name"}, {"api_name": "keras.callbacks.Callback", "line_number": 59, "usage_type": "argument"}, {"api_name": "sklearn.metrics.roc_auc_score", "line_number": 67, "usage_type": "call"}, {"api_name": "keras.layers.Input", "line_number": 72, "usage_type": "call"}, {"api_name": "keras.layers.Embedding", "line_number": 73, "usage_type": "call"}, {"api_name": "keras.layers.SpatialDropout1D", "line_number": 74, "usage_type": "call"}, {"api_name": "keras.layers.Bidirectional", "line_number": 75, "usage_type": "call"}, {"api_name": "keras.layers.GRU", "line_number": 75, "usage_type": "call"}, {"api_name": "keras.layers.GlobalAveragePooling1D", "line_number": 76, "usage_type": "call"}, {"api_name": "keras.layers.GlobalMaxPooling1D", "line_number": 77, "usage_type": "call"}, {"api_name": "keras.layers.concatenate", "line_number": 78, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 79, "usage_type": "call"}, {"api_name": "keras.models.Model", "line_number": 81, "usage_type": "call"}, {"api_name": "keras.utils.multi_gpu_model", "line_number": 82, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 95, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 108, "usage_type": "call"}, {"api_name": "keras.preprocessing.sequence.pad_sequences", "line_number": 112, "usage_type": "call"}, {"api_name": "keras.preprocessing.sequence", "line_number": 112, "usage_type": "name"}, {"api_name": "numpy.argmax", "line_number": 114, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 115, "usage_type": "call"}]} +{"seq_id": "20378195512", "text": "from collections import deque\ndef bfs(graph, start, visited):\n queue = deque([start]) ## bfs 구현 위해서 큐 자료구조 사용\n\n visited[start] = True ## 방문노드상태 True처리\n\n while queue:\n v = queue.popleft() ## 먼저 삽입된 값 출력\n print(v, end=' ')\n\n for i in graph[v]:\n if not visited[i]:\n queue.append(i)\n visited[i] = True\n\ngraph=[\n [], ## 노드 번호를 1부터 시작하는 것으로 설정했기 때문에, 인덱스 0번은 비워두기!\n [2,3,8],\n [1,7],\n [1,4,5],\n [3,5],\n [3,4],\n [7],\n [2,6,8],\n [1,7]\n]\n\nvisited = [False] * 9\n\nbfs(graph, 1, visited)\n", "repo_name": "yougi8/CodingTestStudy", "sub_path": "이코테/ch5_DFS:BFS/bfs.py", "file_name": "bfs.py", "file_ext": "py", "file_size_in_byte": 689, "program_lang": "python", "lang": "ko", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "collections.deque", "line_number": 3, "usage_type": "call"}]} +{"seq_id": "73674672428", "text": "from flask import Flask\nfrom .initdb import init_app\nfrom os import path\n\ndef create_app():\n app = Flask(__name__)\n rpath = path.abspath(path.join(app.root_path, '../'))\n app.config.from_mapping(\n IMAGES_USERS = 'IMAGES_USERS'\n )\n app.config.from_pyfile(path.join(rpath, 'app.cfg'))\n app.config.from_mapping(\n IMAGES_USERS_ABS = path.join(app.static_folder, app.config['IMAGES_USERS'])\n )\n init_app(app)\n\n from . import auth\n app.register_blueprint(auth.bp)\n\n return app\n\napp = create_app()\nfrom app.controllers import routers", "repo_name": "leoPirpiri/nevermore", "sub_path": "app/__init__.py", "file_name": "__init__.py", "file_ext": "py", "file_size_in_byte": 573, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "flask.Flask", "line_number": 6, "usage_type": "call"}, {"api_name": "os.path.abspath", "line_number": 7, "usage_type": "call"}, {"api_name": "os.path", "line_number": 7, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 7, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 11, "usage_type": "call"}, {"api_name": "os.path", "line_number": 11, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 13, "usage_type": "call"}, {"api_name": "os.path", "line_number": 13, "usage_type": "name"}, {"api_name": "initdb.init_app", "line_number": 15, "usage_type": "call"}]} +{"seq_id": "2765337122", "text": "# -*- coding: utf-8 -*-\nfrom __future__ import absolute_import\n\n#from main.celery import app\nimport pickle\nfrom celery import shared_task\nfrom notify.ggmail import GGMail\nfrom notify.gline import GLine\n\nntyitems = ['gmail', 'line']\n\n@shared_task(time_limit=60)\ndef collect_ntyitem(stream):\n args, kwargs = pickle.loads(stream)\n\n for target in targets:\n if target == 'gmail':\n gmail = GGMail(**kwargs)\n msg = gmail.create_msg()\n gmail.send(msg)\n\n if target == 'line':\n line = GLine(**kwargs)\n msg = line.create_msg()\n line.send(msg)", "repo_name": "funningboy/scrapy_giant", "sub_path": "notify/tasks.py", "file_name": "tasks.py", "file_ext": "py", "file_size_in_byte": 617, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 8, "dataset": "github-code", "pt": "37", "api": [{"api_name": "pickle.loads", "line_number": 14, "usage_type": "call"}, {"api_name": "notify.ggmail.GGMail", "line_number": 18, "usage_type": "call"}, {"api_name": "notify.gline.GLine", "line_number": 23, "usage_type": "call"}, {"api_name": "celery.shared_task", "line_number": 12, "usage_type": "call"}]} +{"seq_id": "12643834723", "text": "import discord, asyncio\nfrom discord import client\n\nimport requests, json\nfrom commands import *\nfrom tools import *\n\nclass Client(discord.Client):\n\tdef __init__(self, *args, **kwargs):\n\t\tsuper().__init__(*args, **kwargs)\n\n\t\t# create the background task and run it\n\t\tself.bg_task = self.loop.create_task(self.print_info())\n\n\t# Prints into console on start\n\tasync def on_ready(self):\n\t\tprint(\"Logged in as\")\n\t\tprint(self.user.name)\n\t\tprint(self.user.id)\n\t\tprint(\"----------------\")\n\t\n\t# Prints the info\n\tasync def print_info(self):\n\t\tawait self.wait_until_ready()\n\t\tminutes = 2\n\t\tcounter = 0\n\t\tchannel = self.get_channel(919390138868584489)\n\t\twhile not self.is_closed():\n\t\t\tcounter += 1\n\t\t\tawait channel.send(get_server_info(content) + '\\n' + get_next_occurences(content))\n\t\t\tawait asyncio.sleep(60 * minutes)\n\nreq = requests.get(url=\"https://stellarflyff.com/server-status\")\ncontent = json.loads(req.content) # the json content from the website\n\nclient = Client()\nclient.run(get_token())", "repo_name": "EllisBarnes00/SF-Server-Status-Bot", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 987, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "discord.Client", "line_number": 8, "usage_type": "attribute"}, {"api_name": "asyncio.sleep", "line_number": 31, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 33, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 34, "usage_type": "call"}, {"api_name": "discord.client", "line_number": 36, "usage_type": "name"}, {"api_name": "discord.client.run", "line_number": 37, "usage_type": "call"}, {"api_name": "discord.client", "line_number": 37, "usage_type": "name"}]} +{"seq_id": "42677114500", "text": "# Plote as funções arcoseno, arcocosseno e arcotangente hiperbólicas nos domínios x=[−2,2],[1,2] e ]−1,1[ ,\n# respectivamente. Nomeie os eixos x e y e dê um título e uma legenda a seu gráfico.\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n####################################################\n\n# Valores do domínio da função arcoseno hiperbólico:\nx = np.linspace(-1*np.pi, 2*np.pi, 50)\n\n# Valores da imagem da função arcoseno hiperbólico:\ny = np.arcsinh(x)\n\n# Título do gráfico:\nplt.title('Gráfico do Arcoseno Hiperbólico')\n\n# Legendas dos eixos:\nplt.xlabel('Valores do eixo \"X\" para o Arcoseno Hiperboólico')\nplt.ylabel('Valores do eixo \"Y\" para o Arcoseno Hiperboólico')\n\n# Plotar a curva da função arcoseno hiperbólico:\nplt.plot(x, y)\nplt.show()\n\n####################################################\n\n# Valores do domínio da função arcocoseno hiperbólico:\nx = np.linspace(np.pi, 2*np.pi, 50)\n\n# Valores da imagem da função arcocoseno hiperbólico:\ny = np.arccosh(x)\n\n# Título do gráfico:\nplt.title('Gráfico do Arcocoseno Hiperbólico')\n\n# Legendas dos eixos:\nplt.xlabel('Valores do eixo \"X\" para o Arcocoseno Hiperboólico')\nplt.ylabel('Valores do eixo \"Y\" para o Arcocoseno Hiperboólico')\n\n# Plotar curva da função arcocoseno hiperbólico:\nplt.plot(x, y)\nplt.show()\n\n####################################################\n\n# Valores do domínio da função arcotangente hiperbólico:\nx = np.linspace(-1*np.pi, np.pi, 50)\n\n# Valores da imagem da função arcotangente hiperbólico:\ny = np.arctanh(x)\n\n# Título do gráfico:\nplt.title('Gráfico do Arcotangente Hiperbólico')\n\n# Legendas dos eixos:\nplt.xlabel('Valores do eixo \"X\" para o Arcotangente Hiperboólico')\nplt.ylabel('Valores do eixo \"Y\" para o Arcotangente Hiperboólico')\n\n# Plotar curva da função arcotangente hiperbólico:\nplt.plot(x, y)\nplt.show()\n", "repo_name": "SindyPin/Python_Exercises", "sub_path": "Ex10.py", "file_name": "Ex10.py", "file_ext": "py", "file_size_in_byte": 1861, "program_lang": "python", "lang": "pt", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "numpy.linspace", "line_number": 10, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 10, "usage_type": "attribute"}, {"api_name": "numpy.arcsinh", "line_number": 13, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.title", "line_number": 16, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 16, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 19, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 19, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 20, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 20, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 23, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 23, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 24, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 24, "usage_type": "name"}, {"api_name": "numpy.linspace", "line_number": 29, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 29, "usage_type": "attribute"}, {"api_name": "numpy.arccosh", "line_number": 32, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.title", "line_number": 35, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 35, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 38, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 38, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 39, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 39, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 42, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 42, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 43, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 43, "usage_type": "name"}, {"api_name": "numpy.linspace", "line_number": 48, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 48, "usage_type": "attribute"}, {"api_name": "numpy.arctanh", "line_number": 51, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.title", "line_number": 54, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 54, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 57, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 57, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 58, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 58, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 61, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 61, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 62, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 62, "usage_type": "name"}]} +{"seq_id": "39266393568", "text": "import os\nimport json\n\nwith open(f\"data/emojiCategories.json\", \"r\") as file:\n categoryData = json.load(file)\n\nwith open(f\"data/ignoreEmojiUnicodeList.json\", \"r\") as file:\n ignoreEmojis = json.load(file)\n\nskipCounter = 0\nuseCounter = 0\nerr = []\n\nfor _, (categoryName, emojiList) in enumerate(categoryData.items()):\n categoryData = {}\n for emo in emojiList:\n if emo in ignoreEmojis:\n print(f\"Skip: {emo}\")\n skipCounter += 1\n else:\n useCounter += 1\n if emo[-5:] == \"_fe0f\" and len(emo.split(\"_\")) == 2:\n print(f\"Remove fe0f in tail: {emo}\")\n name = emo[:-5]\n else:\n name = emo\n\n try:\n with open(f\"json/{name}.json\", \"r\") as file:\n data = json.load(file)\n categoryData[name] = data\n except:\n err.append(emo)\n\n print(f\"{categoryName}: {len(categoryData)}\")\n with open(f\"data/{categoryName}.json\", \"w\") as outfile:\n json.dump(categoryData, outfile)\n\nnl = \"\\n\"\nprint(f\"Success: {useCounter} | Skip: {skipCounter} | Error: {len(err)}\")\nprint(f\"Error ({len(err)}): {nl.join(err)}\")\n", "repo_name": "rutopio/EmojiSalon", "sub_path": "utils/getPathAndColorDataByCategories.py", "file_name": "getPathAndColorDataByCategories.py", "file_ext": "py", "file_size_in_byte": 1198, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 5, "dataset": "github-code", "pt": "37", "api": [{"api_name": "json.load", "line_number": 5, "usage_type": "call"}, {"api_name": "json.load", "line_number": 8, "usage_type": "call"}, {"api_name": "json.load", "line_number": 30, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 37, "usage_type": "call"}]} +{"seq_id": "362715039", "text": "# ------------------------------------------------\nimport cv2 # first import cv2, then torch\ncv2.setNumThreads(0)\n# https://github.com/pytorch/pytorch/issues/1838\n# ------------------------------------------------\nimport os\nfrom typing import Dict\nimport numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nimport MinkowskiEngine as ME\nfrom torch import Tensor\nfrom torch.optim import lr_scheduler\nfrom torch.utils.data import DataLoader\n\nfrom loss import point_info_nce_loss, _gather\nfrom utils.sparse_helper import collate_dense_sparse, to_device, align_sparse_features\nfrom loss_moco import (build_dense_head, build_mlp_head, NCELossMocoV2, momentum_update, \n batch_shuffle_ddp, batch_unshuffle_ddp)\nfrom scannet.scannet_pretrain import ScanNetDepthVoxelDataset\nfrom model.cnn3d.minkunet import MinkUNet34C\nfrom model.cnn2d.conv2d import UNet, BasicBlock\nfrom meters import ProgressMeter\n\n\nclass UNetHead(nn.Module):\n def __init__(\n self, \n fusion,\n feature_dims=[64, 64],\n dim_model : int = 128,\n dim_inter : int = 512,\n dim_out : int = 128\n ):\n super().__init__()\n self.fusion = fusion\n self.encoder = UNet(fusion=fusion)\n self.upsample1 = nn.Sequential(nn.ConvTranspose2d(256, 128, kernel_size=2, stride=2, padding=0, bias=False),\n nn.BatchNorm2d(128),\n nn.LeakyReLU(),\n BasicBlock(128, 128))\n self.upsample2 = nn.Sequential(nn.ConvTranspose2d(128+feature_dims[1], 128, kernel_size=2, stride=2, padding=0, bias=False),\n nn.BatchNorm2d(128),\n nn.LeakyReLU(),\n BasicBlock(128, 128))\n self.conv = BasicBlock(128+feature_dims[0], 128)\n self.dense_head = build_dense_head(dim_model, dim_inter, dim_out)\n self.ghead = build_mlp_head(dim_model, dim_inter, dim_out)\n self.pool = nn.AdaptiveMaxPool2d((1, 1))\n\n def forward(self, depth : Tensor, ind : Tensor, rgb : Tensor = None):\n if self.fusion:\n input = {\"depthmap\":depth, \"rgb\":rgb}\n else:\n input = depth\n # s_y=8, s_x1=4, s_x2=2\n y, x1, x2 = self.encoder(input, True)\n y = self.upsample1(y) # s=4\n y = torch.cat([y, x2], dim=1)\n y = self.upsample2(y) # s=2\n y = torch.cat([y, x1], dim=1)\n y = self.conv(y) # s=2, B,C,H,W\n B, C, _, _ = y.size()\n y_lin = y.view(B, C, -1)\n feats = _gather(y_lin, ind)\n local_feats = self.dense_head(feats)\n local_feats = torch.nn.functional.normalize(local_feats, dim=1)\n global_feats = self.pool(y).view(B, C)\n global_feats = self.ghead(global_feats)\n global_feats = torch.nn.functional.normalize(global_feats, dim=1)\n return global_feats, local_feats\n\n\nclass SparseUNetHead(nn.Module):\n def __init__(\n self, \n rgb : bool = True,\n dim_model : int = 256,\n dim_inter : int = 512,\n dim_out : int = 128\n ):\n super().__init__()\n dim_in = 3 if rgb else 1\n self.encoder = MinkUNet34C(dim_in, dim_model)\n self.dense_head = build_dense_head(dim_model, dim_inter, dim_out)\n self.ghead = build_mlp_head(dim_model, dim_inter, dim_out)\n self.pool = nn.AdaptiveMaxPool1d(1)\n \n def forward(self, sparse_tensor : ME.SparseTensor, index : Tensor) :\n sout = self.encoder(sparse_tensor)\n # BxCxN\n feats = align_sparse_features(sout, index)\n local_feats = self.dense_head(feats)\n local_feats = torch.nn.functional.normalize(local_feats, dim=1)\n global_feats = self.pool(feats).squeeze(2)\n global_feats = self.ghead(global_feats)\n global_feats = torch.nn.functional.normalize(global_feats, dim=1)\n return global_feats, local_feats\n\n\nclass DepthVoxelContrast(nn.Module):\n def __init__(self,\n dim_out,\n dim_inter,\n num_neg,\n temperature,\n momentum=0.999,\n fusion=True,\n local_loss=True,\n global_loss=True,\n within_format=False,\n ddp=True,\n warmup=-1):\n super().__init__()\n self.dim_out = dim_out\n self.dim_inter = dim_inter\n self.num_neg = num_neg\n self.t = temperature\n self.momentum = momentum\n assert (local_loss or global_loss), \"at least one of local and global loss have to be true\"\n self.local_loss = local_loss\n self.global_loss = global_loss\n self.within_format = within_format\n self.fusion = fusion\n self.ddp = ddp\n if self.global_loss and self.local_loss:\n self.warmup = warmup\n else:\n self.warmup = -1\n # initialize models\n self.net2d = UNetHead(fusion, dim_inter=dim_inter, dim_out=dim_out)\n self.net3d = SparseUNetHead(fusion, dim_inter=dim_inter, dim_out=dim_out)\n\n if self.global_loss:\n self.net2d_m = UNetHead(fusion, dim_inter=dim_inter, dim_out=dim_out)\n self._copy(self.net2d, self.net2d_m)\n self.net3d_m = SparseUNetHead(fusion, dim_inter=dim_inter, dim_out=dim_out)\n self._copy(self.net3d, self.net3d_m)\n self.loss_func1 = NCELossMocoV2(num_neg, dim_out, temperature)\n self.loss_func2 = NCELossMocoV2(num_neg, dim_out, temperature)\n if within_format:\n self.loss_func3 = NCELossMocoV2(num_neg, dim_out, temperature)\n self.loss_func4 = NCELossMocoV2(num_neg, dim_out, temperature)\n\n def _copy(self, model:nn.Module, model_m:nn.Module):\n for p, pm in zip(model.parameters(), model_m.parameters()):\n pm.data.copy_(p.data)\n pm.requires_grad = False\n \n def update_momentum_models(self):\n momentum_update(self.net2d, self.net2d_m, self.momentum)\n momentum_update(self.net3d, self.net3d_m, self.momentum)\n\n def forward(self, data : Dict[str, Tensor], epoch=None):\n q2d, f2d = self.net2d(\n depth = data[\"depthmap1\"],\n rgb = data[\"rgb1\"] if self.fusion else None,\n ind = data[\"ind_depthmap1\"]\n )\n q3d, f3d = self.net3d(data[\"sin1\"], data[\"ind_vox1\"])\n if self.local_loss:\n loss_pc = point_info_nce_loss(f2d, f3d, self.t)[\"loss\"]\n else:\n loss_pc = torch.tensor(0.).to(q2d.device)\n \n if self.global_loss:\n with torch.no_grad():\n dm = data[\"depthmap2\"]\n rgb = data[\"rgb2\"]\n idm = data[\"ind_depthmap2\"]\n sin = data[\"sin2\"]\n iv = data[\"ind_vox2\"]\n if self.ddp:\n # BN shuffle\n # NOTE: no need for Sparse CNN, according to DepthContrast code\n dm, idx_unshuffle, idx_shuffle = batch_shuffle_ddp(dm, return_idx_shuffle=True)\n rgb, _ = batch_shuffle_ddp(rgb, idx=idx_shuffle)\n idm, _ = batch_shuffle_ddp(idm, idx=idx_shuffle)\n k2d, _ = self.net2d_m(depth=dm, rgb=rgb, ind=idm)\n k3d, _ = self.net3d_m(sin, iv)\n if self.ddp:\n # unshuffle\n k2d = batch_unshuffle_ddp(k2d, idx_unshuffle)\n \n # 3d model generates keys, 2d model generates queries\n loss23 = self.loss_func1(q2d, k3d)\n # 3d model generates queries, 2d model generates keys\n loss32 = self.loss_func2(q3d, k2d)\n if self.within_format:\n # consider within format\n loss22 = self.loss_func3(q2d, k2d)\n loss33 = self.loss_func4(q3d, k3d)\n loss_g = (loss23 + loss32 + loss22 + loss33)/4.\n else:\n # only consider cross-format\n loss_g = (loss23 + loss32)/2\n \n if self.warmup > 0 and epoch is not None:\n wl = 0.5 * min(epoch/self.warmup, 1)\n wg = 1 - wl\n loss = loss_g * wg + loss_pc * wl\n else:\n loss = (loss_pc + loss_g)/(float(self.local_loss)+float(self.global_loss)) \n \n metric = {\"loss32\": loss32.item(), \n \"loss23\": loss23.item(),\n \"loss_g\": loss_g.item(),\n \"loss_pc\": loss_pc.item(),\n \"loss\": loss.item()}\n if self.within_format:\n metric[\"loss22\"] = loss22.item()\n metric[\"loss33\"] = loss33.item()\n else:\n # only PointInfoNCE Loss\n loss = loss_pc\n metric = {\"loss_pc\": loss_pc.item(),\n \"loss\": loss.item()}\n return loss, metric \n\n\ndef main():\n # hyperparamterts\n BATCH_SIZE = 8\n EPOCH = 50\n TEMPERATURE = 0.07\n LOCAL_LOSS = True\n GLOBAL_LOSS = True\n NUM_MATCH = 1024\n SPARSE_KEY = [\"coords\", \"feats\"]\n DEVICE = \"cuda:0\"\n VOXEL_SIZE = 0.05\n BASE_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), \"log\")\n SAVE = os.path.join(BASE_DIR, \"DVCo_test\")\n WARMUP = 10\n if not os.path.exists(SAVE):\n os.mkdir(SAVE)\n\n net = DepthVoxelContrast(dim_out=128, dim_inter=512, num_neg=4096*8,\n ddp=False, local_loss=LOCAL_LOSS, global_loss=GLOBAL_LOSS,\n temperature=TEMPERATURE, warmup=WARMUP)\n net.to(DEVICE)\n ds = ScanNetDepthVoxelDataset(\n \"train\", num_match=NUM_MATCH, match_thresh=0.05, voxel_size=VOXEL_SIZE, num_pairs=2)\n dl = DataLoader(ds, batch_size=BATCH_SIZE, shuffle=True,\n drop_last=True, collate_fn=collate_dense_sparse(SPARSE_KEY, 2), num_workers=6)\n \n val_ds = ScanNetDepthVoxelDataset(\n \"val\", num_match=NUM_MATCH, match_thresh=0.05, voxel_size=VOXEL_SIZE, num_pairs=2)\n val_dl = DataLoader(val_ds, batch_size=BATCH_SIZE, shuffle=True,\n drop_last=True, collate_fn=collate_dense_sparse(SPARSE_KEY, 2), num_workers=6) \n \n optimizer = optim.SGD(net.parameters(), lr=0.03,\n weight_decay=1e-4, momentum=0.9)\n scheduler = lr_scheduler.CosineAnnealingLR(optimizer, EPOCH)\n for epoch in range(EPOCH):\n net.train()\n progress = ProgressMeter(\n len(dl),\n prefix=\"Epoch: [{}]\".format(epoch))\n \n for i, data in enumerate(dl):\n to_device(data, DEVICE)\n data[\"sin1\"] = ME.SparseTensor(\n data[\"feats1\"], data[\"coords1\"]\n )\n data[\"sin2\"] = ME.SparseTensor(\n data[\"feats2\"], data[\"coords2\"]\n )\n \n loss, metric = net(data, epoch+1)\n progress.update(i, metric)\n \n optimizer.zero_grad()\n loss.backward()\n optimizer.step()\n \n if GLOBAL_LOSS:\n net.update_momentum_models()\n \n if i % 20 == 0:\n progress.display(i)\n torch.cuda.empty_cache()\n scheduler.step()\n \n torch.save({\n \"epoch\": epoch, \n \"model_state_dict\": net.net2d.encoder.state_dict()},\n os.path.join(SAVE, \"ckpt_{:03d}_depth.pth\".format(epoch)))\n torch.save({\n \"epoch\": epoch, \n \"model_state_dict\": net.net3d.encoder.state_dict()},\n os.path.join(SAVE, \"ckpt_{:03d}_pcd.pth\".format(epoch)))\n \n with torch.no_grad():\n for i, data in enumerate(val_dl):\n # do nothing. hack to avoid OOM caused by MinkowskiEngine\n to_device(data, DEVICE)\n data[\"sin1\"] = ME.SparseTensor(\n data[\"feats1\"], data[\"coords1\"]\n )\n data[\"sin2\"] = ME.SparseTensor(\n data[\"feats2\"], data[\"coords2\"]\n )\n out = net.net3d(data[\"sin1\"], data[\"ind_vox1\"])\n \n if i > 20:\n break\n \n\nif __name__ == \"__main__\":\n main()\n \n", "repo_name": "lilanxiao/Invar3D", "sub_path": "train_dv.py", "file_name": "train_dv.py", "file_ext": "py", "file_size_in_byte": 12368, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 7, "dataset": "github-code", "pt": "37", "api": [{"api_name": "cv2.setNumThreads", "line_number": 3, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 27, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 27, "usage_type": "name"}, {"api_name": "model.cnn2d.conv2d.UNet", "line_number": 38, "usage_type": "call"}, {"api_name": "torch.nn.Sequential", "line_number": 39, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 39, "usage_type": "name"}, {"api_name": "torch.nn.ConvTranspose2d", "line_number": 39, "usage_type": "call"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 40, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 40, "usage_type": "name"}, {"api_name": "torch.nn.LeakyReLU", "line_number": 41, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 41, "usage_type": "name"}, {"api_name": "model.cnn2d.conv2d.BasicBlock", "line_number": 42, "usage_type": "call"}, {"api_name": "torch.nn.Sequential", "line_number": 43, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 43, "usage_type": "name"}, {"api_name": "torch.nn.ConvTranspose2d", "line_number": 43, "usage_type": "call"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 44, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 44, "usage_type": "name"}, {"api_name": "torch.nn.LeakyReLU", "line_number": 45, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 45, "usage_type": "name"}, {"api_name": "model.cnn2d.conv2d.BasicBlock", "line_number": 46, "usage_type": "call"}, {"api_name": "model.cnn2d.conv2d.BasicBlock", "line_number": 47, "usage_type": "call"}, {"api_name": "loss_moco.build_dense_head", "line_number": 48, "usage_type": "call"}, {"api_name": "loss_moco.build_mlp_head", "line_number": 49, "usage_type": "call"}, {"api_name": "torch.nn.AdaptiveMaxPool2d", "line_number": 50, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 50, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 52, "usage_type": "name"}, {"api_name": "torch.cat", "line_number": 60, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 62, "usage_type": "call"}, {"api_name": "loss._gather", "line_number": 66, "usage_type": "call"}, {"api_name": "torch.nn.functional.normalize", "line_number": 68, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 68, "usage_type": "attribute"}, {"api_name": "torch.nn.functional.normalize", "line_number": 71, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 71, "usage_type": "attribute"}, {"api_name": "torch.nn.Module", "line_number": 75, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 75, "usage_type": "name"}, {"api_name": "model.cnn3d.minkunet.MinkUNet34C", "line_number": 85, "usage_type": "call"}, {"api_name": "loss_moco.build_dense_head", "line_number": 86, "usage_type": "call"}, {"api_name": "loss_moco.build_mlp_head", "line_number": 87, "usage_type": "call"}, {"api_name": "torch.nn.AdaptiveMaxPool1d", "line_number": 88, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 88, "usage_type": "name"}, {"api_name": "MinkowskiEngine.SparseTensor", "line_number": 90, "usage_type": "attribute"}, {"api_name": "torch.Tensor", "line_number": 90, "usage_type": "name"}, {"api_name": "utils.sparse_helper.align_sparse_features", "line_number": 93, "usage_type": "call"}, {"api_name": "torch.nn.functional.normalize", "line_number": 95, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 95, "usage_type": "attribute"}, {"api_name": "torch.nn.functional.normalize", "line_number": 98, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 98, "usage_type": "attribute"}, {"api_name": "torch.nn.Module", "line_number": 102, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 102, "usage_type": "name"}, {"api_name": "loss_moco.NCELossMocoV2", "line_number": 140, "usage_type": "call"}, {"api_name": "loss_moco.NCELossMocoV2", "line_number": 141, "usage_type": "call"}, {"api_name": "loss_moco.NCELossMocoV2", "line_number": 143, "usage_type": "call"}, {"api_name": "loss_moco.NCELossMocoV2", "line_number": 144, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 146, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 146, "usage_type": "name"}, {"api_name": "model.cnn3d.minkunet.parameters", "line_number": 147, "usage_type": "call"}, {"api_name": "model.cnn3d.minkunet", "line_number": 147, "usage_type": "name"}, {"api_name": "loss_moco.momentum_update", "line_number": 152, "usage_type": "call"}, {"api_name": "loss_moco.momentum_update", "line_number": 153, "usage_type": "call"}, {"api_name": "typing.Dict", "line_number": 155, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 155, "usage_type": "name"}, {"api_name": "loss.point_info_nce_loss", "line_number": 163, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 165, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 168, "usage_type": "call"}, {"api_name": "loss_moco.batch_shuffle_ddp", "line_number": 177, "usage_type": "call"}, {"api_name": "loss_moco.batch_shuffle_ddp", "line_number": 178, "usage_type": "call"}, {"api_name": "loss_moco.batch_shuffle_ddp", "line_number": 179, "usage_type": "call"}, {"api_name": "loss_moco.batch_unshuffle_ddp", "line_number": 184, "usage_type": "call"}, {"api_name": "loss.item", "line_number": 210, "usage_type": "call"}, {"api_name": "loss.item", "line_number": 218, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 233, "usage_type": "call"}, {"api_name": "os.path", "line_number": 233, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 233, "usage_type": "call"}, {"api_name": "os.path.abspath", "line_number": 233, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 234, "usage_type": "call"}, {"api_name": "os.path", "line_number": 234, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 236, "usage_type": "call"}, {"api_name": "os.path", "line_number": 236, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 237, "usage_type": "call"}, {"api_name": "scannet.scannet_pretrain.ScanNetDepthVoxelDataset", "line_number": 243, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 245, "usage_type": "call"}, {"api_name": "utils.sparse_helper.collate_dense_sparse", "line_number": 246, "usage_type": "call"}, {"api_name": "scannet.scannet_pretrain.ScanNetDepthVoxelDataset", "line_number": 248, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 250, "usage_type": "call"}, {"api_name": "utils.sparse_helper.collate_dense_sparse", "line_number": 251, "usage_type": "call"}, {"api_name": "torch.optim.SGD", "line_number": 253, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 253, "usage_type": "name"}, {"api_name": "torch.optim.lr_scheduler.CosineAnnealingLR", "line_number": 255, "usage_type": "call"}, {"api_name": "torch.optim.lr_scheduler", "line_number": 255, "usage_type": "name"}, {"api_name": "meters.ProgressMeter", "line_number": 258, "usage_type": "call"}, {"api_name": "utils.sparse_helper.to_device", "line_number": 263, "usage_type": "call"}, {"api_name": "MinkowskiEngine.SparseTensor", "line_number": 264, "usage_type": "call"}, {"api_name": "MinkowskiEngine.SparseTensor", "line_number": 267, "usage_type": "call"}, {"api_name": "loss.backward", "line_number": 275, "usage_type": "call"}, {"api_name": "torch.cuda.empty_cache", "line_number": 283, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 283, "usage_type": "attribute"}, {"api_name": "torch.save", "line_number": 286, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 289, "usage_type": "call"}, {"api_name": "os.path", "line_number": 289, "usage_type": "attribute"}, {"api_name": "torch.save", "line_number": 290, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 293, "usage_type": "call"}, {"api_name": "os.path", "line_number": 293, "usage_type": "attribute"}, {"api_name": "torch.no_grad", "line_number": 295, "usage_type": "call"}, {"api_name": "utils.sparse_helper.to_device", "line_number": 298, "usage_type": "call"}, {"api_name": "MinkowskiEngine.SparseTensor", "line_number": 299, "usage_type": "call"}, {"api_name": "MinkowskiEngine.SparseTensor", "line_number": 302, "usage_type": "call"}]} +{"seq_id": "36668222099", "text": "# String encodings and numeric representations\nimport json\nimport re\nfrom typing import (\n Any,\n Callable,\n Dict,\n Iterable,\n Optional,\n Sequence,\n Type,\n Union,\n)\n\nfrom eth_abi.encoding import (\n BaseArrayEncoder,\n)\nfrom eth_typing import (\n HexStr,\n Primitives,\n TypeStr,\n)\nfrom eth_utils import (\n add_0x_prefix,\n encode_hex,\n is_bytes,\n is_hex,\n is_list_like,\n remove_0x_prefix,\n to_bytes,\n to_hex,\n)\nfrom eth_utils.toolz import (\n curry,\n)\nfrom hexbytes import (\n HexBytes,\n)\n\nfrom web3._utils.abi import (\n is_address_type,\n is_array_type,\n is_bool_type,\n is_bytes_type,\n is_int_type,\n is_string_type,\n is_uint_type,\n size_of_type,\n sub_type_of_array_type,\n)\nfrom web3._utils.validation import (\n validate_abi_type,\n validate_abi_value,\n)\nfrom web3.datastructures import (\n AttributeDict,\n)\n\n\ndef hex_encode_abi_type(\n abi_type: TypeStr, value: Any, force_size: Optional[int] = None\n) -> HexStr:\n \"\"\"\n Encodes value into a hex string in format of abi_type\n \"\"\"\n validate_abi_type(abi_type)\n validate_abi_value(abi_type, value)\n\n data_size = force_size or size_of_type(abi_type)\n if is_array_type(abi_type):\n sub_type = sub_type_of_array_type(abi_type)\n return HexStr(\n \"\".join(\n [remove_0x_prefix(hex_encode_abi_type(sub_type, v, 256)) for v in value]\n )\n )\n elif is_bool_type(abi_type):\n return to_hex_with_size(value, data_size)\n elif is_uint_type(abi_type):\n return to_hex_with_size(value, data_size)\n elif is_int_type(abi_type):\n return to_hex_twos_compliment(value, data_size)\n elif is_address_type(abi_type):\n return pad_hex(value, data_size)\n elif is_bytes_type(abi_type):\n if is_bytes(value):\n return encode_hex(value)\n else:\n return value\n elif is_string_type(abi_type):\n return to_hex(text=value)\n else:\n raise ValueError(f\"Unsupported ABI type: {abi_type}\")\n\n\ndef to_hex_twos_compliment(value: Any, bit_size: int) -> HexStr:\n \"\"\"\n Converts integer value to twos compliment hex representation with given bit_size\n \"\"\"\n if value >= 0:\n return to_hex_with_size(value, bit_size)\n\n value = (1 << bit_size) + value\n hex_value = hex(value)\n hex_value = HexStr(hex_value.rstrip(\"L\"))\n return hex_value\n\n\ndef to_hex_with_size(value: Any, bit_size: int) -> HexStr:\n \"\"\"\n Converts a value to hex with given bit_size:\n \"\"\"\n return pad_hex(to_hex(value), bit_size)\n\n\ndef pad_hex(value: Any, bit_size: int) -> HexStr:\n \"\"\"\n Pads a hex string up to the given bit_size\n \"\"\"\n value = remove_0x_prefix(value)\n return add_0x_prefix(value.zfill(int(bit_size / 4)))\n\n\ndef trim_hex(hexstr: HexStr) -> HexStr:\n if hexstr.startswith(\"0x0\"):\n hexstr = HexStr(re.sub(\"^0x0+\", \"0x\", hexstr))\n if hexstr == \"0x\":\n hexstr = HexStr(\"0x0\")\n return hexstr\n\n\n@curry\ndef pad_bytes(fill_with: bytes, num_bytes: int, unpadded: bytes) -> bytes:\n return unpadded.rjust(num_bytes, fill_with)\n\n\nzpad_bytes = pad_bytes(b\"\\0\")\n\n\n@curry\ndef text_if_str(\n to_type: Callable[..., str], text_or_primitive: Union[Primitives, HexStr, str]\n) -> str:\n \"\"\"\n Convert to a type, assuming that strings can be only unicode text (not a hexstr)\n\n @param to_type is a function that takes the arguments (primitive, hexstr=hexstr,\n text=text), eg~ to_bytes, to_text, to_hex, to_int, etc\n @param text_or_primitive in bytes, str, or int.\n \"\"\"\n if isinstance(text_or_primitive, str):\n (primitive, text) = (None, text_or_primitive)\n else:\n (primitive, text) = (text_or_primitive, None)\n return to_type(primitive, text=text)\n\n\n@curry\ndef hexstr_if_str(\n to_type: Callable[..., HexStr], hexstr_or_primitive: Union[Primitives, HexStr, str]\n) -> HexStr:\n \"\"\"\n Convert to a type, assuming that strings can be only hexstr (not unicode text)\n\n @param to_type is a function that takes the arguments (primitive, hexstr=hexstr,\n text=text), eg~ to_bytes, to_text, to_hex, to_int, etc\n @param hexstr_or_primitive in bytes, str, or int.\n \"\"\"\n if isinstance(hexstr_or_primitive, str):\n (primitive, hexstr) = (None, hexstr_or_primitive)\n if remove_0x_prefix(HexStr(hexstr)) and not is_hex(hexstr):\n raise ValueError(\n \"when sending a str, it must be a hex string. \"\n f\"Got: {hexstr_or_primitive!r}\"\n )\n else:\n (primitive, hexstr) = (hexstr_or_primitive, None)\n return to_type(primitive, hexstr=hexstr)\n\n\nclass FriendlyJsonSerde:\n \"\"\"\n Friendly JSON serializer & deserializer\n\n When encoding or decoding fails, this class collects\n information on which fields failed, to show more\n helpful information in the raised error messages.\n \"\"\"\n\n def _json_mapping_errors(self, mapping: Dict[Any, Any]) -> Iterable[str]:\n for key, val in mapping.items():\n try:\n self._friendly_json_encode(val)\n except TypeError as exc:\n yield f\"{key!r}: because ({exc})\"\n\n def _json_list_errors(self, iterable: Iterable[Any]) -> Iterable[str]:\n for index, element in enumerate(iterable):\n try:\n self._friendly_json_encode(element)\n except TypeError as exc:\n yield f\"{index}: because ({exc})\"\n\n def _friendly_json_encode(\n self, obj: Dict[Any, Any], cls: Optional[Type[json.JSONEncoder]] = None\n ) -> str:\n try:\n encoded = json.dumps(obj, cls=cls)\n return encoded\n except TypeError as full_exception:\n if hasattr(obj, \"items\"):\n item_errors = \"; \".join(self._json_mapping_errors(obj))\n raise TypeError(\n f\"dict had unencodable value at keys: {{{item_errors}}}\"\n )\n elif is_list_like(obj):\n element_errors = \"; \".join(self._json_list_errors(obj))\n raise TypeError(\n f\"list had unencodable value at index: [{element_errors}]\"\n )\n else:\n raise full_exception\n\n def json_decode(self, json_str: str) -> Dict[Any, Any]:\n try:\n decoded = json.loads(json_str)\n return decoded\n except json.decoder.JSONDecodeError as exc:\n err_msg = f\"Could not decode {json_str!r} because of {exc}.\"\n # Calling code may rely on catching JSONDecodeError to recognize bad json\n # so we have to re-raise the same type.\n raise json.decoder.JSONDecodeError(err_msg, exc.doc, exc.pos)\n\n def json_encode(\n self, obj: Dict[Any, Any], cls: Optional[Type[json.JSONEncoder]] = None\n ) -> str:\n try:\n return self._friendly_json_encode(obj, cls=cls)\n except TypeError as exc:\n raise TypeError(f\"Could not encode to JSON: {exc}\")\n\n\ndef to_4byte_hex(hex_or_str_or_bytes: Union[HexStr, str, bytes, int]) -> HexStr:\n size_of_4bytes = 4 * 8\n byte_str = hexstr_if_str(to_bytes, hex_or_str_or_bytes)\n if len(byte_str) > 4:\n raise ValueError(f\"expected value of size 4 bytes. Got: {len(byte_str)} bytes\")\n hex_str = encode_hex(byte_str)\n return pad_hex(hex_str, size_of_4bytes)\n\n\nclass DynamicArrayPackedEncoder(BaseArrayEncoder):\n is_dynamic = True\n\n def encode(self, value: Sequence[Any]) -> bytes:\n encoded_elements = self.encode_elements(value)\n encoded_value = encoded_elements\n\n return encoded_value\n\n\n# TODO: Replace with eth-abi packed encoder once web3 requires eth-abi>=2\ndef encode_single_packed(_type: TypeStr, value: Any) -> bytes:\n import codecs\n\n from eth_abi import (\n grammar as abi_type_parser,\n )\n from eth_abi.registry import (\n has_arrlist,\n registry,\n )\n\n abi_type = abi_type_parser.parse(_type)\n if has_arrlist(_type):\n item_encoder = registry.get_encoder(abi_type.item_type.to_type_str())\n if abi_type.arrlist[-1] != 1:\n return DynamicArrayPackedEncoder(item_encoder=item_encoder).encode(value)\n else:\n raise NotImplementedError(\n \"Fixed arrays are not implemented in this packed encoder prototype\"\n )\n elif abi_type.base == \"string\":\n return codecs.encode(value, \"utf8\")\n elif abi_type.base == \"bytes\":\n return value\n return None\n\n\nclass Web3JsonEncoder(json.JSONEncoder):\n def default(self, obj: Any) -> Union[Dict[Any, Any], HexStr]:\n if isinstance(obj, AttributeDict):\n return obj.__dict__\n elif isinstance(obj, HexBytes):\n return HexStr(obj.hex())\n elif isinstance(obj, bytes):\n return to_hex(obj)\n return json.JSONEncoder.default(self, obj)\n\n\ndef to_json(obj: Dict[Any, Any]) -> str:\n \"\"\"\n Convert a complex object (like a transaction object) to a JSON string\n \"\"\"\n return FriendlyJsonSerde().json_encode(obj, cls=Web3JsonEncoder)\n", "repo_name": "ethereum/web3.py", "sub_path": "web3/_utils/encoding.py", "file_name": "encoding.py", "file_ext": "py", "file_size_in_byte": 9117, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4510, "dataset": "github-code", "pt": "37", "api": [{"api_name": "eth_typing.TypeStr", "line_number": 61, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 61, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 61, "usage_type": "name"}, {"api_name": "web3._utils.validation.validate_abi_type", "line_number": 66, "usage_type": "call"}, {"api_name": "web3._utils.validation.validate_abi_value", "line_number": 67, "usage_type": "call"}, {"api_name": "web3._utils.abi.size_of_type", "line_number": 69, "usage_type": "call"}, {"api_name": "web3._utils.abi.is_array_type", "line_number": 70, "usage_type": "call"}, {"api_name": "web3._utils.abi.sub_type_of_array_type", "line_number": 71, "usage_type": "call"}, {"api_name": "eth_typing.HexStr", "line_number": 72, "usage_type": "call"}, {"api_name": "eth_utils.remove_0x_prefix", "line_number": 74, "usage_type": "call"}, {"api_name": "web3._utils.abi.is_bool_type", "line_number": 77, "usage_type": "call"}, {"api_name": "web3._utils.abi.is_uint_type", "line_number": 79, "usage_type": "call"}, {"api_name": "web3._utils.abi.is_int_type", "line_number": 81, "usage_type": "call"}, {"api_name": "web3._utils.abi.is_address_type", "line_number": 83, "usage_type": "call"}, {"api_name": "web3._utils.abi.is_bytes_type", "line_number": 85, "usage_type": "call"}, {"api_name": "eth_utils.is_bytes", "line_number": 86, "usage_type": "call"}, {"api_name": "eth_utils.encode_hex", "line_number": 87, "usage_type": "call"}, {"api_name": "web3._utils.abi.is_string_type", "line_number": 90, "usage_type": "call"}, {"api_name": "eth_utils.to_hex", "line_number": 91, "usage_type": "call"}, {"api_name": "eth_typing.HexStr", "line_number": 62, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 96, "usage_type": "name"}, {"api_name": "eth_typing.HexStr", "line_number": 105, "usage_type": "call"}, {"api_name": "eth_typing.HexStr", "line_number": 96, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 109, "usage_type": "name"}, {"api_name": "eth_utils.to_hex", "line_number": 113, "usage_type": "call"}, {"api_name": "eth_typing.HexStr", "line_number": 109, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 116, "usage_type": "name"}, {"api_name": "eth_utils.remove_0x_prefix", "line_number": 120, "usage_type": "call"}, {"api_name": "eth_utils.add_0x_prefix", "line_number": 121, "usage_type": "call"}, {"api_name": "eth_typing.HexStr", "line_number": 116, "usage_type": "name"}, {"api_name": "eth_typing.HexStr", "line_number": 124, "usage_type": "name"}, {"api_name": "eth_typing.HexStr", "line_number": 126, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 126, "usage_type": "call"}, {"api_name": "eth_typing.HexStr", "line_number": 128, "usage_type": "call"}, {"api_name": "eth_utils.toolz.curry", "line_number": 132, "usage_type": "name"}, {"api_name": "typing.Callable", "line_number": 142, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 142, "usage_type": "name"}, {"api_name": "eth_typing.Primitives", "line_number": 142, "usage_type": "name"}, {"api_name": "eth_typing.HexStr", "line_number": 142, "usage_type": "name"}, {"api_name": "eth_utils.toolz.curry", "line_number": 140, "usage_type": "name"}, {"api_name": "typing.Callable", "line_number": 160, "usage_type": "name"}, {"api_name": "eth_typing.HexStr", "line_number": 160, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 160, "usage_type": "name"}, {"api_name": "eth_typing.Primitives", "line_number": 160, "usage_type": "name"}, {"api_name": "eth_utils.remove_0x_prefix", "line_number": 171, "usage_type": "call"}, {"api_name": "eth_typing.HexStr", "line_number": 171, "usage_type": "call"}, {"api_name": "eth_utils.is_hex", "line_number": 171, "usage_type": "call"}, {"api_name": "eth_utils.toolz.curry", "line_number": 158, "usage_type": "name"}, {"api_name": "eth_typing.HexStr", "line_number": 161, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 190, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 190, "usage_type": "name"}, {"api_name": "typing.Iterable", "line_number": 190, "usage_type": "name"}, {"api_name": "typing.Iterable", "line_number": 197, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 197, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 205, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 205, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 205, "usage_type": "name"}, {"api_name": "typing.Type", "line_number": 205, "usage_type": "name"}, {"api_name": "json.JSONEncoder", "line_number": 205, "usage_type": "attribute"}, {"api_name": "json.dumps", "line_number": 208, "usage_type": "call"}, {"api_name": "eth_utils.is_list_like", "line_number": 216, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 226, "usage_type": "call"}, {"api_name": "json.decoder", "line_number": 228, "usage_type": "attribute"}, {"api_name": "json.decoder.JSONDecodeError", "line_number": 232, "usage_type": "call"}, {"api_name": "json.decoder", "line_number": 232, "usage_type": "attribute"}, {"api_name": "typing.Dict", "line_number": 224, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 224, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 235, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 235, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 235, "usage_type": "name"}, {"api_name": "typing.Type", "line_number": 235, "usage_type": "name"}, {"api_name": "json.JSONEncoder", "line_number": 235, "usage_type": "attribute"}, {"api_name": "typing.Union", "line_number": 243, "usage_type": "name"}, {"api_name": "eth_typing.HexStr", "line_number": 243, "usage_type": "name"}, {"api_name": "eth_utils.to_bytes", "line_number": 245, "usage_type": "argument"}, {"api_name": "eth_utils.encode_hex", "line_number": 248, "usage_type": "call"}, {"api_name": "eth_abi.encoding.BaseArrayEncoder", "line_number": 252, "usage_type": "name"}, {"api_name": "typing.Sequence", "line_number": 255, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 255, "usage_type": "name"}, {"api_name": "eth_typing.TypeStr", "line_number": 263, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 263, "usage_type": "name"}, {"api_name": "eth_abi.grammar.parse", "line_number": 274, "usage_type": "call"}, {"api_name": "eth_abi.grammar", "line_number": 274, "usage_type": "name"}, {"api_name": "eth_abi.registry.has_arrlist", "line_number": 275, "usage_type": "call"}, {"api_name": "eth_abi.registry.registry.get_encoder", "line_number": 276, "usage_type": "call"}, {"api_name": "eth_abi.registry.registry", "line_number": 276, "usage_type": "name"}, {"api_name": "codecs.encode", "line_number": 284, "usage_type": "call"}, {"api_name": "json.JSONEncoder", "line_number": 290, "usage_type": "attribute"}, {"api_name": "typing.Any", "line_number": 291, "usage_type": "name"}, {"api_name": "web3.datastructures.AttributeDict", "line_number": 292, "usage_type": "argument"}, {"api_name": "hexbytes.HexBytes", "line_number": 294, "usage_type": "argument"}, {"api_name": "eth_typing.HexStr", "line_number": 295, "usage_type": "call"}, {"api_name": "eth_utils.to_hex", "line_number": 297, "usage_type": "call"}, {"api_name": "json.JSONEncoder.default", "line_number": 298, "usage_type": "call"}, {"api_name": "json.JSONEncoder", "line_number": 298, "usage_type": "attribute"}, {"api_name": "typing.Union", "line_number": 291, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 291, "usage_type": "name"}, {"api_name": "eth_typing.HexStr", "line_number": 291, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 301, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 301, "usage_type": "name"}]} +{"seq_id": "6419406218", "text": "# coding: utf-8\n\n'''\n===============================================================================\nSitegen\n\nAuthor: Karlisson M. Bezerra\nE-mail: contact@hacktoon.com\nURL: https://github.com/hacktoon/sitegen\nLicense: WTFPL - http://sam.zoy.org/wtfpl/COPYING\n===============================================================================\n'''\n\nimport os\nimport json\nimport sys\n\nfrom . import reader\nfrom . import utils\nfrom .paging import Page, PageList\nfrom .categorization import Category, CategoryList\nfrom .stamper.stamper import Stamper\nfrom .exceptions import TemplateError\n\n# set to env global date format\nDATE_FORMAT = '%Y-%m-%d %H:%M:%S'\nTEMPLATES_DIR = 'templates'\nDEFAULT_TEMPLATE = 'default'\nTEMPLATES_EXT = 'tpl'\n\n\nclass Template:\n def __init__(self, id, path):\n self.id = id\n if not os.path.exists(path):\n raise FileNotFoundError('Template {!r}'\n ' not found'.format(path))\n self.content = utils.read_file(path)\n self.path = path\n self.include_path = ''\n\n def render(self, context):\n cache = context['template_cache']\n if self.id in cache.keys():\n tree = cache[self.id]\n else:\n tree = Stamper(self.content, include_path=self.include_path)\n cache[self.id] = tree\n if 'page' in context:\n page_content = context['page'].get('content', '')\n context['page']['content'] = Stamper(page_content).render(context)\n return tree.render(context)\n\n\nclass JSONTemplate(Template):\n def __init__(self):\n pass\n\n def render(self, page):\n page_data = page.data.copy()\n page_data['date'] = page['date'].strftime(DATE_FORMAT)\n return json.dumps(page_data, skipkeys=True)\n\n\nclass HTMLTemplate(Template):\n '''Manage HTML rendering process'''\n def build_external_tags(self, links, tpl):\n '''To help in organization'''\n tag_list = []\n for link in links:\n tag_list.append(tpl.format(link))\n return '\\n'.join(tag_list)\n\n def build_style_tags(self, links):\n '''To organize the book style'''\n if not links:\n return ''\n link_tpl = ''\n links = [f for f in links if f.endswith('.css')]\n return self.build_external_tags(links, link_tpl)\n\n def build_script_tags(self, links):\n '''To organize the behavior scripts'''\n if not links:\n return ''\n script_tpl = ''\n links = [f for f in links if f.endswith('.js')]\n return self.build_external_tags(links, script_tpl)\n\n def render(self, page, env):\n page_data = page.data.copy()\n page_data.update({\n 'styles': self.build_style_tags(page.styles),\n 'scripts': self.build_script_tags(page.scripts)\n })\n env['page'] = page_data\n return super().render(env)\n", "repo_name": "hacktoon/sitegen", "sub_path": "sitegen/template.py", "file_name": "template.py", "file_ext": "py", "file_size_in_byte": 2953, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "os.path.exists", "line_number": 35, "usage_type": "call"}, {"api_name": "os.path", "line_number": 35, "usage_type": "attribute"}, {"api_name": "stamper.stamper.Stamper", "line_number": 47, "usage_type": "call"}, {"api_name": "stamper.stamper.Stamper", "line_number": 51, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 62, "usage_type": "call"}]} +{"seq_id": "36390471529", "text": "from rasterio.warp import reproject, Resampling, calculate_default_transform\nimport rasterio as rio\ndef reproj_match(infile, match, outfile):\n \"\"\"Reproject a file to match the shape and projection of existing raster. \n \n Parameters\n ----------\n infile : (string) path to input file to reproject\n match : (string) path to raster with desired shape and projection \n outfile : (string) path to output file tif\n \"\"\"\n # open input\n with rio.open(infile) as src:\n src_transform = src.transform\n \n # open input to match\n with rio.open(match) as match:\n dst_crs = match.crs\n \n # calculate the output transform matrix\n dst_transform, dst_width, dst_height = calculate_default_transform(\n src.crs, # input CRS\n dst_crs, # output CRS\n match.width, # input width\n match.height, # input height \n *match.bounds, # unpacks input outer boundaries (left, bottom, right, top)\n )\n\n # set properties for output\n dst_kwargs = src.meta.copy()\n dst_kwargs.update({\"crs\": dst_crs,\n \"transform\": dst_transform,\n \"width\": dst_width,\n \"height\": dst_height,\n \"nodata\": 0})\n print(\"Coregistered to shape:\", dst_height,dst_width,'\\n Affine',dst_transform)\n # open output\n with rio.open(outfile, \"w\", **dst_kwargs) as dst:\n # iterate through bands and write using reproject function\n for i in range(1, src.count + 1):\n reproject(\n source=rio.band(src, i),\n destination=rio.band(dst, i),\n src_transform=src.transform,\n src_crs=src.crs,\n dst_transform=dst_transform,\n dst_crs=dst_crs,\n resampling=Resampling.nearest)\n", "repo_name": "mmann1123/geo_python", "sub_path": "Scripts/reproj_match.py", "file_name": "reproj_match.py", "file_ext": "py", "file_size_in_byte": 2001, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "rasterio.open", "line_number": 13, "usage_type": "call"}, {"api_name": "rasterio.open", "line_number": 17, "usage_type": "call"}, {"api_name": "rasterio.warp.calculate_default_transform", "line_number": 21, "usage_type": "call"}, {"api_name": "rasterio.open", "line_number": 38, "usage_type": "call"}, {"api_name": "rasterio.warp.reproject", "line_number": 41, "usage_type": "call"}, {"api_name": "rasterio.band", "line_number": 42, "usage_type": "call"}, {"api_name": "rasterio.band", "line_number": 43, "usage_type": "call"}, {"api_name": "rasterio.warp.Resampling.nearest", "line_number": 48, "usage_type": "attribute"}, {"api_name": "rasterio.warp.Resampling", "line_number": 48, "usage_type": "name"}]} +{"seq_id": "34271929749", "text": "# -*- coding: utf-8 -*-\n\n\n\"\"\"setup.py: setuptools control.\"\"\"\n\n\nimport re\nfrom setuptools import setup\nfrom setuptools import find_packages\n\n\nversion = re.search(\n '^__version__\\s*=\\s*\"(.*)\"',\n open('app/app.py').read(),\n re.M\n ).group(1)\n\n\nwith open(\"README.md\", \"rb\") as f:\n long_descr = f.read().decode(\"utf-8\")\n\n\nsetup(\n name = \"cmdline-my-app\",\n packages=find_packages(include=['app', 'app.*']),\n entry_points = {\n \"console_scripts\": ['my-app = app.app:main']\n },\n include_package_data=True,\n version = version,\n description = \"Python command line application template.\",\n long_description = long_descr,\n # install_requires=[\n # 'PyYAML',\n # 'pandas==0.23.3',\n # 'numpy>=1.14.5'\n # ],\n author = \"Adrian Lyxell\",\n author_email = \"adrian.lyxell@sdnit.se\",\n #url = \"http://github.com/wherethecodeis\",\n )\n", "repo_name": "adrlyx/adrian-base-py-proj", "sub_path": "setup.py", "file_name": "setup.py", "file_ext": "py", "file_size_in_byte": 895, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "re.search", "line_number": 12, "usage_type": "call"}, {"api_name": "re.M", "line_number": 15, "usage_type": "attribute"}, {"api_name": "setuptools.setup", "line_number": 23, "usage_type": "call"}, {"api_name": "setuptools.find_packages", "line_number": 25, "usage_type": "call"}]} +{"seq_id": "73495659306", "text": "import requests\nimport time\nimport datetime\nimport re\nimport threading\nimport json\nimport logging\n\ndef lprint(str):\n print(str)\n logging.info(str)\n\ndef update_list(new_record, dst_file):\n try:\n f = open(dst_file, 'a+')\n f.seek(0)\n if not new_record in f.read():\n f.write(new_record+'\\n')\n s = 'Added to '+dst_file+': '+new_record\n lprint(s)\n f.close()\n return True\n except:\n return False\n\n\ndef do_list(radio_info):\n\n encoding = 'latin1'\n info = ''\n\n s = 'Started task for url: '+radio_info[0]+'\\n\\tresult will stored in file: '+radio_info[1]\n lprint(s)\n \n radio_session = requests.Session()\n\n while True:\n try:\n radio = radio_session.get(radio_info[0], headers={'Icy-MetaData': '1'}, stream=True) #, timeout = 20)\n except requests.exceptions.Timeout:\n logging.info('Timeout.')\n continue\n except requests.exceptions.ConnectionError:\n logging.info('Network Unavailable. Check connection.')\n continue\n except:\n logging.info('Unexpected error.')\n continue\n\n metaint = int(radio.headers['icy-metaint'])\n\n stream = radio.raw\n\n audio_data = stream.read(metaint)\n meta_byte = stream.read(1)\n\n if (meta_byte):\n meta_length = ord(meta_byte) * 16\n\n meta_data = stream.read(meta_length).rstrip(b'\\0')\n\n stream_title = re.search(br\"StreamTitle='([^']*)';\", meta_data)\n\n\n if stream_title:\n\n stream_title = stream_title.group(1).decode(encoding, errors='replace')\n\n if info != stream_title:\n if update_list(stream_title, radio_info[1]):\n\n info = stream_title\n\n time.sleep(1)\n\n\ntasks = []\n\nlogging.basicConfig(level=logging.INFO, filename='logger.log', filemode='a+', format='%(asctime)s - %(message)s', datefmt='%d-%b-%y %H:%M:%S')\nlprint('Started.')\n\ntry:\n conf = open('config.json', 'r')\n \n data = json.load(conf)\n \n for radio_conf in data['radio list']:\n tasks = threading.Thread (target = do_list, args=(radio_conf,))\n tasks.start()\n\nexcept:\n lprint('Wrong config file!')\n\n\n\n\n", "repo_name": "barabacka/RadioSongLogger", "sub_path": "logger.py", "file_name": "logger.py", "file_ext": "py", "file_size_in_byte": 2269, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "logging.info", "line_number": 11, "usage_type": "call"}, {"api_name": "requests.Session", "line_number": 35, "usage_type": "call"}, {"api_name": "requests.exceptions", "line_number": 40, "usage_type": "attribute"}, {"api_name": "logging.info", "line_number": 41, "usage_type": "call"}, {"api_name": "requests.exceptions", "line_number": 43, "usage_type": "attribute"}, {"api_name": "logging.info", "line_number": 44, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 47, "usage_type": "call"}, {"api_name": "re.search", "line_number": 62, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 74, "usage_type": "call"}, {"api_name": "logging.basicConfig", "line_number": 79, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 79, "usage_type": "attribute"}, {"api_name": "json.load", "line_number": 85, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 88, "usage_type": "call"}]} +{"seq_id": "830406576", "text": "import pygame, sys\nfrom pygame.locals import *\nimport random\n\n### Hằng số trong game\nWINDOWWIDTH = 800\nWINDOWHEIGHT = 600\n\n### Các màu cơ bản\nWHITE = (255, 255, 255)\nRED = (255, 0, 0)\nGREEN = ( 0, 255, 0)\nBLACK = ( 0, 0, 0)\nSP = 5\npygame.init()\n\n### Xác định FPS ###\nFPS = 240\nfpsClock = pygame.time.Clock()\n### Thiết lập cửa sổ chơi game và caption\nBackGround = pygame.display.set_mode((800, 600))\npygame.display.set_caption('2048 by Tấn Lộc ^^')\n### Font chữ cho các layer text\nfont = pygame.font.SysFont('consolas', 15)\n\nCoor = [[200,100],[300,100],[400,100],[500,100]\n ,[200,200],[300,200],[400,200],[500,200]\n ,[200,300],[300,300],[400,300],[500,300]\n ,[200,400],[300,400],[400,400],[500,400]]\n\nValue = [0, 0, 0, 0,\n 0, 0, 0, 0,\n 0, 0, 0, 0,\n 0, 0, 0, 0]\nTrash = [0, 0, 0, 0,\n 0, 0, 0, 0,\n 0, 0, 0, 0,\n 0, 0, 0, 0]\nLoad = [[0,100,120],[0,260,120],[0,420,120],[0,580,120]\n ,[0,100,270],[0,260,270],[0,420,270],[0,580,270]\n ,[0,100,420],[0,260,420],[0,420,420],[0,580,420]]\nstop = 0\nup = 1\nleft = 2\ndown = 3\nright = 4\n\n\nLayBGr = pygame.image.load('Background\\\\0.png')\nLay2 = pygame.Surface((400, 400))\nLay2.fill(BLACK)\npygame.draw.line(Lay2, GREEN, (0, 0), (0, 400), 1)\npygame.draw.line(Lay2, GREEN, (100, 0), (100, 400), 1)\npygame.draw.line(Lay2, GREEN, (200, 0), (200, 400), 1)\npygame.draw.line(Lay2, GREEN, (300, 0), (300, 400), 1)\npygame.draw.line(Lay2, GREEN, (400, 0), (400, 400), 1)\n\npygame.draw.line(Lay2, GREEN, (0, 0), (400, 0), 1)\npygame.draw.line(Lay2, GREEN, (0, 100), (400, 100), 1)\npygame.draw.line(Lay2, GREEN, (0, 200), (400, 200), 1)\npygame.draw.line(Lay2, GREEN, (0, 300), (400, 300), 1)\npygame.draw.line(Lay2, GREEN, (0, 400), (400, 400), 1)\n\n### Tạo các lớp có trong game \nclass Powof2():\n def __init__(self, pos, value):\n self.surface = pygame.image.load('Num_IMG\\\\' + str(value) + '.png')\n self.value = value\n self.x = Coor[pos][0]\n self.y = Coor[pos][1]\n self.state = [0, 0, 0, 0, 4]\n self.pos = pos\n self.app = 100\n def Move(self):\n if self.state[self.state[4]] == 0:\n self.state[4] = 4\n if self.state[4] == 4:\n for i in range (0, 4):\n if self.state[i] != 0:\n self.state[4] = i \n if self.state[4] == 4:\n return\n if self.state[4] == 0:\n self.y -= SP\n self.state[self.state[4]] -= SP\n if self.state[4] == 1:\n self.x -= SP\n self.state[self.state[4]] -= SP\n if self.state[4] == 2:\n self.y += SP\n self.state[self.state[4]] -= SP\n if self.state[4] == 3:\n self.x += SP\n self.state[self.state[4]] -= SP\n\n def Up(self):\n while self.pos > 3:\n if Value[self.pos - 4] == 0:\n Value[self.pos - 4] = Value[self.pos]\n Value[self.pos] = 0\n self.pos -= 4\n self.state[0] += 100\n elif Value[self.pos - 4].value == Value[self.pos].value:\n Trash[self.pos] = Value[self.pos]\n Value[self.pos] = 0\n Value[self.pos - 4].value *= 2\n Value[self.pos - 4].surface = pygame.image.load('Num_IMG\\\\' + str(self.value*2) + '.png')\n break\n else:\n break \n def Left(self):\n while self.pos % 4 != 0:\n if Value[self.pos - 1] == 0:\n Value[self.pos - 1] = Value[self.pos]\n Value[self.pos] = 0\n self.pos -= 1\n self.state[1] += 100\n elif Value[self.pos - 1].value == Value[self.pos].value:\n Trash[self.pos] = Value[self.pos]\n Value[self.pos] = 0\n Value[self.pos - 1].value *= 2\n Value[self.pos - 1].surface = pygame.image.load('Num_IMG\\\\' + str(self.value*2) + '.png')\n break\n else:\n break\n def Down(self):\n while self.pos < 12:\n if Value[self.pos + 4] == 0:\n Value[self.pos + 4] = Value[self.pos]\n Value[self.pos] = 0 \n self.pos += 4\n self.state[2] += 100\n elif Value[self.pos + 4].value == Value[self.pos].value:\n Trash[self.pos] = Value[self.pos]\n Value[self.pos] = 0\n Value[self.pos + 4].value *= 2\n Value[self.pos + 4].surface = pygame.image.load('Num_IMG\\\\' + str(self.value*2) + '.png')\n break\n else:\n break \n def Right(self):\n while self.pos % 4 != 3:\n if Value[self.pos + 1] == 0:\n Value[self.pos + 1] = Value[self.pos]\n Value[self.pos] = 0\n self.pos += 1\n self.state[3] += 100\n elif Value[self.pos + 1].value == Value[self.pos].value:\n Trash[self.pos] = Value[self.pos]\n Value[self.pos] = 0\n Value[self.pos + 1].value *= 2\n Value[self.pos + 1].surface = pygame.image.load('Num_IMG\\\\' + str(self.value*2) + '.png')\n break\n else:\n break\n \n def Draw(self):\n if self.app != 0:\n ani = pygame.image.load('Load_IMG\\\\' + 'target-loader' + str(self.app%29+1) + '.png')\n self.app -= 1\n BackGround.blit(ani, (self.x, self.y))\n return\n BackGround.blit(self.surface, (self.x, self.y))\n \nclass Menu():\n def __init__(self):\n self.statecur = 0\n self.data = [[0,250,50],[0,250,160],[0,250,270],[0,250,380],[0,250,490]]\n self.bg = 0\n def draw(self):\n \n for i in range(0, 5):\n if self.statecur == i:\n self.data[i][0] = 1\n else:\n self.data[i][0] = 0\n BackGround.blit(pygame.image.load('Menu_IMG\\\\' + str(self.data[i][0]) + str(i) + '.png'), (self.data[i][1], self.data[i][2]))\n BackGround.blit(pygame.image.load('Menu_IMG\\\\l.png'), (130, self.data[self.statecur][2] + 14))\n BackGround.blit(pygame.image.load('Menu_IMG\\\\r.png'), (600, self.data[self.statecur][2] + 14))\n def move(self, direct):\n if direct == 1:\n self.statecur += 1\n if direct == 2:\n self.statecur -= 1\n \n\n if self.statecur == 5:\n self.statecur = 0\n if self.statecur == -1:\n self.statecur = 4\n def run(self):\n if self.statecur == 0:\n StartGame()\n if self.statecur == 1:\n runMenuLoad()\n \n if self.statecur == 2:\n self.bg += 1\n global LayBGr \n LayBGr = pygame.image.load('Background\\\\' + str(self.bg % 4) + '.png')\n if self.statecur == 4:\n pygame.quit()\n sys.exit()\n\n\n\ndef runMenuLoad():\n while True:\n pick = 99\n for event in pygame.event.get():\n if event.type == QUIT:\n pygame.quit()\n sys.exit()\n if event.type == KEYDOWN:\n if event.key == K_UP:\n VarMenu.move(2)\n if event.key == K_LEFT:\n VarMenu.move(3)\n if event.key == K_DOWN:\n VarMenu.move(1)\n if event.key == K_RIGHT:\n VarMenu.move(4)\n if event.key == 13:\n VarMenu.run()\n if event.key == 27:\n runMenuLoad()\n\n BackGround.fill(BLACK)\n f = open('Secret.txt', 'r')\n while True:\n line = f.readline()\n if not line:\n break\n if line.find(' ') == -1:\n Load[int(line)][0] = f.readline()\n f.close()\n f = open('Secret.txt', 'r')\n while True:\n if not line:\n break\n if line.find(' ') == -1 and int(line) == pick:\n line1 = f.readline()\n while True:\n line2 = f.readline()\n if not line2 or line2.find(' ') == -1:\n break\n pos = int(line2[0:line2.find(' ')])\n value = int(line2[line2.find(' ')+1:])\n Value[pos] = Powof2(pos, value) \n StartGame() \n\n \n\n pygame.display.update() # Cập nhật các chỉnh sửa giao diện\n fpsClock.tick(FPS)\n\n\ndef AddBlock():\n arr = []\n for i in range (0, 16):\n if Value[i] == 0:\n arr.append(i)\n if len(arr) > 0:\n pos = random.choice(arr)\n Value[pos] = Powof2(pos, 2)\n\nValue[5] = Powof2(5, 2)\n### Phần thực thi vòng lặp\ndef StartGame():\n while True:\n for event in pygame.event.get():\n if event.type == QUIT:\n pygame.quit()\n sys.exit()\n if event.type == KEYDOWN:\n if event.key == K_UP:\n for i in range(0, 16):\n if Value[i] != 0:\n Value[i].Up()\n if event.key == K_LEFT:\n for i in range(0, 16):\n if Value[i] != 0:\n Value[i].Left()\n if event.key == K_DOWN:\n for i in range(15, -1, -1):\n if Value[i] != 0:\n Value[i].Down()\n if event.key == K_RIGHT:\n for i in range(15, -1, -1):\n if Value[i] != 0:\n Value[i].Right()\n if event.key == 27:\n pygame.quit()\n sys.exit()\n AddBlock()\n\n\n\n BackGround.fill(BLACK)\n BackGround.blit(LayBGr, (0, 0))\n BackGround.blit(Lay2, (200, 100))\n\n \n for i in range(0, 16):\n if Value[i] != 0:\n Value[i].Move()\n Value[i].Draw()\n if Trash[i] != 0:\n Trash[i].Move()\n for j in range(0, 4):\n if Trash[i].state[j] != 0:\n Trash[i].Draw()\n\n\n\n pygame.display.update() # Cập nhật các chỉnh sửa giao diện\n fpsClock.tick(FPS)\n\n\nVarMenu = Menu()\n\ndef StartMenu():\n while True:\n for event in pygame.event.get():\n if event.type == QUIT:\n pygame.quit()\n sys.exit()\n if event.type == KEYDOWN:\n if event.key == K_UP:\n VarMenu.move(2)\n if event.key == K_LEFT:\n VarMenu.move(3)\n if event.key == K_DOWN:\n VarMenu.move(1)\n if event.key == K_RIGHT:\n VarMenu.move(4)\n if event.key == 13:\n VarMenu.run()\n if event.key == 27:\n pygame.quit()\n sys.exit()\n\n BackGround.fill(BLACK)\n BackGround.blit(LayBGr, (0, 0))\n VarMenu.draw()\n\n\n pygame.display.update() # Cập nhật các chỉnh sửa giao diện\n fpsClock.tick(FPS)\n\ndef main():\n StartMenu()\n StartGame()\n\n\n\nif __name__ == \"__main__\":\n main()\n\n\n\n\n\n\n\n\n\n \n\n", "repo_name": "9alaty-coL/L2048C", "sub_path": "2048.py", "file_name": "2048.py", "file_ext": "py", "file_size_in_byte": 11447, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "pygame.init", "line_number": 15, "usage_type": "call"}, {"api_name": "pygame.time.Clock", "line_number": 19, "usage_type": "call"}, {"api_name": "pygame.time", "line_number": 19, "usage_type": "attribute"}, {"api_name": "pygame.display.set_mode", "line_number": 21, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 21, "usage_type": "attribute"}, {"api_name": "pygame.display.set_caption", "line_number": 22, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 22, "usage_type": "attribute"}, {"api_name": "pygame.font.SysFont", "line_number": 24, "usage_type": "call"}, {"api_name": "pygame.font", "line_number": 24, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 49, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 49, "usage_type": "attribute"}, {"api_name": "pygame.Surface", "line_number": 50, "usage_type": "call"}, {"api_name": "pygame.draw.line", "line_number": 52, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 52, "usage_type": "attribute"}, {"api_name": "pygame.draw.line", "line_number": 53, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 53, "usage_type": "attribute"}, {"api_name": "pygame.draw.line", "line_number": 54, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 54, "usage_type": "attribute"}, {"api_name": "pygame.draw.line", "line_number": 55, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 55, "usage_type": "attribute"}, {"api_name": "pygame.draw.line", "line_number": 56, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 56, "usage_type": "attribute"}, {"api_name": "pygame.draw.line", "line_number": 58, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 58, "usage_type": "attribute"}, {"api_name": "pygame.draw.line", "line_number": 59, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 59, "usage_type": "attribute"}, {"api_name": "pygame.draw.line", "line_number": 60, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 60, "usage_type": "attribute"}, {"api_name": "pygame.draw.line", "line_number": 61, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 61, "usage_type": "attribute"}, {"api_name": "pygame.draw.line", "line_number": 62, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 62, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 67, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 67, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 107, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 107, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 122, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 122, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 137, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 137, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 152, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 152, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 159, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 159, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 177, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 177, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 178, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 178, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 179, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 179, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 200, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 200, "usage_type": "attribute"}, {"api_name": "pygame.quit", "line_number": 202, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 203, "usage_type": "call"}, {"api_name": "pygame.event.get", "line_number": 210, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 210, "usage_type": "attribute"}, {"api_name": "pygame.quit", "line_number": 212, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 213, "usage_type": "call"}, {"api_name": "pygame.display.update", "line_number": 254, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 254, "usage_type": "attribute"}, {"api_name": "random.choice", "line_number": 264, "usage_type": "call"}, {"api_name": "pygame.event.get", "line_number": 271, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 271, "usage_type": "attribute"}, {"api_name": "pygame.quit", "line_number": 273, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 274, "usage_type": "call"}, {"api_name": "pygame.quit", "line_number": 293, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 294, "usage_type": "call"}, {"api_name": "pygame.display.update", "line_number": 316, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 316, "usage_type": "attribute"}, {"api_name": "pygame.event.get", "line_number": 324, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 324, "usage_type": "attribute"}, {"api_name": "pygame.quit", "line_number": 326, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 327, "usage_type": "call"}, {"api_name": "pygame.quit", "line_number": 340, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 341, "usage_type": "call"}, {"api_name": "pygame.display.update", "line_number": 348, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 348, "usage_type": "attribute"}]} +{"seq_id": "16284583101", "text": "import numpy as np\nimport matplotlib.pyplot as plt\n\ndef step(x):\n return np.array(x > 0, dtype=np.int)\nx = np.arange(-5.0, 5.0, 0.1) # -5.0부터 5.0까지 0.1 간격 생성\ny = step(x)\nplt.title('Step Function')\nplt.plot(x,y)\nplt.show()\n\ndef sigmoid(x):\n return 1/(1+np.exp(-x))\nx = np.arange(-5.0, 5.0, 0.1)\ny = sigmoid(x)\nplt.plot(x, y)\nplt.plot([0,0],[1.0,0.0], ':') # 가운데 점선 추가\nplt.title('Sigmoid Function')\nplt.show()\n\ndef relu(x):\n return np.maximum(0, x)\nx = np.arange(-5.0, 5.0, 0.1)\ny = relu(x)\nplt.plot(x, y)\nplt.plot([0,0],[5.0,0.0], ':')\nplt.title('Relu Function')\nplt.show()\n\nx = np.arange(-5.0, 5.0, 0.1) # -5.0부터 5.0까지 0.1 간격 생성\ny = np.tanh(x)\nplt.plot(x, y)\nplt.plot([0,0],[1.0,-1.0], ':')\nplt.axhline(y=0, color='orange', linestyle='--')\nplt.title('Tanh Function')\nplt.show()\n\nx = np.arange(-5.0, 5.0, 0.1) # -5.0부터 5.0까지 0.1 간격 생성\ny = np.exp(x) / np.sum(np.exp(x))\nplt.plot(x, y)\nplt.title('Softmax Function')\nplt.show()", "repo_name": "hanwjdgh/NLU-basic", "sub_path": "8. Deep Learning/Artificial Neural Network/test1.py", "file_name": "test1.py", "file_ext": "py", "file_size_in_byte": 994, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "37", "api": [{"api_name": "numpy.array", "line_number": 5, "usage_type": "call"}, {"api_name": "numpy.int", "line_number": 5, "usage_type": "attribute"}, {"api_name": "numpy.arange", "line_number": 6, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.title", "line_number": 8, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 8, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 9, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 9, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 10, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 10, "usage_type": "name"}, {"api_name": "numpy.exp", "line_number": 13, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 14, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 16, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 16, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 17, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 17, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 18, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 18, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 19, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 19, "usage_type": "name"}, {"api_name": "numpy.maximum", "line_number": 22, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 23, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 25, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 25, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 26, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 26, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 27, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 27, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 28, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 28, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 30, "usage_type": "call"}, {"api_name": "numpy.tanh", "line_number": 31, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 32, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 32, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 33, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 33, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axhline", "line_number": 34, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 34, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 35, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 35, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 36, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 36, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 38, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 39, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 39, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 40, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 40, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 41, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 41, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 42, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 42, "usage_type": "name"}]} +{"seq_id": "10564696221", "text": "import math\nimport os\nimport pickle\nimport re\nimport shutil\nimport time\n\nimport cv2\nimport numpy as np\nimport requests\nfrom PIL import Image\nfrom bs4 import BeautifulSoup\nfrom keras.models import load_model\n\nfrom settings import (MODEL_CAPTCHA_FILENAME, MODEL_DIGIT_FILENAME,\n CAPTCHA_FOLDER, CAPTCHA_IMAGE)\n\nos.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'\n\n\ndef save_captcha_image(soup, save_dir, captcha_url='http://81.23.146.8/'):\n headers = {\n 'Host': '81.23.146.8',\n 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko)'\n 'Chrome/67.0.3396.79 Safari/537.36',\n 'Accept': 'image/webp,image/apng,image/*,*/*;q=0.8',\n 'Referer': 'http://81.23.146.8/default.aspx',\n 'Accept-Encoding': 'gzip, deflate',\n 'Accept-Language': 'ru-RU,ru;q=0.9,en-US;q=0.8,en;q=0.7'\n }\n captcha = requests.get(captcha_url + soup.img.get('src'), headers=headers, stream=True)\n if captcha.status_code == 200:\n if not os.path.exists(save_dir):\n os.makedirs(save_dir)\n with open(os.path.join(save_dir, CAPTCHA_IMAGE), 'wb') as image:\n captcha.raw.decode_content = True\n shutil.copyfileobj(captcha.raw, image)\n return image.name\n\n\ndef slice_image(image, save_dir, slice_size=50):\n if not os.path.exists(save_dir):\n os.makedirs(save_dir)\n img = Image.open(image)\n width, height = img.size\n lower = 0\n left = 0\n slices = int(math.ceil(width / slice_size))\n slice_count = 1\n for i in range(slices):\n if slice_count == slices:\n right = width\n else:\n right = int(slice_count * slice_size)\n bbox = (left, lower, right, height)\n working_slice = img.crop(bbox)\n left += slice_size\n working_slice.save(os.path.join(save_dir, str(i + 1) + '.jpg'))\n slice_count += 1\n\n\ndef solve_captcha(images_dir):\n with open(MODEL_DIGIT_FILENAME, 'rb') as f:\n lb = pickle.load(f)\n model = load_model(MODEL_CAPTCHA_FILENAME)\n captcha_text = ''\n for i in range(1, 5):\n image_file = os.path.join(images_dir, '{}.jpg'.format(i))\n digit_image = cv2.imread(image_file)\n digit_image = cv2.cvtColor(digit_image, cv2.COLOR_BGR2GRAY)\n digit_image = np.expand_dims(digit_image, axis=2)\n digit_image = np.expand_dims(digit_image, axis=0)\n prediction = model.predict(digit_image)\n digit = lb.inverse_transform(prediction)[0]\n print('Digit {} on {} step'.format(digit, i))\n captcha_text += digit\n print('Text of captcha:', captcha_text)\n return captcha_text\n\n\ndef get_info_of_card(card_number, user_id, all_info=False):\n check_card = re.match(r'\\d{10,20}', card_number)\n if check_card is None:\n return 'Введен неверный номер карты 😞'\n main_url = 'http://81.23.146.8/default.aspx'\n headers = {\n 'Host': '81.23.146.8',\n 'Upgrade-Insecure-Requests': '1',\n 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko)'\n 'Chrome/67.0.3396.79 Safari/537.36',\n 'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,image/apng,*/*;q=0.8',\n 'Accept-Encoding': 'gzip, deflate',\n 'Accept-Language': 'ru-RU,ru;q=0.9,en-US;q=0.8,en;q=0.7'\n }\n get_request = requests.get(main_url, headers=headers)\n page = get_request.text\n soup = BeautifulSoup(page, 'html.parser')\n event_validation = soup.find(id='__EVENTVALIDATION').get('value')\n view_state = soup.find(id='__VIEWSTATE').get('value')\n captcha_for_user = os.path.join(CAPTCHA_FOLDER, str(user_id))\n captcha_image = save_captcha_image(soup, captcha_for_user)\n digits_for_user = os.path.join(captcha_for_user, 'digits')\n slice_image(captcha_image, digits_for_user)\n captcha_text = solve_captcha(digits_for_user)\n time.sleep(5)\n headers.update({\n 'Origin': 'http://81.23.146.8',\n 'Referer': 'http://81.23.146.8/default.aspx',\n 'Content-Type': 'application/x-www-form-urlencoded'\n })\n post_request = requests.post(main_url, headers=headers, data={\n '__EVENTTARGET': '',\n 'EVENTARGUMENT': '',\n '__VIEWSTATE': view_state,\n 'cardnum': card_number,\n 'checkcode': captcha_text,\n 'Button2': 'Выполнить запрос',\n '__EVENTVALIDATION': event_validation\n })\n page = post_request.text\n soup = BeautifulSoup(page, 'html.parser')\n items_value = soup.findAll('td', class_='FieldValue')\n items = list()\n length_items = len(items_value)\n if length_items == 10:\n if all_info:\n for item in items_value:\n items.append(item.text)\n return items\n else:\n balance = str(items_value[2].text)\n return 'Баланс: {}'.format(balance)\n elif length_items == 8:\n if all_info:\n for item in items_value:\n items.append(item.text)\n return items\n else:\n balance = 'Для вашей карты не предусмотрен баланс 🤷‍♂️'\n return balance\n else:\n invalid_card = soup.find('div', class_='ErrorMessage')\n invalid_captcha = soup.find(id='CustomValidator1')\n if invalid_card is not None:\n return 'Введен неверный номер карты 😞'\n elif invalid_captcha is not None:\n return 'Не удалось решить капчу 😞'\n else:\n return 'Что-то пошло не так 😞'\n", "repo_name": "dinauu/TransCardBot", "sub_path": "solve_captcha.py", "file_name": "solve_captcha.py", "file_ext": "py", "file_size_in_byte": 5684, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "os.environ", "line_number": 18, "usage_type": "attribute"}, {"api_name": "requests.get", "line_number": 31, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 33, "usage_type": "call"}, {"api_name": "os.path", "line_number": 33, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 34, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 35, "usage_type": "call"}, {"api_name": "settings.CAPTCHA_IMAGE", "line_number": 35, "usage_type": "argument"}, {"api_name": "os.path", "line_number": 35, "usage_type": "attribute"}, {"api_name": "shutil.copyfileobj", "line_number": 37, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 42, "usage_type": "call"}, {"api_name": "os.path", "line_number": 42, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 43, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 44, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 44, "usage_type": "name"}, {"api_name": "math.ceil", "line_number": 48, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 58, "usage_type": "call"}, {"api_name": "os.path", "line_number": 58, "usage_type": "attribute"}, {"api_name": "settings.MODEL_DIGIT_FILENAME", "line_number": 63, "usage_type": "argument"}, {"api_name": "pickle.load", "line_number": 64, "usage_type": "call"}, {"api_name": "keras.models.load_model", "line_number": 65, "usage_type": "call"}, {"api_name": "settings.MODEL_CAPTCHA_FILENAME", "line_number": 65, "usage_type": "argument"}, {"api_name": "os.path.join", "line_number": 68, "usage_type": "call"}, {"api_name": "os.path", "line_number": 68, "usage_type": "attribute"}, {"api_name": "cv2.imread", "line_number": 69, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 70, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 70, "usage_type": "attribute"}, {"api_name": "numpy.expand_dims", "line_number": 71, "usage_type": "call"}, {"api_name": "numpy.expand_dims", "line_number": 72, "usage_type": "call"}, {"api_name": "re.match", "line_number": 82, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 95, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 97, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 100, "usage_type": "call"}, {"api_name": "settings.CAPTCHA_FOLDER", "line_number": 100, "usage_type": "argument"}, {"api_name": "os.path", "line_number": 100, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 102, "usage_type": "call"}, {"api_name": "os.path", "line_number": 102, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 105, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 111, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 121, "usage_type": "call"}]} +{"seq_id": "1124208308", "text": "from flask import Flask\nfrom flask_sqlalchemy import SQLAlchemy\nimport random\nfrom array import array\n# @Author : Mehdi Yc\n\napp = Flask(__name__)\napp.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = False\n# Database hosted in remotemysql.com\napp.config['SQLALCHEMY_DATABASE_URI'] = \"mysql://9EDsNxvuTb:Wz0VOBdaZx@remotemysql.com:3306/9EDsNxvuTb\"\n\n\n# email and password : \"mysql://Vpvv8swDIN:Lwj5xUAFl8@remotemysql.com:3306/Vpvv8swDIN\" table name : 'password'\n# email and token : \"mysql://RE7esJnNWs:xsODaDhhpG@remotemysql.com:3306/RE7esJnNWs\" table name : 'token'\n# email and infos : \"mysql://9EDsNxvuTb:Wz0VOBdaZx@remotemysql.com:3306/9EDsNxvuTb\" table name : 'utilisateur'\n\n\ndb = SQLAlchemy(app)\n\n\nclass Client(db.Model):\n\n __tablename__ = 'utilisateur'\n\n Email = db.Column('email', db.String, primary_key=True)\n Name = db.Column('nom', db.String)\n LastName = db.Column('prénom', db.String)\n CurrentBalance = db.Column('balance', db.String)\n Incomes = db.Column('incomes', db.String)\n Expenses = db.Column('expenses', db.String)\n\n\ngetData = Client.query.all()\n\n\ndef getClientData(user):\n\n x = []\n for b in getData:\n\n if str(b.Email) == user:\n x.append(str(b.Name))\n x.append(str(b.LastName))\n x.append(str(b.CurrentBalance))\n x.append(str(b.Incomes))\n x.append(str(b.Expenses))\n print(x)\n else:\n print('non')\n\n return x\n", "repo_name": "abmounir/flask-security", "sub_path": "Flask/securité/MainPage.py", "file_name": "MainPage.py", "file_ext": "py", "file_size_in_byte": 1444, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "flask.Flask", "line_number": 7, "usage_type": "call"}, {"api_name": "flask_sqlalchemy.SQLAlchemy", "line_number": 18, "usage_type": "call"}]} +{"seq_id": "13024378259", "text": "import io\n\nimport numpy as np\nimport struct\nfrom io import IOBase\n\nimport networkx as nx\nimport logging as log\n\nfrom mo.front.kaldi.loader.utils import find_next_tag, read_placeholder, find_next_component, get_name_from_path, \\\n find_end_of_component, end_of_nnet_tag, read_binary_integer32_token, get_parameters, read_token_value, \\\n collect_until_token, collect_until_token_and_read, create_edge_attrs, get_args_for_specifier\nfrom mo.graph.graph import Node, Graph\nfrom mo.utils.error import Error\nfrom mo.utils.utils import refer_to_faq_msg\n\n\ndef load_parallel_component(file_descr, graph: Graph, prev_layer_id):\n \"\"\"\n Load ParallelComponent of the Kaldi model.\n ParallelComponent contains parallel nested networks.\n Slice is inserted before nested networks.\n Outputs of nested networks concatenate with layer Concat.\n\n :param file_descr: descriptor of the model file\n :param graph: graph with the topology.\n :param prev_layer_id: id of the input layers for parallel component layer\n :return: id of the concat layer - last layer of the parallel component layers\n \"\"\"\n nnet_count = read_token_value(file_descr, b'')\n log.debug('Model contains parallel component with {} nested networks'.format(nnet_count))\n\n slice_id = graph.unique_id(prefix='Slice')\n graph.add_node(slice_id, parameters=None, op='slice', kind='op')\n\n slice_node = Node(graph, slice_id)\n Node(graph, prev_layer_id).add_output_port(0)\n slice_node.add_input_port(0)\n graph.create_edge(Node(graph, prev_layer_id), slice_node, 0, 0)\n slices_points = []\n\n outputs = []\n\n for i in range(nnet_count):\n read_token_value(file_descr, b'')\n collect_until_token(file_descr, b'')\n g = load_kalid_nnet1_model(file_descr, 'Nested_net_{}'.format(i))\n input_nodes = [n for n in graph.nodes(data=True) if n[1]['op'] == 'Parameter']\n shape = input_nodes[0][1]['shape']\n if i != nnet_count - 1:\n slices_points.append(shape[1])\n g.remove_node(input_nodes[0][0])\n mapping = {node: graph.unique_id(node) for node in g.nodes(data=False) if node in graph}\n g = nx.relabel_nodes(g, mapping)\n for val in mapping.values():\n g.node[val]['name'] = val\n graph.add_nodes_from(g.nodes(data=True))\n graph.add_edges_from(g.edges(data=True))\n sorted_nodes = tuple(nx.topological_sort(g))\n edge_attrs = create_edge_attrs(slice_id, sorted_nodes[0])\n edge_attrs['out'] = i\n Node(graph, slice_id).add_output_port(i)\n Node(graph, sorted_nodes[0]).add_input_port(len(Node(graph, sorted_nodes[0]).in_ports()))\n graph.create_edge(Node(graph, slice_id), Node(graph, sorted_nodes[0]), i, 0)\n outputs.append(sorted_nodes[-1])\n packed_sp = struct.pack(\"B\", 4) + struct.pack(\"I\", len(slices_points))\n for i in slices_points:\n packed_sp += struct.pack(\"I\", i)\n slice_node.parameters = io.BytesIO(packed_sp)\n concat_id = graph.unique_id(prefix='Concat')\n graph.add_node(concat_id, parameters=None, op='concat', kind='op')\n for i, output in enumerate(outputs):\n edge_attrs = create_edge_attrs(output, concat_id)\n edge_attrs['in'] = i\n Node(graph, output).add_output_port(0)\n Node(graph, concat_id).add_input_port(i)\n graph.create_edge(Node(graph, output), Node(graph, concat_id), 0, i)\n return concat_id\n\n\ndef load_kaldi_model(nnet_path):\n \"\"\"\n Structure of the file is the following:\n magic-number(16896) weights etc.\n :param nnet_path:\n :return:\n \"\"\"\n nnet_name = None\n if isinstance(nnet_path, str):\n file_desc = open(nnet_path, \"rb\")\n nnet_name = get_name_from_path(nnet_path)\n elif isinstance(nnet_path, IOBase):\n file_desc = nnet_path\n else:\n raise Error('Unsupported type of Kaldi model')\n\n tag = find_next_tag(file_desc)\n # start new model / submodel\n if tag == '':\n load_function = load_kalid_nnet1_model\n elif tag == '':\n while tag != '' and tag != '':\n tag = find_next_tag(file_desc)\n\n if tag == '':\n load_function = load_kaldi_nnet3_model\n else:\n load_function = load_kalid_nnet2_model\n elif tag == '':\n load_function = load_kaldi_nnet3_model\n else:\n raise Error('Kaldi model should start with or tag. ',\n refer_to_faq_msg(89))\n read_placeholder(file_desc, 1)\n\n return load_function(file_desc, nnet_name)\n\n\ndef load_kalid_nnet1_model(file_descr, name):\n graph = Graph(name=name)\n\n prev_layer_id = 'Parameter'\n graph.add_node(prev_layer_id, name=prev_layer_id, kind='op', op='Parameter', parameters=None)\n\n while True:\n component_type = find_next_component(file_descr)\n if component_type == end_of_nnet_tag.lower()[1:-1]:\n break\n\n layer_o = read_binary_integer32_token(file_descr)\n layer_i = read_binary_integer32_token(file_descr)\n\n if component_type == 'parallelcomponent':\n prev_layer_id = load_parallel_component(file_descr, graph, prev_layer_id)\n continue\n\n start_index = file_descr.tell()\n end_tag, end_index = find_end_of_component(file_descr, component_type)\n end_index -= len(end_tag)\n layer_id = graph.unique_id(prefix=component_type)\n graph.add_node(layer_id,\n parameters=get_parameters(file_descr, start_index, end_index),\n op=component_type,\n kind='op',\n layer_i=layer_i,\n layer_o=layer_o)\n\n prev_node = Node(graph, prev_layer_id)\n if prev_node.op == 'Parameter':\n prev_node['shape'] = np.array([1, layer_i], dtype=np.int64)\n\n prev_node.add_output_port(0)\n Node(graph, layer_id).add_input_port(0)\n graph.create_edge(prev_node, Node(graph, layer_id), 0, 0)\n prev_layer_id = layer_id\n log.debug('{} (type is {}) was loaded'.format(prev_layer_id, component_type))\n return graph\n\n\ndef load_kalid_nnet2_model(file_descr, nnet_name):\n graph = Graph(name=nnet_name)\n input_name = 'Input'\n graph.add_node(input_name, name=input_name, kind='op', op='Parameter', parameters=None, shape=None)\n\n prev_layer_id = input_name\n\n all_components = load_components(file_descr, graph)\n\n for layer_id in all_components:\n prev_node = Node(graph, prev_layer_id)\n if prev_node.op == 'Parameter':\n parameters = Node(graph, layer_id).parameters\n input_dim = read_token_value(parameters, b'')\n prev_node['shape'] = np.array([1, input_dim], dtype=np.int64)\n prev_node.add_output_port(0)\n Node(graph, layer_id).add_input_port(0)\n graph.create_edge(prev_node, Node(graph, layer_id), 0, 0)\n prev_layer_id = layer_id\n log.debug('{} and {} were connected'.format(prev_layer_id, layer_id))\n return graph\n\n\ndef load_kaldi_nnet3_model(file_descr, nnet_name):\n graph = Graph(name=nnet_name)\n file_descr.read(1)\n component_layer_map = load_topology_map(file_descr, graph)\n # add information for shape calculation for MemoryOffset\n # shape calculation for MemoryOffset can't be done through shape of previous layer because\n # it is separated in 2 parts to remove cycle from graph\n for node in graph.get_op_nodes(**{'op': 'Parameter'}):\n for o_n_name, params in node.get_outputs():\n o_n = Node(graph, o_n_name)\n if o_n['op'] == 'MemoryOffset':\n o_n['parameters']['element_size'] = node['shape'][1]\n\n load_components(file_descr, graph, component_layer_map)\n return graph\n\n\ndef load_components(file_descr, graph, component_layer_map=None):\n num_components = collect_until_token_and_read(file_descr, b'')\n log.debug('Network contains {} components'.format(num_components))\n is_nnet3 = False if component_layer_map is None else True\n\n if not is_nnet3:\n collect_until_token(file_descr, b'')\n\n all_components = list()\n name = \"\"\n for _ in range(num_components):\n if is_nnet3:\n name = collect_until_token_and_read(file_descr, b'', np.string_)\n\n component_type = find_next_component(file_descr)\n if component_type == end_of_nnet_tag.lower()[1:-1]:\n break\n\n start_index = file_descr.tell()\n end_tag, end_index = find_end_of_component(file_descr, component_type)\n # read dim info where possible to simplify shape calculation for MemoryOffset\n # shape calculation for MemoryOffset can't be done through shape of previous layer because\n # it is separated in 2 parts to remove cycle from graph\n file_descr.seek(start_index)\n dim = 0\n try:\n collect_until_token(file_descr, b'', size_search_zone=end_index-start_index)\n cur_index = file_descr.tell()\n if start_index < cur_index < end_index:\n dim = read_binary_integer32_token(file_descr)\n else:\n file_descr.seek(start_index)\n except Error:\n file_descr.seek(start_index)\n\n if is_nnet3:\n if name in component_layer_map:\n layer_id = component_layer_map[name][0]\n for layer in component_layer_map[name]:\n node = Node(graph, layer)\n node['parameters'] = get_parameters(file_descr, start_index, end_index)\n node['op'] = component_type\n # read dim info where possible to simplify shape calculation for MemoryOffset\n # shape calculation for MemoryOffset can't be done through shape of previous layer because\n # it is separated in 2 parts to remove cycle from graph\n for o_n_name, params in node.get_outputs():\n o_n = Node(graph, o_n_name)\n if o_n['op'] == 'MemoryOffset' and dim != 0:\n o_n['parameters']['element_size'] = dim\n else:\n raise Error(\"Something wrong with layer {}\".format(name))\n else:\n layer_id = graph.unique_id(prefix=component_type)\n graph.add_node(layer_id,\n parameters=get_parameters(file_descr, start_index, end_index),\n op=component_type,\n kind='op')\n\n all_components.append(layer_id)\n log.debug('{} (type is {}) was loaded'.format(layer_id, component_type))\n\n return all_components\n\n\ndef load_topology_map(file_descr, graph):\n not_finished = True\n component_layer_map = {}\n layer_node_map = {}\n while not_finished:\n not_finished = read_node(file_descr, graph, component_layer_map, layer_node_map)\n return component_layer_map\n\n\ndef read_node(file_descr, graph, component_layer_map, layer_node_map):\n s = file_descr.readline()\n if s == b'\\n':\n return False\n tokens = s.split(b' ')\n if tokens[0] == b'input-node':\n in_name = s[s.find(b'name=')+len(b'name='):].split(b' ')[0]\n in_name = str(in_name).strip('b').replace('\\'', \"\")\n in_shape = np.array([1, s[s.find(b'dim=')+len(b'dim='):].split(b' ')[0]], dtype=np.int)\n\n if in_name not in layer_node_map:\n graph.add_node(in_name, name=in_name, kind='op', op='Parameter', parameters=None, shape=in_shape)\n layer_node_map[in_name] = in_name\n else:\n Node(graph, in_name)['op'] = 'Parameter'\n Node(graph, in_name)['shape'] = in_shape\n elif tokens[0] == b'component-node':\n layer_name = s[s.find(b'name=')+len(b'name='):].split(b' ')[0]\n layer_name = str(layer_name).strip('b').replace('\\'', \"\")\n\n component_name = s[s.find(b'component=') + len(b'component='):].split(b' ')[0]\n if layer_name not in layer_node_map:\n node_name = graph.unique_id(prefix=layer_name)\n graph.add_node(node_name,\n parameters=None,\n op=None,\n kind='op')\n layer_node_map[layer_name] = node_name\n else:\n node_name = layer_node_map[layer_name]\n\n if component_name in component_layer_map:\n component_layer_map[component_name].append(node_name)\n else:\n component_layer_map[component_name] = [node_name]\n\n # parse input\n in_node_id = parse_input_for_node(s[s.find(b'input=')+6:], graph, layer_node_map)\n out_port = len(Node(graph, in_node_id).out_nodes())\n in_port = len(Node(graph, node_name).in_nodes())\n\n Node(graph, node_name).add_input_port(in_port)\n Node(graph, in_node_id).add_output_port(out_port)\n\n graph.add_edge(in_node_id, node_name, **create_edge_attrs(in_node_id, node_name, in_port, out_port))\n elif tokens[0] == b'output-node':\n layer_name = s[s.find(b'name=') + len(b'name='):].split(b' ')[0]\n layer_name = str(layer_name).strip('b').replace('\\'', \"\")\n node_name = graph.unique_id(prefix=layer_name)\n graph.add_node(node_name,\n parameters=None,\n op='Identity',\n kind='op')\n out_name = graph.unique_id(prefix=node_name+\"_out\")\n graph.add_node(out_name,\n parameters=None,\n op='Result',\n kind='op')\n Node(graph, node_name).add_input_port(0)\n Node(graph, node_name).add_output_port(0)\n Node(graph, out_name).add_input_port(0)\n graph.add_edge(node_name, out_name, **create_edge_attrs(node_name, out_name))\n\n # parse input\n in_node_id = parse_input_for_node(s[s.find(b'input=') + len(b'input='):], graph, layer_node_map)\n\n out_port = len(Node(graph, in_node_id).out_nodes())\n Node(graph, in_node_id).add_output_port(out_port)\n graph.create_edge(Node(graph, in_node_id), Node(graph, node_name), out_port, 0)\n\n objective_type = s[s.find(b'objective=') + 10:].split(b' ')[0].split(b'\\n')[0]\n if objective_type != b'linear':\n raise Error(\"Unsupported objective-type for output {}\".format(node_name))\n elif tokens[0] == b'dim-range-node':\n layer_name = s[s.find(b'name=') + len(b'name='):].split(b' ')[0]\n layer_name = str(layer_name).strip('b').replace('\\'', \"\")\n offset = int(s[s.find(b'dim-offset=') + len(b'dim-offset='):].split(b' ')[0])\n dim = int(s[s.find(b'dim=') + len(b'dim='):].split(b' ')[0])\n\n if layer_name in layer_node_map:\n node_name = layer_node_map[layer_name]\n node = Node(graph, node_name)\n node['parameters'] = {'offset': np.array([offset]), 'dim': np.array([dim]), 'axis': np.array([1])}\n node['op'] = 'Crop'\n else:\n node_name = graph.unique_id(prefix=layer_name)\n graph.add_node(node_name,\n parameters={'offset': np.array([offset]), 'dim': np.array([dim]), 'axis': np.array([1])},\n op='Crop',\n kind='op')\n layer_node_map[layer_name] = node_name\n node = Node(graph, node_name)\n\n in_node_id = parse_input_for_node(s[s.find(b'input-node=') + len(b'input-node='):], graph, layer_node_map)\n out_port = len(Node(graph, in_node_id).out_nodes())\n in_port = len(Node(graph, node_name).in_nodes())\n\n node.add_input_port(in_port)\n Node(graph, in_node_id).add_output_port(out_port)\n\n graph.create_edge(Node(graph, in_node_id), node, out_port, in_port)\n\n # read dim info where possible to simplify shape calculation for MemoryOffset\n # shape calculation for MemoryOffset can't be done through shape of previous layer because\n # it is separated in 2 parts to remove cycle from graph\n for o_n_name, params in node.get_outputs():\n o_n = Node(graph, o_n_name)\n if o_n['op'] == 'MemoryOffset':\n o_n['parameters']['element_size'] = dim\n else:\n raise Error(\"Unsupported node specifier {}\".format(tokens[0]))\n return True\n\n\ndef parse_input_for_node(string, graph, component_layer_map):\n return parse_specifier(string, graph, component_layer_map)\n\n\ndef parse_specifier(string, graph, layer_node_map):\n pos = string.find(b'(')\n if pos == -1:\n # node name\n input_name = str(string.split(b' ')[0]).strip('b').replace(\"\\'\", '').replace('\\\\n', '')\n\n if input_name not in layer_node_map:\n node_name = graph.unique_id(prefix=input_name)\n graph.add_node(node_name, parameters=[], op=\"\", kind='op')\n layer_node_map[input_name] = node_name\n else:\n node_name = layer_node_map[input_name]\n return node_name\n\n spec = string[:pos]\n args = get_args_for_specifier(string[pos:])\n if spec == b'Append':\n nodes = []\n for i in range(len(args)):\n nodes.append(parse_specifier(args[i], graph, layer_node_map))\n layer_name = 'Append_'\n for node in nodes:\n layer_name = layer_name + node + \"_\"\n\n if layer_name not in layer_node_map:\n concat_name = graph.unique_id(prefix=layer_name)\n graph.add_node(concat_name,\n parameters=None,\n op='concat',\n kind='op')\n layer_node_map[layer_name] = concat_name\n i = 0\n Node(graph, concat_name).add_sequence_of_ports('in', range(len(nodes)))\n for node in nodes:\n out_port = len(Node(graph, node).out_nodes())\n Node(graph, node).add_output_port(out_port)\n graph.create_edge(Node(graph, node), Node(graph, concat_name), out_port, i)\n i = i + 1\n else:\n concat_name = layer_node_map[layer_name]\n return concat_name\n elif spec == b'Offset':\n node = parse_specifier(args[0], graph, layer_node_map)\n t = int(args[1])\n if len(args) > 2:\n raise Error(\"ModelOptimizer supports only 2 arguments for Offset\")\n layer_name = 'Offset_' + node + '_'\n if t < 0:\n layer_name = layer_name + '_' + str(-t)\n else:\n layer_name = layer_name + str(t)\n\n if layer_name not in layer_node_map:\n memory_name = graph.unique_id(prefix=layer_name)\n layer_node_map[layer_name] = memory_name\n memory_name_2 = memory_name + '_out'\n graph.add_node(memory_name,\n parameters=dict(t=t, pair_name=memory_name_2, has_default=False),\n op='MemoryOffset',\n kind='op')\n out_port = len(Node(graph, node).out_nodes())\n in_port = len(Node(graph, memory_name).in_nodes())\n Node(graph, memory_name).add_input_port(in_port)\n Node(graph, node).add_output_port(out_port)\n graph.create_edge(Node(graph, node), Node(graph, memory_name), out_port, in_port)\n else:\n memory_name = layer_node_map[layer_name]\n return memory_name\n elif spec == b'Sum':\n nodes = []\n for i in range(len(args)):\n nodes.append(parse_specifier(args[i], graph, layer_node_map))\n\n layer_name = 'Sum_'\n for node in nodes:\n layer_name = layer_name + node + \"_\"\n\n if layer_name not in layer_node_map:\n sum_name = graph.unique_id(prefix=layer_name)\n graph.add_node(sum_name, parameters=None, op='Add', kind='op')\n layer_node_map[layer_name] = sum_name\n else:\n sum_name = layer_node_map[layer_name]\n\n i = 0\n for node in nodes:\n out_port = len(Node(graph, node).out_nodes())\n Node(graph, node).add_output_port(out_port)\n Node(graph, sum_name).add_input_port(i)\n graph.add_edge(node, sum_name, **create_edge_attrs(node, sum_name, i))\n i = i + 1\n return sum_name\n elif spec == b'IfDefined':\n node_id = parse_specifier(args[0], graph, layer_node_map)\n node = Node(graph, node_id)\n if node.op == 'MemoryOffset':\n node['parameters']['has_default'] = True\n return node_id\n elif spec == b'ReplaceIndex':\n node = parse_specifier(args[0], graph, layer_node_map)\n return node\n", "repo_name": "Namptiter/OpenVINO-Darknet-YOLOv3", "sub_path": "model_optimizer/mo/front/kaldi/loader/loader.py", "file_name": "loader.py", "file_ext": "py", "file_size_in_byte": 20673, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 6, "dataset": "github-code", "pt": "37", "api": [{"api_name": "mo.graph.graph.Graph", "line_number": 18, "usage_type": "name"}, {"api_name": "mo.front.kaldi.loader.utils.read_token_value", "line_number": 30, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 31, "usage_type": "call"}, {"api_name": "mo.graph.graph.Node", "line_number": 36, "usage_type": "call"}, {"api_name": "mo.graph.graph.Node", "line_number": 37, "usage_type": "call"}, {"api_name": "mo.graph.graph.Node", "line_number": 39, "usage_type": "call"}, {"api_name": "mo.front.kaldi.loader.utils.read_token_value", "line_number": 45, "usage_type": "call"}, {"api_name": "mo.front.kaldi.loader.utils.collect_until_token", "line_number": 46, "usage_type": "call"}, {"api_name": "networkx.relabel_nodes", "line_number": 54, "usage_type": "call"}, {"api_name": "networkx.topological_sort", "line_number": 59, "usage_type": "call"}, {"api_name": "mo.front.kaldi.loader.utils.create_edge_attrs", "line_number": 60, "usage_type": "call"}, {"api_name": "mo.graph.graph.Node", "line_number": 62, "usage_type": "call"}, {"api_name": "mo.graph.graph.Node", "line_number": 63, "usage_type": "call"}, {"api_name": "mo.graph.graph.Node", "line_number": 64, "usage_type": "call"}, {"api_name": "struct.pack", "line_number": 66, "usage_type": "call"}, {"api_name": "struct.pack", "line_number": 68, "usage_type": "call"}, {"api_name": "io.BytesIO", "line_number": 69, "usage_type": "call"}, {"api_name": "mo.front.kaldi.loader.utils.create_edge_attrs", "line_number": 73, "usage_type": "call"}, {"api_name": "mo.graph.graph.Node", "line_number": 75, "usage_type": "call"}, {"api_name": "mo.graph.graph.Node", "line_number": 76, "usage_type": "call"}, {"api_name": "mo.graph.graph.Node", "line_number": 77, "usage_type": "call"}, {"api_name": "mo.front.kaldi.loader.utils.get_name_from_path", "line_number": 91, "usage_type": "call"}, {"api_name": "io.IOBase", "line_number": 92, "usage_type": "argument"}, {"api_name": "mo.utils.error.Error", "line_number": 95, "usage_type": "call"}, {"api_name": "mo.front.kaldi.loader.utils.find_next_tag", "line_number": 97, "usage_type": "call"}, {"api_name": "mo.front.kaldi.loader.utils.find_next_tag", "line_number": 103, "usage_type": "call"}, {"api_name": "mo.utils.error.Error", "line_number": 112, "usage_type": "call"}, {"api_name": "mo.utils.utils.refer_to_faq_msg", "line_number": 113, "usage_type": "call"}, {"api_name": "mo.front.kaldi.loader.utils.read_placeholder", "line_number": 114, "usage_type": "call"}, {"api_name": "mo.graph.graph.Graph", "line_number": 120, "usage_type": "call"}, {"api_name": "mo.front.kaldi.loader.utils.find_next_component", "line_number": 126, "usage_type": "call"}, {"api_name": "mo.front.kaldi.loader.utils.end_of_nnet_tag.lower", "line_number": 127, "usage_type": "call"}, {"api_name": "mo.front.kaldi.loader.utils.end_of_nnet_tag", "line_number": 127, "usage_type": "name"}, {"api_name": "mo.front.kaldi.loader.utils.read_binary_integer32_token", "line_number": 130, "usage_type": "call"}, {"api_name": "mo.front.kaldi.loader.utils.read_binary_integer32_token", "line_number": 131, "usage_type": "call"}, {"api_name": "mo.front.kaldi.loader.utils.find_end_of_component", "line_number": 138, "usage_type": "call"}, {"api_name": "mo.front.kaldi.loader.utils.get_parameters", "line_number": 142, "usage_type": "call"}, {"api_name": "mo.graph.graph.Node", "line_number": 148, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 150, "usage_type": "call"}, {"api_name": "numpy.int64", "line_number": 150, "usage_type": "attribute"}, {"api_name": "mo.graph.graph.Node", "line_number": 153, "usage_type": "call"}, {"api_name": "mo.graph.graph.Node", "line_number": 154, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 156, "usage_type": "call"}, {"api_name": "mo.graph.graph.Graph", "line_number": 161, "usage_type": "call"}, {"api_name": "mo.graph.graph.Node", "line_number": 170, "usage_type": "call"}, {"api_name": "mo.graph.graph.Node", "line_number": 172, "usage_type": "call"}, {"api_name": "mo.front.kaldi.loader.utils.read_token_value", "line_number": 173, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 174, "usage_type": "call"}, {"api_name": "numpy.int64", "line_number": 174, "usage_type": "attribute"}, {"api_name": "mo.graph.graph.Node", "line_number": 176, "usage_type": "call"}, {"api_name": "mo.graph.graph.Node", "line_number": 177, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 179, "usage_type": "call"}, {"api_name": "mo.graph.graph.Graph", "line_number": 184, "usage_type": "call"}, {"api_name": "mo.graph.graph.Node", "line_number": 192, "usage_type": "call"}, {"api_name": "mo.front.kaldi.loader.utils.collect_until_token_and_read", "line_number": 201, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 202, "usage_type": "call"}, {"api_name": "mo.front.kaldi.loader.utils.collect_until_token", "line_number": 206, "usage_type": "call"}, {"api_name": "mo.front.kaldi.loader.utils.collect_until_token_and_read", "line_number": 212, "usage_type": "call"}, {"api_name": "numpy.string_", "line_number": 212, "usage_type": "attribute"}, {"api_name": "mo.front.kaldi.loader.utils.find_next_component", "line_number": 214, "usage_type": "call"}, {"api_name": "mo.front.kaldi.loader.utils.end_of_nnet_tag.lower", "line_number": 215, "usage_type": "call"}, {"api_name": "mo.front.kaldi.loader.utils.end_of_nnet_tag", "line_number": 215, "usage_type": "name"}, {"api_name": "mo.front.kaldi.loader.utils.find_end_of_component", "line_number": 219, "usage_type": "call"}, {"api_name": "mo.front.kaldi.loader.utils.collect_until_token", "line_number": 226, "usage_type": "call"}, {"api_name": "mo.front.kaldi.loader.utils.read_binary_integer32_token", "line_number": 229, "usage_type": "call"}, {"api_name": "mo.utils.error.Error", "line_number": 232, "usage_type": "name"}, {"api_name": "mo.graph.graph.Node", "line_number": 239, "usage_type": "call"}, {"api_name": "mo.front.kaldi.loader.utils.get_parameters", "line_number": 240, "usage_type": "call"}, {"api_name": "mo.graph.graph.Node", "line_number": 246, "usage_type": "call"}, {"api_name": "mo.utils.error.Error", "line_number": 250, "usage_type": "call"}, {"api_name": "mo.front.kaldi.loader.utils.get_parameters", "line_number": 254, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 259, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 281, "usage_type": "call"}, {"api_name": "numpy.int", "line_number": 281, "usage_type": "attribute"}, {"api_name": "mo.graph.graph.Node", "line_number": 287, "usage_type": "call"}, {"api_name": "mo.graph.graph.Node", "line_number": 288, "usage_type": "call"}, {"api_name": "mo.graph.graph.Node", "line_number": 311, "usage_type": "call"}, {"api_name": "mo.graph.graph.Node", "line_number": 312, "usage_type": "call"}, {"api_name": "mo.graph.graph.Node", "line_number": 314, "usage_type": "call"}, {"api_name": "mo.graph.graph.Node", "line_number": 315, "usage_type": "call"}, {"api_name": "mo.front.kaldi.loader.utils.create_edge_attrs", "line_number": 317, "usage_type": "call"}, {"api_name": "mo.graph.graph.Node", "line_number": 331, "usage_type": "call"}, {"api_name": "mo.graph.graph.Node", "line_number": 332, "usage_type": "call"}, {"api_name": "mo.graph.graph.Node", "line_number": 333, "usage_type": "call"}, {"api_name": "mo.front.kaldi.loader.utils.create_edge_attrs", "line_number": 334, "usage_type": "call"}, {"api_name": "mo.graph.graph.Node", "line_number": 339, "usage_type": "call"}, {"api_name": "mo.graph.graph.Node", "line_number": 340, "usage_type": "call"}, {"api_name": "mo.graph.graph.Node", "line_number": 341, "usage_type": "call"}, {"api_name": "mo.utils.error.Error", "line_number": 345, "usage_type": "call"}, {"api_name": "mo.graph.graph.Node", "line_number": 354, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 355, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 360, "usage_type": "call"}, {"api_name": "mo.graph.graph.Node", "line_number": 364, "usage_type": "call"}, {"api_name": "mo.graph.graph.Node", "line_number": 367, "usage_type": "call"}, {"api_name": "mo.graph.graph.Node", "line_number": 368, "usage_type": "call"}, {"api_name": "mo.graph.graph.Node", "line_number": 371, "usage_type": "call"}, {"api_name": "mo.graph.graph.Node", "line_number": 373, "usage_type": "call"}, {"api_name": "mo.graph.graph.Node", "line_number": 379, "usage_type": "call"}, {"api_name": "mo.utils.error.Error", "line_number": 383, "usage_type": "call"}, {"api_name": "mo.front.kaldi.loader.utils.get_args_for_specifier", "line_number": 406, "usage_type": "call"}, {"api_name": "mo.graph.graph.Node", "line_number": 423, "usage_type": "call"}, {"api_name": "mo.graph.graph.Node", "line_number": 425, "usage_type": "call"}, {"api_name": "mo.graph.graph.Node", "line_number": 426, "usage_type": "call"}, {"api_name": "mo.graph.graph.Node", "line_number": 427, "usage_type": "call"}, {"api_name": "mo.utils.error.Error", "line_number": 436, "usage_type": "call"}, {"api_name": "mo.graph.graph.Node", "line_number": 451, "usage_type": "call"}, {"api_name": "mo.graph.graph.Node", "line_number": 452, "usage_type": "call"}, {"api_name": "mo.graph.graph.Node", "line_number": 453, "usage_type": "call"}, {"api_name": "mo.graph.graph.Node", "line_number": 454, "usage_type": "call"}, {"api_name": "mo.graph.graph.Node", "line_number": 455, "usage_type": "call"}, {"api_name": "mo.graph.graph.Node", "line_number": 477, "usage_type": "call"}, {"api_name": "mo.graph.graph.Node", "line_number": 478, "usage_type": "call"}, {"api_name": "mo.graph.graph.Node", "line_number": 479, "usage_type": "call"}, {"api_name": "mo.front.kaldi.loader.utils.create_edge_attrs", "line_number": 480, "usage_type": "call"}, {"api_name": "mo.graph.graph.Node", "line_number": 485, "usage_type": "call"}]} +{"seq_id": "24207273158", "text": "from urllib import request\nfrom bs4 import BeautifulSoup\nimport re\n#bs4将档案直接存取在内存中,相对etree则是局部遍历\nurl = \"http://www.baidu.com/\"\n\nrsp = request.urlopen(url)\n#返回html文件\ncontent=rsp.read()\n#使用该文件串建bu4解析器(文档,引擎)格式也整理好了\nsoup = BeautifulSoup(content,'lxml')\n#寻找所有关于此节点名称\ntags = soup.find_all(name = \"meta\")\nprint(tags)\nprint(\"==\"*12)\n#运用正则找出me相关的名称(正则找寻节点,以该节点中巡找对应属性及内容\ntags =soup.find_all(re.compile('^me'),content=\"always\")\nfor tag in tags:\n print(tag)\n", "repo_name": "BigChoCho/untitled1", "sub_path": "py-56/Spider86-2.py", "file_name": "Spider86-2.py", "file_ext": "py", "file_size_in_byte": 623, "program_lang": "python", "lang": "zh", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "urllib.request.urlopen", "line_number": 7, "usage_type": "call"}, {"api_name": "urllib.request", "line_number": 7, "usage_type": "name"}, {"api_name": "bs4.BeautifulSoup", "line_number": 11, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 17, "usage_type": "call"}]} +{"seq_id": "5969665118", "text": "from flask import (\n render_template, request, redirect,\n url_for\n)\nfrom flask_login import current_user, login_required\n\nfrom app import db\nfrom app.dashboard.forms import ConfirmForm\nfrom app.dashboard.organization import bp\nfrom app.dashboard.organization.forms import OrganizationForm\nfrom app.models import Organization, Category\nfrom app.utils import save_file, remove_file\n\n\n@bp.route('/')\n@login_required\ndef organization_list():\n data = Organization.query.all()\n form = ConfirmForm()\n if current_user.role.name == \"admin\":\n return render_template('dashboard/organization/index.html', current_user=current_user, form=form,\n organizations=data)\n else:\n return render_template(\"dashboard/index.html\")\n\n\n@bp.route('/')\n@login_required\ndef get_organization(name):\n organization = Organization.query.filter_by(name=name).first_or_404()\n if current_user.role.name == \"admin\":\n return render_template('dashboard/organization/details.html', organization=organization)\n else:\n return render_template(\"dashboard/index.html\")\n\n\n@bp.route('//delete', methods=['POST'])\n@login_required\ndef delete_organization(name):\n form = ConfirmForm()\n if form.validate_on_submit() and form.value.data == name:\n organization = Organization.query.filter_by(name=name).first_or_404()\n db.session.delete(organization)\n db.session.commit()\n return redirect(url_for(\"dashboard.organization.organization_list\"))\n\n\n@bp.route('/create', methods=[\"GET\", \"POST\"])\n@login_required\ndef create_organization():\n categories = Category.query.filter_by(is_organization=True)\n form = OrganizationForm()\n form.category.choices = [(c.id, c.name) for c in categories]\n form.category.choices.insert(0, (0, \"-- اختر تصنيف --\"))\n if request.method == \"POST\":\n if form.validate_on_submit():\n name = form.name.data\n description = form.description.data\n address = form.address.data\n website_url = form.website_url.data\n category = form.category.data\n phone = form.phone.data\n organization = Organization.query.get(name)\n if organization is None:\n organization = Organization(\n name=name,\n description=description,\n website_url=website_url,\n address=address,\n phone=phone,\n logo_url=save_file(\"organization\", form.logo_url.data) if form.logo_url.data else None,\n category=Category.query.get(category) if not category == 0 else None\n )\n db.session.add(organization)\n db.session.commit()\n return redirect(url_for(\"dashboard.organization.organization_list\"))\n errors = f\"هناك مؤسسة مسجلة باسم: {name}\"\n # render_template does autoescaping html form input data\n return render_template(\"dashboard/organization/form.html\", form=form, errors=errors)\n errors = f\"من فضلك تأكد من صحة البيانات\"\n return render_template(\"dashboard/organization/form.html\", form=form, errors=errors)\n return render_template(\"dashboard/organization/form.html\", form=form)\n\n\n@bp.route('//update', methods=[\"GET\", \"POST\"])\n@login_required\ndef update_organization(name):\n if current_user.role.name == \"admin\":\n categories = Category.query.filter_by(is_organization=True)\n form = OrganizationForm()\n form.category.choices = [(c.id, c.name) for c in categories]\n form.category.choices.insert(0, (0, \"-- اختر تصنيف --\"))\n organization = Organization.query.filter_by(name=name).first_or_404()\n if request.method == \"GET\":\n form.name.data = organization.name\n form.description.data = organization.description\n form.address.data = organization.address\n form.phone.data = organization.phone\n form.website_url.data = organization.website_url\n form.logo_url.data = organization.logo_url\n form.category.data = str(\n organization.category.id) if organization.category is not None else \"0\"\n return render_template(\n 'dashboard/organization/form.html',\n form=form, isUpdate=True, organization=organization\n )\n elif request.method == \"POST\":\n organization.name = form.name.data\n organization.description = form.description.data\n organization.address = form.address.data\n organization.phone = form.phone.data\n organization.website_url = form.website_url.data\n if organization.logo_url:\n remove_file(\"organization\", organization.logo_url.split(\"/\")[-1])\n if form.logo_url.data:\n organization.logo_url = save_file(\"organization\", form.logo_url.data)\n organization.category = Category.query.get(\n form.category.data) if not form.category.data == 0 else None\n db.session.commit()\n return redirect(url_for(\"dashboard.organization.organization_list\"))\n else:\n return redirect(url_for(\"main.main_page\"))\n", "repo_name": "lemb0o/Space-Reservation-System", "sub_path": "app/dashboard/organization/routes.py", "file_name": "routes.py", "file_ext": "py", "file_size_in_byte": 5349, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "app.models.Organization.query.all", "line_number": 18, "usage_type": "call"}, {"api_name": "app.models.Organization.query", "line_number": 18, "usage_type": "attribute"}, {"api_name": "app.models.Organization", "line_number": 18, "usage_type": "name"}, {"api_name": "app.dashboard.forms.ConfirmForm", "line_number": 19, "usage_type": "call"}, {"api_name": "flask_login.current_user.role", "line_number": 20, "usage_type": "attribute"}, {"api_name": "flask_login.current_user", "line_number": 20, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 21, "usage_type": "call"}, {"api_name": "flask_login.current_user", "line_number": 21, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 24, "usage_type": "call"}, {"api_name": "app.dashboard.organization.bp.route", "line_number": 15, "usage_type": "call"}, {"api_name": "app.dashboard.organization.bp", "line_number": 15, "usage_type": "name"}, {"api_name": "flask_login.login_required", "line_number": 16, "usage_type": "name"}, {"api_name": "app.models.Organization.query.filter_by", "line_number": 30, "usage_type": "call"}, {"api_name": "app.models.Organization.query", "line_number": 30, "usage_type": "attribute"}, {"api_name": "app.models.Organization", "line_number": 30, "usage_type": "name"}, {"api_name": "flask_login.current_user.role", "line_number": 31, "usage_type": "attribute"}, {"api_name": "flask_login.current_user", "line_number": 31, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 32, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 34, "usage_type": "call"}, {"api_name": "app.dashboard.organization.bp.route", "line_number": 27, "usage_type": "call"}, {"api_name": "app.dashboard.organization.bp", "line_number": 27, "usage_type": "name"}, {"api_name": "flask_login.login_required", "line_number": 28, "usage_type": "name"}, {"api_name": "app.dashboard.forms.ConfirmForm", "line_number": 40, "usage_type": "call"}, {"api_name": "app.models.Organization.query.filter_by", "line_number": 42, "usage_type": "call"}, {"api_name": "app.models.Organization.query", "line_number": 42, "usage_type": "attribute"}, {"api_name": "app.models.Organization", "line_number": 42, "usage_type": "name"}, {"api_name": "app.db.session.delete", "line_number": 43, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 43, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 43, "usage_type": "name"}, {"api_name": "app.db.session.commit", "line_number": 44, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 44, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 44, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 45, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 45, "usage_type": "call"}, {"api_name": "app.dashboard.organization.bp.route", "line_number": 37, "usage_type": "call"}, {"api_name": "app.dashboard.organization.bp", "line_number": 37, "usage_type": "name"}, {"api_name": "flask_login.login_required", "line_number": 38, "usage_type": "name"}, {"api_name": "app.models.Category.query.filter_by", "line_number": 51, "usage_type": "call"}, {"api_name": "app.models.Category.query", "line_number": 51, "usage_type": "attribute"}, {"api_name": "app.models.Category", "line_number": 51, "usage_type": "name"}, {"api_name": "app.dashboard.organization.forms.OrganizationForm", "line_number": 52, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 55, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 55, "usage_type": "name"}, {"api_name": "app.models.Organization.query.get", "line_number": 63, "usage_type": "call"}, {"api_name": "app.models.Organization.query", "line_number": 63, "usage_type": "attribute"}, {"api_name": "app.models.Organization", "line_number": 63, "usage_type": "name"}, {"api_name": "app.models.Organization", "line_number": 65, "usage_type": "call"}, {"api_name": "app.utils.save_file", "line_number": 71, "usage_type": "call"}, {"api_name": "app.models.Category.query.get", "line_number": 72, "usage_type": "call"}, {"api_name": "app.models.Category.query", "line_number": 72, "usage_type": "attribute"}, {"api_name": "app.models.Category", "line_number": 72, "usage_type": "name"}, {"api_name": "app.db.session.add", "line_number": 74, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 74, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 74, "usage_type": "name"}, {"api_name": "app.db.session.commit", "line_number": 75, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 75, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 75, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 76, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 76, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 79, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 81, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 82, "usage_type": "call"}, {"api_name": "app.dashboard.organization.bp.route", "line_number": 48, "usage_type": "call"}, {"api_name": "app.dashboard.organization.bp", "line_number": 48, "usage_type": "name"}, {"api_name": "flask_login.login_required", "line_number": 49, "usage_type": "name"}, {"api_name": "flask_login.current_user.role", "line_number": 88, "usage_type": "attribute"}, {"api_name": "flask_login.current_user", "line_number": 88, "usage_type": "name"}, {"api_name": "app.models.Category.query.filter_by", "line_number": 89, "usage_type": "call"}, {"api_name": "app.models.Category.query", "line_number": 89, "usage_type": "attribute"}, {"api_name": "app.models.Category", "line_number": 89, "usage_type": "name"}, {"api_name": "app.dashboard.organization.forms.OrganizationForm", "line_number": 90, "usage_type": "call"}, {"api_name": "app.models.Organization.query.filter_by", "line_number": 93, "usage_type": "call"}, {"api_name": "app.models.Organization.query", "line_number": 93, "usage_type": "attribute"}, {"api_name": "app.models.Organization", "line_number": 93, "usage_type": "name"}, {"api_name": "flask.request.method", "line_number": 94, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 94, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 103, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 107, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 107, "usage_type": "name"}, {"api_name": "app.utils.remove_file", "line_number": 114, "usage_type": "call"}, {"api_name": "app.utils.save_file", "line_number": 116, "usage_type": "call"}, {"api_name": "app.models.Category.query.get", "line_number": 117, "usage_type": "call"}, {"api_name": "app.models.Category.query", "line_number": 117, "usage_type": "attribute"}, {"api_name": "app.models.Category", "line_number": 117, "usage_type": "name"}, {"api_name": "app.db.session.commit", "line_number": 119, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 119, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 119, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 120, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 120, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 122, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 122, "usage_type": "call"}, {"api_name": "app.dashboard.organization.bp.route", "line_number": 85, "usage_type": "call"}, {"api_name": "app.dashboard.organization.bp", "line_number": 85, "usage_type": "name"}, {"api_name": "flask_login.login_required", "line_number": 86, "usage_type": "name"}]} +{"seq_id": "30503960663", "text": "\"\"\"Piglr Game module.\"\"\"\n\nimport random\nfrom typing import Union\n\nfrom piglr.state import State, as_dict\nfrom piglr.error import IllegalParameterError\n\n\nclass Game:\n \"\"\"Game class used to interact with a the pig environment.\n\n Example usage:\n \n >>> import json\n >>> import random\n >>> import piglr\n >>> game = piglr.Game()\n >>> obs, winner = game.reset()\n >>> while winner is None:\n >>> roll = random.randint(0, 1)\n >>> if roll:\n >>> obs, winner = game.roll()\n >>> else:\n >>> obs, winner = game.bank()\n >>> print(json.dumps(obs, indent=4))\n >>> print(f'Winner: {winner}')\n\n \"\"\"\n def __init__(self, players: int = 2, num_dice: int = 1, dn: int = 6, target: int = 100):\n \"\"\"`Game` init method.\n\n Args:\n players: Number of players. (Legal range 2-inf)\n num_dice: Number of dice. (Legal range 1-inf)\n dn: Dice sidedness (Think D&D, d6, d8, d20, etc.). (Legal\n range 1-inf)\n target: Target score for the game. (Legal range 1-inf)\n\n Raises:\n TypeError: TypeError is raised whenever one of the passed\n parameters is not of type int.\n piglr.error.IllegalParameterError: is raised whenever a\n parameter is passed an illegal value. For legal values\n see args section of init method.\n\n \"\"\"\n for param in [players, num_dice, dn, target]:\n if not isinstance(param, int):\n param_name = f'{param}='.split('=')[0]\n raise TypeError(f'Param {param_name} must be of type int')\n\n if players < 2:\n raise IllegalParameterError('There must be at least 2 players')\n\n if num_dice < 1:\n raise IllegalParameterError('There must be at least 1 dice')\n\n if dn < 2:\n raise IllegalParameterError('The dice must be at least 2 sided')\n\n if target < 1:\n raise IllegalParameterError('The target must be positive')\n\n self.players = players\n self.num_dice = num_dice\n self.dn = dn\n self.target = target\n\n @property\n def state(self) -> dict:\n \"\"\"State property.\n\n Returns:\n Instance of :obj:`piglr.state.State` converted to a\n dictionary.\n\n \"\"\"\n return as_dict(self._state)\n\n def _increment_turn(self, bank: bool) -> None:\n if bank:\n self._state.score[self._state.turn] += self._state.bank\n\n self._state.rolls = 0\n self._state.bank = 0\n\n if self._state.players - 1 == self._state.turn:\n self._state.turn = 0\n else:\n self._state.turn += 1\n\n def _check_for_winner(self) -> Union[int, None]:\n if max(self._state.score.values()) >= self._state.target:\n return max(self._state.score, key=self._state.score.get)\n return None\n\n def reset(self) -> Union[dict, None]:\n \"\"\"Reset method.\n\n Resets the game state.\n\n Returns:\n - State property :attr:`piglr.game.Game.state`.\n - :obj:`None`.\n\n \"\"\"\n self._state = State(\n players=self.players,\n num_dice=self.num_dice,\n dn=self.dn,\n score={i:0 for i in range(self.players)},\n target=self.target\n )\n\n return self.state, None\n\n def roll(self) -> Union[dict, None]:\n \"\"\"Roll dice.\n\n Rolls dice for current player.\n\n Returns:\n - State property :attr:`piglr.game.Game.state`.\n - :obj:`None`.\n\n \"\"\"\n for _ in range(self._state.num_dice):\n d = random.randint(1, self._state.dn)\n if d == 1:\n self._increment_turn(bank=False)\n return self.state, None\n\n else:\n self._state.rolls += 1\n self._state.bank += d\n\n return self.state, None\n \n def bank(self) -> Union[dict, Union[int, None]]:\n \"\"\"Scores bank.\n\n Scores bank for current player. \n\n Returns:\n - State property :attr:`piglr.game.Game.state`.\n - :obj:`int` if the game has been won else :obj:`None`.\n\n \"\"\"\n self._increment_turn(bank=True)\n\n winner = self._check_for_winner()\n\n return self.state, winner\n", "repo_name": "ChristianWLang/piglr", "sub_path": "piglr/game.py", "file_name": "game.py", "file_ext": "py", "file_size_in_byte": 4355, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "piglr.error.IllegalParameterError", "line_number": 54, "usage_type": "call"}, {"api_name": "piglr.error.IllegalParameterError", "line_number": 57, "usage_type": "call"}, {"api_name": "piglr.error.IllegalParameterError", "line_number": 60, "usage_type": "call"}, {"api_name": "piglr.error.IllegalParameterError", "line_number": 63, "usage_type": "call"}, {"api_name": "piglr.state.as_dict", "line_number": 79, "usage_type": "call"}, {"api_name": "typing.Union", "line_number": 93, "usage_type": "name"}, {"api_name": "piglr.state.State", "line_number": 108, "usage_type": "call"}, {"api_name": "typing.Union", "line_number": 98, "usage_type": "name"}, {"api_name": "random.randint", "line_number": 129, "usage_type": "call"}, {"api_name": "typing.Union", "line_number": 118, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 140, "usage_type": "name"}]} +{"seq_id": "33222590336", "text": "import torch\nimport torch.nn as nn\nimport numpy as np\nimport torchvision\n\nclass AlexNetDectection(nn.Module):\n def __init__(self):\n super(AlexNetDectection, self).__init__()\n self.hidden = 16\n self.conv1 = nn.Conv2d(1, 2*self.hidden, kernel_size=5, stride=2, padding=1)\n self.batchNorm1 = nn.BatchNorm2d(2*self.hidden)\n self.relu1 = nn.ReLU()\n self.maxpool1 = nn.MaxPool2d(kernel_size=2, stride=1)\n self.conv2 = nn.Conv2d(2*self.hidden, 6*self.hidden, kernel_size=5, padding=1, stride=2)\n self.batchNorm2 = nn.BatchNorm2d(6 * self.hidden)\n self.relu2 = nn.ReLU()\n self.maxpool2 = nn.MaxPool2d(kernel_size=2, stride=2)\n self.conv3 = nn.Conv2d(6*self.hidden, 6*self.hidden, kernel_size=3, padding=1)\n self.batchNorm3 = nn.BatchNorm2d(6 * self.hidden)\n self.relu3 = nn.ReLU()\n self.conv4 = nn.Conv2d(6*self.hidden, 6*self.hidden, kernel_size=3, padding=1)\n self.batchNorm4 = nn.BatchNorm2d(6 * self.hidden)\n self.relu4 = nn.ReLU()\n self.conv5 = nn.Conv2d(6*self.hidden, 2*self.hidden, kernel_size=3, padding=1)\n self.batchNorm5 = nn.BatchNorm2d(2 * self.hidden)\n self.relu5 = nn.ReLU()\n self.fc1 = nn.Linear(1152, 3*self.hidden)\n self.relu6 = nn.ReLU()\n self.fc2 = nn.Linear(3*self.hidden, 3*self.hidden)\n self. relu7 = nn.ReLU()\n self.fc3 = nn.Linear(3*self.hidden, 3*7)\n self.sigmoid = nn.Sigmoid()\n\n def forward(self, input):\n x = self.conv1(input)\n x = self.batchNorm1(x)\n x = self.relu1(x)\n x = self.maxpool1(x)\n x = self.conv2(x)\n x = self.batchNorm2(x)\n x = self.relu2(x)\n x = self.maxpool2(x)\n x = self.conv3(x)\n x = self.batchNorm3(x)\n x = self.relu3(x)\n x = self.conv4(x)\n x = self.batchNorm4(x)\n x = self.relu4(x)\n x = self.conv5(x)\n x = self.batchNorm5(x)\n x = self.relu5(x)\n x = torch.flatten(x, 1)\n x = self.fc1(x)\n x = self.relu6(x)\n x = self.fc2(x)\n x = self.relu7(x)\n x = self.fc3(x)\n x = self.sigmoid(x)\n\n output = x.reshape(len(x), 3, 7)\n\n return output\n\n\nclass DetectionCriterion(nn.Module):\n def __init__(self):\n super(DetectionCriterion, self).__init__()\n self.criterionBCE = nn.BCELoss(reduction=\"sum\")\n self.criterionMSE = nn.MSELoss(reduction=\"sum\")\n\n def forward(self, output, target):\n targetObject = (target[:,:,0] == 1.0)*1.0\n targetnoObject = (target[:, :, 0] == 0.0) * 1.0\n\n targetClasse = torch.zeros((len(target), 3, 3))\n for i in range(len(target)):\n for j in range(len(target[0])):\n targetClasse[i, j, int(target[i, j, -1])] = 1\n\n xywhLoss = self.criterionMSE(output[:,:,1:4], target[:,:,1:4])\n classLoss = self.criterionBCE(output[:,:,4:], targetClasse)\n noObjectLoss = self.criterionBCE(output[:,:,0], targetnoObject)\n objectLoss = self.criterionBCE(output[:, :, 0], targetObject)\n criterion = 2*classLoss + 4*xywhLoss + objectLoss #+ 0.5*noObjectLoss\n return criterion\n\n", "repo_name": "PedroScocci/S7_APP2", "sub_path": "models/detection_network.py", "file_name": "detection_network.py", "file_ext": "py", "file_size_in_byte": 3191, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "torch.nn.Module", "line_number": 6, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 6, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 10, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 10, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 11, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 11, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 12, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 12, "usage_type": "name"}, {"api_name": "torch.nn.MaxPool2d", "line_number": 13, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 13, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 14, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 14, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 15, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 15, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 16, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 16, "usage_type": "name"}, {"api_name": "torch.nn.MaxPool2d", "line_number": 17, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 17, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 18, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 18, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 19, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 19, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 20, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 20, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 21, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 21, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 22, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 22, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 23, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 23, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 24, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 24, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 25, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 25, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 26, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 26, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 27, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 27, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 28, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 28, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 29, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 29, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 30, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 30, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 31, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 31, "usage_type": "name"}, {"api_name": "torch.nn.Sigmoid", "line_number": 32, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 32, "usage_type": "name"}, {"api_name": "torch.flatten", "line_number": 52, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 65, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 65, "usage_type": "name"}, {"api_name": "torch.nn.BCELoss", "line_number": 68, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 68, "usage_type": "name"}, {"api_name": "torch.nn.MSELoss", "line_number": 69, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 69, "usage_type": "name"}, {"api_name": "torch.zeros", "line_number": 75, "usage_type": "call"}]} +{"seq_id": "9715815608", "text": "# -*- coding:utf-8 -*-\n\n'''\n仿照labelme的json文件写入自己的数据\n'''\nimport cv2\nimport json\n\n# json_file = './1.json'\n\n# data = json.load(open(json_file))\n\n# 参考labelme的json格式重新生成json文件，\n# 便可以使用labelme的接口解析数据\n\ndef dict_json(imageData,shapes,imagePath,fillColor=None,lineColor=None):\n '''\n\n :param imageData: str\n :param shapes: list\n :param imagePath: str\n :param fillColor: list\n :param lineColor: list\n :return: dict\n '''\n return {\"imageData\":imageData,\"shapes\":shapes,\"fillColor\":fillColor,\n 'imagePath':imagePath,'lineColor':lineColor}\n\ndef dict_shapes(points,label,fill_color=None,line_color=None):\n return {'points':points,'fill_color':fill_color,'label':label,'line_color':line_color}\n\n# 注以下都是虚拟数据，仅为了说明问题\nimageData=\"image data\"\nshapes=[]\n# 第一个对象\npoints=[[10,10],[120,10],[120,120],[10,120]] # 数据模拟\n# fill_color=null\nlabel='cat_1'\n# line_color=null\nshapes.append(dict_shapes(points,label))\n\n# 第二个对象\npoints=[[150,200],[200,200],[200,250],[150,250]] # 数据模拟\nlabel='cat_2'\nshapes.append(dict_shapes(points,label))\n\nfillColor=[255,0,0,128]\n\nimagePath='E:/practice/1.jpg'\n\nlineColor=[0,255,0,128]\n\ndata=dict_json(imageData,shapes,imagePath,fillColor,lineColor)\n\n# 写入json文件\njson_file = 'E:/practice/2.json'\njson.dump(data,open(json_file,'w'))", "repo_name": "wucng/TensorExpand", "sub_path": "TensorExpand/Object detection/Data_interface/Labelme/data2json.py", "file_name": "data2json.py", "file_ext": "py", "file_size_in_byte": 1419, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 348, "dataset": "github-code", "pt": "37", "api": [{"api_name": "json.dump", "line_number": 57, "usage_type": "call"}]} +{"seq_id": "6828485117", "text": "import pandas as pd\nimport numpy as np\nimport time\nimport matplotlib.pyplot as plt\n\ndef plot_validation_accuracy_list(title, das, p):\n plt.title(title)\n plt.xlabel(\"No. of iterations\")\n plt.ylabel(\"Accuracy %\")\n for i in range(p):\n plt.plot(das[i], \"o\")\n plt.legend([\"p=1\", \"p=2\", \"p=3\", \"p=4\", \"p=5\"], loc =\"lower left\")\n plt.show()\n \ndef plot_validation_accuracy(title, tas, das):\n plt.title(title)\n plt.xlabel(\"No. of iterations\")\n plt.ylabel(\"Accuracy %\")\n plt.plot(tas)\n plt.plot(das)\n plt.legend([\"train\", \"dev\"], loc =\"lower left\")\n plt.show()\n \ndef plot_best_validation_accuracy_p(title, best_dev_acc):\n plt.title(title)\n plt.xlabel(\"p\")\n plt.ylabel(\"Accuracy %\")\n plt.plot(best_dev_acc)\n plt.xticks(np.arange(5), ['1', '2', '3', '4', '5'])\n plt.show()\n\ndef plot_time(title, run_time_p_1):\n plt.title(title)\n plt.xlabel(\"No. of iterations\")\n plt.ylabel(\"Time(seconds)\")\n plt.plot(run_time_p_1)\n plt.show()\n\ndef kernel(x_1, x_2, p):\n return (1 + np.dot(x_1, x_2.T))**p\n\nonline_train_acc_list = []\nonline_dev_acc_list = []\nbest_dev_acc = []\nrun_time_p_1 = []\ncumulative_run_time_p_1 = []\n\ndef fit(t_x, t_y, d_x, d_y, maxiter, k, p):\n t_x = t_x.to_numpy()\n t_y = t_y.to_numpy()\n d_x = d_x.to_numpy()\n d_y = d_y.to_numpy()\n \n cumulative_run_time = 0\n \n datasize = len(t_x)\n alpha = np.zeros(datasize)\n \n K = kernel(t_x, t_x, p)\n K_d = kernel(t_x, d_x, p)\n no_iteration = 0\n \n while no_iteration < maxiter:\n i_start = time.time()\n \n no_iteration += 1\n for i, row in enumerate(K):\n u = np.sum(alpha * row * (t_y.T).flatten())\n if u * t_y[i] <= 0:\n alpha[i] += 1\n if p == 1:\n i_end = time.time() - i_start\n run_time_p_1.append(i_end)\n cumulative_run_time += i_end\n cumulative_run_time_p_1.append(cumulative_run_time)\n \n \n # online kernel perceptron accuracy\n t_predict = np.sign(np.dot((alpha * (t_y.T).flatten()), K))\n no_correct_prediction_t = np.sum(t_predict == (t_y.T).flatten())\n acc_rate_t = no_correct_prediction_t/len(t_y)*100\n if len(online_train_acc_list) < p:\n online_train_acc_list.append([])\n online_train_acc_list[p-1].append(acc_rate_t)\n \n d_predict = np.sign(np.dot((alpha * (t_y.T).flatten()), K_d))\n no_correct_prediction_d = np.sum(d_predict == (d_y.T).flatten())\n acc_rate_d = no_correct_prediction_d/len(d_y)*100\n if len(online_dev_acc_list) < p:\n online_dev_acc_list.append([])\n online_dev_acc_list[p-1].append(acc_rate_d)\n \n highest_accuracy = max(online_dev_acc_list[p-1])\n best_iter_index = online_dev_acc_list[p-1].index(highest_accuracy)\n best_dev_acc.append(highest_accuracy)\n print()\n print(\"p:\", p)\n print(\"maxiter:\", maxiter)\n print(\"Best iteration (validation accuracy):\", best_iter_index + 1)\n print(\"Highest validation accuracy rate\", highest_accuracy)\n \n\ntrain_data = pd.read_csv(\"data/train_X.csv\")\ntraindata_label = pd.read_csv(\"data/train_y.csv\")\ndev_data = pd.read_csv(\"data/dev_X.csv\")\ndevdata_label = pd.read_csv(\"data/dev_y.csv\")\n\np = [1,2,3,4,5]\n\nfor i in range(len(p)):\n start = time.time()\n fit(train_data, traindata_label, dev_data, devdata_label, 100, kernel, p[i])\n print(\"running time: p =\", i+1 , time.time()-start, \"s\")\n\nplot_best_validation_accuracy_p(\"Best validation accuracy\", best_dev_acc)\nfor i in range(len(p)):\n print(\"p =\", i+1, \"Best validation accuracy:\", best_dev_acc[i])\n\nfor i in range(len(p)):\n plot_validation_accuracy(\"Online perceptron accuracy for p=\" + str(i+1), online_train_acc_list[i], online_dev_acc_list[i])\nplot_validation_accuracy_list(\"Online perceptron train accuracy\", online_train_acc_list, 5)\nplot_validation_accuracy_list(\"Online perceptron validation accuracy\", online_dev_acc_list, 5)\nplot_time(\"The empirical runtime of p=1\", run_time_p_1)\nplot_time(\"The cumulative runtime of p=1\", cumulative_run_time_p_1)\n", "repo_name": "NuttareeB/MachineLearning", "sub_path": "Perceptron/Perceptron-KernelizedPerceptron.py", "file_name": "Perceptron-KernelizedPerceptron.py", "file_ext": "py", "file_size_in_byte": 4119, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "matplotlib.pyplot.title", "line_number": 7, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 7, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 8, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 8, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 9, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 9, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 11, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 11, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 12, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 12, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 13, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 13, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 16, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 16, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 17, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 17, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 18, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 18, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 19, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 19, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 20, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 20, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 21, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 21, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 22, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 22, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 25, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 25, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 26, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 26, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 27, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 27, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 28, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 28, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 29, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 29, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 29, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 30, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 30, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 33, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 33, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 34, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 34, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 35, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 35, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 36, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 36, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 37, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 37, "usage_type": "name"}, {"api_name": "numpy.dot", "line_number": 40, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 57, "usage_type": "call"}, {"api_name": "time.time", "line_number": 64, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 68, "usage_type": "call"}, {"api_name": "time.time", "line_number": 72, "usage_type": "call"}, {"api_name": "numpy.sign", "line_number": 79, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 79, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 80, "usage_type": "call"}, {"api_name": "numpy.sign", "line_number": 86, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 86, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 87, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 103, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 104, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 105, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 106, "usage_type": "call"}, {"api_name": "time.time", "line_number": 111, "usage_type": "call"}, {"api_name": "time.time", "line_number": 113, "usage_type": "call"}]} +{"seq_id": "37845896029", "text": "import collections\n\n\n# ---- Function for one-shot computation ----\n\ndef compute(array, window, maximize):\n\tif not isinstance(window, int):\n\t\traise TypeError()\n\tif not isinstance(maximize, bool):\n\t\traise TypeError()\n\tif window <= 0:\n\t\traise ValueError(\"Window size must be positive\")\n\t\n\tresult = []\n\tdeque = collections.deque()\n\tfor (i, val) in enumerate(array):\n\t\tval = array[i]\n\t\twhile len(deque) > 0 and ((not maximize and val < deque[-1]) or (maximize and val > deque[-1])):\n\t\t\tdeque.pop()\n\t\tdeque.append(val)\n\t\t\n\t\tj = i + 1 - window\n\t\tif j >= 0:\n\t\t\tresult.append(deque[0])\n\t\t\tif array[j] == deque[0]:\n\t\t\t\tdeque.popleft()\n\treturn result\n\n\n\n# ---- Stateful instance for incremental computation ----\n\nclass SlidingWindowMinMax:\n\t\n\tdef __init__(self):\n\t\tself.mindeque = collections.deque()\n\t\tself.maxdeque = collections.deque()\n\t\n\t\n\tdef get_minimum(self):\n\t\treturn self.mindeque[0]\n\t\n\t\n\tdef get_maximum(self):\n\t\treturn self.maxdeque[0]\n\t\n\t\n\tdef add_tail(self, val):\n\t\twhile len(self.mindeque) > 0 and val < self.mindeque[-1]:\n\t\t\tself.mindeque.pop()\n\t\tself.mindeque.append(val)\n\t\t\n\t\twhile len(self.maxdeque) > 0 and val > self.maxdeque[-1]:\n\t\t\tself.maxdeque.pop()\n\t\tself.maxdeque.append(val)\n\t\n\t\n\tdef remove_head(self, val):\n\t\tif val < self.mindeque[0]:\n\t\t\traise ValueError(\"Wrong value\")\n\t\telif val == self.mindeque[0]:\n\t\t\tself.mindeque.popleft()\n\t\t\n\t\tif val > self.maxdeque[0]:\n\t\t\traise ValueError(\"Wrong value\")\n\t\telif val == self.maxdeque[0]:\n\t\t\tself.maxdeque.popleft()\n", "repo_name": "nayuki/Nayuki-web-published-code", "sub_path": "sliding-window-minimum-maximum-algorithm/slidingwindowminmax.py", "file_name": "slidingwindowminmax.py", "file_ext": "py", "file_size_in_byte": 1473, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 130, "dataset": "github-code", "pt": "37", "api": [{"api_name": "collections.deque", "line_number": 15, "usage_type": "call"}, {"api_name": "collections.deque", "line_number": 36, "usage_type": "call"}, {"api_name": "collections.deque", "line_number": 37, "usage_type": "call"}]} +{"seq_id": "40864694814", "text": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# @Time : 2017/2/9 14:52\n# @Author : Neil.tang\n# @Site : \n# @File : models.py\n# @Software: PyCharm\n#\nfrom __future__ import unicode_literals\n\nfrom django.db import models\n\n\n# Django表结构\n\nclass Menus(models.Model):\n name = models.CharField(max_length=32, verbose_name=u'菜单名')\n parent = models.ForeignKey('self',\n verbose_name=u'父级菜单',\n null=True,\n blank=True,\n default='0',\n help_text=u'如果添加的是子菜单，请选择父菜单')\n show = models.BooleanField(verbose_name=u'是否显示',\n default=False,\n help_text=u'菜单是否显示，默认添加不显示')\n url = models.CharField(max_length=300,\n verbose_name=u'菜单url地址',\n null=True,\n blank=True,\n default='javascript:void(0)',\n help_text=u'是否给菜单设置一个url地址')\n priority = models.IntegerField(verbose_name=u'显示优先级',\n null=True,\n blank=True,\n default=-1,\n help_text=u'菜单的显示顺序，优先级越大显示越靠前')\n permission_id = models.CharField(verbose_name=u'权限编号',\n max_length=250,\n help_text=u'给菜单设置一个编号，用于权限控制',\n error_messages={'field-permission_id': u'输入权限'})\n\n def __str__(self):\n return \"{parent}{name}\".format(name=self.name, parent=u\"%s-->\" % self.parent.name if self.parent else '')\n\n class Meta:\n verbose_name = u\"菜单\"\n verbose_name_plural = u\"菜单\"\n ordering = [\"-priority\", \"id\"]\n", "repo_name": "xiaosi2323/monitor", "sub_path": "apps/common/models.py", "file_name": "models.py", "file_ext": "py", "file_size_in_byte": 2071, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "django.db.models.Model", "line_number": 16, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 16, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 17, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 17, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 18, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 18, "usage_type": "name"}, {"api_name": "django.db.models.BooleanField", "line_number": 24, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 24, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 27, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 27, "usage_type": "name"}, {"api_name": "django.db.models.IntegerField", "line_number": 33, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 33, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 38, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 38, "usage_type": "name"}]} +{"seq_id": "5848975417", "text": "import dask.base\nimport dask.dataframe\nimport pandas\n\nfrom ..server.object_cache import get_object_cache\nfrom ..structures.core import Spec, StructureFamily\nfrom ..structures.table import TableStructure\nfrom .array import ArrayAdapter\n\n\nclass TableAdapter:\n \"\"\"\n Wrap a dataframe-like object in an interface that Tiled can serve.\n\n Examples\n --------\n\n >>> df = pandas.DataFrame({\"a\": [1, 2, 3], \"b\": [4, 5, 6]})\n >>> DataFrameAdapter.from_pandas(df, npartitions=1)\n\n \"\"\"\n\n structure_family = StructureFamily.table\n\n @classmethod\n def from_pandas(\n cls,\n *args,\n metadata=None,\n specs=None,\n access_policy=None,\n npartitions=1,\n **kwargs,\n ):\n ddf = dask.dataframe.from_pandas(*args, npartitions=npartitions, **kwargs)\n if specs is None:\n specs = [Spec(\"dataframe\")]\n return cls.from_dask_dataframe(\n ddf, metadata=metadata, specs=specs, access_policy=access_policy\n )\n\n @classmethod\n def from_dask_dataframe(\n cls,\n ddf,\n metadata=None,\n specs=None,\n access_policy=None,\n ):\n structure = TableStructure.from_dask_dataframe(ddf)\n if specs is None:\n specs = [Spec(\"dataframe\")]\n return cls(\n ddf.partitions,\n structure,\n metadata=metadata,\n specs=specs,\n access_policy=access_policy,\n )\n\n def __init__(\n self,\n partitions,\n structure,\n *,\n metadata=None,\n specs=None,\n access_policy=None,\n ):\n self._metadata = metadata or {}\n self._partitions = list(partitions)\n self._structure = structure\n self.specs = specs or []\n self.access_policy = access_policy\n\n def __repr__(self):\n return f\"{type(self).__name__}({self._structure.columns!r})\"\n\n def __getitem__(self, key):\n # Must compute to determine shape.\n return ArrayAdapter.from_array(self.read([key])[key].values)\n\n def items(self):\n yield from (\n (key, ArrayAdapter.from_array(self.read([key])[key].values))\n for key in self._structure.columns\n )\n\n def metadata(self):\n return self._metadata\n\n def structure(self):\n return self._structure\n\n def read(self, fields=None):\n if any(p is None for p in self._partitions):\n raise ValueError(\"Not all partitions have been stored.\")\n if isinstance(self._partitions[0], dask.dataframe.DataFrame):\n if fields is not None:\n ddf = dask.dataframe.concat(\n [p[fields] for p in self._partitions], axis=0\n )\n else:\n ddf = dask.dataframe.concat(self._partitions, axis=0)\n # Note: If the cache is set to NO_CACHE, this is a null context.\n with get_object_cache().dask_context:\n return ddf.compute()\n df = pandas.concat(self._partitions, axis=0)\n if fields is not None:\n df = df[fields]\n return df\n\n def read_partition(self, partition, fields=None):\n partition = self._partitions[partition]\n if partition is None:\n raise RuntimeError(f\"partition {partition} has not be stored yet\")\n if fields is not None:\n partition = partition[fields]\n # Special case for dask to cache computed result in object cache.\n if isinstance(partition, dask.dataframe.DataFrame):\n # Note: If the cache is set to NO_CACHE, this is a null context.\n with get_object_cache().dask_context:\n return partition.compute()\n return partition\n\n\nDataFrameAdapter = TableAdapter\n", "repo_name": "bluesky/tiled", "sub_path": "tiled/adapters/table.py", "file_name": "table.py", "file_ext": "py", "file_size_in_byte": 3757, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 48, "dataset": "github-code", "pt": "37", "api": [{"api_name": "structures.core.StructureFamily.table", "line_number": 23, "usage_type": "attribute"}, {"api_name": "structures.core.StructureFamily", "line_number": 23, "usage_type": "name"}, {"api_name": "dask.base.dataframe.from_pandas", "line_number": 35, "usage_type": "call"}, {"api_name": "dask.base.dataframe", "line_number": 35, "usage_type": "attribute"}, {"api_name": "dask.base", "line_number": 35, "usage_type": "name"}, {"api_name": "structures.core.Spec", "line_number": 37, "usage_type": "call"}, {"api_name": "structures.table.TableStructure.from_dask_dataframe", "line_number": 50, "usage_type": "call"}, {"api_name": "structures.table.TableStructure", "line_number": 50, "usage_type": "name"}, {"api_name": "structures.core.Spec", "line_number": 52, "usage_type": "call"}, {"api_name": "array.ArrayAdapter.from_array", "line_number": 81, "usage_type": "call"}, {"api_name": "array.ArrayAdapter", "line_number": 81, "usage_type": "name"}, {"api_name": "array.ArrayAdapter.from_array", "line_number": 85, "usage_type": "call"}, {"api_name": "array.ArrayAdapter", "line_number": 85, "usage_type": "name"}, {"api_name": "dask.base.dataframe", "line_number": 98, "usage_type": "attribute"}, {"api_name": "dask.base", "line_number": 98, "usage_type": "name"}, {"api_name": "dask.base.dataframe.concat", "line_number": 100, "usage_type": "call"}, {"api_name": "dask.base.dataframe", "line_number": 100, "usage_type": "attribute"}, {"api_name": "dask.base", "line_number": 100, "usage_type": "name"}, {"api_name": "dask.base.dataframe.concat", "line_number": 104, "usage_type": "call"}, {"api_name": "dask.base.dataframe", "line_number": 104, "usage_type": "attribute"}, {"api_name": "dask.base", "line_number": 104, "usage_type": "name"}, {"api_name": "server.object_cache.get_object_cache", "line_number": 106, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 108, "usage_type": "call"}, {"api_name": "dask.base.dataframe", "line_number": 120, "usage_type": "attribute"}, {"api_name": "dask.base", "line_number": 120, "usage_type": "name"}, {"api_name": "server.object_cache.get_object_cache", "line_number": 122, "usage_type": "call"}]} +{"seq_id": "19717708890", "text": "\"\"\"\nDefinition of forms.\n\"\"\"\nimport os\nfrom django import forms\nfrom django.contrib.auth.forms import AuthenticationForm\nfrom django.utils.translation import gettext_lazy as _\nfrom crispy_forms.helper import FormHelper\nfrom crispy_forms.layout import ButtonHolder, Submit, Layout, Row, Column, Field\nfrom app.models import TranslationRequest, ClientFile, ClientInfo\nfrom django.core.exceptions import ValidationError\nfrom .validators import subject_verb_not_equal\n\nclass MultipleFileInput(forms.ClearableFileInput):\n allow_multiple_selected = True\n\n def __init__(self, attrs=None):\n default_attrs = {'class': 'btn btn-secondary'} # Add the Bootstrap class here\n if attrs:\n default_attrs.update(attrs)\n super().__init__(default_attrs)\n\n\nclass MultipleFileField(forms.FileField):\n def __init__(self, *args, **kwargs):\n kwargs.setdefault(\"widget\", MultipleFileInput())\n super().__init__(*args, **kwargs)\n\n def clean(self, data, initial=None):\n single_file_clean = super().clean\n if isinstance(data, (list, tuple)):\n result = [single_file_clean(d, initial) for d in data]\n else:\n result = single_file_clean(data, initial)\n return result\n\n\n\n\nclass BootstrapAuthenticationForm(AuthenticationForm):\n \"\"\"Authentication form which uses boostrap CSS.\"\"\"\n username = forms.CharField(max_length=254,\n widget=forms.TextInput({\n 'class': 'form-control',\n 'placeholder': 'User name'}))\n password = forms.CharField(label=_(\"Password\"),\n widget=forms.PasswordInput({\n 'class': 'form-control',\n 'placeholder':'Password'}))\n\n\n #User forms\n\n\n\nclass ClientInfoForm(forms.ModelForm):\n class Meta:\n model = ClientInfo\n fields = ['first_name', 'last_name', 'email']\n\n \n\nBLACKLISTED_EXTENSIONS = ['.exe', '.bat', '.cmd'] # Add any other blacklisted extensions here\n\nclass MultipleFileInput(forms.ClearableFileInput):\n allow_multiple_selected = True\n\nclass LimitedMultipleFileField(forms.FileField):\n def __init__(self, max_files=5, max_file_size=10 * 1024 * 1024, *args, **kwargs):\n self.max_files = max_files\n self.max_file_size = max_file_size\n kwargs.setdefault(\"widget\", MultipleFileInput())\n super().__init__(*args, **kwargs)\n\n def clean(self, data, initial=None):\n single_file_clean = super().clean\n if isinstance(data, (list, tuple)):\n if len(data) > self.max_files:\n raise forms.ValidationError(f\"You can upload up to {self.max_files} files only.\")\n for file in data:\n self.validate_file(file)\n else:\n self.validate_file(data)\n\n return data\n\n def validate_file(self, file):\n if file.size > self.max_file_size:\n raise forms.ValidationError(f\"File size should not exceed {self.max_file_size} bytes.\")\n _, ext = os.path.splitext(file.name)\n if ext.lower() in BLACKLISTED_EXTENSIONS:\n raise forms.ValidationError(\"Files with this extension are not allowed.\")\n\nclass ClientFileForm(forms.ModelForm):\n class Meta:\n model = ClientFile\n fields = ['file']\n\n file = LimitedMultipleFileField()\n\n\nclass TranslationRequestForm(forms.ModelForm):\n files = LimitedMultipleFileField()\n\n class Meta:\n model = TranslationRequest\n fields = [\n \n 'source_language',\n 'target_language',\n 'content', \n \n ]\n widgets = {\n 'source_language' : forms.RadioSelect,\n 'target_language' : forms.RadioSelect,\n \n 'content':forms.Textarea(attrs={'class': 'form-control', }), # Set cols attribute to 35\n }\n\n\n\n\n\n\nclass TranslatedFileUploadForm(forms.Form):\n processed_file = forms.FileField()\n\nclass SandboxForm(forms.Form):\n LANGUAGE_CHOICES = [\n ('fr', 'French'),\n ('en', 'English'),\n ('es', 'Spanish'),\n ]\n\n subject = forms.ChoiceField(\n label='Your name',\n choices=LANGUAGE_CHOICES,\n widget=forms.RadioSelect(attrs={'class': 'vertical-select'}),\n error_messages={\n 'required': 'Please select your name.',\n }\n )\n verb = forms.ChoiceField(\n label='Your last name',\n choices=LANGUAGE_CHOICES,\n widget=forms.RadioSelect(attrs={'class': 'vertical-select'}),\n error_messages={\n 'required': 'Please select your last name.',\n }\n )\n\n def clean(self):\n cleaned_data = super().clean()\n subject = cleaned_data.get('subject')\n verb = cleaned_data.get('verb')\n\n if subject == verb and subject is not None:\n raise forms.ValidationError(\"Subject and verb can't be the same.\")\n\n return cleaned_data\n\n\n\n\nclass FileUploadForm(forms.ModelForm):\n class Meta:\n model = ClientFile\n fields = ['original_file']\n\n\n\n\n \nclass MultipleImageInput(forms.ClearableFileInput):\n allow_multiple_selected = True\n\nclass MultipleImageField(forms.ImageField):\n widget = MultipleImageInput\n\nclass ProductImageForm(forms.Form):\n name = forms.CharField(max_length=100)\n description = forms.CharField(widget=forms.Textarea)\n price = forms.DecimalField(max_digits=10, decimal_places=2)\n images = MultipleImageField(widget=MultipleImageInput(attrs={'multiple': True}))", "repo_name": "anditherobot/translationrequest", "sub_path": "app/forms.py", "file_name": "forms.py", "file_ext": "py", "file_size_in_byte": 5572, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "django.forms.ClearableFileInput", "line_number": 14, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 14, "usage_type": "name"}, {"api_name": "django.forms.FileField", "line_number": 24, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 24, "usage_type": "name"}, {"api_name": "django.contrib.auth.forms.AuthenticationForm", "line_number": 40, "usage_type": "name"}, {"api_name": "django.forms.CharField", "line_number": 42, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 42, "usage_type": "name"}, {"api_name": "django.forms.TextInput", "line_number": 43, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 43, "usage_type": "name"}, {"api_name": "django.forms.CharField", "line_number": 46, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 46, "usage_type": "name"}, {"api_name": "django.utils.translation.gettext_lazy", "line_number": 46, "usage_type": "call"}, {"api_name": "django.forms.PasswordInput", "line_number": 47, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 47, "usage_type": "name"}, {"api_name": "django.forms.ModelForm", "line_number": 56, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 56, "usage_type": "name"}, {"api_name": "app.models.ClientInfo", "line_number": 58, "usage_type": "name"}, {"api_name": "django.forms.ClearableFileInput", "line_number": 65, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 65, "usage_type": "name"}, {"api_name": "django.forms.FileField", "line_number": 68, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 68, "usage_type": "name"}, {"api_name": "django.forms.ValidationError", "line_number": 79, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 79, "usage_type": "name"}, {"api_name": "django.forms.ValidationError", "line_number": 89, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 89, "usage_type": "name"}, {"api_name": "django.utils.translation.gettext_lazy", "line_number": 90, "usage_type": "name"}, {"api_name": "os.path.splitext", "line_number": 90, "usage_type": "call"}, {"api_name": "os.path", "line_number": 90, "usage_type": "attribute"}, {"api_name": "django.forms.ValidationError", "line_number": 92, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 92, "usage_type": "name"}, {"api_name": "django.forms.ModelForm", "line_number": 94, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 94, "usage_type": "name"}, {"api_name": "app.models.ClientFile", "line_number": 96, "usage_type": "name"}, {"api_name": "django.forms.ModelForm", "line_number": 102, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 102, "usage_type": "name"}, {"api_name": "app.models.TranslationRequest", "line_number": 106, "usage_type": "name"}, {"api_name": "django.forms.RadioSelect", "line_number": 115, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 115, "usage_type": "name"}, {"api_name": "django.forms.RadioSelect", "line_number": 116, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 116, "usage_type": "name"}, {"api_name": "django.forms.Textarea", "line_number": 118, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 118, "usage_type": "name"}, {"api_name": "django.forms.Form", "line_number": 126, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 126, "usage_type": "name"}, {"api_name": "django.forms.FileField", "line_number": 127, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 127, "usage_type": "name"}, {"api_name": "django.forms.Form", "line_number": 129, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 129, "usage_type": "name"}, {"api_name": "django.forms.ChoiceField", "line_number": 136, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 136, "usage_type": "name"}, {"api_name": "django.forms.RadioSelect", "line_number": 139, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 139, "usage_type": "name"}, {"api_name": "django.forms.ChoiceField", "line_number": 144, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 144, "usage_type": "name"}, {"api_name": "django.forms.RadioSelect", "line_number": 147, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 147, "usage_type": "name"}, {"api_name": "django.forms.ValidationError", "line_number": 159, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 159, "usage_type": "name"}, {"api_name": "django.forms.ModelForm", "line_number": 166, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 166, "usage_type": "name"}, {"api_name": "app.models.ClientFile", "line_number": 168, "usage_type": "name"}, {"api_name": "django.forms.ClearableFileInput", "line_number": 175, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 175, "usage_type": "name"}, {"api_name": "django.forms.ImageField", "line_number": 178, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 178, "usage_type": "name"}, {"api_name": "django.forms.Form", "line_number": 181, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 181, "usage_type": "name"}, {"api_name": "django.forms.CharField", "line_number": 182, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 182, "usage_type": "name"}, {"api_name": "django.forms.CharField", "line_number": 183, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 183, "usage_type": "name"}, {"api_name": "django.forms.Textarea", "line_number": 183, "usage_type": "attribute"}, {"api_name": "django.forms.DecimalField", "line_number": 184, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 184, "usage_type": "name"}]} +{"seq_id": "13519612704", "text": "from django.core.exceptions import ImproperlyConfigured\nfrom django.template import loader\nfrom django.views.generic import TemplateView\n\n\nclass TemplateDebugView(TemplateView):\n template_name = \"v1/template_debug.html\"\n debug_template_name = None\n debug_test_cases = None\n extra_js = None\n\n def get_context_data(self, **kwargs):\n context = super().get_context_data(**kwargs)\n\n if self.debug_template_name is None or self.debug_test_cases is None:\n raise ImproperlyConfigured(\n \"TemplateDebugView requires definition of \"\n \"debug_template_name and debug_test_cases\"\n )\n\n template = loader.get_template(self.debug_template_name)\n\n context.update(\n {\n \"debug_template_name\": self.debug_template_name,\n \"debug_test_cases\": {\n name: template.render(\n {\n \"request\": self.request,\n \"value\": data,\n }\n )\n for name, data in self.debug_test_cases.items()\n },\n }\n )\n\n if self.extra_js:\n context[\"page\"] = {\n \"media_js\": self.extra_js,\n }\n\n return context\n", "repo_name": "cfpb/consumerfinance.gov", "sub_path": "cfgov/v1/views/template_debug.py", "file_name": "template_debug.py", "file_ext": "py", "file_size_in_byte": 1322, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 241, "dataset": "github-code", "pt": "37", "api": [{"api_name": "django.views.generic.TemplateView", "line_number": 6, "usage_type": "name"}, {"api_name": "django.core.exceptions.ImproperlyConfigured", "line_number": 16, "usage_type": "call"}, {"api_name": "django.template.loader.get_template", "line_number": 21, "usage_type": "call"}, {"api_name": "django.template.loader", "line_number": 21, "usage_type": "name"}]} +{"seq_id": "10358822352", "text": "from django.conf.urls import patterns, url;\n\nfrom mylinks import views;\n\nurlpatterns = patterns( \"\",\n\turl(r'^$', views.index, name='categoriedlinks_index'),\n\t\n\turl(r'^update/$',views.update,name='update'), \n\turl(r'^update/add/$',views.categoriesAddUpdate,name='categoriesAddUpdate'),\n\t\n\turl(r'^update/(?P\\d+)/$',views.updateNum,name='updateNum'),\n\turl(r'^update/add/(?P.+)/$',views.categoriesAddUpdateNum,name='categoriesAddUpdateNum'),\n);", "repo_name": "twoodside/categoriedlinks", "sub_path": "mylinks/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 453, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "django.conf.urls.patterns", "line_number": 5, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 6, "usage_type": "call"}, {"api_name": "mylinks.views.index", "line_number": 6, "usage_type": "attribute"}, {"api_name": "mylinks.views", "line_number": 6, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 8, "usage_type": "call"}, {"api_name": "mylinks.views.update", "line_number": 8, "usage_type": "attribute"}, {"api_name": "mylinks.views", "line_number": 8, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 9, "usage_type": "call"}, {"api_name": "mylinks.views.categoriesAddUpdate", "line_number": 9, "usage_type": "attribute"}, {"api_name": "mylinks.views", "line_number": 9, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 11, "usage_type": "call"}, {"api_name": "mylinks.views.updateNum", "line_number": 11, "usage_type": "attribute"}, {"api_name": "mylinks.views", "line_number": 11, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 12, "usage_type": "call"}, {"api_name": "mylinks.views.categoriesAddUpdateNum", "line_number": 12, "usage_type": "attribute"}, {"api_name": "mylinks.views", "line_number": 12, "usage_type": "name"}]} +{"seq_id": "925215555", "text": "# Write a python program to flatten a nested list\n\nimport itertools\nimport copy\n\n# Example 1\n\nmy_list = [[1], [2, 3], [4, 5, 6, 7]]\nprint(\"Example 1. source:\", my_list)\n\nflat_list = list(itertools.chain(*my_list))\nprint(\"Example 1. result:\", flat_list)\n\n# Example 2\n\ndef clean_list(l) -> list:\n for i in range(0,len(l)):\n if type(l[i]) is type([]):\n if len(l[i]) > 0:\n tmp = copy.deepcopy(l[i])\n del l[i]\n l[i:i] = tmp\n clean_list(l)\n break\n else:\n l[i] = None \n return l\n\nmy_list = [[[[[1]]]], [[2, 3]], [4, 5, 6, 7], [[]], 8, 9]\nprint(\"Example 2. source: \", my_list)\n\nflat_list = clean_list(my_list)\nprint(\"Example 2. result: \", flat_list)\n", "repo_name": "areshta/python-edu", "sub_path": "006-additional/lab-2/l03.py", "file_name": "l03.py", "file_ext": "py", "file_size_in_byte": 778, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "itertools.chain", "line_number": 11, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 20, "usage_type": "call"}]} +{"seq_id": "28767786243", "text": "from __future__ import annotations\n\nimport inspect\nfrom typing import TypeVar, Generic\n\nfrom .._local_storage import local_storage\n\nT = TypeVar(\"T\")\n\n\nclass _ValueMeta(type):\n def __call__(cls, *args, **kwargs):\n # Get the line of code where the value was created at.\n back = inspect.currentframe().f_back\n\n # The value was called with an explicit generic type, so we have to go back one more frame\n if back.f_globals.get(\"__name__\") == \"typing\":\n back = back.f_back\n\n # Check if the function one back has a property called \"is_reactive\", for now Values can only be used in\n # reactive functions\n\n calling_function = back.f_globals[back.f_code.co_name]\n if not hasattr(calling_function, \"is_dynamic_function\"):\n raise ValueError(\"StableValue can only be used in a reactive function\")\n\n call_line = back.f_lineno\n\n dynamic_function = local_storage().active_dynamic_function()\n if not dynamic_function.has_stable_value(call_line):\n instance = super(_ValueMeta, cls).__call__(*args, **kwargs)\n dynamic_function.add_stable_value(call_line, instance)\n\n return dynamic_function.get_stable_value(call_line)\n\n\nclass StableValue(Generic[T], metaclass=_ValueMeta):\n \"\"\"\n A stable value is a value that is not reactive. It can be used to store values that are not\n reactive, and can be used in reactive functions.\n The value of a stable value can be changed, but it will not trigger a re-render.\n Stable values are stable and have the same value for every re-render of the function.\n \"\"\"\n\n def __init__(self, value: T, name: str = \"\"):\n self._old_value: T | None = None\n self._value = value\n self._name = name\n\n def get(self) -> T:\n return self._value\n\n def get_old(self) -> T | None:\n return self._old_value\n\n def set(self, value: T) -> None:\n self._old_value = self._value\n self._value = value\n\n def __repr__(self) -> str:\n return f\"{self._name}: {self._value.__repr__()}\"\n", "repo_name": "NiclasHaderer/better_shiny", "sub_path": "better_shiny/reactive/stable_value.py", "file_name": "stable_value.py", "file_ext": "py", "file_size_in_byte": 2084, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "typing.TypeVar", "line_number": 8, "usage_type": "call"}, {"api_name": "inspect.currentframe", "line_number": 14, "usage_type": "call"}, {"api_name": "_local_storage.local_storage", "line_number": 29, "usage_type": "call"}, {"api_name": "typing.Generic", "line_number": 37, "usage_type": "name"}]} +{"seq_id": "34020272173", "text": "from __future__ import annotations\n\nfrom l5r_auto.keywords import Dojo, Imperial\nfrom l5r_auto.legality import (\n IvoryEdition,\n ModernEdition,\n SamuraiEdition,\n TwentyFestivalsEdition,\n)\n\nfrom .common import Holding\n\n\":bow:: Produce 2 Gold.
    Limited, :bow:: If you have two or fewer Provinces, discard any face-up Holdings in your Provinces, refilling those Provinces face-up.\"\nImperial_Dojo = Holding(\n card_id=3689,\n title=\"Imperial Dojo\",\n gold_cost=2,\n keywords=[Dojo, Imperial],\n traits=[],\n abilities=[],\n legality=[\n IvoryEdition,\n TwentyFestivalsEdition,\n SamuraiEdition,\n ModernEdition,\n ModernEdition,\n ],\n gold_production=\"2\",\n)\n", "repo_name": "aubustou/l5r", "sub_path": "l5r_auto/cards/holdings/promotional_samurai.py", "file_name": "promotional_samurai.py", "file_ext": "py", "file_size_in_byte": 732, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "common.Holding", "line_number": 14, "usage_type": "call"}, {"api_name": "l5r_auto.keywords.Dojo", "line_number": 18, "usage_type": "name"}, {"api_name": "l5r_auto.keywords.Imperial", "line_number": 18, "usage_type": "name"}, {"api_name": "l5r_auto.legality.IvoryEdition", "line_number": 22, "usage_type": "name"}, {"api_name": "l5r_auto.legality.TwentyFestivalsEdition", "line_number": 23, "usage_type": "name"}, {"api_name": "l5r_auto.legality.SamuraiEdition", "line_number": 24, "usage_type": "name"}, {"api_name": "l5r_auto.legality.ModernEdition", "line_number": 25, "usage_type": "name"}, {"api_name": "l5r_auto.legality.ModernEdition", "line_number": 26, "usage_type": "name"}]} +{"seq_id": "15723427417", "text": "# -*- coding:utf-8 -*-\n\n__author__ = 'huanghf'\n\n\"\"\"\n输入一个正整数数组，把数组里所有数字拼接起来排成一个数，打印能拼接出的所有数字中最小的一个。\n例如输入数组{3，32，321}，则打印出这三个数字能排成的最小数字为321323。\n\n重新定义排序方式\n\n3+32>32+3 -> 3>32\n321+32<32+321 -> 321<32\nx+y x int(nums[0] + x)]\n return quickSort(l) + ''.join(m) + quickSort(r)\n\n return quickSort(nums)\n\n def PrintMinNumber2(self, numbers):\n \"\"\"\n Python sorted函数的cmp参数\n :param numbers:\n :return:\n \"\"\"\n from functools import cmp_to_key\n func = lambda x, y: int((x + y)) - int((y + x))\n nums = sorted([str(x) for x in numbers], key=cmp_to_key(func))\n return ''.join(nums)\n\n def PrintMinNumber3(self, numbers):\n # write code here\n if not numbers:\n return \"\"\n numbers = list(map(str, numbers))\n n = len(numbers)\n # 冒泡排序\n for i in range(n - 1):\n for j in range(0, n - i - 1):\n # j+1和j拼装出的数//\")\r\n@oidc.require_login\r\ndef add_strip(strip_id, leds_count, display_name): #Process adding a strip\r\n\r\n strip_id = strip_id.lower().strip(\":\").strip(\" \")\r\n leds_count = int(leds_count)\r\n user_object = main_database.return_user_object(g.user)\r\n result = user_object.add_strip(strip_id, leds_count, display_name)\r\n print(result)\r\n\r\n if result == True:\r\n return render_template(\"dashboard.html\", user_object=user_object)\r\n else:\r\n flash(result)\r\n return redirect(\"/dashboard\")\r\n\r\n\r\n@app.route(\"/get_strip_status//\")\r\ndef get_strip_status(strip_id): #Serve the strip status\r\n #print(main_database.strips_database)\r\n #strip_obj = main_database.strips_database.loc[strip_id][\"Strip Object\"]\r\n strip_obj = main_database.strips_database.loc[strip_id][\"Strip Object\"]\r\n return strip_obj.return_light_status_as_json()\r\n try:\r\n strip_obj = main_database.strips_database.loc[strip_id][\"Strip Object\"]\r\n return strip_obj.return_light_status_as_json()\r\n except Exception as e: #could do better exceptionn handling\r\n\r\n return f\"{e}:Strip not found\"\r\n\r\n\r\n@app.route(\"/add_section///\")\r\ndef add_section(strip_id,start_led,end_led): #Process adding a section\r\n #print(main_database.strips_database)\r\n #strip_obj = main_database.strips_database.loc[strip_id][\"Strip Object\"]\r\n #strip_obj.add_new_section_and_update(int(start_led), int(end_led))\r\n #user_object = main_database.return_user_object(g.user)\r\n #return render_template(\"dashboard.html\", user_object=user_object)\r\n\r\n try:\r\n strip_obj = main_database.strips_database.loc[strip_id][\"Strip Object\"]\r\n result = strip_obj.add_new_section_and_update(int(start_led), int(end_led))\r\n if result == True:\r\n user_object = main_database.return_user_object(g.user)\r\n return render_template(\"dashboard.html\", user_object=user_object)\r\n else:\r\n flash(result)\r\n return redirect(\"/dashboard\")\r\n except Exception as e: #could do better exceptionn handling\r\n\r\n return f\"{e}:Strip not found or other error\"\r\n\r\n\r\n@app.route(\"/set_section_solid_pattern///,,\") #Only for SolidPatterns at the moment\r\n@oidc.require_login\r\ndef set_section_solid_pattern(strip_id, section_id,R,G,B): #Process setting a section pattern\r\n #print(main_database.strips_database)\r\n strip_obj = main_database.strips_database.loc[strip_id][\"Strip Object\"]\r\n section_obj = strip_obj.get_section_by_id(section_id)\r\n if section_obj != False:\r\n section_obj.set_pattern(SolidPattern({'r':int(R),'g':int(G),'b':int(B)},str(uuid.uuid4())))\r\n return redirect(\"/dashboard\")\r\n\r\n else:\r\n return \"Error finding that section\"\r\n\r\n\r\n@app.route(\"/set_section_dynamic_pattern///\")\r\n@oidc.require_login\r\ndef set_section_dynamic_pattern(strip_id, section_id,dynamic_mode_code): #Process setting a section pattern\r\n #print(main_database.strips_database)\r\n strip_obj = main_database.strips_database.loc[strip_id][\"Strip Object\"]\r\n section_obj = strip_obj.get_section_by_id(section_id)\r\n if section_obj != False:\r\n section_obj.set_pattern(DynamicPattern(dynamic_mode_code,str(uuid.uuid4())))\r\n return redirect(\"/dashboard\")\r\n\r\n else:\r\n return \"Error finding that section\"\r\n\r\n@app.route(\"/set_pattern_solid_color///,,\") #JUST FOR TESting, need to accommodate different info block parameters\r\n@oidc.require_login\r\ndef set_pattern_solid_color(strip_id, pattern_id,R,G,B): #Process moodifying a SolidPattern's color\r\n #print(main_database.strips_database)\r\n strip_obj = main_database.strips_database.loc[strip_id][\"Strip Object\"]\r\n pattern_obj = strip_obj.get_pattern_by_id(pattern_id)\r\n if pattern_obj != False:\r\n pattern_obj.color_dict = {'r':R,'g':G,\"b\":B}\r\n return redirect(\"/dashboard\")\r\n\r\n else:\r\n return \"Error finding that section\"\r\n\r\n@app.route(\"/set_section_sun_pattern//\") #JUST FOR TESting, need to accommodate different info block parameters\r\n@oidc.require_login\r\ndef create_sunrise_sunset_page(strip_id, section_id):\r\n #print(reader.get(request.remote_addr))\r\n try:\r\n lat = reader.get(request.environ.get('HTTP_X_REAL_IP', request.remote_addr))['location']['latitude']\r\n lon = reader.get(request.environ.get('HTTP_X_REAL_IP', request.remote_addr))['location']['longitude']\r\n except:\r\n lat=41.5\r\n lon=-81.7\r\n\r\n strip_obj = main_database.strips_database.loc[strip_id][\"Strip Object\"]\r\n section_obj = strip_obj.get_section_by_id(section_id)\r\n section_obj.set_pattern(\r\n SunPattern(float(lat), float(lon), \"US/Eastern\", {'r': int(225), 'g': int(50), 'b': int(0)},\r\n {'r': int(114), 'g': int(158), 'b': int(223)}, str(uuid.uuid4())))\r\n return redirect(\"/dashboard\")\r\n\r\n@app.route(\"/add_alarm_to_strip//\")\r\n@oidc.require_login\r\ndef add_alarm_to_strip(strip_id, minutes_from_midnight):\r\n\r\n minutes_from_midnight = int(minutes_from_midnight)\r\n\r\n strip_obj = main_database.strips_database.loc[strip_id][\"Strip Object\"]\r\n\r\n strip_obj.add_alarm(minutes_from_midnight)\r\n flash(\"Alarm set!\");\r\n return redirect(\"/dashboard\")\r\n\r\n\"\"\"\r\n@app.route(\"/set_sun_pattern////////////\") #JUST FOR TESting, need to accommodate different info block parameters\r\ndef set_sunrise_sunset_pattern(strip_id, section_id, lat, lon, time_zone, R_up, G_up, B_up, R_down, G_down, B_down):\r\n strip_obj = main_database.strips_database.loc[strip_id][\"Strip Object\"]\r\n section_obj = strip_obj.get_section_by_id(section_id)\r\n section_obj.set_pattern(SunPattern(float(lat), float(lon), time_zone, {'r':int(R_down), 'g':int(G_down), 'b':int(B_down)}, {'r':int(R_up), 'g':int(G_up), 'b':int(B_up)}, str(uuid.uuid4())))\r\n\r\n return section_obj.current_mode.pattern_id\r\n\"\"\"\r\n@app.route(\"/print_section_ids/\") #JUST FOR TESting, need to accommodate different info block parameters\r\ndef print_section_ids(strip_id):\r\n strip_obj = main_database.strips_database.loc[strip_id][\"Strip Object\"]\r\n for section in strip_obj.sections_list:\r\n print(section.section_id)\r\n\r\n return \"done\"", "repo_name": "danielweidman/Suleis", "sub_path": "app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 8049, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "37", "api": [{"api_name": "pickle.load", "line_number": 15, "usage_type": "call"}, {"api_name": "SuleisLightObjects.MainDatabase", "line_number": 17, "usage_type": "call"}, {"api_name": "maxminddb.open_database", "line_number": 19, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 22, "usage_type": "call"}, {"api_name": "flask.g.user", "line_number": 28, "usage_type": "attribute"}, {"api_name": "flask.g", "line_number": 28, "usage_type": "name"}, {"api_name": "flask.g.user", "line_number": 30, "usage_type": "attribute"}, {"api_name": "flask.g", "line_number": 30, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 35, "usage_type": "call"}, {"api_name": "flask.g.user", "line_number": 41, "usage_type": "attribute"}, {"api_name": "flask.g", "line_number": 41, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 43, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 49, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 49, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 55, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 55, "usage_type": "call"}, {"api_name": "flask.g.user", "line_number": 63, "usage_type": "attribute"}, {"api_name": "flask.g", "line_number": 63, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 68, "usage_type": "call"}, {"api_name": "flask.flash", "line_number": 70, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 71, "usage_type": "call"}, {"api_name": "flask.g.user", "line_number": 100, "usage_type": "attribute"}, {"api_name": "flask.g", "line_number": 100, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 101, "usage_type": "call"}, {"api_name": "flask.flash", "line_number": 103, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 104, "usage_type": "call"}, {"api_name": "SuleisLightObjects.SolidPattern", "line_number": 117, "usage_type": "call"}, {"api_name": "uuid.uuid4", "line_number": 117, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 118, "usage_type": "call"}, {"api_name": "SuleisLightObjects.DynamicPattern", "line_number": 131, "usage_type": "call"}, {"api_name": "uuid.uuid4", "line_number": 131, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 132, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 145, "usage_type": "call"}, {"api_name": "flask.request.environ.get", "line_number": 155, "usage_type": "call"}, {"api_name": "flask.request.environ", "line_number": 155, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 155, "usage_type": "name"}, {"api_name": "flask.request.remote_addr", "line_number": 155, "usage_type": "attribute"}, {"api_name": "flask.request.environ.get", "line_number": 156, "usage_type": "call"}, {"api_name": "flask.request.environ", "line_number": 156, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 156, "usage_type": "name"}, {"api_name": "flask.request.remote_addr", "line_number": 156, "usage_type": "attribute"}, {"api_name": "SuleisLightObjects.SunPattern", "line_number": 164, "usage_type": "call"}, {"api_name": "uuid.uuid4", "line_number": 165, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 166, "usage_type": "call"}, {"api_name": "flask.flash", "line_number": 177, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 178, "usage_type": "call"}]} +{"seq_id": "6712543432", "text": "from lxml import etree\nimport requests\nimport sys\n\ndef MakePoem(word):\n url_base = \"http://so.gushiwen.org/search.aspx?value=\"\n key = word\n url = url_base+key\n res = requests.get(url)\n res.encoding = 'utf-8'\n #print(res.text)\n root = etree.HTML(res.content)\n items = root.xpath('//div[@class=\"sons\"][2]/p[@style=\"margin-bottom:0px;\"]')[0]\n item = items.xpath('string(.)')\n \n content = item.replace('\\n','').replace(' ','')\n length = len(content)\n answer = content[:length-1]\n\n return answer\n \n\n\n#print(content)", "repo_name": "littlewizardLI/IBM-Waston-apply", "sub_path": "poem.py", "file_name": "poem.py", "file_ext": "py", "file_size_in_byte": 525, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 7, "dataset": "github-code", "pt": "37", "api": [{"api_name": "requests.get", "line_number": 9, "usage_type": "call"}, {"api_name": "lxml.etree.HTML", "line_number": 12, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 12, "usage_type": "name"}]} +{"seq_id": "20703981247", "text": "from django.urls import path\n\nfrom . import views\n\napp_name = 'dnswrl'\nurlpatterns = [\n # path('', views.index, name='index'),\n path('choice/', views.choice, name='choice'),\n path('input/', views.inputText, name='inputText'),\n path('result/', views.result, name='result'),\n]", "repo_name": "DNSWRL/DNSWRL-python", "sub_path": "dnswrl_app/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 286, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "django.urls.path", "line_number": 8, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 9, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 10, "usage_type": "call"}]} +{"seq_id": "3496901292", "text": "from TableOfPages import TableOfPages\nfrom Ram import Ram\nfrom Tlb import Tlb\nfrom Disk import Disk\nfrom config import DEBUG_MODE\n\n\nclass CPU:\n\n def __init__(self, MEM_FIS_SIZE, MEM_VIR_SIZE, PAGE_SIZE, SUBSTITUTION,\n NUM_LINE, CORR, SUBS, NUM_PROG, PROG):\n self.mem_fis_size = MEM_FIS_SIZE\n self.mem_vir_size = MEM_VIR_SIZE\n self.page_size = PAGE_SIZE\n self.subtitution = SUBSTITUTION\n self.num_line = NUM_LINE\n self.corr = CORR\n self.subs = SUBS\n self.num_prog = NUM_PROG\n self.prog = PROG\n\n if DEBUG_MODE:\n print(\"*\" * 50 + \"\\n\")\n\n # Stores index of current program running\n self.current_program_index = 0\n\n # Dict to store last addr for each program\n # {program_index : last_addr_used_index}\n self.last_addr_used_register = {prog: 0 for prog in range(self.num_prog)}\n\n # Works as a timestamp for FIFO, LFU, LRU stats\n self.iteration = 0\n\n self.load_components()\n\n @property\n def is_finished(self):\n ''' Returns true if all programs did everything they had to do.\n Checks if last index of program is equal to the length - 1 for\n every program'''\n last_address_programs = [len(prog) - 1 for prog in self.prog]\n last_used = self.last_addr_used_register.values()\n comparations = [i == j for i, j in zip(last_address_programs, last_used)]\n\n return len(set(comparations)) == 1 and comparations[0]\n\n @property\n def current_program_list(self):\n ''' Returns current program list '''\n return self.prog[self.current_program_index]\n\n @property\n def current_program_is_finished(self):\n ''' Compares indexes of last addr used and len of program - 1 '''\n last_addr_current_program = len(self.current_program_list) - 1\n last_addr_used = self.last_addr_used_register[self.current_program_index]\n\n return last_addr_used == last_addr_current_program\n\n def load_components(self):\n ''' Loads all essentials components'''\n # print(\"Loading components\")\n self.table_of_pages = TableOfPages(self.mem_vir_size, self.page_size,\n self.num_prog)\n self.ram = Ram(self.mem_fis_size, self.page_size, self.subtitution, self.num_prog)\n self.tlb = Tlb(self.num_line, self.subs, self.corr, self.num_prog)\n self.disk = Disk(self.num_prog)\n\n def print_components(self):\n # print(repr(self.table_of_pages))\n print(repr(self.ram))\n print(repr(self.tlb))\n # print(repr(self.disk))\n\n def run_programs(self):\n ''' Flow control to run programs in correct order '''\n\n while not self.is_finished:\n # If current program is finished go to next program\n if self.current_program_is_finished:\n # print(\"Program {} already finished\".format(self.current_program_index))\n self.next_program()\n continue\n\n # If it is not finished, use it\n if DEBUG_MODE:\n print(\"-\" * 25)\n print(\"Current program {}\".format(self.current_program_index))\n\n # Get last addres used by this program\n last_used_this_program = self.last_addr_used_register[self.current_program_index]\n\n # If program starts with yield, save info and go to next program\n if last_used_this_program == 0 and self.current_program_list[last_used_this_program] == -1:\n if DEBUG_MODE:\n print(\"[YIELD] Programa {} ha ejecutado YIELD\".format(self.current_program_index))\n self.tlb.clear()\n\n # Save last addres used in this program and add one so it does not resumes from\n # index 0 and addres -1\n self.last_addr_used_register[self.current_program_index] = last_used_this_program + 1\n\n # Go to next program\n self.next_program()\n\n # Skip iteration\n continue\n\n # If not, check if it resumes from a -1.\n # Add one to the index if cpu takes this program from a -1\n last_used_this_program = last_used_this_program + 1 if self.current_program_list[last_used_this_program] == -1 else last_used_this_program\n\n # For every address not used, run it\n for j, addr in enumerate(self.current_program_list):\n # Omit addresses already used\n if j < last_used_this_program:\n continue\n\n if DEBUG_MODE:\n print()\n print(\"Iteration: {}\".format(self.iteration))\n print(\"Addr: {}, program: {}\".format(addr, self.current_program_index))\n print()\n\n # Save last addres used in this program\n self.last_addr_used_register[self.current_program_index] = j\n\n # If addr is a yield, clean TLB and go to next program\n if addr < 0:\n if DEBUG_MODE:\n print(\"[YIELD] Programa {} ha ejecutado YIELD\".format(self.current_program_index))\n\n self.tlb.clear()\n\n # Go to next program\n self.next_program()\n\n # Break out of for loop\n break\n\n # If it is not a yield, increase iteration counter\n self.iteration += 1\n\n # Check first digits of addr to get page number\n bin_addr = self.table_of_pages.to_binary(addr)\n bits_to_check = self.table_of_pages.num_of_bits_page\n page_digits = bin_addr[:bits_to_check]\n offset_digits = bin_addr[bits_to_check:]\n page_number = self.table_of_pages.to_decimal(page_digits)\n offset_number = self.table_of_pages.to_decimal(offset_digits)\n if DEBUG_MODE:\n print(\"[ACCESO] Memoria virtual {}({}) -> Pagina virtual {}({}) Offset {}({})\".format(\n bin_addr, addr, page_digits, page_number, offset_digits, offset_number))\n\n # Check page in TLB, if it finds the page updates hit count and returns it\n page = self.tlb.get_page(page_number, self.current_program_index, self.iteration)\n\n if not page:\n # If it not found, go to table of pages and add to TLB\n page = self.table_of_pages.get_page(page_number, self.current_program_index)\n\n # self.tlb.add_page(page, bin_address, self.iteration)\n self.tlb.add_page(page, page_digits, self.iteration)\n\n # Check if it has a marco in RAM\n if not page.has_marco:\n if DEBUG_MODE:\n print(\"[Pagina SIN ASOCIAR] Memoria full: {}\".format(self.ram.is_full))\n # Page fault\n print(\"[PAGE FAULT] Página no tiene marco.\")\n\n self.table_of_pages.update_page_faults(self.current_program_index)\n\n # Assign a marco\n self.ram.add_page(page, self.iteration, self.disk)\n else:\n # If it has marco it can be on disk or ram\n if page.marco_on_disk:\n if DEBUG_MODE:\n print(\"[PAGE FAULT] La pagina se encuentra en un marco del disco\")\n\n # Page fault\n self.table_of_pages.update_page_faults(self.current_program_index)\n\n # Bring back page to ram, and backup one marco to disk\n self.ram.swap_in_out(page, self.disk, self.iteration)\n\n # It counts as a use for page that came back (LFU, LRU)\n # self.ram.update_counters(page, self.iteration)\n else:\n # It has a marco on ram (LFU, LRU)\n self.ram.update_counters(page, self.iteration)\n\n if DEBUG_MODE:\n print(repr(self.ram))\n print(repr(self.tlb))\n print()\n print(\"self.ram.swap_out_stats, \", self.ram.swap_out_stats)\n print(\"self.ram.swap_in_stats, \", self.ram.swap_in_stats)\n print(\"#\" * 66)\n\n if self.current_program_is_finished:\n if DEBUG_MODE:\n print(\"*****************PROGRAMA {} HA FINALIZADO!************\".format(self.current_program_index))\n self.tlb.clear()\n\n self.print_statistics()\n\n def print_statistics(self):\n for p in range(self.num_prog):\n print(\"PROGRAMA {}\".format(p + 1))\n # Hit TLB\n hit_tlb = self.tlb.hit_stats[p]\n page_fault = self.table_of_pages.page_fault_stats[p]\n swap_in = self.ram.swap_in_stats[p]\n swap_out = self.ram.swap_out_stats[p]\n page_disk = self.disk.get_pages_on_disk(p)\n pages_ram = self.ram.get_pages_on_ram(p)\n page_valid = page_disk + pages_ram\n print(\"hit TLB: {}\".format(hit_tlb))\n print(\"page fault: {}\".format(page_fault))\n print(\"swap in: {}\".format(swap_in))\n print(\"swap out: {}\".format(swap_out))\n print(\"page valid: {}\".format(page_valid))\n print(\"page disk: {}\\n\".format(page_disk))\n\n def next_program(self):\n self.current_program_index += 1\n self.current_program_index = 0 if self.current_program_index == self.num_prog else self.current_program_index\n", "repo_name": "lechodiman/t2-iic2343", "sub_path": "CPU.py", "file_name": "CPU.py", "file_ext": "py", "file_size_in_byte": 9732, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "config.DEBUG_MODE", "line_number": 22, "usage_type": "name"}, {"api_name": "TableOfPages.TableOfPages", "line_number": 64, "usage_type": "call"}, {"api_name": "Ram.Ram", "line_number": 66, "usage_type": "call"}, {"api_name": "Tlb.Tlb", "line_number": 67, "usage_type": "call"}, {"api_name": "Disk.Disk", "line_number": 68, "usage_type": "call"}, {"api_name": "config.DEBUG_MODE", "line_number": 87, "usage_type": "name"}, {"api_name": "config.DEBUG_MODE", "line_number": 96, "usage_type": "name"}, {"api_name": "config.DEBUG_MODE", "line_number": 120, "usage_type": "name"}, {"api_name": "config.DEBUG_MODE", "line_number": 131, "usage_type": "name"}, {"api_name": "config.DEBUG_MODE", "line_number": 152, "usage_type": "name"}, {"api_name": "config.DEBUG_MODE", "line_number": 168, "usage_type": "name"}, {"api_name": "config.DEBUG_MODE", "line_number": 180, "usage_type": "name"}, {"api_name": "config.DEBUG_MODE", "line_number": 195, "usage_type": "name"}, {"api_name": "config.DEBUG_MODE", "line_number": 204, "usage_type": "name"}]} +{"seq_id": "12514695957", "text": "from pathlib import Path\nimport os\nimport watts\n\n\"\"\"\nA three-step three-level MultiApps system for a Microreactor Unit Cell is run here\nFirst step is steady state simulation\nSecond step is a Null transient simulation to confirm the steady state obtained\nThird step is a real transient simulation induced by loss of cooling capacity\n\"\"\"\n\n# set your SuperMoose directory as SUPER_MOOSE_DIR\n# As Sockeye is run through dynamic linking\n# set your Sockeye directory as SOCKEYE_DIR\n\nif not os.getenv(\"SOCKEYE_DIR\"):\n raise RuntimeError(\"SOCKEYE_DIR must be set to enable this example.\")\n# MOOSE app type to run\napp_dir = Path(os.enrivon[\"SUPER_MOOSE_DIR\"])\nmoose_exec = app_dir / \"super_moose-opt\"\n\n# Steady State Parameters\nparams_ss = watts.Parameters()\n\n# Griffin\nparams_ss['Griffin_Init_Fuel_Temperature'] = 800.0 # K\n\n# Bison\nparams_ss['BISON_Initial_Temperature'] = 800.0 # K.\nparams_ss['BISON_Inifinit_Temperature'] = 800.0 # K.\nparams_ss['BISON_Outside_HTC'] = 100.0\n\n# Sockeye\nparams_ss['Sockeye_Evap_Elem_Num'] =15\nparams_ss['Sockeye_Adia_Elem_Num'] =5\nparams_ss['Sockeye_Cond_Elem_Num'] =10\n\nparams_ss.show_summary(show_metadata=False, sort_by='key')\n\n# MOOSE Workflow for steady state\nprint(\"Steady-state calculation\")\nmpi_args = ['mpiexec', '-n', '40']\nmoose_plugin_ss = watts.PluginMOOSE(\n 'MP_ss_griffin.tmpl',\n executable=moose_exec,\n extra_inputs=['MP_ss_moose.i', 'MP_ss_sockeye.i', '3D_unit_cell_FY21_level-1_bison.e', '3D_unit_cell_FY21_supersimple.e', 'unitcell_nogap_hom_xml_G11_df_MP.xml']\n)\nmoose_result_ss = moose_plugin_ss(params_ss, mpi_args=mpi_args)\nfor key in moose_result_ss.csv_data:\n print(key, moose_result_ss.csv_data[key])\nprint(moose_result_ss.inputs)\nprint(moose_result_ss.outputs)\n\n# set up an enviroment variable to pass SS output path to trN and tr workflow\nos.environ[\"SS_PATH\"] = str(moose_result_ss.base_path)\n\n# Null Transient Parameters\nparams_trN = params_ss\n\n# MOOSE Workflow for Null transient\nprint(\"Null transient calculation\")\nmoose_plugin_trN = watts.PluginMOOSE(\n 'MP_trN_griffin.tmpl',\n executable=moose_exec,\n show_stdout=False,\n extra_inputs=['MP_trN_moose.i', 'MP_trN_sockeye.i', 'unitcell_nogap_hom_xml_G11_df_MP.xml']\n)\nmoose_result_trN = moose_plugin_trN(params_trN, mpi_args=mpi_args)\nfor key in moose_result_trN.csv_data:\n print(key, moose_result_trN.csv_data[key])\nprint(moose_result_trN.inputs)\nprint(moose_result_trN.outputs)\n\n# tolerance for the Null Transient run to tell if the steady state obtained before is valid\nrel_diff_tol = 1.0e-8\n\npower_rel_diff = (moose_result_trN.csv_data['integrated_power'][0] - moose_result_trN.csv_data['integrated_power'][-1])/moose_result_trN.csv_data['integrated_power'][0]\nif power_rel_diff > rel_diff_tol:\n raise RuntimeError(\"The Null Transient Run has a transient power; please check the steady state run.\")\n\n# Transient Parameters\nparams_tr = params_ss\n\n# MOOSE Workflow for transient\nprint(\"Transient calculation\")\nmoose_plugin_tr = watts.PluginMOOSE(\n 'MP_tr_griffin.tmpl',\n executable=moose_exec,\n show_stdout=True,\n extra_inputs=['MP_tr_moose.i', 'MP_tr_sockeye.i', 'unitcell_nogap_hom_xml_G11_df_MP.xml']\n)\nmoose_result_tr = moose_plugin_tr(params_tr, mpi_args=mpi_args)\nfor key in moose_result_tr.csv_data:\n print(key, moose_result_tr.csv_data[key])\nprint(moose_result_tr.inputs)\nprint(moose_result_tr.outputs)\n", "repo_name": "watts-dev/watts", "sub_path": "examples/MultiStep_Griffin-BISON-Sockeye_MR/watts_exec.py", "file_name": "watts_exec.py", "file_ext": "py", "file_size_in_byte": 3367, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 18, "dataset": "github-code", "pt": "37", "api": [{"api_name": "os.getenv", "line_number": 16, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 19, "usage_type": "call"}, {"api_name": "os.enrivon", "line_number": 19, "usage_type": "attribute"}, {"api_name": "watts.Parameters", "line_number": 23, "usage_type": "call"}, {"api_name": "watts.PluginMOOSE", "line_number": 43, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 55, "usage_type": "attribute"}, {"api_name": "watts.PluginMOOSE", "line_number": 62, "usage_type": "call"}, {"api_name": "watts.PluginMOOSE", "line_number": 86, "usage_type": "call"}]} +{"seq_id": "40646137694", "text": "import json\nfrom tqdm import tqdm\nimport openai\nimport backoff\nimport os\nimport numpy as np\nfrom typing import Iterable, Dict\nimport gzip\nimport json\nimport os\nimport argparse\nfrom agents.gpt.gpt_wrapper import GPTAgent\n\nGEN_STOP_WORDS = {\n \"python\":['def'],\n\n}\n\n\nCOR_STOP_WORDS = {\n \"python\":['def'],\n}\n\n\n\ndef read_problems(problem_file: str) -> Dict[str, Dict]:\n return {task[\"task_id\"]: task for task in stream_jsonl(problem_file)}\n\ndef read_results(result_file: str) -> Dict[str, Dict]:\n return {task[\"task_id\"]: task for task in stream_jsonl(result_file)}\n\ndef stream_jsonl(filename: str) -> Iterable[Dict]:\n \"\"\"\n Parses each jsonl line and yields it as a dictionary\n \"\"\"\n if filename.endswith(\".gz\"):\n with open(filename, \"rb\") as gzfp:\n with gzip.open(gzfp, 'rt') as fp:\n for line in fp:\n if any(not x.isspace() for x in line):\n yield json.loads(line)\n else:\n with open(filename, \"r\") as fp:\n for line in fp:\n if any(not x.isspace() for x in line):\n yield json.loads(line)\n\ndef write_jsonl(filename: str, data: Iterable[Dict], append: bool = False):\n \"\"\"\n Writes an iterable of dictionaries to jsonl\n \"\"\"\n if append:\n mode = 'ab'\n else:\n mode = 'wb'\n filename = os.path.expanduser(filename)\n if filename.endswith(\".gz\"):\n with open(filename, mode) as fp:\n with gzip.GzipFile(fileobj=fp, mode='wb') as gzfp:\n for x in data:\n gzfp.write((json.dumps(x) + \"\\n\").encode('utf-8'))\n else:\n with open(filename, mode) as fp:\n for x in data:\n fp.write((json.dumps(x) + \"\\n\").encode('utf-8'))\n\ndef print_options(args,parser):\n message = 'Arguments:\\n'\n for k, v in sorted(vars(args).items()):\n comment = ''\n default_value = parser.get_default(k)\n if v != default_value:\n comment = f'\\t(default: {default_value})'\n message += f'{str(k):>30}: {str(v):<40}{comment}\\n'\n\n print(message)\n\n\ndef code_generation(problems,args):\n generator = GPTAgent(args.model)\n stop = GEN_STOP_WORDS[args.language]\n samples = []\n for task_id in tqdm(problems, desc=\"Generating code\", total=len(problems)):\n for _ in range(args.num_samples_per_task):\n code_signature = problems[task_id][\"prompt\"]\n # print(\"Task_id: \",task_id)\n # print(code_signature)\n response, usage = generator(code_signature, args.max_tokens, args.temperature, stop)\n # print(\"--------------------response-------------------\")\n # print(response)\n sample = {\"task_id\": task_id, \"completion\": response}\n samples.append(sample)\n\n save_path = os.path.join(\".\", \"results\",\"code_generation\",args.dataset, f\"{args.language}.jsonl\")\n os.makedirs(os.path.dirname(save_path), exist_ok=True)\n print(\"Write the code generation results to file :\\n\",save_path)\n write_jsonl(save_path, samples)\n\n\n\n\ndef get_cor_prompt(buggy_code,error_message):\n with open('./prompt/code_teacher.txt', 'r') as f:\n prompt = f.read()\n\n prompt = prompt.replace(\"%%%buggy_code%%%\",buggy_code)\n prompt = prompt.replace(\"%%%error_message%%%\",error_message)\n return prompt\n\ndef cor_generation(problems,results,args):\n generator = GPTAgent(args.model)\n stop = COR_STOP_WORDS[args.language]\n samples = []\n for task_id in tqdm(problems, desc=\"Generate Chain-of-Repairing(CoR)\", total=len(problems)):\n for _ in range(args.num_samples_per_task):\n if results[task_id][\"result\"] == \"passed\":\n sample = {\"task_id\": task_id, \"completion\": results[task_id][\"completion\"],\"result\":results[task_id][\"result\"],\"passed\":results[task_id][\"passed\"],\"method\": \"\"}\n samples.append(sample)\n else:\n code_signature = problems[task_id][\"prompt\"]\n buggy_code = code_signature + results[task_id][\"completion\"] + \"\\n\"\n error_message = results[task_id][\"result\"]\n prompt = get_cor_prompt(buggy_code,error_message)\n # print(prompt)\n response, usage = generator(prompt, args.max_tokens, args.temperature, stop)\n # print(\"--------------------response-------------------\")\n # print(response)\n sample = {\"task_id\": task_id, \"completion\": results[task_id][\"completion\"],\"result\":results[task_id][\"result\"],\"passed\":results[task_id][\"passed\"], \"method\": response}\n samples.append(sample)\n\n save_path = os.path.join(\".\", \"results\", \"cor_generation\", args.dataset, f\"{args.language}_cor.jsonl\")\n os.makedirs(os.path.dirname(save_path), exist_ok=True)\n print(\"Write the Chain-of-Repairing(CoR) to file :\\n\", save_path)\n write_jsonl(save_path, samples)\n\ndef get_repair_prompt(buggy_code,error_message,repair_method,code_signature):\n with open('./prompt/code_learner.txt', 'r') as f:\n prompt = f.read()\n\n prompt = prompt.replace(\"%%%buggy_code%%%\", buggy_code)\n prompt = prompt.replace(\"%%%error_message%%%\", error_message)\n prompt = prompt.replace(\"%%%repair_method%%%\", repair_method)\n prompt = prompt +\"\\n\\n\"+code_signature\n return prompt\ndef code_reapiring(problems,results,args):\n generator = GPTAgent(args.model)\n stop = GEN_STOP_WORDS[args.language]\n samples = []\n for task_id in tqdm(problems, desc=\"Code Repairing\", total=len(problems)):\n for _ in range(args.num_samples_per_task):\n if results[task_id][\"result\"] == \"passed\":\n sample = {\"task_id\": task_id, \"completion\": results[task_id][\"completion\"],\n \"result\": results[task_id][\"result\"], \"passed\": results[task_id][\"passed\"], \"method\": \"\"}\n samples.append(sample)\n else:\n code_signature = problems[task_id][\"prompt\"]\n buggy_code = code_signature + results[task_id][\"completion\"] + \"\\n\"\n error_message = results[task_id][\"result\"]\n repair_method = results[task_id][\"method\"]\n prompt = get_repair_prompt(buggy_code,error_message,repair_method,code_signature)\n # print(prompt)\n response, usage = generator(code_signature, args.max_tokens, args.temperature, stop)\n # print(\"--------------------response-------------------\")\n # print(response)\n sample = {\"task_id\": task_id, \"completion\": response,\n \"result\": results[task_id][\"result\"], \"passed\": results[task_id][\"passed\"],\n \"method\": results[task_id][\"method\"]}\n samples.append(sample)\n\n save_path = os.path.join(\".\", \"results\", \"code_repair\", args.dataset, f\"{args.language}.jsonl\")\n os.makedirs(os.path.dirname(save_path), exist_ok=True)\n print(\"Write the code repair results to file :\\n\", save_path)\n write_jsonl(save_path, samples)\n\n\ndef main():\n parser = argparse.ArgumentParser(\"Run the interactive loop.\")\n parser.add_argument(\n \"--problem_file\",\n type=str,\n required=True,\n default=\"\",\n help=\"The file containing the problems.\",\n )\n parser.add_argument(\n \"--dataset\",\n type=str,\n default=\"humaneval\",\n choices=[\"humaneval\", \"mbpp\", \"humanevalx\", \"codeerror\"],\n help=\"The dataset to use.\",\n )\n parser.add_argument(\n \"--language\",\n type=str,\n default=\"python\",\n choices=[\"python\", \"cpp\", \"java\", \"js\"],\n help=\"The language to use.\",\n )\n parser.add_argument(\n \"--model\",\n type=str,\n default=\"gpt-3.5-turbo-instruct-0914\",\n help=\"The model to use.\",\n )\n parser.add_argument(\n \"--max_tokens\",\n type=int,\n default=\"512\",\n help=\"The maximum number of tokens to generate.\",\n )\n parser.add_argument(\n \"--temperature\",\n type=float,\n default=\"0.2\",\n help=\"The temperature to use.\",\n )\n parser.add_argument(\n \"--num_samples_per_task\",\n type=int,\n default=\"1\",\n help=\"The number of samples to generate per task.\",\n )\n\n parser.add_argument(\n \"--todo\",\n type=str,\n choices=[\"code_generation\", \"cor_generation\", \"code_reapir\"],\n required=True,\n default=None,\n help=\"Code generation or CoR generation or code reapiring.\",\n )\n args = parser.parse_args()\n print_options(args,parser)\n problems = read_problems(args.problem_file)\n\n if args.todo == \"code_generation\":\n print(\"Start code generation...\\n\\n\")\n code_generation(problems,args)\n elif args.todo == \"cor_generation\":\n print(\"Start CoR generation...\\n\\n\")\n result_file = os.path.join(\".\", \"results\",\"code_generation\",args.dataset, f\"{args.language}.jsonl_results.jsonl\")\n results = read_results(result_file)\n cor_generation(problems,results,args)\n elif args.todo == \"code_reapir\":\n print(\"Start code reapiring...\\n\\n\")\n result_file = os.path.join(\".\", \"results\", \"cor_generation\", args.dataset, f\"{args.language}_cor.jsonl\")\n results = read_results(result_file)\n code_reapiring(problems,results,args)\nif __name__ == '__main__':\n main()\n", "repo_name": "NEUIR/INTERVENOR", "sub_path": "intervenor.py", "file_name": "intervenor.py", "file_ext": "py", "file_size_in_byte": 9403, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "37", "api": [{"api_name": "typing.Dict", "line_number": 26, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 29, "usage_type": "name"}, {"api_name": "gzip.open", "line_number": 38, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 41, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 46, "usage_type": "call"}, {"api_name": "typing.Iterable", "line_number": 32, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 32, "usage_type": "name"}, {"api_name": "typing.Iterable", "line_number": 48, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 48, "usage_type": "name"}, {"api_name": "os.path.expanduser", "line_number": 56, "usage_type": "call"}, {"api_name": "os.path", "line_number": 56, "usage_type": "attribute"}, {"api_name": "gzip.GzipFile", "line_number": 59, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 61, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 65, "usage_type": "call"}, {"api_name": "agents.gpt.gpt_wrapper.GPTAgent", "line_number": 80, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 83, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 94, "usage_type": "call"}, {"api_name": "os.path", "line_number": 94, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 95, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 95, "usage_type": "call"}, {"api_name": "os.path", "line_number": 95, "usage_type": "attribute"}, {"api_name": "agents.gpt.gpt_wrapper.GPTAgent", "line_number": 111, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 114, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 131, "usage_type": "call"}, {"api_name": "os.path", "line_number": 131, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 132, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 132, "usage_type": "call"}, {"api_name": "os.path", "line_number": 132, "usage_type": "attribute"}, {"api_name": "agents.gpt.gpt_wrapper.GPTAgent", "line_number": 146, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 149, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 170, "usage_type": "call"}, {"api_name": "os.path", "line_number": 170, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 171, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 171, "usage_type": "call"}, {"api_name": "os.path", "line_number": 171, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentParser", "line_number": 177, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 241, "usage_type": "call"}, {"api_name": "os.path", "line_number": 241, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 246, "usage_type": "call"}, {"api_name": "os.path", "line_number": 246, "usage_type": "attribute"}]} +{"seq_id": "32509390568", "text": "# Import package and data\nfrom collections import defaultdict\nwith open('data/day7.txt') as file:\n data = file.read().splitlines()\n\n# Initialize our current location and an empty list we'll use here.\ncurrent_location = ''\ndata_with_abs_dir = list()\n\n# We work our way through the data line by line. Every time a directory changes, we update our 'current location'.\n# We then replace every directory reference in the data with a reference to the absolute path, rather than the local path.\nfor line in data:\n line_parsed = line.split(' ')\n if line_parsed[0].isdigit():\n data_with_abs_dir.append(line)\n elif line_parsed[0] == 'dir':\n new_dir = current_location + '-' + line_parsed[1]\n new_line = ' '.join([line_parsed[0], new_dir])\n data_with_abs_dir.append(new_line)\n else:\n if line_parsed[0] == '$':\n if line_parsed[1] == 'ls':\n data_with_abs_dir.append(line)\n elif line_parsed[1] == 'cd':\n if line_parsed[2] != '..':\n current_location = current_location + '-' + line_parsed[2]\n new_line = ' '.join([line_parsed[0], line_parsed[1], current_location])\n data_with_abs_dir.append(new_line)\n else:\n current_location = '-'.join(current_location.rsplit('-')[:-1])\n data_with_abs_dir.append(line)\n else:\n print('You have missed a line! This one:' + str(line))\n break\n else:\n print('You have missed a line! This one:' + str(line))\n\n# Now initialize a few values again.\ndictionary_data_value = 0\ndir_dict = defaultdict(int)\n\n# Work our way backwards through the data. If a data file, update our current directory's memory value.\n# If a directory, store that directory's total memory and reset the current directory's memory to zero.\n# Keep going all the way up until we know every directory's value.\nwhile len(data_with_abs_dir) > 0:\n entry = data_with_abs_dir.pop()\n entry_parsed = entry.split(' ')\n if entry_parsed[0].isdigit():\n dictionary_data_value += int(entry_parsed[0])\n elif entry_parsed[0] == 'dir':\n dictionary_data_value += dir_dict[entry_parsed[1]]\n else:\n if entry_parsed[0] == '$':\n if entry_parsed[1] == 'cd':\n if entry_parsed[2] == '..':\n pass\n else:\n dir_dict[entry_parsed[2]] += dictionary_data_value\n dictionary_data_value = 0\n else:\n pass\n\n### Okay this is the incremental part for Part B\n\n# Load the memory values\ntotal_mem = 70000000\nneeded_mem = 30000000\n\n# Find the current system data, use that to get the total free and the incremental need.\ntotal_system_data = max(dir_dict.values())\ntotal_free = total_mem - total_system_data\nincremental_needed_mem = needed_mem - total_free\n\n# Loop through the directories to keep only those big enough to be deletion candidates.\nsmall_dir = defaultdict(int)\nfor directory, mem in dir_dict.items():\n if mem >= incremental_needed_mem:\n small_dir[directory] = mem\n\n# Find the smallest of those.\nprint(min(small_dir.values()))\n", "repo_name": "conordurkin/adventofcode22", "sub_path": "day_7B.py", "file_name": "day_7B.py", "file_ext": "py", "file_size_in_byte": 3218, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "collections.defaultdict", "line_number": 40, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 75, "usage_type": "call"}]} +{"seq_id": "36668009589", "text": "import asyncio\nimport pytest\n\nfrom web3._utils.contract_sources.contract_data.emitter_contract import (\n EMITTER_CONTRACT_ABI,\n EMITTER_CONTRACT_BYTECODE,\n)\nfrom web3._utils.contract_sources.contract_data.math_contract import (\n MATH_CONTRACT_ABI,\n MATH_CONTRACT_BYTECODE,\n)\nfrom web3._utils.contract_sources.contract_data.offchain_lookup import (\n OFFCHAIN_LOOKUP_ABI,\n OFFCHAIN_LOOKUP_BYTECODE,\n)\nfrom web3._utils.contract_sources.contract_data.revert_contract import (\n REVERT_CONTRACT_ABI,\n REVERT_CONTRACT_BYTECODE,\n)\n\n# --- integration test configurations --- #\n\n\ndef pytest_collection_modifyitems(items, config):\n \"\"\"\n It is ideal to keep this configuration as simple as possible so that we don't\n risk missing some tests.\n \"\"\"\n # TODO: See if there is a better way to address the timeout issues present\n # in unlocked account tests.\n\n flaky_tests = []\n non_flaky_tests = []\n\n for item in items:\n if (\n # Unlocked account tests are problematic - separate them into their own\n # test run.\n any(\n _ in item.fixturenames\n for _ in (\n \"async_unlocked_account\",\n \"async_unlocked_account_dual_type\",\n \"unlocked_account\",\n \"unlocked_account_dual_type\",\n )\n )\n # Leave offchain_lookup tests split between eth sync and async tests as\n # those can conflict with each other as well.\n and \"offchain_lookup\" not in item.name\n ):\n flaky_tests.append(item)\n else:\n non_flaky_tests.append(item)\n\n if config.option.flaky:\n items[:] = flaky_tests\n config.hook.pytest_deselected(items=non_flaky_tests)\n else:\n items[:] = non_flaky_tests\n config.hook.pytest_deselected(items=flaky_tests)\n\n\n@pytest.fixture(scope=\"module\")\ndef math_contract_factory(w3):\n contract_factory = w3.eth.contract(\n abi=MATH_CONTRACT_ABI, bytecode=MATH_CONTRACT_BYTECODE\n )\n return contract_factory\n\n\n@pytest.fixture(scope=\"module\")\ndef emitter_contract_factory(w3):\n contract_factory = w3.eth.contract(\n abi=EMITTER_CONTRACT_ABI, bytecode=EMITTER_CONTRACT_BYTECODE\n )\n return contract_factory\n\n\n@pytest.fixture(scope=\"module\")\ndef revert_contract_factory(w3):\n contract_factory = w3.eth.contract(\n abi=REVERT_CONTRACT_ABI, bytecode=REVERT_CONTRACT_BYTECODE\n )\n return contract_factory\n\n\n@pytest.fixture(scope=\"module\")\ndef offchain_lookup_contract_factory(w3):\n contract_factory = w3.eth.contract(\n abi=OFFCHAIN_LOOKUP_ABI, bytecode=OFFCHAIN_LOOKUP_BYTECODE\n )\n return contract_factory\n\n\n@pytest.fixture(scope=\"module\")\ndef async_offchain_lookup_contract_factory(async_w3):\n contract_factory = async_w3.eth.contract(\n abi=OFFCHAIN_LOOKUP_ABI, bytecode=OFFCHAIN_LOOKUP_BYTECODE\n )\n return contract_factory\n\n\n@pytest.fixture(scope=\"module\")\ndef event_loop():\n loop = asyncio.get_event_loop_policy().new_event_loop()\n yield loop\n loop.close()\n", "repo_name": "ethereum/web3.py", "sub_path": "tests/integration/conftest.py", "file_name": "conftest.py", "file_ext": "py", "file_size_in_byte": 3119, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4510, "dataset": "github-code", "pt": "37", "api": [{"api_name": "web3._utils.contract_sources.contract_data.math_contract.MATH_CONTRACT_ABI", "line_number": 67, "usage_type": "name"}, {"api_name": "web3._utils.contract_sources.contract_data.math_contract.MATH_CONTRACT_BYTECODE", "line_number": 67, "usage_type": "name"}, {"api_name": "pytest.fixture", "line_number": 64, "usage_type": "call"}, {"api_name": "web3._utils.contract_sources.contract_data.emitter_contract.EMITTER_CONTRACT_ABI", "line_number": 75, "usage_type": "name"}, {"api_name": "web3._utils.contract_sources.contract_data.emitter_contract.EMITTER_CONTRACT_BYTECODE", "line_number": 75, "usage_type": "name"}, {"api_name": "pytest.fixture", "line_number": 72, "usage_type": "call"}, {"api_name": "web3._utils.contract_sources.contract_data.revert_contract.REVERT_CONTRACT_ABI", "line_number": 83, "usage_type": "name"}, {"api_name": "web3._utils.contract_sources.contract_data.revert_contract.REVERT_CONTRACT_BYTECODE", "line_number": 83, "usage_type": "name"}, {"api_name": "pytest.fixture", "line_number": 80, "usage_type": "call"}, {"api_name": "web3._utils.contract_sources.contract_data.offchain_lookup.OFFCHAIN_LOOKUP_ABI", "line_number": 91, "usage_type": "name"}, {"api_name": "web3._utils.contract_sources.contract_data.offchain_lookup.OFFCHAIN_LOOKUP_BYTECODE", "line_number": 91, "usage_type": "name"}, {"api_name": "pytest.fixture", "line_number": 88, "usage_type": "call"}, {"api_name": "web3._utils.contract_sources.contract_data.offchain_lookup.OFFCHAIN_LOOKUP_ABI", "line_number": 99, "usage_type": "name"}, {"api_name": "web3._utils.contract_sources.contract_data.offchain_lookup.OFFCHAIN_LOOKUP_BYTECODE", "line_number": 99, "usage_type": "name"}, {"api_name": "pytest.fixture", "line_number": 96, "usage_type": "call"}, {"api_name": "asyncio.get_event_loop_policy", "line_number": 106, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 104, "usage_type": "call"}]} +{"seq_id": "830350181", "text": "# -*- coding: utf-8 -*-\n\nfrom __future__ import absolute_import\nimport time\nimport falcon\nimport logging\nimport requests\nfrom bson import json_util, ObjectId\nfrom google.cloud import storage as gc_storage\n\nfrom uggipuggi.libs.error import HTTPBadRequest, HTTPInternalServerError\nfrom uggipuggi.constants import GROUP, GROUP_MEMBERS, USER_GROUPS, GCS_GROUP_BUCKET, \\\n GAE_IMG_SERVER, IMG_STORE_PATH\nfrom uggipuggi.controllers.image_store import ImageStore\nfrom uggipuggi.helpers.logs_metrics import init_logger, init_statsd\nfrom uggipuggi.controllers.hooks import deserialize, serialize, supply_redis_conn\nfrom uggipuggi.messaging.group_kafka_producers import group_kafka_item_put_producer, \\\n group_kafka_item_post_producer, group_kafka_item_delete_producer, \\\n group_kafka_collection_post_producer, group_kafka_collection_delete_producer \n\n\nlogger = init_logger()\nstatsd = init_statsd('up.controllers.group')\n\n@falcon.before(supply_redis_conn)\n@falcon.after(serialize)\nclass Collection(object):\n def __init__(self):\n self.img_store = ImageStore(IMG_STORE_PATH)\n self.kafka_topic_name = 'group_collection'\n self.gcs_client = gc_storage.Client() \n self.gcs_bucket = self.gcs_client.bucket(GCS_GROUP_BUCKET)\n\n if not self.gcs_bucket.exists():\n logger.debug(\"GCS Bucket %s does not exist, creating one\" %GCS_GROUP_BUCKET)\n self.gcs_bucket.create()\n\n @falcon.before(deserialize)\n @statsd.timer('get_groups_get')\n def on_get(self, req, resp):\n # Get all groups of user\n req.kafka_topic_name = '_'.join([self.kafka_topic_name, req.method.lower()])\n user_groups_id = USER_GROUPS + req.user_id\n resp.body['user_groups'] = list(req.redis_conn.smembers(user_groups_id))\n resp.status = falcon.HTTP_OK\n \n #@falcon.before(deserialize_create)\n @falcon.before(deserialize)\n @falcon.after(group_kafka_collection_post_producer)\n @statsd.timer('create_groups_post')\n def on_post(self, req, resp):\n statsd.incr('create_group.invocations')\n req.kafka_topic_name = '_'.join([self.kafka_topic_name, req.method.lower()])\n # Get new group ID\n group_id = str(req.redis_conn.incr(GROUP))\n logger.debug('New group id created: %s' %group_id)\n group_id_name = GROUP + group_id\n group_members_id_name = GROUP_MEMBERS + group_id\n \n img_url = ''\n if 'multipart/form-data' in req.content_type:\n img_data = req.get_param('group_pic') \n group_name = req.get_param('group_name')\n group_members_list = req.get_param_as_list('member_id')\n image_name = '_'.join([group_id_name, str(int(time.time())), 'group_pic'])\n try:\n img_url = self.img_store.save(img_data.file, image_name, img_data.type)\n except IOError:\n raise HTTPBadRequest(title='Group_pic upload failed', \n description='Group_pic upload to cloud storage failed!') \n \n else:\n group_name = req.params['body']['group_name']\n group_members_list = req.params['body']['member_id']\n \n pipeline = req.redis_conn.pipeline(True) \n pipeline.hmset(group_id_name, {\n 'group_name' : group_name,\n 'group_pic' : img_url,\n 'created_time': time.time(),\n 'admin' : req.user_id\n })\n \n # Add admin (current user) to group_members \n group_members_list.append(req.user_id)\n # Add members to the groups' members list\n pipeline.sadd(group_members_id_name, *group_members_list)\n \n # Add this group to set of groups a user belongs to\n # Note that now group_members_list include admin\n for member_id in group_members_list:\n user_groups_id = USER_GROUPS + member_id\n pipeline.sadd(user_groups_id, group_id_name)\n \n pipeline.execute()\n resp.body = {\"group_id\": group_id}\n resp.status = falcon.HTTP_OK\n \n @falcon.before(deserialize)\n @falcon.after(group_kafka_collection_delete_producer)\n @statsd.timer('delete_groups_delete')\n def on_delete(self, req, resp):\n statsd.incr('delete_group.invocations')\n req.kafka_topic_name = '_'.join([self.kafka_topic_name, req.method.lower()])\n logger.debug(\"Deleting group data in database ...\")\n group_id_name = GROUP + req.params['query']['group_id']\n admin = req.redis_conn.hget(group_id_name, 'admin')\n if admin != req.user_id:\n logger.debug(\"User is not the admin: %s , %s\" %(admin, req.user_id))\n resp.status = falcon.HTTP_UNAUTHORIZED\n return\n else:\n group_members_id_name = GROUP_MEMBERS + group_id\n pipeline = req.redis_conn.pipeline(True)\n group_keys = req.redis_conn.hgetall(group_id_name).keys()\n req.redis_conn.hdel(group_id_name, *group_keys)\n group_members = req.redis_conn.smembers(group_members_id_name)\n \n # Remove this group from all members group list\n for member in group_members:\n user_groups_id = USER_GROUPS + member\n pipeline.srem(user_groups_id, group_id_name)\n \n # Now remove all group members\n pipeline.srem(group_members_id_name, *group_members)\n pipeline.execute()\n logger.debug(\"Deleted group data in database\")\n resp.status = falcon.HTTP_OK\n\n\n@falcon.before(supply_redis_conn)\n@falcon.after(serialize)\nclass Item(object):\n def __init__(self):\n self.kafka_topic_name = 'group_item'\n self.gcs_client = gc_storage.Client() \n self.gcs_bucket = self.gcs_client.bucket(GCS_GROUP_BUCKET)\n\n if not self.gcs_bucket.exists():\n logger.debug(\"GCS Bucket %s does not exist, creating one\" %GCS_GROUP_BUCKET)\n self.gcs_bucket.create()\n\n @falcon.before(deserialize)\n #@falcon.after(group_kafka_item_get_producer)\n @statsd.timer('get_group_info_get')\n def on_get(self, req, resp, id):\n req.kafka_topic_name = '_'.join([self.kafka_topic_name, req.method.lower()])\n group_id_name = GROUP + id\n group_members_id_name = GROUP_MEMBERS + id\n resp.body = req.redis_conn.hgetall(group_id_name)\n # Should we also get members? \n # This is a get request, so body in req.params\n if 'members' in req.params['query']:\n resp.body['members'] = list(req.redis_conn.smembers(group_members_id_name))\n \n resp.status = falcon.HTTP_OK\n \n @falcon.before(deserialize)\n @falcon.after(group_kafka_item_delete_producer)\n @statsd.timer('delete_group_member_delete')\n def on_delete(self, req, resp, id):\n # Delete a member of a group. For deleting group use collection \n # delete request with group_id in the body\n statsd.incr('delete_group_member.invocations')\n req.kafka_topic_name = '_'.join([self.kafka_topic_name, req.method.lower()])\n \n group_id_name = GROUP + id\n admin = req.redis_conn.hget(group_id_name, 'admin')\n if admin != req.user_id:\n logger.debug(\"User is not the admin: %s , %s\" %(admin, req.user_id))\n resp.status = falcon.HTTP_UNAUTHORIZED\n return\n else:\n group_members_id_name = GROUP_MEMBERS + group_id\n logger.debug(\"Deleting member from group data in database ...\")\n pipeline = req.redis_conn.pipeline(True) \n pipeline.srem(group_members_id_name, *req.params['query']['member_id'])\n # Remove this group from this member's group list\n for group_member in req.params['query']['member_id']:\n user_groups_id = USER_GROUPS + group_member\n pipeline.srem(user_groups_id, group_id_name)\n pipeline.execute()\n logger.debug(\"Deleted member from group data in database\")\n resp.status = falcon.HTTP_OK\n\n #@falcon.before(deserialize_update)\n @falcon.before(deserialize)\n @falcon.after(group_kafka_item_put_producer)\n @statsd.timer('update_group_put')\n def on_put(self, req, resp, id):\n # Update group profile like pic\n statsd.incr('update_group.invocations')\n req.kafka_topic_name = '_'.join([self.kafka_topic_name, req.method.lower()])\n logger.debug(\"Finding group in database ... %s\" %repr(id))\n group_id_name = GROUP + id\n admin = req.redis_conn.hget(group_id_name, 'admin')\n if admin != req.user_id:\n logger.debug(\"User is not the admin: %s , %s\" %(admin, req.user_id))\n resp.status = falcon.HTTP_UNAUTHORIZED\n return\n else:\n pipeline = req.redis_conn.pipeline(True)\n if 'multipart/form-data' in req.content_type:\n img_data = req.get_param('group_pic')\n image_name = '_'.join([group_id_name, str(int(time.time())), 'group_pic'])\n try:\n img_url = self.img_store.save(img_data.file, image_name, img_data.type)\n pipeline.hset(group_id_name, 'group_pic', img_url)\n except IOError:\n raise HTTPInternalServerError(title='Group_pic upload failed', \n description='Group_pic upload to cloud storage failed!') \n\n else: \n for key in req.params['body']:\n pipeline.hset(group_id_name, key, req.params['body'][key])\n pipeline.execute()\n logger.debug(\"Updated group data in database\")\n resp.status = falcon.HTTP_OK\n\n @falcon.before(deserialize)\n @falcon.after(group_kafka_item_post_producer)\n @statsd.timer('add_group_member_post')\n def on_post(self, req, resp, id):\n # Add a member to group\n statsd.incr('add_group_member.invocations')\n req.kafka_topic_name = '_'.join([self.kafka_topic_name, req.method.lower()])\n logger.debug(\"Adding member to group in database ... %s\" %repr(id))\n group_id_name = GROUP + id\n admin = req.redis_conn.hget(group_id_name, 'admin')\n if admin != req.user_id:\n logger.debug(\"User is not the admin: %s , %s\" %(admin, req.user_id))\n resp.status = falcon.HTTP_UNAUTHORIZED\n return\n else:\n group_members_id_name = GROUP_MEMBERS + id\n logger.debug(\"Adding members to the group: \")\n pipeline = req.redis_conn.pipeline(True)\n pipeline.sadd(group_members_id_name, *req.params['body']['member_id'])\n for member in req.params['body']['member_id']:\n user_groups_id = USER_GROUPS + member\n pipeline.srem(user_groups_id, group_id_name) \n pipeline.execute()\n logger.debug(\"Added members to group in database\")\n resp.status = falcon.HTTP_OK", "repo_name": "krishnadubba/up_be_falcon", "sub_path": "uggipuggi/controllers/redis_group.py", "file_name": "redis_group.py", "file_ext": "py", "file_size_in_byte": 11182, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "uggipuggi.helpers.logs_metrics.init_logger", "line_number": 22, "usage_type": "call"}, {"api_name": "uggipuggi.helpers.logs_metrics.init_statsd", "line_number": 23, "usage_type": "call"}, {"api_name": "uggipuggi.controllers.image_store.ImageStore", "line_number": 29, "usage_type": "call"}, {"api_name": "uggipuggi.constants.IMG_STORE_PATH", "line_number": 29, "usage_type": "argument"}, {"api_name": "google.cloud.storage.Client", "line_number": 31, "usage_type": "call"}, {"api_name": "google.cloud.storage", "line_number": 31, "usage_type": "name"}, {"api_name": "uggipuggi.constants.GCS_GROUP_BUCKET", "line_number": 32, "usage_type": "argument"}, {"api_name": "uggipuggi.constants.GCS_GROUP_BUCKET", "line_number": 35, "usage_type": "name"}, {"api_name": "uggipuggi.constants.USER_GROUPS", "line_number": 43, "usage_type": "name"}, {"api_name": "falcon.HTTP_OK", "line_number": 45, "usage_type": "attribute"}, {"api_name": "falcon.before", "line_number": 38, "usage_type": "call"}, {"api_name": "uggipuggi.controllers.hooks.deserialize", "line_number": 38, "usage_type": "argument"}, {"api_name": "uggipuggi.constants.GROUP", "line_number": 55, "usage_type": "argument"}, {"api_name": "uggipuggi.constants.GROUP", "line_number": 57, "usage_type": "name"}, {"api_name": "uggipuggi.constants.GROUP_MEMBERS", "line_number": 58, "usage_type": "name"}, {"api_name": "time.time", "line_number": 65, "usage_type": "call"}, {"api_name": "uggipuggi.libs.error.HTTPBadRequest", "line_number": 69, "usage_type": "call"}, {"api_name": "time.time", "line_number": 80, "usage_type": "call"}, {"api_name": "uggipuggi.constants.USER_GROUPS", "line_number": 92, "usage_type": "name"}, {"api_name": "falcon.HTTP_OK", "line_number": 97, "usage_type": "attribute"}, {"api_name": "falcon.before", "line_number": 48, "usage_type": "call"}, {"api_name": "uggipuggi.controllers.hooks.deserialize", "line_number": 48, "usage_type": "argument"}, {"api_name": "falcon.after", "line_number": 49, "usage_type": "call"}, {"api_name": "uggipuggi.messaging.group_kafka_producers.group_kafka_collection_post_producer", "line_number": 49, "usage_type": "argument"}, {"api_name": "uggipuggi.constants.GROUP", "line_number": 106, "usage_type": "name"}, {"api_name": "falcon.HTTP_UNAUTHORIZED", "line_number": 110, "usage_type": "attribute"}, {"api_name": "uggipuggi.constants.GROUP_MEMBERS", "line_number": 113, "usage_type": "name"}, {"api_name": "uggipuggi.constants.USER_GROUPS", "line_number": 121, "usage_type": "name"}, {"api_name": "falcon.HTTP_OK", "line_number": 128, "usage_type": "attribute"}, {"api_name": "falcon.before", "line_number": 99, "usage_type": "call"}, {"api_name": "uggipuggi.controllers.hooks.deserialize", "line_number": 99, "usage_type": "argument"}, {"api_name": "falcon.after", "line_number": 100, "usage_type": "call"}, {"api_name": "uggipuggi.messaging.group_kafka_producers.group_kafka_collection_delete_producer", "line_number": 100, "usage_type": "argument"}, {"api_name": "falcon.before", "line_number": 25, "usage_type": "call"}, {"api_name": "uggipuggi.controllers.hooks.supply_redis_conn", "line_number": 25, "usage_type": "argument"}, {"api_name": "falcon.after", "line_number": 26, "usage_type": "call"}, {"api_name": "uggipuggi.controllers.hooks.serialize", "line_number": 26, "usage_type": "argument"}, {"api_name": "google.cloud.storage.Client", "line_number": 136, "usage_type": "call"}, {"api_name": "google.cloud.storage", "line_number": 136, "usage_type": "name"}, {"api_name": "uggipuggi.constants.GCS_GROUP_BUCKET", "line_number": 137, "usage_type": "argument"}, {"api_name": "uggipuggi.constants.GCS_GROUP_BUCKET", "line_number": 140, "usage_type": "name"}, {"api_name": "uggipuggi.constants.GROUP", "line_number": 148, "usage_type": "name"}, {"api_name": "uggipuggi.constants.GROUP_MEMBERS", "line_number": 149, "usage_type": "name"}, {"api_name": "falcon.HTTP_OK", "line_number": 156, "usage_type": "attribute"}, {"api_name": "falcon.before", "line_number": 143, "usage_type": "call"}, {"api_name": "uggipuggi.controllers.hooks.deserialize", "line_number": 143, "usage_type": "argument"}, {"api_name": "uggipuggi.constants.GROUP", "line_number": 167, "usage_type": "name"}, {"api_name": "falcon.HTTP_UNAUTHORIZED", "line_number": 171, "usage_type": "attribute"}, {"api_name": "uggipuggi.constants.GROUP_MEMBERS", "line_number": 174, "usage_type": "name"}, {"api_name": "uggipuggi.constants.USER_GROUPS", "line_number": 180, "usage_type": "name"}, {"api_name": "falcon.HTTP_OK", "line_number": 184, "usage_type": "attribute"}, {"api_name": "falcon.before", "line_number": 158, "usage_type": "call"}, {"api_name": "uggipuggi.controllers.hooks.deserialize", "line_number": 158, "usage_type": "argument"}, {"api_name": "falcon.after", "line_number": 159, "usage_type": "call"}, {"api_name": "uggipuggi.messaging.group_kafka_producers.group_kafka_item_delete_producer", "line_number": 159, "usage_type": "argument"}, {"api_name": "uggipuggi.constants.GROUP", "line_number": 195, "usage_type": "name"}, {"api_name": "falcon.HTTP_UNAUTHORIZED", "line_number": 199, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 205, "usage_type": "call"}, {"api_name": "uggipuggi.libs.error.HTTPInternalServerError", "line_number": 210, "usage_type": "call"}, {"api_name": "falcon.HTTP_OK", "line_number": 218, "usage_type": "attribute"}, {"api_name": "falcon.before", "line_number": 187, "usage_type": "call"}, {"api_name": "uggipuggi.controllers.hooks.deserialize", "line_number": 187, "usage_type": "argument"}, {"api_name": "falcon.after", "line_number": 188, "usage_type": "call"}, {"api_name": "uggipuggi.messaging.group_kafka_producers.group_kafka_item_put_producer", "line_number": 188, "usage_type": "argument"}, {"api_name": "uggipuggi.constants.GROUP", "line_number": 228, "usage_type": "name"}, {"api_name": "falcon.HTTP_UNAUTHORIZED", "line_number": 232, "usage_type": "attribute"}, {"api_name": "uggipuggi.constants.GROUP_MEMBERS", "line_number": 235, "usage_type": "name"}, {"api_name": "uggipuggi.constants.USER_GROUPS", "line_number": 240, "usage_type": "name"}, {"api_name": "falcon.HTTP_OK", "line_number": 244, "usage_type": "attribute"}, {"api_name": "falcon.before", "line_number": 220, "usage_type": "call"}, {"api_name": "uggipuggi.controllers.hooks.deserialize", "line_number": 220, "usage_type": "argument"}, {"api_name": "falcon.after", "line_number": 221, "usage_type": "call"}, {"api_name": "uggipuggi.messaging.group_kafka_producers.group_kafka_item_post_producer", "line_number": 221, "usage_type": "argument"}, {"api_name": "falcon.before", "line_number": 131, "usage_type": "call"}, {"api_name": "uggipuggi.controllers.hooks.supply_redis_conn", "line_number": 131, "usage_type": "argument"}, {"api_name": "falcon.after", "line_number": 132, "usage_type": "call"}, {"api_name": "uggipuggi.controllers.hooks.serialize", "line_number": 132, "usage_type": "argument"}]} +{"seq_id": "15002816772", "text": "import math\nfrom models.cell import Cell\nimport torch\nimport torch.nn as nn\nfrom tqdm import tqdm\nfrom . import Metric, classification_accuracy\n\n\n\nclass Manager(object):\n\n \n def __init__(self, args, model, data_loaders, filepath) -> None:\n super().__init__()\n self.args = args\n self.model = model\n self.data_loaders = data_loaders\n self.filepath = filepath\n self.reg = self.args.reg\n\n self.channel_ratio = 1.\n self.init_optimizer()\n self.criterion = nn.CrossEntropyLoss()\n\n if 'CIFAR' in self.args.dataset:\n self.input_size = [1,3,32,32]\n elif 'ImageNet' in self.args.dataset:\n self.input_size = [1,3,224,224]\n else:\n raise ValueError('TODO for other datasets')\n self.total_flops = self.compute_flops()\n self.flops_ratio = 1.\n self.sparsity = 1.\n self.layer_num = 0\n for m in self.model.modules():\n if isinstance(m, Cell):\n self.layer_num += 1\n print('Layer number: {}'.format(self.layer_num))\n print('Original FLOPs calculation: {}'.format(self.total_flops))\n if 'flop' in self.args.reg:\n self.update_base_flop()\n\n def update_base_flop(self):\n if 'flop' not in self.args.reg:\n return\n self.base_flop = 0.\n if self.args.reg == 'flop_0.5':\n for m in self.model.modules():\n if isinstance(m, Cell):\n self.base_flop += m.__flops_sqrt__\n else:\n raise ValueError('TODO : regularization {}.'.format(self.args.reg))\n self.base_flop /= self.layer_num\n\n def update_free_conv_mask(self):\n for m in self.model.modules():\n if isinstance(m, Cell):\n m.free_conv_mask = (m.free_conv_mask & ~m.mask)\n\n def init_optimizer(self):\n optimizer_arg = {'params':self.model.parameters(),\n 'lr':self.args.lr}\n if 'Adam' in self.args.optimizer:\n optimizer_arg['betas'] = (0, 0.999)\n self.optimizer = torch.optim.__dict__[self.args.optimizer](**optimizer_arg)\n self.scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(self.optimizer, self.args.epochs - self.args.pruning_iter[-1])\n \n def warm_up_with_multistep_lr(epoch): \n return (epoch+1) / self.args.warmup if epoch < self.args.warmup \\\n else 1 \n self.warmup_scheduler = torch.optim.lr_scheduler.LambdaLR(self.optimizer, lr_lambda=warm_up_with_multistep_lr)\n\n def forward(self, inputs, targets, task_id):\n out = self.model.forward(inputs, task_id)\n loss = self.criterion(out, targets)\n if self.reg == 'ori':\n sparsity_loss = 0.\n elif self.reg == 'l1':\n factors = torch.cat([m.bn.weight.view(-1) for m in self.model.modules() if isinstance(m, Cell)])\n num = torch.cat([m.get_output_mask() for m in self.model.modules() if isinstance(m, Cell)]).sum()\n sparsity_loss = 1. * torch.norm(factors, 1) / num # factors.numel()\n elif self.reg == 'pol':\n sparsity_loss = 0.\n # num = 0\n factors = torch.cat([m.bn.weight.view(-1) for m in self.model.modules() if isinstance(m, Cell)])\n num = torch.cat([m.get_output_mask() for m in self.model.modules() if isinstance(m, Cell)]).sum()\n sparse_weights_mean = factors.sum() / num # factors.numel()\n for m in self.model.modules():\n if isinstance(m, Cell):\n sparsity_term = 1.2 * torch.sum(torch.abs(m.bn.weight)) - torch.sum(\n torch.abs(m.bn.weight - sparse_weights_mean))\n sparsity_loss += 1. * sparsity_term/ num # factors.numel()\n elif self.reg == 'flop_0.5':\n factors = torch.cat(\n [m.bn.weight.view(-1) * m.__flops_sqrt__ / self.base_flop for m in self.model.modules() if\n isinstance(m, Cell)])\n num = torch.cat([m.get_output_mask() for m in self.model.modules() if isinstance(m, Cell)]).sum()\n sparsity_loss = 1. * torch.norm(factors, 1)/ num # factors.numel()\n else:\n raise ValueError('reg {} does not exist!'.format(self.config['reg']))\n\n loss += self.args.lam * sparsity_loss\n \n weights = torch.cat([(m.conv.weight * m.mask).view(-1) for m in self.model.modules() if isinstance(m, Cell)])\n num = torch.cat([m.mask.view(-1) for m in self.model.modules() if isinstance(m, Cell)]).sum()\n l2_loss = 1./torch.sqrt(num) * torch.norm(weights, 2)\n loss += self.args.lam * l2_loss\n\n self.optimizer.zero_grad()\n loss.backward()\n return loss.detach(), out\n \n def get_res_FLOP_iter(self):\n res_FLOP_iter = {}\n tgt = torch.sqrt(torch.tensor(self.args.res_FLOP))\n for e in range(self.args.pruning_iter[0], self.args.pruning_iter[1]):\n res_FLOP_iter[e] = 1 - (e + 1 - self.args.pruning_iter[0])*(1-tgt)/(self.args.pruning_iter[1]-self.args.pruning_iter[0])\n res_FLOP_iter[e] = res_FLOP_iter[e].item() ** 2\n return res_FLOP_iter\n\n def get_res_sparsity_iter(self):\n upper = self.args.rho\n res_sparsity_iter = {}\n for e in range(self.args.pruning_iter[0], self.args.pruning_iter[1]):\n res_sparsity_iter[e] = 1 - (e + 1 - self.args.pruning_iter[0])*(1-upper)/(self.args.pruning_iter[1]-self.args.pruning_iter[0])\n return res_sparsity_iter\n\n def prune(self, task_id, res_FLOP, res_sparsity):\n # flop res\n factors = torch.cat([m.bn.weight.abs().view(-1) for m in self.model.modules() if isinstance(m, Cell)])\n while (self.channel_ratio >0) and (self.flops_ratio > res_FLOP):\n self.channel_ratio -= 0.001\n thred = torch.topk(factors, max(int(factors.shape[0] * self.channel_ratio),1))[0][-1]\n for m in self.model.modules():\n if isinstance(m, Cell):\n m.mask &= (m.bn.weight.abs() > thred).view(-1, 1, 1, 1)\n self.model.update_masks()\n self.flops_ratio = self.compute_flops() / self.total_flops\n \n # for m in self.model.modules():\n # if isinstance(m, Cell):\n # m.bn.weight.data *= m.get_output_mask()\n # if m.bn.bias is not None:\n # m.bn.bias.data *= m.get_output_mask()\n # self.model.classifiers[str(task_id)].weight.data *= self.mask\n\n self.update_base_flop()\n\n # sparsity res\n for m in self.model.modules():\n if isinstance(m, Cell):\n if (m.mask & m.free_conv_mask).sum() <= math.ceil(res_sparsity*m.free_conv_mask.sum()):\n continue\n thred = torch.topk((m.conv.weight * (m.mask & m.free_conv_mask)).abs().view(-1), \\\n max(math.ceil(m.free_conv_mask.sum() * res_sparsity),1))[0][-1]\n m.mask = (~m.free_conv_mask | (m.conv.weight.abs() > thred)) & m.mask\n self.sparsity, _ = self.compute_sparsity()\n\n def warmup(self, epoch, task_id):\n if epoch < 1:\n return\n for m in self.model.modules():\n if isinstance(m, Cell):\n if m.free_conv_mask.sum() > 0:\n m.mask = m.free_conv_mask\n for e in range(epoch):\n self.train(e, task_id, mode='warmup')\n self.warmup_scheduler.step()\n for m in self.model.modules():\n if isinstance(m, Cell):\n m.mask = torch.ones(m.mask.shape, dtype=torch.bool).cuda()\n\n def train(self, epoch_idx, task_id, mode=None):\n if mode in ['warmup', 'finetune']:\n self.reg = 'ori'\n else:\n self.reg = self.args.reg\n self.model.train()\n train_loader = self.data_loaders.train_loader\n train_loss = Metric('train_loss')\n train_accuracy = Metric('train_accuracy')\n\n with tqdm(total=len(train_loader),\n desc='Train Ep. #{}: '.format(epoch_idx + 1),\n disable=False,\n ascii=True) as t:\n for input, target in train_loader:\n input, target = input.cuda(), target.cuda()\n loss, output = self.forward(input, target, task_id)\n \n # update grads\n for m in self.model.modules():\n if isinstance(m, Cell):\n m.conv.weight.grad *= (m.free_conv_mask & m.mask)\n m.bn.weight.grad *= m.get_output_mask()\n if m.bn.bias is not None:\n m.bn.bias.grad *= m.get_output_mask()\n self.model.classifiers[str(task_id)].weight.grad *= self.model.mask\n \n self.optimizer.step()\n \n num = input.size(0)\n train_accuracy.update(classification_accuracy(output, target), num)\n train_loss.update(loss.cpu(), num)\n\n t.set_postfix({\n 'lr': '{:.3f}'.format(self.optimizer.param_groups[0]['lr']),\n 'loss': '{:.2f}'.format(train_loss.avg.item()),\n 'sparsity': '{:.2f}'.format(self.sparsity),\n 'FLOPs': '{:.2f}'.format(self.flops_ratio),\n 'acc': '{:.2f}'.format(100. * train_accuracy.avg.item()),\n })\n t.update(1)\n if mode =='finetune':\n self.scheduler.step()\n summary = {\n 'loss': train_loss.avg.item(),\n 'acc': 100. * train_accuracy.avg.item()\n }\n self.validate(task_id)\n return summary\n\n def validate(self, task_id):\n self.model.eval()\n val_loader = self.data_loaders.val_loader\n loss = Metric('loss')\n accuracy = Metric('accuracy')\n\n with tqdm(total=len(val_loader),\n desc='Val: ',\n ascii=True) as t:\n for data, target in val_loader:\n data, target = data.cuda(), target.cuda()\n\n output = self.model(data, task_id)\n num = data.size(0)\n loss.update(self.criterion(output, target).cpu(), num)\n accuracy.update(classification_accuracy(output, target), num)\n\n t.set_postfix({ \n 'loss': '{:.3f}'.format(loss.avg.item()),\n 'sparsity': '{:.2f}'.format(self.compute_sparsity()[0]),\n 'FLOPs': '{:.2f}'.format((self.compute_flops()/self.total_flops)),\n 'acc': '{:.2f}'.format(100. * accuracy.avg.item()),\n })\n t.update(1)\n\n summary = {\n 'loss': loss.avg.item(),\n 'acc': 100. * accuracy.avg.item(),\n }\n return summary\n \n def set_task(self, task_id):\n self.data_loaders.update_task(task_id)\n self.model.eval()\n checkpoint = torch.load(self.filepath+'/{}.pth.tar'.format(task_id))\n self.model.set_task(task_id, checkpoint)\n \n def add_task(self, task_id, num_classes):\n self.data_loaders.update_task(task_id)\n self.channel_ratio = 1.\n self.init_optimizer()\n self.model.add_task(task_id, num_classes)\n self.update_base_flop()\n\n self.sparsity, _ = self.compute_sparsity()\n self.flops_ratio = self.compute_flops() / self.total_flops\n print('Add task ID: {} with FLOPs: {}, sparsity: {}.'.format(task_id, self.flops_ratio, self.sparsity))\n\n def save_checkpoint(self, task_id):\n channel_weights = {}\n channel_biases = {}\n running_means = {}\n running_vars = {}\n num_batches_tracked = {}\n masks = {}\n for n, m in self.model.named_modules():\n if isinstance(m, Cell):\n channel_weights[n] = m.bn.weight * m.get_output_mask()\n if m.bn.bias is not None:\n channel_biases[n] = m.bn.bias * m.get_output_mask()\n running_means[n] = m.bn.running_mean * m.get_output_mask()\n running_vars[n] = m.bn.running_var * m.get_output_mask()\n num_batches_tracked[n] = m.bn.num_batches_tracked\n masks[n] = m.mask\n checkpoint = {\n 'channel_weights': channel_weights,\n 'channel_biases': channel_biases,\n 'running_means': running_means,\n 'running_vars': running_vars,\n 'num_batches_tracked': num_batches_tracked,\n 'masks': masks,\n }\n torch.save(checkpoint, self.filepath + '/{}.pth.tar'.format(task_id))\n \n def compute_sparsity(self) -> float:\n sum = 0.\n sum_fixed = 0.\n count = 0\n for m in self.model.modules():\n if isinstance(m, Cell):\n sum += m.mask.sum()\n sum_fixed += (~m.free_conv_mask).sum()\n count += m.mask.numel()\n return (sum / count).item(), (sum_fixed / count).item()\n\n def compute_flops(self) -> float:\n global FLOPS\n FLOPS = 0.\n\n def cell_flops_counter_hook(m, input, output):\n global FLOPS\n input = input[0]\n\n # conv flops\n kernel_dims = m.conv.kernel_size\n in_channels = m.get_input_mask().sum()\n out_channels = m.get_output_mask().sum()\n groups = m.conv.groups\n conv_per_position_flops = int(torch.prod(torch.tensor(kernel_dims))) * in_channels // groups\n conv_flops = conv_per_position_flops * output.numel() * out_channels // m.conv.out_channels\n\n # bn flops\n bn_flops = output.numel() * out_channels // m.conv.out_channels\n if m.bn.track_running_stats:\n bn_flops *= 2\n\n # cell flops\n overall_flops = conv_flops + bn_flops\n\n m.__flops_per__ = overall_flops // out_channels if out_channels != 0 else 0\n m.__flops_sqrt__ = math.sqrt(m.__flops_per__)\n FLOPS += overall_flops\n\n def add_hooks(net, hook_handles):\n for net in net.modules():\n if isinstance(net, Cell):\n hook_handles.append(net.register_forward_hook(cell_flops_counter_hook))\n return\n\n handles = []\n add_hooks(self.model, handles)\n demo_input = torch.rand(self.input_size).cuda()\n self.model(demo_input)\n # clear handles\n for h in handles:\n h.remove()\n\n return FLOPS.item()", "repo_name": "ucr-optml/CtRL", "sub_path": "utils/manager.py", "file_name": "manager.py", "file_ext": "py", "file_size_in_byte": 14583, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "37", "api": [{"api_name": "torch.nn.CrossEntropyLoss", "line_number": 23, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 23, "usage_type": "name"}, {"api_name": "models.cell.Cell", "line_number": 36, "usage_type": "argument"}, {"api_name": "models.cell.Cell", "line_number": 49, "usage_type": "argument"}, {"api_name": "models.cell.Cell", "line_number": 57, "usage_type": "argument"}, {"api_name": "torch.optim", "line_number": 65, "usage_type": "attribute"}, {"api_name": "torch.optim.lr_scheduler.CosineAnnealingLR", "line_number": 66, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 66, "usage_type": "attribute"}, {"api_name": "torch.optim.lr_scheduler.LambdaLR", "line_number": 71, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 71, "usage_type": "attribute"}, {"api_name": "torch.cat", "line_number": 79, "usage_type": "call"}, {"api_name": "models.cell.Cell", "line_number": 79, "usage_type": "argument"}, {"api_name": "torch.cat", "line_number": 80, "usage_type": "call"}, {"api_name": "models.cell.Cell", "line_number": 80, "usage_type": "argument"}, {"api_name": "torch.norm", "line_number": 81, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 85, "usage_type": "call"}, {"api_name": "models.cell.Cell", "line_number": 85, "usage_type": "argument"}, {"api_name": "torch.cat", "line_number": 86, "usage_type": "call"}, {"api_name": "models.cell.Cell", "line_number": 86, "usage_type": "argument"}, {"api_name": "models.cell.Cell", "line_number": 89, "usage_type": "argument"}, {"api_name": "torch.sum", "line_number": 90, "usage_type": "call"}, {"api_name": "torch.abs", "line_number": 90, "usage_type": "call"}, {"api_name": "torch.abs", "line_number": 91, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 94, "usage_type": "call"}, {"api_name": "models.cell.Cell", "line_number": 96, "usage_type": "argument"}, {"api_name": "torch.cat", "line_number": 97, "usage_type": "call"}, {"api_name": "models.cell.Cell", "line_number": 97, "usage_type": "argument"}, {"api_name": "torch.norm", "line_number": 98, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 104, "usage_type": "call"}, {"api_name": "models.cell.Cell", "line_number": 104, "usage_type": "argument"}, {"api_name": "torch.cat", "line_number": 105, "usage_type": "call"}, {"api_name": "models.cell.Cell", "line_number": 105, "usage_type": "argument"}, {"api_name": "torch.sqrt", "line_number": 106, "usage_type": "call"}, {"api_name": "torch.norm", "line_number": 106, "usage_type": "call"}, {"api_name": "torch.sqrt", "line_number": 115, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 115, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 130, "usage_type": "call"}, {"api_name": "models.cell.Cell", "line_number": 130, "usage_type": "argument"}, {"api_name": "torch.topk", "line_number": 133, "usage_type": "call"}, {"api_name": "models.cell.Cell", "line_number": 135, "usage_type": "argument"}, {"api_name": "models.cell.Cell", "line_number": 151, "usage_type": "argument"}, {"api_name": "math.ceil", "line_number": 152, "usage_type": "call"}, {"api_name": "torch.topk", "line_number": 154, "usage_type": "call"}, {"api_name": "math.ceil", "line_number": 155, "usage_type": "call"}, {"api_name": "models.cell.Cell", "line_number": 163, "usage_type": "argument"}, {"api_name": "models.cell.Cell", "line_number": 170, "usage_type": "argument"}, {"api_name": "torch.ones", "line_number": 171, "usage_type": "call"}, {"api_name": "torch.bool", "line_number": 171, "usage_type": "attribute"}, {"api_name": "tqdm.tqdm", "line_number": 183, "usage_type": "call"}, {"api_name": "models.cell.Cell", "line_number": 193, "usage_type": "argument"}, {"api_name": "tqdm.tqdm", "line_number": 229, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 257, "usage_type": "call"}, {"api_name": "models.cell.Cell", "line_number": 279, "usage_type": "argument"}, {"api_name": "torch.save", "line_number": 295, "usage_type": "call"}, {"api_name": "models.cell.Cell", "line_number": 302, "usage_type": "argument"}, {"api_name": "torch.prod", "line_number": 321, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 321, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 333, "usage_type": "call"}, {"api_name": "models.cell.Cell", "line_number": 338, "usage_type": "argument"}, {"api_name": "torch.rand", "line_number": 344, "usage_type": "call"}]} +{"seq_id": "17570284577", "text": "#!/usr/bin/python\n\"\"\"collect_db\nSe encarga de recolectar los datos de alerta temprana y mantener la\nbase de datos actualizada automáticamente (definido en tiempo_espera).\n\nAlgunos datos a tener en cuenta:\n\n- Este código debería permanecer corriendo contínuamente.\n\n- Tiene un tiempo de última actualización para partir desde ese punto\n en caso de que el proceso se llegara a parar.\n\n- Para el funcionamiento de este código existen 4 bases de datos:\n - Base de datos psql Epic (Sismos alertados por el EEWS)\n - Base de datos CSN (Sismos reportados por CSN)\n - Base de datos TinyDB (Para ser usada en los reportes)\n - Base de datos con falsas alertas.\n\"\"\"\n# Preambulo\nimport psycopg2\nfrom config import config\nimport time\nimport datetime\nimport calendar\nimport math\nfrom tinydb import TinyDB, Query, where\nimport pandas as pd\nfrom obspy.taup import TauPyModel\nfrom obspy.taup import * \nfrom obspy.geodetics import kilometer2degrees\nfrom obspy.geodetics import locations2degrees\n\ntiempo_espera = 60*60 # 1 hora = 3600 s\nreport_db = TinyDB('report_db.json')\nfalse_alert_db = TinyDB('false_alert_db.json')\nevent_check = Query()\ntime_file = 'tiempo_ultima_actualizacion.dat'\n#model = TauPyModel(model=\"hussen.npz\")\nmodel = TauPyModel(model=\"prem\")\nCatalogo_CSN=\"sismos_diciembre-abril.txt\"\n#tiempo_ultima_actualizacion = 1609459200\ntry:\n\twith open(time_file, 'r') as f:\n\t\ttiempo_ultima_actualizacion = int(f.read())\nexcept:\n\t# En caso de que el archivo no exista\n\ttiempo_ultima_actualizacion = 1607040000 # 04-12-2020 0:00:00 EPOCH\n\twith open(time_file, 'w') as f:\n\t\tf.write(str(tiempo_ultima_actualizacion))\n\t\tf.close()\n\ndef tiempoViaje(ev_lat,ev_lon,loc_lat,loc_lon,dep):\n\t#global model\n\tdist_deg = locations2degrees(ev_lat,ev_lon,loc_lat,loc_lon)\n\tarrivals = []\n\tarrivals = model.get_travel_times(source_depth_in_km=dep, distance_in_degree=dist_deg, phase_list='p')\n\tif not arrivals: # Hay que revisar la fase \"p\" y \"P\", similar \"s\" y \"S\"\n\t\tarrivals= model.get_travel_times(source_depth_in_km=dep, distance_in_degree=dist_deg, phase_list='P')\n\t\tif not arrivals: # A veces, algunas prof tienen problemas, así que se prueba con un pequeño cambio\n\t\t\tarrivals= model.get_travel_times(source_depth_in_km=dep+1, distance_in_degree=dist_deg, phase_list='p')\n\t\t\tif not arrivals:\n\t\t\t\tarrivals= model.get_travel_times(source_depth_in_km=dep+1, distance_in_degree=dist_deg, phase_list='P')\n\tarr_p = arrivals[0]\n\tp_wave = arr_p.time\n\tarrivals= model.get_travel_times(source_depth_in_km=dep, distance_in_degree=dist_deg, phase_list='s')\n\tif not arrivals:\n\t\tarrivals= model.get_travel_times(source_depth_in_km=dep, distance_in_degree=dist_deg, phase_list='S')\n\t\tif not arrivals:\n\t\t\tarrivals= model.get_travel_times(source_depth_in_km=dep+1, distance_in_degree=dist_deg, phase_list='s')\n\t\t\tif not arrivals:\n\t\t\t\tarrivals= model.get_travel_times(source_depth_in_km=dep+1, distance_in_degree=dist_deg, phase_list='S')\n\tarr_s = arrivals[0]\n\ts_wave = arr_s.time\n\ttiempos = {\"P\":p_wave,\"S\":s_wave}\n\treturn tiempos\n\t\ndef connect():\n\t\"\"\" Funcion que se encarga de conectarse a las bases de datos \n\ty actualizar la base de datos para repores automaticos. \"\"\"\n\tconn = None\n\ttry:\n\t\tparams = config()\n\t\tconn = psycopg2.connect(**params)\n\texcept (Exception, psycopg2.DatabaseError) as error:\n\t\tprint(error)\n\tcur = conn.cursor()\n\n\twhile True:\n\t\t# Leer base de datos PSQL/EEWS\n\t\t#tiempo_ultima_actualizacion = time.time()\n\t\tglobal tiempo_ultima_actualizacion\n\t\tcur.execute('SELECT lon,lat,mag,dep,time,modtime from epic.e2event where first_alert = true and modtime > %s and mag > 2.5 order by modtime asc;' % (str(tiempo_ultima_actualizacion)))\n\t\tquery = cur.fetchall()\n\t\tif not query:\n\t\t\tprint(\"se cae aca\")\n\t\t\twith open(time_file, 'w') as f:\n\t\t\t\tf.write(str(tiempo_ultima_actualizacion))\n\t\t\t\tf.close()\n\t\t\t#time.sleep(tiempo_espera)\n\t\t\ttiempo_ultima_actualizacion = time.time()\n\t\t\ttime.sleep(tiempo_espera)\n\t\t\tcontinue\n\t\tlon = []\n\t\tlat = []\n\t\tmag = []\n\t\tdep = []\n\t\tev_time = []\n\t\tmodtime = []\n\t\tfor row in query:\n\t\t\tlon.append(row[0])\n\t\t\tlat.append(row[1])\n\t\t\tmag.append(row[2])\n\t\t\tdep.append(row[3])\n\t\t\tev_time.append(row[4])\n\t\t\tmodtime.append(row[5])\n\t\t#lon = query['lon']\n\t\t#lat = query['lat']\n\t\t#mag = query['mag']\n\t\t#ev_time = query['ev_time']\n\t\t#modtime = query['modtime']\t\n\t\t#lon,lat,mag,ev_time,modtime = query\n\t\t#print(\"eew = %d\" % len(lon))\n\t\t# Leer base de datos CSN\n\t\tdf_CSN = pd.read_csv(Catalogo_CSN, dtype=str, sep=',', engine='python')\n\t\t#print(\"csn = %d\" % len(df_CSN)) ####################################################\n\t\tcsn_lon = df_CSN['longitud'].astype(float).tolist()\n\t\tcsn_lat = df_CSN['latitud'].astype(float).tolist()\n\t\tcsn_dep = df_CSN['profundidad'].astype(float).tolist()\n\t\tdf_CSN['Date'] = df_CSN['o_time'].astype(str)\n\t\tcsn_date = []\n\t\tfor date in df_CSN['Date']:\n\t\t\taux = datetime.datetime.strptime(date+\"Z\", '%Y-%m-%d %H:%M:%SZ')\n\t\t\tcsn_date.append(calendar.timegm(aux.timetuple()))\n\t\t\t#print(date, calendar.timegm(aux.timetuple()))\n\t\tdf_CSN['the_mags'] = df_CSN['the_mags'].astype(str)\n\t\t#df_CSN['tipo'] = tipo.tolist()\n\t\tcsn_mag = []\n\t\tdict_prioridad = {'Mww': 0, 'Mw': 1, 'W': 2, 'L': 4, 'Ml': 5, 'b': 6, 'mww': 0, 'mw': 1}\n\t\tfor string in df_CSN['the_mags']:\n\t\t\tstring = string.split()\n\t\t\tprioridad = 100\n\t\t\tfor i in range(1, len(string), 2):\n\t\t\t\ttry:\n\t\t\t\t\tprioridad_segun_mag = dict_prioridad[string[i]]\n\t\t\t\texcept:\n\t\t\t\t\tprioridad_segun_mag = 7\n\t\t\t\tif prioridad_segun_mag < prioridad:\n\t\t\t\t\taux = float(string[i-1])\n\t\t\t\t\tprioridad = prioridad_segun_mag\n\t\t\tcsn_mag.append(aux)\n\t\t# Cruzar las dos bases de datos y actualizar bases de datos de reporte y falsa alerta\n\t\tfor i in range(0,len(csn_date)):\n\t\t\treport_db.insert({'csn_date': csn_date[i], 'csn_lon': csn_lon[i], 'csn_lat': csn_lat[i], \n\t\t\t\t\t'csn_dep': csn_dep[i], 'csn_mag': csn_mag[i],\n\t\t\t\t\t'eew_date': 0,'eew_lon': 0,'eew_lat': 0,'eew_mag': 0, 'eew_dep' : 0,\n\t\t\t\t\t'err_mag': 0,'err_dist': 0,'err_dep': 0,'err_otime': 0,\n\t\t\t\t\t'alert_time_centinela_P': 0, 'alert_time_centinela_S': 0,\n\t\t\t\t\t'alert_time_santiago_P': 0, 'alert_time_santiago_S': 0,\n\t\t\t\t\t'eew_comp_time': 0,'alertado': False, 'doble_alerta' : False})\n\t\t#aux = {'csn_date': csn_date, 'csn_lon': csn_lon, 'csn_lat': csn_lat, 'csn_dep': csn_dep, 'csn_mag': csn_mag}\n\t\t#sismo = pd.DataFrame(data=aux)\n\t\t#sismo = sismo.assign(eew_date=0)\n\t\t#sismo = sismo.assign(eew_lon=0)\n\t\t#sismo = sismo.assign(eew_lat=0)\n\t\t#sismo = sismo.assign(eew_mag=0)\n\t\t#sismo = sismo.assign(alert_time_centinela_P=0)\n\t\t#sismo = sismo.assign(alert_time_centinela_S=0)\n\t\t#sismo = sismo.assign(alert_time_santiago_P=0)\n\t\t#sismo = sismo.assign(alert_time_santiago_S=0)\n\t\t#sismo = sismo.assign(eew_comp_time=0)\n\t\t#sismo = sismo.assign(alertado=False)\n\t\t#sismo = sismo.assign(doble_alerta=False)\n\t\t#df = pd.DataFrame(data=d)\n\t\tdesde = 0\n\t\t#global false_alert_db, report_db\n\t\tfor i in range(0,len(ev_time)):\n\t\t\t# i: EEW\n\t\t\t# j: CSN\n\t\t\tEEW_date = ev_time[i]\n\t\t\tposibles_eventos = []\n\t\t\tfor j in range(desde,len(csn_date)):\n\t\t\t\tCSN_date = csn_date[j]\n\t\t\t\tdiferencia_tiempo = CSN_date - EEW_date\n\t\t\t\t#print(CSN_date, EEW_date, diferencia_tiempo)\n\t\t\t\tif (abs(CSN_date - EEW_date) <= 60.0) and (modtime[i] > EEW_date):\n\t\t\t\t\t#print(\"sos\")\n\t\t\t\t\tposibles_eventos.append(j)\n\t\t\t\t\tdesde = j\n\t\t\tif not posibles_eventos: \n\t\t\t\tfalse_alert_db.insert({'origin_time':ev_time[i], 'lon':lon[i], 'lat':lat[i], 'mag':mag[i], 'dep':dep[i], 'time':modtime[i]})\n\t\t\t\tcontinue\n\t\t\tdistancia = 300.0\n\t\t\tevento = []\n\t\t\tfor ev in posibles_eventos:\n\t\t\t\taux = 111.0*math.sqrt((lon[i] - csn_lon[ev])**2.0+(lat[i] - csn_lat[ev])**2.0)\n\t\t\t\t#print(lon[i],csn_lon[ev],lat[i],csn_lat[ev],aux)\n\t\t\t\tif aux < distancia:\n\t\t\t\t\tevento = ev\n\t\t\t\t\tdistancia = aux\n\t\t\tif not evento: \n\t\t\t\tfalse_alert_db.insert({'origin_time':ev_time[i], 'lon':lon[i], 'lat':lat[i], 'mag':mag[i], 'dep':dep[i], 'time':modtime[i]})\n\t\t\t\tcontinue\n\t\t\t#false_alert_db.insert({'origin_time':ev_time[i], 'lon':lon[i], 'lat':lat[i], 'mag':mag[i], 'time':modtime[i]})\n\t\t\teew_comp_time = modtime[i] - csn_date[evento]\n\t\t\t#print(lat[i],lon[i],-23.01, -69.10,csn_dep[evento])\n\t\t\taux = tiempoViaje(lat[i],lon[i],-23.01, -69.10,csn_dep[evento])\n\t\t\talert_time_centinela_P = aux['P'] - eew_comp_time\n\t\t\talert_time_centinela_S = aux['S'] - eew_comp_time\n\t\t\taux = tiempoViaje(lat[i],lon[i],-33.45, -70.67,csn_dep[evento])\n\t\t\talert_time_santiago_P = aux['P'] - eew_comp_time\n\t\t\talert_time_santiago_S = aux['S'] - eew_comp_time\n\t\t\tif report_db.get((where('csn_date') == csn_date[evento]) & ~(where('eew_date') == 0)) == None:\n\t\t\t\tprint(1,ev_time[i])\n\t\t\t\t\n\t\t\t\treport_db.update({'eew_date': ev_time[i],'eew_lon': lon[i],'eew_lat': lat[i],'eew_mag': mag[i],'eew_dep': dep[i],\n\t\t\t\t\t\t \t'err_mag' : csn_mag[evento] - mag[i], 'err_dist' : distancia, \n\t\t\t\t\t\t \t'err_dep' : csn_dep[evento] - dep[i], 'err_otime' : csn_date[evento] - ev_time[i],\n\t\t\t\t\t \t\t'alert_time_centinela_P': alert_time_centinela_P, 'alert_time_centinela_S': alert_time_centinela_S,\n\t\t\t\t\t\t\t'alert_time_santiago_P': alert_time_santiago_P, 'alert_time_santiago_S': alert_time_santiago_S,\n\t\t\t\t\t \t\t'eew_comp_time': eew_comp_time,'alertado': True}, where('csn_date') == csn_date[evento])\n\t\t\telse:\n\t\t\t\taux = report_db.get(where('csn_date') == csn_date[evento])\n\t\t\t\tloc_csn = (aux['csn_lat'],aux['csn_lon'])\n\t\t\t\tdist_nuevo = 111.19*locations2degrees(lat[i], lon[i], aux['eew_lat'], aux['eew_lon'])\n\t\t\t\tdist_anterior = 111.19*locations2degrees(lat[i], lon[i], aux['eew_lat'], aux['eew_lon'])\n\t\t\t\tnota_nueva = abs(mag[i]-aux['csn_mag'])/2.0 + abs(ev_time[i]-aux['csn_date'])/20.0 + dist_nuevo/100\n\t\t\t\tnota_anterior = abs(aux['eew_mag']-aux['csn_mag'])/2.0 + abs(aux['eew_date']-aux['csn_date'])/10.0 + dist_anterior/70.0\n\t\t\t\tif 2*nota_nueva < nota_anterior:\n\t\t\t\t\tprint(2,ev_time[i])\n\t\t\t\t\treport_db.update({'rep_date': aux['eew_date'],'rep_lon': aux['eew_lon'],'rep_lat': aux['eew_lat'],'rep_dep': aux['eew_dep'],\n\t\t\t\t\t\t\t 'rep_mag': aux['eew_mag'], 'doble_alerta': True}, where('csn_date') == csn_date[evento])\n\t\t\t\t\treport_db.update({'eew_date': ev_time[i],'eew_lon': lon[i],'eew_lat': lat[i],'eew_mag': mag[i],'eew_dep': dep[i],\n\t\t\t\t\t\t\t\t'err_mag' : csn_mag[evento] - mag[i], 'err_dist' : dist_nuevo, \n\t\t\t\t\t\t\t\t'err_dep' : csn_dep[evento] - dep[i], 'err_otime' : csn_date[evento] - ev_time[i],\n\t\t\t\t\t \t\t\t'alert_time_centinela_P': alert_time_centinela_P, 'alert_time_centinela_S': alert_time_centinela_S,\n\t\t\t\t\t\t\t\t'alert_time_santiago_P': alert_time_santiago_P, 'alert_time_santiago_S': alert_time_santiago_S,\n\t\t\t\t\t \t\t\t'eew_comp_time': eew_comp_time,'alertado': True}, where('csn_date') == csn_date[evento])\n\t\t\t\telse:\n\t\t\t\t\tprint(3,ev_time[i])\n\t\t\t\t\t#print(i,3,csn_date[evento],report_db.get((where('csn_date') == csn_date[evento]) & ~ (where('eew_date') == 0)))\n\t\t\t\t\treport_db.update({'rep_date': ev_time[i],'rep_lon': lon[i],'rep_lat': lat[i], 'rep_dep': dep[i],\n\t\t\t\t\t\t\t 'rep_mag': mag[i], 'doble_alerta': True}, where('csn_date') == csn_date[evento])\n\t\t\t# Cambiar tiempo de ultima actualizacion y esperar para no sobrecargar la base psql\n\t\twith open(time_file, 'w') as f:\n\t\t\tf.write(str(tiempo_ultima_actualizacion))\n\t\t\tf.close()\n\t\tprint(\"listo\")\n\t\t#time.sleep(tiempo_espera)\n\t\ttiempo_ultima_actualizacion = time.time()\n\t\ttime.sleep(tiempo_espera)\n\nif __name__ == '__main__':\n\tconnect()\n", "repo_name": "miguel-mf/eews-reports", "sub_path": "collect_db.py", "file_name": "collect_db.py", "file_ext": "py", "file_size_in_byte": 11173, "program_lang": "python", "lang": "es", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "tinydb.TinyDB", "line_number": 34, "usage_type": "call"}, {"api_name": "tinydb.TinyDB", "line_number": 35, "usage_type": "call"}, {"api_name": "tinydb.Query", "line_number": 36, "usage_type": "call"}, {"api_name": "obspy.taup.TauPyModel", "line_number": 39, "usage_type": "call"}, {"api_name": "obspy.geodetics.locations2degrees", "line_number": 54, "usage_type": "call"}, {"api_name": "config.config", "line_number": 82, "usage_type": "call"}, {"api_name": "psycopg2.connect", "line_number": 83, "usage_type": "call"}, {"api_name": "psycopg2.DatabaseError", "line_number": 84, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 100, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 101, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 124, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 132, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 132, "usage_type": "attribute"}, {"api_name": "calendar.timegm", "line_number": 133, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 195, "usage_type": "call"}, {"api_name": "tinydb.where", "line_number": 212, "usage_type": "call"}, {"api_name": "tinydb.where", "line_number": 220, "usage_type": "call"}, {"api_name": "tinydb.where", "line_number": 222, "usage_type": "call"}, {"api_name": "obspy.geodetics.locations2degrees", "line_number": 224, "usage_type": "call"}, {"api_name": "obspy.geodetics.locations2degrees", "line_number": 225, "usage_type": "call"}, {"api_name": "tinydb.where", "line_number": 231, "usage_type": "call"}, {"api_name": "tinydb.where", "line_number": 237, "usage_type": "call"}, {"api_name": "tinydb.where", "line_number": 242, "usage_type": "call"}, {"api_name": "time.time", "line_number": 249, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 250, "usage_type": "call"}]} +{"seq_id": "10601982219", "text": "# -*- coding: utf-8 -*-\n#DRF框架演示功能\n# @Author : joker\n# @Date : 2019-01-18\nfrom rest_framework.routers import DefaultRouter\n\nfrom books import views使用ApiView改写RestApi接口\n\napp_name = \"books\"\n\nurlpatterns = [\n # url(r'^books/$', views.BookListView.as_view(),name='books'),\n #\n # url(r'^books/(?P\\d+)$',views.BookDetailView.as_view(),name='books_detail')\n]\n\nrouter = DefaultRouter() # 可以处理视图的路由器\nrouter.register('books', views使用ApiView改写RestApi接口.BookInfoViewSet, base_name =\"books\") # 向路由器中注册视图集\n\nurlpatterns += router.urls # 将路由器中的所以路由信息追到到django的路由列表中\n", "repo_name": "JokerHai/haystack", "sub_path": "books/urls-v2.py", "file_name": "urls-v2.py", "file_ext": "py", "file_size_in_byte": 688, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "37", "api": [{"api_name": "rest_framework.routers.DefaultRouter", "line_number": 17, "usage_type": "call"}, {"api_name": "books.views使用ApiView改写RestApi接口.BookInfoViewSet", "line_number": 18, "usage_type": "attribute"}, {"api_name": "books.views使用ApiView改写RestApi接口", "line_number": 18, "usage_type": "name"}]} +{"seq_id": "36452007960", "text": "\"\"\"\nNode synchronization with central server.\nNode providing hash lookup service on local database, for peer nodes.\n\"\"\"\n\nimport os\nimport DB\nimport sys\nfrom _thread import *\nimport socket\nimport datetime\nimport time\n\n\ndef main():\n database = './Data/nodeDB.db'\n dictionary = '/home/phed/Resources/rockyou.txt'\n # Create default database file if it does not exist.\n if not os.path.isfile(database):\n DB.build_database(database, dictionary)\n\n # ------------------------------------------------------------------------ #\n dIP_CentralServer = get_dIP_CentralServer()\n dPort_CentralServer = 50000\n syncTimer = 30 # Synchronization request.\n sIP_listener = get_ip() # Source IP. Peers requesting hash lookup.\n sPort_listener = 50001 # Source Port. Peers requesting hash lookup.\n # ------------------------------------------------------------------------ #\n\n # Synchronization with server.\n start_new_thread(sync_node, (dIP_CentralServer, dPort_CentralServer, database, syncTimer)) # Background thread.\n\n time.sleep(2) # CLI Message output.\n\n # Receive queries from peer nodes.\n start_new_thread(receive_hash_lookup, (database, sIP_listener, sPort_listener)) # Background thread.\n\n # Keep program running.\n while True:\n time.sleep(1)\n\n\n\n\ndef receive_hash_lookup(database, sIP, sPort):\n \"\"\"\n Listener, receive and accept peer nodes request for hash lookup.\n \"\"\"\n s = socket.socket()\n try:\n s.bind((sIP, sPort))\n except socket.error as e:\n print(str(e))\n print(str(datetime.datetime.now()) + ' | Listening on ' + str(sIP) + ':' + str(sPort) + ' [HASH LOOKUP].')\n s.listen(5) # Listen for connections.\n # Accept clients.\n while True:\n conn, addr = s.accept() # Accept connection. (This is where the process waits.)\n print(str(datetime.datetime.now()) + ' | Connected to: ' + addr[0] + ':' + str(addr[1]) + ' [HASH LOOKUP].')\n DB.add_peer(database, addr[0]) # Add the connected client (IP) as a peer in the record of peers.\n start_new_thread(client_conn, (database, conn, addr[0], addr[1])) # Each connected node separate thread.\n s.close()\n\n\ndef client_conn(database, conn, ip, port):\n \"\"\"\n Connected client. Search database for password, send password to connected client.\n \"\"\"\n while True: # Keep trying to receive data.\n rData = conn.recv(2048).decode('utf-8') # Buffer size?\n if rData: # If data has been received.\n break\n qResult = DB.query_hash(database, rData) # Query local database.\n # Send result to remote peer if found password.\n if qResult: # If password found, not empty list.\n sData = ';'.join(qResult) # Prep sent data as string. Each element separated by \";\".\n conn.sendall(sData.encode('utf-8')) # Send data.\n else:\n conn.sendall('Null'.encode('utf-8')) # Necessary for remote node to stop receiving data.\n conn.close()\n print(str(datetime.datetime.now()) + ' | Connection closed to: ' + ip + ':' + str(port) + ' [HASH LOOKUP].')\n\n\ndef sync_node(dIP, dPort, database, timer):\n \"\"\"\n Main synchronization program. Get the latest peer list.\n \"\"\"\n while True: # Never exit, keep running with sleep intervall.\n print(str(datetime.datetime.now()) + ' | Connecting to server at ' + str(dIP) + ':' + str(dPort) + ' [PEER SYNC].')\n s = socket.socket()\n try:\n s.connect((dIP, dPort))\n except socket.error as e:\n print(str(e))\n time.sleep(timer)\n continue # New connection attempt.\n while True: # Keep trying to receive data.\n rData = s.recv(2048).decode('utf-8')\n if rData: # If data has been received.\n break\n s.close()\n print(str(datetime.datetime.now()) + ' | Connection to server closed [PEER SYNC].')\n # Update local peer record.\n count = 0\n peer_list = rData.split(';') # List of peers.\n for peer in peer_list:\n count += DB.add_peer(database, peer) # Add peers to database, returns 1 if new peer added, else 0.\n if count > 0:\n print(str(datetime.datetime.now()) + ' | + ' + str(count), ' new peer(s) added to database [PEER SYNC].')\n # Synchronization interval.\n time.sleep(timer)\n\n\ndef get_ip():\n \"\"\"\n Get the ip address of the local machine.\n https://stackoverflow.com/questions/166506/finding-local-ip-addresses-using-pythons-stdlib\n \"\"\"\n s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\n try:\n # doesn't even have to be reachable\n s.connect(('10.255.255.255', 1))\n ip = s.getsockname()[0]\n except Exception:\n ip = '127.0.0.1'\n finally:\n s.close()\n return ip\n\n\ndef get_dIP_CentralServer():\n \"\"\"\n User prompt to give IP of central synchronization server.\n \"\"\"\n print('Provide IP of peer synchronization server.')\n while True:\n try:\n ip = input('IP: ')\n except Exception as e:\n print(str(e))\n break\n return ip\n\n\nif __name__ == '__main__':\n main()\n", "repo_name": "UlfHed/DI8003-project", "sub_path": "node_lookup_listener.py", "file_name": "node_lookup_listener.py", "file_ext": "py", "file_size_in_byte": 5144, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "os.path.isfile", "line_number": 19, "usage_type": "call"}, {"api_name": "os.path", "line_number": 19, "usage_type": "attribute"}, {"api_name": "DB.build_database", "line_number": 20, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 33, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 40, "usage_type": "call"}, {"api_name": "socket.socket", "line_number": 49, "usage_type": "call"}, {"api_name": "socket.error", "line_number": 52, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 54, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 54, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 59, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 59, "usage_type": "attribute"}, {"api_name": "DB.add_peer", "line_number": 60, "usage_type": "call"}, {"api_name": "DB.query_hash", "line_number": 73, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 81, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 81, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 89, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 89, "usage_type": "attribute"}, {"api_name": "socket.socket", "line_number": 90, "usage_type": "call"}, {"api_name": "socket.error", "line_number": 93, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 95, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 102, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 102, "usage_type": "attribute"}, {"api_name": "DB.add_peer", "line_number": 107, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 109, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 109, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 111, "usage_type": "call"}, {"api_name": "socket.socket", "line_number": 119, "usage_type": "call"}, {"api_name": "socket.AF_INET", "line_number": 119, "usage_type": "attribute"}, {"api_name": "socket.SOCK_DGRAM", "line_number": 119, "usage_type": "attribute"}]} +{"seq_id": "27089742655", "text": "from django.http.response import HttpResponse\nfrom django.shortcuts import render, HttpResponseRedirect\nfrom django.contrib.auth import authenticate, login, logout, update_session_auth_hash\nfrom django.contrib import messages\nfrom django.contrib.auth.decorators import login_required\nfrom django.contrib.auth.forms import PasswordChangeForm\nfrom django.core.mail import send_mail\nfrom django.contrib.auth import get_user_model\nfrom django.utils.http import urlsafe_base64_encode, urlsafe_base64_decode\nfrom django.contrib.sites.shortcuts import get_current_site\nfrom django.template.loader import render_to_string\nfrom django.utils.encoding import force_bytes, force_text\nfrom .tokens import account_activation_token\nfrom .forms import *\nfrom .models import *\nfrom order.models import *\nfrom product.models import *\n\n# Create your views here.\n\n\ndef login_form(request):\n if request.method == 'POST':\n username = request.POST['username']\n password = request.POST['password']\n user = authenticate(request, username=username, password=password)\n if user is not None:\n login(request, user)\n current_user = request.user\n # userprofile = UserProfile.objects.get(user_id=current_user.id)\n # request.session['userimage'] = userprofile.image.url\n\n # Redirect to a success page.\n return HttpResponseRedirect('/')\n else:\n messages.warning(\n request, \"Login error!! Username or password is incorrect\")\n return HttpResponseRedirect('/login')\n # Return an 'invalid login' error message.\n #category = Category.objects.all()\n context = { # 'category': category\n }\n return render(request, 'user/login_form.html', context)\n\n\n@login_required(login_url='/user/login') # Check login\ndef index(request):\n #category = Category.objects.all()\n current_user = request.user # Access User Session information\n profile = UserProfile.objects.get(user_id=current_user.id)\n context = { # 'category': category,\n 'profile': profile}\n return render(request, 'user_profile.html', context)\n\n\ndef logout_func(request):\n logout(request)\n return HttpResponseRedirect('/')\n\ndef registerPage(request):\n User = get_user_model()\n form = SignUpForm()\n if request.method == 'POST':\n form = SignUpForm(request.POST, request.FILES)\n if form.is_valid():\n email = form.cleaned_data.get('email')\n if User.objects.filter(email=email).exists():\n messages.warning(request, \"Signup error!! Invalid email or email already taken\")\n else: \n user = form.save()\n user.is_active = False\n user.save()\n current_site = get_current_site(request)\n mail_subject = 'Activate your account.'\n message = render_to_string('user/email_template.html', {\n 'user': user,\n 'domain': current_site.domain,\n 'uid': urlsafe_base64_encode(force_bytes(user.pk)),\n 'token': account_activation_token.make_token(user),\n })\n to_email = form.cleaned_data.get('email')\n send_mail(mail_subject, message, 'expresstotell@gmail.com', [to_email], fail_silently=False,)\n print(send_mail)\n print(to_email)\n print(message)\n return render(request, 'user/confirm_email.html')\n \n context = {'form': form}\n return render(request, 'user/signup_form.html', context)\n\ndef activate(request, uidb64, token):\n User = get_user_model()\n try:\n uid = force_text(urlsafe_base64_decode(uidb64))\n user = User.objects.get(pk=uid)\n except(TypeError, ValueError, OverflowError, User.DoesNotExist):\n user = None\n if user is not None and account_activation_token.check_token(user, token):\n user.is_active = True\n user.save()\n return render(request, 'user/email_confirmed.html')\n else:\n return render(request, 'user/verification_failed.html')\n\ndef signup_form(request):\n if request.method == 'POST':\n form = SignUpForm(request.POST)\n if form.is_valid():\n form.save() # completed sign up\n username = form.cleaned_data.get('username')\n password = form.cleaned_data.get('password1')\n user = authenticate(username=username, password=password)\n login(request, user)\n # Create data in profile table for user\n current_user = request.user\n data = UserProfile()\n data.user_id = current_user.id\n data.image = \"images/users/user.png\"\n data.save()\n messages.success(request, 'Your account has been created!')\n return HttpResponseRedirect('/')\n else:\n messages.warning(request, form.errors)\n return HttpResponseRedirect('/signup')\n\n form = SignUpForm()\n #category = Category.objects.all()\n context = { # 'category': category,\n 'form': form,\n }\n return render(request, 'user/signup_form.html', context)\n\n\n@login_required(login_url='/user/login') # Check login\ndef user_update(request):\n if request.method == 'POST':\n # request.user is user data\n user_form = UserUpdateForm(request.POST, instance=request.user)\n profile_form = ProfileUpdateForm(\n request.POST, request.FILES, instance=request.user.userprofile)\n if user_form.is_valid() and profile_form.is_valid():\n user_form.save()\n profile_form.save()\n messages.success(request, 'Your account has been updated!')\n return HttpResponseRedirect('/user')\n else:\n category = Category.objects.all()\n user_form = UserUpdateForm(instance=request.user)\n # \"userprofile\" model -> OneToOneField relatinon with user\n profile_form = ProfileUpdateForm(instance=request.user.userprofile)\n context = {\n 'category': category,\n 'user_form': user_form,\n 'profile_form': profile_form\n }\n return render(request, 'user_update.html', context)\n\n@login_required(login_url='/login') # Check login\ndef user_orders(request):\n #category = Category.objects.all()\n current_user = request.user\n orders=Order.objects.filter(user_id=current_user.id).order_by('-id')\n context = {#'category': category,\n 'orders': orders,\n }\n return render(request, 'user/user_orders.html', context)\n\n@login_required(login_url='/login') # Check login\ndef user_orderdetail(request,id):\n #category = Category.objects.all()\n current_user = request.user\n order = Order.objects.get(user_id=current_user.id, id=id)\n orderitems = OrderProduct.objects.filter(order_id=id)\n context = {\n #'category': category,\n 'order': order,\n 'orderitems': orderitems,\n }\n return render(request, 'user/user_order_details.html', context)\n\n@login_required(login_url='/login') # Check login\ndef user_order_product(request):\n #category = Category.objects.all()\n current_user = request.user\n order_product = OrderProduct.objects.filter(user_id=current_user.id).order_by('-id')\n context = {#'category': category,\n 'order_product': order_product,\n }\n return render(request, 'user/user_order_products.html', context)\n\n@login_required(login_url='/login') # Check login\ndef user_order_product_detail(request,id,oid):\n #category = Category.objects.all()\n current_user = request.user\n order = Order.objects.get(user_id=current_user.id, id=oid)\n orderitems = OrderProduct.objects.filter(id=id,user_id=current_user.id)\n context = {\n #'category': category,\n 'order': order,\n 'orderitems': orderitems,\n }\n return render(request, 'user/user_order_detail.html', context)\n", "repo_name": "Deepjyoti13/eCommerce", "sub_path": "user/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 8000, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "django.contrib.auth.authenticate", "line_number": 26, "usage_type": "call"}, {"api_name": "django.contrib.auth.login", "line_number": 28, "usage_type": "call"}, {"api_name": "django.shortcuts.HttpResponseRedirect", "line_number": 34, "usage_type": "call"}, {"api_name": "django.contrib.messages.warning", "line_number": 36, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 36, "usage_type": "name"}, {"api_name": "django.shortcuts.HttpResponseRedirect", "line_number": 38, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 43, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 53, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 46, "usage_type": "call"}, {"api_name": "django.contrib.auth.logout", "line_number": 57, "usage_type": "call"}, {"api_name": "django.shortcuts.HttpResponseRedirect", "line_number": 58, "usage_type": "call"}, {"api_name": "django.contrib.auth.get_user_model", "line_number": 61, "usage_type": "call"}, {"api_name": "django.contrib.messages.warning", "line_number": 68, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 68, "usage_type": "name"}, {"api_name": "django.contrib.sites.shortcuts.get_current_site", "line_number": 73, "usage_type": "call"}, {"api_name": "django.template.loader.render_to_string", "line_number": 75, "usage_type": "call"}, {"api_name": "django.utils.http.urlsafe_base64_encode", "line_number": 78, "usage_type": "call"}, {"api_name": "django.utils.encoding.force_bytes", "line_number": 78, "usage_type": "call"}, {"api_name": "tokens.account_activation_token.make_token", "line_number": 79, "usage_type": "call"}, {"api_name": "tokens.account_activation_token", "line_number": 79, "usage_type": "name"}, {"api_name": "django.core.mail.send_mail", "line_number": 82, "usage_type": "call"}, {"api_name": "django.core.mail.send_mail", "line_number": 83, "usage_type": "argument"}, {"api_name": "django.shortcuts.render", "line_number": 86, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 89, "usage_type": "call"}, {"api_name": "django.contrib.auth.get_user_model", "line_number": 92, "usage_type": "call"}, {"api_name": "django.utils.encoding.force_text", "line_number": 94, "usage_type": "call"}, {"api_name": "django.utils.http.urlsafe_base64_decode", "line_number": 94, "usage_type": "call"}, {"api_name": "tokens.account_activation_token.check_token", "line_number": 98, "usage_type": "call"}, {"api_name": "tokens.account_activation_token", "line_number": 98, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 101, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 103, "usage_type": "call"}, {"api_name": "django.contrib.auth.authenticate", "line_number": 112, "usage_type": "call"}, {"api_name": "django.contrib.auth.login", "line_number": 113, "usage_type": "call"}, {"api_name": "django.contrib.messages.success", "line_number": 120, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 120, "usage_type": "name"}, {"api_name": "django.shortcuts.HttpResponseRedirect", "line_number": 121, "usage_type": "call"}, {"api_name": "django.contrib.messages.warning", "line_number": 123, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 123, "usage_type": "name"}, {"api_name": "django.shortcuts.HttpResponseRedirect", "line_number": 124, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 131, "usage_type": "call"}, {"api_name": "django.contrib.messages.success", "line_number": 144, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 144, "usage_type": "name"}, {"api_name": "django.shortcuts.HttpResponseRedirect", "line_number": 145, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 156, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 134, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 166, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 158, "usage_type": "call"}, {"api_name": "order.models", "line_number": 172, "usage_type": "name"}, {"api_name": "order.models", "line_number": 176, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 179, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 168, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 189, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 181, "usage_type": "call"}, {"api_name": "order.models", "line_number": 195, "usage_type": "name"}, {"api_name": "order.models", "line_number": 199, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 202, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 191, "usage_type": "call"}]} +{"seq_id": "30299563635", "text": "# -*- coding: utf-8 -*-\nimport xarray as xr\nimport numpy as np\nimport pandas as pd\nimport pathlib\nfrom sp02.products.raw_nc import SP02RawData\nimport magic\n\nclass BaselineDatabase(object):\n def __init__(self):\n self.line_table = pd.DataFrame(columns=['site','install', 'line', 'instrument_id', 'comment'])\n self.instrument_table = pd.DataFrame(columns = ['instrument_id','type_id', 'sn', 'config'])\n\n def add2line_table(self, site,install_datetime, line_id, instrument_id, comment = ''):#20200205\n install_datetime = pd.to_datetime(install_datetime)\n new_line_table_entry = pd.DataFrame({'site':site,'install': install_datetime, 'line': line_id, 'instrument_id': instrument_id, 'comment': comment}, index = [instrument_id])\n # self.line_table = self.line_table.append(new_line_table_entry, ignore_index=True)\n self.line_table = pd.concat([self.line_table, new_line_table_entry], ignore_index=True)\n return\n \n def addnewinstrument(self, instrument_id, type_id, sn, config):\n # self.instrument_table = self.instrument_table.append({'instrument_id': instrument_id,'type_id':type_id, 'sn': sn, 'config':config_id}, ignore_index=True)\n # new_instrument = pd.DataFrame({'instrument_id': instrument_id,'type_id':type_id, 'sn': sn, 'config':config}, index = [instrument_id])\n new_instrument = pd.DataFrame( [[instrument_id, type_id, sn, config]], columns = ['instrument_id', 'type_id', 'sn', 'config'],index = [instrument_id])\n self.instrument_table = pd.concat([self.instrument_table, new_instrument])#, ignore_index=True)\n return \n \n def get_instrument(self, site, line, date, when_instrument_not_installed = 'error'):\n \"\"\"\n \n\n Parameters\n ----------\n site : TYPE\n DESCRIPTION.\n line : TYPE\n DESCRIPTION.\n date : TYPE\n DESCRIPTION.\n when_instrument_not_installed : string, optional ('error', 'warn', 'silent')\n When silent or warn None is returned. The default is 'error'.\n\n Returns\n -------\n None.\n\n \"\"\"\n # site = 'mlo'\n # line = 121\n# date = df_all.index[0]\n lt_site_line = self.line_table[np.logical_and(self.line_table.site == site, self.line_table.line == line)]\n previous_installs = lt_site_line[lt_site_line.install <= date]\n if previous_installs.shape[0] == 0:\n txt = f'Instrument not installed (line:{line}, site: {site}, date: {date}'\n if when_instrument_not_installed == 'error':\n raise IndexError(txt)\n elif when_instrument_not_installed == 'warn':\n Warning.warn(txt)\n return None\n elif when_instrument_not_installed == 'silent':\n return None\n else:\n assert(False), f'{when_instrument_not_installed} not an option for when_instrument_not_installed. (error, warn,or silent)'\n lt_found = previous_installs.iloc[-1]\n\n instrument_found = self.instrument_table[self.instrument_table.instrument_id == lt_found.instrument_id].iloc[0]\n return instrument_found\n \ndatabase = BaselineDatabase()\n#### filter comfigurations\nconf_1= {'A': 368, 'B': 1050, 'C': 610, 'D': 778}\nconf_2= {'A': 412, 'B': 500, 'C': 675, 'D': 862}\n\n#### Instruments \ndatabase.addnewinstrument(1,1,1032,conf_2)\ndatabase.addnewinstrument(2,1,1046,conf_1)\ndatabase.addnewinstrument(3,1,1022,conf_2) #typically at SPO\n\n#### instrument linups\ninstalldate = '20131126'\ndatabase.add2line_table('mlo', installdate, 121, 2)\ndatabase.add2line_table('mlo', installdate, 221, 1)\n\ninstalldate = '20140104' # something is statring to go wrong on that day!\ndatabase.add2line_table('mlo', installdate, 121, 1)\ndatabase.add2line_table('mlo', installdate, 221, 2)\n\ninstalldate = '20141204' \ndatabase.add2line_table('mlo', installdate, 121, 2)\ndatabase.add2line_table('mlo', installdate, 221, 1)\n\ninstalldate = '20151203' \ndatabase.add2line_table('mlo', installdate, 121, 1)\ndatabase.add2line_table('mlo', installdate, 221, 2)\n\ninstalldate = '20161211' \ndatabase.add2line_table('mlo', installdate, 121, 1)\ndatabase.add2line_table('mlo', installdate, 221, 2)\n\ninstalldate = '20171207' \ndatabase.add2line_table('mlo', installdate, 121, 2)\ndatabase.add2line_table('mlo', installdate, 221, 1)\n\ndatabase.add2line_table('mlo', '20200205', 121, 1)\ndatabase.add2line_table('mlo', '20200205', 221, 2)\n\ndatabase.add2line_table('mlo', '20200620', 121, 2)\ndatabase.add2line_table('mlo', '20200620', 221, 1)\n\ninstalldate = '20210204' \ndatabase.add2line_table('mlo', installdate, 121, 1)\ndatabase.add2line_table('mlo', installdate, 221, 2)\n\n#### testing: installation in BRW... \ninstalldate = '20210318' \nuninstalldate = '20211008' \ndatabase.add2line_table('brw', installdate, 121, 1)\ndatabase.add2line_table('brw', installdate, 221, 2)\n# database.add2line_table('brw', installdate, 221, 2)\n# database.add2line_table('brw', installdate, 221, 2)\n\ninstalldate = '20220101' \ndatabase.add2line_table('mlo', installdate, 121, 1)\ndatabase.add2line_table('mlo', installdate, 221, 2)\n\ninstalldate = '20220309' \ndatabase.add2line_table('mlo', installdate, 121, 3)\n\ndef get_lines_from_station_header(path = '/nfs/grad/gradobs/documentation/station_headers/MLO_header.xlsx', line_ids = [121, 221]):\n path2header = pathlib.Path(path)\n\n df = pd.read_excel(path2header)\n\n col_names = {}\n lines = []\n for line_id in line_ids:\n idx = (df['Unnamed: 1'] == line_id).argmax()\n header = df.iloc[idx-1].dropna().values[1:]\n col_names[line_id] = header\n lines.append(dict(line_id = line_id, column_labels = header))\n \n return lines\n\ndef read_file(path2raw, lines = None, \n # collabels = ['lineID', 'Year', 'DOY', 'HHMM', 'A', 'B', 'C', 'D','temp'],\n collabels = ['lineID', 'Year', 'DOY', 'HHMM'],\n database = None,\n site = None,\n when_instrument_not_installed = 'error'\n ):\n \"\"\"\n The particular way I am reading here allows for later implementation of\n reading old data from Longenecker. And also allows to read other raw files\n\n Parameters\n ----------\n path2raw : str, list, pathlib.Path\n Single or list of path(s) to file(s).\n lines : list, optional\n List of lines to consider (e.g. [121, 221] for sp02 at MLO). The default is None -> all.\n collabels : TYPE, optional\n DESCRIPTION. The default is ['lineID', 'Year', 'DOY', 'HHMM'].\n database : TYPE, optional\n DESCRIPTION. The default is None.\n site : str, optional\n DESCRIPTION. The default is None. If None the site is infered from the\n file path. Set if the path is not the standard path\n\n Returns\n -------\n out_list : TYPE\n DESCRIPTION.\n\n \"\"\"\n \n out = {}\n collabels = np.array(collabels)\n \n #open\n if not isinstance(path2raw, list):\n path2raw = [path2raw,]\n \n firstfile = path2raw[0]\n if magic.from_file(firstfile.as_posix()) == 'Hierarchical Data Format (version 5) data':\n ds = xr.open_mfdataset(path2raw) \n if ('product' not in ds.attrs) or ds.attrs['product'] == 'SP02 raw':\n return SP02RawData(ds)\n\n else:\n df_all = pd.concat([pd.read_csv(p2r, header=None) for p2r in path2raw])\n \n # df_all = pd.read_csv(path2raw, header=None\n # # names = False\n # )\n # out['df_all_01'] = df_all.copy()\n colsis = df_all.columns.values\n colsis = [int(col) for col in colsis]\n \n # todo: assigne collumn labels accoreding to instrument info\n # if 0:\n colsis[:collabels.shape[0]] = collabels\n df_all.columns = colsis \n # out['df_all_02'] = df_all.copy()\n # df_all = pd.read_csv(path2raw, names=lines[0]['column_labels'])\n \n # make datetime index\n df_all['HHMM'] = df_all.apply(lambda row: f'{int(row.HHMM):04d}', axis=1)\n df_all.index = df_all.apply(lambda row: pd.to_datetime(f'{int(row.Year)}0101') + pd.to_timedelta(row.DOY - 1 , 'd') + pd.to_timedelta(int(row.HHMM[:2]), 'h') + pd.to_timedelta(int(row.HHMM[2:]), 'm'), axis=1)\n df_all.index.name = 'datetime'\n \n # data_list = []\n # df_inst_temp = pd.DataFrame()\n # df_inst_channels = pd.DataFrame()\n out['df_all'] = df_all.copy()\n # return out\n out_list = []\n date = df_all.index[0]\n # print(df_all.lineID.unique())\n for lid in df_all.lineID.unique():\n if isinstance(lines, list):\n if lid not in lines:\n print(f'{lid} not in lines ({lines})')\n continue\n \n df_lid = df_all[df_all.lineID == lid].copy()\n \n # there was the case that Longenecker must have created an overlab when stiching two days together ... therefor ->\n df_lid = df_lid[~df_lid.index.duplicated()]\n df_lid.sort_index(inplace=True)\n \n \n instrument = database.get_instrument(site, lid, date,\n when_instrument_not_installed = when_instrument_not_installed)\n if isinstance(instrument, type(None)):\n continue\n \n df_lid = df_lid.drop(['lineID', 'Year','DOY', 'HHMM'], axis=1)\n df_lid.columns = ['A', 'B', 'C', 'D', 'temp']\n \n # replace invalid values with nan\n df_lid[df_lid == -99999] = np.nan\n df_lid[df_lid == -7999.0] = np.nan\n \n # seperate photo detector readings from temp \n df_temp = df_lid.temp\n df_voltages = df_lid.reindex(['A', 'B', 'C', 'D'], axis= 1)\n \n df_voltages.columns.name = 'channel'\n \n # create dataset\n ds = xr.Dataset()\n ds['raw_data'] = df_voltages\n ds['internal_temperature'] = df_temp\n ser = pd.Series(instrument.config)\n ser.index.name = 'channel'\n ds['channle_wavelengths'] = ser\n \n ds['line_id'] = lid\n sn = instrument['sn']\n ds['serial_no'] = sn\n ds.attrs['product'] = 'SP02 raw'\n # ds_by_instrument[f'sp02_{lid}_{sn}'] = ds\n out_list.append(ds)\n \n return out_list\n\n# for line in lines:\n# lid = line['line_id']\n# dft = df_all[df_all.lineID == lid].copy()\n# dft = dft.dropna(axis = 1)\n\n# # replace placeholder with correct column labels\n# dft.columns = line['column_labels']\n# line['df'] = dft.copy()\n\n# # clean up the channel voltages\n# df_channels = dft.drop(['lineID', 'Year', 'DOY', 'HHMM', 'SPO2 internal temp [degC]'], axis=1)\n# channels = [int(col.split(' ')[2]) for col in df_channels.columns]\n# df_channels.columns = channels\n# # df_channels.columns.name = f'wavelength_lid{lid}'\n# df_channels[df_channels == -99999] = np.nan\n# df_channels[df_channels == -7999.0] = np.nan\n# data_list.append(df_channels.copy())\n# # clean up temp\n# temp = dft['SPO2 internal temp [degC]'].copy()\n# temp[temp == -99999] = np.nan\n# temp[temp == -7999.0] = np.nan\n# df_inst_temp[lid] = temp\n# # print(len(channels))\n# # print(channels)\n# df_inst_channels[lid] = channels\n# # line['raw_data'] = df_channels\n# # ds[f'rawdata_line_id_{lid}'] = df_channels\n# # ds[f'instrument_temperature_line_id_{lid}'] = temp\n# # ds['line_ids'] = lines\n\n# ds = xr.Dataset()\n# data = pd.concat(data_list, axis=1).sort_index(axis=1)\n# data.columns.name = 'channel_wavelength'\n# ds['raw_data'] = data\n# df_inst_temp.columns.name = 'line_id'\n# ds['instrument_temperatures'] = df_inst_temp\n# df_inst_channels.columns.name = 'line_id'\n# df_inst_channels.index = [chr(ord('A') + i) for i in df_inst_channels.index]\n# df_inst_channels.index.name = 'channel'\n# ds['instrument_channels'] = df_inst_channels\n# return ds\n\ndef convert_raw2nc(path2rawfolder = '/nfs/grad/gradobs/raw/mlo/2020/', path2netcdf = '/mnt/telg/data/baseline/mlo/2020/', \n # database = None,\n start_date = '2020-02-06',\n pattern = '*sp02.*',\n sernos = [1032, 1046],\n site = 'mlo',\n overwrite = False, \n verbose = False, \n raise_error = True,\n when_instrument_not_installed = 'error',\n test = False):\n \"\"\"\n \n\n Parameters\n ----------\n path2rawfolder : TYPE, optional\n DESCRIPTION. The default is '/nfs/grad/gradobs/raw/mlo/2020/'.\n path2netcdf : TYPE, optional\n DESCRIPTION. The default is '/mnt/telg/data/baseline/mlo/2020/'.\n # database : TYPE, optional\n DESCRIPTION. The default is None.\n start_date : TYPE, optional\n DESCRIPTION. The default is '2020-02-06'.\n pattern : str, optional\n Only files with this pattern are considered. In newer raw data \n versions this would be '*sp02.*'. In older ones: 'MLOD*'\n sernos : TYPE, optional\n DESCRIPTION. The default is [1032, 1046].\n overwrite : TYPE, optional\n DESCRIPTION. The default is False.\n verbose : TYPE, optional\n DESCRIPTION. The default is False.\n when_instrument_not_installed: str ('error', 'warn', 'silent')\n what to do when a file contains lines without an instrument asigned to \n it. When silent or warn file is skipped. The default is \"error\".\n test : TYPE, optional\n If True only one file is processed. The default is False.\n\n Returns\n -------\n None.\n\n \"\"\"\n try:\n import atmPy.data_archives.NOAA_ESRL_GMD_GRAD.baseline.baseline as atmbsl\n except ModuleNotFoundError:\n raise ModuleNotFoundError('atmPy is requried to execute this function')\n # lines = get_lines_from_station_header()\n path2rawfolder = pathlib.Path(path2rawfolder)\n path2netcdf = pathlib.Path(path2netcdf)\n try:\n path2netcdf.mkdir(exist_ok=True)\n except FileNotFoundError:\n path2netcdf.parent.mkdir()\n path2netcdf.mkdir()\n\n file_list = list(path2rawfolder.glob(pattern))\n # print(len(file_list))\n\n # file_contents = []\n # return file_list\n \n df_in = pd.DataFrame(file_list, columns=['path_in'])\n\n \n \n # test what format, old or new.\n p2f = file_list[0]\n nl = p2f.name.split('.')\n \n if len(nl) == 2:\n # old format like /nfs/grad/gradobs/raw/mlo/2013/sp02/MLOD013A.113\n # get year from path\n def path2date(path2file):\n year = path2file.parent.parent.name\n jul = int(''.join(filter(str.isdigit, path2file.name.split('.')[0])))\n date = pd.to_datetime(year) + pd.to_timedelta(jul-1, 'd')\n return date\n # df_in.index = df_in.path_in.apply(lambda x: pd.to_datetime(year) + pd.to_timedelta((int(''.join(filter(str.isdigit, x.name.split('.')[0]))))-1, 'd'))\n \n else:\n # new format: gradobs.mlo-sp02.20200126.raw.dat\n # df_in.index = df_in.path_in.apply(lambda x: pd.to_datetime(x.name.split('.')[2]))\n path2date = lambda x: pd.to_datetime(x.name.split('.')[2])\n \n # set index based on format\n df_in.index = df_in.path_in.apply(path2date)\n df_in.sort_index(inplace=True)\n df_in = df_in.truncate(before=start_date)\n \n #### gernerate Workplan\n df_out = pd.DataFrame(columns=['path_out'])\n \n # generate output path\n for sn in sernos:\n for idx, row in df_in.iterrows():\n date = idx\n fnnc = f'gradobs.{site}.sp02.sn{sn}.{date.year}{date.month:02d}{date.day:02d}.raw.nc'\n path2netcdf_file = path2netcdf.joinpath(fnnc)\n df_add = pd.DataFrame({'path_in': row.path_in, 'path_out':path2netcdf_file}, index = [idx]\n # ignore_index=True\n )\n \n # df_out = df_out.append(df_add) #deprecated\n df_out = pd.concat([df_out, df_add])\n \n # check if file exists. Process only those that do not exist\n df_out['exists'] = df_out.path_out.apply(lambda x: x.is_file())\n if not overwrite:\n df_work = df_out[~df_out.exists]\n else:\n df_work = df_out\n \n work_array = df_work.path_in.unique()\n\n print(f'No of files that need to be processed: {len(work_array)}')\n\n # exists = 0\n # new = 0\n for e, file in enumerate(work_array):\n # if e == 3: break\n # ds = read_file(file, lines)\n df_sel = df_work[df_work.path_in == file]\n try:\n dslist = read_file(file, database = database, \n site = site,\n when_instrument_not_installed = when_instrument_not_installed)\n except IndexError:\n if raise_error:\n raise\n else:\n print('Instrument not installed ... skip', end = '...')\n if test:\n return {'file': file, 'database': database}\n else:\n continue\n ### generate output file name \n # processing\n for ds in dslist:\n # fnnc = file.name.replace('.dat','.nc')\n # fnnc = fnnc.replace('-sp02', '.sp02')\n # fnns = fnnc.split('.')\n # fnns = fnns[:3] + [f'sn{str(ds.serial_no.values)}'] + fnns[3:]\n # fnnc = '.'.join(fnns)\n # path2netcdf_file = path2netcdf.joinpath(fnnc)\n \n # check which of the output files is the right ... still, i am not convinced this is the most elegant way to do this.... add the lineno in the work table?\n sn = str(ds.serial_no.values)\n try:\n path2netcdf_file = [p2fo for p2fo in df_sel.path_out.values if sn in p2fo.name][0]\n except IndexError:\n assert(False), 'This Error is usually caused because one of the netcdf files (for a serial number) is deleted, but not the other.'\n \n # save to file\n #### add some more info to the data array\n ds.attrs['site'] = site\n si = [s for s in atmbsl.network.stations._stations_list if s.abb == site.upper()][0]\n ds.attrs['site_latitude'] = si.lat\n ds.attrs['site_longitude'] = si.lon\n ds.attrs['site_elevation'] = si.alt\n ds.attrs['site_name'] = si.name\n ds.to_netcdf(path2netcdf_file)\n if test:\n if e==2:\n break\n # out = dict(processed = new,\n # skipped = exists,\n # last_ds_list = dslist)\n \n if not test:\n #### check if all has been processed\n df_out['exists'] = df_out.path_out.apply(lambda x: x.is_file())\n df_work = df_out[~df_out.exists]\n work_array = df_work.path_in.unique()\n \n assert(df_work.shape[0] == 0), f'df_work should be empty at the end. Still has {df_work.shape[0]} entries.'\n return \n\n\n", "repo_name": "hagne/sp02", "sub_path": "sp02/file_io.py", "file_name": "file_io.py", "file_ext": "py", "file_size_in_byte": 19269, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "pandas.DataFrame", "line_number": 11, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 12, "usage_type": "call"}, {"api_name": "pandas.to_datetime", "line_number": 15, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 16, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 18, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 24, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 25, "usage_type": "call"}, {"api_name": "numpy.logical_and", "line_number": 51, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 130, "usage_type": "call"}, {"api_name": "pandas.read_excel", "line_number": 132, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 177, "usage_type": "call"}, {"api_name": "magic.from_file", "line_number": 184, "usage_type": "call"}, {"api_name": "xarray.open_mfdataset", "line_number": 185, "usage_type": "call"}, {"api_name": "sp02.products.raw_nc.SP02RawData", "line_number": 187, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 190, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 190, "usage_type": "call"}, {"api_name": "pandas.to_datetime", "line_number": 208, "usage_type": "call"}, {"api_name": "pandas.to_timedelta", "line_number": 208, "usage_type": "call"}, {"api_name": "numpy.nan", "line_number": 241, "usage_type": "attribute"}, {"api_name": "numpy.nan", "line_number": 242, "usage_type": "attribute"}, {"api_name": "xarray.Dataset", "line_number": 251, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 254, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 358, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 359, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 372, "usage_type": "call"}, {"api_name": "pandas.to_datetime", "line_number": 386, "usage_type": "call"}, {"api_name": "pandas.to_timedelta", "line_number": 386, "usage_type": "call"}, {"api_name": "pandas.to_datetime", "line_number": 393, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 401, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 409, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 414, "usage_type": "call"}, {"api_name": "atmPy.data_archives.NOAA_ESRL_GMD_GRAD.baseline.baseline.network", "line_number": 466, "usage_type": "attribute"}, {"api_name": "atmPy.data_archives.NOAA_ESRL_GMD_GRAD.baseline.baseline", "line_number": 466, "usage_type": "name"}]} +{"seq_id": "24184846626", "text": "import json\nimport os\nimport sys\n\n# Local imports\nsys.path = [\"./\", \"../\"] + sys.path\nfrom GenConfigs import *\n\n\ndef CheckJSONConfig(json_file):\n \"\"\"\n Checks whether a correct json file is provided.\n \"\"\"\n if json_file is None:\n return False\n if not os.path.isfile(json_file):\n return False\n return True\n\n\ndef ReadJSONConfig(json_file):\n \"\"\"\n Reads the JSON config file and returns a list.\n \"\"\"\n workload = None\n try:\n with open(json_file) as f:\n workload = json.load(f)\n except:\n print(\"The JSON config file cannot be read\")\n\n return workload\n\n\ndef WriteJSONConfig(workload, json_file):\n \"\"\"\n Writes the workload description to a json file.\n \"\"\"\n with open(FAAS_ROOT + \"/\" + json_file, \"w\") as outfile:\n json.dump(workload, outfile)\n", "repo_name": "PrincetonUniversity/faas-profiler", "sub_path": "commons/JSONConfigHelper.py", "file_name": "JSONConfigHelper.py", "file_ext": "py", "file_size_in_byte": 830, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 100, "dataset": "github-code", "pt": "37", "api": [{"api_name": "sys.path", "line_number": 6, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 16, "usage_type": "call"}, {"api_name": "os.path", "line_number": 16, "usage_type": "attribute"}, {"api_name": "json.load", "line_number": 28, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 40, "usage_type": "call"}]} +{"seq_id": "22026363794", "text": "# Vision Based Navigation Research\n# Augusta University School of Computer and Cyber Sciences\n# Date Started 5/14/21\n###############################################################################\n\nimport cv2\nimport numpy as np\nimport math\n\n\n###############################################################################\n# Functions\n###############################################################################\n\n\ndef snap(camera_index):\n \"\"\"\n Connects to the camera to take a picture.\n Takes one argument, camera_index, that is the index of the desired camera.\n Returns a frame\n \"\"\"\n\n # connect to the camera\n cam = cv2.VideoCapture(camera_index)\n\n # grab the frame\n ret, frame = cam.read()\n\n # if it could grab the frame, it returns the grabbed frame\n if ret:\n return frame\n\n\ndef live_snap(camera_index):\n \"\"\"\n Debugging function that displays the video feed of the camera\n Takes one argument, camera_index, that is the index of the desired camera\n \"\"\"\n\n # connect to the camera\n cam = cv2.VideoCapture(camera_index)\n\n # create a loop to loop through frames\n while True:\n\n # grab the frame\n ret, frame = cam.read()\n\n # show the frame \n cv2.imshow('live_frames', frame)\n\n # quit on q \n if cv2.waitKey(1) == ord('q'):\n break\n\n # release the camera from the program control, and destroy all lingering windows being displayed\n cam.release()\n cv2.destroyAllWindows()\n\n\ndef find_color(color, img):\n \"\"\"\n Function that will find a color in a image.\n Takes two arguments.\n color is the string color you want to find. 'red' 'green' or 'blue'\n img is the image you want to find the color in.\n Returns a filtered image showing only the desired color\n \"\"\"\n\n # convert the image to an HSV color range \n hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)\n\n # dictionaries for both lower and upper masks of red, green, and blue colors in hsv color \n # hsv is normally 360 degrees of colors, but cv2 only uses 180 degrees. Divide the 360 circle values by 2. \n upper_masks = {\n 'red': [185, 255, 255],\n 'green': [90, 255, 255],\n 'blue': [150, 255, 255],\n }\n\n lower_masks = {\n 'red': [140, 50, 200],\n 'green': [30, 50, 100],\n 'blue': [80, 25, 200],\n }\n\n # define the range of color\n lower_mask = np.array(lower_masks[color])\n upper_mask = np.array(upper_masks[color])\n\n # create the mask that will lay over the original image\n mask = cv2.inRange(hsv, lower_mask, upper_mask)\n # return mask\n\n # combine the photos\n combined = cv2.bitwise_and(img, img, mask=mask)\n return combined\n\n\ndef is_contour_rectangle(c):\n \"\"\"\n Function used to determine if a contour is a rectangle\n Takes the argument c for contour\n Returns a bool\n\n Code found at https://www.pyimagesearch.com/2016/02/08/opencv-shape-detection/\n \"\"\"\n\n perimeter = cv2.arcLength(c, True)\n approx = cv2.approxPolyDP(c, 0.02 * perimeter, True)\n return len(approx) == 4\n\n\ndef find_rect_center(filtered):\n \"\"\"\n Function that will find a colored rectangle in a filtered image and compute its center\n Takes one argument, filtered, that is the color filtered image\n Returns a list [x, y] and the image with only the rectangles\n \"\"\"\n\n # convert the image to gray, blur it slightly, and threshold it \n gray = cv2.cvtColor(filtered, cv2.COLOR_BGR2GRAY)\n blurred = cv2.GaussianBlur(gray, (5, 5), 0)\n thresh = cv2.threshold(blurred, 60, 255, cv2.THRESH_BINARY)[1]\n\n # find the contours in the binary image\n contours, hierarchy = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n\n # create a mask for contours we want to ignore\n ignored_contours = np.ones(thresh.shape[:2], dtype='uint8') * 255\n\n # loop through contours and ignore ones smaller than 500 area and if they are not a rectangle\n for c in contours:\n if cv2.contourArea(c) < 500:\n cv2.drawContours(ignored_contours, [c], -1, 0, -1)\n continue\n if not is_contour_rectangle(c):\n cv2.drawContours(ignored_contours, [c], -1, 0, -1)\n\n # create an image with only good contours and refined the contours\n rectangles = cv2.bitwise_and(thresh, thresh, mask=ignored_contours)\n contours, hierarchy = cv2.findContours(rectangles, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n\n # create a list to hold [x,y] \n centers = []\n\n for c in contours:\n # calculates the 'image moment' for each contour\n m = cv2.moments(c)\n\n # calculates x and y values\n if m[\"m00\"] != 0:\n center_x = int(m[\"m10\"] / m[\"m00\"])\n center_y = int(m[\"m01\"] / m[\"m00\"])\n else:\n center_x, center_y = 0, 0\n\n # draw the outline of the rectangle\n cv2.drawContours(rectangles, [c], -1, (0, 255, 0), 2)\n cv2.circle(rectangles, (center_x, center_y), 1, (0, 0, 255), -1)\n\n # append the centers to the list\n centers.append(center_x)\n centers.append(center_y)\n\n return centers, rectangles\n\n\ndef live_tracking(delay, camera_index):\n \"\"\"\n Function to track the different points in real time\n Takes two arguments\n delay is the number of milliseconds between each update\n camera_index is the index of the desired camera used to track\n Tracks blue and green points.\n Green is the position of the drone\n Blue is the destination point\n \"\"\"\n\n # connect to the camera\n cam = cv2.VideoCapture(camera_index)\n\n # start a loop for the frames\n while True:\n # grab the frame\n ret, img = cam.read()\n\n # find the blue and red in the pictures\n blue_filtered = find_color('blue', img)\n green_filtered = find_color('green', img)\n\n # find the coordinates of the rectangles for the blue and green points\n blue_coords, blue_rectangle = find_rect_center(blue_filtered)\n green_coords, green_rectangle = find_rect_center(green_filtered)\n\n # print the coordinates to the console and wait the desired number of milliseconds\n print(f\"Blue Cords: {blue_coords}\\n Green Cords: {green_coords}\")\n cv2.waitKey(delay)\n\n cam.realease()\n cv2.destroyAllWindows()\n\n\ndef find_all_centers(camera_index):\n \"\"\"\n Function to find the centers of the blue, green, and red points\n Takes the argument camera_index\n Returns three lists in the order red, green, blue and a combined image\n \"\"\"\n\n # get a picture\n img = snap(camera_index)\n\n # filter the different colors from the picture\n red_filtered = find_color('red', img)\n blue_filtered = find_color('blue', img)\n green_filtered = find_color('green', img)\n\n # find the coordinates of the rectangles\n red_center, red_rectangle = find_rect_center(red_filtered)\n blue_center, blue_rectangle = find_rect_center(blue_filtered)\n green_center, green_rectangle = find_rect_center(green_filtered)\n\n # combine the photos\n\n image_data = [red_rectangle, blue_rectangle, green_rectangle]\n dst = image_data[0]\n\n for i in range(len(image_data)):\n if i == 0:\n pass\n else:\n alpha = 1.0 / (i + 1)\n beta = 1.0 - alpha\n dst = cv2.addWeighted(image_data[i], alpha, dst, beta, 0.0)\n\n return red_center, green_center, blue_center, dst\n\n\ndef distance_formula(point1, point2):\n \"\"\"\n Function to compute the distance between two points.\n Takes two arguments\n Point1 and Point2 and separate lists in the form of [x,y]\n returns distance\n \"\"\"\n\n distance = math.sqrt(((point2[0] - point1[0]) ** 2) + ((point2[1] - point1[1]) ** 2))\n return distance\n\n\ndef find_mid_point(point1, point2):\n \"\"\"\n Function to find the midpoint of two points\n Takes two arguments\n point1 and point2 are a list in the form [x, y]\n Returns a list in the form of [x, y]\n \"\"\"\n\n # x = (x1 + x2)/2 and y = (y1 + y2)/2\n x = (point1[0] + point2[0]) / 2\n y = (point1[0] + point2[0]) / 2\n mid_point = [x, y]\n return mid_point\n\n\ndef move_origin(point, axis_point):\n \"\"\"\n Translates a point to a new point relative to an axis.\n Takes two arguments in the form of [x,y]\n returns a point [x,y] relative to the axis point [x, y]\n \"\"\"\n\n # axis_point is treated as [h,k]. It is the origin of the new axis\n # point p(x,y) relative to the new axis is thus described as p(x-h, y-k)\n\n point_rta = [(point[0] - axis_point[0]), (point[1] - axis_point[1])]\n return point_rta\n\n\ndef calculate_angle(point):\n \"\"\"\n Function to calculate an angle of rotation given a point [x,y]\n special arctan rules found here https://learnandlearn.com/python-programming/python-reference/how-to-find-inverse-tan-or-arc-tan-in-python\n \"\"\"\n\n # because inverse tangent has limitations, we have to do a little bit of checking to get a proper angle.\n angle = math.atan((point[1] / point[0])) * (180 / math.pi)\n\n # if both are positive, the angle is good\n if point[0] >= 0 and point[1] >= 0:\n return angle\n # if both are negative add 180\n elif point[1] <= 0 and point[0] <= 0:\n return angle + 180\n # if y is positive and x is negative\n elif point[1] >= 0 and point[0] <= 0:\n return angle + 180\n # if y is negative and x is positive\n elif point[1] <= 0 and point[0] >= 0:\n return angle + 360\n\n\ndef nothing(x):\n \"\"\"\n A function needed for the use of cv2's trackbars\n Used as a call back function\n \"\"\"\n pass\n\n\ndef test_mask_values(camera_index, color):\n \"\"\"\n Function to test what hsv values are needed in a room.\n Takes the argument camera_index and string color you are looking for.\n \"\"\"\n\n # connect to the camera\n cam = cv2.VideoCapture(camera_index)\n\n # make a list of bars needed\n bars = ['l_h_' + color, 'l_s_' + color, 'l_v_' + color,\n 'u_h_' + color, 'u_s_' + color, 'u_v_' + color]\n\n # create the track bars\n win_name = color + '_track'\n cv2.namedWindow(win_name)\n cv2.createTrackbar(bars[0], win_name, 0, 179, nothing)\n cv2.createTrackbar(bars[1], win_name, 0, 255, nothing)\n cv2.createTrackbar(bars[2], win_name, 0, 255, nothing)\n cv2.createTrackbar(bars[3], win_name, 179, 179, nothing)\n cv2.createTrackbar(bars[4], win_name, 255, 255, nothing)\n cv2.createTrackbar(bars[5], win_name, 255, 255, nothing)\n\n # continue updating frames\n while True:\n ret, frame = cam.read()\n if ret:\n hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)\n\n lower_mask = np.array([cv2.getTrackbarPos(bars[0], win_name),\n cv2.getTrackbarPos(bars[1], win_name),\n cv2.getTrackbarPos(bars[2], win_name)])\n upper_mask = np.array([cv2.getTrackbarPos(bars[3], win_name),\n cv2.getTrackbarPos(bars[4], win_name),\n cv2.getTrackbarPos(bars[5], win_name)])\n\n mask = cv2.inRange(hsv, lower_mask, upper_mask)\n result = cv2.bitwise_and(frame, frame, mask=mask)\n\n mask = cv2.cvtColor(mask, cv2.COLOR_GRAY2BGR)\n combined = np.concatenate((mask, result), axis=1)\n cv2.imshow(win_name, combined)\n\n if cv2.waitKey(2) == ord('q'):\n cam.release()\n cv2.destroyAllWindows()\n break\n", "repo_name": "Marknoodle/ML-Drone", "sub_path": "functions.py", "file_name": "functions.py", "file_ext": "py", "file_size_in_byte": 11630, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "cv2.VideoCapture", "line_number": 24, "usage_type": "call"}, {"api_name": "cv2.VideoCapture", "line_number": 41, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 50, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 53, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 58, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 71, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2HSV", "line_number": 71, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 88, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 89, "usage_type": "call"}, {"api_name": "cv2.inRange", "line_number": 92, "usage_type": "call"}, {"api_name": "cv2.bitwise_and", "line_number": 96, "usage_type": "call"}, {"api_name": "cv2.arcLength", "line_number": 109, "usage_type": "call"}, {"api_name": "cv2.approxPolyDP", "line_number": 110, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 122, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 122, "usage_type": "attribute"}, {"api_name": "cv2.GaussianBlur", "line_number": 123, "usage_type": "call"}, {"api_name": "cv2.threshold", "line_number": 124, "usage_type": "call"}, {"api_name": "cv2.THRESH_BINARY", "line_number": 124, "usage_type": "attribute"}, {"api_name": "cv2.findContours", "line_number": 127, "usage_type": "call"}, {"api_name": "cv2.RETR_EXTERNAL", "line_number": 127, "usage_type": "attribute"}, {"api_name": "cv2.CHAIN_APPROX_SIMPLE", "line_number": 127, "usage_type": "attribute"}, {"api_name": "numpy.ones", "line_number": 130, "usage_type": "call"}, {"api_name": "cv2.contourArea", "line_number": 134, "usage_type": "call"}, {"api_name": "cv2.drawContours", "line_number": 135, "usage_type": "call"}, {"api_name": "cv2.drawContours", "line_number": 138, "usage_type": "call"}, {"api_name": "cv2.bitwise_and", "line_number": 141, "usage_type": "call"}, {"api_name": "cv2.findContours", "line_number": 142, "usage_type": "call"}, {"api_name": "cv2.RETR_EXTERNAL", "line_number": 142, "usage_type": "attribute"}, {"api_name": "cv2.CHAIN_APPROX_SIMPLE", "line_number": 142, "usage_type": "attribute"}, {"api_name": "cv2.moments", "line_number": 149, "usage_type": "call"}, {"api_name": "cv2.drawContours", "line_number": 159, "usage_type": "call"}, {"api_name": "cv2.circle", "line_number": 160, "usage_type": "call"}, {"api_name": "cv2.VideoCapture", "line_number": 181, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 198, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 201, "usage_type": "call"}, {"api_name": "cv2.addWeighted", "line_number": 235, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 248, "usage_type": "call"}, {"api_name": "math.atan", "line_number": 288, "usage_type": "call"}, {"api_name": "math.pi", "line_number": 288, "usage_type": "attribute"}, {"api_name": "cv2.VideoCapture", "line_number": 319, "usage_type": "call"}, {"api_name": "cv2.namedWindow", "line_number": 327, "usage_type": "call"}, {"api_name": "cv2.createTrackbar", "line_number": 328, "usage_type": "call"}, {"api_name": "cv2.createTrackbar", "line_number": 329, "usage_type": "call"}, {"api_name": "cv2.createTrackbar", "line_number": 330, "usage_type": "call"}, {"api_name": "cv2.createTrackbar", "line_number": 331, "usage_type": "call"}, {"api_name": "cv2.createTrackbar", "line_number": 332, "usage_type": "call"}, {"api_name": "cv2.createTrackbar", "line_number": 333, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 339, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2HSV", "line_number": 339, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 341, "usage_type": "call"}, {"api_name": "cv2.getTrackbarPos", "line_number": 341, "usage_type": "call"}, {"api_name": "cv2.getTrackbarPos", "line_number": 342, "usage_type": "call"}, {"api_name": "cv2.getTrackbarPos", "line_number": 343, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 344, "usage_type": "call"}, {"api_name": "cv2.getTrackbarPos", "line_number": 344, "usage_type": "call"}, {"api_name": "cv2.getTrackbarPos", "line_number": 345, "usage_type": "call"}, {"api_name": "cv2.getTrackbarPos", "line_number": 346, "usage_type": "call"}, {"api_name": "cv2.inRange", "line_number": 348, "usage_type": "call"}, {"api_name": "cv2.bitwise_and", "line_number": 349, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 351, "usage_type": "call"}, {"api_name": "cv2.COLOR_GRAY2BGR", "line_number": 351, "usage_type": "attribute"}, {"api_name": "numpy.concatenate", "line_number": 352, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 353, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 355, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 357, "usage_type": "call"}]} +{"seq_id": "1928696012", "text": "import inspect\nimport textwrap\nimport traceback\nimport typing as tp\n\nimport attrs\nfrom cattrs.errors import IterableValidationError\n\n\n@attrs.define\nclass StructuresError(Exception):\n ...\n\n\n@attrs.define\nclass NoCreatorFound(StructuresError):\n want: object\n available: list[type]\n\n\n@attrs.define\nclass UnableToConvert(StructuresError):\n creator: tp.Callable\n converting: object\n into: object\n reason: str\n error: Exception | None = None\n\n def __str__(self) -> str:\n if isinstance(self.error, IterableValidationError):\n error_string = (\n \"\\n\"\n + \"\\n\".join(\n f\" || {line}\"\n for line in \"\".join(traceback.format_exception(self.error)).split(\"\\n\")\n )\n + \"\\n | \\n\"\n )\n elif self.error is None or isinstance(self.error, UnableToConvert):\n error_string = \"\\n\"\n else:\n error_string = f\": {self.error}\\n | \\n\"\n\n return (\n \"\\n\\n |>> \"\n + (\n self.reason\n if self.error is None or not isinstance(self.error, UnableToConvert)\n else \"\"\n )\n + error_string\n + \"\\n\".join(\n f\" | {line}\"\n for line in textwrap.dedent(\n f\"\"\"\n Trying to convert '{self.converting}' into '{self.into}'\n\n Using creator '{self.creator}'{self.creator_location}\n \"\"\"\n )\n .strip()\n .split(\"\\n\")\n )\n )\n\n @property\n def creator_location(self) -> str:\n try:\n source_file = inspect.getsourcefile(self.creator)\n except:\n source_file = None\n\n if source_file is None:\n return \"\"\n\n try:\n line_numbers = inspect.getsourcelines(self.creator)\n except:\n return f\" at {source_file}\"\n else:\n return f\" at {source_file}:{line_numbers[1]}\"\n\n\n@attrs.define\nclass NoDataByTypeName(StructuresError):\n want: object\n patterns: list[str]\n available: dict[str, type]\n\n\n@attrs.define\nclass RequiredParam(StructuresError):\n why: str\n need: str\n have: list[str]\n\n\n@attrs.define\nclass MultipleNamesForType(StructuresError):\n want: object\n found: list[str]\n\n\n@attrs.define\nclass CanOnlyRegisterTypes(StructuresError):\n got: object\n\n\n@attrs.define\nclass FoundWithWrongType(StructuresError):\n want: object\n patterns: list[str]\n\n\n@attrs.define\nclass SupertypeNotValid(StructuresError):\n want: object\n got: object\n reason: str\n\n\n@attrs.define\nclass NotValidType(StructuresError):\n pass\n", "repo_name": "delfick/strcs", "sub_path": "strcs/errors.py", "file_name": "errors.py", "file_ext": "py", "file_size_in_byte": 2701, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "37", "api": [{"api_name": "attrs.define", "line_number": 10, "usage_type": "attribute"}, {"api_name": "attrs.define", "line_number": 15, "usage_type": "attribute"}, {"api_name": "typing.Callable", "line_number": 23, "usage_type": "attribute"}, {"api_name": "cattrs.errors.IterableValidationError", "line_number": 30, "usage_type": "argument"}, {"api_name": "traceback.format_exception", "line_number": 35, "usage_type": "call"}, {"api_name": "textwrap.dedent", "line_number": 54, "usage_type": "call"}, {"api_name": "inspect.getsourcefile", "line_number": 69, "usage_type": "call"}, {"api_name": "inspect.getsourcelines", "line_number": 77, "usage_type": "call"}, {"api_name": "attrs.define", "line_number": 21, "usage_type": "attribute"}, {"api_name": "attrs.define", "line_number": 84, "usage_type": "attribute"}, {"api_name": "attrs.define", "line_number": 91, "usage_type": "attribute"}, {"api_name": "attrs.define", "line_number": 98, "usage_type": "attribute"}, {"api_name": "attrs.define", "line_number": 104, "usage_type": "attribute"}, {"api_name": "attrs.define", "line_number": 109, "usage_type": "attribute"}, {"api_name": "attrs.define", "line_number": 115, "usage_type": "attribute"}, {"api_name": "attrs.define", "line_number": 122, "usage_type": "attribute"}]} +{"seq_id": "39889026281", "text": "import json\n\nwith open('locations.json', 'r') as location_file:\n locations = json.load(location_file)\n\nlocation_id = {}\nresults = locations[\"results\"]\n\nfor i in range(len(results)):\n location_id[results[i]['name']] = results[i]['id']\n\nwith open(\"location_id.json\", 'w') as id_file:\n json.dump(location_id, id_file)", "repo_name": "MercMayhem/Mumbai-Air-Pollution-analysis-and-AQI-forecasting", "sub_path": "location_id_scraping.py", "file_name": "location_id_scraping.py", "file_ext": "py", "file_size_in_byte": 323, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "json.load", "line_number": 4, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 13, "usage_type": "call"}]} +{"seq_id": "8989862622", "text": "# ---------------------------------------------------------------\n# Loss for YOLO Person Search\n#\n# Author: Liangqi Li\n# Creating Date: Jun 8, 2018\n# Latest rectified: Jun 9, 2018\n# ---------------------------------------------------------------\nimport math\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch.autograd import Variable\nfrom utils import convert2cpu, bbox_ious, bbox_iou\n\n\ndef build_targets(pred_boxes, target, anchors, num_anchors, n_h, n_w, config):\n n_b = target.size(0)\n n_a = num_anchors\n anchor_step = 2\n noobject_scale = config['noobject_scale']\n object_scale = config['object_scale']\n sil_thresh = config['sil_thresh']\n\n conf_mask = torch.ones(n_b, n_a, n_h, n_w) * noobject_scale\n coord_mask = torch.zeros(n_b, n_a, n_h, n_w)\n cls_mask = torch.zeros(n_b, n_a, n_h, n_w)\n tx = torch.zeros(n_b, n_a, n_h, n_w)\n ty = torch.zeros(n_b, n_a, n_h, n_w)\n tw = torch.zeros(n_b, n_a, n_h, n_w)\n th = torch.zeros(n_b, n_a, n_h, n_w)\n tconf = torch.zeros(n_b, n_a, n_h, n_w)\n tcls = torch.zeros(n_b, n_a, n_h, n_w)\n\n n_all_anchors = n_a * n_h * n_w\n n_pixels = n_h * n_w\n\n for b in range(n_b):\n cur_pred_boxes = pred_boxes[b*n_all_anchors: (b+1)*n_all_anchors].t()\n cur_ious = torch.zeros(n_all_anchors)\n for t in range(50):\n if target[b][t*5+1] == 0:\n break\n gx = target[b][t*5+1] * n_w\n gy = target[b][t*5+2] * n_h\n gw = target[b][t*5+3] * n_w\n gh = target[b][t*5+4] * n_h\n cur_gt_boxes = torch.FloatTensor([gx, gy, gw, gh]).repeat(\n n_all_anchors, 1).t()\n cur_ious = torch.max(cur_ious, bbox_ious(\n cur_pred_boxes, cur_gt_boxes, x1y1x2y2=False))\n conf_mask[b][cur_ious > sil_thresh] = 0\n\n tx.fill_(0.5)\n ty.fill_(0.5)\n tw.zero_()\n th.zero_()\n coord_mask.fill_(1)\n\n n_gt = 0\n n_correct = 0\n for b in range(n_b):\n for t in range(50):\n if target[b][t*5+1] == 0:\n break\n n_gt += 1\n best_iou = 0.0\n best_n = -1\n gx = target[b][t*5+1] * n_w\n gy = target[b][t*5+2] * n_h\n gi = int(gx)\n gj = int(gy)\n gw = target[b][t*5+3] * n_w\n gh = target[b][t*5+4] * n_h\n gt_box = [0, 0, gw, gh]\n for n in range(n_a):\n aw = anchors[anchor_step*n]\n ah = anchors[anchor_step*n+1]\n anchor_box = [0, 0, aw, ah]\n iou = bbox_iou(anchor_box, gt_box, x1y1x2y2=False)\n if iou > best_iou:\n best_iou = iou\n best_n = n\n\n gt_box = [gx, gy, gw, gh]\n pred_box = pred_boxes[b*n_all_anchors+best_n*n_pixels+gj*n_w+gi]\n\n coord_mask[b][best_n][gj][gi] = 1\n cls_mask[b][best_n][gj][gi] = 1\n conf_mask[b][best_n][gj][gi] = object_scale\n tx[b][best_n][gj][gi] = target[b][t*5+1] * n_w - gi\n ty[b][best_n][gj][gi] = target[b][t*5+2] * n_h - gj\n tw[b][best_n][gj][gi] = math.log(\n gw / anchors[anchor_step*best_n])\n th[b][best_n][gj][gi] = math.log(\n gh / anchors[anchor_step*best_n + 1])\n iou = bbox_iou(gt_box, pred_box, x1y1x2y2=False) # best_iou\n tconf[b][best_n][gj][gi] = iou\n tcls[b][best_n][gj][gi] = target[b][t*5]\n if iou > 0.5:\n n_correct = n_correct + 1\n\n return n_gt, n_correct, coord_mask, conf_mask, cls_mask, tx, ty, tw, th,\\\n tconf, tcls\n\n\ndef region_loss(output, target, config):\n\n anchors = config['anchors']\n n_b = output.data.size(0)\n n_a = len(anchors) // 2\n n_c = config['num_classes']\n n_h = output.data.size(2)\n n_w = output.data.size(3)\n\n output = output.view(n_b, n_a, (5 + n_c), n_h, n_w)\n x = F.sigmoid(output.index_select(2, Variable(\n torch.cuda.LongTensor([0]))).view(n_b, n_a, n_h, n_w))\n y = F.sigmoid(output.index_select(2, Variable(\n torch.cuda.LongTensor([1]))).view(n_b, n_a, n_h, n_w))\n w = output.index_select(2, Variable(\n torch.cuda.LongTensor([2]))).view(n_b, n_a, n_h, n_w)\n h = output.index_select(2, Variable(\n torch.cuda.LongTensor([3]))).view(n_b, n_a, n_h, n_w)\n conf = F.sigmoid(output.index_select(2, Variable(\n torch.cuda.LongTensor([4]))).view(n_b, n_a, n_h, n_w))\n cls = output.index_select(2, Variable(\n torch.linspace(5, 5+n_c-1, n_c).long().cuda()))\n cls = cls.view(n_b*n_a, n_c, n_h*n_w).transpose(1, 2).contiguous().view(\n n_b*n_a*n_h*n_w, n_c)\n\n pred_boxes = torch.cuda.FloatTensor(4, n_b*n_a*n_h*n_w)\n grid_x = torch.linspace(0, n_w-1, n_w).repeat(n_h, 1).repeat(\n n_b*n_a, 1, 1).view(n_b*n_a*n_h*n_w).cuda()\n grid_y = torch.linspace(0, n_h-1, n_h).repeat(n_w, 1).t().repeat(\n n_b*n_a, 1, 1).view(n_b*n_a*n_h*n_w).cuda()\n anchor_w = torch.Tensor(anchors).view(n_a, 2).index_select(\n 1, torch.LongTensor([0])).cuda()\n anchor_h = torch.Tensor(anchors).view(n_a, 2).index_select(\n 1, torch.LongTensor([1])).cuda()\n anchor_w = anchor_w.repeat(n_b, 1).repeat(1, 1, n_h*n_w).view(\n n_b*n_a*n_h*n_w)\n anchor_h = anchor_h.repeat(n_b, 1).repeat(1, 1, n_h*n_w).view(\n n_b*n_a*n_h*n_w)\n\n pred_boxes[0] = x.data + grid_x\n pred_boxes[1] = y.data + grid_y\n pred_boxes[2] = torch.exp(w.data) * anchor_w\n pred_boxes[3] = torch.exp(h.data) * anchor_h\n pred_boxes = convert2cpu(\n pred_boxes.transpose(0, 1).contiguous().view(-1 ,4))\n\n n_gt, n_correct, coord_mask, conf_mask, cls_mask, tx, ty, tw, th, tconf,\\\n tcls = build_targets(pred_boxes, target.data, anchors, n_a, n_h, n_w,\n config)\n cls_mask = (cls_mask == 1)\n n_proposals = int((conf > .25).sum().data[0])\n\n tx = Variable(tx.cuda())\n ty = Variable(ty.cuda())\n tw = Variable(tw.cuda())\n th = Variable(th.cuda())\n tconf = Variable(tconf.cuda())\n tcls = Variable(tcls.view(-1)[cls_mask].long().cuda())\n\n coord_mask = Variable(coord_mask.cuda())\n conf_mask = Variable(conf_mask.cuda().sqrt())\n cls_mask = Variable(cls_mask.view(-1, 1).repeat(1, n_c).cuda())\n cls = cls[cls_mask].view(-1, n_c)\n\n coord_scale = config['coord_scale']\n class_scale = config['class_scale']\n loss_x = coord_scale * nn.MSELoss(size_average=False)(\n x * coord_mask, tx * coord_mask) / 2\n loss_y = coord_scale * nn.MSELoss(size_average=False)(\n y * coord_mask, ty * coord_mask) / 2\n loss_w = coord_scale * nn.MSELoss(size_average=False)(\n w * coord_mask, tw * coord_mask) / 2\n loss_h = coord_scale * nn.MSELoss(size_average=False)(\n h * coord_mask, th * coord_mask) / 2\n loss_conf = nn.MSELoss(size_average=False)(\n conf * conf_mask, tconf * conf_mask) / 2\n loss_cls = class_scale * nn.CrossEntropyLoss(size_average=False)(cls, tcls)\n loss = loss_x + loss_y + loss_w + loss_h + loss_conf + loss_cls\n print('nGT %d, recall %d, proposals %d, loss: x %f, y %f, w %f, h %f,'\n ' conf %f, cls %f, total %f' % (\n n_gt, n_correct, n_proposals, loss_x.data[0], loss_y.data[0],\n loss_w.data[0], loss_h.data[0], loss_conf.data[0], loss_cls.data[0],\n loss.data[0]))\n\n return loss", "repo_name": "liliangqi/yolo_person_search", "sub_path": "loss.py", "file_name": "loss.py", "file_ext": "py", "file_size_in_byte": 7372, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "37", "api": [{"api_name": "torch.ones", "line_number": 25, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 26, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 27, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 28, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 29, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 30, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 31, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 32, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 33, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 40, "usage_type": "call"}, {"api_name": "torch.FloatTensor", "line_number": 48, "usage_type": "call"}, {"api_name": "torch.max", "line_number": 50, "usage_type": "call"}, {"api_name": "utils.bbox_ious", "line_number": 50, "usage_type": "call"}, {"api_name": "utils.bbox_iou", "line_number": 80, "usage_type": "call"}, {"api_name": "math.log", "line_number": 93, "usage_type": "call"}, {"api_name": "math.log", "line_number": 95, "usage_type": "call"}, {"api_name": "utils.bbox_iou", "line_number": 97, "usage_type": "call"}, {"api_name": "torch.nn.functional.sigmoid", "line_number": 117, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 117, "usage_type": "name"}, {"api_name": "torch.autograd.Variable", "line_number": 117, "usage_type": "call"}, {"api_name": "torch.cuda.LongTensor", "line_number": 118, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 118, "usage_type": "attribute"}, {"api_name": "torch.nn.functional.sigmoid", "line_number": 119, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 119, "usage_type": "name"}, {"api_name": "torch.autograd.Variable", "line_number": 119, "usage_type": "call"}, {"api_name": "torch.cuda.LongTensor", "line_number": 120, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 120, "usage_type": "attribute"}, {"api_name": "torch.autograd.Variable", "line_number": 121, "usage_type": "call"}, {"api_name": "torch.cuda.LongTensor", "line_number": 122, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 122, "usage_type": "attribute"}, {"api_name": "torch.autograd.Variable", "line_number": 123, "usage_type": "call"}, {"api_name": "torch.cuda.LongTensor", "line_number": 124, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 124, "usage_type": "attribute"}, {"api_name": "torch.nn.functional.sigmoid", "line_number": 125, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 125, "usage_type": "name"}, {"api_name": "torch.autograd.Variable", "line_number": 125, "usage_type": "call"}, {"api_name": "torch.cuda.LongTensor", "line_number": 126, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 126, "usage_type": "attribute"}, {"api_name": "torch.autograd.Variable", "line_number": 127, "usage_type": "call"}, {"api_name": "torch.linspace", "line_number": 128, "usage_type": "call"}, {"api_name": "torch.cuda.FloatTensor", "line_number": 132, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 132, "usage_type": "attribute"}, {"api_name": "torch.linspace", "line_number": 133, "usage_type": "call"}, {"api_name": "torch.linspace", "line_number": 135, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 137, "usage_type": "call"}, {"api_name": "torch.LongTensor", "line_number": 138, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 139, "usage_type": "call"}, {"api_name": "torch.LongTensor", "line_number": 140, "usage_type": "call"}, {"api_name": "torch.exp", "line_number": 148, "usage_type": "call"}, {"api_name": "torch.exp", "line_number": 149, "usage_type": "call"}, {"api_name": "utils.convert2cpu", "line_number": 150, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 159, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 160, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 161, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 162, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 163, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 164, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 166, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 167, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 168, "usage_type": "call"}, {"api_name": "torch.nn.MSELoss", "line_number": 173, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 173, "usage_type": "name"}, {"api_name": "torch.nn.MSELoss", "line_number": 175, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 175, "usage_type": "name"}, {"api_name": "torch.nn.MSELoss", "line_number": 177, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 177, "usage_type": "name"}, {"api_name": "torch.nn.MSELoss", "line_number": 179, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 179, "usage_type": "name"}, {"api_name": "torch.nn.MSELoss", "line_number": 181, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 181, "usage_type": "name"}, {"api_name": "torch.nn.CrossEntropyLoss", "line_number": 183, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 183, "usage_type": "name"}]} +{"seq_id": "39719986681", "text": "import pytest\n\nfrom mappings.core import Core\n\n\nclass TestCoreMapping:\n @pytest.fixture\n def testMapping(self, mocker):\n class TestCore(Core):\n def ___init__(self, source, statics):\n super(self, TestCore).__init__(source, statics)\n\n def createMapping(self):\n pass\n\n mockFormatter = mocker.patch('mappings.core.CustomFormatter')\n mockFormatter.return_value = 'mockFormatter'\n return TestCore('test', {'static': 'values'})\n\n def test_initializer(self, testMapping):\n assert testMapping.mapping == {}\n assert testMapping.source == 'test'\n assert testMapping.record == None\n assert testMapping.staticValues == {'static': 'values'}\n assert testMapping.formatter == 'mockFormatter'\n\n def test_initEmptyRecord(self, testMapping, mocker):\n mockUUID = mocker.patch('mappings.core.uuid4')\n mockUUID.return_value = 'testUUID'\n mockDate = mocker.patch('mappings.core.datetime')\n mockDate.utcnow.side_effect = ['testCreated', 'testModified']\n mockRecord = mocker.patch('mappings.core.Record')\n mockRecord.return_value = 'testRecord'\n\n testRecord = testMapping.initEmptyRecord()\n \n assert testRecord == 'testRecord'\n mockRecord.assert_called_once_with(\n uuid='testUUID', date_created='testCreated', date_modified='testModified',\n frbr_status='to_do', cluster_status=False\n )\n\n def test_applyMapping(self, testMapping, mocker):\n mockInitEmpty = mocker.patch.object(Core, 'initEmptyRecord')\n\n testMapping.applyMapping()\n\n mockInitEmpty.assert_called_once\n\n def test_applyFormatting(self, testMapping):\n assert testMapping.applyFormatting() is None\n\n def test_updateExisting(self, testMapping, mocker):\n mockRecord = mocker.MagicMock()\n mockRecord.__iter__.return_value = [\n ('uuid', 'uuid1'), ('title', 'Test Title')\n ]\n testMapping.record = mockRecord\n\n mockExisting = mocker.MagicMock(uuid='uuid2', source='test', frbr_status='complete')\n\n testMapping.updateExisting(mockExisting)\n\n assert mockExisting.uuid == 'uuid2'\n assert mockExisting.title == 'Test Title'\n assert mockExisting.frbr_status == 'to_do'\n assert mockExisting.cluster_status == False\n\n def test_updateExisting_oclc(self, testMapping, mocker):\n mockRecord = mocker.MagicMock()\n mockRecord.__iter__.return_value = [\n ('uuid', 'uuid1'), ('title', 'Test Title')\n ]\n testMapping.record = mockRecord\n\n mockExisting = mocker.MagicMock(uuid='uuid2', source='oclcClassify', frbr_status='complete')\n\n testMapping.updateExisting(mockExisting)\n\n assert mockExisting.uuid == 'uuid2'\n assert mockExisting.title == 'Test Title'\n assert mockExisting.frbr_status == 'complete'\n assert mockExisting.cluster_status == False\n", "repo_name": "NYPL/drb-etl-pipeline", "sub_path": "tests/unit/test_core_mapping.py", "file_name": "test_core_mapping.py", "file_ext": "py", "file_size_in_byte": 2986, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 7, "dataset": "github-code", "pt": "37", "api": [{"api_name": "mappings.core.Core", "line_number": 9, "usage_type": "name"}, {"api_name": "pytest.fixture", "line_number": 7, "usage_type": "attribute"}, {"api_name": "mappings.core.Core", "line_number": 44, "usage_type": "argument"}]} +{"seq_id": "14548604954", "text": "import os\nimport sys\n\nfrom robot import run as robot_run\nfrom robot.testdoc import testdoc\n\nfrom cumulusci.core.exceptions import RobotTestFailure\nfrom cumulusci.core.tasks import BaseTask\nfrom cumulusci.core.utils import process_bool_arg\nfrom cumulusci.core.utils import process_list_arg\nfrom cumulusci.robotframework.utils import set_pdb_trace\nfrom cumulusci.tasks.salesforce import BaseSalesforceTask\nfrom cumulusci.tasks.robotframework.debugger import DebugListener\n\n\nclass Robot(BaseSalesforceTask):\n task_options = {\n \"suites\": {\n \"description\": 'Paths to test case files/directories to be executed similarly as when running the robot command on the command line. Defaults to \"tests\" to run all tests in the tests directory',\n \"required\": True,\n },\n \"test\": {\n \"description\": \"Run only tests matching name patterns. Can be comma separated and use robot wildcards like *\"\n },\n \"include\": {\"description\": \"Includes tests with a given tag\"},\n \"exclude\": {\"description\": \"Excludes tests with a given tag\"},\n \"vars\": {\n \"description\": \"Pass values to override variables in the format VAR1:foo,VAR2:bar\"\n },\n \"xunit\": {\"description\": \"Set an XUnit format output file for test results\"},\n \"options\": {\n \"description\": \"A dictionary of options to robot.run method. See docs here for format. NOTE: There is no cci CLI support for this option since it requires a dictionary. Use this option in the cumulusci.yml when defining custom tasks where you can easily create a dictionary in yaml.\"\n },\n \"name\": {\"description\": \"Sets the name of the top level test suite\"},\n \"pdb\": {\"description\": \"If true, run the Python debugger when tests fail.\"},\n \"verbose\": {\"description\": \"If true, log each keyword as it runs.\"},\n \"debug\": {\n \"description\": \"If true, enable the `breakpoint` keyword to enable the robot debugger\"\n },\n }\n\n def _init_options(self, kwargs):\n super(Robot, self)._init_options(kwargs)\n\n for option in (\"test\", \"include\", \"exclude\", \"vars\"):\n if option in self.options:\n self.options[option] = process_list_arg(self.options[option])\n if \"vars\" not in self.options:\n self.options[\"vars\"] = []\n\n # Initialize options as a dict\n if \"options\" not in self.options:\n self.options[\"options\"] = {}\n\n # There are potentially many robot options that are or could\n # be lists, but the only one we currently care about is the\n # listener option since we may need to append additional values\n # onto it.\n for option in (\"listener\",):\n if option in self.options[\"options\"]:\n self.options[\"options\"][option] = process_list_arg(\n self.options[\"options\"][option]\n )\n\n listeners = self.options[\"options\"].setdefault(\"listener\", [])\n if process_bool_arg(self.options.get(\"verbose\")):\n listeners.append(KeywordLogger())\n\n if process_bool_arg(self.options.get(\"debug\")):\n listeners.append(DebugListener())\n\n if process_bool_arg(self.options.get(\"pdb\")):\n patch_statusreporter()\n\n def _run_task(self):\n self.options[\"vars\"].append(\"org:{}\".format(self.org_config.name))\n options = self.options[\"options\"].copy()\n for option in (\"test\", \"include\", \"exclude\", \"xunit\", \"name\"):\n if option in self.options:\n options[option] = self.options[option]\n options[\"variable\"] = self.options.get(\"vars\") or []\n options[\"outputdir\"] = os.path.relpath(\n os.path.join(self.working_path, options.get(\"outputdir\", \".\")), os.getcwd()\n )\n\n num_failed = robot_run(self.options[\"suites\"], **options)\n\n # These numbers are from the robot framework user guide:\n # http://robotframework.org/robotframework/latest/RobotFrameworkUserGuide.html#return-codes\n if 0 < num_failed < 250:\n raise RobotTestFailure(\n f\"{num_failed} test{'' if num_failed == 1 else 's'} failed.\"\n )\n elif num_failed == 250:\n raise RobotTestFailure(\"250 or more tests failed.\")\n elif num_failed == 251:\n raise RobotTestFailure(\"Help or version information printed.\")\n elif num_failed == 252:\n raise RobotTestFailure(\"Invalid test data or command line options.\")\n elif num_failed == 253:\n raise RobotTestFailure(\"Test execution stopped by user.\")\n elif num_failed >= 255:\n raise RobotTestFailure(\"Unexpected internal error\")\n\n\nclass RobotTestDoc(BaseTask):\n task_options = {\n \"path\": {\n \"description\": \"The path containing .robot test files\",\n \"required\": True,\n },\n \"output\": {\n \"description\": \"The output html file where the documentation will be written\",\n \"required\": True,\n },\n }\n\n def _run_task(self):\n return testdoc(self.options[\"path\"], self.options[\"output\"])\n\n\nclass KeywordLogger(object):\n ROBOT_LISTENER_API_VERSION = 2\n\n def start_keyword(self, name, attrs):\n sys.stdout.write(\" {} {}\\n\".format(attrs[\"kwname\"], \" \".join(attrs[\"args\"])))\n sys.stdout.flush()\n\n\ndef patch_statusreporter():\n \"\"\"Monkey patch robotframework to do postmortem debugging\n \"\"\"\n from robot.running.statusreporter import StatusReporter\n\n orig_exit = StatusReporter.__exit__\n\n def __exit__(self, exc_type, exc_val, exc_tb):\n if exc_val and isinstance(exc_val, Exception):\n set_pdb_trace(pm=True)\n return orig_exit(self, exc_type, exc_val, exc_tb)\n\n StatusReporter.__exit__ = __exit__\n\n\ndef patch_executescript():\n # convert executeScript calls into executeAsyncScript\n # to work around an issue in chromedriver 77\n # https://bugs.chromium.org/p/chromedriver/issues/detail?id=3103\n from selenium.webdriver.remote.webdriver import WebDriver\n\n def execute_script(self, script, *args):\n # the last argument is the function called to say the async operation is done\n script = (\n \"arguments[arguments.length - 1](function(){\"\n + script\n + \"}.apply(null, Array.prototype.slice.call(arguments, 0, -1)));\"\n )\n return self.execute_async_script(script, *args)\n\n WebDriver.execute_script = execute_script\n\n\npatch_executescript()\n", "repo_name": "justindixon/CumulusCI", "sub_path": "cumulusci/tasks/robotframework/robotframework.py", "file_name": "robotframework.py", "file_ext": "py", "file_size_in_byte": 6535, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "github-code", "pt": "37", "api": [{"api_name": "cumulusci.tasks.salesforce.BaseSalesforceTask", "line_number": 16, "usage_type": "name"}, {"api_name": "cumulusci.core.utils.process_list_arg", "line_number": 47, "usage_type": "call"}, {"api_name": "cumulusci.core.utils.process_list_arg", "line_number": 61, "usage_type": "call"}, {"api_name": "cumulusci.core.utils.process_bool_arg", "line_number": 66, "usage_type": "call"}, {"api_name": "cumulusci.core.utils.process_bool_arg", "line_number": 69, "usage_type": "call"}, {"api_name": "cumulusci.tasks.robotframework.debugger.DebugListener", "line_number": 70, "usage_type": "call"}, {"api_name": "cumulusci.core.utils.process_bool_arg", "line_number": 72, "usage_type": "call"}, {"api_name": "os.path.relpath", "line_number": 82, "usage_type": "call"}, {"api_name": "os.path", "line_number": 82, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 83, "usage_type": "call"}, {"api_name": "os.path", "line_number": 83, "usage_type": "attribute"}, {"api_name": "os.getcwd", "line_number": 83, "usage_type": "call"}, {"api_name": "robot.run", "line_number": 86, "usage_type": "call"}, {"api_name": "cumulusci.core.exceptions.RobotTestFailure", "line_number": 91, "usage_type": "call"}, {"api_name": "cumulusci.core.exceptions.RobotTestFailure", "line_number": 95, "usage_type": "call"}, {"api_name": "cumulusci.core.exceptions.RobotTestFailure", "line_number": 97, "usage_type": "call"}, {"api_name": "cumulusci.core.exceptions.RobotTestFailure", "line_number": 99, "usage_type": "call"}, {"api_name": "cumulusci.core.exceptions.RobotTestFailure", "line_number": 101, "usage_type": "call"}, {"api_name": "cumulusci.core.exceptions.RobotTestFailure", "line_number": 103, "usage_type": "call"}, {"api_name": "cumulusci.core.tasks.BaseTask", "line_number": 106, "usage_type": "name"}, {"api_name": "robot.testdoc.testdoc", "line_number": 119, "usage_type": "call"}, {"api_name": "sys.stdout.write", "line_number": 126, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 126, "usage_type": "attribute"}, {"api_name": "sys.stdout.flush", "line_number": 127, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 127, "usage_type": "attribute"}, {"api_name": "robot.running.statusreporter.StatusReporter.__exit__", "line_number": 135, "usage_type": "attribute"}, {"api_name": "robot.running.statusreporter.StatusReporter", "line_number": 135, "usage_type": "name"}, {"api_name": "cumulusci.robotframework.utils.set_pdb_trace", "line_number": 139, "usage_type": "call"}, {"api_name": "robot.running.statusreporter.StatusReporter.__exit__", "line_number": 142, "usage_type": "attribute"}, {"api_name": "robot.running.statusreporter.StatusReporter", "line_number": 142, "usage_type": "name"}, {"api_name": "selenium.webdriver.remote.webdriver.WebDriver.execute_script", "line_number": 160, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.remote.webdriver.WebDriver", "line_number": 160, "usage_type": "name"}]} +{"seq_id": "5904018358", "text": "from app import db\nfrom sqlalchemy.ext.hybrid import hybrid_property\n\n\nartist_artwork = db.Table(\n 'artist_artwork',\n db.Column('artwork_id', db.Integer, db.ForeignKey('artwork.id')),\n db.Column('artist_id', db.Integer, db.ForeignKey('artist.id')),\n db.UniqueConstraint(\n 'artwork_id',\n 'artist_id',\n name='UC_artist_id_artwork_id'\n )\n)\n\n\nexh_org = db.Table(\n 'exh_org',\n db.Column('exhibition_id', db.Integer, db.ForeignKey('exhibition.id')),\n db.Column('organization_id', db.Integer, db.ForeignKey('org.id')),\n db.UniqueConstraint(\n 'exhibition_id',\n 'organization_id',\n name='UC_exhibition_id_organization_id'\n )\n)\n\n\nclass Exhibition(db.Model):\n # Bio\n id = db.Column(db.Integer, primary_key=True)\n name = db.Column(db.String(240), default='', nullable=False)\n start_date = db.Column(db.Date())\n end_date = db.Column(db.Date())\n opening = db.Column(db.Date())\n comments = db.Column(db.String(), default='')\n\n # Install\n install_start = db.Column(db.Date())\n install_end = db.Column(db.Date())\n prm = db.Column(db.String(5), default='')\n approval = db.Column(db.String(5), default='')\n walkthrough = db.Column(db.String(10), default='')\n cb_presentation = db.Column(db.String(10), default='')\n license_mailed = db.Column(db.String(5), default='')\n license_signed = db.Column(db.String(5), default='')\n license_borough = db.Column(db.String(5), default='')\n bond = db.Column(db.String(10), default='')\n coi = db.Column(db.String(10), default='')\n coi_renewal = db.Column(db.String(10), default='')\n signage_submit = db.Column(db.String(5), default='')\n signage_received = db.Column(db.String(5), default='')\n press_draft = db.Column(db.String(5), default='')\n press_approved = db.Column(db.String(), default='')\n web_text = db.Column(db.String(5), default='')\n work_images = db.Column(db.String(5), default='')\n\n # De-Install\n deinstall_date = db.Column(db.Date())\n deinstall_check = db.Column(db.String(5), default='')\n bond_return = db.Column(db.String(5), default='')\n press_clippings = db.Column(db.String(5), default='')\n\n # Related\n parks = db.relationship(\n 'Park',\n secondary='exh_art_park',\n backref=db.backref('exhibitions'),\n viewonly=True\n )\n artworks = db.relationship(\n 'Artwork',\n secondary='exh_art_park',\n backref=db.backref('exhibitions')\n )\n organizations = db.relationship(\n 'Org',\n secondary=exh_org,\n backref=db.backref('exhibitions', lazy='dynamic')\n )\n\n def __repr__(self):\n return \"\"\n\n @property\n def serialize(self):\n\n return {\n 'id': self.id,\n 'name': self.name,\n 'start_date': self.start_date,\n 'end_date': self.end_date,\n 'opening': self.opening,\n 'comments': self.comments,\n 'install_start': self.install_start,\n 'install_end': self.install_end,\n 'prm': self.prm,\n 'approval': self.approval,\n 'walkthrough': self.walkthrough,\n 'cb_presentation': self.cb_presentation,\n 'license_mailed': self.license_mailed,\n 'license_signed': self.license_signed,\n 'license_borough': self.license_borough,\n 'bond': self.bond,\n 'coi': self.coi,\n 'coi_renewal': self.coi_renewal,\n 'signage_submit': self.signage_submit,\n 'signage_received': self.signage_received,\n 'press_draft': self.press_draft,\n 'press_approved': self.press_approved,\n 'web_text': self.web_text,\n 'work_images': self.work_images,\n 'deinstall_date': self.deinstall_date,\n 'deinstall_check': self.deinstall_check,\n 'bond_return': self.bond_return,\n 'press_clippings': self.press_clippings\n }\n\n\nclass Park(db.Model):\n __searchable__ = ['name']\n id = db.Column(db.Integer, primary_key=True)\n name = db.Column(db.String(240), nullable=False)\n park_id = db.Column(db.String(15), default='')\n borough = db.Column(db.String(15), default='')\n address = db.Column(db.String(500), default='')\n cb = db.Column(db.String(40), default='')\n\n def __repr__(self):\n return \"\"\n\n @property\n def serialize(self):\n\n return {\n 'id': self.id,\n 'name': self.name,\n 'park_id': self.park_id,\n 'borough': self.borough,\n 'address': self.address,\n 'cb': self.cb\n }\n\n\nclass Artwork(db.Model):\n id = db.Column(db.Integer, primary_key=True)\n name = db.Column(db.String(240), nullable=False)\n parks = db.relationship(\n 'Park',\n secondary='exh_art_park',\n backref=db.backref('artworks')\n )\n artists = db.relationship(\n 'Artist',\n secondary='artist_artwork',\n backref=db.backref('artworks', lazy='dynamic')\n )\n\n def __repr__(self):\n return ''.format(self.name)\n\n @property\n def serialize(self):\n return {\n 'id': self.id,\n 'name': self.name\n }\n\n\nclass Exh_art_park(db.Model):\n exhibition_id = db.Column(\n db.Integer,\n db.ForeignKey('exhibition.id'),\n primary_key=True\n )\n artwork_id = db.Column(\n db.Integer,\n db.ForeignKey('artwork.id'),\n primary_key=True\n )\n park_id = db.Column(\n db.Integer,\n db.ForeignKey('park.id'),\n primary_key=True\n )\n\n # db.UniqueConstraint('exhibition_id', 'artwork_id')\n exhib = db.relationship('Exhibition')\n artw = db.relationship('Artwork')\n nycpark = db.relationship('Park')\n\n def __repr__(self):\n return \"\"\n\n\nclass Artist(db.Model):\n id = db.Column(db.Integer, primary_key=True)\n pName = db.Column(db.String(120), nullable=False)\n fName = db.Column(db.String(120), default=None)\n email = db.Column(db.String(80), default='')\n phone = db.Column(db.String(12), default='')\n website = db.Column(db.String(80), default='')\n\n @hybrid_property\n def name(self):\n if self.fName is not None:\n return self.fName + \" \" + self.pName\n else:\n return self.pName\n\n def __repr__(self):\n return \"\"\n\n @property\n def serialize(self):\n return {\n 'id': self.id,\n 'pName': self.pName,\n 'fName': self.fName,\n 'name': self.name,\n 'email': self.email,\n 'phone': self.phone,\n 'website': self.website\n }\n\n\nclass Org(db.Model):\n id = db.Column(db.Integer, primary_key=True)\n name = db.Column(db.String(240), nullable=False)\n website = db.Column(db.String(40), default='')\n phone = db.Column(db.String(12), default='')\n\n @property\n def serialize(self):\n\n return {\n 'id': self.id,\n 'name': self.name,\n 'phone': self.phone,\n 'website': self.website\n }\n\n\ndef init_db():\n db.create_all()\n\n\nif __name__ == '__main__':\n init_db()\n", "repo_name": "purwin/Parks-Database", "sub_path": "app/parks_db.py", "file_name": "parks_db.py", "file_ext": "py", "file_size_in_byte": 6613, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "app.db.Table", "line_number": 5, "usage_type": "call"}, {"api_name": "app.db", "line_number": 5, "usage_type": "name"}, {"api_name": "app.db.Column", "line_number": 7, "usage_type": "call"}, {"api_name": "app.db", "line_number": 7, "usage_type": "name"}, {"api_name": "app.db.Integer", "line_number": 7, "usage_type": "attribute"}, {"api_name": "app.db.ForeignKey", "line_number": 7, "usage_type": "call"}, {"api_name": "app.db.Column", "line_number": 8, "usage_type": "call"}, {"api_name": "app.db", "line_number": 8, "usage_type": "name"}, {"api_name": "app.db.Integer", "line_number": 8, "usage_type": "attribute"}, {"api_name": "app.db.ForeignKey", "line_number": 8, "usage_type": "call"}, {"api_name": "app.db.UniqueConstraint", "line_number": 9, "usage_type": "call"}, {"api_name": "app.db", "line_number": 9, "usage_type": "name"}, {"api_name": "app.db.Table", "line_number": 17, "usage_type": "call"}, {"api_name": "app.db", "line_number": 17, "usage_type": "name"}, {"api_name": "app.db.Column", "line_number": 19, "usage_type": "call"}, {"api_name": "app.db", "line_number": 19, "usage_type": "name"}, {"api_name": "app.db.Integer", "line_number": 19, "usage_type": "attribute"}, {"api_name": "app.db.ForeignKey", "line_number": 19, "usage_type": "call"}, {"api_name": "app.db.Column", "line_number": 20, "usage_type": "call"}, {"api_name": "app.db", "line_number": 20, "usage_type": "name"}, {"api_name": "app.db.Integer", "line_number": 20, "usage_type": "attribute"}, {"api_name": "app.db.ForeignKey", "line_number": 20, "usage_type": "call"}, {"api_name": "app.db.UniqueConstraint", "line_number": 21, "usage_type": "call"}, {"api_name": "app.db", "line_number": 21, "usage_type": "name"}, {"api_name": "app.db.Model", "line_number": 29, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 29, "usage_type": "name"}, {"api_name": "app.db.Column", "line_number": 31, "usage_type": "call"}, {"api_name": "app.db", "line_number": 31, "usage_type": "name"}, {"api_name": "app.db.Integer", "line_number": 31, "usage_type": "attribute"}, {"api_name": "app.db.Column", "line_number": 32, "usage_type": "call"}, {"api_name": "app.db", "line_number": 32, "usage_type": "name"}, {"api_name": "app.db.String", "line_number": 32, "usage_type": "call"}, {"api_name": "app.db.Column", "line_number": 33, "usage_type": "call"}, {"api_name": "app.db", "line_number": 33, "usage_type": "name"}, {"api_name": "app.db.Date", "line_number": 33, "usage_type": "call"}, {"api_name": "app.db.Column", "line_number": 34, "usage_type": "call"}, {"api_name": "app.db", "line_number": 34, "usage_type": "name"}, {"api_name": "app.db.Date", "line_number": 34, "usage_type": "call"}, {"api_name": "app.db.Column", "line_number": 35, "usage_type": "call"}, {"api_name": "app.db", "line_number": 35, "usage_type": "name"}, {"api_name": "app.db.Date", "line_number": 35, "usage_type": "call"}, {"api_name": "app.db.Column", "line_number": 36, "usage_type": "call"}, {"api_name": "app.db", "line_number": 36, "usage_type": "name"}, {"api_name": "app.db.String", "line_number": 36, "usage_type": "call"}, {"api_name": "app.db.Column", "line_number": 39, "usage_type": "call"}, {"api_name": "app.db", "line_number": 39, "usage_type": "name"}, {"api_name": "app.db.Date", "line_number": 39, "usage_type": "call"}, {"api_name": "app.db.Column", "line_number": 40, "usage_type": "call"}, {"api_name": "app.db", "line_number": 40, "usage_type": "name"}, {"api_name": "app.db.Date", "line_number": 40, "usage_type": "call"}, {"api_name": "app.db.Column", "line_number": 41, "usage_type": "call"}, {"api_name": "app.db", "line_number": 41, "usage_type": "name"}, {"api_name": "app.db.String", "line_number": 41, "usage_type": "call"}, {"api_name": "app.db.Column", "line_number": 42, "usage_type": "call"}, {"api_name": "app.db", "line_number": 42, "usage_type": "name"}, {"api_name": "app.db.String", "line_number": 42, "usage_type": "call"}, {"api_name": "app.db.Column", "line_number": 43, "usage_type": "call"}, {"api_name": "app.db", "line_number": 43, "usage_type": "name"}, {"api_name": "app.db.String", "line_number": 43, "usage_type": "call"}, {"api_name": "app.db.Column", "line_number": 44, "usage_type": "call"}, {"api_name": "app.db", "line_number": 44, "usage_type": "name"}, {"api_name": "app.db.String", "line_number": 44, "usage_type": "call"}, {"api_name": "app.db.Column", "line_number": 45, "usage_type": "call"}, {"api_name": "app.db", "line_number": 45, "usage_type": "name"}, {"api_name": "app.db.String", "line_number": 45, "usage_type": "call"}, {"api_name": "app.db.Column", "line_number": 46, "usage_type": "call"}, {"api_name": "app.db", "line_number": 46, "usage_type": "name"}, {"api_name": "app.db.String", "line_number": 46, "usage_type": "call"}, {"api_name": "app.db.Column", "line_number": 47, "usage_type": "call"}, {"api_name": "app.db", "line_number": 47, "usage_type": "name"}, {"api_name": "app.db.String", "line_number": 47, "usage_type": "call"}, {"api_name": "app.db.Column", "line_number": 48, "usage_type": "call"}, {"api_name": "app.db", "line_number": 48, "usage_type": "name"}, {"api_name": "app.db.String", "line_number": 48, "usage_type": "call"}, {"api_name": "app.db.Column", "line_number": 49, "usage_type": "call"}, {"api_name": "app.db", "line_number": 49, "usage_type": "name"}, {"api_name": "app.db.String", "line_number": 49, "usage_type": "call"}, {"api_name": "app.db.Column", "line_number": 50, "usage_type": "call"}, {"api_name": "app.db", "line_number": 50, "usage_type": "name"}, {"api_name": "app.db.String", "line_number": 50, "usage_type": "call"}, {"api_name": "app.db.Column", "line_number": 51, "usage_type": "call"}, {"api_name": "app.db", "line_number": 51, "usage_type": "name"}, {"api_name": "app.db.String", "line_number": 51, "usage_type": "call"}, {"api_name": "app.db.Column", "line_number": 52, "usage_type": "call"}, {"api_name": "app.db", "line_number": 52, "usage_type": "name"}, {"api_name": "app.db.String", "line_number": 52, "usage_type": "call"}, {"api_name": "app.db.Column", "line_number": 53, "usage_type": "call"}, {"api_name": "app.db", "line_number": 53, "usage_type": "name"}, {"api_name": "app.db.String", "line_number": 53, "usage_type": "call"}, {"api_name": "app.db.Column", "line_number": 54, "usage_type": "call"}, {"api_name": "app.db", "line_number": 54, "usage_type": "name"}, {"api_name": "app.db.String", "line_number": 54, "usage_type": "call"}, {"api_name": "app.db.Column", "line_number": 55, "usage_type": "call"}, {"api_name": "app.db", "line_number": 55, "usage_type": "name"}, {"api_name": "app.db.String", "line_number": 55, "usage_type": "call"}, {"api_name": "app.db.Column", "line_number": 56, "usage_type": "call"}, {"api_name": "app.db", "line_number": 56, "usage_type": "name"}, {"api_name": "app.db.String", "line_number": 56, "usage_type": "call"}, {"api_name": "app.db.Column", "line_number": 59, "usage_type": "call"}, {"api_name": "app.db", "line_number": 59, "usage_type": "name"}, {"api_name": "app.db.Date", "line_number": 59, "usage_type": "call"}, {"api_name": "app.db.Column", "line_number": 60, "usage_type": "call"}, {"api_name": "app.db", "line_number": 60, "usage_type": "name"}, {"api_name": "app.db.String", "line_number": 60, "usage_type": "call"}, {"api_name": "app.db.Column", "line_number": 61, "usage_type": "call"}, {"api_name": "app.db", "line_number": 61, "usage_type": "name"}, {"api_name": "app.db.String", "line_number": 61, "usage_type": "call"}, {"api_name": "app.db.Column", "line_number": 62, "usage_type": "call"}, {"api_name": "app.db", "line_number": 62, "usage_type": "name"}, {"api_name": "app.db.String", "line_number": 62, "usage_type": "call"}, {"api_name": "app.db.relationship", "line_number": 65, "usage_type": "call"}, {"api_name": "app.db", "line_number": 65, "usage_type": "name"}, {"api_name": "app.db.backref", "line_number": 68, "usage_type": "call"}, {"api_name": "app.db", "line_number": 68, "usage_type": "name"}, {"api_name": "app.db.relationship", "line_number": 71, "usage_type": "call"}, {"api_name": "app.db", "line_number": 71, "usage_type": "name"}, {"api_name": "app.db.backref", "line_number": 74, "usage_type": "call"}, {"api_name": "app.db", "line_number": 74, "usage_type": "name"}, {"api_name": "app.db.relationship", "line_number": 76, "usage_type": "call"}, {"api_name": "app.db", "line_number": 76, "usage_type": "name"}, {"api_name": "app.db.backref", "line_number": 79, "usage_type": "call"}, {"api_name": "app.db", "line_number": 79, "usage_type": "name"}, {"api_name": "app.db.Model", "line_number": 120, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 120, "usage_type": "name"}, {"api_name": "app.db.Column", "line_number": 122, "usage_type": "call"}, {"api_name": "app.db", "line_number": 122, "usage_type": "name"}, {"api_name": "app.db.Integer", "line_number": 122, "usage_type": "attribute"}, {"api_name": "app.db.Column", "line_number": 123, "usage_type": "call"}, {"api_name": "app.db", "line_number": 123, "usage_type": "name"}, {"api_name": "app.db.String", "line_number": 123, "usage_type": "call"}, {"api_name": "app.db.Column", "line_number": 124, "usage_type": "call"}, {"api_name": "app.db", "line_number": 124, "usage_type": "name"}, {"api_name": "app.db.String", "line_number": 124, "usage_type": "call"}, {"api_name": "app.db.Column", "line_number": 125, "usage_type": "call"}, {"api_name": "app.db", "line_number": 125, "usage_type": "name"}, {"api_name": "app.db.String", "line_number": 125, "usage_type": "call"}, {"api_name": "app.db.Column", "line_number": 126, "usage_type": "call"}, {"api_name": "app.db", "line_number": 126, "usage_type": "name"}, {"api_name": "app.db.String", "line_number": 126, "usage_type": "call"}, {"api_name": "app.db.Column", "line_number": 127, "usage_type": "call"}, {"api_name": "app.db", "line_number": 127, "usage_type": "name"}, {"api_name": "app.db.String", "line_number": 127, "usage_type": "call"}, {"api_name": "app.db.Model", "line_number": 145, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 145, "usage_type": "name"}, {"api_name": "app.db.Column", "line_number": 146, "usage_type": "call"}, {"api_name": "app.db", "line_number": 146, "usage_type": "name"}, {"api_name": "app.db.Integer", "line_number": 146, "usage_type": "attribute"}, {"api_name": "app.db.Column", "line_number": 147, "usage_type": "call"}, {"api_name": "app.db", "line_number": 147, "usage_type": "name"}, {"api_name": "app.db.String", "line_number": 147, "usage_type": "call"}, {"api_name": "app.db.relationship", "line_number": 148, "usage_type": "call"}, {"api_name": "app.db", "line_number": 148, "usage_type": "name"}, {"api_name": "app.db.backref", "line_number": 151, "usage_type": "call"}, {"api_name": "app.db", "line_number": 151, "usage_type": "name"}, {"api_name": "app.db.relationship", "line_number": 153, "usage_type": "call"}, {"api_name": "app.db", "line_number": 153, "usage_type": "name"}, {"api_name": "app.db.backref", "line_number": 156, "usage_type": "call"}, {"api_name": "app.db", "line_number": 156, "usage_type": "name"}, {"api_name": "app.db.Model", "line_number": 170, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 170, "usage_type": "name"}, {"api_name": "app.db.Column", "line_number": 171, "usage_type": "call"}, {"api_name": "app.db", "line_number": 171, "usage_type": "name"}, {"api_name": "app.db.Integer", "line_number": 172, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 172, "usage_type": "name"}, {"api_name": "app.db.ForeignKey", "line_number": 173, "usage_type": "call"}, {"api_name": "app.db", "line_number": 173, "usage_type": "name"}, {"api_name": "app.db.Column", "line_number": 176, "usage_type": "call"}, {"api_name": "app.db", "line_number": 176, "usage_type": "name"}, {"api_name": "app.db.Integer", "line_number": 177, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 177, "usage_type": "name"}, {"api_name": "app.db.ForeignKey", "line_number": 178, "usage_type": "call"}, {"api_name": "app.db", "line_number": 178, "usage_type": "name"}, {"api_name": "app.db.Column", "line_number": 181, "usage_type": "call"}, {"api_name": "app.db", "line_number": 181, "usage_type": "name"}, {"api_name": "app.db.Integer", "line_number": 182, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 182, "usage_type": "name"}, {"api_name": "app.db.ForeignKey", "line_number": 183, "usage_type": "call"}, {"api_name": "app.db", "line_number": 183, "usage_type": "name"}, {"api_name": "app.db.relationship", "line_number": 188, "usage_type": "call"}, {"api_name": "app.db", "line_number": 188, "usage_type": "name"}, {"api_name": "app.db.relationship", "line_number": 189, "usage_type": "call"}, {"api_name": "app.db", "line_number": 189, "usage_type": "name"}, {"api_name": "app.db.relationship", "line_number": 190, "usage_type": "call"}, {"api_name": "app.db", "line_number": 190, "usage_type": "name"}, {"api_name": "app.db.Model", "line_number": 196, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 196, "usage_type": "name"}, {"api_name": "app.db.Column", "line_number": 197, "usage_type": "call"}, {"api_name": "app.db", "line_number": 197, "usage_type": "name"}, {"api_name": "app.db.Integer", "line_number": 197, "usage_type": "attribute"}, {"api_name": "app.db.Column", "line_number": 198, "usage_type": "call"}, {"api_name": "app.db", "line_number": 198, "usage_type": "name"}, {"api_name": "app.db.String", "line_number": 198, "usage_type": "call"}, {"api_name": "app.db.Column", "line_number": 199, "usage_type": "call"}, {"api_name": "app.db", "line_number": 199, "usage_type": "name"}, {"api_name": "app.db.String", "line_number": 199, "usage_type": "call"}, {"api_name": "app.db.Column", "line_number": 200, "usage_type": "call"}, {"api_name": "app.db", "line_number": 200, "usage_type": "name"}, {"api_name": "app.db.String", "line_number": 200, "usage_type": "call"}, {"api_name": "app.db.Column", "line_number": 201, "usage_type": "call"}, {"api_name": "app.db", "line_number": 201, "usage_type": "name"}, {"api_name": "app.db.String", "line_number": 201, "usage_type": "call"}, {"api_name": "app.db.Column", "line_number": 202, "usage_type": "call"}, {"api_name": "app.db", "line_number": 202, "usage_type": "name"}, {"api_name": "app.db.String", "line_number": 202, "usage_type": "call"}, {"api_name": "sqlalchemy.ext.hybrid.hybrid_property", "line_number": 204, "usage_type": "name"}, {"api_name": "app.db.Model", "line_number": 227, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 227, "usage_type": "name"}, {"api_name": "app.db.Column", "line_number": 228, "usage_type": "call"}, {"api_name": "app.db", "line_number": 228, "usage_type": "name"}, {"api_name": "app.db.Integer", "line_number": 228, "usage_type": "attribute"}, {"api_name": "app.db.Column", "line_number": 229, "usage_type": "call"}, {"api_name": "app.db", "line_number": 229, "usage_type": "name"}, {"api_name": "app.db.String", "line_number": 229, "usage_type": "call"}, {"api_name": "app.db.Column", "line_number": 230, "usage_type": "call"}, {"api_name": "app.db", "line_number": 230, "usage_type": "name"}, {"api_name": "app.db.String", "line_number": 230, "usage_type": "call"}, {"api_name": "app.db.Column", "line_number": 231, "usage_type": "call"}, {"api_name": "app.db", "line_number": 231, "usage_type": "name"}, {"api_name": "app.db.String", "line_number": 231, "usage_type": "call"}, {"api_name": "app.db.create_all", "line_number": 245, "usage_type": "call"}, {"api_name": "app.db", "line_number": 245, "usage_type": "name"}]} +{"seq_id": "12316502230", "text": "\nfrom OpenGL.GL import *\nimport glm as glm\n\nimport idk\nfrom sdl2 import *\n\nimport numpy as np\nimport configparser\nimport libgeometry as geom\n\nfrom face_vertices import *\n\nimport definitions as defs\n\n\ndef collect_avg( vertices ) -> glm.vec3:\n avg = glm.vec3(0.0)\n count = 0\n for i in range(0, len(vertices), 8):\n avg += glm.vec3(\n vertices[i+0],\n vertices[i+1],\n vertices[i+2],\n )\n count += 1\n return avg / count\n\n\nclass FaceRenderer:\n\n def __reload_ini(self) -> None:\n config = configparser.ConfigParser()\n config.read(self.config_path)\n\n self.iris_color = glm.vec3(\n float(config[\"visual\"][\"iris_r\"]) / 255.0,\n float(config[\"visual\"][\"iris_g\"]) / 255.0,\n float(config[\"visual\"][\"iris_b\"]) / 255.0\n )\n\n texture_path = config[\"visual\"][\"texture_path\"]\n texture_path = texture_path.encode('utf-8')\n self.face_mh.glTextureID = idk.loadTexture(texture_path)\n\n self.lerp_alpha = float(config[\"tweaks\"][\"interpolation\"])\n\n\n def __reload_shaders(self) -> None:\n self.iris_shader = idk.compileShaderProgram(\n \"src/shaders/\", \"general.vs\", \"face/iris.fs\"\n )\n self.shader = idk.compileShaderProgram(\n \"src/shaders/\", \"general.vs\", self.face_shader_tex_path\n )\n # self.face_shader = idk.compileShaderProgram(\n # \"src/shaders/\", \"general.vs\", self.face_shader_path\n # )\n\n\n def __init__(self, configpath: str, eyeholes: bool = True) -> None:\n self.config_path = configpath\n\n self.iris_shader_path = \"face/iris.fs\"\n\n # if defs.USE_PYTHON:\n self.face_shader_tex_path = \"face/face-tex-py.fs\"\n self.face_shader_path = \"face/face-py.fs\"\n # else:\n # self.face_shader_tex_path = \"face/face-tex.fs\"\n # self.face_shader_path = \"face/face.fs\"\n\n filepath = \"data/indices.txt\"\n if eyeholes:\n filepath = \"data/indices2.txt\"\n\n self.vertices, self.indices = geom.load_CFM(\"data/vertices.txt\", filepath)\n self.vbackbuffer = np.empty_like(self.vertices, dtype=np.float32)\n\n self.__landmarks2D = [glm.vec2(0)] * self.vertices.size\n self.__landmarks3D = []\n\n\n self.face_mh = idk.loadVerticesIndexed(self.vertices, self.indices, GL_DYNAMIC_DRAW)\n\n self.iris_verts = np.array([0]*8*12, dtype=np.float32)\n self.iris_mh = idk.loadVertices(self.iris_verts, GL_DYNAMIC_DRAW)\n self.iris_verts = self.iris_verts.reshape((12, 8))\n self.iris_l_pos = glm.vec3(0.0)\n self.iris_l_nrm = glm.vec3(0.0)\n self.iris_r_pos = glm.vec3(0.0)\n self.iris_r_nrm = glm.vec3(0.0)\n\n self.__reload_shaders()\n self.__reload_ini()\n\n self.ready = False\n self.theta = 3.14159\n\n\n def __preprocess_vertices(self, facelms) -> None:\n aspect = defs.IMG_W / defs.IMG_H\n\n self.vbackbuffer = geom.lmarks_to_np(facelms.landmark, self.vbackbuffer, aspect, glm.vec2(-0.5, -0.5))\n self.__landmarks2D = geom.lmarks_to_glm(facelms.landmark, self.__landmarks2D, aspect, glm.vec2(-0.5, -0.5))\n\n geom.lerp_verts(self.vertices, self.vbackbuffer, self.lerp_alpha)\n geom.calculate_normals(self.vertices, self.indices)\n\n\n def landmarks2D(self) -> list[glm.vec2]:\n return self.__landmarks2D\n\n\n def __draw_iris(self, facelms, cam: idk.Camera, translation = glm.vec3(0.0)) -> None:\n aspect = defs.IMG_W / defs.IMG_H\n\n v0 = facelms.landmark[FACEMESH_RIGHT_IRIS[0][0]]\n v1 = facelms.landmark[FACEMESH_RIGHT_IRIS[1][0]]\n v2 = facelms.landmark[FACEMESH_RIGHT_IRIS[2][0]]\n v3 = facelms.landmark[FACEMESH_RIGHT_IRIS[3][0]]\n\n vertices1 = [ ]\n vertices1 += [ aspect*(v0.x-0.5), v0.y-0.5, v0.z, 0, 0, 0, 0, 0 ]\n vertices1 += [ aspect*(v1.x-0.5), v1.y-0.5, v1.z, 0, 0, 0, 0, 0 ]\n vertices1 += [ aspect*(v2.x-0.5), v2.y-0.5, v2.z, 0, 0, 0, 0, 0 ]\n vertices1 += [ aspect*(v0.x-0.5), v0.y-0.5, v0.z, 0, 0, 0, 0, 0 ]\n vertices1 += [ aspect*(v2.x-0.5), v2.y-0.5, v2.z, 0, 0, 0, 0, 0 ]\n vertices1 += [ aspect*(v3.x-0.5), v3.y-0.5, v3.z, 0, 0, 0, 0, 0 ]\n self.iris_r_pos = collect_avg(vertices1)\n\n v0 = glm.vec3(v0.x, v0.y, v0.z)\n v1 = glm.vec3(v1.x, v1.y, v1.z)\n v2 = glm.vec3(v2.x, v2.y, v2.z)\n v3 = glm.vec3(v3.x, v3.y, v3.z)\n self.iris_r_nrm = glm.normalize(glm.cross(v2-v0, v3-v0))\n\n\n v0 = facelms.landmark[FACEMESH_LEFT_IRIS[0][0]]\n v1 = facelms.landmark[FACEMESH_LEFT_IRIS[1][0]]\n v2 = facelms.landmark[FACEMESH_LEFT_IRIS[2][0]]\n v3 = facelms.landmark[FACEMESH_LEFT_IRIS[3][0]]\n\n vertices2 = [ ]\n vertices2 += [ aspect*(v0.x-0.5), v0.y-0.5, v0.z, 0, 0, 0, 0, 0 ]\n vertices2 += [ aspect*(v1.x-0.5), v1.y-0.5, v1.z, 0, 0, 0, 0, 0 ]\n vertices2 += [ aspect*(v2.x-0.5), v2.y-0.5, v2.z, 0, 0, 0, 0, 0 ]\n vertices2 += [ aspect*(v0.x-0.5), v0.y-0.5, v0.z, 0, 0, 0, 0, 0 ]\n vertices2 += [ aspect*(v2.x-0.5), v2.y-0.5, v2.z, 0, 0, 0, 0, 0 ]\n vertices2 += [ aspect*(v3.x-0.5), v3.y-0.5, v3.z, 0, 0, 0, 0, 0 ]\n self.iris_l_pos = collect_avg(vertices2)\n\n v0 = glm.vec3(v0.x, v0.y, v0.z)\n v1 = glm.vec3(v1.x, v1.y, v1.z)\n v2 = glm.vec3(v2.x, v2.y, v2.z)\n v3 = glm.vec3(v3.x, v3.y, v3.z)\n self.iris_l_nrm = glm.normalize(glm.cross(v2-v0, v3-v0))\n\n vertices = np.array(vertices1+vertices2, dtype=np.float32).reshape((12, 8))\n geom.lerp_verts(self.iris_verts, vertices, self.lerp_alpha)\n idk.subData(self.iris_mh, self.iris_verts)\n\n rotation = glm.rotate(self.theta, glm.vec3(0.0, 1.0, 0.0))\n trans = glm.translate(translation)\n scale = glm.scale(glm.vec3(2.0))\n transform = trans * scale * rotation\n\n glUseProgram(self.iris_shader)\n idk.setmat4(self.iris_shader, \"un_proj\", cam.projection())\n idk.setmat4(self.iris_shader, \"un_view\", cam.viewMatrix())\n idk.setmat4(self.iris_shader, \"un_model\", transform*glm.scale(glm.vec3(-1, 1, 1)))\n idk.setvec3(self.iris_shader, \"un_color\", self.iris_color)\n idk.drawVertices(self.iris_mh)\n\n\n def __draw_face(self, cam: idk.Camera, translation = glm.vec3(0.0)) -> None:\n # current_shader = self.face_shader\n\n rotation = glm.rotate(self.theta, glm.vec3(0.0, 1.0, 0.0))\n trans = glm.translate(translation)\n scale = glm.scale(glm.vec3(2.0))\n transform = trans * rotation * scale\n\n glUseProgram(self.shader)\n idk.setmat4(self.shader, \"un_view\", cam.viewMatrix())\n idk.setmat4(self.shader, \"un_proj\", cam.projection())\n idk.setmat4(self.shader, \"un_model\", transform*glm.scale(glm.vec3(-1, 1, 1)))\n idk.setTexture(self.shader, 0, self.face_mh.glTextureID, \"un_texture\")\n\n idk.indexedSubData(self.face_mh.VAO, self.face_mh.VBO, self.vertices)\n\n idk.drawVerticesIndexedTextured(self.face_mh, self.shader)\n\n\n\n def draw(self, faceDetector, cam: idk.Camera, dtime) -> None:\n\n results = faceDetector.m_results\n if not results or not results.multi_face_landmarks:\n return\n\n self.ready = True\n\n for facelms in results.multi_face_landmarks:\n self.__preprocess_vertices(facelms)\n\n self.__draw_face(cam, glm.vec3(6.0, -1.5, -2.0))\n self.__draw_face(cam, glm.vec3(12.0, -1.5, -2.0))\n\n for facelms in results.multi_face_landmarks:\n self.__draw_iris(facelms, cam, glm.vec3(6.0, -1.5, -2.0))\n self.__draw_iris(facelms, cam, glm.vec3(12.0, -1.5, -2.0))\n\n\n def onEvent(self, state, dtime=1.0) -> None:\n if state[SDL_SCANCODE_F5]:\n self.__reload_ini()\n self.__reload_shaders()\n\n", "repo_name": "mellic03/2812ict-monocular-hci", "sub_path": "src/facerenderer.py", "file_name": "facerenderer.py", "file_ext": "py", "file_size_in_byte": 7854, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "glm.vec3", "line_number": 18, "usage_type": "call"}, {"api_name": "glm.vec3", "line_number": 21, "usage_type": "call"}, {"api_name": "glm.vec3", "line_number": 17, "usage_type": "attribute"}, {"api_name": "configparser.ConfigParser", "line_number": 33, "usage_type": "call"}, {"api_name": "glm.vec3", "line_number": 36, "usage_type": "call"}, {"api_name": "idk.loadTexture", "line_number": 44, "usage_type": "call"}, {"api_name": "idk.compileShaderProgram", "line_number": 50, "usage_type": "call"}, {"api_name": "idk.compileShaderProgram", "line_number": 53, "usage_type": "call"}, {"api_name": "libgeometry.load_CFM", "line_number": 77, "usage_type": "call"}, {"api_name": "numpy.empty_like", "line_number": 78, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 78, "usage_type": "attribute"}, {"api_name": "glm.vec2", "line_number": 80, "usage_type": "call"}, {"api_name": "idk.loadVerticesIndexed", "line_number": 84, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 86, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 86, "usage_type": "attribute"}, {"api_name": "idk.loadVertices", "line_number": 87, "usage_type": "call"}, {"api_name": "glm.vec3", "line_number": 89, "usage_type": "call"}, {"api_name": "glm.vec3", "line_number": 90, "usage_type": "call"}, {"api_name": "glm.vec3", "line_number": 91, "usage_type": "call"}, {"api_name": "glm.vec3", "line_number": 92, "usage_type": "call"}, {"api_name": "definitions.IMG_W", "line_number": 102, "usage_type": "attribute"}, {"api_name": "definitions.IMG_H", "line_number": 102, "usage_type": "attribute"}, {"api_name": "libgeometry.lmarks_to_np", "line_number": 104, "usage_type": "call"}, {"api_name": "glm.vec2", "line_number": 104, "usage_type": "call"}, {"api_name": "libgeometry.lmarks_to_glm", "line_number": 105, "usage_type": "call"}, {"api_name": "glm.vec2", "line_number": 105, "usage_type": "call"}, {"api_name": "libgeometry.lerp_verts", "line_number": 107, "usage_type": "call"}, {"api_name": "libgeometry.calculate_normals", "line_number": 108, "usage_type": "call"}, {"api_name": "glm.vec2", "line_number": 111, "usage_type": "attribute"}, {"api_name": "idk.Camera", "line_number": 115, "usage_type": "attribute"}, {"api_name": "glm.vec3", "line_number": 115, "usage_type": "call"}, {"api_name": "definitions.IMG_W", "line_number": 116, "usage_type": "attribute"}, {"api_name": "definitions.IMG_H", "line_number": 116, "usage_type": "attribute"}, {"api_name": "glm.vec3", "line_number": 132, "usage_type": "call"}, {"api_name": "glm.vec3", "line_number": 133, "usage_type": "call"}, {"api_name": "glm.vec3", "line_number": 134, "usage_type": "call"}, {"api_name": "glm.vec3", "line_number": 135, "usage_type": "call"}, {"api_name": "glm.normalize", "line_number": 136, "usage_type": "call"}, {"api_name": "glm.cross", "line_number": 136, "usage_type": "call"}, {"api_name": "glm.vec3", "line_number": 153, "usage_type": "call"}, {"api_name": "glm.vec3", "line_number": 154, "usage_type": "call"}, {"api_name": "glm.vec3", "line_number": 155, "usage_type": "call"}, {"api_name": "glm.vec3", "line_number": 156, "usage_type": "call"}, {"api_name": "glm.normalize", "line_number": 157, "usage_type": "call"}, {"api_name": "glm.cross", "line_number": 157, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 159, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 159, "usage_type": "attribute"}, {"api_name": "libgeometry.lerp_verts", "line_number": 160, "usage_type": "call"}, {"api_name": "idk.subData", "line_number": 161, "usage_type": "call"}, {"api_name": "glm.rotate", "line_number": 163, "usage_type": "call"}, {"api_name": "glm.vec3", "line_number": 163, "usage_type": "call"}, {"api_name": "glm.translate", "line_number": 164, "usage_type": "call"}, {"api_name": "glm.scale", "line_number": 165, "usage_type": "call"}, {"api_name": "glm.vec3", "line_number": 165, "usage_type": "call"}, {"api_name": "idk.setmat4", "line_number": 169, "usage_type": "call"}, {"api_name": "idk.setmat4", "line_number": 170, "usage_type": "call"}, {"api_name": "idk.setmat4", "line_number": 171, "usage_type": "call"}, {"api_name": "glm.scale", "line_number": 171, "usage_type": "call"}, {"api_name": "glm.vec3", "line_number": 171, "usage_type": "call"}, {"api_name": "idk.setvec3", "line_number": 172, "usage_type": "call"}, {"api_name": "idk.drawVertices", "line_number": 173, "usage_type": "call"}, {"api_name": "idk.Camera", "line_number": 176, "usage_type": "attribute"}, {"api_name": "glm.vec3", "line_number": 176, "usage_type": "call"}, {"api_name": "glm.rotate", "line_number": 179, "usage_type": "call"}, {"api_name": "glm.vec3", "line_number": 179, "usage_type": "call"}, {"api_name": "glm.translate", "line_number": 180, "usage_type": "call"}, {"api_name": "glm.scale", "line_number": 181, "usage_type": "call"}, {"api_name": "glm.vec3", "line_number": 181, "usage_type": "call"}, {"api_name": "idk.setmat4", "line_number": 185, "usage_type": "call"}, {"api_name": "idk.setmat4", "line_number": 186, "usage_type": "call"}, {"api_name": "idk.setmat4", "line_number": 187, "usage_type": "call"}, {"api_name": "glm.scale", "line_number": 187, "usage_type": "call"}, {"api_name": "glm.vec3", "line_number": 187, "usage_type": "call"}, {"api_name": "idk.setTexture", "line_number": 188, "usage_type": "call"}, {"api_name": "idk.indexedSubData", "line_number": 190, "usage_type": "call"}, {"api_name": "idk.drawVerticesIndexedTextured", "line_number": 192, "usage_type": "call"}, {"api_name": "idk.Camera", "line_number": 196, "usage_type": "attribute"}, {"api_name": "glm.vec3", "line_number": 207, "usage_type": "call"}, {"api_name": "glm.vec3", "line_number": 208, "usage_type": "call"}, {"api_name": "glm.vec3", "line_number": 211, "usage_type": "call"}, {"api_name": "glm.vec3", "line_number": 212, "usage_type": "call"}]} +{"seq_id": "4655121204", "text": "#Email:fanyucai1@126.com\n#2019.11.14\n\nimport os\nimport argparse\nimport subprocess\nimport time\nimport configparser\n\n\nclass Myconf(configparser.ConfigParser):\n def __init__(self, defaults=None):\n configparser.ConfigParser.__init__(self, defaults=defaults)\n\n def optionxform(self, optionstr):\n return optionstr\n\ndef run(bam,outdir,bed,configfile,prefix):\n config = Myconf()\n config.read(configfile)\n java = config.get('software', 'java')\n gatk4 = config.get('software', 'gatk4.1.3')\n gatk3 = config.get('software', 'gatk3.7')\n dbsnp138 = config.get('database', 'dbsnp138')\n mill_indel = config.get('database', 'mill_indel')\n phase1_indel = config.get('database', 'phase1_indel')\n hg19_ref = config.get('database', 'hg19_ref')\n if not os.path.exists(outdir):\n os.mkdir(outdir)\n outdir=os.path.abspath(outdir)\n bam=os.path.abspath(bam)\n par=\"\"\n if bed !=\"0\":\n bed=os.path.abspath(bed)\n par+=\" -L %s \" %(bed)\n start=time.time()\n ####################Realign Indels - Realignment\n out=outdir+\"/\"+prefix\n cmd=\"%s -Xmx10G -jar %s -T RealignerTargetCreator -nt 20 -R %s -I %s -known %s -known %s -o %s.target.list %s\" \\\n %(java,gatk3,hg19_ref,bam,phase1_indel,mill_indel,out,par)\n subprocess.check_call(cmd,shell=True)\n cmd=\"%s -Xmx10G -jar %s -T IndelRealigner -R %s -I %s -targetIntervals %s.target.list -known %s -known %s -o %s.realign.bam\" \\\n %(java,gatk3,hg19_ref,bam,out,phase1_indel,mill_indel,out)\n subprocess.check_call(cmd,shell=True)\n ####################Recalibrate Bases\n subprocess.check_call(\"%s -Xmx10G -jar %s BaseRecalibrator --use-original-qualities -R %s -I %s.realign.bam --known-sites %s --known-sites %s -O %s.recal_data.table %s\"\n %(java,gatk4,hg19_ref,out,dbsnp138,mill_indel,out,par),shell=True)\n subprocess.check_call(\"%s -Xmx10G -jar %s ApplyBQSR -R %s -I %s.realign.bam --bqsr-recal-file %s.recal_data.table -O %s.recal.bam && rm %s.realign.bai %s.realign.bam %s.target.list %s.recal_data.table\"\n %(java,gatk4,hg19_ref,out,out,out,out,out,out,out),shell=True)\n end=time.time()\n print(\"Elapse time is %g seconds\" %(end-start))\n\nif __name__==\"__main__\":\n parser = argparse.ArgumentParser(\"Run GATK BQSR\")\n parser.add_argument(\"-b\", \"--bam\", help=\"bam file\", required=True)\n parser.add_argument(\"-l\", \"--bed\", help=\"target region bed file\",default=0)\n parser.add_argument(\"-o\", \"--outdir\", help=\"output directory\", default=os.getcwd())\n parser.add_argument(\"-p\", \"--prefix\", help=\"prefix of output\", default=\"out\")\n parser.add_argument(\"-c\", \"--config\", help=\"config file\", required=True)\n args = parser.parse_args()\n run(args.bam, args.outdir, args.bed, args.config, args.prefix)\n\"\"\"\nURL:\nData pre-processing for variant discovery:\nhttps://software.broadinstitute.org/gatk/best-practices/workflow?id=11165\nhttps://github.com/gatk-workflows/gatk4-data-processing/blob/master/processing-for-variant-discovery-gatk4.b37.wgs.inputs.json\n\nWhat should I use as known variants/sites for running tool X?\nhttps://software.broadinstitute.org/gatk/documentation/article.php?id=1247\n\nWelcome to Sentieon Appnotes's documentation!\nhttps://support.sentieon.com/appnotes/arguments/\n\nFrampton G M, Fichtenholtz A, Otto G A, et al. Development and validation of a clinical cancer genomic profiling test based on massively parallel DNA sequencing[J]. Nature biotechnology, 2013, 31(11): 1023.\n\"\"\"", "repo_name": "fanyucai1/Tumor_BMC", "sub_path": "core/BQSR.py", "file_name": "BQSR.py", "file_ext": "py", "file_size_in_byte": 3498, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "37", "api": [{"api_name": "configparser.ConfigParser", "line_number": 11, "usage_type": "attribute"}, {"api_name": "configparser.ConfigParser.__init__", "line_number": 13, "usage_type": "call"}, {"api_name": "configparser.ConfigParser", "line_number": 13, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 28, "usage_type": "call"}, {"api_name": "os.path", "line_number": 28, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 29, "usage_type": "call"}, {"api_name": "os.path.abspath", "line_number": 30, "usage_type": "call"}, {"api_name": "os.path", "line_number": 30, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 31, "usage_type": "call"}, {"api_name": "os.path", "line_number": 31, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 34, "usage_type": "call"}, {"api_name": "os.path", "line_number": 34, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 36, "usage_type": "call"}, {"api_name": "subprocess.check_call", "line_number": 41, "usage_type": "call"}, {"api_name": "subprocess.check_call", "line_number": 44, "usage_type": "call"}, {"api_name": "subprocess.check_call", "line_number": 46, "usage_type": "call"}, {"api_name": "subprocess.check_call", "line_number": 48, "usage_type": "call"}, {"api_name": "time.time", "line_number": 50, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 54, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 57, "usage_type": "call"}]} +{"seq_id": "9152276746", "text": "'''\nUnit tests for command line interface.\n\n@author: Toni Magni\n'''\nimport unittest\nimport logging\nimport os\nimport importlib.resources\nimport dicom4ortho.__main__\nclass Test(unittest.TestCase):\n\n def setUp(self):\n logging.basicConfig(format='%(asctime)s - %(levelname)s - %(funcName)s: %(message)s',\n level=logging.INFO)\n\n\n def tearDown(self):\n pass\n\n def testCli(self):\n resource_path = None\n with importlib.resources.path(\"test.resources\",\"input_from.csv\") as input_csv:\n testargs = ['',str(input_csv)]\n resource_path = os.path.dirname(input_csv)\n return_status = dicom4ortho.__main__.main(testargs)\n self.assertEqual(return_status, 0)\n output_file1 = os.path.join(resource_path,'EV-01_EO.RP.LR.CO.dcm') \n output_file2 = (os.path.join(resource_path,'EV-17_EO.FF.LC.CO.dcm'))\n output_file3 = (os.path.join(resource_path,'IV-25_IO.MX.MO.OV.WM.BC.dcm'))\n assert os.path.exists(output_file1)\n os.remove(output_file1)\n assert os.path.exists(output_file2)\n os.remove(output_file2)\n assert os.path.exists(output_file3)\n os.remove(output_file3)\n\n def testHelp(self):\n testargs = ['','-h']\n with self.assertRaises(SystemExit) as systemexit:\n dicom4ortho.__main__.main(testargs)\n self.assertEqual(systemexit.exception.code, 0)\n", "repo_name": "open-ortho/dicom4ortho", "sub_path": "test/test_cli.py", "file_name": "test_cli.py", "file_ext": "py", "file_size_in_byte": 1410, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "37", "api": [{"api_name": "unittest.TestCase", "line_number": 11, "usage_type": "attribute"}, {"api_name": "logging.basicConfig", "line_number": 14, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 15, "usage_type": "attribute"}, {"api_name": "importlib.resources.resources.path", "line_number": 23, "usage_type": "call"}, {"api_name": "importlib.resources.resources", "line_number": 23, "usage_type": "attribute"}, {"api_name": "importlib.resources", "line_number": 23, "usage_type": "name"}, {"api_name": "os.path.dirname", "line_number": 25, "usage_type": "call"}, {"api_name": "os.path", "line_number": 25, "usage_type": "attribute"}, {"api_name": "dicom4ortho.__main__.__main__.main", "line_number": 26, "usage_type": "call"}, {"api_name": "dicom4ortho.__main__.__main__", "line_number": 26, "usage_type": "attribute"}, {"api_name": "dicom4ortho.__main__", "line_number": 26, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 28, "usage_type": "call"}, {"api_name": "os.path", "line_number": 28, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 29, "usage_type": "call"}, {"api_name": "os.path", "line_number": 29, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 30, "usage_type": "call"}, {"api_name": "os.path", "line_number": 30, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 31, "usage_type": "call"}, {"api_name": "os.path", "line_number": 31, "usage_type": "attribute"}, {"api_name": "os.remove", "line_number": 32, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 33, "usage_type": "call"}, {"api_name": "os.path", "line_number": 33, "usage_type": "attribute"}, {"api_name": "os.remove", "line_number": 34, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 35, "usage_type": "call"}, {"api_name": "os.path", "line_number": 35, "usage_type": "attribute"}, {"api_name": "os.remove", "line_number": 36, "usage_type": "call"}, {"api_name": "dicom4ortho.__main__.__main__.main", "line_number": 41, "usage_type": "call"}, {"api_name": "dicom4ortho.__main__.__main__", "line_number": 41, "usage_type": "attribute"}, {"api_name": "dicom4ortho.__main__", "line_number": 41, "usage_type": "name"}]} +{"seq_id": "9432235805", "text": "import pyodbc\nimport datetime\n\n\nclass OdbcConnection(object):\n def __init__(self, source_dsn_name, profile_log_dsn_name):\n self.source_dsn_name = source_dsn_name\n self.profile_log_dsn_name = profile_log_dsn_name\n self.source_connection_string = r'DSN={}'.format(self.source_dsn_name)\n print('connecting to ODBC DSN: {}'.format(self.source_connection_string))\n self.source_odbc_connection = pyodbc.connect(self.source_connection_string)\n self.source_meta_cursor = None\n self.profile_log_odbc_connection = None\n self.profile_log_cursor = None\n self.table_profile_queries = {}\n self.column_profile_queries = {}\n self.column_histogram_queries = {}\n self.profile_date = datetime.datetime.today().isoformat()[0:22]\n self.pkg_key = 123\n\n def execute_meta_query(self, query_str):\n print('execute_meta_query(\"{}\")'.format(query_str))\n self.source_meta_cursor = self.source_odbc_connection.cursor()\n self.source_meta_cursor.execute(\"{}\".format(query_str))\n \n def generate_profiling_queries(self):\n current_database = None\n prev_database = None\n current_table = None\n prev_table = None\n current_column = None\n prev_column = None\n rows = self.source_meta_cursor.fetchall()\n for row in rows:\n current_database = row[2]\n current_table = row[3]\n current_column = row[5]\n fq_table = '\"{}\".\"{}\"'.format(current_database, current_table)\n fq_column = '{}.{}.{}'.format(current_database, current_table,current_column)\n if fq_table not in self.table_profile_queries:\n self.table_profile_queries[fq_table] = \"\"\"SELECT '{profile_date}' AS TableProfileDate, 'DENODO' AS PackageName, '{database_name}' AS DatabaseName, '{schema_name}' AS SchemaName, '{table_name}' AS TableName, COUNT(*) AS RecordCount, {pkg_key} AS PkgKey FROM {fq_table_name};\"\"\".format(profile_date=str(self.profile_date), database_name=current_database, schema_name='gotcha', table_name=current_table, pkg_key=self.pkg_key, fq_table_name=fq_table)\n print('\\n\\n{} views found.')\n print('\\twith {} columns')\n def connect_to_profile_log(self):\n self.profile_log_connection_string = r'DSN={}'.format(self.profile_log_dsn_name)\n print('\\n\\nconnecting to ODBC DSN: {}'.format(self.profile_log_connection_string))\n self.profile_log_odbc_connection = pyodbc.connect(self.profile_log_connection_string)\n self.profile_log_cursor = self.profile_log_odbc_connection.cursor()\n proc_cmd = \"\"\"\\\n EXEC AutoTest.dbo.uspInsTableProfile @pProfileDateIsoStr=?, @pPackageName=?, @pDatabaseName=?, @pSchemaName=?, @pTableName=?, @pRowCount=?, @pPkgExecKey=?;\n commit;\n \"\"\"\n for key,value in self.table_profile_queries.items():\n print('{}:\\n\\t{}'.format(key, value))\n self.profile_cursor = self.source_odbc_connection.cursor()\n try:\n self.profile_cursor.execute(\"{}\".format(value))\n proc_params = tuple(self.profile_cursor.fetchall())[0]\n print('\\t{}'.format(proc_params))\n self.profile_log_cursor.execute(proc_cmd, proc_params)\n except pyodbc.Error as e:\n print(e)\n\n def __del__(self):\n print('closing {}'.format(self.source_dsn_name))\n self.source_odbc_connection.close()\n if self.profile_log_odbc_connection != None:\n print('closing {}'.format(self.profile_log_dsn_name))\n self.profile_log_odbc_connection.close()\n\nif __name__ == '__main__':\n so = OdbcConnection('DenodoODBC','DevOdbcSqlServer')\n so.execute_meta_query(\"SELECT * FROM CATALOG_METADATA_VIEWS() WHERE view_name IS NOT NULL;\")\n so.generate_profiling_queries()\n so.connect_to_profile_log()\n print(so)", "repo_name": "VCHDecisionSupport/MsSqlAutoTest", "sub_path": "pyOdbc/DenodoTableProfiles.py", "file_name": "DenodoTableProfiles.py", "file_ext": "py", "file_size_in_byte": 3917, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "37", "api": [{"api_name": "pyodbc.connect", "line_number": 11, "usage_type": "call"}, {"api_name": "datetime.datetime.today", "line_number": 18, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 18, "usage_type": "attribute"}, {"api_name": "pyodbc.connect", "line_number": 47, "usage_type": "call"}, {"api_name": "pyodbc.Error", "line_number": 61, "usage_type": "attribute"}]} +{"seq_id": "21445759570", "text": "from django.conf import settings\nfrom django.conf.urls.static import static\nfrom django.conf.urls import url\nfrom . import views\n\nurlpatterns=[\n url(r'^$',views.index,name ='index'),\n url(r'^program/$',views.program,name='program'),\n url(r'^about/$',views.about,name='about'),\n url(r'^contact/$',views.contact,name='contact'),\n url(r'^enroll/$',views.enroll,name='enroll'),\n url(r'^sponsor/$',views.sponsor,name='sponsor'),\n url(r'^location/$',views.location,name='location'),\n url(r'^annual/$',views.annual,name='annual'),\n url(r'^learn/$',views.learn,name='learn'),\n url(r'^foundation/$',views.foundation,name='foundation'),\n url(r'^annual2018/$',views.annual2018,name='annual2018'),\n url(r'^ajax/newsletter/$', views.newsletter, name='newsletter'),\n ]\n\n\nif settings.DEBUG:\n urlpatterns+= static(settings.MEDIA_URL, document_root = settings.MEDIA_ROOT)\n", "repo_name": "vincentmuya/Little-Einsteins", "sub_path": "app/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 897, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "django.conf.urls.url", "line_number": 7, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 8, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 9, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 10, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 11, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 12, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 13, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 14, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 15, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 16, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 17, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 18, "usage_type": "call"}, {"api_name": "django.conf.settings.DEBUG", "line_number": 22, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 22, "usage_type": "name"}, {"api_name": "django.conf.urls.static.static", "line_number": 23, "usage_type": "call"}, {"api_name": "django.conf.settings.MEDIA_URL", "line_number": 23, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 23, "usage_type": "name"}, {"api_name": "django.conf.settings.MEDIA_ROOT", "line_number": 23, "usage_type": "attribute"}]} +{"seq_id": "35766162810", "text": "import numpy as np\nimport logging\nimport os\nimport numbers\nimport tempfile\nimport time\nimport torch\nimport torch.distributed as dist\n\nimport ray\nfrom ray.exceptions import RayActorError\nfrom ray.tune import Trainable\nfrom ray.tune.resources import Resources\nfrom ray.tune.utils.util import merge_dicts\nfrom ray.util.sgd.torch.distributed_torch_runner import (\n DistributedTorchRunner, LocalDistributedRunner, DeactivatedRunner)\nfrom ray.util.sgd.utils import check_for_failure, NUM_SAMPLES, BATCH_SIZE\nfrom ray.util.sgd.torch.torch_runner import TorchRunner\nfrom ray.util.sgd.torch.constants import VALID_SCHEDULER_STEP\nfrom ray.util.sgd.data import Dataset\n\nlogger = logging.getLogger(__name__)\nRESIZE_COOLDOWN_S = 10\n\n\ndef _validate_scheduler_step_freq(scheduler_step_freq):\n \"\"\"This validation check only happens if a scheduler is passed in.\"\"\"\n if scheduler_step_freq not in VALID_SCHEDULER_STEP:\n raise ValueError(\"Scheduler step freq must be in {}. Got {}\".format(\n VALID_SCHEDULER_STEP, scheduler_step_freq))\n\n\ndef _remind_gpu_usage(use_gpu):\n if not use_gpu and torch.cuda.is_available():\n logger.info(\"GPUs detected but not using them. Set `use_gpu` to \"\n \"enable GPU usage. \")\n\n\nclass TorchTrainer:\n \"\"\"Train a PyTorch model using distributed PyTorch.\n\n Launches a set of actors which connect via distributed PyTorch and\n coordinate gradient updates to train the provided model. If Ray is not\n initialized, TorchTrainer will automatically initialize a local Ray\n cluster for you. Be sure to run `ray.init(address=\"auto\")` to leverage\n multi-node training.\n\n .. code-block:: python\n\n def model_creator(config):\n return nn.Linear(1, 1)\n\n\n def optimizer_creator(model, config):\n return torch.optim.SGD(\n model.parameters(), lr=config.get(\"lr\", 1e-4))\n\n\n def data_creator(config):\n batch_size = config[\"batch_size\"]\n train_data, val_data = LinearDataset(2, 5), LinearDataset(2, 5)\n train_loader = DataLoader(train_data, batch_size=batch_size)\n val_loader = DataLoader(val_data, batch_size=batch_size)\n return train_loader, val_loader\n\n\n trainer = TorchTrainer(\n model_creator=model_creator,\n data_creator=data_creator,\n optimizer_creator=optimizer_creator,\n loss_creator=nn.MSELoss,\n config={\"batch_size\": 32},\n use_gpu=True\n )\n for i in range(4):\n trainer.train()\n\n The creator functions will execute before distributed coordination and\n training is setup. This is so that creator functions that download\n large datasets will not trigger any timeouts.\n\n The order of operations for creator functions are:\n\n ``data_creator`` -> ``model_creator`` -> ``optimizer_creator`` ->\n ``scheduler_creator`` -> ``loss_creator``.\n\n Args:\n model_creator (dict -> Model(s)): Constructor function that takes in\n config and returns the model(s) to be optimized. These must be\n ``torch.nn.Module`` objects. If multiple models are returned,\n a ``training_operator_cls`` must be specified. You do not need to\n handle GPU/devices in this function; RaySGD will do that under\n the hood.\n data_creator (dict -> Iterable(s)): Constructor function\n that takes in the passed config and returns one or\n two Iterable objects. Note that even though two Iterable objects\n can be returned, only one will be used for training, and the\n other will be used for validation. If not provided, you must\n provide a custom TrainingOperator.\n optimizer_creator ((models, dict) -> optimizers): Constructor\n function that takes in the return values from\n ``model_creator`` and the passed config and returns One or\n more Torch optimizer objects. You do not need to handle\n GPU/devices in this function; ``RaySGD`` will do that for you.\n loss_creator (torch.nn.*Loss class | dict -> loss): A constructor\n function for the training loss. This can be either a function that\n takes in the provided config for customization or a subclass\n of ``torch.nn.modules.loss._Loss``, which is most Pytorch\n loss classes. For example, ``loss_creator=torch.nn.BCELoss``.\n If not provided, you must provide a custom TrainingOperator.\n scheduler_creator ((optimizers, dict) -> scheduler):\n A constructor function for the torch scheduler. This is\n a function that takes in the generated optimizers (from\n ``optimizer_creator``) provided config for customization.\n Be sure to set ``scheduler_step_freq`` to increment the\n scheduler correctly.\n training_operator_cls (type): Custom training operator class\n that subclasses the TrainingOperator class. This class\n will be copied onto all remote workers and used to specify\n custom training and validation operations. Defaults to\n TrainingOperator.\n config (dict): Custom configuration value to be passed to\n all creator and operator constructors.\n num_workers (int): the number of workers used in distributed\n training. If 1, the worker will not be wrapped with\n DistributedDataParallel.\n use_gpu (bool): Sets resource allocation for workers to 1 GPU\n if true, and automatically moves both the model and optimizer\n to the available CUDA device.\n backend (string): backend used by distributed PyTorch. Currently\n support \"nccl\", \"gloo\", and \"auto\". If \"auto\", RaySGD will\n automatically use \"nccl\" if `use_gpu` is True, and \"gloo\"\n otherwise.\n serialize_data_creation (bool): A filelock will be used\n to ensure no race conditions in data downloading among\n different workers on the same node (using the local file system).\n Defaults to True.\n wrap_ddp (bool): Whether to automatically wrap DistributedDataParallel\n over each model. If False, you are expected to call it yourself.\n add_dist_sampler (bool): Whether to automatically add a\n DistributedSampler to all created dataloaders. Only applicable\n if num_workers > 1.\n use_fp16 (bool): Enables mixed precision training via apex if apex\n is installed. This is automatically done after the model and\n optimizers are constructed and will work for multi-model training.\n Please see https://github.com/NVIDIA/apex for more details.\n apex_args (dict|None): Dict containing keyword args for amp.initialize.\n See https://nvidia.github.io/apex/amp.html#module-apex.amp. By\n default, the models and optimizers are passed in. Consider using\n \"num_losses\" if operating over multiple models and optimizers.\n scheduler_step_freq: \"batch\", \"epoch\", \"manual\", or None. This will\n determine when ``scheduler.step`` is called. If \"batch\",\n ``step`` will be called after every optimizer step. If \"epoch\",\n ``step`` will be called after one pass of the DataLoader. If\n \"manual\", the scheduler will not be incremented automatically -\n you are expected to call ``trainer.update_schedulers`` manually.\n If a scheduler is passed in, this value is expected to not be None.\n\n \"\"\"\n\n # TODO: Implement autoscaling. If num_workers=-1, the trainer will use as\n # many resources as available. Upon each train call, TorchTrainer will\n # query the Ray global state for total available resources and resize\n # its remote workers to consume all available resources.\n\n def __init__(\n self,\n *,\n model_creator,\n data_creator,\n optimizer_creator,\n loss_creator=None,\n scheduler_creator=None,\n training_operator_cls=None,\n initialization_hook=None,\n config=None,\n num_workers=1,\n use_gpu=\"auto\",\n backend=\"auto\",\n wrap_ddp=True,\n serialize_data_creation=True,\n use_fp16=False,\n use_tqdm=False,\n apex_args=None,\n add_dist_sampler=True,\n scheduler_step_freq=None,\n num_replicas=None,\n batch_size=None,\n data_loader_args=None,\n ):\n if num_workers > 1 and not dist.is_available():\n raise ValueError(\n (\"Distributed PyTorch is not supported on macOS. \"\n \"To run without distributed PyTorch, set 'num_workers=1'. \"\n \"For more information, see \"\n \"https://github.com/pytorch/examples/issues/467.\"))\n\n if not (callable(model_creator) and callable(optimizer_creator)):\n raise ValueError(\n \"Must provide a callable model_creator and optimizer_creator.\")\n\n if num_replicas is not None:\n raise DeprecationWarning(\n \"num_replicas is deprecated. Use num_workers instead.\")\n\n if batch_size is not None:\n raise DeprecationWarning(\n \"batch_size is deprecated. Use config={'batch_size': N} \"\n \"specify a batch size for each worker or \"\n \"config={ray.util.sgd.utils.BATCH_SIZE: N} to specify a \"\n \"batch size to be used across all workers.\")\n\n if data_loader_args:\n raise ValueError(\n \"data_loader_args is deprecated. You can return a \"\n \"torch.utils.data.DataLoader in data_creator. Ray will \"\n \"automatically set a DistributedSampler if a DataLoader is \"\n \"returned and num_workers > 1.\")\n\n self.model_creator = model_creator\n self.optimizer_creator = optimizer_creator\n self.loss_creator = loss_creator\n self.data_creator = data_creator\n self.scheduler_creator = scheduler_creator\n self.training_operator_cls = training_operator_cls\n\n if not training_operator_cls and not loss_creator:\n raise ValueError(\"If a loss_creator is not provided, you must \"\n \"provide a custom training operator.\")\n\n self.initialization_hook = initialization_hook\n self.config = {} if config is None else config\n if use_gpu == \"auto\":\n use_gpu = torch.cuda.is_available()\n\n _remind_gpu_usage(use_gpu)\n\n if backend == \"auto\":\n backend = \"nccl\" if use_gpu else \"gloo\"\n\n logger.debug(\"Using {} as backend.\".format(backend))\n self.backend = backend\n self.use_gpu = use_gpu\n self.max_replicas = num_workers\n\n self.serialize_data_creation = serialize_data_creation\n self.wrap_ddp = wrap_ddp\n self.use_fp16 = use_fp16\n self.use_tqdm = use_tqdm\n self.add_dist_sampler = add_dist_sampler\n\n if apex_args and not isinstance(apex_args, dict):\n raise ValueError(\"apex_args needs to be a dict object.\")\n\n self.apex_args = apex_args\n self.temp_dir = tempfile.mkdtemp(prefix=\"raysgd\")\n self._num_failures = 0\n self._last_resize = float(\"-inf\")\n\n self.local_worker = DeactivatedRunner()\n self.remote_workers = []\n\n if scheduler_creator:\n _validate_scheduler_step_freq(scheduler_step_freq)\n\n self.scheduler_step_freq = scheduler_step_freq\n\n if not ray.is_initialized() and self.max_replicas > 1:\n logger.info(\"Automatically initializing single-node Ray. To use \"\n \"multi-node training, be sure to run `ray.init(\"\n \"address='auto')` before instantiating the Trainer.\")\n ray.init()\n self._start_workers(self.max_replicas)\n\n def _configure_and_split_batch(self, num_workers):\n \"\"\"If sgd.utils.BATCH_SIZE is provided, split among workers.\"\"\"\n if BATCH_SIZE not in self.config:\n return\n # Compute batch size per worker\n logger.debug(\"BATCH_SIZE parameter detected. Splitting among workers.\")\n batch_size = self.config[BATCH_SIZE]\n batch_size_per_worker = batch_size // num_workers\n if batch_size % num_workers > 0:\n new_batch_size = batch_size_per_worker * num_workers\n logger.warning(\n (\"Changing batch size from {old_batch_size} to \"\n \"{new_batch_size} to evenly distribute batches across \"\n \"{num_workers} workers.\").format(\n old_batch_size=batch_size,\n new_batch_size=new_batch_size,\n num_workers=num_workers))\n self.config[BATCH_SIZE] = new_batch_size\n return batch_size_per_worker\n\n def _start_workers(self, num_workers):\n logger.debug(f\"start_workers: Setting %d workers.\" % num_workers)\n worker_config = self.config.copy()\n batch_size_per_worker = self._configure_and_split_batch(num_workers)\n if batch_size_per_worker:\n worker_config[BATCH_SIZE] = batch_size_per_worker\n\n params = dict(\n model_creator=self.model_creator,\n data_creator=self.data_creator,\n optimizer_creator=self.optimizer_creator,\n loss_creator=self.loss_creator,\n scheduler_creator=self.scheduler_creator,\n training_operator_cls=self.training_operator_cls,\n config=worker_config,\n serialize_data_creation=self.serialize_data_creation,\n use_fp16=self.use_fp16,\n use_gpu=self.use_gpu,\n use_tqdm=self.use_tqdm,\n apex_args=self.apex_args,\n scheduler_step_freq=self.scheduler_step_freq)\n\n if num_workers == 1:\n # Start local worker\n self.local_worker = TorchRunner(**params)\n if self.initialization_hook:\n self.apply_all_workers(self.initialization_hook)\n self.local_worker.setup()\n else:\n params.update(\n backend=self.backend,\n add_dist_sampler=self.add_dist_sampler,\n wrap_ddp=self.wrap_ddp)\n\n # Start local worker\n self.local_worker = LocalDistributedRunner(\n num_cpus=1, num_gpus=int(self.use_gpu), **params)\n\n # Generate actor class\n RemoteRunner = ray.remote(\n num_cpus=1, num_gpus=int(self.use_gpu))(DistributedTorchRunner)\n # Start workers\n self.remote_workers = [\n RemoteRunner.remote(**params) for i in range(num_workers - 1)\n ]\n if self.initialization_hook:\n self.apply_all_workers(self.initialization_hook)\n\n # Compute URL for initializing distributed PyTorch\n ip = ray.services.get_node_ip_address()\n port = self.local_worker.find_free_port()\n\n address = \"tcp://{ip}:{port}\".format(ip=ip, port=port)\n\n # Runs the creator functions.\n remote_component_setup = [\n worker.setup_components.remote()\n for i, worker in enumerate(self.remote_workers)\n ]\n self.local_worker.setup_components()\n # Get setup tasks in order to throw errors on failure\n ray.get(remote_component_setup)\n\n # Setup the process group among all workers.\n remote_pgroup_setups = [\n worker.setup_process_group.remote(address, i + 1, num_workers)\n for i, worker in enumerate(self.remote_workers)\n ]\n self.local_worker.setup_process_group(address, 0, num_workers)\n # Get setup tasks in order to throw errors on failure\n ray.get(remote_pgroup_setups)\n\n # Runs code that requires all creator functions to have run.\n remote_operator_setups = [\n worker.setup_ddp_and_operator.remote()\n for worker in self.remote_workers\n ]\n self.local_worker.setup_ddp_and_operator()\n # Get setup tasks in order to throw errors on failure\n ray.get(remote_operator_setups)\n\n def train(self,\n num_steps=None,\n profile=False,\n reduce_results=True,\n max_retries=3,\n info=None,\n dataset=None):\n \"\"\"Runs a training epoch.\n\n Calls `operator.train_epoch()` on N parallel workers simultaneously\n underneath the hood.\n\n Set `max_retries` to enable fault handling in case of\n instance preemption.\n\n Args:\n num_steps (int): Number of batches to compute update steps on.\n This corresponds also to the number of times\n ``TrainingOperator.train_batch`` is called.\n profile (bool): Returns time stats for the training procedure.\n reduce_results (bool): Whether to average all metrics across\n all workers into one dict. If a metric is a non-numerical\n value (or nested dictionaries), one value will be randomly\n selected among the workers. If False, returns a list of dicts.\n max_retries (int): Must be non-negative. If set to N, TorchTrainer\n will detect and recover from training failure. The recovery\n process will kill all current workers, query the Ray\n global state for total available resources, and re-launch up to\n the available resources. Behavior is not well-defined\n in case of shared cluster usage. Defaults to 3.\n info (dict): Optional dictionary passed to the training\n operator for ``train_epoch`` and ``train_batch``.\n dataset (Dataset): Optional dataset to train with. If specified,\n the dataloader passed in via data_creator will be ignored.\n\n Returns:\n (dict | list) A dictionary of metrics for training.\n You can provide custom metrics by passing in a custom\n ``training_operator_cls``. If ``reduce_results=False``,\n this will return a list of metric dictionaries whose\n length will be equal to ``num_workers``.\n \"\"\"\n assert max_retries >= 0, \"`max_retries` must be non-negative.\"\n assert isinstance(dataset, Dataset) is not None \\\n or self.data_creator, \\\n \"Must specify either a data creator or a dataset\"\n if self._should_resize():\n logger.info(\"Resize opportunity detected. Attempting to scale up.\")\n self._resize_workers()\n success, worker_stats = self._train_epoch(\n num_steps=num_steps, profile=profile, info=info, dataset=dataset)\n # Fault handling\n for i in range(max_retries):\n if success:\n break\n else:\n self._num_failures += 1\n self._resize_workers()\n logger.info(\"Retrying training step with %d workers.\" %\n (len(self.remote_workers) + 1))\n success, worker_stats = self._train_epoch(\n num_steps=num_steps,\n profile=profile,\n info=info,\n dataset=dataset)\n if not success:\n raise RuntimeError(\"Training run failed.\")\n\n if reduce_results:\n return self._process_stats(worker_stats)\n else:\n return worker_stats\n\n def _process_stats(self, worker_stats):\n stats = {\n NUM_SAMPLES: sum(\n stats.pop(NUM_SAMPLES, np.nan) for stats in worker_stats)\n }\n\n for stat_key in worker_stats[0]:\n if isinstance(worker_stats[0], numbers.Number):\n stats[stat_key] = np.nanmean(\n [s.get(stat_key, np.nan) for s in worker_stats])\n else:\n stats[stat_key] = worker_stats[0][stat_key]\n return stats\n\n def _train_epoch(self,\n num_steps=None,\n profile=False,\n info=None,\n dataset=None):\n params = dict(num_steps=num_steps, profile=profile, info=info)\n remote_worker_stats = []\n if dataset:\n dataset.set_num_shards(self.max_replicas)\n for i, w in enumerate(self.remote_workers):\n params = dict(num_steps=num_steps, profile=profile, info=info)\n if dataset:\n params[\"iterator\"] = dataset.get_shard(i)\n stats = w.train_epoch.remote(**params)\n remote_worker_stats.append(stats)\n\n try:\n if dataset:\n params[\"iterator\"] = dataset.get_shard(\n len(self.remote_workers))\n local_worker_stats = self.local_worker.train_epoch(**params)\n except RuntimeError as err:\n if \"gloo\" in err.args[0] and \"Timed out\" in err.args[0]:\n logger.warning(err)\n return False, None\n if \"NCCL\" in err.args[0]: # there is no specific error message\n logger.warning(err)\n return False, None\n\n raise err\n\n success = check_for_failure(remote_worker_stats)\n if success:\n return success, [local_worker_stats] + ray.get(remote_worker_stats)\n\n return success, None\n\n def apply_all_workers(self, fn):\n \"\"\"Run a function on all operators on the workers.\n\n Args:\n fn (Callable): A function that takes in no arguments.\n\n Returns:\n A list of objects returned by ``fn`` on each worker.\n\n \"\"\"\n remote_calls = [w.apply.remote(fn) for w in self.remote_workers]\n local_call = self.local_worker.apply(fn)\n return [local_call] + ray.get(remote_calls)\n\n def apply_all_operators(self, fn):\n \"\"\"Run a function on all operators on the workers.\n\n Args:\n fn (Callable[TrainingOperator]): A function that takes in a\n TrainingOperator.\n\n Returns:\n A list of objects returned by ``fn`` on each operator.\n\n \"\"\"\n remote_calls = [\n w.apply_operator.remote(fn) for w in self.remote_workers\n ]\n local_call = self.local_worker.apply_operator(fn)\n return [local_call] + ray.get(remote_calls)\n\n def validate(self,\n num_steps=None,\n profile=False,\n reduce_results=True,\n info=None):\n \"\"\"Evaluates the model on the validation data set.\n\n Args:\n num_steps (int): Number of batches to compute update steps on.\n This corresponds also to the number of times\n ``TrainingOperator.validate_batch`` is called.\n profile (bool): Returns time stats for the evaluation procedure.\n reduce_results (bool): Whether to average all metrics across\n all workers into one dict. If a metric is a non-numerical\n value (or nested dictionaries), one value will be randomly\n selected among the workers. If False, returns a list of dicts.\n info (dict): Optional dictionary passed to the training\n operator for `validate` and `validate_batch`.\n\n Returns:\n A dictionary of metrics for validation.\n You can provide custom metrics by passing in a custom\n ``training_operator_cls``.\n \"\"\"\n params = dict(num_steps=num_steps, profile=profile, info=info)\n\n remote_worker_stats = [\n w.validate.remote(**params) for w in self.remote_workers\n ]\n local_worker_stats = self.local_worker.validate(**params)\n worker_stats = [local_worker_stats] + ray.get(remote_worker_stats)\n\n if reduce_results:\n return self._process_stats(worker_stats)\n else:\n return worker_stats\n\n def update_scheduler(self, metric):\n \"\"\"Calls ``scheduler.step(metric)`` on all schedulers.\n\n This is useful for lr_schedulers such as ``ReduceLROnPlateau``.\n \"\"\"\n self.apply_all_operators(\n lambda op: [sched.step(metric) for sched in op.schedulers])\n\n def get_model(self):\n \"\"\"Returns the learned model(s).\"\"\"\n unwrapped = []\n for model in self.local_worker.models:\n unwrapped += [model.module if hasattr(model, \"module\") else model]\n if len(unwrapped) == 1:\n return unwrapped[0]\n return unwrapped\n\n def get_local_operator(self):\n \"\"\"Returns the local TrainingOperator object.\n\n Be careful not to perturb its state, or else you can cause the system\n to enter an inconsistent state.\n\n Returns:\n TrainingOperator: The local TrainingOperator object.\n \"\"\"\n return self.local_worker.training_operator\n\n def state_dict(self):\n return self.local_worker.state_dict()\n\n def load_state_dict(self, state_dict, blocking=False):\n # This is not the most efficient because you have to wait for\n # the local worker to save then dump to buffer.\n self.local_worker.load_state_dict(state_dict)\n state_id = ray.put(self.local_worker.state_stream())\n\n remote_calls = [\n worker.load_state_stream.remote(state_id)\n for worker in self.remote_workers\n ]\n if blocking:\n ray.get(remote_calls)\n\n def save(self, checkpoint):\n \"\"\"Saves the Trainer state to the provided checkpoint path.\n\n Args:\n checkpoint (str): Path to target checkpoint file.\n \"\"\"\n torch.save(self.state_dict(), checkpoint)\n return checkpoint\n\n def load(self, checkpoint):\n \"\"\"Loads the Trainer and all workers from the provided checkpoint.\n\n Args:\n checkpoint (str): Path to target checkpoint file.\n \"\"\"\n state_dict = torch.load(checkpoint)\n self.load_state_dict(state_dict)\n\n def restore(self, *args):\n raise DeprecationWarning(\"Use `TorchTrainer.load()` instead.\")\n\n def shutdown(self, force=False):\n \"\"\"Shuts down workers and releases resources.\"\"\"\n if not force:\n cleanup = [\n worker.shutdown.remote() for worker in self.remote_workers\n ]\n self.local_worker.shutdown()\n try:\n ray.get(cleanup)\n [\n worker.__ray_terminate__.remote()\n for worker in self.remote_workers\n ]\n except RayActorError:\n logger.warning(\n \"Failed to shutdown gracefully, forcing a shutdown.\")\n\n for worker in self.remote_workers:\n logger.warning(\"Killing worker {}.\".format(worker))\n ray.kill(worker)\n else:\n self.local_worker.shutdown()\n for worker in self.remote_workers:\n logger.debug(\"Killing worker {}.\".format(worker))\n ray.kill(worker)\n\n self.local_worker = DeactivatedRunner()\n self.remote_workers = []\n\n def _reset(self):\n \"\"\"Terminates models without giving up local resource reservation.\"\"\"\n self.local_worker.shutdown(cleanup=False)\n for worker in self.remote_workers:\n logger.debug(\"Killing worker {}.\".format(worker))\n ray.kill(worker)\n self.local_worker = DeactivatedRunner()\n self.remote_workers = []\n\n def _check_potential_remote_workers_size(self):\n # ASSUME 1 GPU + 1 CPU is already reserved for the local worker\n remote_resources = ray.available_resources()\n max_remote_workers = self.max_replicas - 1\n new_remote_workers = min(\n remote_resources.get(\"CPU\", 0), max_remote_workers)\n if self.use_gpu:\n new_remote_workers = min(\n remote_resources.get(\"GPU\", 0), new_remote_workers)\n return new_remote_workers\n\n def _resize_workers(self, max_retries=10):\n self._reset()\n\n time.sleep(1)\n for i in range(max_retries):\n new_remote_workers = self._check_potential_remote_workers_size()\n if new_remote_workers:\n self._last_resize = time.time()\n self._start_workers(int(new_remote_workers) + 1)\n self.load_state_dict(self.state_dict())\n return\n else:\n delay = 2**i\n logger.warning(\n \"No new workers found. Retrying in %d sec.\" % delay)\n time.sleep(delay)\n raise RuntimeError(\"Exceeded max_retries for relaunching workers.\")\n\n def _should_resize(self):\n \"\"\"Returns True if past cooldown and exists resources to scale up.\"\"\"\n worker_gap = self.max_replicas - 1 - len(self.remote_workers)\n past_cooldown = (time.time() - self._last_resize) > RESIZE_COOLDOWN_S\n if past_cooldown and worker_gap:\n # Assume 1 resource is already reserved for local worker.\n potential_remote_size = self._check_potential_remote_workers_size()\n return potential_remote_size > 0\n return False\n\n @classmethod\n def as_trainable(cls, *args, **kwargs):\n \"\"\"Creates a BaseTorchTrainable class compatible with Tune.\n\n Any configuration parameters will be overriden by the Tune\n Trial configuration. You can also subclass the provided Trainable\n to implement your own iterative optimization routine.\n\n .. code-block:: python\n\n TorchTrainable = TorchTrainer.as_trainable(\n model_creator=ResNet18,\n data_creator=cifar_creator,\n optimizer_creator=optimizer_creator,\n loss_creator=nn.CrossEntropyLoss,\n num_gpus=2\n )\n analysis = tune.run(\n TorchTrainable,\n config={\"lr\": tune.grid_search([0.01, 0.1])}\n )\n\n \"\"\"\n\n class TorchTrainable(BaseTorchTrainable):\n @classmethod\n def default_resource_request(cls, config):\n num_workers = config.get(\"num_workers\",\n kwargs.get(\"num_workers\", 1))\n use_gpu = config.get(\"use_gpu\", kwargs.get(\"use_gpu\"))\n\n remote_worker_count = num_workers - 1\n\n return Resources(\n cpu=1,\n gpu=int(use_gpu),\n extra_cpu=int(remote_worker_count),\n extra_gpu=int(int(use_gpu) * remote_worker_count))\n\n def _create_trainer(self, tune_config):\n \"\"\"Overrides the provided config with Tune config.\"\"\"\n provided_config = kwargs.get(\"config\", {}).copy()\n provided_config.update(tune_config)\n kwargs[\"config\"] = provided_config\n trainer = TorchTrainer(*args, **kwargs)\n return trainer\n\n return TorchTrainable\n\n\nclass BaseTorchTrainable(Trainable):\n \"\"\"Base class for converting TorchTrainer to a Trainable class.\n\n This class is produced when you call ``TorchTrainer.as_trainable(...)``.\n\n You can override the produced Trainable to implement custom iterative\n training procedures:\n\n .. code-block:: python\n\n TorchTrainable = TorchTrainer.as_trainable(\n model_creator=ResNet18,\n data_creator=cifar_creator,\n optimizer_creator=optimizer_creator,\n loss_creator=nn.CrossEntropyLoss,\n num_gpus=2\n )\n # TorchTrainable is subclass of BaseTorchTrainable.\n\n class CustomTrainable(TorchTrainable):\n def _train(self):\n for i in range(5):\n train_stats = self.trainer.train()\n validation_stats = self.trainer.validate()\n train_stats.update(validation_stats)\n return train_stats\n\n analysis = tune.run(\n CustomTrainable,\n config={\"lr\": tune.grid_search([0.01, 0.1])}\n )\n\n \"\"\"\n\n def _setup(self, config):\n \"\"\"Constructs a TorchTrainer object as `self.trainer`.\"\"\"\n self._trainer = self._create_trainer(config)\n\n def _train(self):\n \"\"\"Calls `self.trainer.train()` and `self.trainer.validate()` once.\n\n You may want to override this if using a custom LR scheduler.\n \"\"\"\n train_stats = self.trainer.train(max_retries=10, profile=True)\n validation_stats = self.trainer.validate(profile=True)\n stats = merge_dicts(train_stats, validation_stats)\n return stats\n\n def _save(self, checkpoint_dir):\n \"\"\"Returns a path containing the trainer state.\"\"\"\n checkpoint_path = os.path.join(checkpoint_dir, \"trainer.checkpoint\")\n self.trainer.save(checkpoint_path)\n return checkpoint_path\n\n def _restore(self, checkpoint_path):\n \"\"\"Restores the trainer state.\n\n Override this if you have state external to the Trainer object.\n \"\"\"\n return self.trainer.load(checkpoint_path)\n\n def _stop(self):\n \"\"\"Shuts down the trainer.\"\"\"\n self.trainer.shutdown()\n\n def _create_trainer(self, config):\n raise NotImplementedError\n\n @property\n def trainer(self):\n \"\"\"An instantiated TorchTrainer object.\n\n Use this when specifying custom training procedures for Tune.\n \"\"\"\n return self._trainer\n", "repo_name": "HuantWang/SUPERSONIC", "sub_path": "third_party/ray/util/sgd/torch/torch_trainer.py", "file_name": "torch_trainer.py", "file_ext": "py", "file_size_in_byte": 33764, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 119, "dataset": "github-code", "pt": "37", "api": [{"api_name": "logging.getLogger", "line_number": 22, "usage_type": "call"}, {"api_name": "ray.util.sgd.torch.constants.VALID_SCHEDULER_STEP", "line_number": 28, "usage_type": "name"}, {"api_name": "ray.util.sgd.torch.constants.VALID_SCHEDULER_STEP", "line_number": 30, "usage_type": "argument"}, {"api_name": "torch.cuda.is_available", "line_number": 34, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 34, "usage_type": "attribute"}, {"api_name": "torch.distributed.is_available", "line_number": 191, "usage_type": "call"}, {"api_name": "torch.distributed", "line_number": 191, "usage_type": "name"}, {"api_name": "torch.cuda.is_available", "line_number": 234, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 234, "usage_type": "attribute"}, {"api_name": "tempfile.mkdtemp", "line_number": 256, "usage_type": "call"}, {"api_name": "ray.util.sgd.torch.distributed_torch_runner.DeactivatedRunner", "line_number": 260, "usage_type": "call"}, {"api_name": "ray.is_initialized", "line_number": 268, "usage_type": "call"}, {"api_name": "ray.init", "line_number": 272, "usage_type": "call"}, {"api_name": "ray.util.sgd.utils.BATCH_SIZE", "line_number": 277, "usage_type": "name"}, {"api_name": "ray.util.sgd.utils.BATCH_SIZE", "line_number": 281, "usage_type": "name"}, {"api_name": "ray.util.sgd.utils.BATCH_SIZE", "line_number": 292, "usage_type": "name"}, {"api_name": "ray.util.sgd.utils.BATCH_SIZE", "line_number": 300, "usage_type": "name"}, {"api_name": "ray.util.sgd.torch.torch_runner.TorchRunner", "line_number": 319, "usage_type": "call"}, {"api_name": "ray.util.sgd.torch.distributed_torch_runner.LocalDistributedRunner", "line_number": 330, "usage_type": "call"}, {"api_name": "ray.util.sgd.torch.distributed_torch_runner.DistributedTorchRunner", "line_number": 335, "usage_type": "argument"}, {"api_name": "ray.remote", "line_number": 334, "usage_type": "call"}, {"api_name": "ray.services.get_node_ip_address", "line_number": 344, "usage_type": "call"}, {"api_name": "ray.services", "line_number": 344, "usage_type": "attribute"}, {"api_name": "ray.get", "line_number": 356, "usage_type": "call"}, {"api_name": "ray.get", "line_number": 365, "usage_type": "call"}, {"api_name": "ray.get", "line_number": 374, "usage_type": "call"}, {"api_name": "ray.util.sgd.data.Dataset", "line_number": 419, "usage_type": "argument"}, {"api_name": "ray.util.sgd.utils.NUM_SAMPLES", "line_number": 451, "usage_type": "name"}, {"api_name": "ray.util.sgd.utils.NUM_SAMPLES", "line_number": 452, "usage_type": "argument"}, {"api_name": "numpy.nan", "line_number": 452, "usage_type": "attribute"}, {"api_name": "numbers.Number", "line_number": 456, "usage_type": "attribute"}, {"api_name": "numpy.nanmean", "line_number": 457, "usage_type": "call"}, {"api_name": "numpy.nan", "line_number": 458, "usage_type": "attribute"}, {"api_name": "ray.util.sgd.utils.check_for_failure", "line_number": 494, "usage_type": "call"}, {"api_name": "ray.get", "line_number": 496, "usage_type": "call"}, {"api_name": "ray.get", "line_number": 512, "usage_type": "call"}, {"api_name": "ray.get", "line_number": 529, "usage_type": "call"}, {"api_name": "ray.get", "line_number": 561, "usage_type": "call"}, {"api_name": "ray.put", "line_number": 603, "usage_type": "call"}, {"api_name": "ray.get", "line_number": 610, "usage_type": "call"}, {"api_name": "torch.save", "line_number": 618, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 627, "usage_type": "call"}, {"api_name": "ray.get", "line_number": 641, "usage_type": "call"}, {"api_name": "ray.exceptions.RayActorError", "line_number": 646, "usage_type": "name"}, {"api_name": "ray.kill", "line_number": 652, "usage_type": "call"}, {"api_name": "ray.kill", "line_number": 657, "usage_type": "call"}, {"api_name": "ray.util.sgd.torch.distributed_torch_runner.DeactivatedRunner", "line_number": 659, "usage_type": "call"}, {"api_name": "ray.kill", "line_number": 667, "usage_type": "call"}, {"api_name": "ray.util.sgd.torch.distributed_torch_runner.DeactivatedRunner", "line_number": 668, "usage_type": "call"}, {"api_name": "ray.available_resources", "line_number": 673, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 685, "usage_type": "call"}, {"api_name": "time.time", "line_number": 689, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 697, "usage_type": "call"}, {"api_name": "time.time", "line_number": 703, "usage_type": "call"}, {"api_name": "ray.tune.resources.Resources", "line_number": 743, "usage_type": "call"}, {"api_name": "ray.tune.Trainable", "line_number": 760, "usage_type": "name"}, {"api_name": "ray.tune.utils.util.merge_dicts", "line_number": 805, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 810, "usage_type": "call"}, {"api_name": "os.path", "line_number": 810, "usage_type": "attribute"}]} +{"seq_id": "41364520865", "text": "import os\nfrom pathlib import Path\nfrom typing import List, Callable\n\nfrom track_generator import xml_reader\nfrom track_generator.painter import Painter\nfrom track_generator.gazebo_model_generator import GazeboModelGenerator\nfrom track_generator.ground_truth_generator import GroundTruthGenerator\n\nfrom watchdog.observers import Observer\nfrom watchdog.events import FileSystemEventHandler\n\n\ndef _create_output_directory_if_required(output_dirpath: Path):\n output_dirpath.mkdir(parents=True, exist_ok=True)\n\n\ndef _get_track_name_from_file_path(track_filepath: Path) -> str:\n filename = os.path.basename(track_filepath)\n filename_without_extension, _ = os.path.splitext(filename)\n return filename_without_extension\n\n\ndef generate_track(\n track_filepaths: List[Path],\n root_output_dirpath: Path,\n generate_png=False,\n generate_gazebo_project=False,\n generate_ground_truth=False,\n) -> List[Path]:\n \"\"\"\n Generate tracks (SVG, Gazebo project, etc) from given track files (XML)\n :param track_filepaths: List of track files\n :param root_output_dirpath: The output directory to write results to. Subdirectories for every track will be\n generated.\n :param generate_png: Flag whether a png image should be created for the track\n :param generate_gazebo_project:Flag whether gazebo project files should be created for the track\n :return: List of output directories for the tracks\n \"\"\"\n track_output_directories: List[Path] = []\n for track_filepath in track_filepaths:\n track = xml_reader.read_track(track_filepath)\n track.calc()\n\n track_name = _get_track_name_from_file_path(track_filepath)\n track_output_directory = root_output_dirpath / track_name\n _create_output_directory_if_required(track_output_directory)\n track_output_directories.append(track_output_directory)\n\n painter = Painter()\n painter.draw_track(track)\n painter.save_svg(track_name, track_output_directory)\n if generate_png:\n painter.save_png(track_name, track_output_directory)\n\n if generate_gazebo_project:\n gazebo_model_generator = GazeboModelGenerator(track_name, track_output_directory)\n gazebo_model_generator.generate_gazebo_model(track)\n\n painter.save_png(track_name, gazebo_model_generator.track_materials_textures_directory)\n\n painter.draw_track_verbose(track)\n painter.save_svg(track_name, track_output_directory, file_name_postfix=\"_verbose\")\n\n if generate_ground_truth:\n ground_truth_generator = GroundTruthGenerator(track_name, track_output_directory)\n ground_truth_generator.generate_ground_truth(track)\n return track_output_directories\n\n\nclass FileChangedHandler(FileSystemEventHandler):\n def __init__(self, input_filepath: Path, callback: Callable):\n self.input_filepath = input_filepath\n self.callback = callback\n\n def on_closed(self, event) -> None:\n event_filepath = Path(event.src_path)\n if event_filepath == self.input_filepath:\n self.callback()\n return None\n\n\ndef generate_track_live(track_file: Path, root_output_directory: Path) -> None:\n \"\"\"\n Generate tracks (SVG, Gazebo project, etc) from given track files (XML)\n :param track_file: Track file\n :param root_output_directory: The output directory to write results to. Subdirectories for every track will be\n generated.\n :return: Output directory for the track\n \"\"\"\n track_file_directory = track_file.parent\n track_name = _get_track_name_from_file_path(track_file)\n output_file_path = root_output_directory / track_name / f\"{track_name}.svg\"\n from track_generator.gui.track_live_view import TrackLiveView\n\n track_live_view = TrackLiveView(output_file_path)\n\n def update():\n print(f\"Track file changed, regenerating track ({track_file})\")\n generate_track([track_file], root_output_directory, generate_png=False, generate_gazebo_project=False)\n track_live_view.update()\n\n event_handler = FileChangedHandler(track_file, update)\n\n update()\n\n observer = Observer()\n observer.schedule(event_handler, track_file_directory, recursive=False)\n observer.start()\n\n track_live_view.run()\n\n observer.stop()\n observer.join()\n\n\ndef generate_trajectory():\n print(\"generate_trajectory not implemented yet\")\n", "repo_name": "twyleg/track_generator", "sub_path": "track_generator/generator.py", "file_name": "generator.py", "file_ext": "py", "file_size_in_byte": 4383, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "pathlib.Path", "line_number": 14, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 18, "usage_type": "name"}, {"api_name": "os.path.basename", "line_number": 19, "usage_type": "call"}, {"api_name": "os.path", "line_number": 19, "usage_type": "attribute"}, {"api_name": "os.path.splitext", "line_number": 20, "usage_type": "call"}, {"api_name": "os.path", "line_number": 20, "usage_type": "attribute"}, {"api_name": "typing.List", "line_number": 25, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 25, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 26, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 40, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 40, "usage_type": "name"}, {"api_name": "track_generator.xml_reader.read_track", "line_number": 42, "usage_type": "call"}, {"api_name": "track_generator.xml_reader", "line_number": 42, "usage_type": "name"}, {"api_name": "track_generator.painter.Painter", "line_number": 50, "usage_type": "call"}, {"api_name": "track_generator.gazebo_model_generator.GazeboModelGenerator", "line_number": 57, "usage_type": "call"}, {"api_name": "track_generator.ground_truth_generator.GroundTruthGenerator", "line_number": 66, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 30, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 30, "usage_type": "name"}, {"api_name": "watchdog.events.FileSystemEventHandler", "line_number": 71, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 72, "usage_type": "name"}, {"api_name": "typing.Callable", "line_number": 72, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 77, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 83, "usage_type": "name"}, {"api_name": "track_generator.gui.track_live_view.TrackLiveView", "line_number": 96, "usage_type": "call"}, {"api_name": "watchdog.observers.Observer", "line_number": 107, "usage_type": "call"}]} +{"seq_id": "11646245597", "text": "import json\nimport requests.packages.urllib3\nfrom pprint import pprint\nimport os, sys\nfrom tqdm import tqdm\nfrom fmc_rest_client import FMCRestClient\nfrom fmc_rest_client import ResourceException\nfrom fmc_rest_client.resources import *\nfrom tetpyclient import RestClient\nfrom TetPolicy2 import Environment, InventoryFilter, Cluster\nfrom __future__ import absolute_import, division, print_function\n\n__author__ = \"Oxana Sannikova \"\n__contributors__ = [\n \"Chris Mchenry \"\n]\n__copyright__ = \"Copyright (c) 2018 Cisco and/or its affiliates.\"\n__license__ = \"Cisco Sample Code License, Version 1.0\"\n\nclass NetworkGroup(ObjectResource):\n def __init__(self, name=None, objects=None, literals=None, description=\" \"):\n super().__init__(name)\n self.objects = objects\n self.literals = literals\n self.description = description\n\nprint(\"Connecting to Tetration to receive configuration\")\n\n#TO DO: INSERT YOUR TETRATION URL AND API KEY HERE\nTET_API_ENDPOINT=\"https://URL:PORT\"\nTET_API_CREDS=\"PATH/FILENAME.json\"\n\ntetEnv = Environment(TET_API_ENDPOINT,TET_API_CREDS)\nrestclient = RestClient(TET_API_ENDPOINT,\n credentials_file=TET_API_CREDS,\n verify=False)\n\nrequests.packages.urllib3.disable_warnings()\nresp = restclient.get('/openapi/v1/applications')\n\nif not resp:\n sys.exit(\"No data returned for Tetration Apps! HTTP {}\".format(resp.status_code))\n\nappIDs = []\nappNames = []\n\n#INSERT YOUR APPLICATION KEY HERE\nAPP_KEY = \"APPKEY\" \nappIDs = [APP_KEY]\n\n#Collect Policies for selected Tetration Apps\n\ntetEnv.loadPolicy(appIDs)\n \n#Connect to FMC\n\n#TO DO: INSERT FMC URL AND CREDENTIALS HERE\nfmc_server_url = \"https://URL:PORT\"\nfmc_username = \"USER\"\nfmc_password = \"PWD\"\n\nprint('Connecting to FMC {} ...'.format(fmc_server_url))\nfmc = FMCRestClient(fmc_server_url, fmc_username, fmc_password)\nprint('Connected Successfully')\n\n#Create blank Access Control Policy\n\nfmc_acp = AccessPolicy('Tetration ACP','BLOCK')\nfmc_acp = fmc.create(fmc_acp)\nprint('Created Access Control Policy ' + fmc_acp.name)\n\n#Create Host Objects and Network Group Objects based on Tetration clusters for selected App\n\napp = tetEnv.primaryApps[APP_KEY]\nclusters = app.clusters\nfilters = app.inventoryFilters\npolicies = app.defaultPolicies\n\nfmc_host_objects = []\nfmc_networkgroups = {}\nflag = False\n\nfor key in clusters.keys():\n cluster = clusters[key]\n fmc_bulk_hosts = []\n fmc_existing_hosts = []\n fmc_hosts = []\n fmc_networkgroup = []\n print(cluster.name)\n pprint(cluster.hosts)\n if cluster.hosts: #Checking if the cluster configuration is not empty\n for host in cluster.hosts:\n flag = False\n host['name']='tet-'+host['name']\n for x in range(len(fmc_host_objects)):\n if host['name'] == fmc_host_objects[x].name:\n fmc_existing_hosts.append(fmc_host_objects[x])\n flag = True\n if not flag:\n fmc_bulk_hosts.append(Host(host['name'],host['ip']))\n print('Creating hosts for cluster {}'.format(cluster.name))\n if len(fmc_bulk_hosts) != 0:\n fmc_hosts = fmc.create(fmc_bulk_hosts)\n fmc_host_objects = fmc_host_objects + fmc_hosts\n if len(fmc_hosts) != 0:\n if len(fmc_existing_hosts) == 0:\n #for i in range(len(fmc_hosts)):\n # print(fmc_hosts[i].name+' '+fmc_hosts[i].id)\n fmc_networkgroup = fmc.create(NetworkGroup('tet-cluster-'+cluster.uid,fmc_hosts))\n else:\n #print('Existing hosts')\n #for i in range(len(fmc_existing_hosts)):\n # print(fmc_existing_hosts[i].name,' ',fmc_existing_hosts[i].id)\n #print('New hosts')\n #for i in range(len(fmc_hosts)):\n # print(fmc_hosts[i].name+' '+fmc_hosts[i].id)\n group = fmc_existing_hosts+fmc_hosts\n fmc_networkgroup = fmc.create(NetworkGroup('tet-cluster-'+cluster.uid,group))\n else:\n if len(fmc_existing_hosts) != 0:\n #for i in range(len(fmc_existing_hosts)):\n # print(fmc_existing_hosts[i].name,' ',fmc_existing_hosts[i].id)\n fmc_networkgroup = fmc.create(NetworkGroup('tet-cluster-'+cluster.uid,fmc_existing_hosts))\n fmc_networkgroups[cluster.uid] = fmc_networkgroup\n\nfmc_networkgroup = []\nfor key in filters.keys():\n invFilter = filters[key]\n fmc_bulk_ips = []\n fmc_networkgroup = []\n #print('Inv Filter: '+invFilter.name)\n if invFilter.name != 'securedc-tet':\n if invFilter.ipSet: #Check if inventory filter is not empty\n print('Creating network group for inv filter {}'.format(invFilter.name))\n for ip in invFilter.ipSet:\n fmc_bulk_ips.append({\"type\": \"Host\", \"value\": ip})\n else:\n for filter in invFilter.filter['filters']:\n if filter['field'] == 'ip':\n if filter['type'] == 'eq':\n fmc_bulk_ips.append({\"type\": \"Host\", \"value\": filter['value']})\n elif filter['type'] == 'subnet':\n fmc_bulk_ips.append({\"type\": \"Network\", \"value\": filter['value']})\n fmc_networkgroup = fmc.create(NetworkGroup('tet-filter-'+invFilter.uid,[],fmc_bulk_ips))\n fmc_networkgroups[invFilter.uid] = fmc_networkgroup\n\n#Create Access Policy Rules\n\nprint('Creating Access Policy Rules for Policy: '+fmc_acp.name)\nfmc_rules = []\nrule_index = 0\nfor policy in policies:\n rule = AccessRule('tet-rule-'+str(rule_index),fmc_acp)\n rule_index = rule_index + 1\n if policy.action == 'ALLOW':\n rule.action = policy.action\n else:\n rule.action = 'BLOCK'\n #Check Clusters and InvFilters for source networks\n if policy.consumerFilterID in clusters.keys(): #Is source network a cluster?\n rule.sourceNetworks = {'objects': [fmc_networkgroups[policy.consumerFilterID]]}\n #print('Source network is a cluster')\n elif policy.consumerFilterID in filters.keys(): #Is source network an inventory filter?\n invFilter = filters[policy.consumerFilterID]\n #print('Source network is an inventory filter '+invFilter.name + ' ' + str(len(invFilter.ipSet)))\n nets = []\n if policy.consumerFilterName != 'securedc-tet':\n rule.sourceNetworks = {'objects': [fmc_networkgroups[policy.consumerFilterID]]}\n else:\n rule.sourceNetworks = {'literals': nets}\n #Check Clusters and InvFilters for destination networks\n if policy.providerFilterID in clusters.keys(): #Is destination network is a cluster\n rule.destinationNetworks = {'objects': [fmc_networkgroups[policy.providerFilterID]]}\n #print('Destination network is a cluster')\n elif policy.providerFilterID in filters.keys(): #Is destination network is an inventory filter?\n invFilter = filters[policy.providerFilterID]\n #print('Destination network is a filter '+invFilter.name+' '+str(len(invFilter.ipSet)))\n nets = []\n if policy.providerFilterName != 'securedc-tet':\n rule.destinationNetworks = {'objects': [fmc_networkgroups[policy.providerFilterID]]}\n else:\n rule.destinationNetworks = {'literals': nets}\n #Adding destination ports to the FMC rule\n rule.destinationPorts = {'objects': [],'literals': []}\n for l4param in policy.l4params:\n if policy.consumerFilterName != policy.providerFilterName:\n if (l4param['proto'] == 6) or (l4param['proto'] == 17):\n if l4param['port_min'] == l4param['port_max']: #If protocol is TCP or UDP\n rule.destinationPorts['literals'].append({'port': str(l4param['port_min']),\n 'protocol': str(l4param['proto']),\n 'type': 'PortLiteral'})\n else:\n rule.destinationPorts['literals'].append({\"port\": str(l4param['port_min'])+'-'+str(l4param['port_max']),\n 'protocol': str(l4param['proto']), 'type': 'PortLiteral'})\n elif l4param['proto'] == 1: #If protocol is ICMP\n rule.destinationPorts['literals'].append({\"type\": \"ICMPv4PortLiteral\",\"protocol\": \"1\",\"icmpType\": \"Any\"})\n fmc_rules.append(rule)\n\n#Push rules to FMC\n\nfor fmc_rule in tqdm(fmc_rules):\n fmc_acp_rules = fmc.create(fmc_rule)\nprint('Rules were created successfully')\n\n", "repo_name": "oxsannikova/fmc_asa_tetration_policy_import_export", "sub_path": "tetration_to_fmc.py", "file_name": "tetration_to_fmc.py", "file_ext": "py", "file_size_in_byte": 8606, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "TetPolicy2.Environment", "line_number": 33, "usage_type": "call"}, {"api_name": "tetpyclient.RestClient", "line_number": 34, "usage_type": "call"}, {"api_name": "requests.packages.urllib3.packages.urllib3.disable_warnings", "line_number": 38, "usage_type": "call"}, {"api_name": "requests.packages.urllib3.packages", "line_number": 38, "usage_type": "attribute"}, {"api_name": "requests.packages.urllib3", "line_number": 38, "usage_type": "name"}, {"api_name": "sys.exit", "line_number": 42, "usage_type": "call"}, {"api_name": "fmc_rest_client.FMCRestClient", "line_number": 63, "usage_type": "call"}, {"api_name": "pprint.pprint", "line_number": 90, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 201, "usage_type": "call"}]} +{"seq_id": "16560829803", "text": "from pprint import pprint\nfrom collections import namedtuple, Counter\nimport math\nimport statistics\nimport functools\nimport numpy as np\nimport scipy.signal\n\nimport os\nimport time\n\nfrom matplotlib import pyplot as plt\n\nnp.set_printoptions(linewidth=1000)\nnp.set_printoptions(formatter={'bool': lambda x: '█' if x else ' '})\n\nplt.ion()\n\n\ndef cls():\n os.system('clear')\n\n\ndef pt1():\n # f = open('example.txt')\n f = open('input.txt')\n\n dots = []\n folds = []\n for line in f:\n line = line.rstrip()\n if not line:\n break\n x, y = line.split(',')\n dots.append((int(x), int(y)))\n\n for line in f:\n assert(line.startswith('fold along '))\n axis, ofs = line.rstrip().split(' ')[-1].split('=')\n axis = 0 if axis == 'y' else 1\n ofs = int(ofs)\n folds.append((axis, ofs))\n\n shape = [1 + 2 * max(ofs for axis, ofs in folds if axis == i)\n for i in range(2)]\n\n sheet = np.zeros(shape, dtype=bool)\n for x, y in dots:\n sheet[y][x] = 1\n\n for fold in folds:\n sheet = fold_sheet(sheet, *fold)\n plt.imshow(sheet, interpolation='nearest'); plt.draw(); plt.pause(0.5)\n\n print(sheet)\n\n num_dots = np.count_nonzero(sheet)\n print(num_dots)\n\n\ndef fold_sheet(sheet, axis, ofs):\n top, _, bot = np.split(sheet, [ofs, ofs+1], axis)\n bot = np.flip(bot, axis=axis)\n assert(top.shape == bot.shape)\n return top + bot\n\n\nif __name__ == '__main__':\n pt1()\n # pt2()\n", "repo_name": "qqrs/aoc", "sub_path": "2021/day13/day13.py", "file_name": "day13.py", "file_ext": "py", "file_size_in_byte": 1489, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "37", "api": [{"api_name": "numpy.set_printoptions", "line_number": 14, "usage_type": "call"}, {"api_name": "numpy.set_printoptions", "line_number": 15, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.ion", "line_number": 17, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 17, "usage_type": "name"}, {"api_name": "os.system", "line_number": 21, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 47, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 53, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 53, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.draw", "line_number": 53, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.pause", "line_number": 53, "usage_type": "call"}, {"api_name": "numpy.count_nonzero", "line_number": 57, "usage_type": "call"}, {"api_name": "numpy.split", "line_number": 62, "usage_type": "call"}, {"api_name": "numpy.flip", "line_number": 63, "usage_type": "call"}]} +{"seq_id": "37813239518", "text": "\"\"\"Module unittests.\"\"\"\n\nfrom module import ResLinear, ResConv1d, ResConvTranspose1d\nfrom module import Encoder1d, Decoder1d, Discriminator1d\nfrom module import StandardNormalizer, SigmoidTransfer\nfrom module import NormalInjector, RandomNormalInjector\nfrom module import UniformInjector, RandomUniformInjector\nfrom module import NormalRandom, UniformRandom\nfrom module import CategoricalLoss, AdversarialLoss, CategoricalError\n\nfrom absl.testing import absltest\nimport torch\n\n\nclass ResLinearTest(absltest.TestCase):\n def test_forward(self):\n module = ResLinear(4, 8, 16)\n inputs = torch.rand(2, 2, 4)\n outputs = module(inputs)\n self.assertEqual(outputs.shape, (2, 2, 16))\n\n\nclass ResConv1dTest(absltest.TestCase):\n def test_forward(self):\n module = ResConv1d(2, 4, 2, 3, 3, padding1=1, padding2=1)\n inputs = torch.rand(2, 2, 32)\n outputs = module(inputs)\n self.assertEqual(outputs.shape, (2, 2, 32))\n\n\nclass ResConvTranspose1dTest(absltest.TestCase):\n def test_forward(self):\n module = ResConvTranspose1d(\n 2, 4, 2, 3, 3, padding1=1, padding2=1, stride1=2)\n inputs = torch.rand(2, 2, 8)\n outputs = module(inputs)\n self.assertEqual(outputs.shape, (2, 2, 16))\n\n\nclass Encoder1dTest(absltest.TestCase):\n def test_forward(self):\n module = Encoder1d(2, 2, 2, 4, 8, kernel=3, pool=2)\n inputs = torch.rand(2, 2, 16)\n outputs = module(inputs)\n self.assertEqual(outputs.shape, (2, 8, 4))\n\n\nclass Decoder1dTest(absltest.TestCase):\n def test_forward(self):\n module = Decoder1d(2, 2, 2, 4, 8, kernel=3, stride=2)\n inputs = torch.rand(2, 2, 4)\n outputs = module(inputs)\n self.assertEqual(outputs.shape, (2, 8, 16))\n\n\nclass Discriminator1dTest(absltest.TestCase):\n def test_forward(self):\n module = Discriminator1d(2, 2, 2, 4, 8, kernel=3, pool=2)\n inputs = torch.rand(2, 2, 16)\n outputs = module(inputs)\n self.assertEqual(len(outputs), 8)\n self.assertEqual(outputs[0].shape, (2, 8, 16))\n self.assertEqual(outputs[1].shape, (2, 8, 16))\n self.assertEqual(outputs[2].shape, (2, 8, 16))\n self.assertEqual(outputs[3].shape, (2, 8, 8))\n self.assertEqual(outputs[4].shape, (2, 8, 8))\n self.assertEqual(outputs[5].shape, (2, 8, 4))\n self.assertEqual(outputs[6].shape, (2, 8, 4))\n self.assertEqual(outputs[6].shape, (2, 8, 4))\n\n\nclass StandardNormalizerTest(absltest.TestCase):\n def test_forward(self):\n module = StandardNormalizer()\n inputs = torch.rand(2, 2, 16)\n outputs = module(inputs)\n self.assertEqual(outputs.shape, (2, 2, 16))\n\n\nclass NormalInjectorTest(absltest.TestCase):\n def test_forward(self):\n module = NormalInjector()\n inputs = torch.rand(2, 2, 16)\n outputs = module(inputs)\n self.assertEqual(outputs.shape, (2, 2, 16))\n\n\nclass RandomNormalInjectorTest(absltest.TestCase):\n def test_forward(self):\n module = RandomNormalInjector()\n inputs = torch.rand(2, 2, 16)\n outputs = module(inputs)\n self.assertEqual(outputs.shape, (2, 2, 16))\n\n\nclass UniformInjectorTest(absltest.TestCase):\n def test_forward(self):\n module = UniformInjector()\n inputs = torch.rand(2, 2, 16)\n outputs = module(inputs)\n self.assertEqual(outputs.shape, (2, 2, 16))\n\n\nclass RandomUniformInjectorTest(absltest.TestCase):\n def test_forward(self):\n module = RandomUniformInjector()\n inputs = torch.rand(2, 2, 16)\n outputs = module(inputs)\n self.assertEqual(outputs.shape, (2, 2, 16))\n\n\nclass SigmoidTransferTest(absltest.TestCase):\n def test_forward(self):\n module = SigmoidTransfer()\n inputs = torch.rand(2, 2, 16)\n outputs = module(inputs)\n self.assertEqual(outputs.shape, (2, 2, 16))\n\n\nclass NormalRandomTest(absltest.TestCase):\n def test_forward(self):\n module = NormalRandom()\n inputs = torch.rand(2, 2, 16)\n outputs = module(inputs)\n self.assertEqual(outputs.shape, (2, 2, 16))\n\n\nclass UniformRandomTest(absltest.TestCase):\n def test_forward(self):\n module = UniformRandom()\n inputs = torch.rand(2, 2, 16)\n outputs = module(inputs)\n self.assertEqual(outputs.shape, (2, 2, 16))\n\n\nclass CategoricalLossTest(absltest.TestCase):\n def test_forward(self):\n module = CategoricalLoss()\n inputs = torch.rand(2, 16, 7)\n targets = torch.rand(2, 16, 7)\n outputs = module(inputs, targets)\n self.assertEqual(outputs.shape, (2,))\n\n\nclass AdversarialLossTest(absltest.TestCase):\n def test_forward(self):\n module = AdversarialLoss()\n real_outputs = [torch.rand(2, 8, 8), torch.rand(2, 8, 4)]\n fake_outputs = [torch.rand(2, 8, 8), torch.rand(2, 8, 4)]\n outputs = module(real_outputs, fake_outputs)\n self.assertEqual(outputs.shape, (2,))\n\n\nclass CategoricalErrorTest(absltest.TestCase):\n def test_forward(self):\n module = CategoricalError()\n inputs = torch.rand(2, 16, 7)\n targets = torch.rand(2, 16, 7)\n outputs = module(inputs, targets)\n self.assertEqual(outputs.shape, (2,))\n\n\nif __name__ == '__main__':\n absltest.main()\n", "repo_name": "zhangxiangxiao/babble", "sub_path": "module_test.py", "file_name": "module_test.py", "file_ext": "py", "file_size_in_byte": 5293, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "37", "api": [{"api_name": "absl.testing.absltest.TestCase", "line_number": 15, "usage_type": "attribute"}, {"api_name": "absl.testing.absltest", "line_number": 15, "usage_type": "name"}, {"api_name": "module.ResLinear", "line_number": 17, "usage_type": "call"}, {"api_name": "torch.rand", "line_number": 18, "usage_type": "call"}, {"api_name": "absl.testing.absltest.TestCase", "line_number": 23, "usage_type": "attribute"}, {"api_name": "absl.testing.absltest", "line_number": 23, "usage_type": "name"}, {"api_name": "module.ResConv1d", "line_number": 25, "usage_type": "call"}, {"api_name": "torch.rand", "line_number": 26, "usage_type": "call"}, {"api_name": "absl.testing.absltest.TestCase", "line_number": 31, "usage_type": "attribute"}, {"api_name": "absl.testing.absltest", "line_number": 31, "usage_type": "name"}, {"api_name": "module.ResConvTranspose1d", "line_number": 33, "usage_type": "call"}, {"api_name": "torch.rand", "line_number": 35, "usage_type": "call"}, {"api_name": "absl.testing.absltest.TestCase", "line_number": 40, "usage_type": "attribute"}, {"api_name": "absl.testing.absltest", "line_number": 40, "usage_type": "name"}, {"api_name": "module.Encoder1d", "line_number": 42, "usage_type": "call"}, {"api_name": "torch.rand", "line_number": 43, "usage_type": "call"}, {"api_name": "absl.testing.absltest.TestCase", "line_number": 48, "usage_type": "attribute"}, {"api_name": "absl.testing.absltest", "line_number": 48, "usage_type": "name"}, {"api_name": "module.Decoder1d", "line_number": 50, "usage_type": "call"}, {"api_name": "torch.rand", "line_number": 51, "usage_type": "call"}, {"api_name": "absl.testing.absltest.TestCase", "line_number": 56, "usage_type": "attribute"}, {"api_name": "absl.testing.absltest", "line_number": 56, "usage_type": "name"}, {"api_name": "module.Discriminator1d", "line_number": 58, "usage_type": "call"}, {"api_name": "torch.rand", "line_number": 59, "usage_type": "call"}, {"api_name": "absl.testing.absltest.TestCase", "line_number": 72, "usage_type": "attribute"}, {"api_name": "absl.testing.absltest", "line_number": 72, "usage_type": "name"}, {"api_name": "module.StandardNormalizer", "line_number": 74, "usage_type": "call"}, {"api_name": "torch.rand", "line_number": 75, "usage_type": "call"}, {"api_name": "absl.testing.absltest.TestCase", "line_number": 80, "usage_type": "attribute"}, {"api_name": "absl.testing.absltest", "line_number": 80, "usage_type": "name"}, {"api_name": "module.NormalInjector", "line_number": 82, "usage_type": "call"}, {"api_name": "torch.rand", "line_number": 83, "usage_type": "call"}, {"api_name": "absl.testing.absltest.TestCase", "line_number": 88, "usage_type": "attribute"}, {"api_name": "absl.testing.absltest", "line_number": 88, "usage_type": "name"}, {"api_name": "module.RandomNormalInjector", "line_number": 90, "usage_type": "call"}, {"api_name": "torch.rand", "line_number": 91, "usage_type": "call"}, {"api_name": "absl.testing.absltest.TestCase", "line_number": 96, "usage_type": "attribute"}, {"api_name": "absl.testing.absltest", "line_number": 96, "usage_type": "name"}, {"api_name": "module.UniformInjector", "line_number": 98, "usage_type": "call"}, {"api_name": "torch.rand", "line_number": 99, "usage_type": "call"}, {"api_name": "absl.testing.absltest.TestCase", "line_number": 104, "usage_type": "attribute"}, {"api_name": "absl.testing.absltest", "line_number": 104, "usage_type": "name"}, {"api_name": "module.RandomUniformInjector", "line_number": 106, "usage_type": "call"}, {"api_name": "torch.rand", "line_number": 107, "usage_type": "call"}, {"api_name": "absl.testing.absltest.TestCase", "line_number": 112, "usage_type": "attribute"}, {"api_name": "absl.testing.absltest", "line_number": 112, "usage_type": "name"}, {"api_name": "module.SigmoidTransfer", "line_number": 114, "usage_type": "call"}, {"api_name": "torch.rand", "line_number": 115, "usage_type": "call"}, {"api_name": "absl.testing.absltest.TestCase", "line_number": 120, "usage_type": "attribute"}, {"api_name": "absl.testing.absltest", "line_number": 120, "usage_type": "name"}, {"api_name": "module.NormalRandom", "line_number": 122, "usage_type": "call"}, {"api_name": "torch.rand", "line_number": 123, "usage_type": "call"}, {"api_name": "absl.testing.absltest.TestCase", "line_number": 128, "usage_type": "attribute"}, {"api_name": "absl.testing.absltest", "line_number": 128, "usage_type": "name"}, {"api_name": "module.UniformRandom", "line_number": 130, "usage_type": "call"}, {"api_name": "torch.rand", "line_number": 131, "usage_type": "call"}, {"api_name": "absl.testing.absltest.TestCase", "line_number": 136, "usage_type": "attribute"}, {"api_name": "absl.testing.absltest", "line_number": 136, "usage_type": "name"}, {"api_name": "module.CategoricalLoss", "line_number": 138, "usage_type": "call"}, {"api_name": "torch.rand", "line_number": 139, "usage_type": "call"}, {"api_name": "torch.rand", "line_number": 140, "usage_type": "call"}, {"api_name": "absl.testing.absltest.TestCase", "line_number": 145, "usage_type": "attribute"}, {"api_name": "absl.testing.absltest", "line_number": 145, "usage_type": "name"}, {"api_name": "module.AdversarialLoss", "line_number": 147, "usage_type": "call"}, {"api_name": "torch.rand", "line_number": 148, "usage_type": "call"}, {"api_name": "torch.rand", "line_number": 149, "usage_type": "call"}, {"api_name": "absl.testing.absltest.TestCase", "line_number": 154, "usage_type": "attribute"}, {"api_name": "absl.testing.absltest", "line_number": 154, "usage_type": "name"}, {"api_name": "module.CategoricalError", "line_number": 156, "usage_type": "call"}, {"api_name": "torch.rand", "line_number": 157, "usage_type": "call"}, {"api_name": "torch.rand", "line_number": 158, "usage_type": "call"}, {"api_name": "absl.testing.absltest.main", "line_number": 164, "usage_type": "call"}, {"api_name": "absl.testing.absltest", "line_number": 164, "usage_type": "name"}]} +{"seq_id": "6406997245", "text": "import os\nfrom unittest.mock import mock_open, patch\n\nfrom django.core.cache import cache\nfrom django.core.files.storage import default_storage\nfrom django.test import TestCase\n\nfrom import_export.tmp_storages import (\n BaseStorage,\n CacheStorage,\n MediaStorage,\n TempFolderStorage,\n)\n\n\nclass TestBaseStorage(TestCase):\n def setUp(self):\n self.storage = BaseStorage()\n\n def test_save(self):\n with self.assertRaises(NotImplementedError):\n self.storage.save(None)\n\n def test_read(self):\n with self.assertRaises(NotImplementedError):\n self.storage.read()\n\n def test_remove(self):\n with self.assertRaises(NotImplementedError):\n self.storage.remove()\n\n\nclass TestTempFolderStorage(TempFolderStorage):\n def get_full_path(self):\n return \"/tmp/f\"\n\n\nclass TestMediaStorage(MediaStorage):\n def get_full_path(self):\n return \"f\"\n\n\nclass TempStoragesTest(TestCase):\n def setUp(self):\n self.test_string = b\"\"\"\nid,name,author,author_email,imported,published,price,categories\n2,Bar,1,,0,,,\n1,Foo,,,0,,,\n\"\"\"\n\n def test_temp_folder_storage(self):\n tmp_storage = TempFolderStorage()\n tmp_storage.save(self.test_string)\n name = tmp_storage.name\n\n tmp_storage = TempFolderStorage(name=name)\n self.assertEqual(self.test_string.decode(), tmp_storage.read())\n\n self.assertTrue(os.path.isfile(tmp_storage.get_full_path()))\n tmp_storage.remove()\n self.assertFalse(os.path.isfile(tmp_storage.get_full_path()))\n\n def test_temp_folder_storage_read_with_encoding(self):\n tmp_storage = TestTempFolderStorage(encoding=\"utf-8\")\n tmp_storage.name = \"f\"\n with patch(\"builtins.open\", mock_open(read_data=\"data\")) as mock_file:\n tmp_storage.read()\n mock_file.assert_called_with(\"/tmp/f\", \"r\", encoding=\"utf-8\")\n\n def test_cache_storage(self):\n tmp_storage = CacheStorage()\n tmp_storage.save(self.test_string)\n name = tmp_storage.name\n\n tmp_storage = CacheStorage(name=name)\n self.assertEqual(self.test_string, tmp_storage.read())\n\n self.assertIsNotNone(cache.get(tmp_storage.CACHE_PREFIX + tmp_storage.name))\n tmp_storage.remove()\n self.assertIsNone(cache.get(tmp_storage.CACHE_PREFIX + tmp_storage.name))\n\n def test_cache_storage_read_with_encoding(self):\n tmp_storage = CacheStorage()\n tmp_storage.name = \"f\"\n cache.set(\"django-import-export-f\", 101)\n res = tmp_storage.read()\n self.assertEqual(101, res)\n\n def test_cache_storage_read_with_encoding_unicode_chars(self):\n tmp_storage = CacheStorage()\n tmp_storage.name = \"f\"\n tmp_storage.save(\"àèìòùçñ\")\n res = tmp_storage.read()\n self.assertEqual(\"àèìòùçñ\", res)\n\n def test_media_storage(self):\n tmp_storage = MediaStorage()\n tmp_storage.save(self.test_string)\n name = tmp_storage.name\n\n tmp_storage = MediaStorage(name=name)\n self.assertEqual(self.test_string, tmp_storage.read())\n\n self.assertTrue(default_storage.exists(tmp_storage.get_full_path()))\n tmp_storage.remove()\n self.assertFalse(default_storage.exists(tmp_storage.get_full_path()))\n\n def test_media_storage_read_with_encoding(self):\n tmp_storage = TestMediaStorage()\n tmp_storage.name = \"f\"\n with patch(\"import_export.tmp_storages.default_storage.open\") as mock_open:\n tmp_storage.read()\n mock_open.assert_called_with(\"f\", mode=\"rb\")\n", "repo_name": "django-import-export/django-import-export", "sub_path": "tests/core/tests/test_tmp_storages.py", "file_name": "test_tmp_storages.py", "file_ext": "py", "file_size_in_byte": 3587, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2778, "dataset": "github-code", "pt": "37", "api": [{"api_name": "django.test.TestCase", "line_number": 16, "usage_type": "name"}, {"api_name": "import_export.tmp_storages.BaseStorage", "line_number": 18, "usage_type": "call"}, {"api_name": "import_export.tmp_storages.TempFolderStorage", "line_number": 33, "usage_type": "name"}, {"api_name": "import_export.tmp_storages.MediaStorage", "line_number": 38, "usage_type": "name"}, {"api_name": "django.test.TestCase", "line_number": 43, "usage_type": "name"}, {"api_name": "import_export.tmp_storages.TempFolderStorage", "line_number": 52, "usage_type": "call"}, {"api_name": "import_export.tmp_storages.TempFolderStorage", "line_number": 56, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 59, "usage_type": "call"}, {"api_name": "os.path", "line_number": 59, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 61, "usage_type": "call"}, {"api_name": "os.path", "line_number": 61, "usage_type": "attribute"}, {"api_name": "unittest.mock.patch", "line_number": 66, "usage_type": "call"}, {"api_name": "unittest.mock.mock_open", "line_number": 66, "usage_type": "call"}, {"api_name": "import_export.tmp_storages.CacheStorage", "line_number": 71, "usage_type": "call"}, {"api_name": "import_export.tmp_storages.CacheStorage", "line_number": 75, "usage_type": "call"}, {"api_name": "django.core.cache.cache.get", "line_number": 78, "usage_type": "call"}, {"api_name": "django.core.cache.cache", "line_number": 78, "usage_type": "name"}, {"api_name": "django.core.cache.cache.get", "line_number": 80, "usage_type": "call"}, {"api_name": "django.core.cache.cache", "line_number": 80, "usage_type": "name"}, {"api_name": "import_export.tmp_storages.CacheStorage", "line_number": 83, "usage_type": "call"}, {"api_name": "django.core.cache.cache.set", "line_number": 85, "usage_type": "call"}, {"api_name": "django.core.cache.cache", "line_number": 85, "usage_type": "name"}, {"api_name": "import_export.tmp_storages.CacheStorage", "line_number": 90, "usage_type": "call"}, {"api_name": "import_export.tmp_storages.MediaStorage", "line_number": 97, "usage_type": "call"}, {"api_name": "import_export.tmp_storages.MediaStorage", "line_number": 101, "usage_type": "call"}, {"api_name": "django.core.files.storage.default_storage.exists", "line_number": 104, "usage_type": "call"}, {"api_name": "django.core.files.storage.default_storage", "line_number": 104, "usage_type": "name"}, {"api_name": "django.core.files.storage.default_storage.exists", "line_number": 106, "usage_type": "call"}, {"api_name": "django.core.files.storage.default_storage", "line_number": 106, "usage_type": "name"}, {"api_name": "unittest.mock.patch", "line_number": 111, "usage_type": "call"}, {"api_name": "unittest.mock.mock_open", "line_number": 111, "usage_type": "name"}, {"api_name": "unittest.mock.mock_open.assert_called_with", "line_number": 113, "usage_type": "call"}, {"api_name": "unittest.mock.mock_open", "line_number": 113, "usage_type": "name"}]} +{"seq_id": "23881909584", "text": "\nimport os, sys, numpy as np\nfrom PIL import Image\nfrom pfm_helper import load_pfm, save_pfm\nfrom exr_helper import *\nimport argparse\n\n\ndef load_mvec(file, mvscalex, mvscaley):\n if file.endswith('.pfm'):\n mv_img = load_pfm(file)\n mvx = mv_img[...,0:1] * mvscalex\n mvy = mv_img[...,1:2] * mvscaley\n\n # mvx = np.clip(mvx, float(-512), float(512))\n # mvy = np.clip(mvy, float(-512), float(512))\n return np.concatenate([mvy, mvx], axis = -1)\n else:\n mv_img = load_exr(file)\n mvx = mv_img[...,0:1] * mvscalex\n mvy = mv_img[...,1:2] * mvscaley\n\n # save_pfm(file.replace(\".exr\", \"_test.pfm\"), mv_img)\n\n # mvx = np.clip(mvx, float(-512), float(512))\n # mvy = np.clip(mvy, float(-512), float(512))\n return np.concatenate([mvy, mvx], axis = -1)\n\n\n# Stack a list of images vertically\ndef StackVertical(images):\n widths, heights = zip(*(i.size for i in images))\n max_width = max(widths)\n total_height = sum(heights)\n newImage = Image.new('RGB', (max_width, total_height))\n\n y_offset = 0\n for im in images:\n newImage.paste(im, (0,y_offset))\n y_offset += im.size[1]\n return newImage\n\n# Stack a list of images horizontally\ndef StackHorizontal(images):\n widths, heights = zip(*(i.size for i in images))\n total_width = sum(widths)\n max_height = max(heights)\n newImage = Image.new('RGB', (total_width, max_height))\n\n x_offset = 0\n for im in images:\n newImage.paste(im, (x_offset,0))\n x_offset += im.size[0]\n return newImage\n\ndef doWarp(src, mvec, width, height):\n absoluteMvec = np.round(mvec + np.stack(np.split(np.indices((height,width), dtype='float32'), 2), axis=-1)[0]).astype('int32')\n\n output = np.zeros((height,width,3))\n \n for y in range(0, height):\n for x in range(0, width):\n ref = absoluteMvec[y,x]\n\n if np.isfinite(mvec[y,x,0]) and np.isfinite(mvec[y,x,1]):\n ry = np.clip(ref[0], 0, height-1)\n rx = np.clip(ref[1], 0, width-1)\n\n output[y,x,:] = src[ry, rx, 0:3]\n else:\n output[y,x,:] = [1.0, 0.0, 0.0]\n\n return output\n\n\n# ---- main ---- \n\n\ndef parseCommandLine():\n\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--color\", type=str, action='store', dest='color', required=True, help=\"color images (use %%d-like formatter for frame ID)\")\n parser.add_argument(\"--mvec\", type=str, action='store', dest='mvec', required=True, help=\"motion images (use %%d-like formatter for frame ID)\")\n parser.add_argument(\"--frame\", type=int, action='store', dest='frame', default=7, help=\"which frame ID to use\")\n parser.add_argument(\"--lookback\", type=int, action='store', dest='lookback', default=1, help=\"how many frames to look back\")\n parser.add_argument(\"--mvscalex\", type=float, action='store', dest='mvscalex', default=1.0, help=\"mv scale x\")\n parser.add_argument(\"--mvscaley\", type=float, action='store', dest='mvscaley', default=1.0, help=\"mv scale y\")\n\n return parser.parse_args()\n\ndef load_image(file):\n if file.endswith('.exr'): \n return load_exr(file) \n elif file.endswith('.pfm'):\n return load_pfm(file) \n else:\n return np.array(Image.open(file)).astype('float32') * 1.0/255.0\n\ndef save_image(arr, file):\n Image.fromarray((np.clip(arr, 0.0, 1.0) * 255.0).astype('uint8')).save(file)\n\ndef mvcheck(color, mv, frame, lookback, mvscalex, mvscaley):\n \n getcolor = lambda idx : (color % idx)\n getmvec = lambda idx : (mv % idx)\n\n tgt = load_image(getcolor(frame))\n src = load_image(getcolor(frame-lookback))\n\n width = src.shape[1]\n height = src.shape[0]\n\n print(\"Warping %s to %s\" % (color % (frame-lookback), color % (frame)))\n\n warp = src.copy()\n for tgtFrame in range(frame-lookback+1,frame+1):\n print(\" - %s\" % getmvec(tgtFrame))\n mvec = load_mvec(getmvec(tgtFrame), mvscalex, mvscaley)\n \n warp = doWarp(warp, mvec, width, height)\n \n\n meanval = np.mean (np.concatenate([src,tgt,warp], axis=-1))\n stdev = np.std (np.concatenate([src,tgt,warp], axis=-1))\n\n save_image(src / (meanval + stdev * 2), \"source.png\")\n save_image(tgt / (meanval + stdev * 2), \"target.png\")\n save_image(warp / (meanval + stdev * 2), \"warped.png\")\n\n print(\"saved source.png, target.png and warped.png\")\n \ncmd = parseCommandLine()\ncolor = cmd.color\nmvec = cmd.mvec\nframe = cmd.frame\nlookback = cmd.lookback\nmvscalex = cmd.mvscalex\nmvscaley = cmd.mvscaley\n\nmvcheck(color, mvec, frame, lookback, mvscalex, mvscaley)", "repo_name": "ericx134/pytorch_examples", "sub_path": "test/mvcheck/mvcheck.py", "file_name": "mvcheck.py", "file_ext": "py", "file_size_in_byte": 4653, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "pfm_helper.load_pfm", "line_number": 11, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 17, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 27, "usage_type": "call"}, {"api_name": "PIL.Image.new", "line_number": 35, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 35, "usage_type": "name"}, {"api_name": "PIL.Image.new", "line_number": 48, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 48, "usage_type": "name"}, {"api_name": "numpy.round", "line_number": 57, "usage_type": "call"}, {"api_name": "numpy.stack", "line_number": 57, "usage_type": "call"}, {"api_name": "numpy.split", "line_number": 57, "usage_type": "call"}, {"api_name": "numpy.indices", "line_number": 57, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 59, "usage_type": "call"}, {"api_name": "numpy.isfinite", "line_number": 65, "usage_type": "call"}, {"api_name": "numpy.clip", "line_number": 66, "usage_type": "call"}, {"api_name": "numpy.clip", "line_number": 67, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 81, "usage_type": "call"}, {"api_name": "pfm_helper.load_pfm", "line_number": 95, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 97, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 97, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 97, "usage_type": "name"}, {"api_name": "PIL.Image.fromarray", "line_number": 100, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 100, "usage_type": "name"}, {"api_name": "numpy.clip", "line_number": 100, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 123, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 123, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 124, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 124, "usage_type": "call"}]} +{"seq_id": "72949671467", "text": "from .models import *\nfrom django import forms\nimport datetime\n\nclass UsuarioForm(forms.ModelForm):\n \"\"\"Form definition for UsuarioForm.\"\"\"\n\n class Meta:\n \"\"\"Meta definition for UsuarioForm.\"\"\"\n\n model = Usuario\n fields = '__all__'\n exclude = [\n 'empresa',\n 'user',\n 'img_perfil'\n ]\n widgets = {\n 'cedula': forms.TextInput(attrs={'required':'true'}),\n }\n\n\nclass UserForm(forms.ModelForm):\n \"\"\"Form definition for UserForm.\"\"\"\n\n class Meta:\n \"\"\"Meta definition for UserForm.\"\"\"\n model = User\n fields = [\n 'first_name',\n 'last_name',\n 'email',\n 'groups',\n 'username',\n ]\n labels = {\n 'first_name':'Nombres',\n 'last_name':'Apellidos',\n 'email':'Correo',\n 'groups':'Rol',\n }\n widgets = {\n 'groups': forms.SelectMultiple(attrs={'id':'multiselect3'}),\n 'first_name': forms.TextInput(attrs={'required':'true'}),\n 'email':forms.EmailInput(attrs={'required':'true'})\n }\n\nclass ClienteForm(forms.ModelForm):\n \"\"\"Form definition for ClienteForm.\"\"\"\n\n class Meta:\n \"\"\"Meta definition for ClienteForm.\"\"\"\n\n model = Cliente\n fields = '__all__'\n labels = {\n 'nombre_apellido':'Nombres y Apellidos',\n }\n widgets = {\n 'nombre_apellido': forms.TextInput(attrs={'required':'true', 'onkeyup':'buscarCliente(this, 2)', 'autocomplete':'off', 'list':'usuario_by_nombre'}),\n 'cedula':forms.TextInput(attrs={'onkeyup':'buscarCliente(this, 1)', 'autocomplete':'off', 'list':'usuario_by_cedula'}),\n }", "repo_name": "JolRobles/mecanica", "sub_path": "usuarios/forms.py", "file_name": "forms.py", "file_ext": "py", "file_size_in_byte": 1754, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "django.forms.ModelForm", "line_number": 5, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 5, "usage_type": "name"}, {"api_name": "django.forms.TextInput", "line_number": 19, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 19, "usage_type": "name"}, {"api_name": "django.forms.ModelForm", "line_number": 23, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 23, "usage_type": "name"}, {"api_name": "django.forms.SelectMultiple", "line_number": 43, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 43, "usage_type": "name"}, {"api_name": "django.forms.TextInput", "line_number": 44, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 44, "usage_type": "name"}, {"api_name": "django.forms.EmailInput", "line_number": 45, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 45, "usage_type": "name"}, {"api_name": "django.forms.ModelForm", "line_number": 48, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 48, "usage_type": "name"}, {"api_name": "django.forms.TextInput", "line_number": 60, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 60, "usage_type": "name"}, {"api_name": "django.forms.TextInput", "line_number": 61, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 61, "usage_type": "name"}]} +{"seq_id": "42013028763", "text": "import cv2\nimport argparse\nimport math\nimport progressbar\nfrom pointillism import *\n\nparser = argparse.ArgumentParser(description='...')\nparser.add_argument('--palette-size', default=20, type=int, help=\"Number of colors of the base palette\")\nparser.add_argument('--stroke-scale', default=0, type=int, help=\"Scale of the brush strokes (0 = automatic)\")\nparser.add_argument('--gradient-smoothing-radius', default=0, type=int, help=\"Radius of the smooth filter applied to the gradient (0 = automatic)\")\nparser.add_argument('--limit-image-size', default=0, type=int, help=\"Limit the image size (0 = no limits)\")\nparser.add_argument('img_path', nargs='?', default=\"images/lake.jpg\")\n\nargs = parser.parse_args()\n\nres_path = args.img_path.rsplit(\".\", -1)[0] + \"_drawing.jpg\"\nimg = cv2.imread(args.img_path)\n\nif args.limit_image_size > 0:\n img = limit_size(img, args.limit_image_size)\n\nif args.stroke_scale == 0:\n stroke_scale = int(math.ceil(max(img.shape) / 1000))\n print(\"Automatically chosen stroke scale: %d\" % stroke_scale)\nelse:\n stroke_scale = args.stroke_scale\n\nif args.gradient_smoothing_radius == 0:\n gradient_smoothing_radius = int(round(max(img.shape) / 50))\n print(\"Automatically chosen gradient smoothing radius: %d\" % gradient_smoothing_radius)\nelse:\n gradient_smoothing_radius = args.gradient_smoothing_radius\n\n# convert the image to grayscale to compute the gradient\ngray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n\nprint(\"Computing color palette...\")\npalette = ColorPalette.from_image(img, args.palette_size)\n\nprint(\"Extending color palette...\")\npalette = palette.extend([(0, 50, 0), (15, 30, 0), (-15, 30, 0)])\n\n# display the color palette\ncv2.imshow(\"palette\", palette.to_image())\ncv2.waitKey(200)\n\nprint(\"Computing gradient...\")\ngradient = VectorField.from_gradient(gray)\n\nprint(\"Smoothing gradient...\")\ngradient.smooth(gradient_smoothing_radius)\n\nprint(\"Drawing image...\")\n# create a \"cartonized\" version of the image to use as a base for the painting\nres = cv2.medianBlur(img, 11)\n# define a randomized grid of locations for the brush strokes\ngrid = randomized_grid(img.shape[0], img.shape[1], scale=3)\nbatch_size = 10000\n\nbar = progressbar.ProgressBar()\nfor h in bar(range(0, len(grid), batch_size)):\n # get the pixel colors at each point of the grid\n pixels = np.array([img[x[0], x[1]] for x in grid[h:min(h + batch_size, len(grid))]])\n # precompute the probabilities for each color in the palette\n # lower values of k means more randomnes\n color_probabilities = compute_color_probabilities(pixels, palette, k=9)\n\n for i, (y, x) in enumerate(grid[h:min(h + batch_size, len(grid))]):\n color = color_select(color_probabilities[i], palette)\n angle = math.degrees(gradient.direction(y, x)) + 90\n length = int(round(stroke_scale + stroke_scale * math.sqrt(gradient.magnitude(y, x))))\n\n # draw the brush stroke\n cv2.ellipse(res, (x, y), (length, stroke_scale), angle, 0, 360, color, -1, cv2.LINE_AA)\n\n\ncv2.imshow(\"res\", limit_size(res, 1080))\ncv2.imwrite(res_path, res)\ncv2.waitKey(0)\n", "repo_name": "matteo-ronchetti/Pointillism", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 3068, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 142, "dataset": "github-code", "pt": "37", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 7, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 17, "usage_type": "call"}, {"api_name": "math.ceil", "line_number": 23, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 35, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 35, "usage_type": "attribute"}, {"api_name": "cv2.imshow", "line_number": 44, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 45, "usage_type": "call"}, {"api_name": "cv2.medianBlur", "line_number": 55, "usage_type": "call"}, {"api_name": "progressbar.ProgressBar", "line_number": 60, "usage_type": "call"}, {"api_name": "math.degrees", "line_number": 70, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 71, "usage_type": "call"}, {"api_name": "cv2.ellipse", "line_number": 74, "usage_type": "call"}, {"api_name": "cv2.LINE_AA", "line_number": 74, "usage_type": "attribute"}, {"api_name": "cv2.imshow", "line_number": 77, "usage_type": "call"}, {"api_name": "cv2.imwrite", "line_number": 78, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 79, "usage_type": "call"}]} +{"seq_id": "41064645116", "text": "from django.contrib import admin\nfrom . import models\n\nfrom django.urls import path\nfrom django.utils.html import format_html\nfrom django.http.response import HttpResponse\nfrom datetime import datetime\nfrom django.contrib import messages\nfrom django.template.response import TemplateResponse\nfrom django.shortcuts import redirect\nimport requests\nimport re\nimport json\nimport logging\n\nfrom requests.exceptions import ConnectionError\n\nfrom .scrapyd import ScraydAPI\n\nlogger = logging.getLogger(\"django\")\n\n\n@admin.register(models.CrawlerNode)\nclass CrawlerNodeAdmin(admin.ModelAdmin):\n list_display = ('name', 'host', 'port', 'status')\n\n def status(self, obj):\n return format_html('status'.format(obj.id))\n\n status.short_description = 'Status'\n\n def get_urls(self):\n urls = super(CrawlerNodeAdmin, self).get_urls()\n my_urls = [\n path('status/run-spider//',\n self.admin_site.admin_view(self.run_spider),\n name=\"run_spider\"),\n path('status/stop-spider//', self.admin_site.admin_view(self.cancel_job_view),\n name=\"stop_job\"),\n path('status/', self.admin_site.admin_view(self.status_view)),\n path('logs/stats///', self.admin_site.admin_view(self.log_stats_view)),\n path('logs/detail///', self.admin_site.admin_view(self.log_detail_view)),\n ]\n return my_urls + urls\n\n def status_view(self, request, cid):\n context = dict()\n try:\n crawler = models.CrawlerNode.objects.get(pk=cid)\n scrapy_client = ScraydAPI(host=crawler.host, port=crawler.port)\n\n jobs = scrapy_client.listjobs()\n jobs['finished'] = list(reversed(jobs['finished']))[:15]\n for job in jobs['finished']:\n during = datetime.strptime(job[\"end_time\"], \"%Y-%m-%d %H:%M:%S.%f\") - datetime.strptime(\n job[\"start_time\"], \"%Y-%m-%d %H:%M:%S.%f\")\n during = int(during.total_seconds())\n if during < 60:\n during = f'{during} seconds'\n elif during < 3600:\n during = f'{int(during/60)} minutes'\n else:\n hours = int(during / 3600)\n minutes = int((during - hours * 3600) / 60)\n during = f'{hours} hours {minutes} minutes'\n\n job[\"during\"] = during\n\n spiders = scrapy_client.listspiders()[\"spiders\"]\n\n context = dict(\n # Include common variables for rendering the admin template.\n self.admin_site.each_context(request),\n # Anything else you want in the context...\n cid=cid,\n status=scrapy_client.daemonstatus(),\n jobs=jobs,\n spiders=spiders,\n log_url=\"http://{}:{}/logs/default\".format(crawler.host, crawler.port),\n )\n except ConnectionError as e:\n messages.add_message(request, messages.ERROR, \"Error: Can't connect to scrapyd\")\n\n return TemplateResponse(request, \"scrapyd/status.html\", context)\n\n def run_spider(self, request, cid, spider):\n try:\n try:\n params = request.GET.get('params', None)\n args = {}\n if params:\n peices = params.split(\",\")\n for item in peices:\n temp = item.strip().split(\"=\")\n args[temp[0].strip()] = temp[1].strip()\n except Exception:\n raise Exception(\"Parse params error\")\n\n crawler = models.CrawlerNode.objects.get(pk=cid)\n scrapy_client = ScraydAPI(host=crawler.host, port=crawler.port, spider=spider)\n response = scrapy_client.schedule(args=args)\n\n msg = 'Job id: {}'.format(response[\"jobid\"])\n if params:\n msg += '. Params: {}'.format(params)\n messages.add_message(request, messages.SUCCESS, msg)\n except Exception as e:\n messages.add_message(request, messages.ERROR, \"Error: {}\".format(str(e)))\n return redirect(\"/admin/scrapyd/crawlernode/status/{}\".format(cid))\n\n def cancel_job_view(self, request, cid, job_id):\n try:\n crawler = models.CrawlerNode.objects.get(pk=cid)\n scrapy_client = ScraydAPI(host=crawler.host, port=crawler.port)\n response = scrapy_client.cancel(job_id=job_id)\n if response[\"status\"] == \"ok\":\n msg = 'Closing Job id: {}'.format(job_id)\n messages.add_message(request, messages.SUCCESS, msg)\n else:\n msg = \"Error: {}\".format(str(response))\n messages.add_message(request, messages.ERROR, msg)\n except Exception as e:\n logger.exception(e)\n messages.add_message(request, messages.ERROR, \"Error: {}\".format(str(e)))\n return redirect(\"/admin/scrapyd/crawlernode/status/{}\".format(cid))\n\n def log_stats_view(self, request, cid, spider, job_id):\n try:\n crawler = models.CrawlerNode.objects.get(pk=cid)\n url = \"http://{}:{}/logs/default/{}/{}.log\".format(crawler.host, crawler.port, spider, job_id)\n response = requests.get(url)\n content = response.text\n matches = re.search(r\"Dumping Scrapy stats:(.*)\\d\\d\\d\\d-\\d\\d\", content.replace(\"\\n\", \"\"))\n stats = matches.group(1)\n\n stats = re.sub(r'(?is)(datetime\\.datetime\\([\\d\\,\\s]+\\))', r'\"\\1\"', stats).replace(\"'\", '\"')\n stats = json.loads(stats)\n\n result = {\n \"result\": {\n \"item_scraped_count\": 0,\n \"response_received_count\": 0,\n \"finish_reason\": \"\"\n },\n \"downloader\": {},\n \"log_count\": {},\n \"scheduler\": {},\n \"item_scraped_count\": 0,\n \"finish_reason\": \"\",\n \"more\": {},\n }\n for k, v in stats.items():\n if k.startswith(\"downloader\"):\n result[\"downloader\"][k[11:]] = v\n elif k.startswith(\"log_count\"):\n result[\"log_count\"][k[10:]] = v\n elif k.startswith(\"scheduler\"):\n result[\"scheduler\"][k[10:]] = v\n elif k in result['result'].keys():\n result['result'][k] = v\n else:\n if k in ['start_time', 'finish_time']:\n # result[\"more\"][k] = str(eval(v))\n continue\n else:\n result[\"more\"][k] = v\n\n return TemplateResponse(request, \"scrapyd/log_stats.html\", {\"data\": result})\n except Exception as e:\n # logger.exception(e)\n raise e\n\n def log_detail_view(self, request, cid, spider, job_id):\n crawler = models.CrawlerNode.objects.get(pk=cid)\n url = \"http://{}:{}/logs/default/{}/{}.log\".format(crawler.host, crawler.port, spider, job_id)\n response = requests.get(url)\n content = response.text\n return HttpResponse(content, content_type='text/plain')\n", "repo_name": "devopszcom/django-scrapyd", "sub_path": "scrapyd/admin.py", "file_name": "admin.py", "file_ext": "py", "file_size_in_byte": 7422, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "37", "api": [{"api_name": "logging.getLogger", "line_number": 20, "usage_type": "call"}, {"api_name": "django.contrib.admin.ModelAdmin", "line_number": 24, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 24, "usage_type": "name"}, {"api_name": "django.utils.html.format_html", "line_number": 28, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 35, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 38, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 40, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 41, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 42, "usage_type": "call"}, {"api_name": "scrapyd.ScraydAPI", "line_number": 50, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 55, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 55, "usage_type": "name"}, {"api_name": "requests.exceptions.ConnectionError", "line_number": 81, "usage_type": "name"}, {"api_name": "django.contrib.messages.add_message", "line_number": 82, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 82, "usage_type": "name"}, {"api_name": "django.contrib.messages.ERROR", "line_number": 82, "usage_type": "attribute"}, {"api_name": "django.template.response.TemplateResponse", "line_number": 84, "usage_type": "call"}, {"api_name": "scrapyd.ScraydAPI", "line_number": 100, "usage_type": "call"}, {"api_name": "django.contrib.messages.add_message", "line_number": 106, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 106, "usage_type": "name"}, {"api_name": "django.contrib.messages.SUCCESS", "line_number": 106, "usage_type": "attribute"}, {"api_name": "django.contrib.messages.add_message", "line_number": 108, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 108, "usage_type": "name"}, {"api_name": "django.contrib.messages.ERROR", "line_number": 108, "usage_type": "attribute"}, {"api_name": "django.shortcuts.redirect", "line_number": 109, "usage_type": "call"}, {"api_name": "scrapyd.ScraydAPI", "line_number": 114, "usage_type": "call"}, {"api_name": "django.contrib.messages.add_message", "line_number": 118, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 118, "usage_type": "name"}, {"api_name": "django.contrib.messages.SUCCESS", "line_number": 118, "usage_type": "attribute"}, {"api_name": "django.contrib.messages.add_message", "line_number": 121, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 121, "usage_type": "name"}, {"api_name": "django.contrib.messages.ERROR", "line_number": 121, "usage_type": "attribute"}, {"api_name": "django.contrib.messages.add_message", "line_number": 124, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 124, "usage_type": "name"}, {"api_name": "django.contrib.messages.ERROR", "line_number": 124, "usage_type": "attribute"}, {"api_name": "django.shortcuts.redirect", "line_number": 125, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 131, "usage_type": "call"}, {"api_name": "re.search", "line_number": 133, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 136, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 137, "usage_type": "call"}, {"api_name": "django.template.response.TemplateResponse", "line_number": 168, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 176, "usage_type": "call"}, {"api_name": "django.http.response.HttpResponse", "line_number": 178, "usage_type": "call"}, {"api_name": "django.contrib.admin.register", "line_number": 23, "usage_type": "call"}, {"api_name": "django.contrib.admin", "line_number": 23, "usage_type": "name"}]} +{"seq_id": "7190618368", "text": "import re\r\nimport sqlite3\r\n\r\nnumber=input('Enter article number again')\r\n\r\nconn = sqlite3.connect('markersrating.sqlite')\r\ncur = conn.cursor()\r\n\r\ncur.execute('SELECT id, hypothesis_1, Value FROM MarkersRating')\r\nmrsstatus=dict()\r\nwhile True:\r\n row=cur.fetchone()\r\n if row is None:\r\n break\r\n cellname=row[1]\r\n mrsrating=row[2]\r\n mrsstatus[cellname]=mrsrating\r\n\r\ncur.close()\r\n\r\nconn_1 = sqlite3.connect('markersrating.sqlite')\r\ncur_1 = conn_1.cursor()\r\n\r\ncur_1.execute('SELECT id, hypothesis_2, Value FROM CellnameRating')\r\nnamestatus=dict()\r\nwhile True:\r\n row=cur_1.fetchone()\r\n if row is None:\r\n break\r\n cellsname=row[1]\r\n namerating=row[2]\r\n namestatus[cellsname]=namerating\r\n\r\ncur_1.close()\r\n\r\nfinalstatus=mrsstatus\r\nfor k,v in finalstatus.items():\r\n if k in namestatus.keys():\r\n finalstatus[k]=(int(namestatus[k])+int(v))\r\n else:\r\n finalstatus[k]=int(v)\r\n \r\nfor key,val in namestatus.items():\r\n if key not in finalstatus.keys():\r\n finalstatus[key]=int(val)\r\n else:\r\n continue\r\n \r\nfinalrating={k: finalstatus[k] for k in sorted(finalstatus, reverse=True, key=finalstatus.get)}\r\nprint(finalrating)\r\n \r\nconn_2 = sqlite3.connect('markersrating.sqlite')\r\ncur_2 = conn_2.cursor()\r\n\r\nfor k,v in finalrating.items():\r\n cur_2.execute('UPDATE FinalRating SET Article_'+number+ '=(?) WHERE CellNames=(?)',(v,k))\r\n\r\nconn_2.commit()\r\ncur_2.close()\r\n", "repo_name": "AlenaSt97/Article_research_v1", "sub_path": "finalcalc.py", "file_name": "finalcalc.py", "file_ext": "py", "file_size_in_byte": 1443, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "sqlite3.connect", "line_number": 6, "usage_type": "call"}, {"api_name": "sqlite3.connect", "line_number": 21, "usage_type": "call"}, {"api_name": "sqlite3.connect", "line_number": 52, "usage_type": "call"}]} +{"seq_id": "5464676640", "text": "from nalaf.utils.readers import HTMLReader\nfrom nalaf.utils.annotation_readers import AnnJsonAnnotationReader\nfrom nalaf.learning.taggers import StubSameSentenceRelationExtractor\nfrom nalaf.learning.evaluators import DocumentLevelRelationEvaluator, Evaluations\nfrom nalaf.structures.relation_pipelines import RelationExtractionPipeline\nfrom nalaf.preprocessing.tokenizers import TmVarTokenizer, NLTK_TOKENIZER\nfrom nalaf.learning.svmlight import SVMLightTreeKernels\nfrom nalaf.preprocessing.parsers import SpacyParser\nfrom spacy.en import English\n# from relna.learning.taggers import TranscriptionFactorTagger\nfrom relna.learning.taggers import RelnaRelationExtractor\nimport argparse\nimport math\n\n\ndef parse_arguments(argv):\n\n parser = argparse.ArgumentParser(description='Simple-evaluate relna corpus corpus')\n\n parser.add_argument('--corpus', default=\"relna\", choices=[\"relna\"])\n parser.add_argument('--corpus_percentage', default=0.5, type=float, help='e.g. 1 == full corpus; 0.5 == 50%% of corpus')\n parser.add_argument('--minority_class', type=int, default=1, choices=[-1, 1])\n parser.add_argument('--majority_class_undersampling', type=float, default=0.4)\n parser.add_argument('--use_test_set', default=False, action='store_true')\n parser.add_argument('--k_num_folds', type=int, default=5)\n parser.add_argument('--use_tk', default=False, action='store_true')\n\n args = parser.parse_args(argv)\n\n if args.corpus == \"relna\":\n args.dataset_folder_html = './resources/corpora/relna/corrected/'\n args.dataset_folder_annjson = args.dataset_folder_html\n args.e_id_1 = 'e_1'\n args.e_id_2 = 'e_2'\n args.r_id = 'r_4'\n\n print(args)\n\n return args\n\n\ndef read_dataset(args):\n\n dataset = HTMLReader(args.dataset_folder_html).read()\n AnnJsonAnnotationReader(\n args.dataset_folder_annjson,\n read_only_class_id=None,\n read_relations=True,\n delete_incomplete_docs=False,\n raise_exception_on_incosistencies=False).annotate(dataset)\n\n return dataset\n\n\ndef test_baseline(argv=None):\n argv = [] if argv is None else argv\n args = parse_arguments(argv)\n\n dataset = read_dataset(args)\n\n # Computation(precision=0.389351081530782, precision_SE=0.0021024361502353277, recall=0.9790794979079498, recall_SE=0.0007302523934357751, f_measure=0.5571428571428572, f_measure_SE=0.0021357013961776057)\n # Full corpus\n EXPECTED_F = 0.5571\n EXPECTED_F_SE = 0.0021\n\n annotator_gen_fun = (lambda _: StubSameSentenceRelationExtractor(args.e_id_1, args.e_id_2, args.r_id).annotate)\n evaluator = DocumentLevelRelationEvaluator(rel_type=args.r_id)\n\n evaluations = Evaluations.cross_validate(annotator_gen_fun, dataset, evaluator, k_num_folds=5, use_validation_set=True)\n rel_evaluation = evaluations(args.r_id).compute(strictness=\"exact\")\n\n assert math.isclose(rel_evaluation.f_measure, EXPECTED_F, abs_tol=EXPECTED_F_SE * 1.1), rel_evaluation.f_measure\n\n\ndef test_relna(argv=None):\n argv = [] if argv is None else argv\n args = parse_arguments(argv)\n\n if (args.corpus_percentage == 1.0):\n dataset = read_dataset(args)\n # Beware that performance depends a lot on the undersampling and svm threshold\n\n # Current performance :(\n # # class\ttp\tfp\tfn\tfp_ov\tfn_ov\te|P\te|R\te|F\te|F_SE\to|P\to|R\to|F\to|F_SE\n # r_4\t175\t113\t64\t0\t0\t0.6076\t0.7322\t0.6641\t0.0021\t0.6076\t0.7322\t0.6641\t0.0020\n EXPECTED_F = 0.6979\n EXPECTED_F_SE = 0.0019\n\n else:\n dataset, _ = read_dataset(args).percentage_split(args.corpus_percentage)\n\n if (args.corpus_percentage == 0.5):\n EXPECTED_F = 0.6094\n EXPECTED_F_SE = 0.0029\n else:\n # This is not to be tested and will fail\n EXPECTED_F = 0.5\n EXPECTED_F_SE = 0.00001\n\n if args.use_tk:\n nlp = English(entity=False)\n parser = SpacyParser(nlp, constituency_parser=True)\n else:\n parser = None\n\n def train(training_set):\n feature_generators = RelnaRelationExtractor.default_feature_generators(args.e_id_1, args.e_id_2)\n pipeline = RelationExtractionPipeline(args.e_id_1, args.e_id_2, args.r_id, parser=parser, tokenizer=TmVarTokenizer(), feature_generators=feature_generators)\n\n pipeline.execute(training_set, train=True)\n\n # CAUTION! previous relna svm_light had the threshold of prediction at '-0.1' -- nalaf changed it to 0 (assumed to be correct) -- This does change the performance and actually reduce it in this example\n # http://svmlight.joachims.org For classification, the sign of this value determines the predicted class -- CAUTION, relna (Ashish), had it set before to exactly: '-0.1' (was this a bug or a conscious decision to move the threshold of classification?)\n # See more information in: https://github.com/Rostlab/relna/issues/21\n svmlight = SVMLightTreeKernels(classification_threshold=-0.1, use_tree_kernel=args.use_tk)\n instancesfile = svmlight.create_input_file(training_set, 'train', pipeline.feature_set, minority_class=args.minority_class, majority_class_undersampling=args.majority_class_undersampling)\n svmlight.learn(instancesfile, c=0.5)\n\n def annotator(validation_set):\n pipeline.execute(validation_set, train=False)\n instancesfile = svmlight.create_input_file(validation_set, 'predict', pipeline.feature_set)\n predictionsfile = svmlight.classify(instancesfile)\n\n svmlight.read_predictions(validation_set, predictionsfile)\n return validation_set\n\n return annotator\n\n evaluator = DocumentLevelRelationEvaluator(rel_type=args.r_id)\n evaluations = Evaluations.cross_validate(train, dataset, evaluator, args.k_num_folds, use_validation_set=not args.use_test_set)\n rel_evaluation = evaluations(args.r_id).compute(strictness=\"exact\")\n\n assert math.isclose(rel_evaluation.f_measure, EXPECTED_F, abs_tol=EXPECTED_F_SE * 1.1)\n\n\nif __name__ == \"__main__\":\n import sys\n real_args = sys.argv[1:]\n test_baseline(real_args)\n test_relna(real_args)\n", "repo_name": "Rostlab/relna", "sub_path": "tests/test_simple_evaluation.py", "file_name": "test_simple_evaluation.py", "file_ext": "py", "file_size_in_byte": 6122, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 11, "dataset": "github-code", "pt": "37", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 18, "usage_type": "call"}, {"api_name": "nalaf.utils.readers.HTMLReader", "line_number": 44, "usage_type": "call"}, {"api_name": "nalaf.utils.annotation_readers.AnnJsonAnnotationReader", "line_number": 45, "usage_type": "call"}, {"api_name": "nalaf.learning.taggers.StubSameSentenceRelationExtractor", "line_number": 66, "usage_type": "call"}, {"api_name": "nalaf.learning.evaluators.DocumentLevelRelationEvaluator", "line_number": 67, "usage_type": "call"}, {"api_name": "nalaf.learning.evaluators.Evaluations.cross_validate", "line_number": 69, "usage_type": "call"}, {"api_name": "nalaf.learning.evaluators.Evaluations", "line_number": 69, "usage_type": "name"}, {"api_name": "math.isclose", "line_number": 72, "usage_type": "call"}, {"api_name": "spacy.en.English", "line_number": 101, "usage_type": "call"}, {"api_name": "nalaf.preprocessing.parsers.SpacyParser", "line_number": 102, "usage_type": "call"}, {"api_name": "relna.learning.taggers.RelnaRelationExtractor.default_feature_generators", "line_number": 107, "usage_type": "call"}, {"api_name": "relna.learning.taggers.RelnaRelationExtractor", "line_number": 107, "usage_type": "name"}, {"api_name": "nalaf.structures.relation_pipelines.RelationExtractionPipeline", "line_number": 108, "usage_type": "call"}, {"api_name": "nalaf.preprocessing.tokenizers.TmVarTokenizer", "line_number": 108, "usage_type": "call"}, {"api_name": "nalaf.learning.svmlight.SVMLightTreeKernels", "line_number": 115, "usage_type": "call"}, {"api_name": "nalaf.learning.evaluators.DocumentLevelRelationEvaluator", "line_number": 129, "usage_type": "call"}, {"api_name": "nalaf.learning.evaluators.Evaluations.cross_validate", "line_number": 130, "usage_type": "call"}, {"api_name": "nalaf.learning.evaluators.Evaluations", "line_number": 130, "usage_type": "name"}, {"api_name": "math.isclose", "line_number": 133, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 138, "usage_type": "attribute"}]} +{"seq_id": "18156411530", "text": "#for pdf2text\nfrom io import StringIO\nfrom pdfminer.pdfinterp import PDFResourceManager, PDFPageInterpreter\nfrom pdfminer.converter import TextConverter\nfrom pdfminer.layout import LAParams\nfrom pdfminer.pdfpage import PDFPage\nimport os\nimport sys, getopt\n\n#for text2pdf\nfrom docx import Document\nfrom docx.shared import Inches\n\ndef pdf2text(fname):\n pagenums = set()\n output = StringIO()\n manager = PDFResourceManager()\n converter = TextConverter(manager, output, laparams=LAParams())\n interpreter = PDFPageInterpreter(manager, converter)\n\n infile = open(fname, 'rb')\n for page in PDFPage.get_pages(infile, pagenums):\n interpreter.process_page(page)\n infile.close()\n converter.close()\n text = output.getvalue()\n output.close\n return text\n\ndef text2docx(text,path):\n document = Document()\n document.add_paragraph(text)\n document.save(path + '/' + \"output.docx\")\n\ndef text2file(text):\n file = open(\"/media/legendary-acp/Development/Git/Intelligent-OCR-Scanner-Website/static/Output/output.txt\",\"w+\")\n file.write(text)\n file.close() \n\n\ndef pdf2docx(pathl,path):\n text = pdf2text(pathl)\n #text2docx(text,path)\n text2file(text)\n\n\n#pdf2docx(\"/media/legendary-acp/Development/Git/Intelligent-OCR-Scanner-Website/static/Uploads/DBMS_Syllabus.pdf\", \"/media/legendary-acp/Development/Git/Intelligent-OCR-Scanner-Website/static/Uploads\")", "repo_name": "zhengdeding/Intelligent-OCR-Scanner-Website", "sub_path": "conversion.py", "file_name": "conversion.py", "file_ext": "py", "file_size_in_byte": 1397, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "io.StringIO", "line_number": 16, "usage_type": "call"}, {"api_name": "pdfminer.pdfinterp.PDFResourceManager", "line_number": 17, "usage_type": "call"}, {"api_name": "pdfminer.converter.TextConverter", "line_number": 18, "usage_type": "call"}, {"api_name": "pdfminer.layout.LAParams", "line_number": 18, "usage_type": "call"}, {"api_name": "pdfminer.pdfinterp.PDFPageInterpreter", "line_number": 19, "usage_type": "call"}, {"api_name": "pdfminer.pdfpage.PDFPage.get_pages", "line_number": 22, "usage_type": "call"}, {"api_name": "pdfminer.pdfpage.PDFPage", "line_number": 22, "usage_type": "name"}, {"api_name": "docx.Document", "line_number": 31, "usage_type": "call"}]} +{"seq_id": "19141742293", "text": "from main_func import *\n\n\nfrom argparse import ArgumentParser\n\nparser = ArgumentParser()\nparser.add_argument('which',type=int)\nargs = parser.parse_args()\n\n# with concurrent.futures.ProcessPoolExecutor(max_workers=32) as executor: \n# res=[]\n# for d in Path(r'/data/scsnake/ccta/').glob(r'S*/'):\nd = sorted(Path(r'/data/scsnake/ccta/').glob(r'S?????/'))[args.which]\nprint('Processing: {}'.format(str(d)))\nct_dir = ''\nres_dir = ''\n\nfor d1 in d.glob(r'originalDATA_name*/'):\n for d2 in d1.iterdir():\n ct_dir = str(d2)\n break\n break\nfor d1 in d.glob(r'centerlineDATA*/'):\n for d2 in d1.iterdir():\n res_dir = str(d2)\n break\n break\n\n# %%pixie_debugger\nct = CtVolume()\nct.load_image_data(ct_dir + '/')\n\nresult = parse_results(res_dir + '/')\n\n\n## benchmark\ncor = Coronary(result['M1'])\ns_mask, s_mpr = straighten_data_mask(\n ct, cor, output_dim=(100, 100), precision=(2, 2), output_spacing=0.2)\n\nif 0:\n try:\n for vessel in result.keys():\n # res.append(executor.submit(save_mask, ct, result, vessel, save_dir ='/data/scsnake/ccta/'+ct.id)) \n save_mask(ct, result, vessel, save_dir ='/data/scsnake/ccta/'+ d.name + '_' +ct.id)\n\n except Exception as ex:\n print(ex)\n \n# save_straightened_mask(ct, result, save_dir = '/data/scsnake/ccta/'+ct.id +'_straight' ,precision=(3,3))\n# save_mask(ct, result, save_dir ='/data/scsnake/ccta/_'+ct.id ,precision=(3,2,2))\n# for f in futures.as_completed(wait_for):\n# pass\n", "repo_name": "scsnake/philips_read", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 1525, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 6, "usage_type": "call"}]} +{"seq_id": "29567649673", "text": "import pony.orm as pny\nfrom Database import db\nfrom Database.users import User\nimport datetime\nimport uuid\n\n#these are all db tables related to the workflow system\n\nclass MessageLog(db.Entity):\n uuid = pny.PrimaryKey(str)\n status = pny.Required(str)\n date_submitted = pny.Required(datetime.datetime, precision=6)\n date_received = pny.Optional(datetime.datetime, precision=6)\n date_completed = pny.Optional(datetime.datetime, precision=6)\n completion_time = pny.Optional(datetime.timedelta, precision=6)\n originator = pny.Required(str)\n destination = pny.Required(str)\n incident_id = pny.Required(str)\n message = pny.Required(str)\n comment = pny.Optional(pny.LongStr)\n consumer = pny.Optional(str)\n src_tag = pny.Optional(str) #optional name for the sender (for workflow graph visualisation)\n dest_tag = pny.Optional(str) #optional name for the reciever (for workflow graph visualisation)\n\nclass StoredDataset(db.Entity):\n uuid = pny.PrimaryKey(str)\n name = pny.Optional(str)\n type = pny.Optional(str)\n comment = pny.Optional(str)\n incident = pny.Required(\"Incident\")\n date_created = pny.Optional(datetime.datetime)\n\nclass Incident(db.Entity):\n uuid = pny.PrimaryKey(str)\n kind = pny.Required(str)\n name = pny.Required(str)\n status = pny.Required(str, default=\"PENDING\")\n comment = pny.Optional(str)\n user_id = pny.Optional(User)\n upper_left_latlong = pny.Optional(str)\n lower_right_latlong = pny.Optional(str)\n duration = pny.Optional(int)\n\n date_started = pny.Required(datetime.datetime)\n date_completed = pny.Optional(datetime.datetime)\n\n incident_date = pny.Required(datetime.datetime)\n\n parameters = pny.Optional(str)\n\n simulations = pny.Set(\"Simulation\") \n\n associated_datasets = pny.Set(StoredDataset)\n\n#Stores records of simulations submitted to HPC machines\nclass Simulation(db.Entity):\n uuid = pny.PrimaryKey(str)\n incident = pny.Required(Incident)\n date_created = pny.Required(datetime.datetime)\n status = pny.Required(str,default=\"PENDING\")\n status_updated = pny.Required(datetime.datetime)\n directory = pny.Required(str)\n comment = pny.Optional(str)\n status_message = pny.Optional(str)\n machine = pny.Optional(\"Machine\")\n queue = pny.Optional(str)\n jobID = pny.Optional(str)\n wkdir = pny.Optional(str)\n executable = pny.Required(str)\n kind = pny.Required(str)\n results_handler = pny.Optional(str)\n requested_walltime = pny.Optional(str)\n walltime = pny.Optional(str)\n machine_queue_time = pny.Optional(str)\n machine_run_time = pny.Optional(str)\n num_nodes = pny.Optional(int)\n queue_state_calls = pny.Set(\"SimulationStateWorkflowCalls\")\n performance_data = pny.Set(\"PerformanceData\")\n simulation_group = pny.Optional(\"SimulationGroup\")\n \nclass SimulationStateWorkflowCalls(db.Entity):\n id = pny.PrimaryKey(int, auto=True)\n queue_state = pny.Required(str)\n call_name = pny.Required(str)\n simulation = pny.Required(Simulation)\n\nclass SimulationGroup(db.Entity):\n id = pny.PrimaryKey(int, auto=True)\n completion_callback_issued = pny.Required(bool, default=False)\n simulation = pny.Set(Simulation)\n\n#lock for workflow handlers\nclass Lock(db.Entity):\n name = pny.PrimaryKey(str)\n date = pny.Optional(datetime.datetime)\n locked = pny.Required(bool, default=False)\n\n#a log for the handlers to persist some data\nclass HandlerLog(db.Entity):\n incident = pny.Required(str)\n originator = pny.Required(str)\n data = pny.Required(pny.LongStr)\n\nclass RegisteredWorkflow(db.Entity):\n kind=pny.Required(str)\n init_queue_name=pny.Required(str)\n data_queue_name=pny.Optional(str)\n shutdown_queue_name=pny.Optional(str)\n test_workflow = pny.Required(bool, default=False, sql_default='0')\n users = pny.Set(\"User\")\n", "repo_name": "VESTEC-EU/vestec-system", "sub_path": "Database/workflow.py", "file_name": "workflow.py", "file_ext": "py", "file_size_in_byte": 3843, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "37", "api": [{"api_name": "Database.db.Entity", "line_number": 9, "usage_type": "attribute"}, {"api_name": "Database.db", "line_number": 9, "usage_type": "name"}, {"api_name": "pony.orm.PrimaryKey", "line_number": 10, "usage_type": "call"}, {"api_name": "pony.orm", "line_number": 10, "usage_type": "name"}, {"api_name": "pony.orm.Required", "line_number": 11, "usage_type": "call"}, {"api_name": "pony.orm", "line_number": 11, "usage_type": "name"}, {"api_name": "pony.orm.Required", "line_number": 12, "usage_type": "call"}, {"api_name": "pony.orm", "line_number": 12, "usage_type": "name"}, {"api_name": "datetime.datetime", "line_number": 12, "usage_type": "attribute"}, {"api_name": "pony.orm.Optional", "line_number": 13, "usage_type": "call"}, {"api_name": "pony.orm", "line_number": 13, "usage_type": "name"}, {"api_name": "datetime.datetime", "line_number": 13, "usage_type": "attribute"}, {"api_name": "pony.orm.Optional", "line_number": 14, "usage_type": "call"}, {"api_name": "pony.orm", "line_number": 14, "usage_type": "name"}, {"api_name": "datetime.datetime", "line_number": 14, "usage_type": "attribute"}, {"api_name": "pony.orm.Optional", "line_number": 15, "usage_type": "call"}, {"api_name": "pony.orm", "line_number": 15, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 15, "usage_type": "attribute"}, {"api_name": "pony.orm.Required", "line_number": 16, "usage_type": "call"}, {"api_name": "pony.orm", "line_number": 16, "usage_type": "name"}, {"api_name": "pony.orm.Required", "line_number": 17, "usage_type": "call"}, {"api_name": "pony.orm", "line_number": 17, "usage_type": "name"}, {"api_name": "pony.orm.Required", "line_number": 18, "usage_type": "call"}, {"api_name": "pony.orm", "line_number": 18, "usage_type": "name"}, {"api_name": "pony.orm.Required", "line_number": 19, "usage_type": "call"}, {"api_name": "pony.orm", "line_number": 19, "usage_type": "name"}, {"api_name": "pony.orm.Optional", "line_number": 20, "usage_type": "call"}, {"api_name": "pony.orm", "line_number": 20, "usage_type": "name"}, {"api_name": "pony.orm.LongStr", "line_number": 20, "usage_type": "attribute"}, {"api_name": "pony.orm.Optional", "line_number": 21, "usage_type": "call"}, {"api_name": "pony.orm", "line_number": 21, "usage_type": "name"}, {"api_name": "pony.orm.Optional", "line_number": 22, "usage_type": "call"}, {"api_name": "pony.orm", "line_number": 22, "usage_type": "name"}, {"api_name": "pony.orm.Optional", "line_number": 23, "usage_type": "call"}, {"api_name": "pony.orm", "line_number": 23, "usage_type": "name"}, {"api_name": "Database.db.Entity", "line_number": 25, "usage_type": "attribute"}, {"api_name": "Database.db", "line_number": 25, "usage_type": "name"}, {"api_name": "pony.orm.PrimaryKey", "line_number": 26, "usage_type": "call"}, {"api_name": "pony.orm", "line_number": 26, "usage_type": "name"}, {"api_name": "pony.orm.Optional", "line_number": 27, "usage_type": "call"}, {"api_name": "pony.orm", "line_number": 27, "usage_type": "name"}, {"api_name": "pony.orm.Optional", "line_number": 28, "usage_type": "call"}, {"api_name": "pony.orm", "line_number": 28, "usage_type": "name"}, {"api_name": "pony.orm.Optional", "line_number": 29, "usage_type": "call"}, {"api_name": "pony.orm", "line_number": 29, "usage_type": "name"}, {"api_name": "pony.orm.Required", "line_number": 30, "usage_type": "call"}, {"api_name": "pony.orm", "line_number": 30, "usage_type": "name"}, {"api_name": "pony.orm.Optional", "line_number": 31, "usage_type": "call"}, {"api_name": "pony.orm", "line_number": 31, "usage_type": "name"}, {"api_name": "datetime.datetime", "line_number": 31, "usage_type": "attribute"}, {"api_name": "Database.db.Entity", "line_number": 33, "usage_type": "attribute"}, {"api_name": "Database.db", "line_number": 33, "usage_type": "name"}, {"api_name": "pony.orm.PrimaryKey", "line_number": 34, "usage_type": "call"}, {"api_name": "pony.orm", "line_number": 34, "usage_type": "name"}, {"api_name": "pony.orm.Required", "line_number": 35, "usage_type": "call"}, {"api_name": "pony.orm", "line_number": 35, "usage_type": "name"}, {"api_name": "pony.orm.Required", "line_number": 36, "usage_type": "call"}, {"api_name": "pony.orm", "line_number": 36, "usage_type": "name"}, {"api_name": "pony.orm.Required", "line_number": 37, "usage_type": "call"}, {"api_name": "pony.orm", "line_number": 37, "usage_type": "name"}, {"api_name": "pony.orm.Optional", "line_number": 38, "usage_type": "call"}, {"api_name": "pony.orm", "line_number": 38, "usage_type": "name"}, {"api_name": "pony.orm.Optional", "line_number": 39, "usage_type": "call"}, {"api_name": "Database.users.User", "line_number": 39, "usage_type": "argument"}, {"api_name": "pony.orm", "line_number": 39, "usage_type": "name"}, {"api_name": "pony.orm.Optional", "line_number": 40, "usage_type": "call"}, {"api_name": "pony.orm", "line_number": 40, "usage_type": "name"}, {"api_name": "pony.orm.Optional", "line_number": 41, "usage_type": "call"}, {"api_name": "pony.orm", "line_number": 41, "usage_type": "name"}, {"api_name": "pony.orm.Optional", "line_number": 42, "usage_type": "call"}, {"api_name": "pony.orm", "line_number": 42, "usage_type": "name"}, {"api_name": "pony.orm.Required", "line_number": 44, "usage_type": "call"}, {"api_name": "pony.orm", "line_number": 44, "usage_type": "name"}, {"api_name": "datetime.datetime", "line_number": 44, "usage_type": "attribute"}, {"api_name": "pony.orm.Optional", "line_number": 45, "usage_type": "call"}, {"api_name": "pony.orm", "line_number": 45, "usage_type": "name"}, {"api_name": "datetime.datetime", "line_number": 45, "usage_type": "attribute"}, {"api_name": "pony.orm.Required", "line_number": 47, "usage_type": "call"}, {"api_name": "pony.orm", "line_number": 47, "usage_type": "name"}, {"api_name": "datetime.datetime", "line_number": 47, "usage_type": "attribute"}, {"api_name": "pony.orm.Optional", "line_number": 49, "usage_type": "call"}, {"api_name": "pony.orm", "line_number": 49, "usage_type": "name"}, {"api_name": "pony.orm.Set", "line_number": 51, "usage_type": "call"}, {"api_name": "pony.orm", "line_number": 51, "usage_type": "name"}, {"api_name": "pony.orm.Set", "line_number": 53, "usage_type": "call"}, {"api_name": "pony.orm", "line_number": 53, "usage_type": "name"}, {"api_name": "Database.db.Entity", "line_number": 56, "usage_type": "attribute"}, {"api_name": "Database.db", "line_number": 56, "usage_type": "name"}, {"api_name": "pony.orm.PrimaryKey", "line_number": 57, "usage_type": "call"}, {"api_name": "pony.orm", "line_number": 57, "usage_type": "name"}, {"api_name": "pony.orm.Required", "line_number": 58, "usage_type": "call"}, {"api_name": "pony.orm", "line_number": 58, "usage_type": "name"}, {"api_name": "pony.orm.Required", "line_number": 59, "usage_type": "call"}, {"api_name": "pony.orm", "line_number": 59, "usage_type": "name"}, {"api_name": "datetime.datetime", "line_number": 59, "usage_type": "attribute"}, {"api_name": "pony.orm.Required", "line_number": 60, "usage_type": "call"}, {"api_name": "pony.orm", "line_number": 60, "usage_type": "name"}, {"api_name": "pony.orm.Required", "line_number": 61, "usage_type": "call"}, {"api_name": "pony.orm", "line_number": 61, "usage_type": "name"}, {"api_name": "datetime.datetime", "line_number": 61, "usage_type": "attribute"}, {"api_name": "pony.orm.Required", "line_number": 62, "usage_type": "call"}, {"api_name": "pony.orm", "line_number": 62, "usage_type": "name"}, {"api_name": "pony.orm.Optional", "line_number": 63, "usage_type": "call"}, {"api_name": "pony.orm", "line_number": 63, "usage_type": "name"}, {"api_name": "pony.orm.Optional", "line_number": 64, "usage_type": "call"}, {"api_name": "pony.orm", "line_number": 64, "usage_type": "name"}, {"api_name": "pony.orm.Optional", "line_number": 65, "usage_type": "call"}, {"api_name": "pony.orm", "line_number": 65, "usage_type": "name"}, {"api_name": "pony.orm.Optional", "line_number": 66, "usage_type": "call"}, {"api_name": "pony.orm", "line_number": 66, "usage_type": "name"}, {"api_name": "pony.orm.Optional", "line_number": 67, "usage_type": "call"}, {"api_name": "pony.orm", "line_number": 67, "usage_type": "name"}, {"api_name": "pony.orm.Optional", "line_number": 68, "usage_type": "call"}, {"api_name": "pony.orm", "line_number": 68, "usage_type": "name"}, {"api_name": "pony.orm.Required", "line_number": 69, "usage_type": "call"}, {"api_name": "pony.orm", "line_number": 69, "usage_type": "name"}, {"api_name": "pony.orm.Required", "line_number": 70, "usage_type": "call"}, {"api_name": "pony.orm", "line_number": 70, "usage_type": "name"}, {"api_name": "pony.orm.Optional", "line_number": 71, "usage_type": "call"}, {"api_name": "pony.orm", "line_number": 71, "usage_type": "name"}, {"api_name": "pony.orm.Optional", "line_number": 72, "usage_type": "call"}, {"api_name": "pony.orm", "line_number": 72, "usage_type": "name"}, {"api_name": "pony.orm.Optional", "line_number": 73, "usage_type": "call"}, {"api_name": "pony.orm", "line_number": 73, "usage_type": "name"}, {"api_name": "pony.orm.Optional", "line_number": 74, "usage_type": "call"}, {"api_name": "pony.orm", "line_number": 74, "usage_type": "name"}, {"api_name": "pony.orm.Optional", "line_number": 75, "usage_type": "call"}, {"api_name": "pony.orm", "line_number": 75, "usage_type": "name"}, {"api_name": "pony.orm.Optional", "line_number": 76, "usage_type": "call"}, {"api_name": "pony.orm", "line_number": 76, "usage_type": "name"}, {"api_name": "pony.orm.Set", "line_number": 77, "usage_type": "call"}, {"api_name": "pony.orm", "line_number": 77, "usage_type": "name"}, {"api_name": "pony.orm.Set", "line_number": 78, "usage_type": "call"}, {"api_name": "pony.orm", "line_number": 78, "usage_type": "name"}, {"api_name": "pony.orm.Optional", "line_number": 79, "usage_type": "call"}, {"api_name": "pony.orm", "line_number": 79, "usage_type": "name"}, {"api_name": "Database.db.Entity", "line_number": 81, "usage_type": "attribute"}, {"api_name": "Database.db", "line_number": 81, "usage_type": "name"}, {"api_name": "pony.orm.PrimaryKey", "line_number": 82, "usage_type": "call"}, {"api_name": "pony.orm", "line_number": 82, "usage_type": "name"}, {"api_name": "pony.orm.Required", "line_number": 83, "usage_type": "call"}, {"api_name": "pony.orm", "line_number": 83, "usage_type": "name"}, {"api_name": "pony.orm.Required", "line_number": 84, "usage_type": "call"}, {"api_name": "pony.orm", "line_number": 84, "usage_type": "name"}, {"api_name": "pony.orm.Required", "line_number": 85, "usage_type": "call"}, {"api_name": "pony.orm", "line_number": 85, "usage_type": "name"}, {"api_name": "Database.db.Entity", "line_number": 87, "usage_type": "attribute"}, {"api_name": "Database.db", "line_number": 87, "usage_type": "name"}, {"api_name": "pony.orm.PrimaryKey", "line_number": 88, "usage_type": "call"}, {"api_name": "pony.orm", "line_number": 88, "usage_type": "name"}, {"api_name": "pony.orm.Required", "line_number": 89, "usage_type": "call"}, {"api_name": "pony.orm", "line_number": 89, "usage_type": "name"}, {"api_name": "pony.orm.Set", "line_number": 90, "usage_type": "call"}, {"api_name": "pony.orm", "line_number": 90, "usage_type": "name"}, {"api_name": "Database.db.Entity", "line_number": 93, "usage_type": "attribute"}, {"api_name": "Database.db", "line_number": 93, "usage_type": "name"}, {"api_name": "pony.orm.PrimaryKey", "line_number": 94, "usage_type": "call"}, {"api_name": "pony.orm", "line_number": 94, "usage_type": "name"}, {"api_name": "pony.orm.Optional", "line_number": 95, "usage_type": "call"}, {"api_name": "pony.orm", "line_number": 95, "usage_type": "name"}, {"api_name": "datetime.datetime", "line_number": 95, "usage_type": "attribute"}, {"api_name": "pony.orm.Required", "line_number": 96, "usage_type": "call"}, {"api_name": "pony.orm", "line_number": 96, "usage_type": "name"}, {"api_name": "Database.db.Entity", "line_number": 99, "usage_type": "attribute"}, {"api_name": "Database.db", "line_number": 99, "usage_type": "name"}, {"api_name": "pony.orm.Required", "line_number": 100, "usage_type": "call"}, {"api_name": "pony.orm", "line_number": 100, "usage_type": "name"}, {"api_name": "pony.orm.Required", "line_number": 101, "usage_type": "call"}, {"api_name": "pony.orm", "line_number": 101, "usage_type": "name"}, {"api_name": "pony.orm.Required", "line_number": 102, "usage_type": "call"}, {"api_name": "pony.orm", "line_number": 102, "usage_type": "name"}, {"api_name": "pony.orm.LongStr", "line_number": 102, "usage_type": "attribute"}, {"api_name": "Database.db.Entity", "line_number": 104, "usage_type": "attribute"}, {"api_name": "Database.db", "line_number": 104, "usage_type": "name"}, {"api_name": "pony.orm.Required", "line_number": 105, "usage_type": "call"}, {"api_name": "pony.orm", "line_number": 105, "usage_type": "name"}, {"api_name": "pony.orm.Required", "line_number": 106, "usage_type": "call"}, {"api_name": "pony.orm", "line_number": 106, "usage_type": "name"}, {"api_name": "pony.orm.Optional", "line_number": 107, "usage_type": "call"}, {"api_name": "pony.orm", "line_number": 107, "usage_type": "name"}, {"api_name": "pony.orm.Optional", "line_number": 108, "usage_type": "call"}, {"api_name": "pony.orm", "line_number": 108, "usage_type": "name"}, {"api_name": "pony.orm.Required", "line_number": 109, "usage_type": "call"}, {"api_name": "pony.orm", "line_number": 109, "usage_type": "name"}, {"api_name": "pony.orm.Set", "line_number": 110, "usage_type": "call"}, {"api_name": "pony.orm", "line_number": 110, "usage_type": "name"}]} +{"seq_id": "18694247442", "text": "# encoding: utf-8\nimport os\nimport sys\n\nsys.path.append(os.path.abspath(os.path.join('..', 'resources')))\nfrom find_applicable_sandhis import FindApplicableSandhis\nfrom collections import OrderedDict\n\nfind_sandhis = FindApplicableSandhis('sanskrit', True)\n\n\ndef find_uninflected_stem(stem, form):\n \"\"\"\n Finds all the shared caracters from left to right.\n find_uninflected_stem('rAmaH', 'rAmo') => -1+aH\n\n :param stem: form to reach by applying the diff\n :param form: given form\n :return: a diff: '-+'\n \"\"\"\n i = 0\n while i <= len(stem) - 1 and i <= len(form) - 1 and stem[i] == form[i]:\n i += 1\n stem_ending = stem[i:]\n form_ending = form[i:]\n if stem_ending == '' and form_ending == '':\n operation = ''\n else:\n form_ending_len = len(form_ending)\n operation = '-{}+{}'.format(form_ending_len, stem_ending)\n return operation\n\n\ndef singled_entries(entries):\n singled = OrderedDict()\n for line in entries:\n form, value = line.split(',')\n value = adjust_new_initial_in_consonant1_sandhi(value)\n if form not in singled.keys():\n singled[form] = [value]\n else:\n if value not in singled[form]:\n singled[form].append(value)\n output = []\n for k, v in singled.items():\n output.append(k + ',' + '|'.join(v))\n return output\n\n\ndef adjust_new_initial_in_consonant1_sandhi(cmd):\n if '/=' not in cmd and '-+=' not in cmd and '- +=' not in cmd:\n initial, remainder = cmd.split('$')\n new_initial = cmd.split('/-')[1].split('+')[0].strip()\n if initial != new_initial:\n return '{}${}'.format(new_initial, remainder)\n return cmd\n\n\ndef sandhify(inflected_form):\n sandhied = find_sandhis.all_possible_sandhis(inflected_form)\n singled = singled_entries(sandhied)\n return singled\n\n\ndef sandhied_n_lemmatized_total(raw_pairs):\n \"\"\"\n applies apply_all_sandhis() on every entry in raw_pairs\n creates a new diff with the lemma from which the inflected form was derived\n discarding the diff produced to find the unsandhied inflected form.\n\n outputformat: ',,/'\n : ';;…'\n : '-+'\n : '-+'\n\n :param raw_pairs: [(inflected_form, lemma), …] generated by raw_parse_Heritage_XML.py\n :return: ex. ['prezyate,a$-1+;-6+I/-'+', 'aprezyata,A:i:u:U:f:e:E:o:O$-1+;-6+I/', …]\n \"\"\"\n\n def is_unknown_lemma(lemma, lemmas):\n if lemma not in lemmas.keys():\n lemmas[lemma] = True\n return True\n return False\n\n lemmas = {}\n\n total_sandhied = []\n for infl, lemma in raw_pairs:\n all_non_infl = []\n if '⟾' in lemma:\n lem, POS = lemma, '-1'\n all_non_infl.append((lem, POS))\n elif '/' in lemma:\n for l in lemma.split('/'):\n lem, POS = l[:-1], l[-1]\n all_non_infl.append((lem, POS))\n else:\n lem, POS = lemma[:-1], lemma[-1]\n all_non_infl.append((lem, POS))\n\n # adding the lemmas to the total output\n for l, pos in all_non_infl:\n if is_unknown_lemma(l, lemmas):\n if '—' in l:\n total_sandhied.append('{},${}/=0#{}'.format(infl, find_uninflected_stem(l, infl), pos))\n else:\n if l == infl:\n total_sandhied.append('{},$-0+/=0#{}'.format(l, pos))\n else:\n total_sandhied.append('{},${}/=0£9#{}'.format(infl, find_uninflected_stem(l, infl), POS))\n\n sandhied = []\n if '⟾' in lemma:\n l, pos = all_non_infl[0]\n # sandhied = ['{},${}/=0£9'.format(infl, find_uninflected_stem(l, infl))]\n total_sandhied.append('{},${}/=0£9#{}'.format(infl, find_uninflected_stem(l, infl), POS))\n else:\n # sandhied = ['{},$-0+/=0£9'.format(infl)] # include the inflected form.\n for l, pos in all_non_infl:\n total_sandhied.append('{},${}/=0£9#{}'.format(infl, find_uninflected_stem(l, infl), POS))\n sandhied.extend([f for f in find_sandhis.all_possible_sandhis(infl) if '⟾' not in f])\n for entry in sandhied:\n parts = entry.split(',')\n partss = parts[1].split('$')\n partsss = partss[1].split('=')\n sandhied_form = parts[0]\n initial = partss[0]\n new_initials = partsss[0].split('/')[1]\n sandhi_type = partsss[1]\n operations = []\n for stem, POS in all_non_infl:\n operation = find_uninflected_stem(stem, sandhied_form)\n if operation != '':\n operations.append(operation)\n else:\n operations.append('-0+')\n to_add = '{},{}${}/{}={}#{}'.format(sandhied_form, initial, ';'.join(operations), new_initials,\n sandhi_type, POS)\n total_sandhied.append(to_add)\n\n singled = singled_entries(total_sandhied)\n return singled\n\n\ndef import_inflected_pairs():\n \"\"\"\n\n :return: a list of tuples (inflected form, lemma, POS)\n POS values are from 1 to 4 for normal part of speech tags,\n -1 in case of multi-token lemmas.\n \"\"\"\n folders = ['../input/custom_entries', '../input/maxmatch_workaround']\n\n input_files = ['{}/{}'.format(folder, f) for folder in folders for f in os.listdir(folder)]\n input_files.append('../input/preverbs.txt')\n input_files.append('../output/heritage_raw_pairs.txt') # Sanskrit Heritage data\n\n total = []\n for in_file in input_files:\n with open(in_file) as f:\n for a in f.readlines():\n if '/' in a:\n form, lemmas = a.strip().split(',')\n total.append((form, lemmas))\n else:\n form, l = a.strip().split(',')\n total.append((form, l))\n return total\n\n\nif __name__ == \"__main__\":\n # opening the inflected forms\n inflected = import_inflected_pairs()\n\n total_sandhied = sandhied_n_lemmatized_total(inflected)\n\n with open('../output/trie_content.txt', 'w') as g:\n output = '\\n'.join(total_sandhied)\n g.write(output)\n", "repo_name": "buda-base/sanskrit-stemming-data", "sub_path": "sandhify/sandhifier.py", "file_name": "sandhifier.py", "file_ext": "py", "file_size_in_byte": 6499, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "37", "api": [{"api_name": "sys.path.append", "line_number": 5, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 5, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 5, "usage_type": "call"}, {"api_name": "os.path", "line_number": 5, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 5, "usage_type": "call"}, {"api_name": "find_applicable_sandhis.FindApplicableSandhis", "line_number": 9, "usage_type": "call"}, {"api_name": "collections.OrderedDict", "line_number": 35, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 155, "usage_type": "call"}]} +{"seq_id": "72697328427", "text": "import itertools\nimport os\nimport pickle\nimport warnings\n\nimport matplotlib.pyplot as plt\nimport statsmodels.api as sm\nfrom scipy.stats import truncnorm, pearsonr, shapiro, norm, anderson\nimport numpy as np\nfrom statsmodels.distributions import ECDF\n\nfrom baetorch.baetorch.models_v2.base_layer import flatten_np\n\nwarnings.simplefilter(action=\"ignore\", category=FutureWarning)\n\n# ======SPECIFY DATASET=======\ndataset = \"ZEMA\"\n# dataset = \"STRATH\"\n# dataset = \"ODDS\"\n# dataset = \"Images\"\n\n\ncheck_row_exists = True\n# check_row_exists = False\n\nexp_names = {\n \"ZEMA\": \"ZEMA_INF_FULL_\",\n \"STRATH\": \"STRATH_INF_FULL_\",\n \"ODDS\": \"ODDS_INF_FULL_\",\n \"Images\": \"IMAGES_INF_FULL_\",\n}\nexp_name_prefix = exp_names[dataset]\n\n# ========LOAD DATASET===========\n\nfilenames = {\n \"ZEMA\": \"ZEMA_np_data.p\",\n \"STRATH\": \"STRATH_np_data.p\",\n \"Images\": \"Images_np_data.p\",\n \"ODDS\": \"ODDS_np_data.p\",\n}\ndataset_folder = \"np_datasets\"\npickled_dataset = pickle.load(\n open(os.path.join(dataset_folder, filenames[dataset]), \"rb\")\n)\ntasks_col_map = {\n \"ZEMA\": \"target_dim\",\n \"STRATH\": \"target_dim\",\n \"ODDS\": \"dataset\",\n \"Images\": \"id_dataset\",\n}\ntasks_col_name = tasks_col_map[dataset]\n# ================================\n\n\n# =========SPECIFY GRID===========\n\n## FULL GRID\n# grid = {\n# \"W_std\": [1.4, 1.2, 1.0, 0.8],\n# \"diag_reg\": [1e-5, 1e-4, 1e-3],\n# \"norm\": [\"layer\", \"none\"],\n# \"skip\": [False],\n# \"num_layers\": [2, 3, 4, 5],\n# \"activation\": [\"leakyrelu\", \"gelu\", \"erf\"],\n# }\n\n## STANDARD SINGLE TRY\ngrid = {\n \"W_std\": [1.2],\n \"diag_reg\": [1e-5],\n \"norm\": [\"layer\"],\n \"skip\": [False],\n \"num_layers\": [4],\n \"activation\": [\"leakyrelu\"],\n}\n\ngrid_keys = grid.keys()\ngrid_list = list(itertools.product(*grid.values()))\n# ==================COUNT GRID SIZE====================\nprint(\"TOTAL TASKS:\")\nprint(len(grid_list))\n\n# exp_man = ExperimentManager(folder_name=\"thesis_experiments/inf_experiments\")\n# start_exp_time = time.time()\n# final_res = []\n\n\nfor target_key, data_list in pickled_dataset.items():\n ## handle images nested structure differently\n if dataset == \"Images\":\n iterate_list = data_list[\"train\"]\n else:\n iterate_list = data_list\n\n # N data lists , 1 for each random seed split\n for data_dict in iterate_list:\n random_seed = data_dict[\"random_seed\"]\n x_id_train = data_dict[\"x_id_train\"]\n\n # unpack for images differently\n if dataset == \"Images\":\n x_id_train = x_id_train[: len(x_id_train) // 3]\n x_id_test = data_list[\"x_id_test\"]\n x_ood_test = data_list[\"x_ood_test\"]\n else:\n x_id_test = data_dict[\"x_id_test\"]\n x_ood_test = data_dict[\"x_ood_test\"]\n\n# plt.figure()\n# sm.qqplot(x_id_train[0].reshape(-1), line=\"45\")\n# sm.qqplot(x_ood_test.reshape(-1), line=\"45\")\n# sm.qqplot(df.data, line=\"45\")\n\n# # dt_samples = x_id_train[:, 0].reshape(-1)\n# dt_samples = x_id_train[:, 0, 5]\n# # dt_samples = x_id_train.reshape(-1)\n#\n# plt.figure()\n# plt.hist(dt_samples, density=True)\n#\n# # ============================================\n# xa = np.min(dt_samples)\n# xb = np.max(dt_samples)\n#\n# x = np.linspace(xa, xb, 10000)\n# par = truncnorm.fit(dt_samples)\n# # par = truncnorm.fit(dt_samples, a=0, b=1)\n# fig, ax = plt.subplots(1, 1)\n# ax.plot(x, truncnorm.pdf(x, *par), \"b-\", lw=1, alpha=0.6, label=\"truncnorm fit\")\n# ax.hist(dt_samples, density=True, histtype=\"stepfilled\", alpha=0.3)\n#\n#\n# # plt.figure()\n# # sm.qqplot(dt_samples, line=\"45\")\n#\n# # ============================================\n\n# mean, std = norm.fit(dt_samples)\n#\n# plt.figure()\n# # plt.hist(dt_samples, bins=30, density=True)\n# plt.hist(dt_samples, density=True)\n# xmin, xmax = plt.xlim()\n# x = np.linspace(xmin, xmax, 100)\n# y_1 = norm.pdf(x, mean, 1)\n# y_homo = norm.pdf(x, mean, std)\n#\n# plt.plot(x, y_1)\n# plt.plot(x, y_homo)\n# plt.show()\n\n# ===========================\ntarget_dim = 2\nx_id_train = pickled_dataset[target_dim][0][\"x_id_train\"]\n\n\ni_feature = 0\nnum_samples = x_id_train.shape[0]\ndt_samples = flatten_np(x_id_train)\ndt_samples = dt_samples[:, i_feature]\n# dt_samples = x_id_train.reshape(num_samples, -1)\n\nmean, std = norm.fit(dt_samples)\necdf_f = ECDF(dt_samples)\nx = np.linspace(np.min(dt_samples), np.max(dt_samples))\ny_ecdf = ecdf_f(x)\ny_cdf = norm.cdf(x, mean, std)\ny_cdf_1 = norm.cdf(x, mean, 1)\n\ncalibration_error = np.mean(np.abs(y_ecdf - y_cdf))\nprint(\"CALIB ERROR: \" + str(calibration_error))\n\n# calculate ECE for all\n# target_dim = \"thyroid\"\n# target_dim = \"pendigits\"\n# target_dim = \"optdigits\"\n# target_dim = \"pima\"\n# target_dim = \"vowels\"\n# target_dim = \"ionosphere\"\n# target_dim = 2\n\n# x_id_train = pickled_dataset[target_dim][0][\"x_id_train\"]\n# x_id_train = pickled_dataset[target_dim][0][\"x_id_test\"]\n# x_id_train = pickled_dataset[target_dim][0][\"x_ood_test\"]\n\n\ndef get_calibration_stats(x_data):\n total_features = np.product(x_data.shape[1:])\n flattened_x_id = flatten_np(x_data)\n ece_features = []\n p_corrs = []\n shapiro_vals = []\n for i_feature in range(total_features):\n dt_samples = flattened_x_id[:, i_feature]\n\n mean, std = norm.fit(dt_samples)\n ecdf_f = ECDF(dt_samples)\n y_ecdf = ecdf_f(dt_samples)\n y_cdf = norm.cdf(dt_samples, mean, std)\n calibration_error = np.mean(np.abs(y_ecdf - y_cdf))\n p_corr = pearsonr(y_cdf, y_ecdf)[0]\n shapiro_val = shapiro(dt_samples)\n # shapiro_val = anderson(dt_samples)\n shapiro_vals.append(shapiro_val)\n if not np.isnan(calibration_error) and not np.isnan(p_corr):\n ece_features.append(calibration_error)\n p_corrs.append(p_corr)\n ece_features = np.array(ece_features)\n p_corrs = np.array(p_corrs)\n high_corr_perc = len(np.argwhere(np.array(p_corrs) > 0.9)) / total_features\n\n return {\n \"ece\": ece_features,\n \"p_corr\": p_corrs,\n \"high_corr\": high_corr_perc,\n \"shapiro\": shapiro_vals,\n }\n\n\n# calculate ECE for all\n# target_dim = \"thyroid\"\n# target_dim = \"pendigits\"\n# target_dim = \"optdigits\"\n# target_dim = \"pima\"\n# target_dim = \"vowels\"\n# target_dim = \"ionosphere\"\ntarget_dim = 1\n\nall_ece_diff = []\nall_pcorr_diff = []\nnum_splits = len(pickled_dataset[target_dim])\nfor split_i in range(num_splits):\n # id_stats = get_calibration_stats(pickled_dataset[target_dim][split_i][\"x_id_train\"])\n id_stats = get_calibration_stats(\n np.concatenate(\n (\n pickled_dataset[target_dim][split_i][\"x_id_train\"],\n pickled_dataset[target_dim][split_i][\"x_id_test\"],\n )\n )\n )\n ood_stats = get_calibration_stats(\n pickled_dataset[target_dim][split_i][\"x_ood_test\"]\n )\n\n ece_diffs = id_stats[\"ece\"] - ood_stats[\"ece\"]\n pcorr_diffs = id_stats[\"p_corr\"] - ood_stats[\"p_corr\"]\n\n ece_diff_mean = np.mean(np.abs(ece_diffs))\n pcorr_diff_mean = np.mean(np.abs(pcorr_diffs))\n\n # ece_diff_mean = np.mean(ece_diffs)\n # pcorr_diff_mean = np.mean(pcorr_diffs)\n\n # print(\"CALIB ERROR: \" + str(ece_diff_mean))\n # print(\"MEAN CORR: \" + str(pcorr_diff_mean))\n all_ece_diff.append(ece_diff_mean)\n all_pcorr_diff.append(pcorr_diff_mean)\n\nall_ece_diff = np.array(all_ece_diff)\nall_pcorr_diff = np.array(all_pcorr_diff)\n\nprint(\"CALIB ERROR: \" + str(np.mean(all_ece_diff) * 100))\nprint(\"MEAN CORR: \" + str(np.mean(all_pcorr_diff)))\n\n# =======================\n# flatten_x_id = flatten_np(x_id_train)\n#\n# shapiro_test = shapiro(pickled_dataset[target_dim][split_i][\"x_id_train\"][:, 0, 0])\n\n# target_dim = \"thyroid\"\n# target_dim = \"pendigits\"\n# target_dim = \"optdigits\"\n# target_dim = \"pima\"\n# target_dim = \"vowels\"\n# target_dim = \"ionosphere\"\ntarget_dim = 3\n\nall_shapiros = {\"id\": [], \"ood\": []}\n\nnum_splits = len(pickled_dataset[target_dim])\nfor split_i in range(num_splits):\n id_stats = get_calibration_stats(\n np.concatenate(\n (\n pickled_dataset[target_dim][split_i][\"x_id_train\"],\n pickled_dataset[target_dim][split_i][\"x_id_test\"],\n )\n )\n )\n ood_stats = get_calibration_stats(\n pickled_dataset[target_dim][split_i][\"x_ood_test\"]\n )\n all_shapiros[\"id\"].append(np.copy(id_stats[\"shapiro\"]))\n all_shapiros[\"ood\"].append(np.copy(ood_stats[\"shapiro\"]))\n\nshapiros_id = np.array(all_shapiros[\"id\"]).mean(0)[:, 0]\nshapiros_ood = np.array(all_shapiros[\"ood\"]).mean(0)[:, 0]\n\ndiv_ = np.nanmean(shapiros_ood / shapiros_id)\n\nprint(np.nanmean(shapiros_id))\nprint(np.nanmean(shapiros_ood))\nprint(np.nanmean(div_))\n\n# =======================\n\n\n# total_features = np.product(x_id_train.shape[1:])\n# flattened_x_id = flatten_np(x_id_train)\n# ece_features = []\n# p_corrs = []\n# for i_feature in range(total_features):\n# dt_samples = flattened_x_id[:, i_feature]\n#\n# mean, std = norm.fit(dt_samples)\n# ecdf_f = ECDF(dt_samples)\n# y_ecdf = ecdf_f(dt_samples)\n# y_cdf = norm.cdf(dt_samples, mean, std)\n# calibration_error = np.mean(np.abs(y_ecdf - y_cdf))\n# p_corr = pearsonr(y_cdf, y_ecdf)[0]\n# if not np.isnan(calibration_error):\n# ece_features.append(calibration_error)\n# p_corrs.append(p_corr)\n# ece_features = np.array(ece_features)\n# p_corrs = np.array(p_corrs)\n# # mean_ece = np.mean(ece_features)\n# # mean_corr = np.mean(p_corrs)\n# # mean_ece = np.mean(ece_features)\n# # mean_corr = np.mean(p_corrs)\n# high_corr_perc = len(np.argwhere(np.array(p_corrs) > 0.9)) / total_features\n#\n\n# print(\"CALIB ERROR: \" + str(mean_ece))\n# print(\"MEAN CORR: \" + str(mean_corr))\n# print(len(np.argwhere(np.array(p_corrs) > 0.9)) / total_features)\n\n\n# plt.figure()\n# plt.plot(y_ecdf, y_ecdf)\n# plt.scatter(y_ecdf, y_cdf)\n\n\n# plt.figure()\n# plt.plot(x, y_ecdf)\n# plt.plot(x, y_cdf)\n# plt.plot(x, y_cdf_1)\n\n\n# plt.figure()\n# plt.scatter(y_cdf, y_ecdf)\n# plt.plot(y_cdf, y_cdf)\n# plt.plot(y_cdf, y_cdf_1)\n\nplt.figure()\nplt.plot(y_ecdf, y_ecdf)\nplt.scatter(y_ecdf, y_cdf)\nplt.scatter(y_ecdf, y_cdf_1)\n", "repo_name": "bangxiangyong/bottleneck_ae", "sub_path": "00-TEST-NORMALITY.py", "file_name": "00-TEST-NORMALITY.py", "file_ext": "py", "file_size_in_byte": 9961, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "37", "api": [{"api_name": "warnings.simplefilter", "line_number": 14, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 43, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 44, "usage_type": "call"}, {"api_name": "os.path", "line_number": 44, "usage_type": "attribute"}, {"api_name": "itertools.product", "line_number": 79, "usage_type": "call"}, {"api_name": "baetorch.baetorch.models_v2.base_layer.flatten_np", "line_number": 160, "usage_type": "call"}, {"api_name": "scipy.stats.norm.fit", "line_number": 164, "usage_type": "call"}, {"api_name": "scipy.stats.norm", "line_number": 164, "usage_type": "name"}, {"api_name": "statsmodels.distributions.ECDF", "line_number": 165, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 166, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 166, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 166, "usage_type": "call"}, {"api_name": "scipy.stats.norm.cdf", "line_number": 168, "usage_type": "call"}, {"api_name": "scipy.stats.norm", "line_number": 168, "usage_type": "name"}, {"api_name": "scipy.stats.norm.cdf", "line_number": 169, "usage_type": "call"}, {"api_name": "scipy.stats.norm", "line_number": 169, "usage_type": "name"}, {"api_name": "numpy.mean", "line_number": 171, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 171, "usage_type": "call"}, {"api_name": "numpy.product", "line_number": 189, "usage_type": "call"}, {"api_name": "baetorch.baetorch.models_v2.base_layer.flatten_np", "line_number": 190, "usage_type": "call"}, {"api_name": "scipy.stats.norm.fit", "line_number": 197, "usage_type": "call"}, {"api_name": "scipy.stats.norm", "line_number": 197, "usage_type": "name"}, {"api_name": "statsmodels.distributions.ECDF", "line_number": 198, "usage_type": "call"}, {"api_name": "scipy.stats.norm.cdf", "line_number": 200, "usage_type": "call"}, {"api_name": "scipy.stats.norm", "line_number": 200, "usage_type": "name"}, {"api_name": "numpy.mean", "line_number": 201, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 201, "usage_type": "call"}, {"api_name": "scipy.stats.pearsonr", "line_number": 202, "usage_type": "call"}, {"api_name": "scipy.stats.shapiro", "line_number": 203, "usage_type": "call"}, {"api_name": "numpy.isnan", "line_number": 206, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 209, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 210, "usage_type": "call"}, {"api_name": "numpy.argwhere", "line_number": 211, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 211, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 236, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 250, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 250, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 251, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 251, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 261, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 262, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 264, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 265, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 285, "usage_type": "call"}, {"api_name": "numpy.copy", "line_number": 295, "usage_type": "call"}, {"api_name": "numpy.copy", "line_number": 296, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 298, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 299, "usage_type": "call"}, {"api_name": "numpy.nanmean", "line_number": 301, "usage_type": "call"}, {"api_name": "numpy.nanmean", "line_number": 303, "usage_type": "call"}, {"api_name": "numpy.nanmean", "line_number": 304, "usage_type": "call"}, {"api_name": "numpy.nanmean", "line_number": 305, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 356, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 356, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 357, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 357, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 358, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 358, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 359, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 359, "usage_type": "name"}]} +{"seq_id": "31320222334", "text": "#!/usr/bin/env python\n\n\"\"\"visualize_bbox_coco.py: Visualize the annotations from a COCO style dataset\n\n Note: Requires that that COCO annotation have the exact same filenames as the files in the data folder\n Note: pycoco implementation of visualizing annotations as found at https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocoDemo.ipynb\n\n Arguments:\n Required:\n -f, --file = location of annotation file\n -d, --data = location of data\n\n Optional:\n -i, --index = index of image to visualize if more than one (default = 0)\n\n Usage: visualize_bbox_coco.py [-h] -f FILE -d DATA [-i INDEX]\n Example usage: python src/visualize/visualize_bbox_coco.py -f data/processed/incision_1/annotations/instances_default.json -d data/processed/incision_1/ \n\"\"\"\n\nimport cv2\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport argparse\nfrom os.path import isfile, isdir\n\nfrom pycocotools.coco import COCO\n\n\ndef main():\n parser = argparse.ArgumentParser(description='Seperate video file into individual frames')\n parser.add_argument('-f', '--file', type=str, required=True, help='name and location of file you want to parse')\n parser.add_argument('-d', '--data', type=str, required=True, help='location of data')\n parser.add_argument('-i', '--index', type=int, default=0, help='index of image you want to see (default = 0)')\n args = parser.parse_args()\n\n annotation_file = args.file\n data_folder = args.data\n image_index = args.index \n\n # Check if input file exists\n if not isfile(annotation_file):\n print('Annotation file does not exist')\n return\n \n if not isdir(data_folder):\n print('Data folder does not exist')\n return\n\n\n # initialize COCO api for instance annotations\n coco = COCO(annotation_file)\n\n # display COCO categories and supercategories\n cats = coco.loadCats(coco.getCatIds())\n nms=[cat['name'] for cat in cats]\n print('COCO categories: \\n{}\\n'.format(' '.join(nms)))\n\n #nms = set([cat['supercategory'] for cat in cats])\n #print('COCO supercategories: \\n{}'.format(' '.join(nms)))\n \n # Get all images containing given categories, select one at random\n catIds = coco.getCatIds(catNms = ['Tool'])\n imgIds = coco.getImgIds(catIds = catIds )\n\n img = coco.loadImgs(imgIds[image_index])[0]\n\n # Use OpenCV to read the image file and draw to matplotlib\n print('Reading ' + img['file_name'])\n read_image = cv2.imread(data_folder + img['file_name'])\n plt.axis('off')\n plt.imshow(cv2.cvtColor(read_image, cv2.COLOR_BGR2RGB))\n\n # Get annotaton from COCO and draw it matplotlib\n annIds = coco.getAnnIds(imgIds=img['id'], catIds=catIds, iscrowd=None)\n anns = coco.loadAnns(annIds)\n coco.showAnns(anns, draw_bbox=True)\n plt.show()\n\nif __name__ == '__main__':\n main()", "repo_name": "StafaH/graph-imitation-learning", "sub_path": "src/visualize/visualize_bbox_coco.py", "file_name": "visualize_bbox_coco.py", "file_ext": "py", "file_size_in_byte": 2853, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "37", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 30, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 41, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 45, "usage_type": "call"}, {"api_name": "pycocotools.coco.COCO", "line_number": 51, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 69, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.axis", "line_number": 70, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 70, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 71, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 71, "usage_type": "name"}, {"api_name": "cv2.cvtColor", "line_number": 71, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2RGB", "line_number": 71, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.show", "line_number": 77, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 77, "usage_type": "name"}]} +{"seq_id": "2524824124", "text": "# -*- coding: utf-8 -*-\n\"\"\"\n@Author : WeiDongcheng @tonse\n@Time : 2023/2/7 18:04\n@File : meter_detect_util.py\n@Desc : \n\"\"\"\nimport base64\nimport math\n\nimport cv2\nimport numpy as np\nfrom math import cos, pi, sin\n\nfrom PySide2.QtCore import QSettings\n\nfrom src.myutils.airtest.core.cv import Template, TemplateCv2\n\n\nclass ImgUtil:\n\n @staticmethod\n def cvImg2Base64(frame, fmt=\".png\"):\n \"\"\"\n opencv图片转base64字符串\n :param frame: opencv图片\n :param fmt: 图片原格式\n :return: base64图片字符串\n \"\"\"\n image = cv2.imencode(fmt, frame)[1]\n image_code = str(base64.b64encode(image))[2:-1]\n return image_code\n\n @staticmethod\n def base64ToCvImg(base64_code):\n \"\"\"\n base64图片字符串转opencv图片\n :param base64_code: base64编码\n :return: opencv图片\n \"\"\"\n # base64解码\n img_data = base64.b64decode(base64_code)\n # 转换为np数组\n # img_array = np.fromstring(img_data, np.uint8) # is deprecated\n img_array = np.frombuffer(img_data, np.uint8)\n # 转换成opencv可用格式\n img = cv2.imdecode(img_array, cv2.COLOR_RGB2BGR)\n return img\n\nclass MeterUtil:\n\n @staticmethod\n def cross_point(line1, line2):\n \"\"\"\n 计算两条直线的交点\n :param line1: 第一条直线\n :param line2: 第二条直线\n :return: list : 交点 [x, y]\n \"\"\"\n x1 = line1[0] # 取直线1的第一个点坐标\n y1 = line1[1]\n x2 = line1[2] # 取直线1的第二个点坐标\n y2 = line1[3]\n\n x3 = line2[0] # 取直线2的第一个点坐标\n y3 = line2[1]\n x4 = line2[2] # 取直线2的第二个点坐标\n y4 = line2[3]\n\n if x2 - x1 == 0: # L1 直线斜率不存在\n k1 = None\n b1 = 0\n else:\n k1 = (y2 - y1) * 1.0 / (x2 - x1) # 计算k1,由于点均为整数，需要进行浮点数转化\n b1 = y1 * 1.0 - x1 * k1 * 1.0 # 整型转浮点型是关键\n\n if (x4 - x3) == 0: # L2直线斜率不存在操作\n k2 = None\n b2 = 0\n else:\n k2 = (y4 - y3) * 1.0 / (x4 - x3) # 斜率存在操作\n b2 = y3 * 1.0 - x3 * k2 * 1.0\n\n if k1 is None and k2 is None: # L1与L2直线斜率都不存在，两条直线均与y轴平行\n if x1 == x3: # 两条直线实际为同一直线\n return [x1, y1] # 均为交点，返回任意一个点\n else:\n return None # 平行线无交点\n elif k1 is not None and k2 is None: # 若L2与y轴平行，L1为一般直线，交点横坐标为L2的x坐标\n x = x3\n y = k1 * x * 1.0 + b1 * 1.0\n elif k1 is None and k2 is not None: # 若L1与y轴平行，L2为一般直线，交点横坐标为L1的x坐标\n x = x1\n y = k2 * x * 1.0 + b2 * 1.0\n else: # 两条一般直线\n if k1 == k2: # 两直线斜率相同\n if b1 == b2: # 截距相同，说明两直线为同一直线，返回任一点\n return [x1, y1]\n else: # 截距不同，两直线平行，无交点\n return None\n else: # 两直线不平行，必然存在交点\n x = (b2 - b1) * 1.0 / (k1 - k2)\n y = k1 * x * 1.0 + b1 * 1.0\n return [x, y]\n\n @staticmethod\n def get_point_pos(image, center):\n \"\"\"\n 霍夫曼直线检测找出直线\n :param imagePath: 图片路径,在该图片上查找指针指尖的点\n :param center: 指针旋转中心\n :return: 指针指尖的点\n \"\"\"\n # 转换成灰度图\n height, width = image.shape[:2] # 测试图片高度和宽度\n # image = cv2.resize(image, (500, 500), interpolation=cv2.INTER_CUBIC)\n scale = 1\n centerX = center[0]\n centerY = center[1]\n widthMax = 600\n if width > widthMax: # 宽度大于400的图片，进行缩放\n scale = widthMax/width\n print(f\"scale = {scale}\")\n height = (height*widthMax)/width\n width = width*scale\n image = cv2.resize(image, (widthMax, int(height)), interpolation=cv2.INTER_CUBIC)\n centerX = center[0]*scale\n centerY = center[1]*scale\n cX = centerX\n cY = centerY\n xLeft = width - cX # 指针旋转点 到 右边的距离\n yLeft = height - cY # 指针旋转点 到 底部的距离\n minValue = min([cX, cY, xLeft, yLeft])\n rValue = minValue * 0.6 # 指针旋转半径\n print(f\"minValue = {minValue}\")\n image_gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n\n # 边缘检测, Sobel算子大小为3\n \"\"\"\n 其中：\n edges 为计算得到的边缘图像。\n image 为 8 位输入图像。\n threshold1 表示处理过程中的第一个阈值。\n threshold2 表示处理过程中的第二个阈值。\n apertureSize 表示 Sobel 算子的孔径大小。\n L2gradient 为计算图像梯度幅度（gradient magnitude）的标识。其默认值为 False。如果为 True，则使用更精确的 L2 范数进行计算（即两个方向的导数的平方和再开方），否则使用 L1 范数（直接将两个方向导数的绝对值相加）。\n \"\"\"\n edges = cv2.Canny(image_gray, 170, 220, apertureSize=3)\n # edges = cv2.Canny(image_gray, 170, 150, apertureSize=3)\n\n # 霍夫曼直线检测(统计霍夫变换)\n \"\"\"\n 参数：\n image: 边缘检测的输出图像. 它应该是个灰度图 (但事实上是个二值化图) * \n lines: 储存着检测到的直线的参数对  的容器，也就是线段两个端点的坐标\n rho : 　参数极径  以像素值为单位的分辨率. 我们使用 1 像素.\n theta: 参数极角  以弧度为单位的分辨率. 我们使用 1度 (即CV_PI/180)\n threshold: 要”检测” 一条直线所需最少的的曲线交点 \n minLinLength: 能组成一条直线的最少点的数量. 点数量不足的直线将被抛弃.线段的最小长度\n maxLineGap:线段上最近两点之间的阈值 线段之间的最大允许间隙，将它们视为一条线\n \"\"\"\n # lines = cv2.HoughLinesP(edges, 1, np.pi / 180, 100, minLineLength=100, maxLineGap=10)\n # lines = cv2.HoughLinesP(edges, 1, np.pi / 180, 100, minLineLength=int(width/8), maxLineGap=10)\n # print(int(width/2))\n # 这个系数适用大多数情况，不适用的可以使用 threshold=100，minLineLength=100， maxLineGap=10测试\n threshold = int(width / 8) # 8 #\n minLineLength = int(width / 8) # 8\n maxLineGap = int(width / 80) # 80 待处理宽度小于80时，线段之间的最大允许间隙，将它们视为一条线\n # threshold = 100 # 测试用\n # minLineLength = 100\n # maxLineGap = 10\n print(f\"threshold={threshold}, minLineLength={minLineLength}, maxLineGap={maxLineGap}\")\n lines = cv2.HoughLinesP(edges, 1, np.pi / 180, threshold, minLineLength=minLineLength, maxLineGap=maxLineGap)\n # 遍历\n targetPoint = [0, 0]\n if lines is not None:\n lineInPointer = []\n for lIndex, line in enumerate(lines):\n line1x1, line1y1, line1x2, line1y2 = line[0]\n # 判断线段两个点到指针中心的距离，要至少有一个点在该距离内，否则判断为不是指针的线\n disP1 = math.sqrt(pow(centerX - line1x1, 2) + pow(centerY - line1y1, 2))\n disP2 = math.sqrt(pow(centerX - line1x2, 2) + pow(centerY - line1y2, 2))\n minDistance = minValue*0.4\n if disP1 <= minDistance or disP2 <= minDistance:\n lineInPointer.append(line)\n lines = lineInPointer\n cv2.imshow(\"edges\", edges)\n print(f\"lines len = {len(lines)}\")\n if len(lines) >= 2: # 找到的线大于等于两条\n lineRet = []\n for lIndex, line in enumerate(lines):\n line1x1, line1y1, line1x2, line1y2 = line[0]\n lDis = math.sqrt(pow(line1x2 - line1x1, 2) + pow(line1y2 - line1y1, 2)) # 计算线段长度\n lineRet.append([lIndex, lDis, line])\n lineRet.sort(key=lambda x: x[1], reverse=True) # 按线段长度排序\n # lines = [lineRet[0][2], lineRet[1][2]]\n line1x1, line1y1, line1x2, line1y2 = lineRet[0][2][0]\n line2x1, line2y1, line2x2, line2y2 = lineRet[1][2][0]\n line1 = [line1x1, line1y1, line1x2, line1y2]\n line2 = [line2x1, line2y1, line2x2, line2y2]\n targetPoint = MeterUtil.cross_point(line1, line2) # 计算延长线交点\n print(f\"targetPoint line > = 2: {targetPoint}\")\n if targetPoint is not None:\n centerToTarDis = math.sqrt(pow(centerX - targetPoint[0], 2) + pow(centerY - targetPoint[1], 2))\n print(f\"centerToTarDis={centerToTarDis}\")\n if targetPoint[0] < 0 or targetPoint[1] < 0 or centerToTarDis > (minValue*0.8): # 交点为负值或太远\n centerToTarScale = rValue / centerToTarDis\n if targetPoint[0] > centerX and targetPoint[1] < centerY: # 第一象限\n xOffset = ((targetPoint[0] - centerX) * centerToTarScale)\n yOffset = ((centerY - targetPoint[1]) * centerToTarScale)\n targetPoint[0] = centerX + xOffset\n targetPoint[1] = targetPoint[1] + (centerY - targetPoint[1] - yOffset)\n print(f\"第一象限：（{targetPoint[0]}， {targetPoint[1]}）\")\n elif targetPoint[0] < centerX and targetPoint[1] < centerY: # 第二象限\n xOffset = ((centerX - targetPoint[0]) * centerToTarScale)\n yOffset = ((centerY - targetPoint[1]) * centerToTarScale)\n targetPoint[0] = targetPoint[0] + (centerX - targetPoint[0] - xOffset)\n targetPoint[1] = targetPoint[1] + (centerY - targetPoint[1] - yOffset)\n print(f\"第二象限：（{targetPoint[0]}， {targetPoint[1]}）\")\n elif targetPoint[0] < centerX and targetPoint[1] > centerY: # 第三象限\n xOffset = ((centerX - targetPoint[0]) * centerToTarScale)\n yOffset = ((targetPoint[1] - centerY) * centerToTarScale)\n targetPoint[0] = centerX - xOffset\n targetPoint[1] = centerY + yOffset\n print(f\"第三象限：（{targetPoint[0]}， {targetPoint[1]}）\")\n elif targetPoint[0] > centerX and targetPoint[1] > center[1]: # 第四象限\n xOffset = ((targetPoint[0] - centerX) * centerToTarScale)\n yOffset = ((targetPoint[1] - centerY) * centerToTarScale)\n targetPoint[0] = centerX + xOffset\n targetPoint[1] = centerY + yOffset\n print(f\"第四象限：（{targetPoint[0]}， {targetPoint[1]}）\")\n else:\n print(\"无象限\")\n else:\n print(f\"centerToTarDis={centerToTarDis}, minValue*0.8={minValue*0.8}\")\n else:\n line1x1, line1y1, line1x2, line1y2 = lines[0][0]\n disP1 = math.sqrt(pow(centerX - line1x1, 2) + pow(centerY - line1y1, 2))\n disP2 = math.sqrt(pow(centerX - line1x2, 2) + pow(centerY - line1y2, 2))\n if disP1 >= disP2:\n targetPoint = [line1x1, line1y1]\n else:\n targetPoint = [line1x2, line1y2]\n print(f\"targetPoint is none get again: {targetPoint}\")\n elif len(lines) == 1:\n line1x1, line1y1, line1x2, line1y2 = lines[0][0]\n disP1 = math.sqrt(pow(centerX - line1x1, 2) + pow(centerY - line1y1, 2))\n disP2 = math.sqrt(pow(centerX - line1x2, 2) + pow(centerY - line1y2, 2))\n if disP1 >= disP2:\n targetPoint = [line1x1, line1y1]\n else:\n targetPoint = [line1x2, line1y2]\n for i, line in enumerate(lines):\n # 获取坐标\n x1, y1, x2, y2 = line[0]\n cv2.line(image, (x1, y1), (x2, y2), (0, 0, 255), thickness=3)\n cv2.putText(image, str(i), (x2, y2), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2)\n if len(targetPoint) == 2:\n cv2.circle(image, (int(targetPoint[0]), int(targetPoint[1])), 3, (0, 255, 0), -1)\n # cv2.imshow(\"lines\", image)\n # cv2.waitKey(0)\n print(f\"targetPoint={targetPoint}\")\n return [int(targetPoint[0]), int(targetPoint[1])], image, scale\n\n @staticmethod\n def get_meter_value(center, pointZero, pointMax, pointTarget, maxValue):\n \"\"\"\n 获取某点对应的数值\n :param center: 中心点\n :param pointZero: 0值坐标\n :param pointMax: 最大值坐标\n :param pointTarget: 目标值坐标\n :param maxValue: 刻度值范围\n :return:\n \"\"\"\n # 实际中心点\n c_y, c_x = center[1], center[0]\n degreeZeroValue = 0\n degreeMaxValue = 0\n degreeTargetValue = 0\n zeroFound = False\n targetFound = False\n maxFound = False\n x1 = c_x + c_x\n targetValue = 0\n\n for i in range(361):\n x = (x1 - c_x) * cos(i * pi / 180) + c_x\n y = (x1 - c_x) * sin(i * pi / 180) + c_y\n xThen = (x1 - c_x) * cos((i + 1) * pi / 180) + c_x\n yThen = (x1 - c_x) * sin((i + 1) * pi / 180) + c_y\n # temp = img.copy()\n # cv2.line(temp, (c_x, c_y), (int(x), int(y)), (0, 0, 255), thickness=3) # 画线\n # 判断点在多边形上\n pts = np.array([[c_x, c_y], [x, y], [xThen, yThen], [c_x, c_y]], np.int32) # 数据类型必须为 int32\n pts = pts.reshape((-1, 1, 2))\n # 设置为True时，返回实际距离值。若返回值为正，表示点在多边形内部，返回值为负，表示在多边形外部，返回值为0，表示在多边形上。\n # 设置为False,返回 -1、0、1三个固定值。若返回值为+1，表示点在多边形内部，返回值为-1，表示在多边形外部，返回值为0，表示在多边形上。\n retZero = cv2.pointPolygonTest(pts, (pointZero[0], pointZero[1]), False)\n retMax = cv2.pointPolygonTest(pts, (pointMax[0], pointMax[1]), False)\n retTarget = cv2.pointPolygonTest(pts, (pointTarget[0], pointTarget[1]), False)\n if retZero >= 0 and zeroFound is False:\n zeroFound = True\n degreeZeroValue = i\n print(f\"0值的角度={i}, 圆心({c_x}, {c_y}), 线的点({x}, {y}), degreeZeroValue={degreeZeroValue}\")\n print(f\"0值坐标：{pointZero}\")\n # cv2.drawContours(temp, [pts], -1, (0, 255, 0), -1) # 画轮廓\n # cv2.imshow('drawContours', temp)\n # cv2.waitKey(0)\n if retMax >= 0 and maxFound is False:\n maxFound = True\n degreeMaxValue = i\n print(f\"最大值的角度={i}, 圆心({c_x}, {c_y}), 线的点({x}, {y}), degreeMaxValue={degreeMaxValue}\")\n print(f\"最大值坐标：{pointMax}\")\n # cv2.drawContours(temp, [pts], -1, (0, 255, 0), -1) # 画轮廓\n # cv2.imshow('drawContours', temp)\n # cv2.waitKey(0)\n if retTarget >= 0 and targetFound is False:\n targetFound = True\n degreeTargetValue = i\n print(f\"目标值的角度={i}, 圆心({c_x}, {c_y}), 线的点({x}, {y}), degreeTargetValue={degreeTargetValue}\")\n print(f\"目标值坐标：{pointTarget}\")\n # cv2.drawContours(temp, [pts], -1, (0, 255, 0), -1) # 画轮廓\n # cv2.imshow('drawContours', temp)\n # cv2.waitKey(0)\n # cv2.destroyAllWindows()\n print(f\"0值角度={degreeZeroValue}, 最大值角度={degreeMaxValue}, 目标值角度={degreeTargetValue}\")\n # 一般情况，degreeMaxValue < degreeZeroValue < degreeTargetValue\n if degreeMaxValue < degreeZeroValue < degreeTargetValue:\n # 先到最大刻度，再到0刻度，再到目标刻度\n valueDegrees = 360 - (degreeZeroValue - degreeMaxValue) # 有效刻度所占的所有角度值 = 360 - 无效角度\n perDegreeValue = maxValue / valueDegrees # 每个角度占的值\n targetValue = (degreeTargetValue - degreeZeroValue) * perDegreeValue # 目标角度对应的值\n elif degreeZeroValue < degreeTargetValue < degreeMaxValue:\n # 先找到0刻度，再到目标刻度，再到最大刻度\n valueDegrees = degreeMaxValue - degreeZeroValue # 有效刻度所占的所有角度值 = 360 - 无效角度\n perDegreeValue = maxValue / valueDegrees # 每个角度占的值\n targetValue = (degreeTargetValue - degreeZeroValue) * perDegreeValue # 目标角度对应的值\n elif degreeTargetValue < degreeMaxValue < degreeZeroValue:\n # 先找到目标刻度，再到最大刻度，再到0刻度\n valueDegrees = 360 - (degreeZeroValue - degreeMaxValue) # 有效刻度所占的所有角度值 = 360 - 无效角度\n leftDegree = degreeMaxValue - degreeTargetValue # 目标刻度到最大刻度之差\n realTargetDegree = valueDegrees - leftDegree # 目标刻度总角度\n perDegreeValue = maxValue / valueDegrees # 每个角度占的值\n targetValue = realTargetDegree * perDegreeValue # 目标角度对应的值\n elif degreeMaxValue < degreeTargetValue < degreeZeroValue:\n # 先找到最大值，再到目标值，再到0值，说明可能因为误差原因，目标在0值之前，直接置0\n targetValue = 0\n elif degreeTargetValue == degreeZeroValue:\n targetValue = 0\n elif degreeTargetValue == degreeTargetValue:\n targetValue = maxValue\n if targetValue > maxValue:\n targetValue = maxValue\n print(f\"targetValue = {targetValue}\")\n return targetValue\n\n @staticmethod\n def readValueFormFrame(frame, centerPos, zeroPos, maxPox, meterMaxValue):\n imageTmp = frame.copy()\n poiPoint, posImg, scale = MeterUtil.get_point_pos(imageTmp, centerPos) # 获取指针指尖点\n centerX = centerPos[0]*scale\n centerY = centerPos[1]*scale\n newCenter = (centerX, centerY)\n zeroX = zeroPos[0]*scale\n zeroY = zeroPos[1]*scale\n newZero = (zeroX, zeroY)\n maxX = maxPox[0]*scale\n maxY = maxPox[1]*scale\n newMax = (maxX, maxY)\n cv2.circle(posImg, (int(centerX), int(centerY)), 3, (127, 255, 255), -1)\n cv2.circle(posImg, (int(zeroX), int(zeroY)), 3, (255, 0, 0), -1)\n cv2.circle(posImg, (int(maxX), int(maxY)), 3, (255, 255, 85), -1)\n return MeterUtil.get_meter_value(newCenter, newZero, newMax, poiPoint, meterMaxValue), posImg\n\n @staticmethod\n def demo001():\n # 读数测试\n imagePath = r\"D:\\projects\\python\\pylearning\\files\\pics\\meter\\ico_50c.png\" # 50\n image = cv2.imread(imagePath)\n center = [303, 307] # 指针旋转中心点\n pointZero = [95, 425] # 0值指针坐标\n pointMax = [506, 425] # 最大值指针坐标\n maxValue = 100 # 刻度值最大值\n MeterUtil.readValueFormFrame(image, center, pointZero, pointMax, maxValue)\n\n @staticmethod\n def demo002():\n # 读数测试\n imagePath = r\"D:\\projects\\python\\pylearning\\files\\pics\\meter\\ico_38c_tmp.png\" # 50\n # imageTestPath = r\"D:\\projects\\python\\pylearning\\files\\pics\\meter\\ico_75c_scale.png\" # 50\n imageTestPath = r\"D:\\projects\\python\\pylearning\\files\\pics\\meter\\ico_75c_test.png\" # 50\n tmp = Template(imagePath)\n png_frame = cv2.imdecode(np.fromfile(imageTestPath, dtype=np.uint8), cv2.IMREAD_COLOR)\n cv2.imshow('OriPic', png_frame)\n match_ret = tmp.match_in_result(png_frame)\n if match_ret is not None:\n startX = match_ret[\"rectangle\"][0][0]\n startY = match_ret[\"rectangle\"][0][1]\n endX = match_ret[\"rectangle\"][2][0]\n endY = match_ret[\"rectangle\"][2][1]\n copyTestFram = png_frame.copy()\n cv2.rectangle(png_frame,\n (startX, startY), # 左上角坐标\n (endX, endY), # 右下角坐标\n (0, 255, 0), # 线颜色\n 2) # 线粗细\n print(match_ret)\n testFrame = copyTestFram[startY:endY, startX:endX]\n cv2.imshow('testFrame', testFrame)\n height, width = testFrame.shape[:2] # 测试图片高度和宽度\n print(f\"测试图片width={width}, height={height}\")\n\n image = cv2.imread(imagePath)\n tmpHeight, tmpWidht = image.shape[:2] # 模板图片高度和宽度\n print(f\"模板图片width={tmpWidht}, height={tmpHeight}\")\n # 模板图片信息：\n center = [298, 298] # 指针旋转中心点\n pointZero = [92, 415] # 0值指针坐标\n pointMax = [503, 414] # 最大值指针坐标\n maxValue = 100 # 刻度值最大值\n\n # 测试图片信息\n scale = height / tmpHeight\n testCenter = [int(scale * center[0]), int(scale * center[1])]\n testZeroPos = [int(scale * pointZero[0]), int(scale * pointZero[1])]\n testMaxPos = [int(scale * pointMax[0]), int(scale * pointMax[1])]\n # xCenter = (width * center[0]) / tmpWidht\n # yCenter = (height * center[1]) / tmpHeight\n # testCenter = [xCenter, yCenter]\n # xZero = (width * center[0]) / tmpWidht\n # yZero = (width * center[0]) / tmpWidht\n value, posImg = MeterUtil.readValueFormFrame(testFrame, testCenter, testZeroPos, testMaxPos, maxValue)\n cv2.imshow(\"posImg\", posImg)\n cv2.waitKey(0)\n cv2.destroyAllWindows()\n\n @staticmethod\n def demo003():\n # 读数测试\n imagePath = r\"D:\\projects\\python\\pylearning\\files\\pics\\meter\\002_tmp_ori.png\" # 50\n # imageTestPath = r\"D:\\projects\\python\\pylearning\\files\\pics\\meter\\ico_75c_scale.png\" # 50\n imageTestPath = r\"D:\\projects\\python\\pylearning\\files\\pics\\meter\\002_tmp_ori.png\" # 50\n tmp = Template(imagePath)\n png_frame = cv2.imdecode(np.fromfile(imageTestPath, dtype=np.uint8), cv2.IMREAD_COLOR)\n cv2.imshow('OriPic', png_frame)\n match_ret = tmp.match_in_result(png_frame)\n if match_ret is not None:\n startX = match_ret[\"rectangle\"][0][0]\n startY = match_ret[\"rectangle\"][0][1]\n endX = match_ret[\"rectangle\"][2][0]\n endY = match_ret[\"rectangle\"][2][1]\n copyTestFram = png_frame.copy()\n cv2.rectangle(png_frame,\n (startX, startY), # 左上角坐标\n (endX, endY), # 右下角坐标\n (0, 255, 0), # 线颜色\n 2) # 线粗细\n print(match_ret)\n testFrame = copyTestFram[startY:endY, startX:endX]\n cv2.imshow('testFrame', testFrame)\n height, width = testFrame.shape[:2] # 测试图片高度和宽度\n print(f\"测试图片width={width}, height{height}\")\n\n image = cv2.imread(imagePath)\n tmpHeight, tmpWidht = image.shape[:2] # 模板图片高度和宽度\n print(f\"模板图片width={tmpWidht}, height{tmpHeight}\")\n # 模板图片信息：\n center = [410, 408] # 指针旋转中心点\n pointZero = [206, 576] # 0值指针坐标\n pointMax = [580, 596] # 最大值指针坐标\n maxValue = 160 # 刻度值最大值\n\n # 测试图片信息\n scale = height / tmpHeight\n testCenter = [int(scale * center[0]), int(scale * center[1])]\n testZeroPos = [int(scale * pointZero[0]), int(scale * pointZero[1])]\n testMaxPos = [int(scale * pointMax[0]), int(scale * pointMax[1])]\n # xCenter = (width * center[0]) / tmpWidht\n # yCenter = (height * center[1]) / tmpHeight\n # testCenter = [xCenter, yCenter]\n # xZero = (width * center[0]) / tmpWidht\n # yZero = (width * center[0]) / tmpWidht\n value, posImg = MeterUtil.readValueFormFrame(testFrame, testCenter, testZeroPos, testMaxPos, maxValue)\n cv2.imshow(\"posImg\", posImg)\n cv2.waitKey(0)\n cv2.destroyAllWindows()\n\n @staticmethod\n def demo004():\n # 读数测试\n imagePath = r\"D:\\projects\\python\\pylearning\\files\\pics\\meter\\003_press_meter_tmp_small.png\" # 50\n # imageTestPath = r\"D:\\projects\\python\\pylearning\\files\\pics\\meter\\003_press_meter1.jpg\" # 50\n imageTestPath = r\"D:\\projects\\python\\pylearning\\files\\pics\\meter\\003_press_meter2.jpg\" # 50\n # imageTestPath = r\"D:\\projects\\python\\pylearning\\files\\pics\\meter\\003_press_meter_tmp_small.png\" # 50\n tmp = Template(imagePath)\n png_frame = cv2.imdecode(np.fromfile(imageTestPath, dtype=np.uint8), cv2.IMREAD_COLOR)\n cv2.imshow('OriPic', png_frame)\n match_ret = tmp.match_in_result(png_frame)\n if match_ret is not None:\n startX = match_ret[\"rectangle\"][0][0]\n startY = match_ret[\"rectangle\"][0][1]\n endX = match_ret[\"rectangle\"][2][0]\n endY = match_ret[\"rectangle\"][2][1]\n copyTestFram = png_frame.copy()\n cv2.rectangle(png_frame,\n (startX, startY), # 左上角坐标\n (endX, endY), # 右下角坐标\n (0, 255, 0), # 线颜色\n 2) # 线粗细\n print(match_ret)\n testFrame = copyTestFram[startY:endY, startX:endX]\n cv2.imshow('testFrame', testFrame)\n height, width = testFrame.shape[:2] # 测试图片高度和宽度\n print(f\"测试图片width={width}, height{height}\")\n\n image = cv2.imread(imagePath)\n tmpHeight, tmpWidht = image.shape[:2] # 模板图片高度和宽度\n print(f\"模板图片width={tmpWidht}, height{tmpHeight}\")\n # 模板图片信息：\n center = [95, 93] # 指针旋转中心点\n pointZero = [34, 134] # 0值指针坐标\n pointMax = [150, 157] # 最大值指针坐标\n maxValue = 0.6 # 刻度值最大值\n\n # 测试图片信息\n scale = height / tmpHeight\n testCenter = [int(scale * center[0]), int(scale * center[1])]\n testZeroPos = [int(scale * pointZero[0]), int(scale * pointZero[1])]\n testMaxPos = [int(scale * pointMax[0]), int(scale * pointMax[1])]\n # xCenter = (width * center[0]) / tmpWidht\n # yCenter = (height * center[1]) / tmpHeight\n # testCenter = [xCenter, yCenter]\n # xZero = (width * center[0]) / tmpWidht\n # yZero = (width * center[0]) / tmpWidht\n value, posImg = MeterUtil.readValueFormFrame(testFrame, testCenter, testZeroPos, testMaxPos, maxValue)\n cv2.imshow(\"posImg\", posImg)\n cv2.waitKey(0)\n cv2.destroyAllWindows()\n else:\n print(\"not match\")\n\n @staticmethod\n def demo005():\n # 读数测试\n imagePath = r\"D:\\projects\\python\\pylearning\\files\\pics\\meter\\speed_meter_temp.png\" # 50\n imageTestPath = r\"D:\\projects\\python\\pylearning\\files\\pics\\meter\\speed_meter_real.jpg\" # 50\n tmp = Template(imagePath)\n png_frame = cv2.imdecode(np.fromfile(imageTestPath, dtype=np.uint8), cv2.IMREAD_COLOR)\n cv2.imshow('OriPic', png_frame)\n match_ret = tmp.match_in_result(png_frame)\n if match_ret is not None:\n startX = match_ret[\"rectangle\"][0][0]\n startY = match_ret[\"rectangle\"][0][1]\n endX = match_ret[\"rectangle\"][2][0]\n endY = match_ret[\"rectangle\"][2][1]\n copyTestFram = png_frame.copy()\n cv2.rectangle(png_frame,\n (startX, startY), # 左上角坐标\n (endX, endY), # 右下角坐标\n (0, 255, 0), # 线颜色\n 2) # 线粗细\n print(match_ret)\n testFrame = copyTestFram[startY:endY, startX:endX]\n cv2.imshow('testFrame', testFrame)\n height, width = testFrame.shape[:2] # 测试图片高度和宽度\n print(f\"测试图片width={width}, height{height}\")\n\n image = cv2.imread(imagePath)\n tmpHeight, tmpWidht = image.shape[:2] # 模板图片高度和宽度\n print(f\"模板图片width={tmpWidht}, height{tmpHeight}\")\n # 模板图片信息：\n center = [170, 164] # 指针旋转中心点\n pointZero = [68, 243] # 0值指针坐标\n pointMax = [269, 251] # 最大值指针坐标\n maxValue = 260 # 刻度值最大值\n\n # 测试图片信息\n scale = height / tmpHeight\n testCenter = [int(scale * center[0]), int(scale * center[1])]\n testZeroPos = [int(scale * pointZero[0]), int(scale * pointZero[1])]\n testMaxPos = [int(scale * pointMax[0]), int(scale * pointMax[1])]\n # xCenter = (width * center[0]) / tmpWidht\n # yCenter = (height * center[1]) / tmpHeight\n # testCenter = [xCenter, yCenter]\n # xZero = (width * center[0]) / tmpWidht\n # yZero = (width * center[0]) / tmpWidht\n value, posImg = MeterUtil.readValueFormFrame(testFrame, testCenter, testZeroPos, testMaxPos, maxValue)\n cv2.imshow(\"posImg\", posImg)\n cv2.waitKey(0)\n cv2.destroyAllWindows()\n else:\n print(\"not match\")\n\n @staticmethod\n def demo006_001_meter():\n # 读数测试\n imagePath = r\"D:\\projects\\python\\pylearning\\files\\pics\\meter\\001_meter.png\" # 50\n imageTestPath = r\"D:\\projects\\python\\pylearning\\files\\pics\\meter\\001_meter.png\" # 50\n tmp = Template(imagePath)\n png_frame = cv2.imdecode(np.fromfile(imageTestPath, dtype=np.uint8), cv2.IMREAD_COLOR)\n cv2.imshow('OriPic', png_frame)\n match_ret = tmp.match_in_result(png_frame)\n if match_ret is not None:\n startX = match_ret[\"rectangle\"][0][0]\n startY = match_ret[\"rectangle\"][0][1]\n endX = match_ret[\"rectangle\"][2][0]\n endY = match_ret[\"rectangle\"][2][1]\n copyTestFram = png_frame.copy()\n cv2.rectangle(png_frame,\n (startX, startY), # 左上角坐标\n (endX, endY), # 右下角坐标\n (0, 255, 0), # 线颜色\n 2) # 线粗细\n print(match_ret)\n testFrame = copyTestFram[startY:endY, startX:endX]\n cv2.imshow('testFrame', testFrame)\n height, width = testFrame.shape[:2] # 测试图片高度和宽度\n print(f\"测试图片width={width}, height{height}\")\n\n image = cv2.imread(imagePath)\n tmpHeight, tmpWidht = image.shape[:2] # 模板图片高度和宽度\n print(f\"模板图片width={tmpWidht}, height{tmpHeight}\")\n # 模板图片信息：\n center = [297, 280] # 指针旋转中心点\n pointZero = [122, 386] # 0值指针坐标\n pointMax = [478, 386] # 最大值指针坐标\n maxValue = 260 # 刻度值最大值\n\n # 测试图片信息\n scale = height / tmpHeight\n testCenter = [int(scale * center[0]), int(scale * center[1])]\n testZeroPos = [int(scale * pointZero[0]), int(scale * pointZero[1])]\n testMaxPos = [int(scale * pointMax[0]), int(scale * pointMax[1])]\n # xCenter = (width * center[0]) / tmpWidht\n # yCenter = (height * center[1]) / tmpHeight\n # testCenter = [xCenter, yCenter]\n # xZero = (width * center[0]) / tmpWidht\n # yZero = (width * center[0]) / tmpWidht\n value, posImg = MeterUtil.readValueFormFrame(testFrame, testCenter, testZeroPos, testMaxPos, maxValue)\n cv2.imshow(\"posImg\", posImg)\n cv2.waitKey(0)\n cv2.destroyAllWindows()\n else:\n print(\"not match\")\n\n @staticmethod\n def demo_013_meter():\n # 读数测试\n tmpPath = r\"D:\\projects\\python\\pylearning\\files\\pics\\meter\\013_meter.png\" # 50\n imageTestPath = r\"D:\\projects\\python\\pylearning\\files\\pics\\meter\\013_meter.png\" # 50\n testMeterframe = cv2.imdecode(np.fromfile(imageTestPath, dtype=np.uint8), cv2.IMREAD_COLOR)\n # 模板图片信息：\n center = (230, 255) # 指针旋转中心点\n pointZero = (102, 372) # 0值指针坐标\n pointMax = (353, 388) # 最大值指针坐标\n maxValue = 60000 # 刻度值最大值\n meterValue, retImg = MeterUtil.readMeter(testMeterframe, tmpPath, center, pointZero, pointMax, maxValue)\n if meterValue is not None:\n print(f\"meter read value is: {meterValue}\")\n cv2.imshow(\"ResultImage\", retImg)\n cv2.waitKey(0)\n cv2.destroyAllWindows()\n else:\n print(\"error, read meter failed!\")\n\n @staticmethod\n def readMeter(testFrame, tmpMeterPicPath, centerPoint, zeroPoint, maxPoint, maxValue):\n \"\"\"\n 仪表读数\n :param testFrame: 待读取的仪表图片\n :param tmpMeterPicPath: 模板图片路径\n :param centerPoint: 指针旋转中心点\n :param zeroPoint: 0值刻度坐标\n :param maxPoint: 最大值刻度坐标\n :param maxValue: 数值最大值\n :return: value：读数结果\n image ： 读数过程分析结果图片\n \"\"\"\n value, image = None, None\n tmp = Template(tmpMeterPicPath)\n match_ret = tmp.match_in_result(testFrame)\n if match_ret is not None:\n startX = match_ret[\"rectangle\"][0][0]\n startY = match_ret[\"rectangle\"][0][1]\n endX = match_ret[\"rectangle\"][2][0]\n endY = match_ret[\"rectangle\"][2][1]\n copyTestFram = testFrame.copy()\n cv2.rectangle(testFrame,\n (startX, startY), # 左上角坐标\n (endX, endY), # 右下角坐标\n (0, 255, 0), # 线颜色\n 2) # 线粗细\n print(match_ret)\n testFrame = copyTestFram[startY:endY, startX:endX]\n # cv2.imshow('testFrame', testFrame)\n height, width = testFrame.shape[:2] # 测试图片高度和宽度\n print(f\"测试图片width={width}, height{height}\")\n\n image = cv2.imdecode(np.fromfile(tmpMeterPicPath, dtype=np.uint8), cv2.IMREAD_COLOR)\n tmpHeight, tmpWidht = image.shape[:2] # 模板图片高度和宽度\n print(f\"模板图片width={tmpWidht}, height{tmpHeight}\")\n # 测试图片信息\n scale = height / tmpHeight\n testCenter = [int(scale * centerPoint[0]), int(scale * centerPoint[1])]\n testZeroPos = [int(scale * zeroPoint[0]), int(scale * zeroPoint[1])]\n testMaxPos = [int(scale * maxPoint[0]), int(scale * maxPoint[1])]\n value, image = MeterUtil.readValueFormFrame(testFrame, testCenter, testZeroPos, testMaxPos, maxValue)\n return value, image\n else:\n print(\"not match\")\n return value, image\n\n @staticmethod\n def readMeterFrame(testFrame, tmpMeterPicFrame, centerPoint, zeroPoint, maxPoint, maxValue):\n \"\"\"\n 仪表读数\n :param testFrame: 待读取的仪表图片\n :param tmpMeterPicFrame: 模板图片路径\n :param centerPoint: 指针旋转中心点\n :param zeroPoint: 0值刻度坐标\n :param maxPoint: 最大值刻度坐标\n :param maxValue: 数值最大值\n :return: value：读数结果\n image ： 读数过程分析结果图片\n \"\"\"\n value, image = None, None\n tmp = TemplateCv2(tmpMeterPicFrame)\n match_ret = tmp.match_in_result(testFrame)\n if match_ret is not None:\n startX = match_ret[\"rectangle\"][0][0]\n startY = match_ret[\"rectangle\"][0][1]\n endX = match_ret[\"rectangle\"][2][0]\n endY = match_ret[\"rectangle\"][2][1]\n copyTestFram = testFrame.copy()\n # cv2.rectangle(testFrame,\n # (startX, startY), # 左上角坐标\n # (endX, endY), # 右下角坐标\n # (0, 255, 0), # 线颜色\n # 2) # 线粗细\n print(match_ret)\n testFrame = copyTestFram[startY:endY, startX:endX]\n # cv2.imshow('testFrame', testFrame)\n height, width = testFrame.shape[:2] # 测试图片高度和宽度\n print(f\"测试图片width={width}, height{height}\")\n\n tmpHeight, tmpWidht = tmpMeterPicFrame.shape[:2] # 模板图片高度和宽度\n print(f\"模板图片width={tmpWidht}, height{tmpHeight}\")\n # 测试图片信息\n scale = height / tmpHeight\n testCenter = [int(scale * centerPoint[0]), int(scale * centerPoint[1])]\n testZeroPos = [int(scale * zeroPoint[0]), int(scale * zeroPoint[1])]\n testMaxPos = [int(scale * maxPoint[0]), int(scale * maxPoint[1])]\n value, image = MeterUtil.readValueFormFrame(testFrame, testCenter, testZeroPos, testMaxPos, maxValue)\n return value, image\n else:\n print(\"not match\")\n return value, image\n\n @staticmethod\n def generateTemplate(tempPicPath, center, pointZero, pointMax, maxValue):\n cvFrame = cv2.imdecode(np.fromfile(tempPicPath, dtype=np.uint8), cv2.IMREAD_COLOR)\n templatePath = \"qt_meter_temp.ini\"\n METER_TEMPLATE = QSettings(templatePath, QSettings.IniFormat) # 校准设置,如果替换ini文件，需要重新赋值一次才会更新\n tempPic = \"MeterTemplate/temp_pic\"\n tempCenterPos = \"MeterTemplate/temp_center\"\n tempZeroPos = \"MeterTemplate/temp_zero\"\n tempMaxPos = \"MeterTemplate/temp_max_pox\"\n tempMaxValue = \"MeterTemplate/temp_max_value\"\n\n METER_TEMPLATE.setValue(tempPic, ImgUtil.cvImg2Base64(cvFrame)) # 图片\n METER_TEMPLATE.setValue(tempCenterPos, center) # 中心点\n METER_TEMPLATE.setValue(tempZeroPos, pointZero) # 缩放比例\n METER_TEMPLATE.setValue(tempMaxPos, pointMax) # 画框时的宽度\n METER_TEMPLATE.setValue(tempMaxValue, maxValue) # 画框时的宽度\n\n @staticmethod\n def getTemplate():\n templatePath = \"meter_qt.ini\"\n METER_TEMPLATE = QSettings(templatePath, QSettings.IniFormat) # 校准设置,如果替换ini文件，需要重新赋值一次才会更新\n tempPic = \"MeterTemplate/temp_pic\"\n tempCenterPos = \"MeterTemplate/temp_center\"\n tempZeroPos = \"MeterTemplate/temp_zero\"\n tempMaxPos = \"MeterTemplate/temp_max_pox\"\n tempMaxValue = \"MeterTemplate/temp_max_value\"\n\n tempPicBase64 = str(METER_TEMPLATE.value(tempPic)) # 图片\n tempFrame = ImgUtil.base64ToCvImg(tempPicBase64)\n getCenter = METER_TEMPLATE.value(tempCenterPos, None) # 中心点\n getZero = METER_TEMPLATE.value(tempZeroPos, None) # 缩放比例\n getMaxPos = METER_TEMPLATE.value(tempMaxPos, None) # 画框时的宽度\n getMaxValue = float(METER_TEMPLATE.value(tempMaxValue, None)) # 画框时的宽度\n print(type(getCenter))\n print(type(getZero))\n print(type(getMaxPos))\n print(type(getMaxValue))\n return tempFrame, getCenter, getZero, getMaxPos, getMaxValue\n # cv2.imshow(\"ResultImage\", tempFrame)\n # cv2.waitKey(0)\n # cv2.destroyAllWindows()\n\n\nif __name__ == \"__main__\":\n # MeterUtil.demo002() # ico_38c_tmp 两种都OK\n # MeterUtil.demo003() # 识别了多个直线，待增加判断有多少条线经过圆心范围，然后求两条直线交点 002_tmp_ori， 两种都可以\n # MeterUtil.demo004() # 直线交点为负数，找不到目标指针 003_press_meter 计算ok\n # MeterUtil.demo005() # speed_meter_temp.png 两种都可以\n # MeterUtil.demo006_001_meter() # 001_meter.png 两种都可以\n # MeterUtil.demo_013_meter()\n\n # 生成模板测试\n tempPath = r\"D:\\projects\\python\\pylearning\\files\\pics\\meter\\qt_meter_temp.png\" # 50\n center = (509, 502) # 指针旋转中心点\n pointZero = (179, 697) # 0值指针坐标\n pointMax = (841, 691) # 最大值指针坐标\n maxValue = 100 # 刻度值最大值\n MeterUtil.generateTemplate(tempPath, center, pointZero, pointMax, maxValue)\n\n # 读取模板测试\n # imageTestPath = r\"D:\\projects\\python\\pylearning\\files\\pics\\meter\\003_press_meter_tmp_small.png\" # 50\n # testMeterframe = cv2.imdecode(np.fromfile(imageTestPath, dtype=np.uint8), cv2.IMREAD_COLOR)\n # tempFrame, getCenter, getZero, getMaxPos, getMaxValue = MeterUtil.getTemplate()\n # meterValue, retImg = MeterUtil.readMeterFrame(testMeterframe, tempFrame, getCenter, getZero, getMaxPos, getMaxValue)\n # if meterValue is not None:\n # print(f\"meter read value is: {meterValue}\")\n # cv2.imshow(\"ResultImage\", retImg)\n # cv2.waitKey(0)\n # cv2.destroyAllWindows()\n # else:\n # print(\"error, read meter failed!\")\n\n\n", "repo_name": "TonsenWei/pyhelper", "sub_path": "src/examples/demos/meter_read/meter_util.py", "file_name": "meter_util.py", "file_ext": "py", "file_size_in_byte": 42607, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "cv2.imencode", "line_number": 30, "usage_type": "call"}, {"api_name": "base64.b64encode", "line_number": 31, "usage_type": "call"}, {"api_name": "base64.b64decode", "line_number": 42, "usage_type": "call"}, {"api_name": "numpy.frombuffer", "line_number": 45, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 45, "usage_type": "attribute"}, {"api_name": "cv2.imdecode", "line_number": 47, "usage_type": "call"}, {"api_name": "cv2.COLOR_RGB2BGR", "line_number": 47, "usage_type": "attribute"}, {"api_name": "cv2.resize", "line_number": 126, "usage_type": "call"}, {"api_name": "cv2.INTER_CUBIC", "line_number": 126, "usage_type": "attribute"}, {"api_name": "cv2.cvtColor", "line_number": 136, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 136, "usage_type": "attribute"}, {"api_name": "cv2.Canny", "line_number": 148, "usage_type": "call"}, {"api_name": "cv2.HoughLinesP", "line_number": 173, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 173, "usage_type": "attribute"}, {"api_name": "math.sqrt", "line_number": 181, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 182, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 187, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 193, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 204, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 238, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 239, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 247, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 248, "usage_type": "call"}, {"api_name": "cv2.line", "line_number": 256, "usage_type": "call"}, {"api_name": "cv2.putText", "line_number": 257, "usage_type": "call"}, {"api_name": "cv2.FONT_HERSHEY_SIMPLEX", "line_number": 257, "usage_type": "attribute"}, {"api_name": "cv2.circle", "line_number": 259, "usage_type": "call"}, {"api_name": "math.cos", "line_number": 288, "usage_type": "call"}, {"api_name": "math.pi", "line_number": 288, "usage_type": "name"}, {"api_name": "math.sin", "line_number": 289, "usage_type": "call"}, {"api_name": "math.pi", "line_number": 289, "usage_type": "name"}, {"api_name": "math.cos", "line_number": 290, "usage_type": "call"}, {"api_name": "math.pi", "line_number": 290, "usage_type": "name"}, {"api_name": "math.sin", "line_number": 291, "usage_type": "call"}, {"api_name": "math.pi", "line_number": 291, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 295, "usage_type": "call"}, {"api_name": "numpy.int32", "line_number": 295, "usage_type": "attribute"}, {"api_name": "cv2.pointPolygonTest", "line_number": 299, "usage_type": "call"}, {"api_name": "cv2.pointPolygonTest", "line_number": 300, "usage_type": "call"}, {"api_name": "cv2.pointPolygonTest", "line_number": 301, "usage_type": "call"}, {"api_name": "cv2.circle", "line_number": 371, "usage_type": "call"}, {"api_name": "cv2.circle", "line_number": 372, "usage_type": "call"}, {"api_name": "cv2.circle", "line_number": 373, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 380, "usage_type": "call"}, {"api_name": "src.myutils.airtest.core.cv.Template", "line_number": 393, "usage_type": "call"}, {"api_name": "cv2.imdecode", "line_number": 394, "usage_type": "call"}, {"api_name": "numpy.fromfile", "line_number": 394, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 394, "usage_type": "attribute"}, {"api_name": "cv2.IMREAD_COLOR", "line_number": 394, "usage_type": "attribute"}, {"api_name": "cv2.imshow", "line_number": 395, "usage_type": "call"}, {"api_name": "cv2.rectangle", "line_number": 403, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 410, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 414, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 434, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 435, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 436, "usage_type": "call"}, {"api_name": "src.myutils.airtest.core.cv.Template", "line_number": 444, "usage_type": "call"}, {"api_name": "cv2.imdecode", "line_number": 445, "usage_type": "call"}, {"api_name": "numpy.fromfile", "line_number": 445, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 445, "usage_type": "attribute"}, {"api_name": "cv2.IMREAD_COLOR", "line_number": 445, "usage_type": "attribute"}, {"api_name": "cv2.imshow", "line_number": 446, "usage_type": "call"}, {"api_name": "cv2.rectangle", "line_number": 454, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 461, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 465, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 485, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 486, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 487, "usage_type": "call"}, {"api_name": "src.myutils.airtest.core.cv.Template", "line_number": 496, "usage_type": "call"}, {"api_name": "cv2.imdecode", "line_number": 497, "usage_type": "call"}, {"api_name": "numpy.fromfile", "line_number": 497, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 497, "usage_type": "attribute"}, {"api_name": "cv2.IMREAD_COLOR", "line_number": 497, "usage_type": "attribute"}, {"api_name": "cv2.imshow", "line_number": 498, "usage_type": "call"}, {"api_name": "cv2.rectangle", "line_number": 506, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 513, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 517, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 537, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 538, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 539, "usage_type": "call"}, {"api_name": "src.myutils.airtest.core.cv.Template", "line_number": 548, "usage_type": "call"}, {"api_name": "cv2.imdecode", "line_number": 549, "usage_type": "call"}, {"api_name": "numpy.fromfile", "line_number": 549, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 549, "usage_type": "attribute"}, {"api_name": "cv2.IMREAD_COLOR", "line_number": 549, "usage_type": "attribute"}, {"api_name": "cv2.imshow", "line_number": 550, "usage_type": "call"}, {"api_name": "cv2.rectangle", "line_number": 558, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 565, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 569, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 589, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 590, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 591, "usage_type": "call"}, {"api_name": "src.myutils.airtest.core.cv.Template", "line_number": 600, "usage_type": "call"}, {"api_name": "cv2.imdecode", "line_number": 601, "usage_type": "call"}, {"api_name": "numpy.fromfile", "line_number": 601, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 601, "usage_type": "attribute"}, {"api_name": "cv2.IMREAD_COLOR", "line_number": 601, "usage_type": "attribute"}, {"api_name": "cv2.imshow", "line_number": 602, "usage_type": "call"}, {"api_name": "cv2.rectangle", "line_number": 610, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 617, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 621, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 641, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 642, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 643, "usage_type": "call"}, {"api_name": "cv2.imdecode", "line_number": 652, "usage_type": "call"}, {"api_name": "numpy.fromfile", "line_number": 652, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 652, "usage_type": "attribute"}, {"api_name": "cv2.IMREAD_COLOR", "line_number": 652, "usage_type": "attribute"}, {"api_name": "cv2.imshow", "line_number": 661, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 662, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 663, "usage_type": "call"}, {"api_name": "src.myutils.airtest.core.cv.Template", "line_number": 681, "usage_type": "call"}, {"api_name": "cv2.rectangle", "line_number": 689, "usage_type": "call"}, {"api_name": "cv2.imdecode", "line_number": 700, "usage_type": "call"}, {"api_name": "numpy.fromfile", "line_number": 700, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 700, "usage_type": "attribute"}, {"api_name": "cv2.IMREAD_COLOR", "line_number": 700, "usage_type": "attribute"}, {"api_name": "src.myutils.airtest.core.cv.TemplateCv2", "line_number": 728, "usage_type": "call"}, {"api_name": "cv2.imdecode", "line_number": 762, "usage_type": "call"}, {"api_name": "numpy.fromfile", "line_number": 762, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 762, "usage_type": "attribute"}, {"api_name": "cv2.IMREAD_COLOR", "line_number": 762, "usage_type": "attribute"}, {"api_name": "PySide2.QtCore.QSettings", "line_number": 764, "usage_type": "call"}, {"api_name": "PySide2.QtCore.QSettings.IniFormat", "line_number": 764, "usage_type": "attribute"}, {"api_name": "PySide2.QtCore.QSettings", "line_number": 780, "usage_type": "call"}, {"api_name": "PySide2.QtCore.QSettings.IniFormat", "line_number": 780, "usage_type": "attribute"}]} +{"seq_id": "38330650168", "text": "from django.urls import path\nfrom . import views\n\nurlpatterns = [\n path('profile/', views.profile),\n path('shop/', views.shop),\n path('example/', views.example),\n path('secondpage/', views.secondpage),\n path('firstpage/', views.firstpage),\n path('home/', views.home),\n path('shopsuamilist/', views.shopsuamilist),\n]\n", "repo_name": "adhearizta/latihan-web", "sub_path": "mainapp/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 337, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "django.urls.path", "line_number": 5, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 6, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 7, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 8, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 9, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 10, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 11, "usage_type": "call"}]} +{"seq_id": "31946115488", "text": "import requests\nimport time\nimport csv\nimport logging\nimport re\nimport os.path\nfrom bs4 import BeautifulSoup\nimport bs4\n\n# logging.basicConfig(filename= os.path.join(os.getcwd(), \"../logs\", \"tender_collector.log\"),\n# format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',\n# level=logging.DEBUG)\nlogging.basicConfig()\n\nin_file = \"../owner-list-test.txt\"\n# in_file = \"owner-list-unique-alphabetical.txt\"\ndelay = 5 # in-between fetches\nout_folder = \"raw\"\nskip_owner_count = 266\nmax_failure_count = 10\n\n\ndef build_url(owner_id: str) -> str:\n \"\"\"\n https://armp.cm/recherche_avancee?maitre_ouvrage=1761®ion=0&departement=0\n \"\"\"\n\n return f\"https://armp.cm/recherche_avancee?maitre_ouvrage={owner_id}®ion=0&departement=0\"\n\n\ndef build_output_filename(in_url, folder_name) -> str:\n\n escaped_url = re.sub(\"[\\\\W]\", \"_\", in_url)\n return os.path.join(folder_name, escaped_url+\".html\")\n\n\ndef save_content(content, out_filename: str):\n\n with open(out_filename, \"w\", encoding=\"utf-8\") as f_html:\n f_html.write(content)\n\n\ndef get_next_url(soup: bs4.element.Tag) -> str:\n \"\"\"\n :param content:\n :return:\n \"\"\"\n\n #›\n\n next_url_tag = soup.find(attrs={\"rel\": \"next\"})\n\n if next_url_tag:\n\n return next_url_tag.get(\"href\")\n\n return \"\"\n\n\ndef main():\n\n with open(in_file, \"r\", encoding=\"utf-8\") as f:\n reader = csv.reader(f)\n\n ii = 0\n failure_count = 0\n for row in reader:\n\n if skip_owner_count > 0 and ii < skip_owner_count:\n ii += 1\n continue\n\n if len(row) < 2:\n logging.warning(\"Empty row, %s\", row)\n continue\n\n url = build_url(row[1])\n\n while url:\n\n logging.info(\"Fetching from %s\", url)\n r = requests.get(url)\n\n if r.status_code != 200:\n failure_count += 1\n logging.warning(\"Response code %s unexpected for %s, current failure count %s\",\n r.status_code, url, failure_count)\n if failure_count < max_failure_count:\n time.sleep(60)\n continue\n else:\n logging.error(\"Max failure count reached, exiting\")\n return\n\n failure_count = 0\n\n html_content = r.text\n\n html_filename = build_output_filename(url, out_folder)\n logging.info(f\"Saving content to {html_filename}\")\n\n save_content(html_content, html_filename)\n soup = BeautifulSoup(html_content, \"html.parser\")\n url = get_next_url(soup)\n\n time.sleep(delay)\n\n\nif __name__ == \"__main__\":\n main()\n", "repo_name": "2trc/armp-lib", "sub_path": "src/armp/tender_collector.py", "file_name": "tender_collector.py", "file_ext": "py", "file_size_in_byte": 3005, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "logging.basicConfig", "line_number": 13, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 33, "usage_type": "call"}, {"api_name": "os.path.path.join", "line_number": 34, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 34, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 34, "usage_type": "name"}, {"api_name": "bs4.element", "line_number": 43, "usage_type": "attribute"}, {"api_name": "csv.reader", "line_number": 63, "usage_type": "call"}, {"api_name": "logging.warning", "line_number": 74, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 81, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 82, "usage_type": "call"}, {"api_name": "logging.warning", "line_number": 86, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 89, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 92, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 100, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 103, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 106, "usage_type": "call"}]} +{"seq_id": "4185815733", "text": "import numpy as np\nfrom oscillator import hopf_integrate, hopf_parameters, hopf_plot\nfrom mapper import tc_joint, ctr_joint\nimport matplotlib.pyplot as plt\nimport time\n\n\n# CPG constants\nsample_time = 0.03 # Sample time\nctr_offsset = 10\n\n\ndef get_angles(t):\n osc = np.zeros([6])\n osc[:] = hopf_integrate(t)\n return tc_joint(osc[0:2]), ctr_joint(osc[3:6], ctr_offsset)\n\n\nif __name__ == '__main__':\n\n # Variables de prueba\n d_range = 300\n tc_out = np.zeros([d_range , 6])\n ctr_out = np.zeros([d_range , 6])\n\n # Get first two values separately (to compile C functions)\n tc_out[0, :], ctr_out[0, :] = get_angles(0)\n tc_out[1, :], ctr_out[1, :] = get_angles(sample_time)\n\n # Variables de prueba\n t_t = np.zeros([d_range])\n ti = 2*sample_time\n t_t[1] = ti\n\n # Variables de prueba\n max_t = float('-inf')\n\n for i in range(2, d_range):\n time.sleep(sample_time)\n start = time.time()\n\n tc_angles, ctr_angles = get_angles(ti)\n tc_out[i, :] = tc_angles\n ctr_out[i, :] = ctr_angles\n\n stop = time.time() - start\n print('Step time({}): {}'.format(i, stop))\n max_t = max(max_t, stop)\n\n ti += sample_time\n t_t[i] = ti\n\n print('Max step CPG time)', max_t)\n plt.plot(t_t, tc_out[:, 0], t_t, tc_out[:, 1], t_t, ctr_out[:, 0], t_t, ctr_out[:,1])\n plt.show()\n\n", "repo_name": "istarendil/vekima", "sub_path": "scripts/cpg.py", "file_name": "cpg.py", "file_ext": "py", "file_size_in_byte": 1387, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "numpy.zeros", "line_number": 14, "usage_type": "call"}, {"api_name": "oscillator.hopf_integrate", "line_number": 15, "usage_type": "call"}, {"api_name": "mapper.tc_joint", "line_number": 16, "usage_type": "call"}, {"api_name": "mapper.ctr_joint", "line_number": 16, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 23, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 24, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 31, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 39, "usage_type": "call"}, {"api_name": "time.time", "line_number": 40, "usage_type": "call"}, {"api_name": "time.time", "line_number": 46, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 54, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 54, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 55, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 55, "usage_type": "name"}]} +{"seq_id": "22626448929", "text": "import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nimport models\n\n\nclass SRNTT(nn.Module):\n \"\"\"\n PyTorch Module for SRNTT.\n Now x4 is only supported.\n\n Parameters\n ---\n ngf : int, optional\n the number of filterd of generator.\n n_blucks : int, optional\n the number of residual blocks for each module.\n \"\"\"\n def __init__(self, ngf=64, n_blocks=16, use_weights=False):\n super(SRNTT, self).__init__()\n self.content_extractor = ContentExtractor(ngf, n_blocks)\n self.texture_transfer = TextureTransfer(ngf, n_blocks, use_weights)\n models.init_weights(self, init_type='normal', init_gain=0.02)\n\n def forward(self, x, maps, weights=None):\n \"\"\"\n Parameters\n ---\n x : torch.Tensor\n the input image of SRNTT.\n maps : dict of torch.Tensor\n the swapped feature maps on relu3_1, relu2_1 and relu1_1.\n depths of the maps are 256, 128 and 64 respectively.\n \"\"\"\n\n base = F.interpolate(x, None, 4, 'bilinear', False)\n upscale_plain, content_feat = self.content_extractor(x)\n\n if maps is not None:\n if hasattr(self.texture_transfer, 'a'): # if weight is used\n upscale_srntt = self.texture_transfer(\n content_feat, maps, weights)\n else:\n upscale_srntt = self.texture_transfer(\n content_feat, maps)\n return upscale_plain + base, upscale_srntt + base\n else:\n return upscale_plain + base, None\n\n\nclass ContentExtractor(nn.Module):\n \"\"\"\n Content Extractor for SRNTT, which outputs maps before-and-after upscale.\n more detail: https://github.com/ZZUTK/SRNTT/blob/master/SRNTT/model.py#L73.\n Currently this module only supports `scale_factor=4`.\n\n Parameters\n ---\n ngf : int, optional\n a number of generator's features.\n n_blocks : int, optional\n a number of residual blocks, see also `ResBlock` class.\n \"\"\"\n\n def __init__(self, ngf=64, n_blocks=16):\n super(ContentExtractor, self).__init__()\n\n self.head = nn.Sequential(\n nn.Conv2d(3, ngf, kernel_size=3, stride=1, padding=1),\n nn.LeakyReLU(0.1, True)\n )\n self.body = nn.Sequential(\n *[ResBlock(ngf) for _ in range(n_blocks)],\n # nn.Conv2d(ngf, ngf, kernel_size=3, stride=1, padding=1),\n # nn.BatchNorm2d(ngf)\n )\n self.tail = nn.Sequential(\n nn.Conv2d(ngf, ngf * 4, kernel_size=3, stride=1, padding=1),\n nn.PixelShuffle(2),\n nn.LeakyReLU(0.1, True),\n nn.Conv2d(ngf, ngf * 4, kernel_size=3, stride=1, padding=1),\n nn.PixelShuffle(2),\n nn.LeakyReLU(0.1, True),\n nn.Conv2d(ngf, ngf, kernel_size=3, stride=1, padding=1),\n nn.LeakyReLU(0.1, True),\n nn.Conv2d(ngf, 3, kernel_size=3, stride=1, padding=1),\n # nn.Tanh()\n )\n\n def forward(self, x):\n h = self.head(x)\n h = self.body(h) + h\n upscale = self.tail(h)\n return upscale, h\n\n\nclass TextureTransfer(nn.Module):\n \"\"\"\n Conditional Texture Transfer for SRNTT,\n see https://github.com/ZZUTK/SRNTT/blob/master/SRNTT/model.py#L116.\n This module is devided 3 parts for each scales.\n\n Parameters\n ---\n ngf : int\n a number of generator's filters.\n n_blocks : int, optional\n a number of residual blocks, see also `ResBlock` class.\n \"\"\"\n\n def __init__(self, ngf=64, n_blocks=16, use_weights=False):\n super(TextureTransfer, self).__init__()\n\n # for small scale\n self.head_small = nn.Sequential(\n nn.Conv2d(ngf + 256, ngf, kernel_size=3, stride=1, padding=1),\n nn.LeakyReLU(0.1, True),\n )\n self.body_small = nn.Sequential(\n *[ResBlock(ngf) for _ in range(n_blocks)],\n # nn.Conv2d(ngf, ngf, kernel_size=3, stride=1, padding=1),\n # nn.BatchNorm2d(ngf)\n )\n self.tail_small = nn.Sequential(\n nn.Conv2d(ngf, ngf * 4, kernel_size=3, stride=1, padding=1),\n nn.PixelShuffle(2),\n nn.LeakyReLU(0.1, True),\n )\n\n # for medium scale\n self.head_medium = nn.Sequential(\n nn.Conv2d(ngf + 128, ngf, kernel_size=3, stride=1, padding=1),\n nn.LeakyReLU(0.1, True),\n )\n self.body_medium = nn.Sequential(\n *[ResBlock(ngf) for _ in range(n_blocks)],\n # nn.Conv2d(ngf, ngf, kernel_size=3, stride=1, padding=1),\n # nn.BatchNorm2d(ngf)\n )\n self.tail_medium = nn.Sequential(\n nn.Conv2d(ngf, ngf * 4, kernel_size=3, stride=1, padding=1),\n nn.PixelShuffle(2),\n nn.LeakyReLU(0.1, True),\n )\n\n # for large scale\n self.head_large = nn.Sequential(\n nn.Conv2d(ngf + 64, ngf, kernel_size=3, stride=1, padding=1),\n nn.LeakyReLU(0.1, True),\n )\n self.body_large = nn.Sequential(\n *[ResBlock(ngf) for _ in range(n_blocks)],\n # nn.Conv2d(ngf, ngf, kernel_size=3, stride=1, padding=1),\n # nn.BatchNorm2d(ngf)\n )\n self.tail_large = nn.Sequential(\n nn.Conv2d(ngf, ngf // 2, kernel_size=3, stride=1, padding=1),\n nn.LeakyReLU(0.1, True),\n nn.Conv2d(ngf // 2, 3, kernel_size=3, stride=1, padding=1),\n # nn.Tanh()\n )\n\n if use_weights:\n self.a = nn.Parameter(torch.ones(3), requires_grad=True)\n self.b = nn.Parameter(torch.ones(3), requires_grad=True)\n\n def forward(self, x, maps, weights=None):\n # compute weighted maps\n if hasattr(self, 'a') and weights is not None:\n for idx, layer in enumerate(['relu3_1', 'relu2_1', 'relu1_1']):\n weights_scaled = F.interpolate(\n F.pad(weights, (1, 1, 1, 1), mode='replicate'),\n scale_factor=2**idx,\n mode='bicubic',\n align_corners=True) * self.a[idx] + self.b[idx]\n maps[layer] *= torch.sigmoid(weights_scaled)\n\n # small scale\n h = torch.cat([x, maps['relu3_1']], 1)\n h = self.head_small(h)\n h = self.body_small(h) + x\n x = self.tail_small(h)\n\n # medium scale\n h = torch.cat([x, maps['relu2_1']], 1)\n h = self.head_medium(h)\n h = self.body_medium(h) + x\n x = self.tail_medium(h)\n\n # large scale\n h = torch.cat([x, maps['relu1_1']], 1)\n h = self.head_large(h)\n h = self.body_large(h) + x\n x = self.tail_large(h)\n\n return x\n\n\nclass ResBlock(nn.Module):\n \"\"\"\n Basic residual block for SRNTT.\n\n Parameters\n ---\n n_filters : int, optional\n a number of filters.\n \"\"\"\n\n def __init__(self, n_filters=64):\n super(ResBlock, self).__init__()\n self.body = nn.Sequential(\n nn.Conv2d(n_filters, n_filters, 3, 1, 1),\n nn.ReLU(True),\n nn.Conv2d(n_filters, n_filters, 3, 1, 1),\n )\n\n def forward(self, x):\n return self.body(x) + x\n\n\nif __name__ == \"__main__\":\n device = torch.device('cuda:0')\n\n x = torch.rand(16, 3, 24, 24).to(device)\n\n maps = {}\n maps.update({'relu3_1': torch.rand(16, 256, 24, 24).to(device)})\n maps.update({'relu2_1': torch.rand(16, 128, 48, 48).to(device)})\n maps.update({'relu1_1': torch.rand(16, 64, 96, 96).to(device)})\n\n model = SRNTT().to(device)\n _, out = model(x, maps)\n", "repo_name": "S-aiueo32/srntt-pytorch", "sub_path": "models/srntt.py", "file_name": "srntt.py", "file_ext": "py", "file_size_in_byte": 7593, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 102, "dataset": "github-code", "pt": "37", "api": [{"api_name": "torch.nn.Module", "line_number": 8, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 8, "usage_type": "name"}, {"api_name": "models.init_weights", "line_number": 24, "usage_type": "call"}, {"api_name": "torch.nn.functional.interpolate", "line_number": 37, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 37, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 52, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 52, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 69, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 69, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 70, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 70, "usage_type": "name"}, {"api_name": "torch.nn.LeakyReLU", "line_number": 71, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 71, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 73, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 73, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 78, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 78, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 79, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 79, "usage_type": "name"}, {"api_name": "torch.nn.PixelShuffle", "line_number": 80, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 80, "usage_type": "name"}, {"api_name": "torch.nn.LeakyReLU", "line_number": 81, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 81, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 82, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 82, "usage_type": "name"}, {"api_name": "torch.nn.PixelShuffle", "line_number": 83, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 83, "usage_type": "name"}, {"api_name": "torch.nn.LeakyReLU", "line_number": 84, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 84, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 85, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 85, "usage_type": "name"}, {"api_name": "torch.nn.LeakyReLU", "line_number": 86, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 86, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 87, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 87, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 98, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 98, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 116, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 116, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 117, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 117, "usage_type": "name"}, {"api_name": "torch.nn.LeakyReLU", "line_number": 118, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 118, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 120, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 120, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 125, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 125, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 126, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 126, "usage_type": "name"}, {"api_name": "torch.nn.PixelShuffle", "line_number": 127, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 127, "usage_type": "name"}, {"api_name": "torch.nn.LeakyReLU", "line_number": 128, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 128, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 132, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 132, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 133, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 133, "usage_type": "name"}, {"api_name": "torch.nn.LeakyReLU", "line_number": 134, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 134, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 136, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 136, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 141, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 141, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 142, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 142, "usage_type": "name"}, {"api_name": "torch.nn.PixelShuffle", "line_number": 143, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 143, "usage_type": "name"}, {"api_name": "torch.nn.LeakyReLU", "line_number": 144, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 144, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 148, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 148, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 149, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 149, "usage_type": "name"}, {"api_name": "torch.nn.LeakyReLU", "line_number": 150, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 150, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 152, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 152, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 157, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 157, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 158, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 158, "usage_type": "name"}, {"api_name": "torch.nn.LeakyReLU", "line_number": 159, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 159, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 160, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 160, "usage_type": "name"}, {"api_name": "torch.nn.Parameter", "line_number": 165, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 165, "usage_type": "name"}, {"api_name": "torch.ones", "line_number": 165, "usage_type": "call"}, {"api_name": "torch.nn.Parameter", "line_number": 166, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 166, "usage_type": "name"}, {"api_name": "torch.ones", "line_number": 166, "usage_type": "call"}, {"api_name": "torch.nn.functional.interpolate", "line_number": 172, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 172, "usage_type": "name"}, {"api_name": "torch.nn.functional.pad", "line_number": 173, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 173, "usage_type": "name"}, {"api_name": "torch.sigmoid", "line_number": 177, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 180, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 186, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 192, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 200, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 200, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 212, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 212, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 213, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 213, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 214, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 214, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 215, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 215, "usage_type": "name"}, {"api_name": "torch.device", "line_number": 223, "usage_type": "call"}, {"api_name": "torch.rand", "line_number": 225, "usage_type": "call"}, {"api_name": "torch.rand", "line_number": 228, "usage_type": "call"}, {"api_name": "torch.rand", "line_number": 229, "usage_type": "call"}, {"api_name": "torch.rand", "line_number": 230, "usage_type": "call"}]} +{"seq_id": "34438758102", "text": "from django import forms\nfrom .models import omr_templates,Exam\n\n\n\n\nclass upload_form(forms.ModelForm):\n\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n self.fields['pdf_name'].widget.attrs.update({'class':\"form-control\",\n 'placeholder':\"template name\",\n 'aria-label':\"template\",\n 'aria-describedby':\"basic-addon2\"})\n self.fields['pdf'].widget.attrs.update({'class':\"custom-file-input\",'id':\"inputGroupFile04\"})\n\n class Meta:\n model = omr_templates\n fields =[\n 'pdf_name',\n 'pdf'\n ]\n\nclass DateInput(forms.DateInput):\n input_type = 'date'\n\n\nclass exam_builder(forms.ModelForm):\n\n def __init__(self,*args,**kwargs):\n super().__init__(*args,**kwargs)\n self.fields['exam_name'].widget.attrs.update({'class':\"form-control\",\n 'placeholder':\"exam name\",\n 'aria-label':\"exam\",\n 'aria-describedby':\"basic-addon2\"})\n\n self.fields['ansKey'].widget.attrs.update({'class':\"custom-file-input\",\n 'id':\"file-upload\"})\n self.fields['ansKeyImg'].widget.attrs.update({'class':\"custom-file-input\",\n 'id':\"file-upload2\"})\n\n self.fields['template'].widget.attrs.update({'class':\"custom-select\",\n 'id':\"inputGroupSelect02\"})\n\n self.fields['date'].widget = DateInput()\n\n class Meta:\n model = Exam\n fields =[\n 'exam_name',\n 'ansKey',\n 'ansKeyImg',\n 'template',\n 'date'\n ]\n #widgets = {\n # 'date': DateInput()\n #}\n\nclass exam_editor(forms.ModelForm):\n\n class Meta:\n model = Exam\n fields =[\n 'exam_name',\n 'ansKey',\n 'ansKeyImg',\n 'template'\n ]\n\n\nclass multiple_files_input(forms.Form):\n inputs = forms.FileField(widget=forms.ClearableFileInput(attrs={'multiple': True}))\n", "repo_name": "barotdhrumil21/b691009ff", "sub_path": "src/functions/forms.py", "file_name": "forms.py", "file_ext": "py", "file_size_in_byte": 2286, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "django.forms.ModelForm", "line_number": 7, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 7, "usage_type": "name"}, {"api_name": "models.omr_templates", "line_number": 18, "usage_type": "name"}, {"api_name": "django.forms.DateInput", "line_number": 24, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 24, "usage_type": "name"}, {"api_name": "django.forms.ModelForm", "line_number": 28, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 28, "usage_type": "name"}, {"api_name": "models.Exam", "line_number": 48, "usage_type": "name"}, {"api_name": "django.forms.ModelForm", "line_number": 60, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 60, "usage_type": "name"}, {"api_name": "models.Exam", "line_number": 63, "usage_type": "name"}, {"api_name": "django.forms.Form", "line_number": 72, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 72, "usage_type": "name"}, {"api_name": "django.forms.FileField", "line_number": 73, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 73, "usage_type": "name"}, {"api_name": "django.forms.ClearableFileInput", "line_number": 73, "usage_type": "call"}]} +{"seq_id": "9820822290", "text": "import pglast\nfrom pglast.enums import AlterTableType, ConstrType\n\nimport squabble.rule\nfrom squabble import RuleConfigurationException\nfrom squabble.message import Message\nfrom squabble.rules import BaseRule\n\n\nclass AddColumnDisallowConstraints(BaseRule):\n \"\"\"\n Prevent adding a column with certain constraints to an existing table.\n\n Configuration: ::\n\n {\n \"AddColumnDisallowConstraints\": {\n \"disallowed\": [\"DEFAULT\", \"FOREIGN\"]\n }\n }\n\n Valid constraint types:\n - DEFAULT\n - NULL\n - NOT NULL\n - FOREIGN\n - UNIQUE\n \"\"\"\n\n _CONSTRAINT_MAP = {\n 'DEFAULT': ConstrType.CONSTR_DEFAULT,\n 'NULL': ConstrType.CONSTR_NULL,\n 'NOT NULL': ConstrType.CONSTR_NOTNULL,\n 'FOREIGN': ConstrType.CONSTR_FOREIGN,\n 'UNIQUE': ConstrType.CONSTR_UNIQUE,\n }\n\n class ConstraintNotAllowed(Message):\n \"\"\"\n When adding a column to an existing table, certain constraints can have\n unintentional side effects, like locking the table or introducing\n performance issues.\n\n For example, adding a ``DEFAULT`` constraint may hold a lock on the\n table while all existing rows are modified to fill in the default\n value.\n\n A ``UNIQUE`` constraint will require scanning the table to confirm\n there are no duplicates.\n\n On a particularly hot table, a ``FOREIGN`` constraint will introduce\n possibly dangerous overhead to confirm the referential integrity of\n each row.\n \"\"\"\n CODE = 1004\n TEMPLATE = 'column \"{col}\" has a disallowed constraint'\n\n def enable(self, ctx, config):\n disallowed = config.get('disallowed', [])\n if disallowed == []:\n raise RuleConfigurationException(\n self, 'must specify `disallowed` constraints')\n\n constraints = set()\n\n for c in disallowed:\n ty = self._CONSTRAINT_MAP.get(c.upper())\n if ty is None:\n raise RuleConfigurationException(\n self, 'unknown constraint: `%s`' % c)\n\n constraints.add(ty)\n\n ctx.register('AlterTableCmd', self._check(constraints))\n\n @squabble.rule.node_visitor\n def _check(self, ctx, node, disallowed_constraints):\n \"\"\"\n Node is an `AlterTableCmd`:\n\n ::\n {\n 'AlterTableCmd': {\n 'def': {\n 'ColumnDef': {\n 'colname': 'bar',\n 'constraints': [{'Constraint': {'contype': 2}}]\n }\n }\n }\n }\n \"\"\"\n\n # We only care about adding a column\n if node.subtype != AlterTableType.AT_AddColumn:\n return\n\n constraints = node['def'].constraints\n\n # No constraints imposed, nothing to do.\n if constraints == pglast.Missing:\n return\n\n for constraint in constraints:\n if constraint.contype.value in disallowed_constraints:\n col = node['def'].colname.value\n\n ctx.report(\n self.ConstraintNotAllowed(col=col),\n node=constraint)\n", "repo_name": "erik/squabble", "sub_path": "squabble/rules/add_column_disallow_constraints.py", "file_name": "add_column_disallow_constraints.py", "file_ext": "py", "file_size_in_byte": 3205, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 64, "dataset": "github-code", "pt": "37", "api": [{"api_name": "squabble.rules.BaseRule", "line_number": 10, "usage_type": "name"}, {"api_name": "pglast.enums.ConstrType.CONSTR_DEFAULT", "line_number": 31, "usage_type": "attribute"}, {"api_name": "pglast.enums.ConstrType", "line_number": 31, "usage_type": "name"}, {"api_name": "pglast.enums.ConstrType.CONSTR_NULL", "line_number": 32, "usage_type": "attribute"}, {"api_name": "pglast.enums.ConstrType", "line_number": 32, "usage_type": "name"}, {"api_name": "pglast.enums.ConstrType.CONSTR_NOTNULL", "line_number": 33, "usage_type": "attribute"}, {"api_name": "pglast.enums.ConstrType", "line_number": 33, "usage_type": "name"}, {"api_name": "pglast.enums.ConstrType.CONSTR_FOREIGN", "line_number": 34, "usage_type": "attribute"}, {"api_name": "pglast.enums.ConstrType", "line_number": 34, "usage_type": "name"}, {"api_name": "pglast.enums.ConstrType.CONSTR_UNIQUE", "line_number": 35, "usage_type": "attribute"}, {"api_name": "pglast.enums.ConstrType", "line_number": 35, "usage_type": "name"}, {"api_name": "squabble.message.Message", "line_number": 38, "usage_type": "name"}, {"api_name": "squabble.RuleConfigurationException", "line_number": 61, "usage_type": "call"}, {"api_name": "squabble.RuleConfigurationException", "line_number": 69, "usage_type": "call"}, {"api_name": "pglast.enums.AlterTableType.AT_AddColumn", "line_number": 95, "usage_type": "attribute"}, {"api_name": "pglast.enums.AlterTableType", "line_number": 95, "usage_type": "name"}, {"api_name": "pglast.Missing", "line_number": 101, "usage_type": "attribute"}, {"api_name": "squabble.rule.rule", "line_number": 76, "usage_type": "attribute"}, {"api_name": "squabble.rule", "line_number": 76, "usage_type": "name"}]} +{"seq_id": "10509333331", "text": "import matplotlib\n# matplotlib.use('AGG')\n\nimport os\nimport matplotlib.animation as animation\nimport matplotlib.pyplot as plt\nfrom datetime import datetime\n\nfrom mpl_toolkits.basemap import Basemap\n\nfrom timutils import midpt_norm\nfrom timutils import colorbar_from_cmap_norm\nimport IDE_locations\nimport spinup_diagnostics as sd\n\n\ndef map_init(data):\n\n cmap, norm = get_norm_cmap(plt.get_cmap('Blues'))\n\n fig = plt.figure()\n ax = plt.subplot2grid((2, 12), (0, 0), rowspan=2, colspan=10)\n cbar_ax = plt.subplot2grid((2, 12), (0, 11), rowspan=2, colspan=1)\n\n clm_map = Basemap(ax=ax)\n clm_map.drawcoastlines()\n clm_map.drawstates()\n clm_map.drawcountries()\n\n locs = IDE_locations.CLMf05g16_get_spatial_info()\n domain = locs[0]\n locs = locs[1:]\n lat = domain.get_lat()\n lon = domain.get_lon()\n map_data = clm_map.pcolormesh(lon, lat,\n data[0, :, :].squeeze(),\n cmap=cmap,\n norm=norm,\n latlon=True)\n\n t_idx = 0\n colorbar_from_cmap_norm.colorbar_from_cmap_norm(\n cmap, norm, cbar_ax, None, data[t_idx, ...])\n cbar_ax.set_title('data')\n cbar_ax = None\n return (clm_map, fig, ax, cbar_ax, map_data)\n\n\ndef get_norm_cmap(cmap_arg=plt.get_cmap('Blues')):\n cmap, norm = midpt_norm.get_discrete_midpt_cmap_norm(vmin=4799,\n vmax=4807,\n midpoint=4803,\n bands_above_mdpt=7,\n bands_below_mdpt=7,\n extend='both')\n return(cmap, norm)\n\n\ndef map_update(i, m, fig, ax, cbar_ax, t_idx, data, map_data):\n\n z_sfc = 0 # array index of surface\n map_data.set_array(data[i, :-1, :-1].squeeze().flatten())\n # ax[0].set_xlim([-160.0, -40.0])\n # ax[0].set_ylim([15.0, 85])\n\n t_str = '{}'.format(t_idx[i])\n ax.set_title(t_str)\n\n print('plotting for {}'.format(t_str))\n # fig.canvas.draw()\n\n\nif __name__ == \"__main__\":\n outfile = 'clm.mp4'\n\n cases = ['IDE_ctl', 'IDE_redpcp']\n data_dirs = [os.path.join(os.getenv('CSCRATCH'), 'archive',\n this_case, 'lnd', 'hist')\n for this_case in cases]\n clm_runs = [sd.CLM_spinup_analyzer(data_dir=this_data,\n CASE=this_case)\n for this_data, this_case in zip(data_dirs, cases)]\n this_run = clm_runs[0]\n this_run.gather_filenames(glob_pat='*h1*')\n vardata = this_run.parse_var('WT')\n\n m, fig, ax, cbar_ax, map_data = map_init(vardata.data)\n\n im_ani = animation.FuncAnimation(fig,\n func=map_update,\n frames=10, # len(cos['t']),\n interval=100,\n # blit=True, # only update parts\n # # that changed\n fargs=[m, fig, ax, cbar_ax, vardata.time,\n vardata.data, map_data])\n # note: not having ffmpeg available (`module load ffmpeg` at\n # NERSC) can create some undecipherable errors - check on that if\n # it's not working\n # im_ani.save(outfile, metadata={'artist': 'CLM'}, bitrate=100000)\n # plt.close(fig)\n", "repo_name": "Timothy-W-Hilton/CLM_IDE_analyses", "sub_path": "CLM_mapper.py", "file_name": "CLM_mapper.py", "file_ext": "py", "file_size_in_byte": 3521, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "matplotlib.pyplot.get_cmap", "line_number": 19, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 19, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 21, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 21, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot2grid", "line_number": 22, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 22, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot2grid", "line_number": 23, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 23, "usage_type": "name"}, {"api_name": "mpl_toolkits.basemap.Basemap", "line_number": 25, "usage_type": "call"}, {"api_name": "IDE_locations.CLMf05g16_get_spatial_info", "line_number": 30, "usage_type": "call"}, {"api_name": "timutils.colorbar_from_cmap_norm.colorbar_from_cmap_norm", "line_number": 42, "usage_type": "call"}, {"api_name": "timutils.colorbar_from_cmap_norm", "line_number": 42, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.get_cmap", "line_number": 49, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 49, "usage_type": "name"}, {"api_name": "timutils.midpt_norm.get_discrete_midpt_cmap_norm", "line_number": 50, "usage_type": "call"}, {"api_name": "timutils.midpt_norm", "line_number": 50, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 77, "usage_type": "call"}, {"api_name": "os.path", "line_number": 77, "usage_type": "attribute"}, {"api_name": "os.getenv", "line_number": 77, "usage_type": "call"}, {"api_name": "spinup_diagnostics.CLM_spinup_analyzer", "line_number": 80, "usage_type": "call"}, {"api_name": "matplotlib.animation.FuncAnimation", "line_number": 89, "usage_type": "call"}, {"api_name": "matplotlib.animation", "line_number": 89, "usage_type": "name"}]} +{"seq_id": "4472217345", "text": "import unicodedata\nimport pandas as pd\nimport html_to_dataframe as td\nfrom bs4 import BeautifulSoup\nimport requests\n\ndef strip_accents(text):\n try:\n text = unicode(text, 'utf-8')\n except NameError: # unicode is a default on python 3 \n pass\n\n text = unicodedata.normalize('NFD', text)\\\n .encode('ascii', 'ignore')\\\n .decode(\"utf-8\")\n\n return str(text)\n\ndef piso(num):\n if num%5>0: \n div=num//5 + 1 \n else:\n div=num//5\n return int(div)\n\ndef extractdata(url, profesor):\n datos=[]\n resp = requests.get(url)\n soup = BeautifulSoup(resp.text, 'html.parser')\n for sopita in soup.find_all('tr')[1:]:\n scores=sopita.find(class_='breakdown').find_all(class_='score')\n grade=sopita.find(class_='grade').find(class_='response').text\n datos.append({'profesor' : profesor, 'calidad' : scores[0].text, 'facilidad' : scores[1].text, 'califest' : grade})\n return datos\ndelta=[]\ndat=data_analyzer('https://www.misprofesores.com/escuelas/UANL-FCFM_2263', 'FCFM')\ndat[\"ID\"]\nfor index, row in dat.iterrows():\n for i in range(1,piso(row['# de calif.'])):\n baseurl='https://www.misprofesores.com/profesores/'\n url=baseurl+strip_accents(re.sub(r'\\s+', '-', row[\"Nombre\"])+'-'+re.sub(r'\\s+', '-', row[\"Apellido\"])+'_'+str(row[\"ID\"])+'?page='+str(i))\n for i in extractdata(url, row[\"Apellido\"]): delta.append(i)\n\ndelta=pd.DataFrame(delta)\ndelta.dropna()\nfor index, row in delta.iterrows():\n if row['califest']=='N/A':\n print('N/A') \n elif float(row['califest'])>10:\n row['califest']=float(row['califest'])/10\n print(row['califest'])\ndelta.to_csv('delta2.csv', index=False)\n", "repo_name": "SandovalAguilar/Tercer-semestre", "sub_path": "poo/ExtraerDatosIndividuales.py", "file_name": "ExtraerDatosIndividuales.py", "file_ext": "py", "file_size_in_byte": 1702, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "unicodedata.normalize", "line_number": 13, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 28, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 29, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 44, "usage_type": "call"}]} +{"seq_id": "3269711960", "text": "from __future__ import unicode_literals\n\nimport logging\n\nfrom django.core.management.base import BaseCommand\nfrom django.contrib.auth import get_user_model\n\nfrom django_keycloak.models import Realm\n\nimport django_keycloak.services.users\n\nlogger = logging.getLogger(__name__)\n\n\ndef realm(name):\n try:\n return Realm.objects.get(name=name)\n except Realm.DoesNotExist:\n raise TypeError('Realm does not exist')\n\n\ndef user(username):\n UserModel = get_user_model()\n try:\n return UserModel.objects.get(username=username)\n except UserModel.DoesNotExist:\n raise TypeError('User does not exist')\n\n\nclass Command(BaseCommand):\n\n def add_arguments(self, parser):\n parser.add_argument('--realm', type=realm, required=True)\n parser.add_argument('--user', type=user, required=True)\n\n def handle(self, *args, **options):\n user = options['user']\n realm = options['realm']\n\n django_keycloak.services.users.add_user(client=realm.client, user=user)\n", "repo_name": "Peter-Slump/django-keycloak", "sub_path": "src/django_keycloak/management/commands/keycloak_add_user.py", "file_name": "keycloak_add_user.py", "file_ext": "py", "file_size_in_byte": 1012, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 117, "dataset": "github-code", "pt": "37", "api": [{"api_name": "logging.getLogger", "line_number": 12, "usage_type": "call"}, {"api_name": "django_keycloak.models.Realm.objects.get", "line_number": 17, "usage_type": "call"}, {"api_name": "django_keycloak.models.Realm.objects", "line_number": 17, "usage_type": "attribute"}, {"api_name": "django_keycloak.models.Realm", "line_number": 17, "usage_type": "name"}, {"api_name": "django_keycloak.models.Realm.DoesNotExist", "line_number": 18, "usage_type": "attribute"}, {"api_name": "django_keycloak.models.Realm", "line_number": 18, "usage_type": "name"}, {"api_name": "django.contrib.auth.get_user_model", "line_number": 23, "usage_type": "call"}, {"api_name": "django.core.management.base.BaseCommand", "line_number": 30, "usage_type": "name"}, {"api_name": "django_keycloak.models.services.users.add_user", "line_number": 40, "usage_type": "call"}, {"api_name": "django_keycloak.models.services", "line_number": 40, "usage_type": "attribute"}, {"api_name": "django_keycloak.models", "line_number": 40, "usage_type": "name"}]} +{"seq_id": "43493206643", "text": "\"\"\"\nBuilds the master station list\n\nSource file for airports.csv and runways.csv can be downloaded from\nhttp://ourairports.com/data/\n\"\"\"\n\n# stdlib\nimport csv\nimport json\nfrom pathlib import Path\n\n# module\nfrom find_bad_stations import BAD_PATH, GOOD_PATH, load_stations\n\nAIRPORT_PATH = Path(\"data\", \"airports.csv\")\nSTATION_PATH = Path(\"data\", \"stations.txt\")\nRUNWAY_PATH = Path(\"data\", \"runways.csv\")\nOUTPUT_PATH = Path(\"..\", \"avwx\", \"stations.json\")\n\nACCEPTED_STATION_TYPES = [\n \"balloonport\",\n \"closed\",\n \"heliport\",\n \"large_airport\",\n \"medium_airport\",\n \"seaplane_base\",\n \"small_airport\",\n]\n\n\ndef nullify(data: dict) -> dict:\n \"\"\"\n Nullify empty strings in a dict\n \"\"\"\n for key, val in data.items():\n if val == \"\":\n data[key] = None\n return data\n\n\ndef format_coord(coord: str) -> float:\n \"\"\"\n Convert coord string to float\n \"\"\"\n neg = -1 if coord[-1] in (\"S\", \"W\") else 1\n return neg * float(coord[:-1].strip().replace(\" \", \".\"))\n\n\ndef format_station(station: [str]) -> dict:\n \"\"\"\n Converts source station list into info dict\n \"\"\"\n try:\n elev_ft = float(station[6])\n elev_m = round(elev_ft * 0.3048)\n elev_ft = round(elev_ft)\n except ValueError:\n elev_ft, elev_m = None, None\n iloc = station[9].find(\"-\")\n ret = {\n \"type\": station[2],\n \"name\": station[3],\n \"reporting\": None,\n \"latitude\": float(station[4]),\n \"longitude\": float(station[5]),\n \"elevation_ft\": elev_ft,\n \"elevation_m\": elev_m,\n \"country\": station[9][:iloc],\n \"state\": station[9][iloc + 1 :],\n \"city\": station[10],\n \"icao\": station[1].upper(),\n \"iata\": station[13].upper(),\n \"website\": station[15],\n \"wiki\": station[16],\n \"note\": station[17],\n }\n return nullify(ret)\n\n\ndef build_stations() -> dict:\n \"\"\"\n Builds the station dict from source file\n \"\"\"\n stations = {}\n data = csv.reader(AIRPORT_PATH.open())\n next(data) # Skip header\n for station in data:\n icao = station[1].upper()\n if len(icao) != 4:\n continue\n if station[2] in ACCEPTED_STATION_TYPES:\n stations[icao] = format_station(station)\n return stations\n\n\ndef add_missing_stations(stations: dict) -> dict:\n \"\"\"\n Add non-airport stations from NOAA\n \"\"\"\n for line in STATION_PATH.open().readlines():\n # Must be data line with METAR reporting\n if len(line) != 84 or line[0] == \"!\" or line[62] != \"X\":\n continue\n icao = line[20:24].strip().upper()\n if not icao or icao in stations: # or icao in BAD_STATIONS:\n continue\n elev_m = int(line[55:59].strip())\n ret = {\n \"type\": None,\n \"name\": line[3:19].strip(),\n \"reporting\": None,\n \"latitude\": format_coord(line[39:45]),\n \"longitude\": format_coord(line[47:54]),\n \"elevation_ft\": round(elev_m * 3.28084),\n \"elevation_m\": elev_m,\n \"country\": line[81:83].strip(),\n \"state\": line[:2],\n \"city\": None,\n \"icao\": icao,\n \"iata\": line[26:29].strip().upper(),\n \"website\": None,\n \"wiki\": None,\n \"note\": None,\n }\n stations[icao] = nullify(ret)\n return stations\n\n\ndef add_runways(stations: dict) -> dict:\n \"\"\"\n Add runway information to station if availabale\n \"\"\"\n data = csv.reader(RUNWAY_PATH.open())\n next(data) # Skip header\n for runway in data:\n data = {\n \"length_ft\": int(runway[3]) if runway[3] else 0,\n \"width_ft\": int(runway[4]) if runway[4] else 0,\n \"ident1\": runway[8],\n \"ident2\": runway[14],\n }\n icao = runway[2]\n if icao in stations:\n if \"runways\" in stations[icao]:\n stations[icao][\"runways\"].append(data)\n else:\n stations[icao][\"runways\"] = [data]\n # Sort runways by longest length and add missing nulls\n for icao in stations:\n if \"runways\" in stations[icao]:\n stations[icao][\"runways\"].sort(key=lambda x: x[\"length_ft\"], reverse=True)\n else:\n stations[icao][\"runways\"] = None\n return stations\n\n\ndef add_reporting(stations: dict) -> dict:\n \"\"\"\n Add reporting boolean to station if available\n \"\"\"\n good = load_stations(GOOD_PATH)\n bad = load_stations(BAD_PATH)\n for icao in stations:\n if icao in good:\n stations[icao][\"reporting\"] = True\n elif icao in bad:\n stations[icao][\"reporting\"] = False\n # else unknown\n return stations\n\n\ndef main() -> int:\n \"\"\"\n Build/update the stations.json master file\n \"\"\"\n stations = build_stations()\n stations = add_missing_stations(stations)\n stations = add_reporting(stations)\n stations = add_runways(stations)\n json.dump(stations, OUTPUT_PATH.open(\"w\"), sort_keys=True)\n return 0\n\n\nif __name__ == \"__main__\":\n main()\n", "repo_name": "honnorat/AVWX-Engine", "sub_path": "util/build_stations.py", "file_name": "build_stations.py", "file_ext": "py", "file_size_in_byte": 5055, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "github-code", "pt": "37", "api": [{"api_name": "pathlib.Path", "line_number": 16, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 17, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 18, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 19, "usage_type": "call"}, {"api_name": "csv.reader", "line_number": 86, "usage_type": "call"}, {"api_name": "csv.reader", "line_number": 134, "usage_type": "call"}, {"api_name": "find_bad_stations.load_stations", "line_number": 162, "usage_type": "call"}, {"api_name": "find_bad_stations.GOOD_PATH", "line_number": 162, "usage_type": "argument"}, {"api_name": "find_bad_stations.load_stations", "line_number": 163, "usage_type": "call"}, {"api_name": "find_bad_stations.BAD_PATH", "line_number": 163, "usage_type": "argument"}, {"api_name": "json.dump", "line_number": 181, "usage_type": "call"}]} +{"seq_id": "34372527768", "text": "#! /usr/bin/env python3\n\nimport time\nimport serial\nimport sys\n\ndef usage():\n return \"%s [serial_port (e.g. ttyACM0)]\"%sys.argv[0]\n\nif __name__ == \"__main__\":\n\n if len(sys.argv) == 2:\n port = sys.argv[1]\n\n else:\n print(usage())\n sys.exit(1)\n\n # configure the serial connections (the parameters differs on the device you are connecting to)\n ser = serial.Serial(\n port='/dev/' + port,\n baudrate=9600,\n parity=serial.PARITY_NONE,\n stopbits=serial.STOPBITS_ONE,\n bytesize=serial.EIGHTBITS\n )\n\n ser.close()\n ser.open()\n ser.isOpen()\n\n print('Enter your commands below.\\r\\nInsert \"exit\" to leave the application.')\n\n inp=1\n while 1 :\n # get keyboard input\n inp = input(\">> \")\n # Python 3 users\n # input = input(\">> \")\n if inp == 'exit':\n ser.close()\n exit()\n\n else:\n # send the character to the device\n # (note that I happend a \\r\\n carriage return and line feed to the characters - this is requested by my device)\n ser.write(b\"%s\\r\\n\" % inp.encode('ascii','ignore'))\n out = b''\n # let's wait one second before reading output (let's give device time to answer)\n time.sleep(0.1)\n while ser.inWaiting() > 0:\n out += ser.read(1)\n\n if out != '':\n print(\">> %s\" % out)", "repo_name": "dudasdavid/mogi_haptic_device", "sub_path": "tools/serial_console.py", "file_name": "serial_console.py", "file_ext": "py", "file_size_in_byte": 1438, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "sys.argv", "line_number": 8, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 12, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 13, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 17, "usage_type": "call"}, {"api_name": "serial.Serial", "line_number": 20, "usage_type": "call"}, {"api_name": "serial.PARITY_NONE", "line_number": 23, "usage_type": "attribute"}, {"api_name": "serial.STOPBITS_ONE", "line_number": 24, "usage_type": "attribute"}, {"api_name": "serial.EIGHTBITS", "line_number": 25, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 50, "usage_type": "call"}]} +{"seq_id": "1620984057", "text": "from django.contrib import admin, messages\nfrom django import forms\n\nfrom .forms import GroupForm\nfrom .models import Season, GroupRound, Group, Matchup, Game\n\n\nclass GroupAdmin(admin.ModelAdmin):\n actions = ['generate_schedule', 'delete_schedule']\n\n def change_view(self, request, object_id, form_url='', extra_context = None):\n extra_context = extra_context or {}\n \n\n return super(GroupAdmin, self).change_view(\n request, object_id, form_url,\n extra_context = extra_context)\n \n\n def generate_schedule(self, request, queryset):\n created_groups = []\n for group in queryset:\n if group.create_group_schedule():\n created_groups.append(group.group_name)\n \n message = \"\"\n level = messages.SUCCESS\n if len(created_groups) == 0:\n message = \"No schedules were generated for the selected groups.\"\n level = messages.ERROR\n else:\n message = \"Schedules were generated for {}\".format(created_groups)\n\n self.message_user(request, message, level)\n self.short_description = \"Generate round-robin schedule for selected groups\"\n\n def delete_schedule(self, request, queryset):\n deleted_groups = []\n for group in queryset:\n if group.delete_group_schedule():\n deleted_groups.append(group.group_name)\n\n message = \"\"\n level = messages.SUCCESS\n if len(deleted_groups) == 0:\n message = \"No schedules were deleted for the selected groups.\"\n level = messages.ERROR\n else:\n message = \"Schedules were deleted for {}\".format(deleted_groups)\n\n self.message_user(request, message, level)\n self.short_description = \"Delete schedule for selected groups\"\n\nadmin.site.register(Season)\nadmin.site.register(GroupRound)\nadmin.site.register(Group, GroupAdmin)\nadmin.site.register(Matchup)\nadmin.site.register(Game)\n", "repo_name": "biddellns/litsl", "sub_path": "season/admin.py", "file_name": "admin.py", "file_ext": "py", "file_size_in_byte": 1975, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "django.contrib.admin.ModelAdmin", "line_number": 8, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 8, "usage_type": "name"}, {"api_name": "django.contrib.messages.SUCCESS", "line_number": 27, "usage_type": "attribute"}, {"api_name": "django.contrib.messages", "line_number": 27, "usage_type": "name"}, {"api_name": "django.contrib.messages.ERROR", "line_number": 30, "usage_type": "attribute"}, {"api_name": "django.contrib.messages", "line_number": 30, "usage_type": "name"}, {"api_name": "django.contrib.messages.SUCCESS", "line_number": 44, "usage_type": "attribute"}, {"api_name": "django.contrib.messages", "line_number": 44, "usage_type": "name"}, {"api_name": "django.contrib.messages.ERROR", "line_number": 47, "usage_type": "attribute"}, {"api_name": "django.contrib.messages", "line_number": 47, "usage_type": "name"}, {"api_name": "django.contrib.admin.site.register", "line_number": 54, "usage_type": "call"}, {"api_name": "models.Season", "line_number": 54, "usage_type": "argument"}, {"api_name": "django.contrib.admin.site", "line_number": 54, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 54, "usage_type": "name"}, {"api_name": "django.contrib.admin.site.register", "line_number": 55, "usage_type": "call"}, {"api_name": "models.GroupRound", "line_number": 55, "usage_type": "argument"}, {"api_name": "django.contrib.admin.site", "line_number": 55, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 55, "usage_type": "name"}, {"api_name": "django.contrib.admin.site.register", "line_number": 56, "usage_type": "call"}, {"api_name": "models.Group", "line_number": 56, "usage_type": "argument"}, {"api_name": "django.contrib.admin.site", "line_number": 56, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 56, "usage_type": "name"}, {"api_name": "django.contrib.admin.site.register", "line_number": 57, "usage_type": "call"}, {"api_name": "models.Matchup", "line_number": 57, "usage_type": "argument"}, {"api_name": "django.contrib.admin.site", "line_number": 57, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 57, "usage_type": "name"}, {"api_name": "django.contrib.admin.site.register", "line_number": 58, "usage_type": "call"}, {"api_name": "models.Game", "line_number": 58, "usage_type": "argument"}, {"api_name": "django.contrib.admin.site", "line_number": 58, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 58, "usage_type": "name"}]} +{"seq_id": "23898792032", "text": "from django.urls import path,include\nfrom django.conf.urls import url\n\nfrom django.contrib.auth import views as auth_views\nfrom . import views\n\n\n# app_name = \"account\"\n\nurlpatterns = [\n path('login/',auth_views.LoginView.as_view(), name='login'),\n path('logout/',auth_views.LogoutView.as_view(), name='logout'),\n # #password change url\n # path('password_change/',auth_views.PasswordChangeView.as_view(),name='password_change'),\n # path('password_change/done/',auth_views.PasswordChangeDoneView.as_view(),name='password_change_done'),\n # #password reset url\n # path('password_reset/', auth_views.PasswordResetView.as_view(), name='password_reset'),\n # path('password_reset/done/', auth_views.PasswordResetDoneView.as_view(), name='password_reset_done'),\n # path('reset///', auth_views.PasswordResetConfirmView.as_view(), name='password_reset_confirm'),\n # path('reset/done/', auth_views.PasswordResetCompleteView.as_view(), name='password_reset_complete'),\n\n path('', include('django.contrib.auth.urls')),\n\n #registration\n path('register/',views.register,name=\"register\"),\n path('',views.dashboard, name='dashboard'),\n\n #profile edit\n path('edit/',views.edit,name='edit'),\n #profile details\n path('@//',views.profile_details,name='profile_details'),\n\n path('all_members/',views.all_members,name='all_members'),\n # url(r'^activate/(?P[0-9A-Za-z_\\-]+)/(?P[0-9A-Za-z]{1,13}-[0-9A-Za-z]{1,20})/$',\n # views.activate, name='activate'),\n path('activate///',views.activate, name='activate'),\n]", "repo_name": "eshafik/itea_v1", "sub_path": "account/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 1615, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "django.urls.path", "line_number": 11, "usage_type": "call"}, {"api_name": "django.contrib.auth.views.LoginView.as_view", "line_number": 11, "usage_type": "call"}, {"api_name": "django.contrib.auth.views.LoginView", "line_number": 11, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.views", "line_number": 11, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 12, "usage_type": "call"}, {"api_name": "django.contrib.auth.views.LogoutView.as_view", "line_number": 12, "usage_type": "call"}, {"api_name": "django.contrib.auth.views.LogoutView", "line_number": 12, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.views", "line_number": 12, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 22, "usage_type": "call"}, {"api_name": "django.urls.include", "line_number": 22, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 25, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 26, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 29, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 31, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 33, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 36, "usage_type": "call"}]} +{"seq_id": "16626904334", "text": "# coding=utf-8\nimport torch\nfrom transformers import (AutoModelForCausalLM, AutoTokenizer)\nfrom transformers.generation.utils import GenerationConfig\n\n\nclass LLM:\n def __init__(self, model_path) -> None:\n self.model_path = model_path\n\n self.model, self.tokenizer = self._load()\n\n def _load(self):\n model = AutoModelForCausalLM.from_pretrained(\n self.model_path, device_map=\"auto\", torch_dtype=torch.float16, trust_remote_code=True)\n tokenizer = AutoTokenizer.from_pretrained(\n self.model_path, use_fast=False, trust_remote_code=True)\n model.generation_config = GenerationConfig.from_pretrained(self.model_path)\n return model, tokenizer\n\n def summary(self, input):\n messages = []\n prompt = f\"用中文总结下面这段话：\\n\\n{input}\"\n messages.append({\"role\": \"user\", \"content\": prompt})\n response = self.model.chat(self.tokenizer, messages)\n return response\n", "repo_name": "litetoooooom/PaperAssistant", "sub_path": "paperassistant/models/llm.py", "file_name": "llm.py", "file_ext": "py", "file_size_in_byte": 970, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "37", "api": [{"api_name": "transformers.AutoModelForCausalLM.from_pretrained", "line_number": 14, "usage_type": "call"}, {"api_name": "transformers.AutoModelForCausalLM", "line_number": 14, "usage_type": "name"}, {"api_name": "torch.float16", "line_number": 15, "usage_type": "attribute"}, {"api_name": "transformers.AutoTokenizer.from_pretrained", "line_number": 16, "usage_type": "call"}, {"api_name": "transformers.AutoTokenizer", "line_number": 16, "usage_type": "name"}, {"api_name": "transformers.generation.utils.GenerationConfig.from_pretrained", "line_number": 18, "usage_type": "call"}, {"api_name": "transformers.generation.utils.GenerationConfig", "line_number": 18, "usage_type": "name"}]} +{"seq_id": "73174923628", "text": "import numpy as np\nimport h5py\nimport glob\nimport librosa\nimport time\n\nfrom scipy.io import wavfile\n\nfs = 22050\nfs = 44100\nscale = np.power(2,16)\n\ndef pic(x,x1):\n k = x.shape(0) // scale\n a = x[:k*scale]\n b = x1[:k*scale]\n\ndef savefile(a,b,num):\n filename = '/data1/littletree/sepvoice_w/%d.h5' % num\n f = h5py.File(filename,'w')\n f['in'] = a\n f['out'] = b\n f.close()\n\ndef tobit(a):\n base = np.zeros((17,4096),dtype=np.uint8)\n base[0] = (a > 0) * 1\n s = a\n for x in range(16): \n base[x+1] = s % 2\n s = s // 2\n \n return base\n\n \n \ndef main():\n res = glob.glob(\"/data1/littletree/DSD100/Mixtures/*/*/*.wav\")\n num = 0\n for f in res:\n print(f)\n now = time.time()\n f1 = f.replace('Mixtures','Sources').replace('mixture','vocals') \n m = wavfile.read(f)\n m1 = wavfile.read(f1)\n print('wav:',time.time() - now)\n now = time.time()\n x,_ = librosa.load(f, sr=fs, mono=False)\n x1,_ = librosa.load(f1, sr=fs, mono=False)\n print(x.shape)\n print(m[1].shape,m[0])\n print(np.sum(np.abs(x.T - m[1]/32768.0)))\n print('librosa:',time.time() - now)\n k = x.shape[1] // scale\n\n a = x[:,:k*scale]\n b = x1[:,:k*scale]\n #savefile(a,b,num)\n num += 1\n\n\nif __name__ == \"__main__\":\n main()\n\n\n", "repo_name": "xi-studio/separation-voice", "sub_path": "scripts/onepic.py", "file_name": "onepic.py", "file_ext": "py", "file_size_in_byte": 1371, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "numpy.power", "line_number": 11, "usage_type": "call"}, {"api_name": "h5py.File", "line_number": 20, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 26, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 26, "usage_type": "attribute"}, {"api_name": "glob.glob", "line_number": 38, "usage_type": "call"}, {"api_name": "time.time", "line_number": 42, "usage_type": "call"}, {"api_name": "scipy.io.wavfile.read", "line_number": 44, "usage_type": "call"}, {"api_name": "scipy.io.wavfile", "line_number": 44, "usage_type": "name"}, {"api_name": "scipy.io.wavfile.read", "line_number": 45, "usage_type": "call"}, {"api_name": "scipy.io.wavfile", "line_number": 45, "usage_type": "name"}, {"api_name": "time.time", "line_number": 46, "usage_type": "call"}, {"api_name": "time.time", "line_number": 47, "usage_type": "call"}, {"api_name": "librosa.load", "line_number": 48, "usage_type": "call"}, {"api_name": "librosa.load", "line_number": 49, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 52, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 52, "usage_type": "call"}, {"api_name": "time.time", "line_number": 53, "usage_type": "call"}]} +{"seq_id": "12846017806", "text": "import dataclasses\nimport json\nimport os\nimport logging\nimport re\nfrom typing import Dict\n\nimport jsonpickle\nfrom pydantic import ValidationError\n\nfrom config import Config\nfrom payfast import PayFast\nfrom transaction import HTTPResponse, Transaction, TransactionRequest\n\nlogger = logging.getLogger()\nlogger.setLevel(logging.INFO)\n\n\n\ndef lambda_handler(event, context):\n logger.info('## ENVIRONMENT VARIABLES\\r' + jsonpickle.encode(dict(**os.environ)))\n logger.info('## EVENT\\r' + jsonpickle.encode(event))\n logger.info('## CONTEXT\\r' + jsonpickle.encode(context))\n\n config = Config()\n config.initialise_from_env()\n\n # Validate and serialise body\n try:\n body = json.loads(event[\"body\"])\n input_dict = {\n key.name: body[snake_case_to_camel_case(key.name)]\n for key in dataclasses.fields(TransactionRequest)\n }\n transaction_request = TransactionRequest(**input_dict)\n except (KeyError, ValidationError, json.JSONDecodeError) as e:\n logger.error(\"Malformed body\", exc_info=1)\n return generate_http_response(\n status_code=400,\n body=f'Malformed body: {e}'\n )\n \n if not transaction_request:\n return generate_http_response(\n status_code=500,\n body='Malformed body. See the logs for details.'\n )\n\n # Build Transaction and send to Payfast\n transaction = Transaction(\n **dataclasses.asdict(transaction_request),\n merchant_id=config.payfast_merchant_id,\n merchant_key=config.payfast_merchant_key,\n email_confirmation=config.payfast_email_confirmation,\n confirmation_address=config.payfast_confirmation_address\n )\n\n # Generate Payfast payment identifier\n payfast = PayFast(config=config, transaction=transaction)\n identifier = payfast.generate_payment_identifier()\n\n if identifier:\n return generate_http_response(\n status_code=200,\n body=json.dumps({'identifier': identifier})\n )\n else:\n return generate_http_response(\n status_code=500,\n body='Payfast Payment Identifier could not be created'\n )\n\ndef snake_case_to_camel_case(key: str) -> str:\n \"\"\"\n Changes 'snake_case_name' to 'SnakeCaseName'\n \"\"\"\n return re.sub(r'(?!^)_([a-zA-Z])', lambda m: m.group(1).upper(), key)\n\n\ndef generate_http_response(status_code: int, body: str) -> Dict:\n response = HTTPResponse(\n statusCode=status_code,\n body=body,\n headers={\n 'Content-Type': 'application/json',\n 'Access-Control-Allow-Origin': '*',\n 'Access-Control-Allow-Methods': 'GET,POST,PUT,DELETE,OPTIONS',\n 'Access-Control-Allow-Headers': 'X-Amz-Date,X-Api-Key,X-Amz-Security-Token,X-Requested-With,X-Auth-Token,Referer,User-Agent,Origin,Content-Type,Authorization,Accept,Access-Control-Allow-Methods,Access-Control-Allow-Origin,Access-Control-Allow-Headers',\n 'Access-Control-Allow-Credentials': 'true',\n },\n isBase64Encoded=False\n )\n logger.info(\"response:\")\n logger.info(dataclasses.asdict(response))\n logger.info('## RETURN\\r' + jsonpickle.encode(dataclasses.asdict(response)))\n return dataclasses.asdict(response)", "repo_name": "dvdl16/simsafari-lodge-booking-payment-lambda", "sub_path": "lambda_function.py", "file_name": "lambda_function.py", "file_ext": "py", "file_size_in_byte": 3261, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "logging.getLogger", "line_number": 15, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 16, "usage_type": "attribute"}, {"api_name": "jsonpickle.encode", "line_number": 21, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 21, "usage_type": "attribute"}, {"api_name": "jsonpickle.encode", "line_number": 22, "usage_type": "call"}, {"api_name": "jsonpickle.encode", "line_number": 23, "usage_type": "call"}, {"api_name": "config.Config", "line_number": 25, "usage_type": "call"}, {"api_name": "config.initialise_from_env", "line_number": 26, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 30, "usage_type": "call"}, {"api_name": "dataclasses.fields", "line_number": 33, "usage_type": "call"}, {"api_name": "transaction.TransactionRequest", "line_number": 33, "usage_type": "argument"}, {"api_name": "transaction.TransactionRequest", "line_number": 35, "usage_type": "call"}, {"api_name": "pydantic.ValidationError", "line_number": 36, "usage_type": "name"}, {"api_name": "json.JSONDecodeError", "line_number": 36, "usage_type": "attribute"}, {"api_name": "transaction.Transaction", "line_number": 50, "usage_type": "call"}, {"api_name": "dataclasses.asdict", "line_number": 51, "usage_type": "call"}, {"api_name": "config.payfast_merchant_id", "line_number": 52, "usage_type": "attribute"}, {"api_name": "config.payfast_merchant_key", "line_number": 53, "usage_type": "attribute"}, {"api_name": "config.payfast_email_confirmation", "line_number": 54, "usage_type": "attribute"}, {"api_name": "config.payfast_confirmation_address", "line_number": 55, "usage_type": "attribute"}, {"api_name": "payfast.PayFast", "line_number": 59, "usage_type": "call"}, {"api_name": "payfast.generate_payment_identifier", "line_number": 60, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 65, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 77, "usage_type": "call"}, {"api_name": "transaction.HTTPResponse", "line_number": 81, "usage_type": "call"}, {"api_name": "dataclasses.asdict", "line_number": 94, "usage_type": "call"}, {"api_name": "jsonpickle.encode", "line_number": 95, "usage_type": "call"}, {"api_name": "dataclasses.asdict", "line_number": 95, "usage_type": "call"}, {"api_name": "dataclasses.asdict", "line_number": 96, "usage_type": "call"}, {"api_name": "typing.Dict", "line_number": 80, "usage_type": "name"}]} +{"seq_id": "37203346416", "text": "# Warning: Execute this code at your own risk. It automates activity in paladins and likely violates it's TOS.\r\n# Key functionalities include determining screen resolution, the centermost pixel of a gui element(s) given an and\r\n# degree of confidence, finding a pixel given coordinates, clicking a pixel, clicking and dragging, click drag release\r\n# and screenshot.\r\nimport pyautogui\r\nimport time\r\n# The script will print the resolution of the host device screen, and print the locations of several buttons\r\n# then click the minimize button on the command prompt screen that opens when running this scripts\r\n\r\n\r\ndef reproportion(o_d, n_d, pixel):\r\n return round((n_d / o_d) * pixel)\r\n# Remember that pyautogui discriminates images based on resolution. So scripts using images taken on other devices\r\n# may fail even if very similar images are provided.\r\n\r\n# Initial state: All apps but pycharm are minimized. Paladins Feb 2022 ver. app is installed and shortcut is on desktop.\r\n# Also the monitor must be x=1920 by y=1080.\r\n# If on another on monitor then for every dimension use rounded(your_dim/my_dim * dim)\r\n\r\n\r\nif __name__ == '__main__':\r\n old_x = 1920\r\n old_y = 1080\r\n path = r'C:\\Users\\Kendrick\\PycharmProjects\\autogui\\Images'\r\n print(\"Hello\")\r\n pyautogui.PAUSE = 2.5\r\n pyautogui.FAILSAFE = True\r\n print(\"Current screen resolution\", pyautogui.size())\r\n new_x = pyautogui.size()[0]\r\n new_y = pyautogui.size()[1]\r\n print(\"Current mouse position\", pyautogui.position())\r\n print(\"Button found at\", pyautogui.locateOnScreen(path + r'\\pycharm_logo.PNG'))\r\n coord = pyautogui.locateOnScreen(path + r'\\pycharm_logo.PNG')\r\n # Standard coordinates are composed of left, top, width, height pixel counts. Each accessible by coord[x] -1 List[List[int]]:\n # If root is invalid, return\n if (root is None):\n return ans\n\n # Initialize the answer array, and two stacks:\n # s1 = Stack used to process the node in the correct order that we want them to be processed in.\n # This one will be the substitute for the call stack that we would have in a recursive solution\n # s2 = Stack that we just push the nodes onto for the final solution.\n # This one will act as the container of our solution\n ans = []\n s1 = []\n s2 = []\n\n # Append the root to s1\n s1.append(root)\n\n # Loop over until s1 is not empty\n while(s1):\n # Get and remove the top of the stack s1\n # Append the element obtained to stack s2\n x = s1[-1]\n s1.pop()\n s2.append(x)\n\n # If there are any child of the current node,\n # append the children to the stack s1\n if (x.left):\n s1.append(x.left)\n if (x.right):\n s1.append(x.right)\n\n # While the stack is not empty\n while(s2):\n # Get and remove the top of the stack s2\n # Append the element obtained to an answer array\n y = s2[-1]\n s2.pop()\n ans.append(y.val)\n\n return ans\n \n\"\"\"\nTime Complexity : O(n)\nSpace Complexity: O(n)\n\"\"\"", "repo_name": "tmdenddl/Algorithm", "sub_path": "Python/Data Strucutre Related Questions/Stacks & Queues/Binary Tree Postorder Traversal.py", "file_name": "Binary Tree Postorder Traversal.py", "file_ext": "py", "file_size_in_byte": 1903, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "typing.List", "line_number": 32, "usage_type": "name"}]} +{"seq_id": "2081851065", "text": "import os \r\nimport numpy as np \r\nimport tensorflow as tf \r\nimport sklearn as sk \r\nimport matplotlib.pyplot as plt\r\nimport pandas as pd \r\ntry:\r\n train_df = pd.read_csv('/kaggle/input/fraud-detection/fraudTrain.csv')\r\n test_df = pd.read_csv('/kaggle/input/fraud-detection/fraudTest.csv')\r\nexcept:\r\n train_df = pd.read_csv('C:/Users/acer/Desktop/CODSOFT/Credit Card Fraud detection/fraudTrain.csv')\r\n test_df = pd.read_csv('C:/Users/acer/Desktop/CODSOFT/Credit Card Fraud detection/fraudTest.csv')\r\ntrain_df.head()\r\ntrain_df.isnull().sum()\r\ndef data_pre(X):\r\n del_col=['merchant','first','last','street','zip','unix_time','Unnamed: 0','trans_num','cc_num']\r\n X.drop(columns=del_col,inplace=True)\r\n \r\n \r\n X['trans_date_trans_time']=pd.to_datetime(X['trans_date_trans_time'])\r\n X['trans_date']=X['trans_date_trans_time'].dt.strftime('%Y-%m-%d')\r\n X['trans_date']=pd.to_datetime(X['trans_date'])\r\n \r\n \r\n X['dob']=pd.to_datetime(X['dob'])\r\n \r\n #Calculate Age of each trans \r\n X[\"age\"] = (X[\"trans_date\"] - X[\"dob\"]).dt.days //365\r\n\r\n \r\n X['trans_month']=X['trans_date'].dt.month\r\n X['trans_year']=X['trans_date'].dt.year\r\n \r\n X['gender']=X['gender'].apply(lambda x : 1 if x=='M' else 0)\r\n X['gender']=X['gender'].astype(int)\r\n X['lat_dis']=abs(X['lat']-X['merch_lat'])\r\n X['long_dis']=abs(X['long']-X['merch_long'])\r\n X=pd.get_dummies(X,columns=['category'])\r\n X=X.drop(columns=['city','trans_date_trans_time','state','job','merch_lat','merch_long','lat','long','dob','trans_date'])\r\n return X\r\n \r\ntrain_df_pre=data_pre(train_df.copy())\r\ntrain_df_pre.head()\r\nx_train=train_df_pre.drop('is_fraud',axis=1)\r\ny_train=train_df_pre['is_fraud']\r\ntest_df_pre=data_pre(test_df.copy())\r\ntest_df_pre.head()\r\nx_test=test_df_pre.drop('is_fraud',axis=1)\r\ny_test=test_df_pre['is_fraud']\r\nfrom sklearn.linear_model import LogisticRegression\r\nfrom sklearn.tree import DecisionTreeClassifier\r\nfrom sklearn.ensemble import RandomForestClassifier\r\nfrom sklearn.metrics import accuracy_score, classification_report\r\nfrom sklearn.preprocessing import StandardScaler\r\n\r\n# Step 1: Fit the StandardScaler on the training data\r\nscaler = StandardScaler()\r\nscaler.fit(x_train)\r\nx_train=scaler.transform(x_train)\r\nx_test=scaler.transform(x_test)\r\nlogistic_regression=LogisticRegression()\r\nlogistic_regression.fit(x_train,y_train)\r\ny_pred_logistic = logistic_regression.predict(x_test)\r\naccuracy_logistic = accuracy_score(y_test, y_pred_logistic)\r\naccuracy_logistic\r\nrandom_forest = RandomForestClassifier(random_state=42,n_estimators=100)\r\nrandom_forest.fit(x_train, y_train)\r\ny_pred_rf = random_forest.predict(x_test)\r\naccuracy_rf = accuracy_score(y_test, y_pred_rf)\r\naccuracy_rf\r\nDecisionTree=DecisionTreeClassifier()\r\nDecisionTree.fit(x_train,y_train)\r\ny_pred_dt = DecisionTree.predict(x_test)\r\naccuracy_dt = accuracy_score(y_test, y_pred_dt)\r\naccuracy_dt\r\nprint(\"\\nClassification Report for Logistic Regression:\\n\", classification_report(y_test, y_pred_logistic))\r\nprint(\"\\nClassification Report for Decision Tree:\\n\", classification_report(y_test, y_pred_dt))\r\nprint(\"\\nClassification Report for Random Forest:\\n\", classification_report(y_test, y_pred_rf))", "repo_name": "07Harjot/CODSOFT_Internship_Task-02_CreditCard_Fraud_detection", "sub_path": "credit.py", "file_name": "credit.py", "file_ext": "py", "file_size_in_byte": 3211, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "pandas.read_csv", "line_number": 8, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 9, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 11, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 12, "usage_type": "call"}, {"api_name": "pandas.to_datetime", "line_number": 20, "usage_type": "call"}, {"api_name": "pandas.to_datetime", "line_number": 22, "usage_type": "call"}, {"api_name": "pandas.to_datetime", "line_number": 25, "usage_type": "call"}, {"api_name": "pandas.get_dummies", "line_number": 38, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.StandardScaler", "line_number": 57, "usage_type": "call"}, {"api_name": "sklearn.linear_model.LogisticRegression", "line_number": 61, "usage_type": "call"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 64, "usage_type": "call"}, {"api_name": "sklearn.ensemble.RandomForestClassifier", "line_number": 66, "usage_type": "call"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 69, "usage_type": "call"}, {"api_name": "sklearn.tree.DecisionTreeClassifier", "line_number": 71, "usage_type": "call"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 74, "usage_type": "call"}, {"api_name": "sklearn.metrics.classification_report", "line_number": 76, "usage_type": "call"}, {"api_name": "sklearn.metrics.classification_report", "line_number": 77, "usage_type": "call"}, {"api_name": "sklearn.metrics.classification_report", "line_number": 78, "usage_type": "call"}]} +{"seq_id": "27883508021", "text": "import logging\n\nfrom dataactcore.interfaces.db import GlobalDB\nfrom dataactcore.broker_logging import configure_logging\n\nfrom dataactcore.models.jobModels import PublishedFilesHistory, CertifyHistory, PublishHistory, Submission\nfrom dataactcore.models.userModel import User # noqa\nfrom dataactcore.models.lookups import PUBLISH_STATUS_DICT\n\nfrom dataactvalidator.health_check import create_app\n\nlogger = logging.getLogger(__name__)\n\n\nif __name__ == '__main__':\n \"\"\" Cleans up duplicated FABS published records and unpublishes the submissions they're associated with if all\n records from a specific submission are deleted.\n \"\"\"\n\n with create_app().app_context():\n configure_logging()\n\n sess = GlobalDB.db().session\n\n logger.info(\"Beginning script to clean up duplicated FABS records. Creating temporary table.\")\n\n # Create a temporary table\n sess.execute(\"\"\"CREATE TEMP TABLE duplicated_fabs AS\n SELECT UPPER(afa_generated_unique) as afa_generated_unique, MAX(submission_id) AS max_id\n FROM published_fabs\n WHERE is_active IS TRUE\n GROUP BY UPPER(afa_generated_unique)\n HAVING COUNT(1) > 1\"\"\")\n\n logger.info(\"Table created, determining which submissions have been affected.\")\n # Figure out exactly which submissions have been affected in any way\n executed = sess.execute(\"\"\" SELECT DISTINCT submission_id\n FROM published_fabs AS pf\n WHERE is_active IS TRUE\n AND EXISTS (SELECT 1\n FROM duplicated_fabs AS df\n WHERE df.afa_generated_unique = UPPER(pf.afa_generated_unique))\"\"\")\n affected_submissions = []\n for row in executed:\n affected_submissions.append(row['submission_id'])\n\n # If no rows are affected, just exit, no need to hit the DB anymore\n if len(affected_submissions) == 0:\n logger.info(\"There are no duplicated submissions, ending script.\")\n exit(0)\n\n logger.info(\"Deleting duplicate records.\")\n # Delete duplicates from the published FABS table, keeping the instance with the highest submission_id\n executed = sess.execute(\"\"\" DELETE FROM published_fabs AS pf\n WHERE is_active IS TRUE\n AND EXISTS (SELECT 1\n FROM duplicated_fabs AS df\n WHERE df.afa_generated_unique = UPPER(pf.afa_generated_unique)\n AND df.max_id != pf.submission_id)\"\"\")\n\n logger.info(\"Deleted {} duplicate rows from published_fabs. Determining if any \"\n \"submissions have been completely invalidated by the deletes.\".format(executed.rowcount))\n\n # Make a list of submissions that have had all published records deleted\n cleared_submissions = []\n for sub in affected_submissions:\n executed = sess.execute(\"\"\" SELECT COUNT(*) as result_count\n FROM published_fabs\n WHERE submission_id = {}\"\"\".format(sub))\n if executed.fetchone()['result_count'] == 0:\n cleared_submissions.append(sub)\n\n # If no submission has been cleared out completely, we can just exit\n if len(cleared_submissions) == 0:\n logger.info(\"No affected submissions have been completely invalidated by the deletes, ending script.\")\n exit(0)\n\n logger.info(\"The following submissions have been completely invalidated by the deletes, unpublishing them: \"\n + \", \".join(str(sub) for sub in cleared_submissions))\n\n # Unpublish each submission that has been cleared out, including deleting any record of it in the\n # certified/published tables\n for sub in cleared_submissions:\n sess.query(PublishedFilesHistory).filter_by(submission_id=sub).delete()\n sess.query(CertifyHistory).filter_by(submission_id=sub).delete()\n sess.query(PublishHistory).filter_by(submission_id=sub).delete()\n sess.query(Submission).filter_by(submission_id=sub).\\\n update({\"publish_status_id\": PUBLISH_STATUS_DICT[\"unpublished\"]})\n sess.commit()\n logger.info(\"Submissions successfully unpublished, script completed.\")\n", "repo_name": "fedspendingtransparency/data-act-broker-backend", "sub_path": "dataactcore/scripts/ad_hoc/remove_fabs_duplicate_submissions.py", "file_name": "remove_fabs_duplicate_submissions.py", "file_ext": "py", "file_size_in_byte": 4636, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 45, "dataset": "github-code", "pt": "37", "api": [{"api_name": "logging.getLogger", "line_number": 12, "usage_type": "call"}, {"api_name": "dataactvalidator.health_check.create_app", "line_number": 20, "usage_type": "call"}, {"api_name": "dataactcore.broker_logging.configure_logging", "line_number": 21, "usage_type": "call"}, {"api_name": "dataactcore.interfaces.db.GlobalDB.db", "line_number": 23, "usage_type": "call"}, {"api_name": "dataactcore.interfaces.db.GlobalDB", "line_number": 23, "usage_type": "name"}, {"api_name": "dataactcore.models.jobModels.PublishedFilesHistory", "line_number": 84, "usage_type": "argument"}, {"api_name": "dataactcore.models.jobModels.CertifyHistory", "line_number": 85, "usage_type": "argument"}, {"api_name": "dataactcore.models.jobModels.PublishHistory", "line_number": 86, "usage_type": "argument"}, {"api_name": "dataactcore.models.jobModels.Submission", "line_number": 87, "usage_type": "argument"}, {"api_name": "dataactcore.models.lookups.PUBLISH_STATUS_DICT", "line_number": 88, "usage_type": "name"}]} +{"seq_id": "33363059374", "text": "# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Sun Apr 3 10:49:43 2022\r\n\r\n@authors: G. Scabbia, A. Abotaleb, A. Sinopoli\r\n\r\n Qatar Environment and Energy Research Institute (QEERI), \r\n Hamad Bin Khalifa University (HBKU), Qatar Foundation, \r\n P.O. Box 34110, Doha, Qatar\r\n \r\n Corresponding authors: asinopoli@hbku.edu.qa\r\n\r\n@project-title: Sulphur Oxidative Coupling of Methane process development \r\n and its modelling via Machine Learning\r\n \r\n@file description: script for tuning the hyperparameter of the ANN.\r\n 1. Load the split data\r\n 2. Tune the Activation function\r\n 3. Tune the ANN architecture\r\n 4. Tune the Optimization function\r\n 5. Tune the Layer weight regularizers\r\n\r\n Input data is loaded from ./input data/\r\n validation results are stored in:\r\n activation function: ./validation_results/activation_function/\r\n architecture: ./validation_results/architecture/\r\n optimizaer: ./validation_results/opt/\r\n regularization term: ./validation_results/L2_reg/\r\n\r\n\r\n\"\"\"\r\n\r\n# import libraries\r\nimport pandas as pd\r\n\r\nfrom pickle import load\r\n\r\nfrom keras.initializers import GlorotNormal\r\nfrom keras.layers import Dense\r\nfrom keras.models import Sequential\r\nfrom keras.regularizers import l2\r\n\r\nfrom tensorflow.keras.layers import LeakyReLU\r\nfrom tensorflow.keras.layers import PReLU\r\n\r\n\r\n\r\ndef design_ANN(num_of_neurons=3, num_hidden_layers=1, activation_func='sigmoid', \r\n num_input=3, num_output=8, opt='adam', \r\n loss_function='mean_squared_error', weight_reg=0):\r\n \r\n initializer = GlorotNormal()\r\n \r\n # Initialising the ANN\r\n model = Sequential()\r\n\r\n # Adding the input layer and the first hidden layer\r\n model.add(Dense(units = num_of_neurons, \r\n activation = activation_func, \r\n kernel_initializer=initializer,\r\n kernel_regularizer=l2(weight_reg), \r\n input_dim = num_input))\r\n\r\n if num_hidden_layers > 1:\r\n for layer in range(1, num_hidden_layers):\r\n # Adding the additional hidden layer\r\n model.add(Dense(units = num_of_neurons, \r\n activation = activation_func, \r\n kernel_initializer=initializer))\r\n\r\n # Adding the output layer\r\n model.add(Dense(units = num_output, \r\n activation = 'linear', \r\n kernel_initializer=initializer))\r\n \r\n # Compiling the ANN\r\n model.compile(optimizer = opt, loss = loss_function)\r\n\r\n return model\r\n\r\n\r\ndef main():\r\n \r\n input_var = ['temperature', 'pressure', 'mass_ratio']\r\n\r\n output_var = ['H2S_conv', 'CS2_conv', 'naphthalene', 'p_Xylene', \r\n 'toluene', 'benzene', 'H2S', 'methane']\r\n \r\n ## Load the data from the train/validation/test split\r\n \r\n X_train = pd.read_csv('./input data/X_train.csv')\r\n \r\n y_train = pd.read_csv('./input data/y_train.csv')\r\n \r\n X_val = pd.read_csv('./input data/X_val.csv')\r\n y_val = pd.read_csv('./input data/y_val.csv')\r\n\r\n print('Train set size: ' +str(X_train.shape[0]))\r\n print('Validation set size: ' +str(X_val.shape[0]))\r\n \r\n ## Hyperparameter tuning Validation\r\n \r\n # fixed settings\r\n num_input = len(input_var)\r\n num_output = len(output_var) \r\n loss_function = 'mean_squared_error'\r\n \r\n # hyperparameters:\r\n num_hidden_layers = [1, 2, 3]\r\n num_of_neurons = [5, 6, 7]\r\n activation_func = ['sigmoid', 'tanh', 'relu', 'LeakyReLU', 'PReLU', 'elu'] \r\n optimizers = ['SGD', 'RMSprop', 'Adam', 'Adadelta', 'Adagrad', 'Adamax', \r\n 'Nadam', 'Ftrl']\r\n weight_reg = [0, 1e-2, 1e-3, 1e-4, 1e-5, 1e-6]\r\n\r\n \r\n # 1. Tune the Activation function\r\n \r\n # hyperparameters setting\r\n neurons = 6\r\n layers = 1\r\n opt = 'adam'\r\n numb_of_epochs = 300\r\n weight_reg = 0\r\n \r\n # store the evaluation results in eval_result\r\n eval_result = pd.DataFrame(columns = ['function', 'train_acc', 'val_acc'])\r\n \r\n for function in activation_func:\r\n \r\n if function == 'LeakyReLU':\r\n act_func = LeakyReLU(alpha=0.01)\r\n elif function == 'PReLU':\r\n act_func = PReLU() \r\n else : \r\n act_func = function\r\n \r\n \r\n print('Validating: '+str(function))\r\n \r\n # design the model\r\n model = design_ANN(neurons, layers, act_func, \r\n num_input, num_output, opt, loss_function, weight_reg)\r\n \r\n # train the model\r\n history = model.fit(X_train, y_train, \r\n batch_size = 32, \r\n epochs = numb_of_epochs, verbose=1, \r\n validation_data=(X_val, y_val))\r\n \r\n # convert the history.history dict to a pandas DataFrame and save it for later use \r\n hist_df = pd.DataFrame(history.history)\r\n \r\n hist_csv_file = './validation_results/activation_function/history_'+str(function)+'.csv'\r\n with open(hist_csv_file, mode='w') as file:\r\n hist_df.to_csv(file)\r\n \r\n # Evaluate the model\r\n \r\n train_acc = model.evaluate(X_train, y_train, verbose=0)\r\n val_acc = model.evaluate(X_val, y_val, verbose=0) \r\n \r\n print('\\ttrain acc: %.3f \\tval acc: %.3f' % ( train_acc, val_acc))\r\n \r\n eval_result.loc[eval_result.shape[0]] = [str(function), train_acc, val_acc]\r\n \r\n # save the result\r\n with open('./validation_results/activation_function/result_models.csv', mode='w') as file:\r\n eval_result.to_csv(file)\r\n \r\n # 2. Tune the ANN architecture\r\n \r\n # hyperparameters setting\r\n act_func = 'tanh'\r\n opt = 'adam'\r\n numb_of_epochs = 2000\r\n weight_reg = 0\r\n \r\n # store the evaluation results in eval_result\r\n eval_result = pd.DataFrame(columns = ['num_hidden_layers', 'num_nodes', \r\n 'train_acc', 'val_acc'])\r\n \r\n for layers in num_hidden_layers:\r\n \r\n for nodes in num_of_neurons:\r\n \r\n architecture = str(layers)+'_'+str(nodes)\r\n \r\n print('Validating: '+str(layers) +'hidden layers, '+str(nodes)+' hidden nodes')\r\n \r\n # design the model\r\n model = design_ANN(nodes, layers, act_func, \r\n num_input, num_output, opt, \r\n loss_function, weight_reg)\r\n \r\n model.summary()\r\n \r\n # train the model\r\n history = model.fit(X_train, y_train, \r\n batch_size = 32, \r\n epochs = numb_of_epochs, \r\n verbose=1, \r\n validation_data=(X_val, y_val))\r\n \r\n # convert the history.history dict to a pandas DataFrame and save it for later use \r\n hist_df = pd.DataFrame(history.history)\r\n \r\n hist_csv_file = './validation_results/architecture/history_'+architecture+'.csv'\r\n with open(hist_csv_file, mode='w') as file:\r\n hist_df.to_csv(file)\r\n \r\n # Evaluate the model\r\n \r\n train_acc = model.evaluate(X_train, y_train, verbose=0)\r\n val_acc = model.evaluate(X_val, y_val, verbose=0) \r\n print('\\ttrain acc: %.3f \\tval acc: %.3f' % ( train_acc, val_acc))\r\n \r\n eval_result.loc[eval_result.shape[0]] = [layers, nodes, train_acc, val_acc]\r\n \r\n # save the result\r\n with open('./validation_results/architecture/result_models.csv', mode='w') as file:\r\n eval_result.to_csv(file)\r\n \r\n # 3. Tune the Optimization function\r\n \r\n # hyperparameters setting\r\n neurons = 5\r\n layers = 2\r\n act_func = 'tanh'\r\n numb_of_epochs = 2000\r\n weight_reg = 0\r\n \r\n # store the evaluation results in eval_result\r\n eval_result = pd.DataFrame(columns = ['opt_function', 'train_acc', 'val_acc'])\r\n \r\n \r\n for opt in optimizers:\r\n \r\n opt_function = opt\r\n \r\n print('Validating: '+str(opt_function))\r\n \r\n # design the model\r\n model = design_ANN(neurons, layers, act_func, \r\n num_input, num_output, opt, \r\n loss_function, weight_reg)\r\n \r\n model.summary()\r\n \r\n # train the model\r\n history = model.fit(X_train, y_train, \r\n batch_size = 32, \r\n epochs = numb_of_epochs, \r\n verbose=1, \r\n validation_data=(X_val, y_val))\r\n \r\n # convert the history.history dict to a pandas DataFrame and save it for later use \r\n hist_df = pd.DataFrame(history.history)\r\n \r\n hist_csv_file = './validation_results/opt/history_'+opt_function+'.csv'\r\n with open(hist_csv_file, mode='w') as file:\r\n hist_df.to_csv(file)\r\n \r\n # Evaluate the model\r\n \r\n train_acc = model.evaluate(X_train, y_train, verbose=0)\r\n val_acc = model.evaluate(X_val, y_val, verbose=0) \r\n print('\\ttrain acc: %.3f \\tval acc: %.3f' % ( train_acc, val_acc))\r\n \r\n eval_result.loc[eval_result.shape[0]] = [opt_function, train_acc, val_acc]\r\n \r\n \r\n # save the result\r\n with open('./validation_results/opt/result_models.csv', mode='w') as file:\r\n eval_result.to_csv(file)\r\n \r\n # 4. Tune the Layer weight regularizers\r\n \r\n # hyperparameters setting\r\n neurons = 5\r\n layers = 2\r\n act_func = 'tanh'\r\n numb_of_epochs = 1000\r\n weight_reg = 0\r\n opt = 'Adamax'\r\n \r\n # store the evaluation results in eval_result\r\n eval_result = pd.DataFrame(columns = ['L2_reg', 'train_acc', 'val_acc']) \r\n \r\n for reg in weight_reg:\r\n \r\n print('Validating: '+str(reg))\r\n \r\n regularization = reg\r\n \r\n # design the model\r\n model = design_ANN(neurons, layers, act_func, \r\n num_input, num_output, opt, \r\n loss_function, reg)\r\n \r\n model.summary()\r\n \r\n # train the model\r\n history = model.fit(X_train, y_train, \r\n batch_size = 32, \r\n epochs = numb_of_epochs, \r\n verbose=1, \r\n validation_data=(X_val, y_val))\r\n \r\n # convert the history.history dict to a pandas DataFrame and save it for later use \r\n hist_df = pd.DataFrame(history.history)\r\n \r\n hist_csv_file = './validation_results/L2_reg/history_'+str(reg)+'.csv'\r\n with open(hist_csv_file, mode='w') as file:\r\n hist_df.to_csv(file)\r\n \r\n # Evaluate the model\r\n \r\n train_acc = model.evaluate(X_train, y_train, verbose=0)\r\n val_acc = model.evaluate(X_val, y_val, verbose=0) \r\n print('\\ttrain acc: %.3f \\tval acc: %.3f' % ( train_acc, val_acc))\r\n \r\n eval_result.loc[eval_result.shape[0]] = [regularization, train_acc, val_acc]\r\n \r\n # save the result\r\n with open('./validation_results/L2_reg/result_models.csv', mode='w') as file:\r\n eval_result.to_csv(file)\r\n \r\n \r\nif __name__ == \"__main__\":\r\n main()\r\n \r\n ", "repo_name": "QEERI/SOCM_ML", "sub_path": "2. model_tuning.py", "file_name": "2. model_tuning.py", "file_ext": "py", "file_size_in_byte": 11595, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "37", "api": [{"api_name": "keras.initializers.GlorotNormal", "line_number": 52, "usage_type": "call"}, {"api_name": "keras.models.Sequential", "line_number": 55, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 58, "usage_type": "call"}, {"api_name": "keras.regularizers.l2", "line_number": 61, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 67, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 72, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 91, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 93, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 95, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 96, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 127, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.LeakyReLU", "line_number": 132, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.PReLU", "line_number": 134, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 152, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 180, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 206, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 234, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 258, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 288, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 311, "usage_type": "call"}]} +{"seq_id": "9820851880", "text": "import re\n\nimport pglast\nfrom pglast.enums import AlterTableType, ConstrType\n\nfrom squabble.message import Message\nfrom squabble.rules import BaseRule\n\n\nclass RequireForeignKey(BaseRule):\n \"\"\"\n New columns that look like references must have a foreign key constraint.\n\n By default, \"looks like\" means that the name of the column matches\n the regex ``.*_id$``, but this is configurable.\n\n .. code-block:: sql\n\n CREATE TABLE comments (\n post_id INT, -- warning here, this looks like a foreign key,\n -- but no constraint was given\n\n -- No warning here\n user_id INT REFERENCES users(id)\n )\n\n ALTER TABLE books\n ADD COLUMN author_id INT; -- warning here\n\n Configuration ::\n\n {\n \"RequireForeignKey\": {\n \"column_regex\": \".*_id$\"\n }\n }\n \"\"\"\n\n class MissingForeignKeyConstraint(Message):\n \"\"\"\n Foreign keys are a good way to guarantee that your database\n retains referential integrity.\n\n When adding a new column that points to another table, make sure to add\n a constraint so that the database can check that it points to a valid\n record.\n\n Foreign keys can either be added when creating a table, or\n after the fact in the case of adding a new column.\n\n .. code-block:: sql\n\n CREATE TABLE admins (user_id INTEGER REFERENCES users(id));\n\n CREATE TABLE admins (\n user_id INTEGER,\n FOREIGN KEY (user_id) REFERENCES users(id)\n );\n\n ALTER TABLE admins ADD COLUMN user_id INTEGER REFERENCES users(id);\n\n ALTER TABLE admins ADD FOREIGN KEY user_id REFERENCES users(id);\n \"\"\"\n TEMPLATE = '\"{col}\" may need a foreign key constraint'\n CODE = 1008\n\n _DEFAULT_REGEX = '.*_id$'\n\n def enable(self, root_ctx, config):\n fk_regex = re.compile(config.get('column_regex', self._DEFAULT_REGEX))\n\n # Keep track of column_name -> column_def node so we can\n # report a sane location for the warning when a new column\n # doesn't have a foreign key.\n missing_fk = {}\n\n # We want to check both columns that are part of CREATE TABLE\n # as well as ALTER TABLE ... ADD COLUMN\n root_ctx.register(\n 'CreateStmt',\n lambda _, node: _create_table_stmt(node, fk_regex, missing_fk))\n\n root_ctx.register(\n 'AlterTableStmt',\n lambda _, node: _alter_table_stmt(node, fk_regex, missing_fk))\n\n def _report_missing(ctx):\n \"\"\"\n When we exit the root context, any elements remaining in\n ``missing_fk`` are known not to have a FOREIGN KEY\n constraint, so report them as errors.\n \"\"\"\n for column, node in missing_fk.items():\n ctx.report(\n self.MissingForeignKeyConstraint(col=column),\n node=node)\n\n root_ctx.register_exit(_report_missing)\n\n\ndef _create_table_stmt(table_node, fk_regex, missing_fk):\n table_name = table_node.relation.relname.value\n if table_node.tableElts == pglast.Missing:\n return\n\n for e in table_node.tableElts:\n # Defining a column, may include an inline constraint.\n if e.node_tag == 'ColumnDef':\n if _column_needs_foreign_key(fk_regex, e):\n key = '{}.{}'.format(table_name, e.colname.value)\n missing_fk[key] = e\n\n # FOREIGN KEY (...) REFERENCES ...\n elif e.node_tag == 'Constraint':\n _remove_satisfied_foreign_keys(e, table_name, missing_fk)\n\n\ndef _alter_table_stmt(node, fk_regex, missing_fk):\n table_name = node.relation.relname.value\n\n for cmd in node.cmds:\n if cmd.subtype == AlterTableType.AT_AddColumn:\n if _column_needs_foreign_key(fk_regex, cmd['def']):\n key = '{}.{}'.format(table_name, cmd['def'].colname.value)\n missing_fk[key] = cmd['def']\n\n elif cmd.subtype in (AlterTableType.AT_AddConstraint,\n AlterTableType.AT_AddConstraintRecurse):\n constraint = cmd['def']\n _remove_satisfied_foreign_keys(constraint, table_name, missing_fk)\n\n\ndef _remove_satisfied_foreign_keys(constraint, table_name, missing_fk):\n # Nothing to do if this isn't a foreign key constraint\n if constraint.contype != ConstrType.CONSTR_FOREIGN:\n return\n\n # Clear out any columns that we earlier identified as\n # needing a foreign key.\n for col_name in constraint.fk_attrs:\n key = '{}.{}'.format(table_name, col_name.string_value)\n missing_fk.pop(key, '')\n\n\ndef _column_needs_foreign_key(fk_regex, column_def):\n \"\"\"\n Return True if the ``ColumnDef`` defines a column with a name that\n matches the foreign key regex but does not specify an inline\n constraint.\n\n >>> import re\n >>> import pglast\n\n >>> fk_regex = re.compile('.*_id$')\n >>> cols = {\n ... # name doesn't match regex\n ... 'email': {'ColumnDef': {'colname': 'email'}},\n ...\n ... # name matches regex, but no foreign key\n ... 'users_id': {'ColumnDef': {'colname': 'users_id'}},\n ...\n ... # name matches regex, but has foreign key (contype == 8)\n ... 'post_id': {'ColumnDef': {\n ... 'colname': 'post_id',\n ... 'constraints': [{'Constraint': {'contype': 8}}]\n ... }}\n ... }\n >>> _column_needs_foreign_key(fk_regex, pglast.Node(cols['email']))\n False\n >>> _column_needs_foreign_key(fk_regex, pglast.Node(cols['users_id']))\n True\n >>> _column_needs_foreign_key(fk_regex, pglast.Node(cols['post_id']))\n False\n \"\"\"\n name = column_def.colname.value\n if not fk_regex.match(name):\n return False\n\n if column_def.constraints == pglast.Missing:\n return True\n\n return not any(\n e.contype == ConstrType.CONSTR_FOREIGN\n for e in column_def.constraints\n )\n", "repo_name": "erik/squabble", "sub_path": "squabble/rules/require_foreign_key.py", "file_name": "require_foreign_key.py", "file_ext": "py", "file_size_in_byte": 5990, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 64, "dataset": "github-code", "pt": "37", "api": [{"api_name": "squabble.rules.BaseRule", "line_number": 10, "usage_type": "name"}, {"api_name": "squabble.message.Message", "line_number": 39, "usage_type": "name"}, {"api_name": "re.compile", "line_number": 70, "usage_type": "call"}, {"api_name": "pglast.Missing", "line_number": 103, "usage_type": "attribute"}, {"api_name": "pglast.enums.AlterTableType.AT_AddColumn", "line_number": 122, "usage_type": "attribute"}, {"api_name": "pglast.enums.AlterTableType", "line_number": 122, "usage_type": "name"}, {"api_name": "pglast.enums.AlterTableType.AT_AddConstraint", "line_number": 127, "usage_type": "attribute"}, {"api_name": "pglast.enums.AlterTableType", "line_number": 127, "usage_type": "name"}, {"api_name": "pglast.enums.AlterTableType.AT_AddConstraintRecurse", "line_number": 128, "usage_type": "attribute"}, {"api_name": "pglast.enums.AlterTableType", "line_number": 128, "usage_type": "name"}, {"api_name": "pglast.enums.ConstrType.CONSTR_FOREIGN", "line_number": 135, "usage_type": "attribute"}, {"api_name": "pglast.enums.ConstrType", "line_number": 135, "usage_type": "name"}, {"api_name": "pglast.Missing", "line_number": 179, "usage_type": "attribute"}, {"api_name": "pglast.enums.ConstrType.CONSTR_FOREIGN", "line_number": 183, "usage_type": "attribute"}, {"api_name": "pglast.enums.ConstrType", "line_number": 183, "usage_type": "name"}]} +{"seq_id": "72725605868", "text": "from flask import Flask, request, jsonify\n\napp = Flask(__name__)\n\n@app.route('/api/submitData', methods=['POST'])\ndef submit_data():\n data = request.json\n\n # Perform any necessary operations with the received data\n # ...\n\n # Prepare the response JSON data\n response_data = {\n 'message': 'Data received successfully',\n 'data': data\n }\n\n return jsonify(response_data)\n\nif __name__ == '__main__':\n app.run()\n", "repo_name": "kjeevesh/Final_SAP_Demo", "sub_path": "my-app/src/app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 443, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "37", "api": [{"api_name": "flask.Flask", "line_number": 3, "usage_type": "call"}, {"api_name": "flask.request.json", "line_number": 7, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 7, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 18, "usage_type": "call"}]} +{"seq_id": "71122555946", "text": "import speech_recognition as sr\nimport os\nimport urllib.parse\nimport wx\nimport wx.adv\n\nTRAY_TOOLTIP = 'Agador'\nTRAY_ICON = 'img/agador.ico'\n\ndef create_menu_item(menu, label, func):\n item = wx.MenuItem(menu, -1, label)\n menu.Bind(wx.EVT_MENU, func, id=item.GetId())\n menu.Append(item)\n return item\n\n\nclass TaskBarIcon(wx.adv.TaskBarIcon):\n def __init__(self,frame):\n wx.adv.TaskBarIcon.__init__(self)\n self.myapp_frame = frame\n self.set_icon(TRAY_ICON)\n self.Bind(wx.adv.EVT_TASKBAR_LEFT_DOWN, self.on_left_down)\n\n def CreatePopupMenu(self):\n menu = wx.Menu()\n create_menu_item(menu, 'Exit', self.on_exit)\n return menu\n\n def set_icon(self, path):\n icon = wx.Icon(wx.Bitmap(path))\n self.SetIcon(icon, TRAY_TOOLTIP)\n\n def on_left_down(self, event):\n r = sr.Recognizer()\n mic = sr.Microphone()\n\n with mic as source:\n r.adjust_for_ambient_noise(source)\n audio = r.listen(source)\n os.system(\"xdg-open https://duckduckgo.com/?q=\" + urllib.parse.quote(r.recognize_google(audio)))\n\n def on_exit(self, event):\n self.myapp_frame.Close()\n\nclass My_Application(wx.Frame):\n\n def __init__(self):\n wx.Frame.__init__(self, None, wx.ID_ANY, \"\", size=(1,1))\n panel = wx.Panel(self)\n self.myapp = TaskBarIcon(self)\n self.Bind(wx.EVT_CLOSE, self.onClose)\n\n def onClose(self, evt):\n \"\"\"\n Destroy the taskbar icon and the frame\n \"\"\"\n self.myapp.RemoveIcon()\n self.myapp.Destroy()\n self.Destroy()\n\nif __name__ == \"__main__\":\n MyApp = wx.App()\n My_Application()\n MyApp.MainLoop()\n\n", "repo_name": "Heather-Herbert/Agador", "sub_path": "Agador.py", "file_name": "Agador.py", "file_ext": "py", "file_size_in_byte": 1693, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "wx.MenuItem", "line_number": 11, "usage_type": "call"}, {"api_name": "wx.EVT_MENU", "line_number": 12, "usage_type": "attribute"}, {"api_name": "wx.adv", "line_number": 17, "usage_type": "attribute"}, {"api_name": "wx.adv.TaskBarIcon.__init__", "line_number": 19, "usage_type": "call"}, {"api_name": "wx.adv", "line_number": 19, "usage_type": "attribute"}, {"api_name": "wx.adv", "line_number": 22, "usage_type": "attribute"}, {"api_name": "wx.Menu", "line_number": 25, "usage_type": "call"}, {"api_name": "wx.Icon", "line_number": 30, "usage_type": "call"}, {"api_name": "wx.Bitmap", "line_number": 30, "usage_type": "call"}, {"api_name": "speech_recognition.Recognizer", "line_number": 34, "usage_type": "call"}, {"api_name": "speech_recognition.Microphone", "line_number": 35, "usage_type": "call"}, {"api_name": "os.system", "line_number": 40, "usage_type": "call"}, {"api_name": "urllib.parse.parse.quote", "line_number": 40, "usage_type": "call"}, {"api_name": "urllib.parse.parse", "line_number": 40, "usage_type": "attribute"}, {"api_name": "urllib.parse", "line_number": 40, "usage_type": "name"}, {"api_name": "wx.Frame", "line_number": 45, "usage_type": "attribute"}, {"api_name": "wx.Frame.__init__", "line_number": 48, "usage_type": "call"}, {"api_name": "wx.Frame", "line_number": 48, "usage_type": "attribute"}, {"api_name": "wx.ID_ANY", "line_number": 48, "usage_type": "attribute"}, {"api_name": "wx.Panel", "line_number": 49, "usage_type": "call"}, {"api_name": "wx.EVT_CLOSE", "line_number": 51, "usage_type": "attribute"}, {"api_name": "wx.App", "line_number": 62, "usage_type": "call"}]} +{"seq_id": "44731924894", "text": "import discord\nimport asyncio\nimport re\nimport traceback\n\nimport config\nimport commands\nfrom tokens import DISCORD_TOKEN as TOKEN\n\nimport lifebuoy\nimport mundane\nimport karma\nimport f1984\nimport leisure\nimport wisdom\nimport ihavenomouth\n\ntry:\n\twith open(\"./god_users.txt\") as f:\n\t\tGOD_USERS = f.read().splitlines()\nexcept IOError:\n\twith open(\"./god_users.txt\",'w') as f:\n\t\tGOD_USERS = []\n\n\nDEFAULT = mundane.game_status_per_message\nmundane.SAVE = [\n\tconfig.save,\n\tkarma.save,\n\twisdom.save,\n]\n\nback_log = []\nLOG_CHANNEL = None\n\ncommands = [\n\t(\n\t\tlambda m: (\n\t\t\tm.channel.name == 'screenshots' or\n\t\t\tm.channel.name == 'inspirations'\n\t\t), \n\t\tf1984.check_screenshot\n\t),\n\n\t# Admin only commands\n\tcommands.register(\n\t\t'reload',\n\t\tmundane.reload,\n\t\tadmin=True, leisure=False,\n\t),\n\tcommands.register(\n\t\t'set_log',\n\t\tmundane.set_log_channel,\n\t\tadmin=True, leisure=False,\n\t),\n\tcommands.register(\n\t\t'add_leisure',\n\t\tcommands.add_leisure_channel,\n\t\tadmin=True, leisure=False,\n\t),\n\tcommands.register(\n\t\t'delete_leisure',\n\t\tcommands.delete_leisure_channel,\n\t\tadmin=True, leisure=False,\n\t),\n\tcommands.register(\n\t\t'add_prefix',\n\t\tcommands.add_prefix,\n\t\tadmin=True, leisure=False,\n\t),\n\tcommands.register(\n\t\t'delete_prefix',\n\t\tcommands.delete_prefix,\n\t\tadmin=True, leisure=False,\n\t),\n\tcommands.register(\n\t\t'error',\n\t\tmundane.do_raise_error,\n\t\tadmin=True, leisure=False,\n\t),\n\tcommands.register(\n\t\t'reset_karma',\n\t\tkarma.reset_karma,\n\t\tadmin=True, leisure=False,\n\t),\n\tcommands.register(\n\t\t'force_save',\n\t\tlifebuoy.force_save,\n\t\tadmin=True, leisure=False,\n\t),\n\tcommands.register(\n\t\t'temp_ban',\n\t\tihavenomouth.temp_ban,\n\t\tadmin=True, leisure=False,\n\t\tdelete=False,\n\t),\n\tcommands.register(\n\t\t'tban',\n\t\tihavenomouth.temp_ban,\n\t\tadmin=True, leisure=False,\n\t\tdelete=False,\n\t),\n\tcommands.register(\n\t\t'temp_mute',\n\t\tihavenomouth.temp_mute,\n\t\tadmin=True, leisure=False,\n\t\tdelete=False,\n\t),\n\tcommands.register(\n\t\t'tmute',\n\t\tihavenomouth.temp_mute,\n\t\tadmin=True, leisure=False,\n\t\tdelete=False,\n\t),\n\tcommands.register(\n\t\t'vote_mute',\n\t\tf1984.vote_mute,\n\t\tadmin=False, leisure=True,\n\t\tdelete=False,\n\t\thelp='\\t!vmute [@User]\\n\\tVotes to mute a user (3 votes required to mute). Requires the Scribe role.'\n\t),\n\tcommands.register(\n\t\t'vmute',\n\t\tf1984.vote_mute,\n\t\tadmin=False, leisure=True,\n\t\tdelete=False,\n\t\thelp='\\t!vmute [@User]\\n\\tVotes to mute a user (3 votes required to mute). Requires the Scribe role.'\n\t),\n\tcommands.register(\n\t\t'check_mute',\n\t\tihavenomouth.check_mute,\n\t\tadmin=False, leisure=True,\n\t\tdelete=False\n\t),\n\n\tcommands.register(\n\t\t'zc',\n\t\tleisure.zerochan_command,\n\t\tadmin=True,\n\t\thelp='\\t!zc \\n\\tTries (once) to post a random image from zerochan with the given tag.'\n\t),\n\t\n\t# Commands for everyone to use\n\tcommands.register(\n\t\t'help',\n\t\tcommands.help_command,\n\t\tleisure=False,\n\t\thelp='\\t!help [command]\\n\\tShows a list of available commands, or detailed help for the given command.'\n\t),\n\tcommands.register(\n\t\t'list_prefix',\n\t\tcommands.list_prefix,\n\t\tleisure=False,\n\t),\n\tcommands.register(\n\t\t'karma',\n\t\tkarma.send_karma_score,\n\t\tleisure=False,\n\t\thelp='\\t!karma\\n\\tShows your current karma score, and how much karma you\\'ve given to others.'\n\t),\n\tcommands.register(\n\t\t'topkarma',\n\t\tkarma.top_karma,\n\t\tdelete=False,\n\t\thelp='\\t!topkarma [@User1] [@User2] [@User3] ...\\n\\t' +\n\t\t\t'With no tagged users, shows the current top karma scores.\\n\\t' +\n\t\t\t'With one tagged user, shows the leaderboard but centered on the user\\'s current placement.\\n\\t' +\n\t\t\t'With multiple tagged users, just shows their current scores and rankings on the leaderboard.'\n\t),\n\tcommands.register(\n\t\t'kwords',\n\t\tkarma.k_words,\n\t\tdelete=False,\n\t\thelp='\\t!kwords\\n\\t' +\n\t\t\t'Checks what words are currently functional for karma'\n\t),\n\tcommands.register(\n\t\t'hug', \n\t\tleisure.hug_command,\n\t\tdelete=False,\n\t\thelp='\\t!hug <@User1> [@User2] [@User3] ...\\n\\t' +\n\t\t\t'Gives someone (or some people) a hug! So nice~\\n\\t' +\n\t\t\t'Just keep in mind you can\\'t hug yourself, nor hug anyone outside of #off-topic.' \n\t),\n\tcommands.register(\n\t\t'rainbow', \n\t\tleisure.rainbow_command,\n\t\tdelete=False,\n\t\thelp='\\t!rainbow\\n' +\n\t\t\t'Adds or removes a rainbow from the end of your name!'\n\t),\n\t\n\t# Some test commands to check that things are actually working\n\tcommands.register(\n\t\t'testping',\n\t\tcommands.test_command,\n\t\tadmin=True, leisure=False\n\t),\n\tcommands.register(\n\t\t'pingrich',\n\t\tcommands.test_rich_command,\n\t\tadmin=True, leisure=False\n\t),\n\tcommands.register(\n\t\t'testexcept',\n\t\tcommands.test_exception_command,\n\t\tadmin=True, leisure=False\n\t),\n\n\t# dragonhax\n\tcommands.register(\n\t\t'dragonhax',\n\t\tkarma.dragonhax,\n\t\tadmin=True, leisure=False\n\t),\n\n\t(f1984.ip_check, f1984.remove_ip),\n\t(karma.check_karma_legal, karma.parse_karma),\n]\n\nclient = discord.Client()\nclient.exiting = False\n\ndef log(s):\n\tglobal back_log\n\tprint(s)\n\tif mundane.LOG_CHANNEL_ID != None:\n\t\tback_log.append(s)\n\nclient.log = log\n\n@client.event\nasync def on_ready():\n\tloop_errored_once = False\n\tclient.log(\n\t\tf'Logged in as\\n{client.user.name}\\n{client.user.id}\\n------'\n\t)\n\twhile not client.exiting:\n\t\ttry:\n\t\t\tawait not_exit_loop()\n\t\texcept BaseException as e:\n\t\t\tif loop_errored_once:\n\t\t\t\tawait asyncio.sleep(30)\n\t\t\t\tcontinue\n\t\t\tloop_errored_once = True\n\t\t\tclient.log(\n\t\t\t\tf'Encountered an exception:\\n```'\n\t\t\t\t+ traceback.format_exc()\n\t\t\t\t+'```'\n\t\t\t)\n\n\tfor message, _ in zip(back_log, range(10)):\n\t\tawait client.send_message(LOG_CHANNEL, message)\n\nasync def not_exit_loop():\n\tawait asyncio.sleep(5)\n\tawait handle_log(client)\n\tlifebuoy.save_if_needed(client)\n\tawait ihavenomouth.check_all(client)\n\t\t\nasync def handle_log(client):\n\tglobal LOG_CHANNEL\n\tglobal back_log\n\tset_log(client)\n\twhile len(back_log) > 0 and LOG_CHANNEL != None:\n\t\tawait try_log(client)\n\t\tawait asyncio.sleep(1)\n\t\tif client.exiting:\n\t\t\tbreak\n\t\t\ndef set_log(client):\n\tglobal LOG_CHANNEL\n\tif mundane.LOG_CHANNEL_ID == None:\n\t\tLOG_CHANNEL = None\n\telif LOG_CHANNEL == None or LOG_CHANNEL.id != mundane.LOG_CHANNEL_ID:\n\t\tLOG_CHANNEL = client.get_channel(mundane.LOG_CHANNEL_ID)\n\nasync def try_log(client):\n\tglobal LOG_CHANNEL\n\tglobal back_log\n\ttry:\n\t\tawait client.send_message(LOG_CHANNEL, back_log.pop(0))\n\texcept:\n\t\tprint(\n\t\t\tf'Something went wrong while logging:\\n',\n\t\t\ttraceback.format_exc()\n\t\t)\n\t\n@client.event\nasync def on_message(message):\n\tif message.author.bot or client.exiting:\n\t\treturn\n\n\tfor command in commands:\n\t\tif command[0](message):\n\t\t\tcontent = await sanitize(message.content)\n\t\t\tclient.log(\n\t\t\t\tf'Executing {command[1]} because of message '\n\t\t\t\tf'{content} by {message.author} in {message.channel}'\n\t\t\t)\n\t\t\ttry:\n\t\t\t\tawait command[1](client, message)\n\t\t\texcept Exception as e:\n\t\t\t\tclient.log(\n\t\t\t\t\tf'Encountered an exception while running command:\\n```'\n\t\t\t\t\t+ traceback.format_exc()\n\t\t\t\t\t+'```'\n\t\t\t\t)\n\t\t\treturn\n\tif DEFAULT != None:\n\t\tawait DEFAULT(client, message)\n\n\nasync def delete_message(message):\n\tif message.channel.is_private:\n\t\treturn\n\tif message.channel.name == \"administration\":\n\t\treturn\n\tif message.channel.name.startswith(\"bot\"):\n\t\treturn\n\tcontent = await sanitize(message.content)\n\tclient.log(\n\t\tf'Deleting message\\n{content}\\n'\n\t\tf'by {message.author} '\n\t\tf'in {message.channel}'\n\t)\n\tawait client.__delete_message__(message)\nclient.__delete_message__ = client.delete_message\nclient.delete_message = delete_message\n\ndef sent_by_admin(message):\n\treturn (\n\t\tmessage.author.id in GOD_USERS\n\t\tif message.channel.is_private\n\t\telse message.author.top_role.id == '261519756417433601'\n\t)\n\n\nclient.sent_by_admin = sent_by_admin\n\n\nasync def sanitize(content):\n\tcontent = content.replace(\"`\", \"`​\")\n\tfound = set(re.findall(\n\t\tr\"<@!?([0-9]*)>\",\n\t\tcontent,\n\t))\n\tfor m in found:\n\t\tuser = await client.get_user_info(m)\n\t\tcontent = re.sub(f'<@!?{m}>', f'@{str(user)}', content)\n\t# was somewhat annoying seeing all those 'empty' messages in the log\n\tif len(content) == 0:\n\t\treturn ''\n\treturn f'```{content}```'\n\nclient.sanitize = sanitize\n\nif __name__ == \"__main__\":\n\tclient.run(TOKEN)\n", "repo_name": "ITR13/BesiegeBot", "sub_path": "sourcecube.py", "file_name": "sourcecube.py", "file_ext": "py", "file_size_in_byte": 7879, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "mundane.game_status_per_message", "line_number": 26, "usage_type": "attribute"}, {"api_name": "mundane.SAVE", "line_number": 27, "usage_type": "attribute"}, {"api_name": "config.save", "line_number": 28, "usage_type": "attribute"}, {"api_name": "karma.save", "line_number": 29, "usage_type": "attribute"}, {"api_name": "wisdom.save", "line_number": 30, "usage_type": "attribute"}, {"api_name": "f1984.check_screenshot", "line_number": 42, "usage_type": "attribute"}, {"api_name": "commands.register", "line_number": 46, "usage_type": "call"}, {"api_name": "mundane.reload", "line_number": 48, "usage_type": "attribute"}, {"api_name": "commands.register", "line_number": 51, "usage_type": "call"}, {"api_name": "mundane.set_log_channel", "line_number": 53, "usage_type": "attribute"}, {"api_name": "commands.register", "line_number": 56, "usage_type": "call"}, {"api_name": "commands.add_leisure_channel", "line_number": 58, "usage_type": "attribute"}, {"api_name": "commands.register", "line_number": 61, "usage_type": "call"}, {"api_name": "commands.delete_leisure_channel", "line_number": 63, "usage_type": "attribute"}, {"api_name": "commands.register", "line_number": 66, "usage_type": "call"}, {"api_name": "commands.add_prefix", "line_number": 68, "usage_type": "attribute"}, {"api_name": "commands.register", "line_number": 71, "usage_type": "call"}, {"api_name": "commands.delete_prefix", "line_number": 73, "usage_type": "attribute"}, {"api_name": "commands.register", "line_number": 76, "usage_type": "call"}, {"api_name": "mundane.do_raise_error", "line_number": 78, "usage_type": "attribute"}, {"api_name": "commands.register", "line_number": 81, "usage_type": "call"}, {"api_name": "karma.reset_karma", "line_number": 83, "usage_type": "attribute"}, {"api_name": "commands.register", "line_number": 86, "usage_type": "call"}, {"api_name": "lifebuoy.force_save", "line_number": 88, "usage_type": "attribute"}, {"api_name": "commands.register", "line_number": 91, "usage_type": "call"}, {"api_name": "ihavenomouth.temp_ban", "line_number": 93, "usage_type": "attribute"}, {"api_name": "commands.register", "line_number": 97, "usage_type": "call"}, {"api_name": "ihavenomouth.temp_ban", "line_number": 99, "usage_type": "attribute"}, {"api_name": "commands.register", "line_number": 103, "usage_type": "call"}, {"api_name": "ihavenomouth.temp_mute", "line_number": 105, "usage_type": "attribute"}, {"api_name": "commands.register", "line_number": 109, "usage_type": "call"}, {"api_name": "ihavenomouth.temp_mute", "line_number": 111, "usage_type": "attribute"}, {"api_name": "commands.register", "line_number": 115, "usage_type": "call"}, {"api_name": "f1984.vote_mute", "line_number": 117, "usage_type": "attribute"}, {"api_name": "commands.register", "line_number": 122, "usage_type": "call"}, {"api_name": "f1984.vote_mute", "line_number": 124, "usage_type": "attribute"}, {"api_name": "commands.register", "line_number": 129, "usage_type": "call"}, {"api_name": "ihavenomouth.check_mute", "line_number": 131, "usage_type": "attribute"}, {"api_name": "commands.register", "line_number": 136, "usage_type": "call"}, {"api_name": "leisure.zerochan_command", "line_number": 138, "usage_type": "attribute"}, {"api_name": "commands.register", "line_number": 144, "usage_type": "call"}, {"api_name": "commands.help_command", "line_number": 146, "usage_type": "attribute"}, {"api_name": "commands.register", "line_number": 150, "usage_type": "call"}, {"api_name": "commands.list_prefix", "line_number": 152, "usage_type": "attribute"}, {"api_name": "commands.register", "line_number": 155, "usage_type": "call"}, {"api_name": "karma.send_karma_score", "line_number": 157, "usage_type": "attribute"}, {"api_name": "commands.register", "line_number": 161, "usage_type": "call"}, {"api_name": "karma.top_karma", "line_number": 163, "usage_type": "attribute"}, {"api_name": "commands.register", "line_number": 170, "usage_type": "call"}, {"api_name": "karma.k_words", "line_number": 172, "usage_type": "attribute"}, {"api_name": "commands.register", "line_number": 177, "usage_type": "call"}, {"api_name": "leisure.hug_command", "line_number": 179, "usage_type": "attribute"}, {"api_name": "commands.register", "line_number": 185, "usage_type": "call"}, {"api_name": "leisure.rainbow_command", "line_number": 187, "usage_type": "attribute"}, {"api_name": "commands.register", "line_number": 194, "usage_type": "call"}, {"api_name": "commands.test_command", "line_number": 196, "usage_type": "attribute"}, {"api_name": "commands.register", "line_number": 199, "usage_type": "call"}, {"api_name": "commands.test_rich_command", "line_number": 201, "usage_type": "attribute"}, {"api_name": "commands.register", "line_number": 204, "usage_type": "call"}, {"api_name": "commands.test_exception_command", "line_number": 206, "usage_type": "attribute"}, {"api_name": "commands.register", "line_number": 211, "usage_type": "call"}, {"api_name": "karma.dragonhax", "line_number": 213, "usage_type": "attribute"}, {"api_name": "f1984.ip_check", "line_number": 217, "usage_type": "attribute"}, {"api_name": "f1984.remove_ip", "line_number": 217, "usage_type": "attribute"}, {"api_name": "karma.check_karma_legal", "line_number": 218, "usage_type": "attribute"}, {"api_name": "karma.parse_karma", "line_number": 218, "usage_type": "attribute"}, {"api_name": "discord.Client", "line_number": 221, "usage_type": "call"}, {"api_name": "mundane.LOG_CHANNEL_ID", "line_number": 227, "usage_type": "attribute"}, {"api_name": "asyncio.sleep", "line_number": 243, "usage_type": "call"}, {"api_name": "traceback.format_exc", "line_number": 248, "usage_type": "call"}, {"api_name": "asyncio.sleep", "line_number": 256, "usage_type": "call"}, {"api_name": "lifebuoy.save_if_needed", "line_number": 258, "usage_type": "call"}, {"api_name": "ihavenomouth.check_all", "line_number": 259, "usage_type": "call"}, {"api_name": "asyncio.sleep", "line_number": 267, "usage_type": "call"}, {"api_name": "mundane.LOG_CHANNEL_ID", "line_number": 273, "usage_type": "attribute"}, {"api_name": "mundane.LOG_CHANNEL_ID", "line_number": 275, "usage_type": "attribute"}, {"api_name": "mundane.LOG_CHANNEL_ID", "line_number": 276, "usage_type": "attribute"}, {"api_name": "traceback.format_exc", "line_number": 286, "usage_type": "call"}, {"api_name": "traceback.format_exc", "line_number": 306, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 344, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 350, "usage_type": "call"}, {"api_name": "tokens.DISCORD_TOKEN", "line_number": 359, "usage_type": "argument"}]} +{"seq_id": "73823711147", "text": "import numpy as np\nimport os\nimport glob\nfrom tensorflow import gfile\nimport tensorflow as tf\nfrom six.moves import range\nfrom utils import adjust_dynamic_range\n\n\nclass GroundTruthData(object):\n \"\"\"Abstract class for data sets that are two-step generative models.\"\"\"\n\n @property\n def num_samples(self):\n raise NotImplementedError()\n\n @property\n def num_factors(self):\n raise NotImplementedError()\n\n @property\n def factors_num_values(self):\n raise NotImplementedError()\n\n @property\n def observation_shape(self):\n raise NotImplementedError()\n\n def sample_factors(self, num, random_state):\n \"\"\"Sample a batch of factors Y.\"\"\"\n raise NotImplementedError()\n\n def sample_observations_from_factors(self, latent_factors, random_state):\n \"\"\"Sample a batch of observations X given a batch of factors Y.\"\"\"\n raise NotImplementedError()\n\n def sample(self, num, random_state):\n \"\"\"Sample a batch of factors Y and observations X.\"\"\"\n latent_factors = self.sample_factors(num, random_state)\n images, labels, labels_mask = self.sample_observations_from_factors(latent_factors, random_state) # images in NHWC\n return images, labels, labels_mask, latent_factors\n\n def sample_images(self, num, random_state):\n \"\"\"Sample a batch of observations X.\"\"\"\n return self.sample(num, random_state)[0]\n\n def sample_labels(self, num, random_state):\n \"\"\"Sample a batch of labels.\"\"\"\n return self.sample(num, random_state)[1]\n\n\nclass SplitDiscreteStateSpace(object):\n \"\"\"State space with factors split between latent variable and observations.\"\"\"\n\n def __init__(self, factor_sizes, latent_factor_indices):\n self.factor_sizes = factor_sizes\n self.num_factors = len(self.factor_sizes)\n self.latent_factor_indices = latent_factor_indices\n self.observation_factor_indices = [i for i in range(self.num_factors) if i not in self.latent_factor_indices]\n\n @property\n def num_latent_factors(self):\n return len(self.latent_factor_indices)\n\n def sample_latent_factors(self, num, random_state):\n \"\"\"Sample a batch of the latent factors.\"\"\"\n factors = np.zeros(shape=(num, len(self.latent_factor_indices)), dtype=np.int64)\n for pos, i in enumerate(self.latent_factor_indices):\n factors[:, pos] = self._sample_factor(i, num, random_state)\n return factors\n\n def sample_all_factors(self, latent_factors, random_state):\n \"\"\"Samples the remaining factors based on the latent factors.\"\"\"\n num_samples = latent_factors.shape[0]\n all_factors = np.zeros(shape=(num_samples, self.num_factors), dtype=np.int64)\n all_factors[:, self.latent_factor_indices] = latent_factors\n # Complete all the other factors\n for i in self.observation_factor_indices:\n all_factors[:, i] = self._sample_factor(i, num_samples, random_state)\n return all_factors\n\n def _sample_factor(self, i, num, random_state):\n return random_state.randint(self.factor_sizes[i], size=num)\n\n\n# ---------------------------------------------------------------------\n\ndef tf_dataset_from_ground_truth_data(ground_truth_data, random_seed):\n \"\"\"Generate a tf.data.DataSet from ground_truth data.\"\"\"\n\n def generator():\n # We need to hard code the random seed so that the data set can be reset.\n random_state = np.random.RandomState(random_seed)\n while True:\n images, labels, labels_mask, _ = ground_truth_data.sample(1, random_state)\n yield images[0], labels[0], labels_mask[0]\n\n data_types = (tf.string, tf.float32, tf.float32) if len(ground_truth_data.observation_shape) == 0 \\\n else (tf.float32, tf.float32, tf.float32) # image filenames or arrays\n return tf.data.Dataset.from_generator(\n generator, data_types,\n output_shapes=(ground_truth_data.observation_shape, [ground_truth_data.num_factors], []))\n\n\ndef make_input_fn(ground_truth_data, image_class, seed, gpu_id, num_batches=None):\n \"\"\"Creates an input function for the experiments.\"\"\"\n\n def load_dataset(batch_size, shuffle=True):\n dataset = tf_dataset_from_ground_truth_data(ground_truth_data, seed)\n # pre-processing\n dataset = dataset.map(image_class.image_processing_tf, num_parallel_calls=64) # equal to number of cpus\n\n if shuffle:\n dataset = dataset.shuffle(ground_truth_data.num_samples)\n\n # We need to drop the remainder as otherwise we lose the batch size in the\n # tensor shape. This has no effect as our data set is infinite.\n dataset = dataset.batch(batch_size, drop_remainder=True)\n dataset = dataset.prefetch(buffer_size=16) # need to tune for optimization\n\n if num_batches is not None:\n dataset = dataset.take(num_batches)\n\n dataset = dataset.apply(tf.data.experimental.prefetch_to_device(device='/gpu:{}'.format(gpu_id)))\n return dataset.make_one_shot_iterator().get_next()\n\n return load_dataset\n\n\ndef tf_labels_from_ground_truth_data(ground_truth_data, random_seed):\n \"\"\"Generate a tf.data.DataSet from ground_truth data.\"\"\"\n\n def generator():\n # We need to hard code the random seed so that the data set can be reset.\n random_state = np.random.RandomState(random_seed)\n while True:\n labels = ground_truth_data.sample_labels(1, random_state)\n yield labels[0]\n\n return tf.data.Dataset.from_generator(\n generator, tf.float32,\n output_shapes=([ground_truth_data.num_factors]))\n\n\ndef make_labels_fn(ground_truth_data, seed, gpu_id, num_batches=None):\n \"\"\"Creates an input function for the experiments.\"\"\"\n\n def sample_labels(batch_size, shuffle=True):\n labels_dst = tf_labels_from_ground_truth_data(ground_truth_data, seed)\n\n if shuffle:\n labels_dst = labels_dst.shuffle(ground_truth_data.num_samples)\n\n # We need to drop the remainder as otherwise we lose the batch size in the\n # tensor shape. This has no effect as our data set is infinite.\n labels_dst = labels_dst.batch(batch_size, drop_remainder=True)\n labels_dst = labels_dst.prefetch(buffer_size=16) # need to tune for optimization\n\n if num_batches is not None:\n labels_dst = labels_dst.take(num_batches)\n\n labels_dst = labels_dst.apply(tf.data.experimental.prefetch_to_device(device='/gpu:{}'.format(gpu_id)))\n return labels_dst.make_one_shot_iterator().get_next()\n\n return sample_labels\n\n\n# ---------------------------------------------------------------------\nclass DSprites(GroundTruthData):\n \"\"\"DSprites dataset.\n\n The data set was originally introduced in \"beta-VAE: Learning Basic Visual\n Concepts with a Constrained Variational Framework\" and can be downloaded from\n https://github.com/deepmind/dsprites-dataset.\n\n The ground-truth factors of variation are (in the default setting):\n 0 - shape (3 different values)\n 1 - scale (6 different values)\n 2 - orientation (40 different values)\n 3 - position x (32 different values)\n 4 - position y (32 different values)\n \"\"\"\n\n def __init__(self, data_path, labels_fine_list=[], labels_coarse_list=[]):\n # By default, all factors (including shape) are considered ground truth factors.\n self.labels_fine_list = labels_fine_list\n self.labels_coarse_list = labels_coarse_list\n self.latent_factor_indices = self.labels_fine_list + self.labels_coarse_list\n\n self.fine_factor_indices = [0, 1]\n self.coarse_factor_indices = [2, 3, 4]\n if not set(self.labels_fine_list).issubset(set(self.fine_factor_indices)):\n print(\"[warning]: labels_fine_list is not a subset of fine ground-truth list\")\n if not set(self.labels_coarse_list).issubset(set(self.coarse_factor_indices)):\n print(\"[warning]: labels_coarse_list is not a subset of coarse ground-truth list\")\n\n self.data_shape = [64, 64, 1]\n # Load the data so that we can sample from it.\n dsprites_file = os.path.join(data_path, 'dsprites', 'dsprites_ndarray_co1sh3sc6or40x32y32_64x64.npz')\n with gfile.Open(dsprites_file, \"rb\") as data_file:\n # Data was saved originally using python2, so we need to set the encoding.\n data = np.load(data_file, encoding=\"latin1\", allow_pickle=True)\n self.images = np.array(data[\"imgs\"])\n self.images = adjust_dynamic_range(self.images, drange_in=[0, 1], drange_out=[0, 255]) # TODO\n self.factor_sizes = np.array(data[\"metadata\"][()][\"latents_sizes\"], dtype=np.int64)\n self.full_factor_sizes = [3, 6, 40, 32, 32]\n self.factor_bases = np.prod(self.full_factor_sizes) / np.cumprod(self.full_factor_sizes)\n self.state_space = SplitDiscreteStateSpace(self.full_factor_sizes, self.latent_factor_indices)\n\n self.labels_mask = np.random.uniform(0., 1., size=len(self.images)) # for semi-supervised learning\n\n @property\n def num_samples(self):\n return self.images.shape[0]\n\n @property\n def num_factors(self):\n return self.state_space.num_latent_factors\n\n @property\n def factors_num_values(self):\n return [self.full_factor_sizes[i] for i in self.latent_factor_indices]\n\n @property\n def observation_shape(self):\n return self.data_shape\n\n def sample_factors(self, num, random_state):\n \"\"\"Sample a batch of factors Y.\"\"\"\n latent_factors = self.state_space.sample_latent_factors(num, random_state)\n return latent_factors\n\n def sample_observations_from_factors(self, latent_factors, random_state):\n \"\"\"Sample a batch of observations X and labels given a batch of factors Y.\"\"\"\n all_factors = self.state_space.sample_all_factors(latent_factors, random_state)\n indices = np.array(np.dot(all_factors, self.factor_bases), dtype=np.int64)\n all_labels = np.divide(all_factors, self.full_factor_sizes) # normalize all_factors => all_labels\n labels = all_labels[:, self.latent_factor_indices]\n images = np.expand_dims(self.images[indices].astype(np.float32), axis=3)\n\n labels_mask = self.labels_mask[indices]\n assert labels_mask.ndim == 1 and labels_mask.shape[0] == images.shape[0]\n return images, labels, labels_mask\n\n def _sample_factor(self, i, num, random_state):\n return random_state.randint(self.full_factor_sizes[i], size=num)\n# ---------------------------------------------------------------------\n\n\n# ---------------------------------------------------------------------\nclass FFHQ(GroundTruthData):\n \"\"\"FFHQ dataset.\n\n The data set was originally introduced in StyleGAN and can be downloaded from\n https://github.com/NVlabs/ffhq-dataset.\n\n There is no ground-truth factors of variation\n \"\"\"\n\n def __init__(self, data_path, label_size_fine=0, label_size_coarse=0):\n self.label_size_fine = label_size_fine\n self.label_size_coarse = label_size_coarse\n self.images = glob.glob(os.path.join(data_path, 'ffhq', '*.png'))\n self.data_shape = [1024, 1024, 3]\n\n @property\n def num_samples(self):\n assert isinstance(self.images, list)\n return len(self.images)\n\n @property\n def num_factors(self):\n return 0\n\n @property\n def observation_shape(self):\n \"\"\"It is because we use image filenames\"\"\"\n return []\n\n def sample_factors(self, num, random_state):\n \"\"\"There is no labels in FFHQ and all_factors are image indices\"\"\"\n latent_factors = random_state.randint(self.num_samples, size=num)\n return latent_factors\n\n def sample_observations_from_factors(self, latent_factors, random_state):\n indices = latent_factors\n labels = np.zeros([indices.shape[0], 0])\n images = np.array([self.images[index] for index in indices], dtype=str)\n labels_mask = np.zeros([indices.shape[0]])\n return images, labels, labels_mask\n# ---------------------------------------------------------------------\n\n\n# ---------------------------------------------------------------------\nclass Isaac3D(GroundTruthData):\n \"\"\"pinkroom dataset.\n\n The data set is created for supervised disentanglement - with the dataset name:\n - images: 'Isaac3D_v1/images/xxxxxx.png'\n - labels: 'Isaac3D_v1/labels.npy'\n\n The ground-truth factors of variation are (in the default setting):\n 0 - object shape (3 different values, discrete)\n 1 - robot horizontal move (8 different values)\n 2 - robot vertical move (5 different values)\n 3 - camera height (4 different values)\n 4 - object scale (4 different values)\n 5 - lighting intensity (4 different values)\n 6 - lighting direction (6 different values)\n 7 - object color (4 different values)\n 8 - wall color (4 different values)\n \"\"\"\n\n def __init__(self, data_path, labels_fine_list=[], labels_coarse_list=[]):\n # By default, all factors (including shape) are considered ground truth factors.\n self.labels_fine_list = labels_fine_list\n self.labels_coarse_list = labels_coarse_list\n self.latent_factor_indices = self.labels_fine_list + self.labels_coarse_list\n\n self.fine_factor_indices = [5, 6, 7, 8]\n self.coarse_factor_indices = [0, 1, 2, 3, 4]\n if not set(self.labels_fine_list).issubset(set(self.fine_factor_indices)):\n print(\"[warning]: labels_fine_list is not a subset of fine ground-truth list\")\n if not set(self.labels_coarse_list).issubset(set(self.coarse_factor_indices)):\n print(\"[warning]: labels_coarse_list is not a subset of coarse ground-truth list\")\n\n self.data_shape = [512, 512, 3]\n # Load the data so that we can sample from it.\n isaac3d_dir = os.path.join(data_path, 'Isaac3D_v1')\n self.images = sorted(glob.glob(os.path.join(isaac3d_dir, 'images', '*.png')))\n\n self.labels = np.load(os.path.join(isaac3d_dir, 'labels.npy'))\n self.factor_sizes = [3, 8, 5, 4, 4, 4, 6, 4, 4]\n self.factor_bases = np.prod(self.factor_sizes) / np.cumprod(self.factor_sizes)\n self.state_space = SplitDiscreteStateSpace(self.factor_sizes, self.latent_factor_indices)\n\n self.labels_mask = np.random.uniform(0., 1., size=len(self.images)) # for semi-supervised learning\n\n @property\n def num_samples(self):\n assert isinstance(self.images, list)\n return len(self.images)\n\n @property\n def num_factors(self):\n return self.state_space.num_latent_factors\n\n @property\n def factors_num_values(self):\n return [self.factor_sizes[i] for i in self.latent_factor_indices]\n\n @property\n def observation_shape(self):\n \"\"\"It is because we use image filenames\"\"\"\n return []\n\n def sample_factors(self, num, random_state):\n \"\"\"Sample a batch of factors Y.\"\"\"\n latent_factors = self.state_space.sample_latent_factors(num, random_state)\n return latent_factors # latent_factors are in the order: fine + coarse\n\n def sample_observations_from_factors(self, latent_factors, random_state):\n \"\"\"Sample a batch of observations X given a batch of factors Y.\"\"\"\n # all_factors are in the original order [0, 1, 2, ...]\n all_factors = self.state_space.sample_all_factors(latent_factors, random_state)\n indices = np.array(np.dot(all_factors, self.factor_bases), dtype=np.int64)\n assert indices.ndim == 1, indices\n all_labels = self.labels[indices].astype(np.float32)\n labels = all_labels[:, self.latent_factor_indices] # labels are in the order: [fine, coarse]\n images = np.array([self.images[index] for index in indices], dtype=str)\n\n labels_mask = self.labels_mask[indices]\n assert labels_mask.ndim == 1 and labels_mask.shape[0] == images.shape[0]\n return images, labels, labels_mask\n\n def _sample_factor(self, i, num, random_state):\n return random_state.randint(self.factor_sizes[i], size=num)\n\n\n# ---------------------------------------------------------------------\nclass Falcor3D(GroundTruthData):\n \"\"\"pinkroom dataset.\n\n The data set is created for supervised disentanglement - with the dataset name:\n - images: 'resized_pink_room_camera7_train/images/xxxxxx.png'\n - labels: 'resized_pink_room_camera7_train/train-rec.labels'\n\n The ground-truth factors of variation are (in the default setting):\n 0 - lighting intensity (5 different values)\n 1 - lighting direction x_l (6 different values)\n 2 - lighting direction y_l (6 different values)\n 3 - lighting direction z_l (6 different values)\n 4 - camera position x_c (6 different values)\n 5 - camera position y_c (6 different values)\n 6 - camera position z_c (6 different values)\n \"\"\"\n\n def __init__(self, data_path, labels_fine_list=[], labels_coarse_list=[]):\n # By default, all factors (including shape) are considered ground truth factors.\n self.labels_fine_list = labels_fine_list\n self.labels_coarse_list = labels_coarse_list\n self.latent_factor_indices = self.labels_fine_list + self.labels_coarse_list\n\n self.fine_factor_indices = [0, 1, 2, 3]\n self.coarse_factor_indices = [4, 5, 6]\n if not set(self.labels_fine_list).issubset(set(self.fine_factor_indices)):\n print(\"[warning]: labels_fine_list is not a subset of fine ground-truth list\")\n if not set(self.labels_coarse_list).issubset(set(self.coarse_factor_indices)):\n print(\"[warning]: labels_coarse_list is not a subset of coarse ground-truth list\")\n\n self.data_shape = [1024, 1024, 3]\n\n # Load the data so that we can sample from it.\n pinkroom_dir = os.path.join(data_path, 'resized_pink_room_camera7_train')\n self.images = sorted(glob.glob(os.path.join(pinkroom_dir, 'images', '*.png')))\n\n self.labels = np.load(os.path.join(pinkroom_dir, 'train-rec.labels'))\n self.factor_sizes = [5, 6, 6, 6, 6, 6, 6]\n self.factor_bases = np.prod(self.factor_sizes) / np.cumprod(self.factor_sizes)\n self.state_space = SplitDiscreteStateSpace(self.factor_sizes, self.latent_factor_indices)\n\n self.labels_mask = np.random.uniform(0., 1., size=len(self.images)) # for semi-supervised learning\n\n @property\n def num_samples(self):\n assert isinstance(self.images, list)\n return len(self.images)\n\n @property\n def num_factors(self):\n return self.state_space.num_latent_factors\n\n @property\n def factors_num_values(self):\n return [self.factor_sizes[i] for i in self.latent_factor_indices]\n\n @property\n def observation_shape(self):\n \"\"\"It is because we use image filenames\"\"\"\n return []\n\n def sample_factors(self, num, random_state):\n \"\"\"Sample a batch of factors Y.\"\"\"\n latent_factors = self.state_space.sample_latent_factors(num, random_state)\n return latent_factors\n\n def sample_observations_from_factors(self, latent_factors, random_state):\n \"\"\"Sample a batch of observations X given a batch of factors Y.\"\"\"\n all_factors = self.state_space.sample_all_factors(latent_factors, random_state)\n indices = np.array(np.dot(all_factors, self.factor_bases), dtype=np.int64)\n assert indices.ndim == 1, indices\n all_labels = self.labels[indices].astype(np.float32)\n labels = all_labels[:, self.latent_factor_indices]\n images = np.array([self.images[index] for index in indices], dtype=str)\n\n labels_mask = self.labels_mask[indices]\n assert labels_mask.ndim == 1 and labels_mask.shape[0] == images.shape[0]\n return images, labels, labels_mask\n\n def _sample_factor(self, i, num, random_state):\n return random_state.randint(self.factor_sizes[i], size=num)\n\n\n# ---------------------------------------------------------------------\ndef get_named_ground_truth_data(data_path, labels_fine_list, labels_coarse_list, name):\n \"\"\"Returns ground truth data set based on name.\"\"\"\n\n if name == \"dsprites\":\n return DSprites(data_path, labels_fine_list, labels_coarse_list)\n elif name == 'ffhq':\n return FFHQ(data_path, labels_fine_list, labels_coarse_list)\n elif name == 'isaac3d':\n return Isaac3D(data_path, labels_fine_list, labels_coarse_list)\n elif name == 'falcor3d':\n return Falcor3D(data_path, labels_fine_list, labels_coarse_list)\n else:\n raise ValueError(\"Invalid data set name.\")\n", "repo_name": "gokulbot/Disentangled_GANs", "sub_path": "data/dataset.py", "file_name": "dataset.py", "file_ext": "py", "file_size_in_byte": 20539, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "six.moves.range", "line_number": 59, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 67, "usage_type": "call"}, {"api_name": "numpy.int64", "line_number": 67, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 75, "usage_type": "call"}, {"api_name": "numpy.int64", "line_number": 75, "usage_type": "attribute"}, {"api_name": "numpy.random.RandomState", "line_number": 93, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 93, "usage_type": "attribute"}, {"api_name": "tensorflow.string", "line_number": 98, "usage_type": "attribute"}, {"api_name": "tensorflow.float32", "line_number": 98, "usage_type": "attribute"}, {"api_name": "tensorflow.float32", "line_number": 99, "usage_type": "attribute"}, {"api_name": "tensorflow.data.Dataset.from_generator", "line_number": 100, "usage_type": "call"}, {"api_name": "tensorflow.data", "line_number": 100, "usage_type": "attribute"}, {"api_name": "tensorflow.data.experimental.prefetch_to_device", "line_number": 124, "usage_type": "call"}, {"api_name": "tensorflow.data", "line_number": 124, "usage_type": "attribute"}, {"api_name": "numpy.random.RandomState", "line_number": 135, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 135, "usage_type": "attribute"}, {"api_name": "tensorflow.data.Dataset.from_generator", "line_number": 140, "usage_type": "call"}, {"api_name": "tensorflow.data", "line_number": 140, "usage_type": "attribute"}, {"api_name": "tensorflow.float32", "line_number": 141, "usage_type": "attribute"}, {"api_name": "tensorflow.data.experimental.prefetch_to_device", "line_number": 162, "usage_type": "call"}, {"api_name": "tensorflow.data", "line_number": 162, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 199, "usage_type": "call"}, {"api_name": "os.path", "line_number": 199, "usage_type": "attribute"}, {"api_name": "tensorflow.gfile.Open", "line_number": 200, "usage_type": "call"}, {"api_name": "tensorflow.gfile", "line_number": 200, "usage_type": "name"}, {"api_name": "numpy.load", "line_number": 202, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 203, "usage_type": "call"}, {"api_name": "utils.adjust_dynamic_range", "line_number": 204, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 205, "usage_type": "call"}, {"api_name": "numpy.int64", "line_number": 205, "usage_type": "attribute"}, {"api_name": "numpy.prod", "line_number": 207, "usage_type": "call"}, {"api_name": "numpy.cumprod", "line_number": 207, "usage_type": "call"}, {"api_name": "numpy.random.uniform", "line_number": 210, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 210, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 236, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 236, "usage_type": "call"}, {"api_name": "numpy.int64", "line_number": 236, "usage_type": "attribute"}, {"api_name": "numpy.divide", "line_number": 237, "usage_type": "call"}, {"api_name": "numpy.expand_dims", "line_number": 239, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 239, "usage_type": "attribute"}, {"api_name": "glob.glob", "line_number": 263, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 263, "usage_type": "call"}, {"api_name": "os.path", "line_number": 263, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 287, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 288, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 289, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 329, "usage_type": "call"}, {"api_name": "os.path", "line_number": 329, "usage_type": "attribute"}, {"api_name": "glob.glob", "line_number": 330, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 330, "usage_type": "call"}, {"api_name": "os.path", "line_number": 330, "usage_type": "attribute"}, {"api_name": "numpy.load", "line_number": 332, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 332, "usage_type": "call"}, {"api_name": "os.path", "line_number": 332, "usage_type": "attribute"}, {"api_name": "numpy.prod", "line_number": 334, "usage_type": "call"}, {"api_name": "numpy.cumprod", "line_number": 334, "usage_type": "call"}, {"api_name": "numpy.random.uniform", "line_number": 337, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 337, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 366, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 366, "usage_type": "call"}, {"api_name": "numpy.int64", "line_number": 366, "usage_type": "attribute"}, {"api_name": "numpy.float32", "line_number": 368, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 370, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 414, "usage_type": "call"}, {"api_name": "os.path", "line_number": 414, "usage_type": "attribute"}, {"api_name": "glob.glob", "line_number": 415, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 415, "usage_type": "call"}, {"api_name": "os.path", "line_number": 415, "usage_type": "attribute"}, {"api_name": "numpy.load", "line_number": 417, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 417, "usage_type": "call"}, {"api_name": "os.path", "line_number": 417, "usage_type": "attribute"}, {"api_name": "numpy.prod", "line_number": 419, "usage_type": "call"}, {"api_name": "numpy.cumprod", "line_number": 419, "usage_type": "call"}, {"api_name": "numpy.random.uniform", "line_number": 422, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 422, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 450, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 450, "usage_type": "call"}, {"api_name": "numpy.int64", "line_number": 450, "usage_type": "attribute"}, {"api_name": "numpy.float32", "line_number": 452, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 454, "usage_type": "call"}]} +{"seq_id": "15493359790", "text": "import copy\nimport json\nimport pkgutil\nfrom types import GeneratorType\nfrom typing import Any, Set\n\nimport jsonschema\nimport yaml\nfrom babelfish import Language\n\nfrom .schema import RootSchema\n\n\ndef is_iterable(obj: Any):\n return hasattr(obj, '__iter__') and not isinstance(obj, str) or isinstance(obj, GeneratorType)\n\n\ndef ensure_list(param: Any):\n if not param:\n param = []\n elif not is_iterable(param):\n param = [param]\n return param\n\n\ndef get_language_groups(languages: Set[Language]):\n groups = set()\n for language in languages:\n ietf = str(language)\n ietf_parts = ietf.split('-')\n\n groups.add(ietf)\n groups.add(ietf_parts[0])\n groups.add('-'.join(ietf_parts[:2]))\n\n return groups\n\n\ndef validate(data: dict):\n jsonschema.validate(data, RootSchema.schema)\n\n\ndef load_config_file(path: str):\n with open(path, 'r') as f:\n data = json.load(f) if path.endswith('.json') else yaml.safe_load(f.read())\n validate(data)\n return data\n\n\ndef load_config_resource(resource_name: str):\n resource_data = pkgutil.get_data('cleanit', resource_name)\n data = json.loads(resource_data) if resource_name.endswith('.json') else yaml.safe_load(resource_data)\n validate(data)\n return data\n\n\ndef merge_options(*options):\n \"\"\"\n Merge options into a single options dict.\n :param options:\n :type options:\n :return:\n :rtype:\n \"\"\"\n\n merged = {}\n if options:\n if options[0]:\n merged.update(copy.deepcopy(options[0]))\n\n for options in options[1:]:\n if options:\n pristine = options.get('pristine')\n\n if pristine is True:\n merged = {}\n elif pristine:\n for to_reset in pristine:\n if to_reset in merged:\n del merged[to_reset]\n\n for (option, value) in options.items():\n merge_option_value(option, value, merged)\n\n return merged\n\n\ndef merge_option_value(option, value, merged):\n \"\"\"\n Merge option value\n :param option:\n :param value:\n :param merged:\n :return:\n \"\"\"\n if value is not None and option != 'pristine':\n if option in merged.keys() and isinstance(merged[option], list):\n for val in value:\n if val not in merged[option] and val is not None:\n merged[option].append(val)\n elif option in merged.keys() and isinstance(merged[option], dict):\n merged[option] = merge_options(merged[option], value)\n elif isinstance(value, list):\n merged[option] = list(value)\n else:\n merged[option] = value\n", "repo_name": "ratoaq2/cleanit", "sub_path": "cleanit/utils.py", "file_name": "utils.py", "file_ext": "py", "file_size_in_byte": 2723, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 19, "dataset": "github-code", "pt": "37", "api": [{"api_name": "typing.Any", "line_number": 14, "usage_type": "name"}, {"api_name": "types.GeneratorType", "line_number": 15, "usage_type": "argument"}, {"api_name": "typing.Any", "line_number": 18, "usage_type": "name"}, {"api_name": "typing.Set", "line_number": 26, "usage_type": "name"}, {"api_name": "babelfish.Language", "line_number": 26, "usage_type": "name"}, {"api_name": "jsonschema.validate", "line_number": 40, "usage_type": "call"}, {"api_name": "schema.RootSchema.schema", "line_number": 40, "usage_type": "attribute"}, {"api_name": "schema.RootSchema", "line_number": 40, "usage_type": "name"}, {"api_name": "json.load", "line_number": 45, "usage_type": "call"}, {"api_name": "yaml.safe_load", "line_number": 45, "usage_type": "call"}, {"api_name": "pkgutil.get_data", "line_number": 51, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 52, "usage_type": "call"}, {"api_name": "yaml.safe_load", "line_number": 52, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 69, "usage_type": "call"}]} +{"seq_id": "17686221046", "text": "\"\"\"\r\n--- Part Two ---\r\n\r\nAs you finish the last group's customs declaration, you notice that you misread one word in the instructions:\r\n\r\nYou don't need to identify the questions to which anyone answered \"yes\";\r\nyou need to identify the questions to which everyone answered \"yes\"!\r\n\r\nUsing the same example as above:\r\n\r\nabc\r\n\r\na\r\nb\r\nc\r\n\r\nab\r\nac\r\n\r\na\r\na\r\na\r\na\r\n\r\nb\r\n\r\nThis list represents answers from five groups:\r\n\r\n In the first group, everyone (all 1 person) answered \"yes\" to 3 questions: a, b, and c.\r\n In the second group, there is no question to which everyone answered \"yes\".\r\n In the third group, everyone answered yes to only 1 question, a. Since some people did not answer \"yes\" to b or c,\r\n they don't count.\r\n In the fourth group, everyone answered yes to only 1 question, a.\r\n In the fifth group, everyone (all 1 person) answered \"yes\" to 1 question, b.\r\n\r\nIn this example, the sum of these counts is 3 + 0 + 1 + 1 + 1 = 6.\r\n\r\nFor each group, count the number of questions to which everyone answered \"yes\". What is the sum of those counts?\r\n\r\n\"\"\"\r\nfrom typing import List, Tuple, Sequence\r\n\r\n\r\ndef restart_search(chars: List[str], group_size: int, yes: int) -> Tuple[List[str], int]:\r\n for char in chars:\r\n if chars.count(char) == group_size:\r\n yes += 1\r\n while char in chars:\r\n chars.pop(chars.index(char))\r\n else:\r\n while char in chars:\r\n chars.pop(chars.index(char))\r\n\r\n return chars, yes\r\n\r\n\r\ndef number_of_yes_in_group(group: Sequence[str]) -> int:\r\n \"\"\"\r\n Finds and returns the number of yeses in a group.\r\n :param group:\r\n :return:\r\n \"\"\"\r\n group_size = len(group)\r\n if group_size == 1:\r\n return len(group[0])\r\n\r\n yeses = 0\r\n\r\n chars = []\r\n for string in group:\r\n for char in string:\r\n chars.append(char)\r\n\r\n while len(chars) > 0:\r\n c, y = restart_search(chars, group_size, yeses)\r\n chars = c\r\n yeses = y\r\n\r\n return yeses\r\n\r\n\r\ndef make_input_convenient(raw_input: Sequence[str]) -> List[str]:\r\n \"\"\"\r\n Makes an input convenient for further processing.\r\n This is done by flattening out the list.\r\n The function returns a list of strings convenient for processing.\r\n\r\n\r\n :param raw_input: Sequence[str] - A list of strings to be made convenient.\r\n :return: - List[str] - Convenient output\r\n \"\"\"\r\n convenient = []\r\n\r\n for lst in raw_input:\r\n convenient.append(lst)\r\n\r\n return convenient\r\n\r\n\r\ndef main():\r\n with open(\"./input.txt\") as f:\r\n puzzle_input = [i.strip() for i in f.readlines()]\r\n groups: List[list] = []\r\n\r\n while \"\" in puzzle_input:\r\n index = puzzle_input.index(\"\")\r\n groups.append(make_input_convenient(puzzle_input[:index]))\r\n puzzle_input = puzzle_input[index + 1:]\r\n else:\r\n groups.append(make_input_convenient(puzzle_input))\r\n\r\n total_number_of_yeses = 0\r\n\r\n for group in groups:\r\n total_number_of_yeses += number_of_yes_in_group(group)\r\n\r\n print(total_number_of_yeses) # Answer = 3158\r\n\r\n\r\nif __name__ == '__main__':\r\n main()\r\n", "repo_name": "king-phyte/aoc2020", "sub_path": "Day 6/part_2.py", "file_name": "part_2.py", "file_ext": "py", "file_size_in_byte": 3148, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "37", "api": [{"api_name": "typing.List", "line_number": 44, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 44, "usage_type": "name"}, {"api_name": "typing.Sequence", "line_number": 57, "usage_type": "name"}, {"api_name": "typing.Sequence", "line_number": 82, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 82, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 103, "usage_type": "name"}]} +{"seq_id": "9707202070", "text": "import functools\nimport os.path\n\nfrom multiprocessing import Pool\n\nimport logging\n\nimport matplotlib.pyplot as plt\nimport matplotlib.transforms\nimport numpy as np\n\nfrom matplotlib import animation\n\nlogging.basicConfig(level=logging.INFO)\nlogger = logging.getLogger(__name__)\n\n\ndef _gen_image(num, image_dir, vals, plot_func, masses, times, labels, kwargs):\n new_gens = list(map(lambda k: (vals[k][j] for j in range(num)),\n range(len(vals))))\n\n new_series = [(new_gens[k], times[k][:num], masses[k], labels[k]) for k in range(len(vals))]\n fig, ax = plot_func(*new_series, show=False, **kwargs)\n\n path = os.path.join(image_dir, f\"{num:0>5}.png\")\n if num % 10 == 0:\n logger.info(f\"writing image {num}\")\n plt.xlim(ax.get_xlim())\n plt.ylim(ax.get_ylim())\n fig.savefig(path, format=\"png\", dpi=800, bbox_inches=\"tight\")\n plt.close()\n\n\ndef animate(plot_func: callable, *series, out: str = None, image_dir: str = None, **kwargs):\n gens, times, masses, labels = zip(*series)\n\n vals = [list(gen) for gen in gens]\n\n tmp_dir = os.path.join(os.path.dirname(__file__), \"tmp\")\n\n if image_dir is None:\n image_dir = tmp_dir\n\n func = functools.partial(_gen_image,\n image_dir=image_dir,\n vals=vals,\n plot_func=plot_func,\n masses=masses,\n times=times,\n labels=labels,\n kwargs=kwargs)\n\n with Pool(4) as pool:\n pool.map(func, range(1, len(vals[0])))\n\n logger.info(f\"writing video to {out}\")\n os.system(f\"ffmpeg -y -v 3 -framerate 30 -pattern_type glob -i '{image_dir}/*.png' -s 1200x1200 {out}\")\n\n\n\"\"\"\n for file in os.listdir(tmp_dir):\n os.remove(os.path.join(tmp_dir, file))\n\"\"\"", "repo_name": "thatGuySpectre/SciCom22", "sub_path": "scicom/render/renderlib.py", "file_name": "renderlib.py", "file_ext": "py", "file_size_in_byte": 1857, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "37", "api": [{"api_name": "logging.basicConfig", "line_number": 14, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 14, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 15, "usage_type": "call"}, {"api_name": "os.path.path.join", "line_number": 25, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 25, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 25, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 28, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 28, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 29, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 29, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.close", "line_number": 31, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 31, "usage_type": "name"}, {"api_name": "os.path.path.join", "line_number": 39, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 39, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 39, "usage_type": "name"}, {"api_name": "os.path.path.dirname", "line_number": 39, "usage_type": "call"}, {"api_name": "functools.partial", "line_number": 44, "usage_type": "call"}, {"api_name": "multiprocessing.Pool", "line_number": 53, "usage_type": "call"}, {"api_name": "os.path.system", "line_number": 57, "usage_type": "call"}, {"api_name": "os.path", "line_number": 57, "usage_type": "name"}]} +{"seq_id": "72329906348", "text": "import os\r\nimport linecache\r\n\r\ntemps = open(\"temp_users.txt\",\"w\")\r\nfile = \"users.txt\"\r\nln = 180\r\n\r\nwhile ln < 221:\r\n line = linecache.getline(file, ln).strip()\r\n temps.write(line + '\\n')\r\n os.system(\"usermod -a -G temp \" + line)\r\n ln +=1\r\n\r\ntemps.close()\r\n\r\n", "repo_name": "SCary120/Linux-Admin-Portfolio", "sub_path": "create_temps.py", "file_name": "create_temps.py", "file_ext": "py", "file_size_in_byte": 270, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "linecache.getline", "line_number": 9, "usage_type": "call"}, {"api_name": "os.system", "line_number": 11, "usage_type": "call"}]} +{"seq_id": "15801079718", "text": "from __future__ import annotations\nfrom dataclasses import dataclass, field\nfrom kiota_abstractions.base_request_builder import BaseRequestBuilder\nfrom kiota_abstractions.get_path_parameters import get_path_parameters\nfrom kiota_abstractions.method import Method\nfrom kiota_abstractions.request_adapter import RequestAdapter\nfrom kiota_abstractions.request_information import RequestInformation\nfrom kiota_abstractions.request_option import RequestOption\nfrom kiota_abstractions.serialization import Parsable, ParsableFactory\nfrom typing import Any, Callable, Dict, List, Optional, TYPE_CHECKING, Union\n\nif TYPE_CHECKING:\n from .....models.o_data_errors.o_data_error import ODataError\n from .....models.organizational_branding_localization import OrganizationalBrandingLocalization\n from .....models.organizational_branding_localization_collection_response import OrganizationalBrandingLocalizationCollectionResponse\n from .count.count_request_builder import CountRequestBuilder\n from .item.organizational_branding_localization_item_request_builder import OrganizationalBrandingLocalizationItemRequestBuilder\n\nclass LocalizationsRequestBuilder(BaseRequestBuilder):\n \"\"\"\n Provides operations to manage the localizations property of the microsoft.graph.organizationalBranding entity.\n \"\"\"\n def __init__(self,request_adapter: RequestAdapter, path_parameters: Optional[Union[Dict[str, Any], str]] = None) -> None:\n \"\"\"\n Instantiates a new LocalizationsRequestBuilder and sets the default values.\n param path_parameters: The raw url or the Url template parameters for the request.\n param request_adapter: The request adapter to use to execute the requests.\n Returns: None\n \"\"\"\n super().__init__(request_adapter, \"{+baseurl}/organization/{organization%2Did}/branding/localizations{?%24top,%24skip,%24search,%24filter,%24count,%24orderby,%24select,%24expand}\", path_parameters)\n \n def by_organizational_branding_localization_id(self,organizational_branding_localization_id: str) -> OrganizationalBrandingLocalizationItemRequestBuilder:\n \"\"\"\n Provides operations to manage the localizations property of the microsoft.graph.organizationalBranding entity.\n param organizational_branding_localization_id: The unique identifier of organizationalBrandingLocalization\n Returns: OrganizationalBrandingLocalizationItemRequestBuilder\n \"\"\"\n if not organizational_branding_localization_id:\n raise TypeError(\"organizational_branding_localization_id cannot be null.\")\n from .item.organizational_branding_localization_item_request_builder import OrganizationalBrandingLocalizationItemRequestBuilder\n\n url_tpl_params = get_path_parameters(self.path_parameters)\n url_tpl_params[\"organizationalBrandingLocalization%2Did\"] = organizational_branding_localization_id\n return OrganizationalBrandingLocalizationItemRequestBuilder(self.request_adapter, url_tpl_params)\n \n async def get(self,request_configuration: Optional[LocalizationsRequestBuilderGetRequestConfiguration] = None) -> Optional[OrganizationalBrandingLocalizationCollectionResponse]:\n \"\"\"\n Retrieve all localization branding objects, including the default branding.\n param request_configuration: Configuration for the request such as headers, query parameters, and middleware options.\n Returns: Optional[OrganizationalBrandingLocalizationCollectionResponse]\n Find more info here: https://learn.microsoft.com/graph/api/organizationalbranding-list-localizations?view=graph-rest-1.0\n \"\"\"\n request_info = self.to_get_request_information(\n request_configuration\n )\n from .....models.o_data_errors.o_data_error import ODataError\n\n error_mapping: Dict[str, ParsableFactory] = {\n \"4XX\": ODataError,\n \"5XX\": ODataError,\n }\n if not self.request_adapter:\n raise Exception(\"Http core is null\") \n from .....models.organizational_branding_localization_collection_response import OrganizationalBrandingLocalizationCollectionResponse\n\n return await self.request_adapter.send_async(request_info, OrganizationalBrandingLocalizationCollectionResponse, error_mapping)\n \n async def post(self,body: Optional[OrganizationalBrandingLocalization] = None, request_configuration: Optional[LocalizationsRequestBuilderPostRequestConfiguration] = None) -> Optional[OrganizationalBrandingLocalization]:\n \"\"\"\n Create a new organizationalBrandingLocalization object. This creates a localized branding and at the same time, the default branding if it doesn't exist. The default branding is created only once. It's loaded when a localized branding isn't configured for the user's browser language. To retrieve the default branding, see Get branding.\n param body: The request body\n param request_configuration: Configuration for the request such as headers, query parameters, and middleware options.\n Returns: Optional[OrganizationalBrandingLocalization]\n Find more info here: https://learn.microsoft.com/graph/api/organizationalbranding-post-localizations?view=graph-rest-1.0\n \"\"\"\n if not body:\n raise TypeError(\"body cannot be null.\")\n request_info = self.to_post_request_information(\n body, request_configuration\n )\n from .....models.o_data_errors.o_data_error import ODataError\n\n error_mapping: Dict[str, ParsableFactory] = {\n \"4XX\": ODataError,\n \"5XX\": ODataError,\n }\n if not self.request_adapter:\n raise Exception(\"Http core is null\") \n from .....models.organizational_branding_localization import OrganizationalBrandingLocalization\n\n return await self.request_adapter.send_async(request_info, OrganizationalBrandingLocalization, error_mapping)\n \n def to_get_request_information(self,request_configuration: Optional[LocalizationsRequestBuilderGetRequestConfiguration] = None) -> RequestInformation:\n \"\"\"\n Retrieve all localization branding objects, including the default branding.\n param request_configuration: Configuration for the request such as headers, query parameters, and middleware options.\n Returns: RequestInformation\n \"\"\"\n request_info = RequestInformation()\n request_info.url_template = self.url_template\n request_info.path_parameters = self.path_parameters\n request_info.http_method = Method.GET\n request_info.headers[\"Accept\"] = [\"application/json\"]\n if request_configuration:\n request_info.add_request_headers(request_configuration.headers)\n request_info.set_query_string_parameters_from_raw_object(request_configuration.query_parameters)\n request_info.add_request_options(request_configuration.options)\n return request_info\n \n def to_post_request_information(self,body: Optional[OrganizationalBrandingLocalization] = None, request_configuration: Optional[LocalizationsRequestBuilderPostRequestConfiguration] = None) -> RequestInformation:\n \"\"\"\n Create a new organizationalBrandingLocalization object. This creates a localized branding and at the same time, the default branding if it doesn't exist. The default branding is created only once. It's loaded when a localized branding isn't configured for the user's browser language. To retrieve the default branding, see Get branding.\n param body: The request body\n param request_configuration: Configuration for the request such as headers, query parameters, and middleware options.\n Returns: RequestInformation\n \"\"\"\n if not body:\n raise TypeError(\"body cannot be null.\")\n request_info = RequestInformation()\n request_info.url_template = self.url_template\n request_info.path_parameters = self.path_parameters\n request_info.http_method = Method.POST\n request_info.headers[\"Accept\"] = [\"application/json\"]\n if request_configuration:\n request_info.add_request_headers(request_configuration.headers)\n request_info.add_request_options(request_configuration.options)\n request_info.set_content_from_parsable(self.request_adapter, \"application/json\", body)\n return request_info\n \n def with_url(self,raw_url: Optional[str] = None) -> LocalizationsRequestBuilder:\n \"\"\"\n Returns a request builder with the provided arbitrary URL. Using this method means any other path or query parameters are ignored.\n param raw_url: The raw URL to use for the request builder.\n Returns: LocalizationsRequestBuilder\n \"\"\"\n if not raw_url:\n raise TypeError(\"raw_url cannot be null.\")\n return LocalizationsRequestBuilder(self.request_adapter, raw_url)\n \n @property\n def count(self) -> CountRequestBuilder:\n \"\"\"\n Provides operations to count the resources in the collection.\n \"\"\"\n from .count.count_request_builder import CountRequestBuilder\n\n return CountRequestBuilder(self.request_adapter, self.path_parameters)\n \n @dataclass\n class LocalizationsRequestBuilderGetQueryParameters():\n \"\"\"\n Retrieve all localization branding objects, including the default branding.\n \"\"\"\n def get_query_parameter(self,original_name: Optional[str] = None) -> str:\n \"\"\"\n Maps the query parameters names to their encoded names for the URI template parsing.\n param original_name: The original query parameter name in the class.\n Returns: str\n \"\"\"\n if not original_name:\n raise TypeError(\"original_name cannot be null.\")\n if original_name == \"count\":\n return \"%24count\"\n if original_name == \"expand\":\n return \"%24expand\"\n if original_name == \"filter\":\n return \"%24filter\"\n if original_name == \"orderby\":\n return \"%24orderby\"\n if original_name == \"search\":\n return \"%24search\"\n if original_name == \"select\":\n return \"%24select\"\n if original_name == \"skip\":\n return \"%24skip\"\n if original_name == \"top\":\n return \"%24top\"\n return original_name\n \n # Include count of items\n count: Optional[bool] = None\n\n # Expand related entities\n expand: Optional[List[str]] = None\n\n # Filter items by property values\n filter: Optional[str] = None\n\n # Order items by property values\n orderby: Optional[List[str]] = None\n\n # Search items by search phrases\n search: Optional[str] = None\n\n # Select properties to be returned\n select: Optional[List[str]] = None\n\n # Skip the first n items\n skip: Optional[int] = None\n\n # Show only the first n items\n top: Optional[int] = None\n\n \n from kiota_abstractions.base_request_configuration import BaseRequestConfiguration\n\n @dataclass\n class LocalizationsRequestBuilderGetRequestConfiguration(BaseRequestConfiguration):\n from kiota_abstractions.base_request_configuration import BaseRequestConfiguration\n\n \"\"\"\n Configuration for the request such as headers, query parameters, and middleware options.\n \"\"\"\n # Request query parameters\n query_parameters: Optional[LocalizationsRequestBuilder.LocalizationsRequestBuilderGetQueryParameters] = None\n\n \n from kiota_abstractions.base_request_configuration import BaseRequestConfiguration\n\n @dataclass\n class LocalizationsRequestBuilderPostRequestConfiguration(BaseRequestConfiguration):\n from kiota_abstractions.base_request_configuration import BaseRequestConfiguration\n\n \"\"\"\n Configuration for the request such as headers, query parameters, and middleware options.\n \"\"\"\n \n\n", "repo_name": "microsoftgraph/msgraph-sdk-python", "sub_path": "msgraph/generated/organization/item/branding/localizations/localizations_request_builder.py", "file_name": "localizations_request_builder.py", "file_ext": "py", "file_size_in_byte": 12067, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 186, "dataset": "github-code", "pt": "37", "api": [{"api_name": "typing.TYPE_CHECKING", "line_number": 12, "usage_type": "name"}, {"api_name": "kiota_abstractions.base_request_builder.BaseRequestBuilder", "line_number": 19, "usage_type": "name"}, {"api_name": "kiota_abstractions.request_adapter.RequestAdapter", "line_number": 23, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 23, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 23, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 23, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 23, "usage_type": "name"}, {"api_name": "kiota_abstractions.get_path_parameters.get_path_parameters", "line_number": 42, "usage_type": "call"}, {"api_name": "item.organizational_branding_localization_item_request_builder.OrganizationalBrandingLocalizationItemRequestBuilder", "line_number": 44, "usage_type": "call"}, {"api_name": "item.organizational_branding_localization_item_request_builder.OrganizationalBrandingLocalizationItemRequestBuilder", "line_number": 32, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 46, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 58, "usage_type": "name"}, {"api_name": "kiota_abstractions.serialization.ParsableFactory", "line_number": 58, "usage_type": "name"}, {"api_name": "models.o_data_errors.o_data_error.ODataError", "line_number": 59, "usage_type": "name"}, {"api_name": "models.o_data_errors.o_data_error.ODataError", "line_number": 60, "usage_type": "name"}, {"api_name": "models.organizational_branding_localization_collection_response.OrganizationalBrandingLocalizationCollectionResponse", "line_number": 66, "usage_type": "argument"}, {"api_name": "models.organizational_branding_localization_collection_response.OrganizationalBrandingLocalizationCollectionResponse", "line_number": 46, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 68, "usage_type": "name"}, {"api_name": "models.organizational_branding_localization.OrganizationalBrandingLocalization", "line_number": 68, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 83, "usage_type": "name"}, {"api_name": "kiota_abstractions.serialization.ParsableFactory", "line_number": 83, "usage_type": "name"}, {"api_name": "models.o_data_errors.o_data_error.ODataError", "line_number": 84, "usage_type": "name"}, {"api_name": "models.o_data_errors.o_data_error.ODataError", "line_number": 85, "usage_type": "name"}, {"api_name": "models.organizational_branding_localization.OrganizationalBrandingLocalization", "line_number": 91, "usage_type": "argument"}, {"api_name": "typing.Optional", "line_number": 93, "usage_type": "name"}, {"api_name": "kiota_abstractions.request_information.RequestInformation", "line_number": 99, "usage_type": "call"}, {"api_name": "kiota_abstractions.method.Method.GET", "line_number": 102, "usage_type": "attribute"}, {"api_name": "kiota_abstractions.method.Method", "line_number": 102, "usage_type": "name"}, {"api_name": "kiota_abstractions.request_information.RequestInformation", "line_number": 93, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 110, "usage_type": "name"}, {"api_name": "models.organizational_branding_localization.OrganizationalBrandingLocalization", "line_number": 110, "usage_type": "name"}, {"api_name": "kiota_abstractions.request_information.RequestInformation", "line_number": 119, "usage_type": "call"}, {"api_name": "kiota_abstractions.method.Method.POST", "line_number": 122, "usage_type": "attribute"}, {"api_name": "kiota_abstractions.method.Method", "line_number": 122, "usage_type": "name"}, {"api_name": "kiota_abstractions.request_information.RequestInformation", "line_number": 110, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 130, "usage_type": "name"}, {"api_name": "{'OrganizationalBrandingLocalizationItemRequestBuilder': 'item.organizational_branding_localization_item_request_builder.OrganizationalBrandingLocalizationItemRequestBuilder', 'ODataError': 'models.o_data_errors.o_data_error.ODataError', 'OrganizationalBrandingLocalizationCollectionResponse': 'models.organizational_branding_localization_collection_response.OrganizationalBrandingLocalizationCollectionResponse', 'OrganizationalBrandingLocalization': 'models.organizational_branding_localization.OrganizationalBrandingLocalization'}", "line_number": 138, "usage_type": "call"}, {"api_name": "count.count_request_builder.CountRequestBuilder", "line_number": 147, "usage_type": "call"}, {"api_name": "count.count_request_builder.CountRequestBuilder", "line_number": 141, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 154, "usage_type": "name"}, {"api_name": "count.count_request_builder", "line_number": 181, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 181, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 184, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 184, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 187, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 190, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 190, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 193, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 196, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 196, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 199, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 202, "usage_type": "name"}, {"api_name": "dataclasses.dataclass", "line_number": 149, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 215, "usage_type": "name"}, {"api_name": "{'OrganizationalBrandingLocalizationItemRequestBuilder': 'item.organizational_branding_localization_item_request_builder.OrganizationalBrandingLocalizationItemRequestBuilder', 'ODataError': 'models.o_data_errors.o_data_error.ODataError', 'OrganizationalBrandingLocalizationCollectionResponse': 'models.organizational_branding_localization_collection_response.OrganizationalBrandingLocalizationCollectionResponse', 'OrganizationalBrandingLocalization': 'models.organizational_branding_localization.OrganizationalBrandingLocalization', 'CountRequestBuilder': 'count.count_request_builder.CountRequestBuilder', 'BaseRequestConfiguration': 'kiota_abstractions.base_request_configuration.BaseRequestConfiguration'}.LocalizationsRequestBuilderGetQueryParameters", "line_number": 215, "usage_type": "attribute"}, {"api_name": "dataclasses.dataclass", "line_number": 207, "usage_type": "name"}, {"api_name": "dataclasses.dataclass", "line_number": 220, "usage_type": "name"}]} +{"seq_id": "11082752561", "text": "import argparse\nimport os\nfrom pathlib import Path\n\nfrom PIL import Image\n\nimport torch\nimport torch.nn as nn\nfrom torchvision import datasets, models, transforms\n\nparser = argparse.ArgumentParser(description='Great Description To Be Here')\nparser.add_argument('--input', action=\"store\")\nargs = parser.parse_args()\n\n\nmodel_ft = torch.load('models/model_squeeze_1_1')\ndevice = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n\nmodel_ft = model_ft.to(device)\nmodel_ft.eval()\n\ndata_transforms = transforms.Compose([\n transforms.Resize(256),\n transforms.CenterCrop(224),\n transforms.ToTensor(),\n transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n ])\n\n\nsource_dir = Path(args.input)\nwith torch.no_grad():\n for file in os.listdir(source_dir):\n test_image = Image.open(source_dir / file) #Image.open(Path(data_dir) / file)\n test_image_tensor = data_transforms(test_image)\n\n inputs = test_image_tensor.to(device)\n outputs = model_ft(inputs.reshape((1,inputs.shape[0],inputs.shape[1],inputs.shape[2])))\n _, preds = torch.max(outputs, 1)\n if preds == 0:\n print(file)", "repo_name": "SultanIsaly/glasses_recognition", "sub_path": "run.py", "file_name": "run.py", "file_ext": "py", "file_size_in_byte": 1177, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "37", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 11, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 16, "usage_type": "call"}, {"api_name": "torch.device", "line_number": 17, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 17, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 17, "usage_type": "attribute"}, {"api_name": "torchvision.transforms.Compose", "line_number": 22, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 22, "usage_type": "name"}, {"api_name": "torchvision.transforms.Resize", "line_number": 23, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 23, "usage_type": "name"}, {"api_name": "torchvision.transforms.CenterCrop", "line_number": 24, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 24, "usage_type": "name"}, {"api_name": "torchvision.transforms.ToTensor", "line_number": 25, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 25, "usage_type": "name"}, {"api_name": "torchvision.transforms.Normalize", "line_number": 26, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 26, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 30, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 31, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 32, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 33, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 33, "usage_type": "name"}, {"api_name": "torch.max", "line_number": 38, "usage_type": "call"}]} +{"seq_id": "72001906028", "text": "from colorama import Fore\nimport os, time, base64, sys\n \ndef printlento(s):\n for c in s + '\\n':\n sys.stdout.write(c)\n sys.stdout.flush()\n time.sleep(1 / 10)\n\nos.system('clear')\n\n\nprint(\"_____________________\")\nprint(\"[1] Encode base64 |\")\nprint(\"[2] Descode base64 |\")\nprint(\"_____________________|\")\nEncode = input(\"Elije pibe >> \")\n\nif Encode == \"1\":\n encript = input(b'Escribe lo que vas a encriptar >> ')\n pito = base64.b64encode(bytes(encript, 'utf-8'))\n print(pito)\n", "repo_name": "JacketDeveloper/ThelVadam", "sub_path": "Encript.py", "file_name": "Encript.py", "file_ext": "py", "file_size_in_byte": 510, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "sys.stdout.write", "line_number": 6, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 6, "usage_type": "attribute"}, {"api_name": "sys.stdout.flush", "line_number": 7, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 7, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 8, "usage_type": "call"}, {"api_name": "os.system", "line_number": 10, "usage_type": "call"}, {"api_name": "base64.b64encode", "line_number": 21, "usage_type": "call"}]} +{"seq_id": "15000097246", "text": "import os\r\nimport json\r\nimport math\r\nfrom tkinter import *\r\nfrom tkinter import ttk\r\nfrom tkinter import messagebox\r\nfrom tkinter import filedialog\r\nimport configparser\r\nimport numpy as np\r\nimport xlsxwriter\r\nimport webbrowser\r\nfrom translate import Translate\r\nfrom PIL import Image\r\n\r\n\r\nclass Stammbaum:\r\n generations = {}\r\n\r\ndef CheckFloat(x):\r\n if x - int(x) == 0:\r\n return True\r\n else:\r\n return False\r\ndef get_all_elements_in_list_of_lists(list):\r\n count = 0\r\n for element in list:\r\n count += len(element)\r\n return count\r\ndef checkListForSymbol(list, symbol):\r\n for i in range(len(list)):\r\n if symbol in list[i]:\r\n return i # list.index(list[i])\r\ndef findLastSymbolInMatrix(matrix, symbol, positionOfTarget):\r\n for i in range(len(matrix)):\r\n if symbol == matrix[i][positionOfTarget]:\r\n return i\r\ndef replaceEmptyWithSpaceInList(matrix):\r\n max = 0\r\n for y in range(len(matrix)):\r\n for x in range(len(matrix[y])):\r\n if max < len(matrix[y][x].replace(\"[Leftcorner.png]\", \"\").replace('[Rightcorner.png]', \"\").replace('[Intersection.png]', \"\").replace('[Horizontal.png]', \"\").replace('[person.jpg]', \"\").replace('[topCellBorder]', \"\").replace('[grey]', \"\").replace('[center]', \"\").replace('[betweenCellBorder]', \"\").replace('[bold]', \"\").replace('[bottomCellBorder]', \"\")):\r\n max = len(matrix[y][x].replace(\"[Leftcorner.png]\", \"\").replace('[Rightcorner.png]', \"\").replace('[Intersection.png]', \"\").replace('[Horizontal.png]', \"\").replace('[person.jpg]', \"\").replace('[topCellBorder]', \"\").replace('[grey]', \"\").replace('[center]', \"\").replace('[betweenCellBorder]', \"\").replace('[bold]', \"\").replace('[bottomCellBorder]', \"\"))\r\n\r\n for y in range(len(matrix)):\r\n for x in range(len(matrix[y])):\r\n matrix[y][x] = str(matrix[y][x]) + ' ' * (max -len(matrix[y][x]))\r\n\r\n matrix = np.ndarray.tolist(np.asarray(matrix))\r\n return matrix\r\ndef replaceEmpty(list):\r\n for n in range(len(list)):\r\n for i in range(len(list[n])):\r\n if list[n][i] == '':\r\n list[n][i] = ' '\r\n return list\r\ndef roundDown(float):\r\n if not CheckFloat(float):\r\n num = int(float) \r\n return num\r\n else:\r\n return int(float)\r\ndef roundUp(float):\r\n if not CheckFloat(float):\r\n num = int(float) + 1\r\n return num\r\n else:\r\n return int(float)\r\ndef create_tree(personen):\r\n #print(personen)\r\n stammbaum = Stammbaum\r\n stammbaum.generations = {}\r\n #print(math.log(int(len(personen.keys()) + 1), 2))\r\n amountGenerations = int(math.log(int(len(personen.keys()) + 1), 2)) # int(sorted(personen.keys())[-1])\r\n alteredPpl = ''\r\n for i in range(len(personen.keys())):\r\n if CheckFloat(math.log(int(sorted(personen.keys())[i]), 2)): # if the Person ID is a power of 2\r\n alteredPpl = alteredPpl + '|'\r\n if alteredPpl[-1] == '|':\r\n alteredPpl = alteredPpl + str(sorted(personen.keys())[i])\r\n else:\r\n alteredPpl = alteredPpl + ', ' + str(sorted(personen.keys())[i])\r\n else:\r\n if alteredPpl[-1] == '|':\r\n alteredPpl = alteredPpl + str(sorted(personen.keys())[i])\r\n else:\r\n alteredPpl = alteredPpl + ', ' + str(sorted(personen.keys())[i])\r\n alteredPpl = alteredPpl[1:]\r\n alteredPpl = alteredPpl.split('|')\r\n #print(alteredPpl)\r\n\r\n for i in range(amountGenerations):\r\n stammbaum.generations.update({i:alteredPpl[i].replace(' ', '').split(',')})\r\n #print(stammbaum.generations)\r\n \r\n IDgens = stammbaum.generations\r\n #print('IDGENS:',IDgens)\r\n generations = len(IDgens.keys())\r\n IDs = get_all_elements_in_list_of_lists(IDgens.values())\r\n CELLCOLUMNS = 11\r\n CELLROWS = 3\r\n HEIGHT = CELLROWS * generations\r\n WIDTH = CELLCOLUMNS * IDs #+ MARGIN * 2\r\n grid = []\r\n for i in range(generations):\r\n List = []\r\n for n in range(IDs):\r\n List.append('')\r\n grid.append(List)\r\n\r\n np.asarray(replaceEmptyWithSpaceInList(grid))\r\n #print(np.asarray(replaceEmptyWithSpaceInList(grid)), '\\n') # debug\r\n\r\n # Create the grid with IDs replaced by 'X'\r\n CELLSYMBOL = 'X'\r\n LINESYMBOL = '-'\r\n INTERSECTIONSYMBOL = '+'\r\n\r\n # Create the X's at the bottom\r\n for x in range(IDs):\r\n if (x % 2) == 0:\r\n # x is even\r\n grid[generations - 1][x] = CELLSYMBOL\r\n\r\n np.asarray(replaceEmptyWithSpaceInList(grid))\r\n #print(np.asarray(replaceEmptyWithSpaceInList(grid)), '\\n') # debug\r\n\r\n # Create the rest of the X's\r\n for i in range(generations - 1, 0, -1):\r\n #print('i:', i) # debug\r\n start = -1\r\n for n in range(IDs):\r\n #print('n:', n) # debug\r\n if grid[i][n] == CELLSYMBOL:\r\n if start == -1:\r\n start = n\r\n else:\r\n middle = int(start + (n - start) / 2) # get middle of start and n\r\n grid[i - 1][middle] = CELLSYMBOL # create the X in the middle of start and n one generation above the two of them\r\n start = -1 # reset start\r\n\r\n np.asarray(replaceEmptyWithSpaceInList(grid))\r\n #print(np.asarray(replaceEmptyWithSpaceInList(grid)), '\\n') # debug\r\n\r\n # # Create the lines, that connect the 'X's\r\n # for i in range(1, generations, 1):\r\n # start = -1\r\n # for n in range(IDs):\r\n # if grid[i][n] == CELLSYMBOL:\r\n # if start == -1:\r\n # start = n\r\n # else:\r\n # line = range(start + 1, n)\r\n # for k in line:\r\n # grid[i][k] = LINESYMBOL\r\n # start = -1 # reset start\r\n\r\n np.asarray(replaceEmptyWithSpaceInList(grid))\r\n #print(np.asarray(replaceEmptyWithSpaceInList(grid)), '\\n') # debug\r\n\r\n # # Create the intersections\r\n # for i in range(1, generations, 1):\r\n # start = -1\r\n # for n in range(IDs):\r\n # if grid[i][n] == CELLSYMBOL:\r\n # if start == -1:\r\n # start = n\r\n # else:\r\n # middle = int(start + (n - start) / 2) # get middle of start and n\r\n # grid[i][middle] = INTERSECTIONSYMBOL # create the X in the middle of start and n one generation above the two of them\r\n # start = -1 # reset start\r\n\r\n np.asarray(replaceEmptyWithSpaceInList(grid))\r\n #print(np.asarray(replaceEmptyWithSpaceInList(grid)), '\\n') # debug\r\n\r\n # Replace the X's with the IDs\r\n ids = list(range(1, IDs + 1, 1))\r\n for i in range(generations):\r\n for n in range(IDs):\r\n if grid[i][n] == CELLSYMBOL:\r\n grid[i][n] = str(ids[0])\r\n ids.pop(0)\r\n\r\n replaceEmptyWithSpaceInList(grid)\r\n np.asarray(replaceEmptyWithSpaceInList(grid))\r\n #print(np.asarray(replaceEmptyWithSpaceInList(grid)), '\\n') # debug\r\n\r\n # If the IDs go past 1 digit, create new characters to fill the space\r\n highest = []\r\n for i in range(generations):\r\n highest.append(max(grid[i], key=len))\r\n highest = max(highest, key=len)\r\n\r\n grid = grid[::-1] # this inverts the list\r\n return grid\r\ndef createTree(personen):\r\n # Create the tree\r\n grid = create_tree(personen)\r\n\r\n # Create lines between the nums\r\n line = []\r\n for i in range(len(grid[0])):\r\n line.append('')\r\n n = 0\r\n while n in range(len(grid)):\r\n if (n % 2) != 0:\r\n grid.insert(n, line)\r\n n += 1\r\n\r\n HORIZONTAL = '─'\r\n LEFTCORNER = '└'\r\n RIGHTCORNER = '┘'\r\n INTERSECTION = '┬'\r\n\r\n np.asarray(replaceEmptyWithSpaceInList(grid))\r\n #print(np.asarray(replaceEmptyWithSpaceInList(grid)), '\\n') # debug\r\n\r\n # Convert the list so multiple list entrys aren't referenced the same...\r\n grid = np.ndarray.tolist(np.asarray(grid)) # THIS TOOK ONE WHOLE DAY TO FIGURE OUT!!!\r\n\r\n for y in range(len(grid)):\r\n if (y % 2) != 0:\r\n LEFT = True\r\n for x in range(len(grid[0])):\r\n if grid[y - 1][x].replace(' ', '').isnumeric():\r\n if LEFT:\r\n grid[y][x] = LEFTCORNER\r\n LEFT = False\r\n elif not LEFT:\r\n grid[y][x] = RIGHTCORNER\r\n LEFT = True\r\n\r\n # Create the lines, that connect the 'X's\r\n for i in range(1, len(grid), 1):\r\n start = -1\r\n for n in range(len(grid[0])):\r\n if grid[i][n].replace(' ', '') in [LEFTCORNER, RIGHTCORNER]:\r\n if start == -1:\r\n start = n\r\n else:\r\n line = range(start + 1, n)\r\n for k in line:\r\n grid[i][k] = HORIZONTAL\r\n start = -1 # reset start\r\n\r\n # Create the intersections\r\n for i in range(1, len(grid), 1):\r\n start = -1\r\n for n in range(len(grid[0])):\r\n if grid[i][n] in [LEFTCORNER, RIGHTCORNER]:\r\n if start == -1:\r\n start = n\r\n else:\r\n middle = int(start + (n - start) / 2) # get middle of start and n\r\n grid[i][middle] = INTERSECTION # create the X in the middle of start and n one generation above the two of them\r\n start = -1 # reset start\r\n\r\n np.asarray(replaceEmptyWithSpaceInList(grid))\r\n #print(np.asarray(replaceEmptyWithSpaceInList(grid))) # debug\r\n return grid\r\n\r\ndef refreshTitle():\r\n root.title(txt.title)\r\ndef translateTree():\r\n global personen\r\n \r\n attributes = [\r\n txt.name,\r\n txt.marriageDate,\r\n txt.marriagePlace,\r\n txt.otherMarriages,\r\n txt.siblings,\r\n txt.birthPlace,\r\n txt.deathPlace,\r\n txt.birthDate,\r\n txt.deathDate,\r\n txt.annotation,\r\n txt.image\r\n ]\r\n for i in range(len(list(personen.values()))):\r\n index = 0\r\n for attr in list(list(personen.values())[i].keys()):\r\n mydict = personen[str(i+1)]\r\n mydict[attributes[index]] = mydict.pop(attr)\r\n index += 1\r\ndef def_factory(arg):\r\n def edit_person():\r\n def on_closing():\r\n textBoxes[imgIndex] = imgPath\r\n for i in range(len(textBoxes)): \r\n if i == 0:\r\n person[txt.name] = u\"\\U0000FFFF\".join([textBoxes[0].get(), textBoxes[1].get()])\r\n elif i == 1 or i > 11:\r\n pass\r\n else:\r\n person[list(person.keys())[i - 1]] = textBoxes[i].get()\r\n personen.update({id:person})\r\n window.destroy()\r\n def browseIMG(): \r\n global currentfile\r\n global personen\r\n global config\r\n\r\n exts = ['.blp','.bmp','.bufr','.cur','.dcx','.dds','.dib','.eps','.ps','.fit','.fits','.flc','.fli','.ftc','.ftu','.gbr','.gif','.grib','.h5','.hdf','.icns','.ico','.im','.iim','.jfif','.jpe','.jpeg','.jpg','.j2c','.j2k','.jp2','.jpc','.jpf','.jpx','.mpeg','.mpg','.msp','.pcd','.pcx','.pxr','.apng','.png','.pbm','.pgm','.pnm','.ppm','.psd','.bw','.rgb','.rgba','.sgi','ras','icb','.tga','.vda','.vst','.tif','.tiff','.webp','.emf','.wmf','.xbm','.xpm']\r\n allImage = list(map('*{}'.format, exts))\r\n filetypes = (\r\n (txt.allFiles,\"*.*\"),\r\n (txt.imageFiles,tuple(allImage)),\r\n (txt.pngFiles,\"*.png\"),\r\n (txt.jpegFiles,(\"*.jpg\", \"*.jpeg\", \"*.jpe\", \"*.jfif\")),\r\n (txt.gifFiles,\"*.gif\"),\r\n (txt.bmpFiles,(\"*.bmp\", \"*.dib\")),\r\n (txt.wmfFiles,\"*.wmf\"),\r\n (txt.emfFiles,\"*.emf\")\r\n )\r\n\r\n img = filedialog.askopenfilename(initialdir = \"/\",title = txt.openTitle,filetypes = filetypes,parent=window) #os.path.join(os.path.dirname(__file__), img)\r\n try: \r\n if os.path.exists(img):\r\n if os.path.splitext(img)[1].lower() in exts:\r\n imgPath.set(os.path.join(os.path.dirname(__file__), img))\r\n else:\r\n messagebox.showerror(txt.error, txt.notImage, parent=window)\r\n else:\r\n messagebox.showerror(txt.error, txt.openError, parent=window)\r\n except:\r\n messagebox.showerror(txt.error, txt.openError, parent=window)\r\n\r\n id = str(arg)\r\n personName = personen.get(id).get(txt.name).replace(u\"\\U0000FFFF\", \" \")\r\n\r\n window = Toplevel(root)\r\n window.title(txt.edit + \" \" + personName)\r\n window.iconbitmap('./img/icon.ico')\r\n\r\n frame = ttk.Frame(window, padding=\"3 3 12 12\")\r\n frame.grid(column=0, row=0, sticky=(N, W, E, S))\r\n window.columnconfigure(0, weight=1) # frame should expand to fill any extra space if the window is resized.\r\n window.rowconfigure(0, weight=1) # frame should expand to fill any extra space if the window is resized.\r\n\r\n person = personen.get(id)\r\n\r\n textBoxes = {}\r\n for i in range(len(list(person))): \r\n if i == 0:\r\n textBoxes.update({i:list(person.values())[i].split(u\"\\U0000FFFF\")[0]})\r\n textBoxes.update({i + 1:list(person.values())[i].split(u\"\\U0000FFFF\")[1]})\r\n else:\r\n textBoxes.update({i + 1:list(person.values())[i]}) # the +1 is bcs we have split Name into 2 boxes\r\n \r\n for i in range(len(list(person))):\r\n if list(person)[i] == txt.name:\r\n ttk.Label(frame, text=list(person)[i] + \":\").grid(column=0, row=i, sticky=E)\r\n \r\n textBoxes[i] = StringVar(value=textBoxes[i])\r\n textBoxes[i + 1] = StringVar(value=textBoxes[i + 1])\r\n ttk.Entry(frame, width=15, textvariable=textBoxes[i]).grid(column=1, row=i, sticky=(W, E))\r\n ttk.Entry(frame, width=15, textvariable=textBoxes[i + 1]).grid(column=2, row=i, sticky=(W, E))\r\n else:\r\n if list(person)[i] == txt.image:\r\n ttk.Label(frame, text=list(person)[i] + \":\").grid(column=0, row=i, sticky=E)\r\n \r\n textBoxes[i + 1] = StringVar(value=textBoxes[i + 1])\r\n imgPath = textBoxes[i + 1]\r\n imgIndex = i + 1\r\n ttk.Entry(frame, width=15, textvariable=imgPath).grid(column=1, row=i, sticky=(W, E))\r\n imgBtn = ttk.Button(frame, text=txt.browse, command=browseIMG)\r\n imgBtn.grid(column=2, row=i, sticky=(W, E))\r\n else:\r\n ttk.Label(frame, text=list(person)[i] + \":\").grid(column=0, row=i, sticky=E)\r\n \r\n textBoxes[i + 1] = StringVar(value=textBoxes[i + 1])\r\n ttk.Entry(frame, width=30, textvariable=textBoxes[i + 1]).grid(column=1, row=i, sticky=(W, E), columnspan=2)\r\n \r\n ttk.Button(frame, text=txt.cancel, command=window.destroy).grid(column=1, row=len(list(person)), sticky=(N, S))\r\n ttk.Button(frame, text=txt.done, command=on_closing).grid(column=2, row=len(list(person)), sticky=(N, S))\r\n \r\n # Polish\r\n for child in frame.winfo_children(): \r\n child.grid_configure(padx=1, pady=1)\r\n \r\n\r\n window.protocol(\"WM_DELETE_WINDOW\", on_closing)\r\n\r\n return edit_person\r\n\r\n\r\nconfig = configparser.ConfigParser()\r\nconfig.read('settings.ini')\r\n\r\ntxt = Translate(config['CURRENT']['language'])\r\nif config['CURRENT']['currentfile']:\r\n currentfile = config['CURRENT']['currentfile']\r\n try: \r\n with open(os.path.join(os.path.dirname(__file__), currentfile), 'r') as input_file:\r\n personen = json.loads(input_file.read())\r\n except:\r\n currentfile = ''\r\n config['CURRENT']['currentfile'] = ''\r\n personen = txt.people\r\nelse:\r\n currentfile = ''\r\n config['CURRENT']['currentfile'] = ''\r\n personen = txt.people\r\n\r\ntranslateTree()\r\n\r\nroot = Tk()\r\nrefreshTitle()\r\nroot.iconbitmap('./img/icon.ico')\r\n\r\n\r\ndef cleanup():\r\n def deleteXLSM():\r\n text.insert(END, \"\\n\")\r\n for item in items:\r\n if item.endswith(\".xlsm\"):\r\n itemPath = os.path.join(thisDir, item)\r\n try:\r\n os.remove(os.path.join(thisDir, item))\r\n text.insert(END, \"\\n\\\"\"+itemPath+\"\\\"\"+txt.removed)\r\n except Exception as e: \r\n text.insert(END, \"\\n\\\"\"+itemPath+\"\\\"\"+txt.removeError+\"\\n\")\r\n text.insert(END, e)\r\n thisDir = os.getcwd()\r\n items = os.listdir(thisDir)\r\n \r\n\r\n newWindow = Toplevel(root)\r\n newWindow.title(txt.info)\r\n\r\n frame = ttk.Frame(newWindow, padding=\"3 3 12 12\")\r\n frame.grid(column=0, row=0, sticky=(N, W, E, S))\r\n newWindow.columnconfigure(0, weight=1)\r\n newWindow.rowconfigure(0, weight=1)\r\n\r\n text = Text(frame)\r\n text.insert(INSERT, txt.cleanupWarning)\r\n for item in items:\r\n if item.endswith(\".xlsm\"):\r\n itemPath = os.path.join(thisDir, item)\r\n #os.remove(os.path.join(thisDir, item))\r\n text.insert(END, \"\\n\"+itemPath)\r\n text.pack(fill=BOTH)\r\n\r\n yesBtn = ttk.Button(frame, text=txt.yes, command=deleteXLSM)\r\n yesBtn.pack(side=RIGHT)\r\n\r\n closeBtn = ttk.Button(frame, text=txt.close, command=newWindow.destroy)\r\n closeBtn.pack(side=RIGHT)\r\n\r\n # Polish\r\n for child in frame.winfo_children(): \r\n child.pack_configure(padx=5, pady=5)\r\ndef refreshMenubar():\r\n menubar = Menu(root)\r\n\r\n filemenu = Menu(menubar, tearoff=0)\r\n filemenu.add_command(label=txt.open, command=onOpen)\r\n filemenu.add_command(label=txt.save, command=quickSave)\r\n filemenu.add_command(label=txt.saveAs, command=onSave)\r\n filemenu.add_command(label=txt.export, command=exportToExcel)\r\n filemenu.add_command(label=txt.settings, command=showSettings)\r\n filemenu.add_command(label=txt.exit, command=onExit)\r\n\r\n helpmenu = Menu(menubar, tearoff=0)\r\n helpmenu.add_command(label=txt.faq, command=showFAQ)\r\n helpmenu.add_command(label=txt.tutorial, command=showTutorial)\r\n helpmenu.add_command(label=txt.version, command=showVersion)\r\n\r\n editmenu = Menu(menubar, tearoff=0)\r\n editmenu.add_command(label=txt.addGeneration, command=addGeneration)\r\n editmenu.add_command(label=txt.removeGeneration, command=removeGeneration)\r\n\r\n menubar.add_cascade(label=txt.file, menu=filemenu)\r\n menubar.add_cascade(label=txt.edit, menu=editmenu)\r\n menubar.add_cascade(label=txt.help, menu=helpmenu)\r\n\r\n root.config(menu=menubar)\r\ndef showTutorial():\r\n webbrowser.open('http://stammbaumgenerator.great-site.net/tutorial.html', new=0)\r\ndef showFAQ():\r\n webbrowser.open('http://stammbaumgenerator.great-site.net/faq.html#', new=0)\r\ndef removeGeneration():\r\n global personen\r\n last = int((len(personen) + 1) / 2 - 1)\r\n #print(last)\r\n a = list(personen.keys())\r\n b = list(personen.values())\r\n del a[last:len(personen)]\r\n del b[last:len(personen)]\r\n personen = dict(zip(a, b))\r\n # while len(personen) > last:\r\n # personen.pop(list(personen.keys())[-1])\r\n #print(personen)\r\n # refresh the tree\r\n refreshTree()\r\ndef addGeneration():\r\n global personen\r\n for i in range((len(personen) + 1) * 2 - 1):\r\n if not str(i+1) in list(personen.keys()):\r\n personen.update({str(i+1):txt.person})\r\n # refresh the tree\r\n refreshTree()\r\ndef refreshTree():\r\n for widget in secondframe.winfo_children():\r\n widget.destroy()\r\n stammbaum = createTree(personen)\r\n\r\n commands = []\r\n for i in range(len(stammbaum[0])):\r\n for y in range(len(stammbaum)):\r\n if stammbaum[y][i].replace(\" \", \"\").isdecimal():\r\n id = int(stammbaum[y][i])\r\n commands.append(def_factory(id))\r\n\r\n for y in range(len(stammbaum)):\r\n for x in range(len(stammbaum[y])):\r\n value = stammbaum[y][x].replace(\" \", \"\")\r\n if value.isdecimal():\r\n ttk.Button(secondframe, text=value, command=commands[x], width=4).grid(column=x, row=y)\r\n else:\r\n if value == \"└\":\r\n value = \" └─\"\r\n ttk.Label(secondframe, text=value, font=\"Ariel 10\").grid(column=x, row=y, sticky=E)\r\n elif value == \"┬\":\r\n value = \"─┬─\"\r\n ttk.Label(secondframe, text=value, font=\"Ariel 10\").grid(column=x, row=y, sticky=(N, E, S, W))\r\n elif value == \"┘\":\r\n value = \"─┘ \"\r\n ttk.Label(secondframe, text=value, font=\"Ariel 10\").grid(column=x, row=y, sticky=W)\r\n elif value == \"─\":\r\n value = \"───\"\r\n ttk.Label(secondframe, text=value, font=\"Ariel 10\").grid(column=x, row=y, sticky=(N, E, S, W))\r\n else:\r\n pass\r\n #ttk.Label(secondframe, text=value, font=\"Ariel 10\").grid(column=x, row=y, sticky=(N, E, S, W))\r\ndef onOpen():\r\n global currentfile\r\n global personen\r\n global config\r\n\r\n inp = filedialog.askopenfilename(initialdir = \"/\",title = txt.openTitle,filetypes = ((txt.jsonFiles,\"*.json\"),(txt.allFiles,\"*.*\")))\r\n try: \r\n with open(os.path.join(os.path.dirname(__file__), inp), 'r') as input_file:\r\n personen = json.loads(input_file.read())\r\n currentfile = inp\r\n config['CURRENT']['currentfile'] = currentfile\r\n\r\n except:\r\n if input:\r\n messagebox.showerror(txt.error, txt.openError)\r\n refreshTree()\r\ndef onSave():\r\n global currentfile\r\n global config\r\n\r\n input = filedialog.asksaveasfilename(initialdir = \"/\",title = txt.saveTitle,filetypes = ((txt.jsonFiles,\"*.json\"),(txt.allFiles,\"*.*\")))\r\n if not '.json' in input:\r\n input = input + '.json'\r\n try: \r\n with open(os.path.join(os.path.dirname(__file__), input), 'w') as output_file:\r\n output_file.write(json.dumps(personen))\r\n currentfile = input\r\n config['CURRENT']['currentfile'] = currentfile\r\n\r\n except:\r\n if input:\r\n messagebox.showerror(txt.error, txt.saveError)\r\ndef quickSave():\r\n try: \r\n with open(os.path.join(os.path.dirname(__file__), currentfile), 'w') as output_file:\r\n output_file.write(json.dumps(personen))\r\n\r\n except:\r\n if input:\r\n messagebox.showerror(txt.error, txt.saveError)\r\ndef bindSave(a):\r\n try: \r\n with open(os.path.join(os.path.dirname(__file__), currentfile), 'w') as output_file:\r\n output_file.write(json.dumps(personen))\r\n\r\n except:\r\n if input:\r\n messagebox.showerror(txt.error, txt.saveError)\r\ndef exportToExcel():\r\n workbook = xlsxwriter.Workbook(txt.excelTitle + personen.get('1').get(txt.name).replace(u\"\\U0000FFFF\", \" \") + '.xlsm')\r\n workbook.set_vba_name('DieseArbeitsMappe')\r\n worksheet = workbook.add_worksheet()\r\n worksheet.set_vba_name('Tabelle1')\r\n\r\n bold = workbook.add_format({'bold':True})\r\n bold.set_left()\r\n bold.set_right()\r\n bold.set_font_size(10)\r\n bold.set_font_name('Bahnschrift Light SemiCondensed')\r\n\r\n anmerkungen = workbook.add_format({'bold':True})\r\n anmerkungen.set_font_size(10)\r\n anmerkungen.set_font_name('Bahnschrift Light SemiCondensed')\r\n\r\n normal = workbook.add_format({'font_size': 10, 'font_name': 'Bahnschrift Light SemiCondensed'})\r\n\r\n title = workbook.add_format({'align': 'center', 'bold': True, 'valign': 'vcenter', 'font_size': 16, 'font_name': 'Bahnschrift Light SemiCondensed'})\r\n\r\n center = workbook.add_format({'font_size': 10, 'font_name': 'Bahnschrift Light SemiCondensed', 'align': 'center'})\r\n\r\n topCellBorder = workbook.add_format()\r\n topCellBorder.set_top()\r\n topCellBorder.set_left()\r\n topCellBorder.set_right()\r\n topCellBorder.set_align('right')\r\n topCellBorder.set_align('vcenter')\r\n topCellBorder.set_bg_color('#C6EFCE')\r\n topCellBorder.set_font_size(10)\r\n topCellBorder.set_font_name('Bahnschrift Light SemiCondensed')\r\n\r\n grey = workbook.add_format()\r\n grey.set_left()\r\n grey.set_right()\r\n grey.set_font_size(10)\r\n grey.set_bg_color('#D6D6D6')\r\n grey.set_font_name('Bahnschrift Light SemiCondensed')\r\n\r\n betweenCellBorder = workbook.add_format()\r\n betweenCellBorder.set_left()\r\n betweenCellBorder.set_right()\r\n betweenCellBorder.set_font_size(10)\r\n betweenCellBorder.set_font_name('Bahnschrift Light SemiCondensed')\r\n\r\n bottomCellBorder = workbook.add_format()\r\n bottomCellBorder.set_bottom()\r\n bottomCellBorder.set_left()\r\n bottomCellBorder.set_right()\r\n bottomCellBorder.set_font_size(10)\r\n bottomCellBorder.set_font_name('Bahnschrift Light SemiCondensed')\r\n\r\n grid = createTree(personen)\r\n\r\n HORIZONTAL = '─'\r\n LEFTCORNER = '└'\r\n RIGHTCORNER = '┘'\r\n INTERSECTION = '┬'\r\n\r\n amountOfAttributes = 9\r\n\r\n amountOfBrosAndSis = 0\r\n for i in range(len(personen.keys())):\r\n if len(list(filter(None, personen.get(list(personen.keys())[i]).get(txt.siblings).replace('; ', ',').replace(';', ',').replace(', ', ',').split(',')))) > amountOfBrosAndSis:\r\n amountOfBrosAndSis = len(list(filter(None, personen.get(list(personen.keys())[i]).get(txt.siblings).replace('; ', ',').replace(';', ',').replace(', ', ',').split(','))))\r\n\r\n if amountOfAttributes >= amountOfBrosAndSis:\r\n CELLHEIGHT = amountOfAttributes\r\n else:\r\n CELLHEIGHT = amountOfBrosAndSis\r\n\r\n IMAGEHEIGHT = 4\r\n CELLWIDTH = 1\r\n #CELLHEIGHT = IMAGEHEIGHT + additive # TODO: W.I.P.\r\n #CELLHEIGHT = 9\r\n\r\n SPACE = ' '\r\n\r\n SP = 0\r\n LC = -1\r\n RC = -2\r\n IS = -3\r\n HL = -4\r\n\r\n sizedGrid = np.ndarray.tolist(np.asarray(grid))\r\n for y in range(len(sizedGrid)):\r\n for x in range(len(sizedGrid[y])):\r\n if sizedGrid[y][x] == ' '*len(sizedGrid[y][x]):\r\n SPACE = sizedGrid[y][x]\r\n sizedGrid[y][x] = SP\r\n elif sizedGrid[y][x].replace(' ', '') == LEFTCORNER:\r\n sizedGrid[y][x] = LC\r\n elif sizedGrid[y][x].replace(' ', '') == RIGHTCORNER:\r\n sizedGrid[y][x] = RC\r\n elif sizedGrid[y][x].replace(' ', '') == INTERSECTION:\r\n sizedGrid[y][x] = IS\r\n elif sizedGrid[y][x].replace(' ', '') == HORIZONTAL:\r\n sizedGrid[y][x] = HL\r\n elif type(sizedGrid[y][x]) == str:\r\n sizedGrid[y][x] = int(sizedGrid[y][x])\r\n\r\n sizedGrid = np.kron(np.asarray(sizedGrid), np.ones((CELLHEIGHT, CELLWIDTH)))\r\n sizedGrid = np.ndarray.tolist(sizedGrid)\r\n\r\n for y in range(len(sizedGrid)):\r\n for x in range(len(sizedGrid[y])):\r\n if sizedGrid[y][x] == SP:\r\n sizedGrid[y][x] = SPACE\r\n elif sizedGrid[y][x] == LC:\r\n sizedGrid[y][x] = LEFTCORNER\r\n elif sizedGrid[y][x] == RC:\r\n sizedGrid[y][x] = RIGHTCORNER\r\n elif sizedGrid[y][x] == IS:\r\n sizedGrid[y][x] = INTERSECTION\r\n elif sizedGrid[y][x] == HL:\r\n sizedGrid[y][x] = HORIZONTAL\r\n elif type(sizedGrid[y][x]) == float:\r\n sizedGrid[y][x] = str(int(sizedGrid[y][x]))\r\n\r\n np.asarray(replaceEmptyWithSpaceInList(sizedGrid))\r\n #print(np.asarray(replaceEmptyWithSpaceInList(sizedGrid)), '\\n') # debug\r\n\r\n for i in range(len(grid)):\r\n grid[i].append(' ')\r\n for i in range(len(sizedGrid)):\r\n sizedGrid[i].append(' ')\r\n\r\n for Y in range(len(grid)):\r\n for X in range(len(grid[Y])):\r\n if grid[Y][X] in ['', ' '*len(str(grid[Y][X]))]:\r\n if str(grid[Y][X-1]).replace(' ', '').isnumeric and not str(grid[Y][X-1]).replace(' ', '') in [LEFTCORNER, INTERSECTION, HORIZONTAL, RIGHTCORNER, '', ' '*len(str(grid[Y][X-1]))]:\r\n iteration = 0\r\n for i in range(Y * CELLHEIGHT, (Y + 1) * CELLHEIGHT, 1):\r\n if not personen.get(str(grid[Y][X-1]).replace(' ', '')).get(txt.siblings) in ['', ' '*len(personen.get(str(grid[Y][X-1]).replace(' ', '')).get(txt.siblings))]:\r\n for k in range(len(list(str(personen.get(str(grid[Y][X-1].replace(' ', ''))).get(txt.siblings)).replace(';',',').replace(', ',',').split(',')))):\r\n try:\r\n sizedGrid[i][X] = ' ' + list(str(personen.get(str(grid[Y][X-1].replace(' ', ''))).get(txt.siblings)).replace(';',',').replace(', ',',').split(','))[iteration]\r\n except IndexError:\r\n pass\r\n iteration += 1\r\n elif grid[Y][X].replace(' ','') == LEFTCORNER:\r\n iteration = 0\r\n for i in range(Y * CELLHEIGHT, (Y + 1) * CELLHEIGHT, 1):\r\n if iteration == 0:\r\n sizedGrid[i][X] = '[Leftcorner.png]'\r\n else:\r\n sizedGrid[i][X] = ' '\r\n iteration += 1\r\n elif grid[Y][X].replace(' ','') == RIGHTCORNER:\r\n iteration = 0\r\n for i in range(Y * CELLHEIGHT, (Y + 1) * CELLHEIGHT, 1):\r\n if iteration == 0:\r\n sizedGrid[i][X] = '[Rightcorner.png]'\r\n else:\r\n sizedGrid[i][X] = ' '\r\n iteration += 1\r\n elif grid[Y][X].replace(' ','') == INTERSECTION:\r\n iteration = 0\r\n for i in range(Y * CELLHEIGHT, (Y + 1) * CELLHEIGHT, 1):\r\n if iteration == 0:\r\n sizedGrid[i][X] = '[Intersection.png]'\r\n elif iteration == 1:\r\n id1 = str(int(grid[Y + 1][X]) * 2)\r\n id2 = str(int(grid[Y + 1][X]) * 2 + 1)\r\n if not personen.get(str(id1)).get(txt.otherMarriages) in ['', ' ' * len(personen.get(str(id1)).get(txt.otherMarriages))] or not personen.get(str(id2)).get(txt.otherMarriages) in ['', ' ' * len(personen.get(str(id2)).get(txt.otherMarriages))]: # weitere Ehe\r\n sizedGrid[i][X] = '[center]' + str(personen.get(str(id1)).get(txt.otherMarriages)) + str(personen.get(str(id2)).get(txt.otherMarriages))\r\n else:\r\n sizedGrid[i][X] = ' '\r\n elif iteration == 2:\r\n id = str(int(grid[Y + 1][X]) * 2)\r\n #get person up then left\r\n # for m in range(X + 1, 0, -1):\r\n # if not str(grid[Y - 1][m]).replace(' ','') in [LEFTCORNER, INTERSECTION, HORIZONTAL, RIGHTCORNER, '', ' '*len(str(grid[Y - 1][m]).replace(' ',''))]:\r\n # id = grid[Y - 1][m].replace(' ','')\r\n\r\n sizedGrid[i][X] = '[center]oo ' + str(personen.get(str(id)).get(txt.marriageDate)) + ' | ' + str(personen.get(str(id)).get(txt.marriagePlace))\r\n else:\r\n sizedGrid[i][X] = ' '\r\n iteration += 1\r\n elif grid[Y][X].replace(' ','') == HORIZONTAL:\r\n iteration = 0\r\n for i in range(Y * CELLHEIGHT, (Y + 1) * CELLHEIGHT, 1):\r\n if iteration == 0:\r\n sizedGrid[i][X] = '[Horizontal.png]'\r\n else:\r\n sizedGrid[i][X] = ' '\r\n iteration += 1\r\n elif not str(grid[Y][X]).replace(' ','') in [LEFTCORNER, INTERSECTION, HORIZONTAL, RIGHTCORNER, '', ' '*len(str(grid[Y][X]).replace(' ',''))]:\r\n #print(grid[Y][X])\r\n ID = int(grid[Y][X])\r\n #print(ID)\r\n attribute = personen.get(str(ID))\r\n #print(attribute)\r\n iteration = 0\r\n for i in range(Y * CELLHEIGHT, (Y + 1) * CELLHEIGHT, 1):\r\n if iteration == 0:\r\n sizedGrid[i][X] = '[topCellBorder][person.jpg] ' + str(ID)\r\n elif iteration in [1, 2, 3]:\r\n sizedGrid[i][X] = '[grey]'\r\n elif iteration == 4:\r\n sizedGrid[i][X] = '[betweenCellBorder][bold]' + str(attribute.get(txt.name).split(u\"\\U0000FFFF\")[1])\r\n elif iteration == 5:\r\n sizedGrid[i][X] = '[betweenCellBorder]' + str(attribute.get(txt.name).split(u\"\\U0000FFFF\")[0])\r\n elif iteration == 6:\r\n sizedGrid[i][X] = '[betweenCellBorder]' + str(attribute.get(txt.birthDate) + ' - ' + attribute.get(txt.deathDate))\r\n elif iteration == 7:\r\n sizedGrid[i][X] = '[betweenCellBorder]' + str('* ' + attribute.get(txt.birthPlace))\r\n elif iteration == 8:\r\n sizedGrid[i][X] = '[bottomCellBorder]' + str('† ' + attribute.get(txt.deathPlace))\r\n iteration += 1\r\n\r\n np.asarray(replaceEmptyWithSpaceInList(sizedGrid))\r\n #print(np.asarray(replaceEmptyWithSpaceInList(sizedGrid)), '\\n') # debug\r\n\r\n\r\n MTOP = 1\r\n MLEFT = 1\r\n\r\n MARGIN_COL = int(MLEFT)\r\n MARGIN_ROW = 2 + int(MTOP)\r\n\r\n # Write the values\r\n for row in range(len(sizedGrid)):\r\n for col in range(len(sizedGrid[row])):\r\n if '[Horizontal.png]' in sizedGrid[row][col]:\r\n worksheet.insert_image(row + MARGIN_ROW, col + MARGIN_COL, './img/Horizontal.png', {'object_position': 1, 'x_scale': 0.501})\r\n elif '[Intersection.png]' in sizedGrid[row][col]:\r\n worksheet.insert_image(row + MARGIN_ROW, col + MARGIN_COL, './img/Intersection.png', {'object_position': 1, 'x_scale': 0.501})\r\n elif '[Rightcorner.png]' in sizedGrid[row][col]:\r\n worksheet.insert_image(row + MARGIN_ROW, col + MARGIN_COL, './img/Rightcorner.png', {'object_position': 1, 'x_scale': 0.501})\r\n elif '[Leftcorner.png]' in sizedGrid[row][col]:\r\n worksheet.insert_image(row + MARGIN_ROW, col + MARGIN_COL, './img/Leftcorner.png', {'object_position': 1, 'x_scale': 0.501})\r\n elif '[person.jpg]' in sizedGrid[row][col]:\r\n worksheet.write(row + MARGIN_ROW, col + MARGIN_COL, sizedGrid[row][col].replace('[person.jpg]', '').replace('[topCellBorder]', '').replace(' ', '') + ' ', topCellBorder)\r\n\r\n imgID = sizedGrid[row][col].replace('[person.jpg]', '').replace('[topCellBorder]', '').replace(' ', '')\r\n imagePath = personen[imgID][txt.image]\r\n if not os.path.exists(imagePath):\r\n imagePath = './img/person.jpg'\r\n else:\r\n imgname, img_extension = os.path.splitext(imagePath)\r\n with Image.open(imagePath) as img:\r\n img = img.resize((84, 75))\r\n img.save(f'./tmp/{imgID}.png')\r\n imagePath = f'./tmp/{imgID}.png'\r\n worksheet.insert_image(row + MARGIN_ROW, col + MARGIN_COL, imagePath, {'x_offset': 1, 'y_offset': 1, 'x_scale':1.05, 'y_scale':1.075})\r\n else:\r\n if '[topCellBorder]' in sizedGrid[row][col]:\r\n worksheet.write(row + MARGIN_ROW, col + MARGIN_COL, sizedGrid[row][col].replace('[topCellBorder]', ''), topCellBorder)\r\n elif '[center]' in sizedGrid[row][col]:\r\n while sizedGrid[row][col][-1] == ' ':\r\n sizedGrid[row][col] = sizedGrid[row][col][:-1]\r\n worksheet.write(row + MARGIN_ROW, col + MARGIN_COL, sizedGrid[row][col].replace('[center]', ''), center)\r\n elif '[grey]' in sizedGrid[row][col]:\r\n worksheet.write(row + MARGIN_ROW, col + MARGIN_COL, sizedGrid[row][col].replace('[grey]', ''), grey)\r\n elif '[betweenCellBorder]' in sizedGrid[row][col]:\r\n if '[bold]' in sizedGrid[row][col]:\r\n worksheet.write(row + MARGIN_ROW, col + MARGIN_COL, sizedGrid[row][col].replace('[betweenCellBorder]', '').replace('[bold]', ''), bold)\r\n else:\r\n worksheet.write(row + MARGIN_ROW, col + MARGIN_COL, sizedGrid[row][col].replace('[betweenCellBorder]', ''), betweenCellBorder)\r\n elif '[bottomCellBorder]' in sizedGrid[row][col]:\r\n worksheet.write(row + MARGIN_ROW, col + MARGIN_COL, sizedGrid[row][col].replace('[bottomCellBorder]', ''), bottomCellBorder)\r\n else:\r\n worksheet.write(row + MARGIN_ROW, col + MARGIN_COL, sizedGrid[row][col], normal)\r\n\r\n # Create the title\r\n worksheet.merge_range(1, 1, 1, len(sizedGrid[0]) - 1, txt.excelTitle + personen.get('1').get(txt.name).replace(u\"\\U0000FFFF\", \" \"), title)\r\n worksheet.set_row(1, 48)\r\n\r\n # Create the annotations\r\n Infos = []\r\n for i in range(len(personen)):\r\n if not personen.get(str(i + 1)).get(txt.annotation) in ['', ' ' * len(personen.get(str(i + 1)).get(txt.annotation))]:\r\n Infos.append(f'{i + 1}: ' + personen.get(str(i + 1)).get(txt.annotation))\r\n\r\n MARGIN_TOP = 2\r\n\r\n worksheet.write(MARGIN_TOP, len(sizedGrid[0]) + MARGIN_COL, txt.excelAnnotation, anmerkungen)\r\n\r\n n = 1\r\n for i in Infos:\r\n worksheet.write(MARGIN_TOP + n, len(sizedGrid[0]) + MARGIN_COL, i, normal)\r\n n += 1\r\n #console.log(f\"[green]Created infos[/green]\")\r\n\r\n # hide the gridlines.\r\n worksheet.hide_gridlines(2)\r\n #console.log(f\"[green]Hid gridlines[/green]\")\r\n\r\n # Create VBA-AutoFit-Makro, that runs automatically when opening the workbook\r\n workbook.add_vba_project('./vbaProject.bin')\r\n #console.log(f\"[green]Added vbaProject binary[/green]\")\r\n\r\n\r\n try:\r\n workbook.close()\r\n except:\r\n messagebox.showerror(txt.error, txt.excelError)\r\n return\r\n\r\n os.startfile(txt.excelTitle + personen.get('1').get(txt.name).replace(u\"\\U0000FFFF\", \" \") + '.xlsm')\r\ndef showVersion():\r\n messagebox.showinfo(txt.version, \"IT-Tech © 2022\\n\" + txt.versionText) # (Major version).(Minor version).(Revision number).(Build number)\r\ndef showSettings():\r\n def changeLang(event):\r\n global config\r\n global txt\r\n\r\n lang = langVar.get()\r\n if lang in languages:\r\n txt = Translate(lang)\r\n translateTree()\r\n refreshMenubar()\r\n refreshTitle()\r\n config['CURRENT']['language'] = lang\r\n\r\n def reset():\r\n langVar.set(config['DEFAULT']['language'])\r\n LangCombo.set(langVar.get())\r\n changeLang(None)\r\n\r\n def done():\r\n # if txt.language != startLang:\r\n # messagebox.showinfo(txt.info, txt.infoLanguageChange)\r\n window.destroy()\r\n\r\n window = Toplevel(root)\r\n window.title(txt.settings)\r\n window.iconbitmap('./img/icon.ico')\r\n \r\n frame = ttk.Frame(window, padding=\"3 3 12 12\")\r\n frame.grid(column=0, row=0, sticky=(N, W, E, S))\r\n window.columnconfigure(0, weight=1) # frame should expand to fill any extra space if the window is resized.\r\n window.rowconfigure(0, weight=1) # frame should expand to fill any extra space if the window is resized.\r\n\r\n #Language settings\r\n # startLang = txt.language\r\n languages = txt.languages\r\n\r\n ttk.Label(frame, text=txt.languageLabel).grid(column=0, row=0, sticky=(N, W, E, S))\r\n langVar = StringVar()\r\n LangCombo = ttk.Combobox(frame, values=languages, textvariable=langVar, state='readonly')\r\n LangCombo.set(txt.language)\r\n LangCombo.bind('<>', changeLang)\r\n LangCombo.grid(column=1, row=0)\r\n\r\n #Cleanup unnecessary xlsm-Files\r\n cleanupBtn = ttk.Button(frame, text=txt.cleanup, command=cleanup) \r\n cleanupBtn.grid(column=0,row=1,columnspan=2,sticky=(N,W,E,S))\r\n\r\n #Buttons\r\n resetBtn = ttk.Button(frame, text=txt.reset, command=reset)\r\n resetBtn.grid(column=0, row=2, sticky=(N,W,E,S))\r\n\r\n doneBtn = ttk.Button(frame, text=txt.done, command=done)\r\n doneBtn.grid(column=1, row=2, sticky=(N,W,E,S))\r\n\r\n # Polish\r\n for child in frame.winfo_children(): \r\n child.grid_configure(padx=1, pady=1)\r\n resetBtn.grid_configure(pady=5)\r\n doneBtn.grid_configure(pady=5)\r\n\r\ndef onExit():\r\n global config\r\n\r\n with open('settings.ini', 'w') as configfile:\r\n config.write(configfile)\r\n root.quit()\r\n\r\nrefreshMenubar()\r\nroot.bind('', bindSave)\r\nroot.protocol(\"WM_DELETE_WINDOW\", onExit)\r\n\r\nfirstframe = Frame(root)\r\nfirstframe.pack(fill=BOTH,expand=1)\r\nmainframe = ttk.Frame(firstframe, padding=\"3 3 12 12\")\r\nmainframe.pack(fill=X,side=BOTTOM)\r\n# mainframe.grid(column=0, row=0, sticky=(N, W, E, S))\r\nroot.columnconfigure(0, weight=1) \r\nroot.rowconfigure(0, weight=1) \r\ncanvas = Canvas(firstframe)\r\ncanvas.pack(side=LEFT,fill=BOTH,expand=1)\r\nsecondframe = Frame(canvas)\r\ncanvas.create_window((0,0),window= secondframe, anchor=\"nw\")\r\nx_scrollbar = ttk.Scrollbar(mainframe,orient=HORIZONTAL,command=canvas.xview)\r\nx_scrollbar.pack(side=BOTTOM,fill=X)\r\n\r\ny_scrollbar = ttk.Scrollbar(firstframe,orient=VERTICAL,command=canvas.yview)\r\ny_scrollbar.pack(side=RIGHT,fill=Y)\r\n\r\nrefreshTree()\r\n\r\nmainloop()", "repo_name": "hcet-ti/stammbaumgenerator", "sub_path": "v1.1.0.0/Stammbaum.py", "file_name": "Stammbaum.py", "file_ext": "py", "file_size_in_byte": 41628, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "numpy.ndarray.tolist", "line_number": 48, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 48, "usage_type": "attribute"}, {"api_name": "numpy.asarray", "line_number": 48, "usage_type": "call"}, {"api_name": "math.log", "line_number": 73, "usage_type": "call"}, {"api_name": "math.log", "line_number": 76, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 110, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 124, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 141, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 157, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 172, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 184, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 214, "usage_type": "call"}, {"api_name": "numpy.ndarray.tolist", "line_number": 218, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 218, "usage_type": "attribute"}, {"api_name": "numpy.asarray", "line_number": 218, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 257, "usage_type": "call"}, {"api_name": "tkinter.filedialog.askopenfilename", "line_number": 316, "usage_type": "call"}, {"api_name": "tkinter.filedialog", "line_number": 316, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 318, "usage_type": "call"}, {"api_name": "os.path", "line_number": 318, "usage_type": "attribute"}, {"api_name": "os.path.splitext", "line_number": 319, "usage_type": "call"}, {"api_name": "os.path", "line_number": 319, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 320, "usage_type": "call"}, {"api_name": "os.path", "line_number": 320, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 320, "usage_type": "call"}, {"api_name": "tkinter.messagebox.showerror", "line_number": 322, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 322, "usage_type": "name"}, {"api_name": "tkinter.messagebox.showerror", "line_number": 324, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 324, "usage_type": "name"}, {"api_name": "tkinter.messagebox.showerror", "line_number": 326, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 326, "usage_type": "name"}, {"api_name": "tkinter.ttk.Frame", "line_number": 335, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 335, "usage_type": "name"}, {"api_name": "tkinter.ttk.Label", "line_number": 352, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 352, "usage_type": "name"}, {"api_name": "tkinter.ttk.Entry", "line_number": 356, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 356, "usage_type": "name"}, {"api_name": "tkinter.ttk.Entry", "line_number": 357, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 357, "usage_type": "name"}, {"api_name": "tkinter.ttk.Label", "line_number": 360, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 360, "usage_type": "name"}, {"api_name": "tkinter.ttk.Entry", "line_number": 365, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 365, "usage_type": "name"}, {"api_name": "tkinter.ttk.Button", "line_number": 366, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 366, "usage_type": "name"}, {"api_name": "tkinter.ttk.Label", "line_number": 369, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 369, "usage_type": "name"}, {"api_name": "tkinter.ttk.Entry", "line_number": 372, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 372, "usage_type": "name"}, {"api_name": "tkinter.ttk.Button", "line_number": 374, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 374, "usage_type": "name"}, {"api_name": "tkinter.ttk.Button", "line_number": 375, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 375, "usage_type": "name"}, {"api_name": "configparser.ConfigParser", "line_number": 387, "usage_type": "call"}, {"api_name": "translate.Translate", "line_number": 390, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 394, "usage_type": "call"}, {"api_name": "os.path", "line_number": 394, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 394, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 395, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 417, "usage_type": "call"}, {"api_name": "os.path", "line_number": 417, "usage_type": "attribute"}, {"api_name": "os.remove", "line_number": 419, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 419, "usage_type": "call"}, {"api_name": "os.path", "line_number": 419, "usage_type": "attribute"}, {"api_name": "os.getcwd", "line_number": 424, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 425, "usage_type": "call"}, {"api_name": "tkinter.ttk.Frame", "line_number": 431, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 431, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 440, "usage_type": "call"}, {"api_name": "os.path", "line_number": 440, "usage_type": "attribute"}, {"api_name": "tkinter.ttk.Button", "line_number": 445, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 445, "usage_type": "name"}, {"api_name": "tkinter.ttk.Button", "line_number": 448, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 448, "usage_type": "name"}, {"api_name": "webbrowser.open", "line_number": 480, "usage_type": "call"}, {"api_name": "webbrowser.open", "line_number": 482, "usage_type": "call"}, {"api_name": "tkinter.ttk.Button", "line_number": 520, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 520, "usage_type": "name"}, {"api_name": "tkinter.ttk.Label", "line_number": 524, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 524, "usage_type": "name"}, {"api_name": "tkinter.ttk.Label", "line_number": 527, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 527, "usage_type": "name"}, {"api_name": "tkinter.ttk.Label", "line_number": 530, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 530, "usage_type": "name"}, {"api_name": "tkinter.ttk.Label", "line_number": 533, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 533, "usage_type": "name"}, {"api_name": "tkinter.filedialog.askopenfilename", "line_number": 542, "usage_type": "call"}, {"api_name": "tkinter.filedialog", "line_number": 542, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 544, "usage_type": "call"}, {"api_name": "os.path", "line_number": 544, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 544, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 545, "usage_type": "call"}, {"api_name": "tkinter.messagebox.showerror", "line_number": 551, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 551, "usage_type": "name"}, {"api_name": "tkinter.filedialog.asksaveasfilename", "line_number": 557, "usage_type": "call"}, {"api_name": "tkinter.filedialog", "line_number": 557, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 561, "usage_type": "call"}, {"api_name": "os.path", "line_number": 561, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 561, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 562, "usage_type": "call"}, {"api_name": "tkinter.messagebox.showerror", "line_number": 568, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 568, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 571, "usage_type": "call"}, {"api_name": "os.path", "line_number": 571, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 571, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 572, "usage_type": "call"}, {"api_name": "tkinter.messagebox.showerror", "line_number": 576, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 576, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 579, "usage_type": "call"}, {"api_name": "os.path", "line_number": 579, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 579, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 580, "usage_type": "call"}, {"api_name": "tkinter.messagebox.showerror", "line_number": 584, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 584, "usage_type": "name"}, {"api_name": "xlsxwriter.Workbook", "line_number": 586, "usage_type": "call"}, {"api_name": "numpy.ndarray.tolist", "line_number": 669, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 669, "usage_type": "attribute"}, {"api_name": "numpy.asarray", "line_number": 669, "usage_type": "call"}, {"api_name": "numpy.kron", "line_number": 686, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 686, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 686, "usage_type": "call"}, {"api_name": "numpy.ndarray.tolist", "line_number": 687, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 687, "usage_type": "attribute"}, {"api_name": "numpy.asarray", "line_number": 704, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 796, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 822, "usage_type": "call"}, {"api_name": "os.path", "line_number": 822, "usage_type": "attribute"}, {"api_name": "os.path.splitext", "line_number": 825, "usage_type": "call"}, {"api_name": "os.path", "line_number": 825, "usage_type": "attribute"}, {"api_name": "PIL.Image.open", "line_number": 826, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 826, "usage_type": "name"}, {"api_name": "tkinter.messagebox.showerror", "line_number": 882, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 882, "usage_type": "name"}, {"api_name": "os.startfile", "line_number": 885, "usage_type": "call"}, {"api_name": "tkinter.messagebox.showinfo", "line_number": 887, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 887, "usage_type": "name"}, {"api_name": "translate.Translate", "line_number": 895, "usage_type": "call"}, {"api_name": "tkinter.ttk.Frame", "line_number": 915, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 915, "usage_type": "name"}, {"api_name": "tkinter.ttk.Label", "line_number": 924, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 924, "usage_type": "name"}, {"api_name": "tkinter.ttk.Combobox", "line_number": 926, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 926, "usage_type": "name"}, {"api_name": "tkinter.ttk.Button", "line_number": 932, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 932, "usage_type": "name"}, {"api_name": "tkinter.ttk.Button", "line_number": 936, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 936, "usage_type": "name"}, {"api_name": "tkinter.ttk.Button", "line_number": 939, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 939, "usage_type": "name"}, {"api_name": "tkinter.ttk.Frame", "line_number": 961, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 961, "usage_type": "name"}, {"api_name": "tkinter.ttk.Scrollbar", "line_number": 970, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 970, "usage_type": "name"}, {"api_name": "tkinter.ttk.Scrollbar", "line_number": 973, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 973, "usage_type": "name"}]} +{"seq_id": "8329429958", "text": "import torchvision\nfrom torchvision import datasets, transforms\nfrom torch.utils.data import DataLoader, Dataset\nimport numpy as np\n\n# a class that was made to take only the 'cat' and 'dog' labels\n\n\nclass SubLoader(torchvision.datasets.CIFAR10):\n def __init__(self, exclude_list, *args, **kwargs):\n super(SubLoader, self).__init__(*args, **kwargs)\n\n if exclude_list == []:\n return\n labels = np.array(self.targets)\n exclude = np.array(exclude_list).reshape(1, -1)\n mask = ~(labels.reshape(-1, 1) == exclude).any(axis=1)\n self.data = self.data[mask]\n self.targets = labels[mask].tolist()\n self.targets = list(map(lambda x: 1 if x == 5 else 0, self.targets))\n", "repo_name": "barifrah1/transfer_learning_hw2", "sub_path": "subloader.py", "file_name": "subloader.py", "file_ext": "py", "file_size_in_byte": 726, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "torchvision.datasets", "line_number": 9, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 15, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 16, "usage_type": "call"}]} +{"seq_id": "4623120968", "text": "import numpy as np\r\nimport torch\r\nimport torch.nn as nn\r\nimport torch.nn.functional as F\r\n\r\n\r\nclass Attention(nn.Module):\r\n \"\"\" Pytorch self-attention layer code inspired from:\r\n Link:\r\n https://discuss.pytorch.org/t/self-attention-on-words-and-masking/5671/4\r\n Original Web Page:\r\n https://www.kaggle.com/dannykliu/lstm-with-attention-clr-in-pytorch\r\n Usage:\r\n In __init__():\r\n self.atten1 = Attention(hidden_dim*2, batch_first=True) # 2 is bidrectional\r\n In forward():\r\n x, _ = self.atten1(x, lengths)\r\n \"\"\"\r\n\r\n def __init__(self, hidden_size, batch_first=True, device=None):\r\n super(Attention, self).__init__()\r\n\r\n self.hidden_size = hidden_size\r\n self.batch_first = batch_first\r\n if device is None:\r\n self.device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\r\n else:\r\n self.device = device\r\n\r\n self.att_weights = nn.Parameter(\r\n torch.Tensor(1, hidden_size), requires_grad=True)\r\n\r\n stdv = 1.0 / np.sqrt(self.hidden_size)\r\n for weight in self.att_weights:\r\n nn.init.uniform_(weight, -stdv, stdv)\r\n\r\n def get_mask(self):\r\n pass\r\n\r\n def forward(self, inputs, lengths):\r\n if self.batch_first:\r\n batch_size, max_len = inputs.size()[:2]\r\n else:\r\n max_len, batch_size = inputs.size()[:2]\r\n\r\n # apply attention layer\r\n weights = torch.bmm(inputs,\r\n self.att_weights # (1, hidden_size)\r\n .permute(1, 0) # (hidden_size, 1)\r\n .unsqueeze(0) # (1, hidden_size, 1)\r\n # (batch_size, hidden_size, 1)\r\n .repeat(batch_size, 1, 1)\r\n )\r\n\r\n attentions = torch.softmax(F.relu(weights.squeeze()), dim=-1)\r\n\r\n # create mask based on the sentence lengths\r\n mask = torch.ones(attentions.size(), requires_grad=True).to(self.device)\r\n for i, l in enumerate(lengths): # skip the first sentence\r\n if l < max_len:\r\n mask[i, l:] = 0\r\n\r\n # apply mask and renormalize attention scores (weights)\r\n masked = attentions * mask\r\n _sums = masked.sum(-1).unsqueeze(-1) # sums per row\r\n\r\n attentions = masked.div(_sums)\r\n\r\n # apply attention weights\r\n weighted = torch.mul(\r\n inputs, attentions.unsqueeze(-1).expand_as(inputs))\r\n\r\n # get the final fixed vector representations of the sentences\r\n representations = weighted.sum(1).squeeze()\r\n\r\n return representations, attentions\r\n", "repo_name": "miracleyoo/mlib", "sub_path": "dl/pytorch/attention.py", "file_name": "attention.py", "file_ext": "py", "file_size_in_byte": 2673, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "37", "api": [{"api_name": "torch.nn.Module", "line_number": 7, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 7, "usage_type": "name"}, {"api_name": "torch.cuda.is_available", "line_number": 26, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 26, "usage_type": "attribute"}, {"api_name": "torch.nn.Parameter", "line_number": 30, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 30, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 31, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 33, "usage_type": "call"}, {"api_name": "torch.nn.init.uniform_", "line_number": 35, "usage_type": "call"}, {"api_name": "torch.nn.init", "line_number": 35, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 35, "usage_type": "name"}, {"api_name": "torch.bmm", "line_number": 47, "usage_type": "call"}, {"api_name": "torch.softmax", "line_number": 55, "usage_type": "call"}, {"api_name": "torch.nn.functional.relu", "line_number": 55, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 55, "usage_type": "name"}, {"api_name": "torch.ones", "line_number": 58, "usage_type": "call"}, {"api_name": "torch.mul", "line_number": 70, "usage_type": "call"}]} +{"seq_id": "25615052379", "text": "import discord\nfrom discord.ext import commands\nfrom discord.utils import get\n\nclass c115(commands.Cog, name=\"c115\"):\n\n def __init__(self, bot: commands.Bot):\n self.bot = bot\n @commands.command(name='Machine_Lord_the_Shining', aliases=['c115', 'Machine_Lord_2'])\n async def example_embed(self, ctx):\n embed = discord.Embed(title='Machine Lord the Shining',\n color=0xcccccc)\n embed.set_thumbnail(url='https://www.duelingbook.com/images/custom-pics/2300000/2321579.jpg')\n\n embed.add_field(name='Status (Archetype)', value='Casual:3/Tournament:3 (Machine Lord)', inline=True)\n embed.add_field(name='Type (Attribute)', value='Machine/Synchro/Effect (EARTH)', inline=False)\n embed.add_field(name='Level (ATK/DEF)', value='7 (2500/2400)', inline=False)\n embed.add_field(name='Monster Effect', value='1 \"Machine Lord\" Tuner + 1+ non Tuner monsters\\nIf this card is Synchro Summoned using only Machine monsters, this card cannot be destroyed by battle or targeted by your opponent\\'s card effects. Once per turn: You can target 1 Level 5 or lower Machine monster in your GY; banish that monster, and if you do, inflict 700 damage to your opponent. You can only Special Summon 1 \"Machine Lord the Shining\" per turn.', inline=False)\n embed.set_footer(text='Set Code: ANCF')\n\n await ctx.send(embed=embed)\n\ndef setup(bot: commands.Bot):\n bot.add_cog(c115(bot))", "repo_name": "ProfessorSean/Kasutamaiza", "sub_path": "upcfcardsearch/c115.py", "file_name": "c115.py", "file_ext": "py", "file_size_in_byte": 1452, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "discord.ext.commands.Cog", "line_number": 5, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 5, "usage_type": "name"}, {"api_name": "discord.ext.commands.Bot", "line_number": 7, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 7, "usage_type": "name"}, {"api_name": "discord.Embed", "line_number": 11, "usage_type": "call"}, {"api_name": "discord.ext.commands.command", "line_number": 9, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 9, "usage_type": "name"}, {"api_name": "discord.ext.commands.Bot", "line_number": 23, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 23, "usage_type": "name"}]} +{"seq_id": "29208908588", "text": "from tqdm import tqdm\nfrom optparse import OptionParser\n\nimport multiprocessing, os, signal, subprocess, time\n\ndef init_worker(tqdm_lock=None):\n signal.signal(signal.SIGINT, signal.SIG_IGN)\n if tqdm_lock is not None:\n tqdm.set_lock(tqdm_lock)\n\ndef count_mutants(nameFile):\n output = subprocess.Popen(['wc', '-l', f'{nameFile}.seq'], stdout=subprocess.PIPE, stderr=subprocess.STDOUT)\n stdout, stderr = output.communicate()\n mutants = stdout.split()[0].decode('utf-8')\n return int(mutants)\n\ndef run_tango(nameFile):\n subprocess.run(f'~/bin/tango -inputfile={nameFile}.seq > log.out', shell=True)\n\ndef clean_tmp(nameFile):\n subprocess.run(f'cp *_aggregation.txt* ../../FINAL_RESULTS/{nameFile}_aggregation.txt', shell=True)\n subprocess.run('rm *.txt', shell=True)\n\ndef main(folder):\n curr_dir = os.chdir(folder)\n nameFile = folder.split('/')[-1]\n if not os.path.exists(f'../../FINAL_RESULTS/{nameFile}_aggregation.txt'):\n if count_mutants(nameFile) > 10000:\n run_tango(nameFile)\n clean_tmp(nameFile)\n else :\n print(f'{nameFile} need to be rerun on cluster')\n\n\n\nif __name__ == '__main__':\n parser = OptionParser()\n parser.add_option(\"-d\", \"--input_directory\", dest=\"curr_path\", default=\"None\", help=\"[Required] Provide the input input_directory\")\n (options, args) = parser.parse_args()\n curr_path = options.curr_path\n\n myfolders = [ os.path.join(curr_path, folder) for folder in sorted(os.listdir(curr_path)) ]\n p = multiprocessing.Pool(initializer=init_worker, initargs=(tqdm.get_lock(),), processes=2)\n try:\n pbar = tqdm(myfolders, maxinterval=1.0, miniters=1, desc=\"Terminated Tango: \", bar_format=\"{desc}:{percentage:3.0f}%|{bar}|\")\n for _, result in enumerate(p.imap_unordered(main, myfolders, chunksize=1)):\n pbar.update(1) # Everytime the iteration finishes, update the global progress bar\n\n pbar.close()\n p.close()\n p.join()\n except KeyboardInterrupt:\n print(\"KeyboardInterrupt, terminating workers.\")\n pbar.close()\n p.terminate()\n p.join()\n exit(1)\n", "repo_name": "dgfug/NKR_lifespan", "sub_path": "code/02_mutation_tolerance/run_tango.py", "file_name": "run_tango.py", "file_ext": "py", "file_size_in_byte": 2151, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "37", "api": [{"api_name": "signal.signal", "line_number": 7, "usage_type": "call"}, {"api_name": "signal.SIGINT", "line_number": 7, "usage_type": "attribute"}, {"api_name": "signal.SIG_IGN", "line_number": 7, "usage_type": "attribute"}, {"api_name": "tqdm.tqdm.set_lock", "line_number": 9, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 9, "usage_type": "name"}, {"api_name": "subprocess.Popen", "line_number": 12, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 12, "usage_type": "attribute"}, {"api_name": "subprocess.STDOUT", "line_number": 12, "usage_type": "attribute"}, {"api_name": "subprocess.run", "line_number": 18, "usage_type": "call"}, {"api_name": "subprocess.run", "line_number": 21, "usage_type": "call"}, {"api_name": "subprocess.run", "line_number": 22, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 25, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 27, "usage_type": "call"}, {"api_name": "os.path", "line_number": 27, "usage_type": "attribute"}, {"api_name": "optparse.OptionParser", "line_number": 37, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 42, "usage_type": "call"}, {"api_name": "os.path", "line_number": 42, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 42, "usage_type": "call"}, {"api_name": "multiprocessing.Pool", "line_number": 43, "usage_type": "call"}, {"api_name": "tqdm.tqdm.get_lock", "line_number": 43, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 43, "usage_type": "name"}, {"api_name": "tqdm.tqdm", "line_number": 45, "usage_type": "call"}]} +{"seq_id": "11140116810", "text": "import os\nimport re\nimport time\nfrom datetime import datetime, timezone, timedelta\nimport numpy as np\nimport pandas as pd\nimport requests\nfrom bs4 import BeautifulSoup\n\n\ndef stock_code():\n res = requests.get(\"https://isin.twse.com.tw/isin/C_public.jsp?strMode=2\")\n soup = BeautifulSoup(res.content.decode(\"MS950\"), \"html.parser\")\n table_rows = soup.find_all(\"tr\")\n stock = []\n for tr in table_rows:\n td = tr.find_all(\"td\")\n row = [tr.text for tr in td]\n stock.append(row)\n code_table = pd.DataFrame(\n stock[2:],\n columns=[\n \"code_name\",\n \"ISINCode\",\n \"date\",\n \"market\",\n \"industry\",\n \"CFIcode\",\n \"note\",\n ],\n )\n code_table.dropna(inplace=True)\n code = []\n name = []\n for i in code_table.code_name.values:\n code.append(i.split(\"\\u3000\")[0])\n name.append(i.split(\"\\u3000\")[1])\n code_table[\"code\"] = code\n code_table[\"code\"] = code_table.code.str.replace(\" \", \"\")\n code_table[\"name\"] = name\n return code_table\n\n\ndef pchome_stock_tick(code, date):\n url = \"http://pchome.megatime.com.tw/stock/sto0/ock3/sid{}.html\".format(code)\n ref = \"http://pchome.megatime.com.tw/stock/sto0/ock2/sid{}.html\".format(code)\n res = requests.get(url, headers={\"Referer\": ref})\n html = re.findall(\n \".*
    \",\n res.text,\n )\n soup = BeautifulSoup(html[0], \"html.parser\")\n table = soup.find_all(\"td\")\n data = []\n for i in table[7:]:\n data.append(i.text)\n data = np.array(data)\n col = [\"time\", \"bid\", \"ask\", \"price\", \"change\", \"volume\", \"total_volume\"]\n data = pd.DataFrame(data.reshape(int(len(data) / 7), 7), columns=col)\n for i in col[1:]:\n data.loc[data[i] == \"--\", i] = None\n data.loc[data[i] == \"市價\", i] = data.loc[data[i] == \"市價\", \"price\"]\n if i == \"volume\" or i == \"total_volume\":\n data[i] = data[i].astype(int)\n else:\n data[i] = data[i].astype(float)\n data[\"code\"] = code\n data[\"date\"] = date\n return data\n\n\ndef download_upload():\n code_table = stock_code()\n date = datetime.now(timezone(timedelta(hours=8))).strftime(\"%Y-%m-%d\")\n file_path = os.path.join(os.getenv(\"HOME\"), \"pchome\", \"pchome_{}.csv\".format(date))\n count = 0\n big_table = pd.DataFrame()\n for i in code_table.code.values:\n try:\n data = pchome_stock_tick(i, date)\n big_table = big_table.append(data)\n except:\n print(\"count:{}, code:{}\".format(count, i))\n print(\"something wrong\")\n\n count += 1\n time.sleep(1)\n if count % 10 == 0:\n if count == 10:\n big_table.to_csv(file_path, index=False)\n else:\n big_table.to_csv(file_path, index=False, header=False, mode=\"a\")\n big_table = pd.DataFrame()\n\n if len(big_table) > 0:\n big_table.to_csv(file_path, index=False, header=False, mode=\"a\")\n\n\nif __name__ == \"__main__\":\n download_upload()", "repo_name": "KuiMing/invest_data", "sub_path": "pchome_stock_tick_nas.py", "file_name": "pchome_stock_tick_nas.py", "file_ext": "py", "file_size_in_byte": 3123, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "requests.get", "line_number": 12, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 13, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 20, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 47, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 48, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 52, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 57, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 59, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 74, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 74, "usage_type": "name"}, {"api_name": "datetime.timezone", "line_number": 74, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 74, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 75, "usage_type": "call"}, {"api_name": "os.path", "line_number": 75, "usage_type": "attribute"}, {"api_name": "os.getenv", "line_number": 75, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 77, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 87, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 93, "usage_type": "call"}]} +{"seq_id": "75035475307", "text": "import torch\nimport torch.nn as nn\nimport torch.utils.data as data\nfrom torch.autograd import Variable\n\nimport pyro\nimport pyro.distributions as dist\nfrom pyro.optim import Adam\nfrom pyro.infer import SVI\nfrom pyro.util import ng_zeros, ng_ones\n\nimport torchvision.datasets as datasets\nimport torchvision.transforms as transforms\n\nimport matplotlib.pyplot as plt\n\n\nclass Decoder(nn.Module):\n def __init__(self, z_dim, hidden_dim, y_dim):\n super(Decoder, self).__init__()\n\n self.fc1 = nn.Linear(z_dim + y_dim, hidden_dim)\n self.fc2 = nn.Linear(hidden_dim, 784)\n\n self.softplus = nn.Softplus()\n self.sigmoid = nn.Sigmoid()\n\n def forward(self, z):\n z = torch.cat(z, 1)\n hidden = self.softplus(self.fc1(z))\n img_mu = self.sigmoid(self.fc2(hidden))\n return img_mu\n\n\nclass Encoder(nn.Module):\n def __init__(self, z_dim, hidden_dim, y_dim):\n super(Encoder, self).__init__()\n\n self.fc1 = nn.Linear(784 + y_dim, hidden_dim)\n self.fc21 = nn.Linear(hidden_dim, z_dim)\n self.fc22 = nn.Linear(hidden_dim, z_dim)\n\n self.softplus = nn.Softplus()\n\n def forward(self, x):\n x = torch.cat(x, 1)\n hidden = self.softplus(self.fc1(x))\n\n z_mu = self.fc21(hidden)\n z_sigma = torch.exp(self.fc22(hidden))\n return z_mu, z_sigma\n\nclass Encoder_y(nn.Module):\n def __init__(self, y_dim, hidden_dim):\n super(Encoder_y, self).__init__()\n\n self.fc1 = nn.Linear(784, hidden_dim)\n self.fc2 = nn.Linear(hidden_dim, y_dim)\n self.softplus = nn.Softplus()\n self.softmax = nn.Softmax()\n\n def forward(self, x):\n hidden = self.softplus(self.fc1(x))\n return self.softmax(self.fc2(hidden))\n\n\nclass VAE(nn.Module):\n def __init__(self, z_dim=50, y_dim=10, hidden_dim=500, use_cuda=False):\n super(VAE, self).__init__()\n\n self.encoder = Encoder(z_dim, hidden_dim, y_dim)\n self.decoder = Decoder(z_dim, hidden_dim, y_dim)\n self.encoder_y = Encoder_y(y_dim, hidden_dim)\n\n if use_cuda:\n self.cuda()\n\n self.use_cuda = use_cuda\n self.z_dim = z_dim\n self.y_dim = y_dim\n self.aux_loss_multiplier = 0.3\n\n def model(self, x, y=None):\n pyro.module('decoder', self.decoder)\n\n z_mu = ng_zeros([x.size(0), self.z_dim], type_as=x.data)\n z_sigma = ng_ones([x.size(0), self.z_dim], type_as=x.data)\n\n z = pyro.sample('latent', dist.normal, z_mu, z_sigma)\n\n alpha_prior = ng_ones([x.size(0), self.y_dim], type_as=y.data) / 10.\n if y is None:\n y = pyro.sample('y', dist.one_hot_categorical, alpha_prior)\n else:\n pyro.observe('y', dist.one_hot_categorical, y, alpha_prior)\n\n img_mu = self.decoder([z, y])\n pyro.observe('x', dist.bernoulli, x, img_mu)\n\n def guide(self, x, y=None):\n pyro.module('encoder', self.encoder)\n\n if y is None:\n alpha = self.encoder_y(x)\n y = pyro.sample('y', dist.one_hot_categorical, alpha)\n\n z_mu, z_sigma = self.encoder([x, y])\n z = pyro.sample(\"latent\", dist.normal, z_mu, z_sigma)\n\n def model_classify(self, x, y):\n alpha = self.encoder_y(x)\n pyro.observe('y_aux', dist.one_hot_categorical,\n y, alpha, log_pdf_mask=self.aux_loss_multiplier)\n\n def guide_classify(self, x, y):\n pass\n\n def reconstruct_img(self, x):\n z_mu, z_sigma = self.encoder(x)\n z = dist.normal(z_mu, z_sigma)\n img_mu = self.decoder(z)\n return img_mu\n\n def generate(self, batch_size=1):\n prior_mu = Variable(torch.zeros([batch_size, self.z_dim]))\n prior_sigma = Variable(torch.ones([batch_size, self.z_dim]))\n\n zs = pyro.sample('z', dist.normal, prior_mu, prior_sigma)\n img_mu = self.decoder(zs)\n xs = pyro.sample('sample', dist.bernoulli, img_mu)\n return img_mu, xs\n\n\nif __name__ == '__main__':\n batch_size = 100\n epochs = 10\n kwargs = {'num_workers': 1, 'pin_memory': True} if False else {}\n train_loader = data.DataLoader(\n datasets.MNIST('./data', train=True, download=True,\n transform=transforms.Compose([\n transforms.ToTensor(),\n ])),\n batch_size=batch_size, shuffle=True, **kwargs)\n test_loader = data.DataLoader(\n datasets.MNIST('./data', train=False, transform=transforms.Compose([\n transforms.ToTensor(),\n ])),\n batch_size=batch_size, shuffle=True, **kwargs)\n\n vae = VAE(use_cuda=False)\n\n adam_args = {'lr': 0.01}\n optimizer = Adam(adam_args)\n\n svi = SVI(vae.model, vae.guide, optimizer, loss='ELBO', enum_discrete=True)\n svi_aux = SVI(vae.model_classify, vae.guide_classify, optimizer, loss='ELBO')\n\n test_img = Variable(test_loader.dataset[0][0])\n\n yp = torch.FloatTensor(batch_size, 10)\n\n for epoch in range(epochs):\n epoch_loss = 0\n for x, y in train_loader:\n yp.zero_()\n yp.scatter_(1, y.view(-1, 1), 1)\n\n x = x.view(-1, 784)\n x, y = Variable(x), Variable(yp)\n\n loss = svi.step(x, y)\n loss = svi_aux.step(x,y)\n epoch_loss += loss\n print(loss)\n print(epoch_loss / len(train_loader.dataset))\n recon_img = vae.reconstruct_img(test_img)\n plt.gray()\n plt.subplot(1, 2, 1)\n plt.imshow(test_img.view(-1, 28, 28).squeeze(0).data.numpy())\n plt.subplot(1, 2, 2)\n plt.imshow(recon_img.view(-1, 28, 28).squeeze(0).data.numpy())\n plt.show()\n\n\n\n", "repo_name": "makora9143/fmvae", "sub_path": "ssvae.py", "file_name": "ssvae.py", "file_ext": "py", "file_size_in_byte": 5661, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "torch.nn.Module", "line_number": 18, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 18, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 22, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 22, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 23, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 23, "usage_type": "name"}, {"api_name": "torch.nn.Softplus", "line_number": 25, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 25, "usage_type": "name"}, {"api_name": "torch.nn.Sigmoid", "line_number": 26, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 26, "usage_type": "name"}, {"api_name": "torch.cat", "line_number": 29, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 35, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 35, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 39, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 39, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 40, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 40, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 41, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 41, "usage_type": "name"}, {"api_name": "torch.nn.Softplus", "line_number": 43, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 43, "usage_type": "name"}, {"api_name": "torch.cat", "line_number": 46, "usage_type": "call"}, {"api_name": "torch.exp", "line_number": 50, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 53, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 53, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 57, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 57, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 58, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 58, "usage_type": "name"}, {"api_name": "torch.nn.Softplus", "line_number": 59, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 59, "usage_type": "name"}, {"api_name": "torch.nn.Softmax", "line_number": 60, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 60, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 67, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 67, "usage_type": "name"}, {"api_name": "pyro.module", "line_number": 84, "usage_type": "call"}, {"api_name": "pyro.util.ng_zeros", "line_number": 86, "usage_type": "call"}, {"api_name": "pyro.util.ng_ones", "line_number": 87, "usage_type": "call"}, {"api_name": "pyro.sample", "line_number": 89, "usage_type": "call"}, {"api_name": "pyro.distributions.normal", "line_number": 89, "usage_type": "attribute"}, {"api_name": "pyro.distributions", "line_number": 89, "usage_type": "name"}, {"api_name": "pyro.util.ng_ones", "line_number": 91, "usage_type": "call"}, {"api_name": "pyro.sample", "line_number": 93, "usage_type": "call"}, {"api_name": "pyro.distributions.one_hot_categorical", "line_number": 93, "usage_type": "attribute"}, {"api_name": "pyro.distributions", "line_number": 93, "usage_type": "name"}, {"api_name": "pyro.observe", "line_number": 95, "usage_type": "call"}, {"api_name": "pyro.distributions.one_hot_categorical", "line_number": 95, "usage_type": "attribute"}, {"api_name": "pyro.distributions", "line_number": 95, "usage_type": "name"}, {"api_name": "pyro.observe", "line_number": 98, "usage_type": "call"}, {"api_name": "pyro.distributions.bernoulli", "line_number": 98, "usage_type": "attribute"}, {"api_name": "pyro.distributions", "line_number": 98, "usage_type": "name"}, {"api_name": "pyro.module", "line_number": 101, "usage_type": "call"}, {"api_name": "pyro.sample", "line_number": 105, "usage_type": "call"}, {"api_name": "pyro.distributions.one_hot_categorical", "line_number": 105, "usage_type": "attribute"}, {"api_name": "pyro.distributions", "line_number": 105, "usage_type": "name"}, {"api_name": "pyro.sample", "line_number": 108, "usage_type": "call"}, {"api_name": "pyro.distributions.normal", "line_number": 108, "usage_type": "attribute"}, {"api_name": "pyro.distributions", "line_number": 108, "usage_type": "name"}, {"api_name": "pyro.observe", "line_number": 112, "usage_type": "call"}, {"api_name": "pyro.distributions.one_hot_categorical", "line_number": 112, "usage_type": "attribute"}, {"api_name": "pyro.distributions", "line_number": 112, "usage_type": "name"}, {"api_name": "pyro.distributions.normal", "line_number": 120, "usage_type": "call"}, {"api_name": "pyro.distributions", "line_number": 120, "usage_type": "name"}, {"api_name": "torch.autograd.Variable", "line_number": 125, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 125, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 126, "usage_type": "call"}, {"api_name": "torch.ones", "line_number": 126, "usage_type": "call"}, {"api_name": "pyro.sample", "line_number": 128, "usage_type": "call"}, {"api_name": "pyro.distributions.normal", "line_number": 128, "usage_type": "attribute"}, {"api_name": "pyro.distributions", "line_number": 128, "usage_type": "name"}, {"api_name": "pyro.sample", "line_number": 130, "usage_type": "call"}, {"api_name": "pyro.distributions.bernoulli", "line_number": 130, "usage_type": "attribute"}, {"api_name": "pyro.distributions", "line_number": 130, "usage_type": "name"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 138, "usage_type": "call"}, {"api_name": "torch.utils.data", "line_number": 138, "usage_type": "name"}, {"api_name": "torchvision.datasets.MNIST", "line_number": 139, "usage_type": "call"}, {"api_name": "torchvision.datasets", "line_number": 139, "usage_type": "name"}, {"api_name": "torchvision.transforms.Compose", "line_number": 140, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 140, "usage_type": "name"}, {"api_name": "torchvision.transforms.ToTensor", "line_number": 141, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 141, "usage_type": "name"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 144, "usage_type": "call"}, {"api_name": "torch.utils.data", "line_number": 144, "usage_type": "name"}, {"api_name": "torchvision.datasets.MNIST", "line_number": 145, "usage_type": "call"}, {"api_name": "torchvision.datasets", "line_number": 145, "usage_type": "name"}, {"api_name": "torchvision.transforms.Compose", "line_number": 145, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 145, "usage_type": "name"}, {"api_name": "torchvision.transforms.ToTensor", "line_number": 146, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 146, "usage_type": "name"}, {"api_name": "pyro.optim.Adam", "line_number": 153, "usage_type": "call"}, {"api_name": "pyro.infer.SVI", "line_number": 155, "usage_type": "call"}, {"api_name": "pyro.infer.SVI", "line_number": 156, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 158, "usage_type": "call"}, {"api_name": "torch.FloatTensor", "line_number": 160, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 169, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.gray", "line_number": 177, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 177, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 178, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 178, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 179, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 179, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 180, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 180, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 181, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 181, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 182, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 182, "usage_type": "name"}]} +{"seq_id": "26907748080", "text": "from django.shortcuts import render, redirect\nfrom django.http import HttpResponse\nfrom django.contrib.auth.decorators import login_required\nfrom .forms import CustomUserCreationForm, ChamadoForm, ComentarioForm\nfrom .models import CustomUser, Chamado, Situacao\nfrom .filters import FiltroChamado\nfrom django.core.paginator import Paginator, EmptyPage, PageNotAnInteger\nfrom datetime import datetime\n\n# Create your views here.\ndef login(request):\n\treturn render(request, 'login.html')\n\ndef inicial(request):\n\treturn redirect('login')\n\n@login_required\ndef perfil(request):\n\tchamados = Chamado.objects.filter(user=request.user).order_by('-id')\n\tpaginator = Paginator(chamados, 7)\n\tpage = request.GET.get('page')\n\tcontacts = paginator.get_page(page)\n\tcontexto = {\n\t'lista_chamado': chamados,\n\t'contacts': contacts\n\t}\n\treturn render(request, 'dashboard/chamados.html', contexto)\n\ndef registro(request):\n\tform = CustomUserCreationForm(request.POST or None)\n\tif form.is_valid():\n\t\tform.save()\n\t\treturn redirect('login')\n\tcontexto = {\n\t\t'form': form\n\t}\n\treturn render(request, 'registration/register.html', contexto)\n\n@login_required\ndef dados(request,id):\n\tif request.user.id == id:\n\t\tuser = CustomUser.objects.get(pk=id)\n\t\tform = CustomUserCreationForm(request.POST or None, instance=user)\n\t\tif form.is_valid():\n\t\t\tform.save()\n\t\t\treturn redirect('perfil')\n\t\tcontexto = {\n\t\t\t'form': form\n\t\t}\n\telse:\n\t\treturn redirect('perfil')\n\treturn render(request, 'registration/register.html', contexto)\n\n@login_required\ndef cadastro(request):\n\tdata_hora_aberto = datetime.now()\n\tform = ChamadoForm(request.POST or None)\n\tif form.is_valid():\n\t\tchamado = form.save(commit=False)\n\t\tchamado.user = request.user\n\t\tchamado.datahora_aberto = data_hora_aberto\n\t\tchamado.save()\n\t\treturn redirect('perfil')\n\tcontexto = {\n\t\t'form': form\n\t}\n\treturn render(request, 'dashboard/registro.html', contexto)\n\n@login_required\ndef editar(request,id):\n\tchamado = Chamado.objects.get(pk=id)\n\tform = ChamadoForm(request.POST or None, instance=chamado)\n\tif form.is_valid():\n\t\tchamado = form.save(commit=False)\n\t\tchamado.user = request.user\n\t\tchamado.save()\n\t\treturn redirect('perfil')\n\tcontexto = {\n\t\t'form': form\n\t}\n\treturn render(request, 'dashboard/registro.html', contexto)\n\n@login_required\ndef apagar(request,id):\n\tchamado = Chamado.objects.get(pk=id)\n\tchamado.delete()\n\treturn redirect('perfil')\n\n@login_required\ndef adminchamados(request):\n\tchamado = Chamado.objects.all().order_by('-id')\n\tmeufiltro = FiltroChamado(request.GET, queryset=chamado)\n\tcontexto = {\n\t'filtro': meufiltro\n\t}\n\treturn render(request, 'admin/admin-chamados.html', contexto)\n\n@login_required\ndef resolverchamado(request, id):\n\tdata_hora_andamento = datetime.now()\n\tchamado = Chamado.objects.get(pk=id)\n\tchamado.situacao_id = 2\n\tchamado.datahora_andamento = data_hora_andamento\n\tchamado.save()\n\treturn redirect('adminchamados')\n\n@login_required\ndef resolvidochamado(request, id):\n\tdata_hora_fechado = datetime.now()\n\tchamado = Chamado.objects.get(pk=id)\n\tchamado.situacao_id = 3\n\tchamado.datahora_fechado = data_hora_fechado\n\tchamado.save()\n\treturn redirect('adminchamados')\n\n@login_required\ndef usuarios(request):\n\tuser = CustomUser.objects.all().order_by('-id')\n\tcontexto = {\n\t'usuarios': user\n\t}\n\treturn render(request, 'admin/usuarios.html', contexto)\n\n@login_required\ndef editaruser(request, id):\n\tuser = CustomUser.objects.get(pk=id)\n\tform = CustomUserCreationForm(request.POST or None, instance=user)\n\tif form.is_valid():\n\t\tform.save()\n\t\treturn redirect('usuarios')\n\tcontexto = {\n\t\t'form': form\n\t}\n\treturn render(request, 'registration/register.html', contexto)\n\n@login_required\ndef apagaruser(request, id):\n\tuser = CustomUser.objects.get(pk=id)\n\tuser.delete()\n\treturn redirect('usuarios')\n\n@login_required\ndef superuser(request, id):\n\tuser = CustomUser.objects.get(pk=id)\n\tuser.is_superuser = 1\n\tuser.save()\n\treturn redirect('usuarios')\n\n@login_required\ndef normaluser(request, id):\n\tuser = CustomUser.objects.get(pk=id)\n\tuser.is_superuser = 0\n\tuser.save()\n\treturn redirect('usuarios')\n\n@login_required\ndef comentario(request, id):\n\tchamado = Chamado.objects.get(pk=id)\n\tform = ComentarioForm(request.POST or None, instance=chamado)\n\tif form.is_valid():\n\t\tform.save()\n\t\treturn redirect('adminchamados')\n\tcontexto = {\n\t\t'form': form\n\t}\n\treturn render(request, 'admin/comentario.html', contexto)\n\n", "repo_name": "antoniocirilo/chamados", "sub_path": "TI/chamados/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 4340, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "37", "api": [{"api_name": "django.shortcuts.render", "line_number": 12, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 15, "usage_type": "call"}, {"api_name": "models.Chamado.objects.filter", "line_number": 19, "usage_type": "call"}, {"api_name": "models.Chamado.objects", "line_number": 19, "usage_type": "attribute"}, {"api_name": "models.Chamado", "line_number": 19, "usage_type": "name"}, {"api_name": "django.core.paginator.Paginator", "line_number": 20, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 27, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 17, "usage_type": "name"}, {"api_name": "forms.CustomUserCreationForm", "line_number": 30, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 33, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 37, "usage_type": "call"}, {"api_name": "models.CustomUser.objects.get", "line_number": 42, "usage_type": "call"}, {"api_name": "models.CustomUser.objects", "line_number": 42, "usage_type": "attribute"}, {"api_name": "models.CustomUser", "line_number": 42, "usage_type": "name"}, {"api_name": "forms.CustomUserCreationForm", "line_number": 43, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 46, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 51, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 52, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 39, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 56, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 56, "usage_type": "name"}, {"api_name": "forms.ChamadoForm", "line_number": 57, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 63, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 67, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 54, "usage_type": "name"}, {"api_name": "models.Chamado.objects.get", "line_number": 71, "usage_type": "call"}, {"api_name": "models.Chamado.objects", "line_number": 71, "usage_type": "attribute"}, {"api_name": "models.Chamado", "line_number": 71, "usage_type": "name"}, {"api_name": "forms.ChamadoForm", "line_number": 72, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 77, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 81, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 69, "usage_type": "name"}, {"api_name": "models.Chamado.objects.get", "line_number": 85, "usage_type": "call"}, {"api_name": "models.Chamado.objects", "line_number": 85, "usage_type": "attribute"}, {"api_name": "models.Chamado", "line_number": 85, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 87, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 83, "usage_type": "name"}, {"api_name": "models.Chamado.objects.all", "line_number": 91, "usage_type": "call"}, {"api_name": "models.Chamado.objects", "line_number": 91, "usage_type": "attribute"}, {"api_name": "models.Chamado", "line_number": 91, "usage_type": "name"}, {"api_name": "filters.FiltroChamado", "line_number": 92, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 96, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 89, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 100, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 100, "usage_type": "name"}, {"api_name": "models.Chamado.objects.get", "line_number": 101, "usage_type": "call"}, {"api_name": "models.Chamado.objects", "line_number": 101, "usage_type": "attribute"}, {"api_name": "models.Chamado", "line_number": 101, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 105, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 98, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 109, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 109, "usage_type": "name"}, {"api_name": "models.Chamado.objects.get", "line_number": 110, "usage_type": "call"}, {"api_name": "models.Chamado.objects", "line_number": 110, "usage_type": "attribute"}, {"api_name": "models.Chamado", "line_number": 110, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 114, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 107, "usage_type": "name"}, {"api_name": "models.CustomUser.objects.all", "line_number": 118, "usage_type": "call"}, {"api_name": "models.CustomUser.objects", "line_number": 118, "usage_type": "attribute"}, {"api_name": "models.CustomUser", "line_number": 118, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 122, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 116, "usage_type": "name"}, {"api_name": "models.CustomUser.objects.get", "line_number": 126, "usage_type": "call"}, {"api_name": "models.CustomUser.objects", "line_number": 126, "usage_type": "attribute"}, {"api_name": "models.CustomUser", "line_number": 126, "usage_type": "name"}, {"api_name": "forms.CustomUserCreationForm", "line_number": 127, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 130, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 134, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 124, "usage_type": "name"}, {"api_name": "models.CustomUser.objects.get", "line_number": 138, "usage_type": "call"}, {"api_name": "models.CustomUser.objects", "line_number": 138, "usage_type": "attribute"}, {"api_name": "models.CustomUser", "line_number": 138, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 140, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 136, "usage_type": "name"}, {"api_name": "models.CustomUser.objects.get", "line_number": 144, "usage_type": "call"}, {"api_name": "models.CustomUser.objects", "line_number": 144, "usage_type": "attribute"}, {"api_name": "models.CustomUser", "line_number": 144, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 147, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 142, "usage_type": "name"}, {"api_name": "models.CustomUser.objects.get", "line_number": 151, "usage_type": "call"}, {"api_name": "models.CustomUser.objects", "line_number": 151, "usage_type": "attribute"}, {"api_name": "models.CustomUser", "line_number": 151, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 154, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 149, "usage_type": "name"}, {"api_name": "models.Chamado.objects.get", "line_number": 158, "usage_type": "call"}, {"api_name": "models.Chamado.objects", "line_number": 158, "usage_type": "attribute"}, {"api_name": "models.Chamado", "line_number": 158, "usage_type": "name"}, {"api_name": "forms.ComentarioForm", "line_number": 159, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 162, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 166, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 156, "usage_type": "name"}]} +{"seq_id": "10259421406", "text": "import plotly.figure_factory as ff\r\nimport plotly.graph_objects as go\r\nimport statistics\r\nimport random\r\nimport pandas as pd\r\nimport csv\r\n\r\ndf = pd.read_csv(\"medium_data.csv\")\r\ndata = df[\"reading_time\"].tolist()\r\n\r\npopulation_mean = statistics.mean(data)\r\n\r\npopulation_st = statistics.stdev(data)\r\nprint(population_mean, population_st)\r\n\r\ndef randomsetofmean(counter):\r\n dataset = []\r\n for i in range(0,counter):\r\n random_index = random.randint(0, len(data)-1)\r\n value = data[random_index]\r\n dataset.append(value)\r\n mean = statistics.mean(dataset)\r\n return mean\r\n\r\ndef show_fig(mean_list):\r\n df = mean_list\r\n \r\n fig = ff.create_distplot([df], ['mean'], show_hist = False)\r\n fig.show()\r\n \r\ndef setup():\r\n mean_list = []\r\n for i in range(0,100):\r\n setofmeans = randomsetofmean(30)\r\n mean_list.append(setofmeans)\r\n show_fig(mean_list)\r\n \r\nsetup()", "repo_name": "SamirDhakal/Project-110", "sub_path": "code.py", "file_name": "code.py", "file_ext": "py", "file_size_in_byte": 917, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "pandas.read_csv", "line_number": 8, "usage_type": "call"}, {"api_name": "statistics.mean", "line_number": 11, "usage_type": "call"}, {"api_name": "statistics.stdev", "line_number": 13, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 19, "usage_type": "call"}, {"api_name": "statistics.mean", "line_number": 22, "usage_type": "call"}, {"api_name": "plotly.figure_factory.create_distplot", "line_number": 28, "usage_type": "call"}, {"api_name": "plotly.figure_factory", "line_number": 28, "usage_type": "name"}]} +{"seq_id": "10427993019", "text": "import random, string\nimport time\n\nfrom grid import Grid\nfrom smart_search import full_search as smart_search\nfrom trie import Trie\n\n\ndef run_smart_search(grid: Grid, dictionary: set) -> set:\n alpha = set(grid.letters) # get unique letters in grid\n alpha_dict = {l: i for i, l in enumerate(alpha)} # convert unique letters to lookup map\n\n word_trie = Trie(alpha_dict) # pass alpha_dict to Trie so as to not waste space with unavailable chars\n filtered_dictionary = set() # keep track of words after filtering for available chars\n for word in dictionary:\n if set(word).difference(alpha) or len(word) > len(grid.letters):\n continue # skip words which are not subset of available alphabet or are longer than grid\n filtered_dictionary.add(word)\n word_trie.insert(word)\n\n print('Available Letters:', alpha)\n print(f'Filtered Dictionary has {len(filtered_dictionary)} words')\n\n all_words = set()\n # Perform a full search starting from each of the cells in the grid\n for i in range(len(grid.letters)):\n all_words = all_words.union(smart_search(grid, i, word_trie))\n\n return all_words\n\n\ndef run():\n C = min(int(input('Desired number of columns in grid (<=10): ')), 10)\n R = min(int(input('Desired number of rows in grid (<=10): ')), 10)\n LETTERS = string.ascii_lowercase\n ROWS = [[random.choice(LETTERS)\n for _ in range(C)]\n for _ in range (R)]\n grid = Grid(ROWS)\n print('\\n'.join([' | '.join(row) for row in ROWS]))\n\n # read in sample dictionary - taken from https://www.mit.edu/~ecprice/wordlist.10000\n with open('./data/english_words.txt') as f:\n word_dictionary = f.read().splitlines()\n\n simple_word_dictionary = set(word_dictionary) # ensure no duplicate words passed in dict\n start_time = time.time()\n all_words = run_smart_search(grid, simple_word_dictionary)\n print(f'Took {time.time() - start_time} to build dictionary and find {len(all_words)} words in grid')\n assert(all_words.issubset(simple_word_dictionary))\n\n all_words = list(all_words)\n print('\\nSample of found words:')\n print_words = all_words if len(all_words) < 20 else all_words[:20]\n print(print_words)\n\n with open('./data/extracted_words.txt', 'w') as f:\n f.writelines([w + '\\n' for w in all_words])\n\t\nif __name__ == '__main__':\n run()\n", "repo_name": "michaelfedell/word_finder", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 2374, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "grid.Grid", "line_number": 9, "usage_type": "name"}, {"api_name": "grid.letters", "line_number": 10, "usage_type": "attribute"}, {"api_name": "trie.Trie", "line_number": 13, "usage_type": "call"}, {"api_name": "grid.letters", "line_number": 16, "usage_type": "attribute"}, {"api_name": "grid.letters", "line_number": 26, "usage_type": "attribute"}, {"api_name": "smart_search.full_search", "line_number": 27, "usage_type": "call"}, {"api_name": "string.ascii_lowercase", "line_number": 35, "usage_type": "attribute"}, {"api_name": "random.choice", "line_number": 36, "usage_type": "call"}, {"api_name": "grid.Grid", "line_number": 39, "usage_type": "call"}, {"api_name": "time.time", "line_number": 47, "usage_type": "call"}, {"api_name": "time.time", "line_number": 49, "usage_type": "call"}]} +{"seq_id": "2800331327", "text": "\"\"\"CLI list command for ModernROOTTools.\"\"\"\nimport pathlib\nimport logging\nimport sys\n\nimport click\n\nfrom mrtools import cache\nfrom mrtools import utils\nfrom mrtools import exceptions\n\nlog = logging.getLogger(\".\".join(__name__.split(\".\")[:2]))\n\n\n@click.command(name=\"list\")\n@click.argument(\n \"dataset-file\",\n metavar=\"YAML\",\n required=True,\n nargs=-1,\n type=click.Path(dir_okay=False, exists=True, readable=True, path_type=pathlib.Path),\n)\n@click.option(\n \"-p\",\n \"--period\",\n metavar=\"PERIOD\",\n default=[],\n multiple=True,\n help=\"Run periods. [default: all periods]\",\n)\n@click.option(\n \"--tree/--no-tree\",\n default=False,\n help=\"List the full dataset tree\",\n show_default=True,\n)\ndef list_datasets(\n dataset_file: list[pathlib.Path],\n period: list[str],\n tree: bool,\n) -> None:\n \"\"\"List the datasets defined in YAML.\"\"\"\n\n if not dataset_file:\n log.error(\"No dataset file given\")\n sys.exit(1)\n\n dc = cache.DatasetCache()\n for df in dataset_file:\n dc.load(df)\n\n try:\n period = dc.verify_period(period)\n except exceptions.MRTError:\n log.exception(\"Unknown period: %s\", \", \".join(period))\n sys.exit()\n\n for p in period:\n print(f\"Period {p}:\")\n if tree:\n for parent, _groups, children in dc.walk(p):\n print(\n f\" {parent} ({parent.type.name},\"\n f\"Size {utils.human_readable_size(parent.size)},\"\n f\"{len(parent)} file(s))\"\n )\n for child in children:\n print(\n f\" {child} ({child.type.name},\"\n f\"Size {utils.human_readable_size(child.size)},\"\n f\"{len(child)} file(s))\"\n )\n else:\n for s in dc.list(p):\n print(\n f\" {s} ({s.type.name},\"\n f\"Size {utils.human_readable_size(s.size)},\"\n f\"{len(s)} file(s))\"\n )\n", "repo_name": "dietrichliko/ModernRootTools", "sub_path": "src/mrtools/commands/list.py", "file_name": "list.py", "file_ext": "py", "file_size_in_byte": 2057, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "logging.getLogger", "line_number": 12, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 38, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 46, "usage_type": "call"}, {"api_name": "mrtools.cache.DatasetCache", "line_number": 48, "usage_type": "call"}, {"api_name": "mrtools.cache", "line_number": 48, "usage_type": "name"}, {"api_name": "mrtools.exceptions.MRTError", "line_number": 54, "usage_type": "attribute"}, {"api_name": "mrtools.exceptions", "line_number": 54, "usage_type": "name"}, {"api_name": "sys.exit", "line_number": 56, "usage_type": "call"}, {"api_name": "mrtools.utils.human_readable_size", "line_number": 64, "usage_type": "call"}, {"api_name": "mrtools.utils", "line_number": 64, "usage_type": "name"}, {"api_name": "mrtools.utils.human_readable_size", "line_number": 70, "usage_type": "call"}, {"api_name": "mrtools.utils", "line_number": 70, "usage_type": "name"}, {"api_name": "mrtools.utils.human_readable_size", "line_number": 77, "usage_type": "call"}, {"api_name": "mrtools.utils", "line_number": 77, "usage_type": "name"}, {"api_name": "click.command", "line_number": 15, "usage_type": "call"}, {"api_name": "click.argument", "line_number": 16, "usage_type": "call"}, {"api_name": "click.Path", "line_number": 21, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 21, "usage_type": "attribute"}, {"api_name": "click.option", "line_number": 23, "usage_type": "call"}, {"api_name": "click.option", "line_number": 31, "usage_type": "call"}]} +{"seq_id": "34888797948", "text": "\"\"\"This is software meant to autoplay the game called 2048.\"\"\"\n\nimport os\nimport sys\nimport argparse\nfrom selenium import webdriver\nfrom selenium.webdriver.common.keys import Keys\nfrom selenium.common.exceptions import NoSuchElementException\n\n\ndef is_game_over(driver):\n \"\"\"Returns true if game is over. Otherwise, returns False.\"\"\"\n try:\n driver.find_element_by_class_name('game-over')\n return True\n except NoSuchElementException:\n return False\n\n\ndef al_strategy(driver):\n \"\"\"This strategy is from the book 'Automate the Boring Stuff with Python' by Al Sweigart.\n\n The strategy is to move up, right, down and then left and then repeat.\n \"\"\"\n html_elem = driver.find_element_by_tag_name('html')\n html_elem.send_keys(Keys.UP)\n\n # need to repeat getting the html elem to avoid error when browser refreshes\n html_elem = driver.find_element_by_tag_name('html')\n html_elem.send_keys(Keys.RIGHT)\n\n html_elem = driver.find_element_by_tag_name('html')\n html_elem.send_keys(Keys.DOWN)\n\n html_elem = driver.find_element_by_tag_name('html')\n html_elem.send_keys(Keys.LEFT)\n\n\ndef check_file_exists(filepath):\n \"\"\"Returns true if filepath exists. Otherwise, returns false.\"\"\"\n if not os.path.isfile(filepath):\n print(f\"\"\"The file does not exist at {filepath}.\"\"\")\n sys.exit()\n\n\ndef init_webdriver(browser_name, webdriver_filepath):\n \"\"\"Returns the Selenium webdriver object with browser's name and\n its webdriver filepath is needed.\"\"\"\n\n browser_name_lower = browser_name.lower()\n if browser_name_lower == \"firefox\":\n # Selenium and Firefox browser needs the geckodriver and its filepath\n\n check_file_exists(webdriver_filepath)\n return webdriver.Firefox(executable_path=webdriver_path)\n if browser_name_lower == \"safari\":\n # Selenium and Safari browser needs user to enable the\n # 'Allow Remote Automation' option in Safari's Develop menu to control Safari via WebDriver\n\n return webdriver.Safari()\n if browser_name_lower in (\"chrome\", \"googlechrome\"):\n # Selenium and Google Chrome browser needs the corresponding chromedriver and its filepath\n\n check_file_exists(webdriver_filepath)\n return webdriver.Chrome(executable_path=webdriver_path)\n\n # Browser is not supported.\n print(f\"\"\"This program does not support {browser_name}.\"\"\")\n sys.exit()\n\n\ndef init_argparse():\n \"\"\"Initalizes the argparser.\"\"\"\n parser = argparse.ArgumentParser(\n usage=\"%(prog)s [BROWSER_NAME] --filepath [WEBDRIVER_PATH if needed]\",\n description=\"Autoplays 2048 via supported browsers that may require \\\n filepath of the webdriver.\"\n )\n parser.add_argument(\n \"-v\",\n \"--version\",\n action=\"version\",\n version=f\"{parser.prog} version 1.0.0\"\n )\n\n parser.add_argument(\n \"--loop\",\n action=\"store_true\",\n help=\"set loop to be True which will keep playing the game till user quits\"\n )\n\n parser.add_argument(\n \"browser\",\n metavar=\"browser\",\n type=str,\n help=\"the name of the web browser. Firefox|Chrome|Safari\"\n )\n\n parser.add_argument(\n \"--filepath\",\n dest=\"filepath\",\n help=\"the filepath to the webdriver\"\n )\n return parser\n\n\ndef click_restart_button(driver):\n \"\"\"Clicks the retry button.\"\"\"\n # Need to press the New game button in anchor tag with class of restart-button.\n try:\n restart_button = driver.find_element_by_class_name('restart-button')\n restart_button.click()\n except NoSuchElementException:\n print(\"Cannot find element with the classs of 'restart-button\")\n\nif __name__ == \"__main__\":\n\n arg_parser = init_argparse()\n args = arg_parser.parse_args()\n browser = args.browser\n webdriver_path = args.filepath\n\n chosen_driver = init_webdriver(browser, webdriver_path)\n chosen_driver.get(\"https://gabrielecirulli.github.io/2048/\")\n\n print(args.loop)\n if not args.loop:\n while not is_game_over(chosen_driver):\n # This is algorithm from book which says to repeat up, right, down and left.\n al_strategy(chosen_driver)\n\n # Either close the browser or stop this program to stop running the program.\n else:\n while True:\n while not is_game_over(chosen_driver):\n # This is algorithm from book which says to repeat up, right, down and left.\n al_strategy(chosen_driver)\n click_restart_button(chosen_driver)\n\n # Either close the browser or stop this program to stop running the program.\n", "repo_name": "wwyl1234/autoplay-2048", "sub_path": "autoplay_2048.py", "file_name": "autoplay_2048.py", "file_ext": "py", "file_size_in_byte": 4637, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "selenium.common.exceptions.NoSuchElementException", "line_number": 16, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.keys.Keys.UP", "line_number": 26, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.keys.Keys", "line_number": 26, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.keys.Keys.RIGHT", "line_number": 30, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.keys.Keys", "line_number": 30, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.keys.Keys.DOWN", "line_number": 33, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.keys.Keys", "line_number": 33, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.keys.Keys.LEFT", "line_number": 36, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.keys.Keys", "line_number": 36, "usage_type": "name"}, {"api_name": "os.path.isfile", "line_number": 41, "usage_type": "call"}, {"api_name": "os.path", "line_number": 41, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 43, "usage_type": "call"}, {"api_name": "selenium.webdriver.Firefox", "line_number": 55, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 55, "usage_type": "name"}, {"api_name": "selenium.webdriver.Safari", "line_number": 60, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 60, "usage_type": "name"}, {"api_name": "selenium.webdriver.Chrome", "line_number": 65, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 65, "usage_type": "name"}, {"api_name": "sys.exit", "line_number": 69, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 74, "usage_type": "call"}, {"api_name": "selenium.common.exceptions.NoSuchElementException", "line_number": 113, "usage_type": "name"}]} +{"seq_id": "21408952397", "text": "from ntpath import join\r\nimport sys\r\nimport random\r\nimport argparse\r\nimport os\r\nimport csv\r\nimport time\r\n\r\nfrom PySide6 import QtCore, QtWidgets, QtGui\r\nfrom PySide6.QtWidgets import QPushButton, QComboBox, QMainWindow, QMenu, QDockWidget, QLabel\r\nfrom PySide6.QtCore import Slot\r\nfrom PySide6.QtGui import QAction, QImage, QPixmap\r\nimport pyqtgraph as pg\r\n\r\nimport numpy as np\r\nimport mediapipe as mp\r\nimport cv2\r\n\r\nfrom image_data_providers import PyRealSenseCameraProvider, PyRealSenseVideoProvider, WebcamProvider\r\nfrom video_tracking.rt_pose_estimation import estimate_pose\r\n\r\nfrom opensim_tools import calculate_angle, AngleTraj, path_planning\r\n\r\nfrom skeleton_display import SkeletonWidget\r\nfrom implant_display import ImplantWidget\r\nfrom coordinate_display import CoordinatePlotWidget\r\n\r\nmp_drawing = mp.solutions.drawing_utils\r\nmp_drawing_styles = mp.solutions.drawing_styles\r\nmp_pose = mp.solutions.pose\r\n\r\nclass MainWindow(QMainWindow):\r\n def __init__(self, pose, image_data_provider):\r\n super().__init__()\r\n\r\n self.setGeometry(100, 100, 1400, 800)\r\n\r\n self.pose = pose\r\n self.image_data_provider = image_data_provider\r\n\r\n self.current_frame = 0\r\n self.frame_indices = list(range(self.current_frame))\r\n\r\n self.menu_bar = self.menuBar()\r\n\r\n self.skeleton_widget = SkeletonWidget()\r\n self.skeleton_widget.resize(800, 600)\r\n\r\n self.setCentralWidget(self.skeleton_widget)\r\n\r\n self.implant_dock_widget = QDockWidget('Implant')\r\n self.implant_dock_widget.setWidget(ImplantWidget())\r\n self.addDockWidget(QtCore.Qt.LeftDockWidgetArea, self.implant_dock_widget)\r\n self.coordinate_plot_dock_widget = QDockWidget('Coordinates', self)\r\n self.coordinate_plot_dock_widget.setWidget(CoordinatePlotWidget())\r\n self.coordinate_plot_dock_widget.hide()\r\n\r\n self.rgb_image_dock_widget = QDockWidget('RGB image', self)\r\n self.rgb_image_dock_widget.setWidget(QLabel())\r\n self.rgb_image_dock_widget.hide()\r\n\r\n self.depth_image_dock_widget = QDockWidget('Depth image', self)\r\n self.depth_image_dock_widget.setWidget(QLabel())\r\n self.depth_image_dock_widget.hide()\r\n\r\n self.view_menu = self.menu_bar.addMenu('View')\r\n self.view_menu.addAction(self.implant_dock_widget.toggleViewAction())\r\n self.view_menu.addAction(self.coordinate_plot_dock_widget.toggleViewAction())\r\n self.view_menu.addAction(self.rgb_image_dock_widget.toggleViewAction())\r\n self.view_menu.addAction(self.depth_image_dock_widget.toggleViewAction())\r\n\r\n wrist, shoulder = self.get_pos()\r\n\r\n wrist_pos_i = [0.2, round(0.4 - wrist[1], 1), round(-wrist[0], 1)]\r\n wrist_pos_f = [0.1, round(0.4 - shoulder[1], 1), round(-shoulder[0], 1)]\r\n\r\n angle_traj, time_traj = path_planning(wrist_pos_i, wrist_pos_f)\r\n\r\n angle_traj_2, time_traj_2 = path_planning(wrist_pos_f, wrist_pos_i)\r\n angle_traj = np.concatenate([angle_traj,angle_traj_2])\r\n time_traj_2 = time_traj_2 + np.max(time_traj)\r\n time_traj = np.concatenate([time_traj,time_traj_2])\r\n\r\n for i in range(10):\r\n angle_traj = np.concatenate([angle_traj,angle_traj])\r\n time_traj_add = time_traj + np.max(time_traj)\r\n time_traj = np.concatenate([time_traj,time_traj_add])\r\n\r\n self.set_trajectory(angle_traj[:,1], time_traj)\r\n\r\n self.timer = QtCore.QTimer(self)\r\n self.connect(self.timer, QtCore.SIGNAL(\"timeout()\"), lambda: self.update())\r\n update_interval = 100\r\n self.timer.start(update_interval)\r\n\r\n self.features_update = None\r\n self.results = None\r\n self.rgb_image = None\r\n\r\n self.target_angle = self.angle_traj_widget.lower_bound\r\n\r\n @Slot()\r\n def update(self):\r\n self.update_model()\r\n self.update_view()\r\n\r\n def update_model(self):\r\n self.current_frame += 1\r\n\r\n #self.frame_indices = self.frame_indices[1:]\r\n #self.frame_indices.append(self.frame_indices[-1] + 1)\r\n self.frame_indices.append(self.current_frame)\r\n\r\n self.rgb_image, self.depth_image = self.image_data_provider.retrieve_rgb_depth_image()\r\n\r\n # Color image of the depth\r\n depth_colormap = cv2.applyColorMap(cv2.convertScaleAbs(self.depth_image, alpha=0.05), cv2.COLORMAP_JET)\r\n\r\n # TODO decide on whether to use the depth_image or the depth_colormap\r\n depth_qimage = QImage(depth_colormap.data, depth_colormap.shape[1], depth_colormap.shape[0], QImage.Format_BGR888)\r\n self.depth_image_dock_widget.widget().setPixmap(QPixmap.fromImage(depth_qimage))\r\n\r\n # Estimate the pose with MediaPipe\r\n self.raw_joint_positions, self.filtered_joint_positions, self.raw_bones, self.filtered_bones, self.results = \\\r\n estimate_pose(self.pose, self.rgb_image, self.depth_image, self.image_data_provider.depth_scale)\r\n\r\n mp_drawing.draw_landmarks(\r\n self.rgb_image,\r\n self.results.pose_landmarks,\r\n mp_pose.POSE_CONNECTIONS,\r\n landmark_drawing_spec=mp_drawing_styles.get_default_pose_landmarks_style())\r\n\r\n rgb_qimage = QImage(self.rgb_image, self.rgb_image.shape[1], self.rgb_image.shape[0], QImage.Format_BGR888)\r\n self.rgb_image_dock_widget.widget().setPixmap(QPixmap.fromImage(rgb_qimage))\r\n\r\n x, y, z = self.raw_joint_positions[self.coordinate_plot_dock_widget.widget().selected_joint]\r\n filtered_x, filtered_y, filtered_z = self.filtered_joint_positions[self.coordinate_plot_dock_widget.widget().selected_joint]\r\n\r\n self.features_update = {'x_val': x, 'y_val': y, 'z_val': z, 'filtered_z_val': filtered_z}\r\n \r\n def update_view(self):\r\n #print(\"Updating view, has traj?\", self.has_traj)\r\n self.coordinate_plot_dock_widget.widget().update(self.frame_indices, self.features_update)\r\n\r\n # Get coordinates\r\n landmarks = self.results.pose_world_landmarks.landmark\r\n shoulder = [landmarks[self.angle_traj_widget.joint1.value].x,\r\n landmarks[self.angle_traj_widget.joint1.value].y]\r\n elbow = [landmarks[self.angle_traj_widget.joint2.value].x,\r\n landmarks[self.angle_traj_widget.joint2.value].y]\r\n wrist = [landmarks[self.angle_traj_widget.joint3.value].x,\r\n landmarks[self.angle_traj_widget.joint3.value].y]\r\n\r\n angle = calculate_angle(shoulder, elbow, wrist)\r\n angle = abs(angle - 180)\r\n\r\n if angle < self.angle_traj_widget.lower_bound:\r\n self.target_angle = self.angle_traj_widget.upper_bound\r\n elif angle > self.angle_traj_widget.upper_bound:\r\n self.target_angle = self.angle_traj_widget.lower_bound\r\n\r\n # Update angle_traj_widget by updating angle and time values:\r\n self.angle_traj_widget.update_plot(angle, self.rgb_image)\r\n\r\n self.skeleton_widget.update_plot_data(self.raw_joint_positions, self.raw_bones, self.filtered_joint_positions, self.filtered_bones)\r\n\r\n self.implant_dock_widget.widget().update(angle, self.target_angle)\r\n\r\n def get_pos(self):\r\n rgb_image, depth_image = self.image_data_provider.retrieve_rgb_depth_image()\r\n _, _, _, _, results = estimate_pose(self.pose, rgb_image, depth_image, self.image_data_provider.depth_scale)\r\n landmarks = results.pose_world_landmarks.landmark\r\n\r\n # Get coordinates\r\n shoulder = [landmarks[mp_pose.PoseLandmark.RIGHT_SHOULDER.value].x, landmarks[mp_pose.PoseLandmark.RIGHT_SHOULDER.value].y]\r\n # elbow = [landmarks[mp_pose.PoseLandmark.RIGHT_ELBOW.value].x,landmarks[mp_pose.PoseLandmark.RIGHT_ELBOW.value].y]\r\n wrist = [landmarks[mp_pose.PoseLandmark.RIGHT_WRIST.value].x, landmarks[mp_pose.PoseLandmark.RIGHT_WRIST.value].y]\r\n\r\n return wrist, shoulder\r\n\r\n def set_trajectory(self, angle_traj, time_traj):\r\n self.angle_traj_widget = AngleTraj(angle_traj, time_traj)\r\n self.angle_traj_widget.setWindowTitle('Trajectory window')\r\n self.angle_traj_widget.time_begin = time.time()\r\n self.angle_traj_widget.resize(800, 400)\r\n self.angle_traj_widget.show()\r\n\r\nif __name__ == '__main__':\r\n parser = argparse.ArgumentParser()\r\n parser.add_argument(\"-s\", \"--source\", default='video')\r\n parser.add_argument(\"-d\", \"--dir\", default=\"C:\\\\Users\\\\cleme\\\\Documents\\\\EPFL\\\\Master\\\\MA-3\\\\sensor\\\\data\\\\\")\r\n parser.add_argument(\"-z\", \"--depth-file\")\r\n parser.add_argument(\"-w\", \"--with-depth\")\r\n args = parser.parse_args()\r\n\r\n try:\r\n if args.source == 'video':\r\n image_data_provider = PyRealSenseVideoProvider(file_path=os.path.join(args.dir, args.depth_file))\r\n elif args.source == 'pyrealsense':\r\n image_data_provider = PyRealSenseCameraProvider()\r\n elif args.source == 'webcam':\r\n image_data_provider = WebcamProvider()\r\n\r\n with mp_pose.Pose(static_image_mode=False,\r\n model_complexity=2,\r\n min_detection_confidence=0.5,\r\n min_tracking_confidence=0.5) as pose:\r\n\r\n app = QtWidgets.QApplication([])\r\n\r\n main_window = MainWindow(pose, image_data_provider)\r\n main_window.show()\r\n\r\n sys.exit(app.exec())\r\n finally:\r\n image_data_provider.stop()", "repo_name": "davidcian/motion-tracking-triggered-ees", "sub_path": "gui/native_gui.py", "file_name": "native_gui.py", "file_ext": "py", "file_size_in_byte": 8788, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "37", "api": [{"api_name": "mediapipe.solutions", "line_number": 28, "usage_type": "attribute"}, {"api_name": "mediapipe.solutions", "line_number": 29, "usage_type": "attribute"}, {"api_name": "mediapipe.solutions", "line_number": 30, "usage_type": "attribute"}, {"api_name": "PySide6.QtWidgets.QMainWindow", "line_number": 32, "usage_type": "name"}, {"api_name": "skeleton_display.SkeletonWidget", "line_number": 46, "usage_type": "call"}, {"api_name": "PySide6.QtWidgets.QDockWidget", "line_number": 51, "usage_type": "call"}, {"api_name": "implant_display.ImplantWidget", "line_number": 52, "usage_type": "call"}, {"api_name": "PySide6.QtCore.Qt", "line_number": 53, "usage_type": "attribute"}, {"api_name": "PySide6.QtCore", "line_number": 53, "usage_type": "name"}, {"api_name": "PySide6.QtWidgets.QDockWidget", "line_number": 54, "usage_type": "call"}, {"api_name": "coordinate_display.CoordinatePlotWidget", "line_number": 55, "usage_type": "call"}, {"api_name": "PySide6.QtWidgets.QDockWidget", "line_number": 58, "usage_type": "call"}, {"api_name": "PySide6.QtWidgets.QLabel", "line_number": 59, "usage_type": "call"}, {"api_name": "PySide6.QtWidgets.QDockWidget", "line_number": 62, "usage_type": "call"}, {"api_name": "PySide6.QtWidgets.QLabel", "line_number": 63, "usage_type": "call"}, {"api_name": "opensim_tools.path_planning", "line_number": 77, "usage_type": "call"}, {"api_name": "opensim_tools.path_planning", "line_number": 79, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 80, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 81, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 82, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 85, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 86, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 87, "usage_type": "call"}, {"api_name": "PySide6.QtCore.QTimer", "line_number": 91, "usage_type": "call"}, {"api_name": "PySide6.QtCore", "line_number": 91, "usage_type": "name"}, {"api_name": "PySide6.QtCore.SIGNAL", "line_number": 92, "usage_type": "call"}, {"api_name": "PySide6.QtCore", "line_number": 92, "usage_type": "name"}, {"api_name": "PySide6.QtCore.Slot", "line_number": 102, "usage_type": "call"}, {"api_name": "cv2.applyColorMap", "line_number": 117, "usage_type": "call"}, {"api_name": "cv2.convertScaleAbs", "line_number": 117, "usage_type": "call"}, {"api_name": "cv2.COLORMAP_JET", "line_number": 117, "usage_type": "attribute"}, {"api_name": "PySide6.QtGui.QImage", "line_number": 120, "usage_type": "call"}, {"api_name": "PySide6.QtGui.QImage.Format_BGR888", "line_number": 120, "usage_type": "attribute"}, {"api_name": "PySide6.QtGui.QPixmap.fromImage", "line_number": 121, "usage_type": "call"}, {"api_name": "PySide6.QtGui.QPixmap", "line_number": 121, "usage_type": "name"}, {"api_name": "video_tracking.rt_pose_estimation.estimate_pose", "line_number": 125, "usage_type": "call"}, {"api_name": "PySide6.QtGui.QImage", "line_number": 133, "usage_type": "call"}, {"api_name": "PySide6.QtGui.QImage.Format_BGR888", "line_number": 133, "usage_type": "attribute"}, {"api_name": "PySide6.QtGui.QPixmap.fromImage", "line_number": 134, "usage_type": "call"}, {"api_name": "PySide6.QtGui.QPixmap", "line_number": 134, "usage_type": "name"}, {"api_name": "opensim_tools.calculate_angle", "line_number": 154, "usage_type": "call"}, {"api_name": "video_tracking.rt_pose_estimation.estimate_pose", "line_number": 171, "usage_type": "call"}, {"api_name": "opensim_tools.AngleTraj", "line_number": 182, "usage_type": "call"}, {"api_name": "time.time", "line_number": 184, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 189, "usage_type": "call"}, {"api_name": "image_data_providers.PyRealSenseVideoProvider", "line_number": 198, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 198, "usage_type": "call"}, {"api_name": "os.path", "line_number": 198, "usage_type": "attribute"}, {"api_name": "image_data_providers.PyRealSenseCameraProvider", "line_number": 200, "usage_type": "call"}, {"api_name": "image_data_providers.WebcamProvider", "line_number": 202, "usage_type": "call"}, {"api_name": "PySide6.QtWidgets.QApplication", "line_number": 209, "usage_type": "call"}, {"api_name": "PySide6.QtWidgets", "line_number": 209, "usage_type": "name"}, {"api_name": "sys.exit", "line_number": 214, "usage_type": "call"}]} +{"seq_id": "16743922027", "text": "import unittest\nfrom pathlib import Path\n\nimport yaml\n\nfrom taskcat.testing import CFNTest\n\n\nclass TestRetain(unittest.TestCase):\n @classmethod\n def setUpClass(cls):\n cur_dir = Path(__file__).parent\n templates = Path(\"../../../tests/data/\")\n cls.template_dir = cur_dir / templates / \"retain-resources\"\n\n def test_from_file(self):\n test = CFNTest.from_file(project_root=self.template_dir.resolve())\n\n with test as stacks:\n\n for stack in stacks:\n\n bucket_name = \"\"\n\n for output in stack.outputs:\n\n if output.key == \"LogsBucketName\":\n bucket_name = output.value\n break\n\n assert \"logs\" in bucket_name\n\n assert stack.region.name in bucket_name\n\n def test_from_dict(self):\n taskcat_config = self.template_dir / \".taskcat.yml\"\n\n with open(taskcat_config.resolve()) as f:\n config = yaml.load(f.read(), Loader=yaml.SafeLoader)\n\n config[\"tests\"][\"log-bucket\"][\"parameters\"][\"KeepBucket\"] = \"TRUE\"\n\n test = CFNTest.from_dict(config, project_root=self.template_dir.resolve())\n\n with test as stacks:\n pass\n\n for stack in stacks:\n session = stack.region.session\n\n s3 = session.resource(\"s3\")\n\n for output in stack.outputs:\n\n if output.key == \"LogsBucketName\":\n bucket = s3.Bucket(output.value)\n bucket.wait_until_exists()\n bucket.delete()\n bucket.wait_until_not_exists()\n break\n", "repo_name": "aws-ia/taskcat", "sub_path": "e2e/tests/test_imported/test_retain.py", "file_name": "test_retain.py", "file_ext": "py", "file_size_in_byte": 1654, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1116, "dataset": "github-code", "pt": "37", "api": [{"api_name": "unittest.TestCase", "line_number": 9, "usage_type": "attribute"}, {"api_name": "pathlib.Path", "line_number": 12, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 13, "usage_type": "call"}, {"api_name": "taskcat.testing.CFNTest.from_file", "line_number": 17, "usage_type": "call"}, {"api_name": "taskcat.testing.CFNTest", "line_number": 17, "usage_type": "name"}, {"api_name": "yaml.load", "line_number": 39, "usage_type": "call"}, {"api_name": "yaml.SafeLoader", "line_number": 39, "usage_type": "attribute"}, {"api_name": "taskcat.testing.CFNTest.from_dict", "line_number": 43, "usage_type": "call"}, {"api_name": "taskcat.testing.CFNTest", "line_number": 43, "usage_type": "name"}]} +{"seq_id": "30669885182", "text": "from kgtt_bot.bot.main import kgtt\nfrom kgtt_bot.vk import Bot\nfrom kgtt_bot import dbutils\nfrom kgtt_bot.bot.data import keyboards,states,emoji\n\nfrom kgtt_bot.schedule import get_text\n\nimport json\nfrom string import punctuation\nimport sqlite3\n\n\n@kgtt.on.multiply(['Расписание'], [states.main])\ndef schedule(self : Bot):\n if self.info.UserGroup:\n try:\n request = self.db.cursor().execute(f\"\"\"SELECT json(Schedule) \n FROM StudGroups \n WHERE StudGroup = '{self.info.UserGroup}';\"\"\").fetchone()[0]\n schedule = get_text(json.loads(request))\n kgtt.utils.user_message(schedule)\n\n except sqlite3.OperationalError:\n kgtt.utils.user_message(f'Расписания для группы {self.info.UserGroup} не найдено!')\n \n except TypeError :\n kgtt.utils.user_message('Группа не найдена!')\n \n except IndexError:\n kgtt.utils.user_message('Группа не найдена!')\n\n else:\n dbutils.set_state(self, states.table_register)\n kgtt.utils.user_message('Введите свою учебную группу', keyboard = keyboards.cancel(), \n link=[\"https://clck.ru/psvfm\"])\n\n\n@kgtt.on.state(states.table_register)\ndef auth(self : Bot):\n \n def general_view(group : str) -> str:\n group = group.upper()\n for char in punctuation:\n group = group.replace(char,'')\n \n return group\n \n \n if self.message.text != emoji.red_circle:\n \n \n group = general_view(self.message.text)\n dbutils.update_field(self,table='UserGroups',category='UserGroup',new=group)\n dbutils.set_state(self,states.main)\n dbutils.on_mailing_status(self)\n kgtt.utils.user_message(\"Приятного использования! Рассылка включена!\\nГруппу всегда можно поменять в параметрах\"\n , keyboard=keyboards.main())", "repo_name": "QuoNaro/kgtt-bot", "sub_path": "src/kgtt_bot/bot/handlers/table.py", "file_name": "table.py", "file_ext": "py", "file_size_in_byte": 2021, "program_lang": "python", "lang": "ru", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "kgtt_bot.vk.Bot", "line_number": 14, "usage_type": "name"}, {"api_name": "kgtt_bot.schedule.get_text", "line_number": 20, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 20, "usage_type": "call"}, {"api_name": "kgtt_bot.bot.main.kgtt.utils.user_message", "line_number": 21, "usage_type": "call"}, {"api_name": "kgtt_bot.bot.main.kgtt.utils", "line_number": 21, "usage_type": "attribute"}, {"api_name": "kgtt_bot.bot.main.kgtt", "line_number": 21, "usage_type": "name"}, {"api_name": "sqlite3.OperationalError", "line_number": 23, "usage_type": "attribute"}, {"api_name": "kgtt_bot.bot.main.kgtt.utils.user_message", "line_number": 24, "usage_type": "call"}, {"api_name": "kgtt_bot.bot.main.kgtt.utils", "line_number": 24, "usage_type": "attribute"}, {"api_name": "kgtt_bot.bot.main.kgtt", "line_number": 24, "usage_type": "name"}, {"api_name": "kgtt_bot.bot.main.kgtt.utils.user_message", "line_number": 27, "usage_type": "call"}, {"api_name": "kgtt_bot.bot.main.kgtt.utils", "line_number": 27, "usage_type": "attribute"}, {"api_name": "kgtt_bot.bot.main.kgtt", "line_number": 27, "usage_type": "name"}, {"api_name": "kgtt_bot.bot.main.kgtt.utils.user_message", "line_number": 30, "usage_type": "call"}, {"api_name": "kgtt_bot.bot.main.kgtt.utils", "line_number": 30, "usage_type": "attribute"}, {"api_name": "kgtt_bot.bot.main.kgtt", "line_number": 30, "usage_type": "name"}, {"api_name": "kgtt_bot.dbutils.set_state", "line_number": 33, "usage_type": "call"}, {"api_name": "kgtt_bot.dbutils", "line_number": 33, "usage_type": "name"}, {"api_name": "kgtt_bot.bot.data.states.table_register", "line_number": 33, "usage_type": "attribute"}, {"api_name": "kgtt_bot.bot.data.states", "line_number": 33, "usage_type": "name"}, {"api_name": "kgtt_bot.bot.main.kgtt.utils.user_message", "line_number": 34, "usage_type": "call"}, {"api_name": "kgtt_bot.bot.main.kgtt.utils", "line_number": 34, "usage_type": "attribute"}, {"api_name": "kgtt_bot.bot.main.kgtt", "line_number": 34, "usage_type": "name"}, {"api_name": "kgtt_bot.bot.data.keyboards.cancel", "line_number": 34, "usage_type": "call"}, {"api_name": "kgtt_bot.bot.data.keyboards", "line_number": 34, "usage_type": "name"}, {"api_name": "kgtt_bot.bot.main.kgtt.on.multiply", "line_number": 13, "usage_type": "call"}, {"api_name": "kgtt_bot.bot.main.kgtt.on", "line_number": 13, "usage_type": "attribute"}, {"api_name": "kgtt_bot.bot.main.kgtt", "line_number": 13, "usage_type": "name"}, {"api_name": "kgtt_bot.bot.data.states.main", "line_number": 13, "usage_type": "attribute"}, {"api_name": "kgtt_bot.bot.data.states", "line_number": 13, "usage_type": "name"}, {"api_name": "kgtt_bot.vk.Bot", "line_number": 39, "usage_type": "name"}, {"api_name": "string.punctuation", "line_number": 43, "usage_type": "name"}, {"api_name": "kgtt_bot.bot.data.emoji.red_circle", "line_number": 49, "usage_type": "attribute"}, {"api_name": "kgtt_bot.bot.data.emoji", "line_number": 49, "usage_type": "name"}, {"api_name": "kgtt_bot.dbutils.update_field", "line_number": 53, "usage_type": "call"}, {"api_name": "kgtt_bot.dbutils", "line_number": 53, "usage_type": "name"}, {"api_name": "kgtt_bot.dbutils.set_state", "line_number": 54, "usage_type": "call"}, {"api_name": "kgtt_bot.dbutils", "line_number": 54, "usage_type": "name"}, {"api_name": "kgtt_bot.bot.data.states.main", "line_number": 54, "usage_type": "attribute"}, {"api_name": "kgtt_bot.bot.data.states", "line_number": 54, "usage_type": "name"}, {"api_name": "kgtt_bot.dbutils.on_mailing_status", "line_number": 55, "usage_type": "call"}, {"api_name": "kgtt_bot.dbutils", "line_number": 55, "usage_type": "name"}, {"api_name": "kgtt_bot.bot.main.kgtt.utils.user_message", "line_number": 56, "usage_type": "call"}, {"api_name": "kgtt_bot.bot.main.kgtt.utils", "line_number": 56, "usage_type": "attribute"}, {"api_name": "kgtt_bot.bot.main.kgtt", "line_number": 56, "usage_type": "name"}, {"api_name": "kgtt_bot.bot.data.keyboards.main", "line_number": 57, "usage_type": "call"}, {"api_name": "kgtt_bot.bot.data.keyboards", "line_number": 57, "usage_type": "name"}, {"api_name": "kgtt_bot.bot.main.kgtt.on.state", "line_number": 38, "usage_type": "call"}, {"api_name": "kgtt_bot.bot.main.kgtt.on", "line_number": 38, "usage_type": "attribute"}, {"api_name": "kgtt_bot.bot.main.kgtt", "line_number": 38, "usage_type": "name"}, {"api_name": "kgtt_bot.bot.data.states.table_register", "line_number": 38, "usage_type": "attribute"}, {"api_name": "kgtt_bot.bot.data.states", "line_number": 38, "usage_type": "name"}]} +{"seq_id": "37417917119", "text": "def run(pipeline_args, known_args):\n \"\"\"\n Run the pipeline\n \"\"\"\n\n import apache_beam as beam\n from apache_beam.io.gcp.internal.clients import bigquery as beam_bigquery\n from apache_beam.options.pipeline_options import PipelineOptions, SetupOptions\n\n from geobeam.io import GeodatabaseSource\n from geobeam.fn import make_valid, filter_invalid, format_record\n\n pipeline_options = PipelineOptions([\n '--experiments', 'use_beam_bq_sink',\n ] + pipeline_args)\n\n with beam.Pipeline(options=pipeline_options) as p:\n (p\n | beam.io.Read(GeodatabaseSource(known_args.gcs_url,\n layer_name=known_args.layer_name,\n gdb_name=known_args.gdb_name))\n | 'MakeValid' >> beam.Map(make_valid)\n | 'FilterInvalid' >> beam.Filter(filter_invalid)\n | 'FormatRecords' >> beam.Map(format_record)\n | 'WriteToBigQuery' >> beam.io.WriteToBigQuery(\n beam_bigquery.TableReference(\n datasetId=known_args.dataset,\n tableId=known_args.table),\n method=beam.io.WriteToBigQuery.Method.FILE_LOADS,\n write_disposition=beam.io.BigQueryDisposition.WRITE_TRUNCATE,\n create_disposition=beam.io.BigQueryDisposition.CREATE_NEVER))\n\n\nif __name__ == '__main__':\n import logging\n import argparse\n\n logging.getLogger().setLevel(logging.INFO)\n\n parser = argparse.ArgumentParser()\n parser.add_argument('--gcs_url')\n parser.add_argument('--dataset')\n parser.add_argument('--table')\n parser.add_argument('--layer_name')\n parser.add_argument('--gdb_name')\n parser.add_argument('--in_epsg', type=int, default=None)\n known_args, pipeline_args = parser.parse_known_args()\n\n run(pipeline_args, known_args)\n", "repo_name": "GoogleCloudPlatform/dataflow-geobeam", "sub_path": "geobeam/examples/geodatabase_frd.py", "file_name": "geodatabase_frd.py", "file_ext": "py", "file_size_in_byte": 1772, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 84, "dataset": "github-code", "pt": "37", "api": [{"api_name": "apache_beam.options.pipeline_options.PipelineOptions", "line_number": 13, "usage_type": "call"}, {"api_name": "apache_beam.Pipeline", "line_number": 17, "usage_type": "call"}, {"api_name": "apache_beam.io.Read", "line_number": 19, "usage_type": "call"}, {"api_name": "apache_beam.io", "line_number": 19, "usage_type": "attribute"}, {"api_name": "geobeam.io.GeodatabaseSource", "line_number": 19, "usage_type": "call"}, {"api_name": "apache_beam.Map", "line_number": 22, "usage_type": "call"}, {"api_name": "geobeam.fn.make_valid", "line_number": 22, "usage_type": "argument"}, {"api_name": "apache_beam.Filter", "line_number": 23, "usage_type": "call"}, {"api_name": "geobeam.fn.filter_invalid", "line_number": 23, "usage_type": "argument"}, {"api_name": "apache_beam.Map", "line_number": 24, "usage_type": "call"}, {"api_name": "geobeam.fn.format_record", "line_number": 24, "usage_type": "argument"}, {"api_name": "apache_beam.io.WriteToBigQuery", "line_number": 25, "usage_type": "call"}, {"api_name": "apache_beam.io", "line_number": 25, "usage_type": "attribute"}, {"api_name": "apache_beam.io.gcp.internal.clients.bigquery.TableReference", "line_number": 26, "usage_type": "call"}, {"api_name": "apache_beam.io.gcp.internal.clients.bigquery", "line_number": 26, "usage_type": "name"}, {"api_name": "apache_beam.io", "line_number": 29, "usage_type": "attribute"}, {"api_name": "apache_beam.io", "line_number": 30, "usage_type": "attribute"}, {"api_name": "apache_beam.io", "line_number": 31, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 38, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 38, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentParser", "line_number": 40, "usage_type": "call"}]} +{"seq_id": "17770670751", "text": "from typing import Any, List, Optional\n\nimport click\nfrom tabulate import tabulate\n\nfrom camundactl.output.base import OutputHandler\n\n\ndef _ensure_length(value: Any, max_length: int = 1000) -> str:\n \"\"\"makes sure the value is no longer then the given lengths\"\"\"\n if isinstance(value, str):\n if len(value) > max_length:\n return value[0:max_length] + \"...\"\n return value\n\n\nclass TableOutputHandler(OutputHandler):\n\n name: str = \"table\"\n\n options = {\n \"output_headers\": click.option(\n \"-oH\",\n \"--output-header\",\n \"output_headers\",\n default=None,\n help=\"comma seperated list of headers\",\n ),\n \"output_cell_max_length\": click.option(\n \"-oCL\",\n \"--output-cell-limit\",\n \"output_cell_max_length\",\n type=int,\n required=False,\n default=40,\n help=\"limit cell value for table output (default=40)\",\n ),\n }\n\n def __init__(\n self,\n table_headers=None,\n table_headers_backlist=None,\n cell_max_length=40,\n ):\n self.default_cell_max_length = cell_max_length\n self.default_table_headers = table_headers\n self.table_headers_backlist = table_headers_backlist or ()\n\n def handle(\n self,\n result: List[Any],\n output_headers: Optional[str],\n output_cell_max_length,\n ):\n if output_headers:\n headers = output_headers.split(\",\")\n else:\n headers = self.default_table_headers\n\n cell_max_length = output_cell_max_length or self.default_cell_max_length\n\n if not headers:\n # use the keys as headers, but remove all backlist headers\n if not result:\n click.echo(\"empty result\")\n return\n first, *_ = result\n if isinstance(first, dict):\n headers = [\n key\n for key in first.keys()\n if key not in self.table_headers_backlist\n ]\n result = [\n [_ensure_length(item.get(key), cell_max_length) for key in headers]\n for item in result\n ]\n else:\n headers = (\"unknown\",)\n result = [(item,) for item in result]\n elif isinstance(headers, (tuple, list)):\n result = [\n [_ensure_length(row[key], cell_max_length) for key in headers]\n for row in result\n ]\n else:\n raise Exception(\n f\"invalid header format type={type(self.headers)}\"\n f\", values={self.headers}\"\n )\n click.echo(tabulate(result, headers=headers))\n\n\nclass ObjectTableOutputHandler(TableOutputHandler):\n def handle(\n self,\n result: dict[str, Any],\n output_headers: Optional[str],\n output_cell_max_length,\n ):\n\n headers = \"key,value\"\n\n output_headers = output_headers or self.default_table_headers\n\n new_result = []\n for key, value in result.items():\n if self.table_headers_backlist and key in self.table_headers_backlist:\n continue\n if output_headers and key not in output_headers:\n continue\n new_result.append({\"key\": key, \"value\": str(value)})\n\n return super().handle(new_result, headers, output_cell_max_length)\n", "repo_name": "jblawatt/camundactl", "sub_path": "camundactl/output/table.py", "file_name": "table.py", "file_ext": "py", "file_size_in_byte": 3488, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "37", "api": [{"api_name": "typing.Any", "line_number": 9, "usage_type": "name"}, {"api_name": "camundactl.output.base.OutputHandler", "line_number": 17, "usage_type": "name"}, {"api_name": "click.option", "line_number": 22, "usage_type": "call"}, {"api_name": "click.option", "line_number": 29, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 52, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 52, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 53, "usage_type": "name"}, {"api_name": "click.echo", "line_number": 66, "usage_type": "call"}, {"api_name": "click.echo", "line_number": 92, "usage_type": "call"}, {"api_name": "tabulate.tabulate", "line_number": 92, "usage_type": "call"}, {"api_name": "typing.Any", "line_number": 98, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 99, "usage_type": "name"}]} +{"seq_id": "9919701312", "text": "from django.test import TestCase\nfrom designmytee.models import Designer, Submission, Competition, Support_Request \nfrom population_script import populate\nfrom designmytee.forms import FeedbackForm, CustomSignupForm, SubmissionForm\n\nimport os\n\n# Various tests for the Models, forms and views, run \"python manage.py test\" to run all tests.\n# Populate function from populate_designmytee is frequently used to create test databases for each set of tests, this may cause the tests\n# to take longer as the populate function needs ~4 seconds to run each time\n\nclass DesignerTests(TestCase):\n \n print(\"Starting tests, this may take a short while!...\")\n \n @classmethod\n def setUpTestData(cls):\n populate()\n\n def test_user_connected_to_designer(self):\n for d in Designer.objects.all():\n self.assertTrue(d.user != None, \"Error, Designer instance exists with no user, please check user: \" + str(d.id))\n \n def test_user_instances_contain_correct_fields(self):\n for d in Designer.objects.all():\n self.assertTrue(d.user.first_name != None, \"Error, Designer intances user has no firstname, check user: \" + str(d.id))\n self.assertTrue(d.user.last_name != None, \"Error, Designer intances user has no lastname, check user: \" + str(d.id))\n self.assertTrue(d.user.username != None, \"Error, Designer intances user has no username, check user: \" + str(d.id))\n self.assertTrue(d.user.password != None, \"Error, Designer intances user has no password, check user: \" + str(d.id))\n \n def test_category_image_directory_is_in_place(self):\n cwd = os.getcwd()\n profileDirectory = os.path.join(cwd, 'Media\\profile_images')\n self.assertTrue(os.path.isdir(profileDirectory), \"Error, profile_images folder not in media directory\")\n \n def test_category_images_folder_not_empty(self):\n cwd = os.getcwd()\n profileDirectory = os.path.join(cwd, 'Media\\profile_images')\n self.assertTrue(len(os.listdir(profileDirectory)) != 0, \"Error, no images are stored in profile_images folder\")\n \n def test_wins_and_participations_not_zero(self):\n for d in Designer.objects.all():\n self.assertTrue(d.wins >= 0, \"Error, Designer instance exists with either no default wins or an invalid wins count, see user: \" + str(d.id))\n self.assertTrue(d.participations >= 0, \"Error, Designer instance exists with either no default participations or an invalid participation count, see user: \" + str(d.id))\n \n def test_wins_not_more_than_participations(self):\n for d in Designer.objects.all():\n self.assertTrue(d.wins <= d.participations, \"Error, designer wins are more than participations, check user: \" + str(d.id))\n \nclass CompetitionTests(TestCase):\n @classmethod\n def setUpTestData(cls):\n populate()\n \n\n def test_lengths_are_set_correctly(self):\n for c in Competition.objects.all():\n maxLength = c._meta.get_field('title').max_length\n self.assertEqual(maxLength, 128, \"Error, Length value not correctly set in model, please check model max length value for title\")\n maxLength = c._meta.get_field('competitionDescription').max_length\n self.assertEqual(maxLength, 200, \"Error, Length value not correctly set in model, please check model max length value for competitionDescription\")\n \n def test_category_image_directory_is_in_place(self):\n cwd = os.getcwd()\n competitionDirectory = os.path.join(cwd, 'Media\\competition_images')\n self.assertTrue(os.path.isdir(competitionDirectory), \"Error, competition_images folder not in media directory\")\n \n def test_category_images_folder_not_empty(self):\n cwd = os.getcwd()\n competitionDirectory = os.path.join(cwd, 'Media\\competition_images')\n self.assertTrue(len(os.listdir(competitionDirectory)) != 0, \"Error, no images are stored in competition_images folder\")\n \n def test_start_date_is_before_end_date(self):\n \n for c in Competition.objects.all():\n self.assertIs(c.start_date_before_end_date(), True, \"Error, Start date is after end date, object id: \" + str(c.id))\n \n def test_end_date_is_before_expiry_date(self):\n \n for c in Competition.objects.all():\n self.assertIs(c.end_date_before_expiry_date(), True, \"Error, End date is after Expiry date, object id: \" + str(c.id))\n \n def test_lengths_are_adheared_to(self):\n \n for c in Competition.objects.all():\n self.assertIs(c.test_length(fieldToTest=c.title, size=128), True, \"Error, object title has an invalid size, object id: \" + str(c.id))\n self.assertIs(c.test_length(fieldToTest=c.competitionDescription, size=200), True, \"Error, object competitionDescription has an invalid size, object id: \" + str(c.id))\n \nclass SubmissonTests(TestCase):\n @classmethod\n def setUpTestData(cls):\n populate()\n \n def test_submission_image_directory_is_in_place(self):\n cwd = os.getcwd()\n submissionDirectory = os.path.join(cwd, 'Media\\submission_images')\n self.assertTrue(os.path.isdir(submissionDirectory), \"Error, submission_images folder not in media directory\")\n \n def test_category_images_folder_not_empty(self):\n cwd = os.getcwd()\n submissionDirectory = os.path.join(cwd, 'Media\\submission_images')\n self.assertTrue(len(os.listdir(submissionDirectory)) != 0, \"Error, no images are stored in submission_images folder\")\n \n def test_lengths_are_set_correctly(self):\n for s in Submission.objects.all():\n maxLength = s._meta.get_field('submissionDescription').max_length\n self.assertEqual(maxLength, 200, \"Error, Length value not correctly set in model, please check model max length value for submissionDescription\")\n \n def test_lengths_are_adheared_to(self):\n \n for s in Submission.objects.all():\n self.assertIs(s.test_length(fieldToTest=s.submissionDescription, size=200), True, \"Error, object submissionDescription has an invalid size, object id: \" + str(s.id))\n \n def test_participant_connected_to_submission(self):\n for s in Submission.objects.all():\n self.assertTrue(s.participant != None, \"Error, Submission instance exists with no participant, please check user: \" + str(s.id))\n \n def test_Competition_connected_to_submission(self):\n for s in Submission.objects.all():\n self.assertTrue(s.competition != None, \"Error, Submission instance exists with no competition, please check user: \" + str(s.id))\n \n def test_votes_not_zero(self):\n for s in Submission.objects.all():\n self.assertTrue(s.votes >= 0, \"Error, Submission instance exists with either no default votes or an invalid votes count, see user: \" + str(s.id))\n \nclass FeedbackTests(TestCase):\n @classmethod\n def setUpTestData(cls):\n populate()\n \n def test_lengths_are_set_correctly(self):\n for f in Support_Request.objects.all():\n maxLength = f._meta.get_field('firstName').max_length\n self.assertEqual(maxLength, 128, \"Error, Length value not correctly set in model, please check model max length value for firstname\")\n maxLength = f._meta.get_field('lastName').max_length\n self.assertEqual(maxLength, 128, \"Error, Length value not correctly set in model, please check model max length value for lastname\")\n maxLength = f._meta.get_field('contactNumber').max_length\n self.assertEqual(maxLength, 11, \"Error, Length value not correctly set in model, please check model max length value for contactnumber\")\n maxLength = f._meta.get_field('contactEmail').max_length\n self.assertEqual(maxLength, 200, \"Error, Length value not correctly set in model, please check model max length value for contactEmail\")\n maxLength = f._meta.get_field('suggestionsOrFeedback').max_length\n self.assertEqual(maxLength, 500, \"Error, Length value not correctly set in model, please check model max length value\")\n \n def test_lengths_are_adheared_to(self):\n \n for f in Support_Request.objects.all():\n self.assertIs(f.test_length(fieldToTest=f.firstName, size=128), True, \"Error, object firstname has an invalid size, object id: \" + str(f.id))\n self.assertIs(f.test_length(fieldToTest=f.lastName, size=128), True, \"Error, object lastname has an invalid size, object id: \" + str(f.id))\n self.assertIs(f.test_length(fieldToTest=f.contactNumber, size=11), True, \"Error, object contactNumber has an invalid size, object id: \" + str(f.id))\n self.assertIs(f.test_length(fieldToTest=f.contactEmail, size=200), True, \"Error, object contactEmail has an invalid size, object id: \" + str(f.id))\n self.assertIs(f.test_length(fieldToTest=f.suggestionsOrFeedback, size=500), True, \"Error, object suggestionsOrFeedback has an invalid size, object id: \" + str(f.id))\n \nclass FormTests(TestCase):\n @classmethod\n def setUpTestData(cls):\n populate()\n \n # checks that feedback and submission form can correctly detect an invalid input\n \n def test_feedback_form_detects_wrong_contact_number(self):\n form = FeedbackForm(data={'contactNumber': \"08217482\"})\n self.assertEqual(form.errors['contactNumber'], ['Error, not a valid phone number, length must be 11 and in the form: \"07124282832\"'], \"Error, feedback form does not correctly identify phone numbers of incorrect length\")\n form = FeedbackForm(data={'contactNumber': \"8274h17f821\"})\n self.assertEqual(form.errors['contactNumber'], ['Error, please enter a number in the form: \"07124282832\" '], \"Error, feedback form does not correctly identify non-number input for contact number\")\n \n def test_feedback_form_help_text(self):\n form = FeedbackForm()\n self.assertEqual(form.fields['firstName'].help_text, \"Please enter your first name:\", \"Error, feedback form help text for firstname is invalid\")\n self.assertEqual(form.fields['lastName'].help_text, \"Please enter your last name:\",\"Error, feedback form help text for lastname is invalid\")\n self.assertEqual(form.fields['contactNumber'].help_text, \"Please enter your phone number:\", \"Error, feedback form help text for contactNumber is invalid\")\n self.assertEqual(form.fields['contactEmail'].help_text, \"Please enter your email:\", \"Error, feedback form help text for contactEmail is invalid\")\n self.assertEqual(form.fields['suggestionsOrFeedback'].help_text, \"Please enter your suggestion/query/feedback:\", \"Error, feedback form help text for suggestionsOrFeedback is invalid\")\n \n def test_Signup_form_lable_text(self):\n form = CustomSignupForm()\n self.assertEqual(form.fields['first_name'].label, 'First Name', \"Error, Sign up form label text for first_name is invalid\")\n self.assertEqual(form.fields['last_name'].label, 'Last Name', \"Error, Sign up form label text for last_name is invalid\")\n \nclass ViewTests(TestCase):\n @classmethod\n def setUpTestData(cls):\n populate()\n \n def test__view_urls_at_correct_area(self):\n response = self.client.get('/designmytee/about/')\n self.assertEqual(response.status_code, 200, \"Error, about response not at correct area\")\n \n response = self.client.get('/designmytee/results/')\n self.assertEqual(response.status_code, 200, \"Error, results response not at correct area\")\n \n response = self.client.get('/designmytee/')\n self.assertEqual(response.status_code, 200, \"Error, home response not at correct area\")\n \n response = self.client.get('/designmytee/help/')\n self.assertEqual(response.status_code, 200, \"Error, help response not at correct area\")\n \n \n for competition in Competition.objects.all():\n \n response = self.client.get('/designmytee/competition/' + competition.slug + \"/\")\n self.assertEqual(response.status_code, 200, \"Error, competition response not at correct area\")\n \n response = self.client.get('/designmytee/competitions/')\n self.assertEqual(response.status_code, 200, \"Error, competitions response not at correct area\")\n \n response = self.client.get('/designmytee/login/')\n self.assertEqual(response.status_code, 200, \"Error, login response not at correct area\")\n \n response = self.client.get('/designmytee/signup/')\n self.assertEqual(response.status_code, 200, \"Error, signup response not at correct area\")\n \n response = self.client.get('/designmytee/password/reset/')\n self.assertEqual(response.status_code, 200, \"Error, password reset response not at correct area\")\n \n \n \n ", "repo_name": "YashSrivastava001/Team7DWebApp", "sub_path": "designmytee/tests.py", "file_name": "tests.py", "file_ext": "py", "file_size_in_byte": 13081, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "django.test.TestCase", "line_number": 12, "usage_type": "name"}, {"api_name": "population_script.populate", "line_number": 18, "usage_type": "call"}, {"api_name": "designmytee.models.Designer.objects.all", "line_number": 21, "usage_type": "call"}, {"api_name": "designmytee.models.Designer.objects", "line_number": 21, "usage_type": "attribute"}, {"api_name": "designmytee.models.Designer", "line_number": 21, "usage_type": "name"}, {"api_name": "designmytee.models.Designer.objects.all", "line_number": 25, "usage_type": "call"}, {"api_name": "designmytee.models.Designer.objects", "line_number": 25, "usage_type": "attribute"}, {"api_name": "designmytee.models.Designer", "line_number": 25, "usage_type": "name"}, {"api_name": "os.getcwd", "line_number": 32, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 33, "usage_type": "call"}, {"api_name": "os.path", "line_number": 33, "usage_type": "attribute"}, {"api_name": "os.path.isdir", "line_number": 34, "usage_type": "call"}, {"api_name": "os.path", "line_number": 34, "usage_type": "attribute"}, {"api_name": "os.getcwd", "line_number": 37, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 38, "usage_type": "call"}, {"api_name": "os.path", "line_number": 38, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 39, "usage_type": "call"}, {"api_name": "designmytee.models.Designer.objects.all", "line_number": 42, "usage_type": "call"}, {"api_name": "designmytee.models.Designer.objects", "line_number": 42, "usage_type": "attribute"}, {"api_name": "designmytee.models.Designer", "line_number": 42, "usage_type": "name"}, {"api_name": "designmytee.models.Designer.objects.all", "line_number": 47, "usage_type": "call"}, {"api_name": "designmytee.models.Designer.objects", "line_number": 47, "usage_type": "attribute"}, {"api_name": "designmytee.models.Designer", "line_number": 47, "usage_type": "name"}, {"api_name": "django.test.TestCase", "line_number": 50, "usage_type": "name"}, {"api_name": "population_script.populate", "line_number": 53, "usage_type": "call"}, {"api_name": "designmytee.models.Competition.objects.all", "line_number": 57, "usage_type": "call"}, {"api_name": "designmytee.models.Competition.objects", "line_number": 57, "usage_type": "attribute"}, {"api_name": "designmytee.models.Competition", "line_number": 57, "usage_type": "name"}, {"api_name": "os.getcwd", "line_number": 64, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 65, "usage_type": "call"}, {"api_name": "os.path", "line_number": 65, "usage_type": "attribute"}, {"api_name": "os.path.isdir", "line_number": 66, "usage_type": "call"}, {"api_name": "os.path", "line_number": 66, "usage_type": "attribute"}, {"api_name": "os.getcwd", "line_number": 69, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 70, "usage_type": "call"}, {"api_name": "os.path", "line_number": 70, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 71, "usage_type": "call"}, {"api_name": "designmytee.models.Competition.objects.all", "line_number": 75, "usage_type": "call"}, {"api_name": "designmytee.models.Competition.objects", "line_number": 75, "usage_type": "attribute"}, {"api_name": "designmytee.models.Competition", "line_number": 75, "usage_type": "name"}, {"api_name": "designmytee.models.Competition.objects.all", "line_number": 80, "usage_type": "call"}, {"api_name": "designmytee.models.Competition.objects", "line_number": 80, "usage_type": "attribute"}, {"api_name": "designmytee.models.Competition", "line_number": 80, "usage_type": "name"}, {"api_name": "designmytee.models.Competition.objects.all", "line_number": 85, "usage_type": "call"}, {"api_name": "designmytee.models.Competition.objects", "line_number": 85, "usage_type": "attribute"}, {"api_name": "designmytee.models.Competition", "line_number": 85, "usage_type": "name"}, {"api_name": "django.test.TestCase", "line_number": 89, "usage_type": "name"}, {"api_name": "population_script.populate", "line_number": 92, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 95, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 96, "usage_type": "call"}, {"api_name": "os.path", "line_number": 96, "usage_type": "attribute"}, {"api_name": "os.path.isdir", "line_number": 97, "usage_type": "call"}, {"api_name": "os.path", "line_number": 97, "usage_type": "attribute"}, {"api_name": "os.getcwd", "line_number": 100, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 101, "usage_type": "call"}, {"api_name": "os.path", "line_number": 101, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 102, "usage_type": "call"}, {"api_name": "designmytee.models.Submission.objects.all", "line_number": 105, "usage_type": "call"}, {"api_name": "designmytee.models.Submission.objects", "line_number": 105, "usage_type": "attribute"}, {"api_name": "designmytee.models.Submission", "line_number": 105, "usage_type": "name"}, {"api_name": "designmytee.models.Submission.objects.all", "line_number": 111, "usage_type": "call"}, {"api_name": "designmytee.models.Submission.objects", "line_number": 111, "usage_type": "attribute"}, {"api_name": "designmytee.models.Submission", "line_number": 111, "usage_type": "name"}, {"api_name": "designmytee.models.Submission.objects.all", "line_number": 115, "usage_type": "call"}, {"api_name": "designmytee.models.Submission.objects", "line_number": 115, "usage_type": "attribute"}, {"api_name": "designmytee.models.Submission", "line_number": 115, "usage_type": "name"}, {"api_name": "designmytee.models.Submission.objects.all", "line_number": 119, "usage_type": "call"}, {"api_name": "designmytee.models.Submission.objects", "line_number": 119, "usage_type": "attribute"}, {"api_name": "designmytee.models.Submission", "line_number": 119, "usage_type": "name"}, {"api_name": "designmytee.models.Submission.objects.all", "line_number": 123, "usage_type": "call"}, {"api_name": "designmytee.models.Submission.objects", "line_number": 123, "usage_type": "attribute"}, {"api_name": "designmytee.models.Submission", "line_number": 123, "usage_type": "name"}, {"api_name": "django.test.TestCase", "line_number": 126, "usage_type": "name"}, {"api_name": "population_script.populate", "line_number": 129, "usage_type": "call"}, {"api_name": "designmytee.models.Support_Request.objects.all", "line_number": 132, "usage_type": "call"}, {"api_name": "designmytee.models.Support_Request.objects", "line_number": 132, "usage_type": "attribute"}, {"api_name": "designmytee.models.Support_Request", "line_number": 132, "usage_type": "name"}, {"api_name": "designmytee.models.Support_Request.objects.all", "line_number": 146, "usage_type": "call"}, {"api_name": "designmytee.models.Support_Request.objects", "line_number": 146, "usage_type": "attribute"}, {"api_name": "designmytee.models.Support_Request", "line_number": 146, "usage_type": "name"}, {"api_name": "django.test.TestCase", "line_number": 153, "usage_type": "name"}, {"api_name": "population_script.populate", "line_number": 156, "usage_type": "call"}, {"api_name": "designmytee.forms.FeedbackForm", "line_number": 161, "usage_type": "call"}, {"api_name": "designmytee.forms.FeedbackForm", "line_number": 163, "usage_type": "call"}, {"api_name": "designmytee.forms.FeedbackForm", "line_number": 167, "usage_type": "call"}, {"api_name": "designmytee.forms.CustomSignupForm", "line_number": 175, "usage_type": "call"}, {"api_name": "django.test.TestCase", "line_number": 179, "usage_type": "name"}, {"api_name": "population_script.populate", "line_number": 182, "usage_type": "call"}, {"api_name": "designmytee.models.Competition.objects.all", "line_number": 198, "usage_type": "call"}, {"api_name": "designmytee.models.Competition.objects", "line_number": 198, "usage_type": "attribute"}, {"api_name": "designmytee.models.Competition", "line_number": 198, "usage_type": "name"}]} +{"seq_id": "72861813867", "text": "#for every number divisible by 3 print fizz\r\n#for every number divisible by 5 print buzz\r\n#for every number divisible by 3 and 5 print fizzbuzz\r\n\r\nimport time\r\nfrom functools import lru_cache\r\nimport operator\r\n\r\n#Limit = 100\r\nprint(\"insert the last number:\")\r\nLimit=input()\r\nLimit=int(Limit)+1\r\n@lru_cache(maxsize=None)\r\ndef Classic():\r\n for i in range(1, Limit):\r\n\r\n if i % 15 == 0:\r\n print(\"fizzbuzz\")\r\n elif i % 5 == 0:\r\n print(\"buzz\")\r\n elif i % 3 == 0:\r\n print(\"fizz\")\r\n else:\r\n print(i)\r\n\r\n\r\n@lru_cache(maxsize=None)\r\ndef Alternative1(): # Not mine\r\n for i in range(1, Limit):\r\n print(\"fizz\" * (i % 3 == 0) + \"buzz\" * (i % 5 == 0) or i)\r\n\r\n\r\n@lru_cache(maxsize=None)\r\ndef Alternative2(): # Not mine\r\n for i in range(1, Limit):\r\n print([i, \"buzz\", \"fizz\", \"fizzbuzz\"][2 * (i % 3 == 0) + (i % 5 == 0)])\r\n\r\n\r\n@lru_cache(maxsize=None)\r\ndef Inline1():\r\n a = \"bazz\"\r\n b = \"fizz\"\r\n c = \"fizzbuzz\"\r\n # outside string declaration\r\n for i in range(1, Limit):\r\n print(a if (i % 5 == 0) else b if (i % 3 == 0) else c if (i % 15 == 0) else str(i))\r\n\r\n\r\n@lru_cache(maxsize=None)\r\ndef Inline2():\r\n for i in range(1, Limit):\r\n a = \"bazz\"\r\n b = \"fizz\"\r\n c = \"fizzbuzz\"\r\n # inside string declaration\r\n print(a if (i % 5 == 0) else b if (i % 3 == 0) else c if (i % 15 == 0) else str(i))\r\n\r\n\r\n@lru_cache(maxsize=None)\r\ndef Inline3():\r\n for i in range(1, Limit):\r\n # no string declaration\r\n print(\"bazz\" if (i % 5 == 0) else \"fizz\" if (i % 3 == 0) else \"fizzbuzz\" if (i % 15 == 0) else str(i))\r\n\r\n\r\n@lru_cache(maxsize=None)\r\ndef RandomTest():\r\n a = \"bazz\"\r\n b = \"fizz\"\r\n c = \"fizzbuzz\"\r\n for i in range(1, Limit):\r\n if i % 3 == 0 or i % 5 == 0:\r\n if i % 5 == 0 and i % 3 == 0:\r\n print(c)\r\n continue\r\n elif i % 3 == 0:\r\n print(a)\r\n elif i % 5 == 0:\r\n print(b)\r\n else:\r\n print(i)\r\n\r\n\r\n##run the tests\r\nFlag0 = time.time() # start\r\nClassic()\r\nFlag1 = time.time() # stop cla\r\nAlternative1()\r\nFlag2 = time.time() # stop alt\r\nAlternative2()\r\nFlag3 = time.time() # stop alt2\r\nInline1()\r\nFlag4 = time.time() # stop in1\r\nInline2()\r\nFlag5 = time.time() # stop in2\r\nInline3()\r\nFlag6 = time.time() # stop in3\r\nRandomTest()\r\nFlag7 = time.time() # stop RT\r\n#\r\nFlag_time1 = Flag1 - Flag0\r\nFlag_time2 = Flag2 - Flag1\r\nFlag_time3 = Flag3 - Flag2\r\nFlag_time4 = Flag4 - Flag3\r\nFlag_time5 = Flag5 - Flag4\r\nFlag_time6 = Flag6 - Flag5\r\nFlag_time7 = Flag7 - Flag6\r\n# create a dict whit flag names end flag times\r\nflag_list = {\"Flag1\": Flag_time1, \"Flag2\": Flag_time2, \"Flag3\": Flag_time3, \"Flag4\": Flag_time4, \"Flag5\": Flag_time5,\r\n \"Flag6\": Flag_time6, \"Flag7\": Flag_time7}\r\n\r\n# total time\r\nFlag7_0 = (Flag7 - Flag0)\r\n# calc the average\r\naverage = Flag7_0 / 7\r\n\r\n# print the total end average times\r\nprint(\"\\n= | Total Time : \", round(Flag7_0, 5))\r\nprint(\"= | average : \", round(average, 5))\r\n\r\n# check if more then average\r\nif Flag_time1 == average:\r\n print(\"= | Classic : \", round(Flag_time1, 5))\r\nif Flag_time1 > average:\r\n print(\"↑ | Classic : \", round(Flag_time1, 5))\r\nif Flag_time1 < average:\r\n print(\"↓ | Classic : \", round(Flag_time1, 5))\r\n# check if more then average\r\nif Flag_time2 == average:\r\n print(\"= | Alternative1 : \", round(Flag_time2, 5))\r\nif Flag_time2 > average:\r\n print(\"↑ | Alternative1 : \", round(Flag_time2, 5))\r\nif Flag_time2 < average:\r\n print(\"↓ | Alternative1 : \", round(Flag_time2, 5))\r\n# check if more then average\r\nif Flag_time3 == average:\r\n print(\"= | Alternative2 : \", round(Flag_time3, 5))\r\nif Flag_time3 > average:\r\n print(\"↑ | Alternative2 : \", round(Flag_time3, 5))\r\nif Flag_time3 < average:\r\n print(\"↓ | Alternative2 : \", round(Flag_time3, 5))\r\n# check if more then average\r\nif Flag_time4 == average:\r\n print(\"= | Inline1 : \", round(Flag_time4, 5))\r\nif Flag_time4 > average:\r\n print(\"↑ | Inline1 : \", round(Flag_time4, 5))\r\nif Flag_time4 < average:\r\n print(\"↓ | Inline1 : \", round(Flag_time4, 5))\r\n# check if more then average\r\nif Flag_time5 == average:\r\n print(\"= | Inline2 : \", round(Flag_time5, 5))\r\nif Flag_time5 > average:\r\n print(\"↑ | Inline2 : \", round(Flag_time5, 5))\r\nif Flag_time5 < average:\r\n print(\"↓ | Inline2 : \", round(Flag_time5, 5))\r\n# check if more then average\r\nif Flag_time6 == average:\r\n print(\"= | Inline3 : \", round(Flag_time6, 5))\r\nif Flag_time6 > average:\r\n print(\"↑ | Inline3 : \", round(Flag_time6, 5))\r\nif Flag_time6 < average:\r\n print(\"↓ | Inline3 : \", round(Flag_time6, 5))\r\n# check if more then average\r\nif Flag_time7 == average:\r\n print(\"= | RandomTest : \", round(Flag_time7, 5))\r\nif Flag_time7 > average:\r\n print(\"↑ | RandomTest : \", round(Flag_time7, 5))\r\nif Flag_time7 < average:\r\n print(\"↓ | RandomTest : \", round(Flag_time7, 5))\r\n\r\n# check the slowest\r\ntemp_x = max(flag_list.items(), key=operator.itemgetter(1))[0]\r\ntemp_xx = str(temp_x)\r\n# check the fastest\r\ntemp_y = min(flag_list.items(), key=operator.itemgetter(1))[0]\r\ntemp_yy = str(temp_y)\r\n# print the slowest\r\nif temp_xx == \"Flag1\":\r\n print(\"↑ | The slowest is: \", \"Classic \\n↑ | Whit:\", round(flag_list[\"Flag1\"], 5))\r\nelif temp_xx == \"Flag2\":\r\n print(\"↑ | The slowest is: \", \"Alternative1\\n↑ | Whit:\", round(flag_list[\"Flag2\"], 5))\r\nelif temp_xx == \"Flag3\":\r\n print(\"↑ | The slowest is: \", \"Alternative2\\n↑ | Whit:\", round(flag_list[\"Flag3\"], 5))\r\nelif temp_xx == \"Flag4\":\r\n print(\"↑ | The slowest is: \", \"Inline1 \\n↑ | Whit:\", round(flag_list[\"Flag4\"], 5))\r\nelif temp_xx == \"Flag5\":\r\n print(\"↑ | The slowest is: \", \"Inline2 \\n↑ | Whit:\", round(flag_list[\"Flag5\"], 5))\r\nelif temp_xx == \"Flag6\":\r\n print(\"↑ | The slowest is: \", \"Inline3 \\n↑ | Whit:\", round(flag_list[\"Flag6\"], 5))\r\nelif temp_xx == \"Flag7\":\r\n print(\"↑ | The slowest is: \", \"RandomTest \\n↑ | Whit:\", round(flag_list[\"Flag7\"], 5))\r\nelse:\r\n pass\r\n# print the fastest\r\nif temp_yy == \"Flag1\":\r\n print(\"↓ | The fastest is: \", \"Classic \\n↓ | Whit:\", round(flag_list[\"Flag1\"], 5))\r\nelif temp_yy == \"Flag2\":\r\n print(\"↓ | The fastest is: \", \"Alternative1\\n↓ | Whit:\", round(flag_list[\"Flag2\"], 5))\r\nelif temp_yy == \"Flag3\":\r\n print(\"↓ | The fastest is: \", \"Alternative2\\n↓ | Whit:\", round(flag_list[\"Flag3\"], 5))\r\nelif temp_yy == \"Flag4\":\r\n print(\"↓ | The fastest is: \", \"Inline1 \\n↓ | Whit:\", round(flag_list[\"Flag4\"], 5))\r\nelif temp_yy == \"Flag5\":\r\n print(\"↓ | The fastest is: \", \"Inline2 \\n↓ | Whit:\", round(flag_list[\"Flag5\"], 5))\r\nelif temp_yy == \"Flag6\":\r\n print(\"↓ | The fastest is: \", \"Inline3 \\n↓ | Whit:\", round(flag_list[\"Flag6\"], 5))\r\nelif temp_yy == \"Flag7\":\r\n print(\"↓ | The fastest is: \", \"RandomTest \\n↓ | Whit:\", round(flag_list[\"Flag7\"], 5))\r\nelse:\r\n pass\r\n", "repo_name": "Arkadio918/Buzz-Fizz-fastest", "sub_path": "BuzzFizz Original.py", "file_name": "BuzzFizz Original.py", "file_ext": "py", "file_size_in_byte": 7076, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "functools.lru_cache", "line_number": 13, "usage_type": "call"}, {"api_name": "functools.lru_cache", "line_number": 27, "usage_type": "call"}, {"api_name": "functools.lru_cache", "line_number": 33, "usage_type": "call"}, {"api_name": "functools.lru_cache", "line_number": 39, "usage_type": "call"}, {"api_name": "functools.lru_cache", "line_number": 49, "usage_type": "call"}, {"api_name": "functools.lru_cache", "line_number": 59, "usage_type": "call"}, {"api_name": "functools.lru_cache", "line_number": 66, "usage_type": "call"}, {"api_name": "time.time", "line_number": 85, "usage_type": "call"}, {"api_name": "time.time", "line_number": 87, "usage_type": "call"}, {"api_name": "time.time", "line_number": 89, "usage_type": "call"}, {"api_name": "time.time", "line_number": 91, "usage_type": "call"}, {"api_name": "time.time", "line_number": 93, "usage_type": "call"}, {"api_name": "time.time", "line_number": 95, "usage_type": "call"}, {"api_name": "time.time", "line_number": 97, "usage_type": "call"}, {"api_name": "time.time", "line_number": 99, "usage_type": "call"}, {"api_name": "operator.itemgetter", "line_number": 172, "usage_type": "call"}, {"api_name": "operator.itemgetter", "line_number": 175, "usage_type": "call"}]} +{"seq_id": "7537574562", "text": "from experiment_secuence import ExperimentSecuence\nfrom pp2 import Pp2\nimport logging\nfrom time import sleep\nfrom setup import SetupModDig\n\nlogger = logging.getLogger(\"modDig\")\n\n\nclass ExperimentRunner (ExperimentSecuence):\n\n def __init__(self, definition):\n ExperimentSecuence.__init__(self, definition)\n self.pp2 = Pp2()\n\n def __iter__(self):\n return ExperimentSecuence.__iter__(self)\n\n def next(self):\n secuence, duration = ExperimentSecuence.next(self)\n logger.info(\"Generated pulse secuence {} seconds\".format(duration))\n self.pp2.upload_program(secuence)\n logger.info(\"Uploaded Program PP2\")\n self.pp2.trigger_program()\n logger.info(\"Triggered Program PP2\")\n if not SetupModDig.debug:\n sleep(duration)\n else:\n logger.info(\"modo simulado no hay sleep inermedio\")\n\n logger.info(\"Finished run PP2\")\n self.ad.read_channels()\n logger.info(\"Already data from AD\")\n return self.ad.data_a, self.ad.data_b\n", "repo_name": "pablobovina/ModuloDigital", "sub_path": "source/experiment_runner.py", "file_name": "experiment_runner.py", "file_ext": "py", "file_size_in_byte": 1039, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "logging.getLogger", "line_number": 7, "usage_type": "call"}, {"api_name": "experiment_secuence.ExperimentSecuence", "line_number": 10, "usage_type": "name"}, {"api_name": "experiment_secuence.ExperimentSecuence.__init__", "line_number": 13, "usage_type": "call"}, {"api_name": "experiment_secuence.ExperimentSecuence", "line_number": 13, "usage_type": "name"}, {"api_name": "pp2.Pp2", "line_number": 14, "usage_type": "call"}, {"api_name": "experiment_secuence.ExperimentSecuence.__iter__", "line_number": 17, "usage_type": "call"}, {"api_name": "experiment_secuence.ExperimentSecuence", "line_number": 17, "usage_type": "name"}, {"api_name": "experiment_secuence.ExperimentSecuence.next", "line_number": 20, "usage_type": "call"}, {"api_name": "experiment_secuence.ExperimentSecuence", "line_number": 20, "usage_type": "name"}, {"api_name": "setup.SetupModDig.debug", "line_number": 26, "usage_type": "attribute"}, {"api_name": "setup.SetupModDig", "line_number": 26, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 27, "usage_type": "call"}]} +{"seq_id": "31968095319", "text": "import logging\nimport requests\nfrom time import sleep\n\nfrom celery import Celery\nfrom flask import Flask, request\nfrom random import randint, uniform\n\nlogger = logging.getLogger(__name__)\n\napp = Flask(__name__)\napp.config['CELERY_BROKER_URL'] = 'amqp://guest@rabbit/'\napp.config['API_DELAY'] = 0.2\n\ncelery = Celery(app.name, broker=app.config['CELERY_BROKER_URL'])\ncelery.conf.update(app.config)\n\n\n@celery.task(bind=True, retry_backoff=True, acks_late=False)\ndef add(self, x, y):\n result = x + y\n data = {'result': result}\n try:\n requests.post('http://api:5000/result/', data, timeout=1.5)\n except Exception as exc:\n logger.exception('API request failed with')\n self.retry(exc=exc)\n\n\n@app.route('/')\ndef hello_world():\n a, b = randint(0, 1000), randint(0, 1000)\n add.delay(a, b)\n return 'Hello, World!'\n\n\n@app.route('/result/', methods=['POST'])\ndef process_result():\n logger.info('Process result %s' % request.data)\n sleep(uniform(0, app.config['API_DELAY']))\n return 'OK'\n\n\n@app.route('/delay/', methods=['GET', 'POST'])\ndef set_delay():\n if request.method == 'GET':\n pass\n elif request.method == 'POST':\n print(request.data)\n delay = float(request.form.get('delay'))\n app.config['API_DELAY'] = delay\n return 'Delay {}'.format(app.config['API_DELAY'])\n", "repo_name": "mrpear/celery-tests", "sub_path": "app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 1342, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "logging.getLogger", "line_number": 9, "usage_type": "call"}, {"api_name": "flask.Flask", "line_number": 11, "usage_type": "call"}, {"api_name": "celery.Celery", "line_number": 15, "usage_type": "call"}, {"api_name": "celery.conf.update", "line_number": 16, "usage_type": "call"}, {"api_name": "celery.conf", "line_number": 16, "usage_type": "attribute"}, {"api_name": "requests.post", "line_number": 24, "usage_type": "call"}, {"api_name": "celery.task", "line_number": 19, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 32, "usage_type": "call"}, {"api_name": "flask.request.data", "line_number": 39, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 39, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 40, "usage_type": "call"}, {"api_name": "random.uniform", "line_number": 40, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 46, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 46, "usage_type": "name"}, {"api_name": "flask.request.method", "line_number": 48, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 48, "usage_type": "name"}, {"api_name": "flask.request.data", "line_number": 49, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 49, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 50, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 50, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 50, "usage_type": "name"}]} +{"seq_id": "73683504747", "text": "import os\nimport argparse\nimport glob\nfrom ts_config_logic import ts_config_file\n\ndef validate_args():\n parser = argparse.ArgumentParser()\n parser.add_argument('--file', help='file to compile and run')\n args = parser.parse_args()\n return args.file\n\ndef valid_file(argFile):\n if(not os.path.isfile('./{}'.format(argFile))):\n print('Unable to find file {}'.format(argFile))\n return False\n\n filename, file_extension = os.path.splitext(argFile)\n print('filename: {}'.format(filename))\n print('file extension {}'.format(file_extension))\n if(file_extension != \".ts\"):\n print('Incorrect file format, unable to compile you DINGUS')\n return False\n \n return True\n\n\ndef locate_and_run_file(argFile):\n\n if(not valid_file(argFile)):\n return\n\n print('You found it Henry FINALLY GAAAAAAAAAAAAAAAAAAAAAAAH!!!!!');\n ts_config_file(argFile[:-3])\n \n\n\ndef run_script():\n print('test')\n\n argsFile = validate_args()\n\n locate_and_run_file(argsFile)\n #Compile the code\n #Check to see if there exists in ts config\n #if so delete it and provide it from this code\n #Seperate file to handle generating the tsconfig\n #Replace the compile object names and outputs\n \n #Invoke a subprocess call and wait till compilition is finished\n #After you get the result if it failed our not, need to figure out if failed or not\n #check output final line, will be fun to figure out\n\nif __name__ == '__main__':\n run_script()\n\n\n\n\n", "repo_name": "Henelllis/UtilityScripts", "sub_path": "compile_and_run.py", "file_name": "compile_and_run.py", "file_ext": "py", "file_size_in_byte": 1520, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 7, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 13, "usage_type": "call"}, {"api_name": "os.path", "line_number": 13, "usage_type": "attribute"}, {"api_name": "os.path.splitext", "line_number": 17, "usage_type": "call"}, {"api_name": "os.path", "line_number": 17, "usage_type": "attribute"}, {"api_name": "ts_config_logic.ts_config_file", "line_number": 33, "usage_type": "call"}]} +{"seq_id": "23826000788", "text": "# -*- coding: utf-8 -*-\nimport re\nfrom types import SimpleNamespace\n\nfrom blessed import Terminal\n\nterm = Terminal()\n\n\ndef repo_output_handler(repo, tabs=1, indent='\\t'):\n\n results = []\n repo_condition = 'green'\n for i in repo.modified:\n results.append(term.yellow(f'{tabs*indent}modified: {i.path}'))\n repo_condition = 'yellow'\n for i in repo.renamed:\n results.append(\n term.yellow(f'{tabs*indent}renamed: {i.path} {i.original_path}')\n )\n repo_condition = 'yellow'\n for i in repo.untracked:\n results.append(term.yellow(f'{tabs*indent}untracked: {i.path}'))\n repo_condition = 'yellow'\n for i in repo.deleted:\n results.append(term.red(f'{tabs*indent}deleted: {i.path}'))\n repo_condition = 'red'\n for i in repo.ignored:\n results.append(term.red(f'{tabs*indent}ignored: {i.path}'))\n\n if not repo.online:\n repo_condition = 'blue'\n\n try:\n header = [(tabs - 1) * indent, repo.name]\n if repo.branch.head:\n header.append(term.magenta(repo.branch.head))\n if repo.ahead:\n header.append(term.cyan(f'↑{repo.ahead}'))\n if repo.behind:\n header.append(term.cyan(f'↓{repo.ahead}'))\n if repo.modified:\n header.append(term.cyan(f'~{len(repo.modified)}'))\n if repo.deleted:\n header.append(term.cyan(f'-{len(repo.deleted)}'))\n if repo.untracked:\n header.append(term.cyan(f'…{len(repo.untracked)}'))\n header = ' '.join(header)\n except Exception: # For Bare Repos\n header = f\"{(tabs-1)*indent} {repo.name} {len(repo.modified):+}\"\n\n conditions = {\n \"green\": lambda iterable, item: iterable.insert(\n 0, term.green(f\"✓ {item}\")\n ),\n \"yellow\": lambda iterable, item: iterable.insert(\n 0, term.yellow(f\"⚠ {header}\")\n ),\n \"red\": lambda iterable, item: iterable.insert(\n 0, term.red(f\"! {header}\")\n ),\n \"blue\": lambda iterable, item: iterable.insert(\n 0, term.blue(f\"? {header}\")\n ),\n \"default\": lambda iterable, item: iterable.insert(0, f\"? {header}\"),\n }\n add_header = conditions.get(repo_condition, conditions['default'])\n add_header(results, header)\n\n return '\\n'.join(results)\n\n\nasync def parse_repo(parsed_output: dict, short: bool = False):\n status_stdout = parsed_output['status']['stdout']\n parsed = parse_git_status(status_stdout)\n parsed.name = parsed_output['name']\n\n if short:\n # To tell if there's any changes that were made.\n any_changes = any(\n i\n for i in [\n parsed.ahead,\n parsed.behind,\n len(parsed.modified),\n len(parsed.renamed),\n len(parsed.deleted),\n len(parsed.untracked),\n len(parsed.ignored),\n ]\n )\n if any_changes:\n print(repo_output_handler(parsed))\n else:\n print(repo_output_handler(parsed))\n\n\ndef parse_git_status(stdout):\n\n lines = stdout.splitlines()\n repo = SimpleNamespace()\n\n branch_info = [i for i in lines if i.startswith('#')]\n modified = [i for i in lines if i.startswith('1')]\n renamed_or_copied = [i for i in lines if i.startswith('2')]\n untracked = [i for i in lines if i.startswith('?')]\n ignored = [i for i in lines if i.startswith('!')]\n\n # Branch\n oid_group = '# branch.oid (?P.*)'\n head_group = '# branch.head (?P.*)'\n upstream_group = '# branch.upstream (?P.*)'\n ahead_behind_group = '# branch.ab (?P.*) (?P.*)'\n space = r'\\s*'\n branch_re = re.compile(\n fr'({oid_group})?'\n + fr'{space}'\n + fr'({head_group})?'\n + fr'{space}'\n + fr'({upstream_group})?'\n + fr'{space}'\n + fr'({ahead_behind_group})?'\n )\n\n branch_info = [i for i in lines if i.startswith('#')]\n branch_match = branch_re.match('\\n'.join(branch_info))\n\n branch = SimpleNamespace(\n oid=branch_match.group('oid'),\n head=branch_match.group('head'),\n upstream=branch_match.group('upstream'),\n ahead=int(\n branch_match.group('ahead') if branch_match.group('ahead') else 0\n ),\n behind=int(\n branch_match.group('behind') if branch_match.group('behind') else 0\n ),\n )\n\n # Changed\n modified = [get_file_info(i.split(maxsplit=9)) for i in modified]\n\n # Renamed or Copied\n renamed_or_copied = [\n get_file_info(i.split(maxsplit=10)) for i in renamed_or_copied\n ]\n\n # Untracked\n untracked = [i.split(maxsplit=1)[1] for i in untracked]\n untracked = [SimpleNamespace(path=i, type='Untracked') for i in untracked]\n\n # Ignored\n # Only if `--ignored=matching` is included\n ignored = [i.split(maxsplit=1)[1] for i in ignored]\n ignored = [SimpleNamespace(path=i, type='Ignored') for i in ignored]\n\n # All Files\n all_files = modified + renamed_or_copied + untracked + ignored\n # Resort by Type\n modified = [i for i in all_files if i.type[0] == 'M']\n renamed = [i for i in all_files if i.type[0] == 'R']\n deleted = [i for i in all_files if i.type[0] == 'D']\n untracked = [i for i in all_files if i.type[0] == 'U']\n ignored = [i for i in all_files if i.type[0] == 'I']\n\n repo.branch = branch\n repo.ahead = branch.ahead\n repo.behind = branch.behind\n repo.modified = modified\n repo.renamed = renamed\n repo.deleted = deleted\n repo.untracked = untracked\n repo.ignored = ignored\n repo.all_changed_files = all_files\n\n # Quickfix\n if repo.branch.oid:\n repo.online = True\n else:\n repo.online = False\n\n return repo\n\n\ndef get_file_info(raw):\n\n if raw[0] == '1':\n type_ = 'changed'\n else:\n type_ = 'renamed_or_copied'\n raw = raw[1:] # Get rid of the type as the docs don't refer to it.\n\n if type_ == 'renamed_or_copied':\n path = raw[8]\n original_path = raw[9]\n else:\n path = raw[7]\n original_path = None\n\n staged = False\n subtype = raw[0][-1] if raw[0][-1] != '.' else raw[0][0]\n if raw[0][0] == '.':\n staged = False\n subtype = raw[0][-1]\n elif raw[0][-1] == '.':\n staged = True\n subtype = raw[0][0]\n if subtype == 'D':\n subtype = 'Deleted'\n elif subtype == 'M':\n subtype = 'Modified'\n elif subtype == 'R':\n subtype = 'Renamed'\n\n file = SimpleNamespace(\n path=path,\n staged=staged,\n original_path=original_path,\n type=subtype,\n )\n\n return file\n", "repo_name": "AceofSpades5757/cs-sync", "sub_path": "src/cs_sync/handlers.py", "file_name": "handlers.py", "file_ext": "py", "file_size_in_byte": 6668, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "blessed.Terminal", "line_number": 7, "usage_type": "call"}, {"api_name": "types.SimpleNamespace", "line_number": 101, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 115, "usage_type": "call"}, {"api_name": "types.SimpleNamespace", "line_number": 128, "usage_type": "call"}, {"api_name": "types.SimpleNamespace", "line_number": 150, "usage_type": "call"}, {"api_name": "types.SimpleNamespace", "line_number": 155, "usage_type": "call"}, {"api_name": "types.SimpleNamespace", "line_number": 215, "usage_type": "call"}]} +{"seq_id": "31703797434", "text": "import os\r\nimport openai\r\n\r\n# openai.api_key = os.getenv(\"OPENAI_API_KEY\")\r\nopenai.api_key = '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'\r\n\r\n\r\nresponse = openai.Completion.create(\r\n model=\"text-davinci-003\",\r\n prompt=\"Write a 800 words romantc horror story\",\r\n temperature=0.7,\r\n max_tokens=2000,\r\n top_p=1,\r\n frequency_penalty=0,\r\n presence_penalty=0\r\n)\r\n\r\nprint(response)", "repo_name": "tf153/chatGPT4", "sub_path": "app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 389, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "openai.api_key", "line_number": 5, "usage_type": "attribute"}, {"api_name": "openai.Completion.create", "line_number": 8, "usage_type": "call"}, {"api_name": "openai.Completion", "line_number": 8, "usage_type": "attribute"}]} +{"seq_id": "8964450190", "text": "import torch\nimport numpy as np\n\n \nclass ModelWrapperTorch:\n def __init__(self, model, device):\n self.model = model.to(device)\n self.device = device\n \n def get_predictions(self, batch_ppl):\n batch_ppl = torch.FloatTensor(batch_ppl).to(self.device)\n batch_conf = self.model(batch_ppl)\n return batch_conf.data.cpu()\n\n def __call__(self, batch_ppl):\n batch_predictions = self.get_predictions(batch_ppl)\n batch_predictions2 = (batch_predictions[:,0]).unsqueeze(1).numpy() #pre-merged logits\n return batch_predictions2\n \nclass MixedModelWrapperTorch:\n def __init__(self, model, device):\n self.model = model.to(device)\n self.device = device\n \n def get_predictions(self, batch_ppl):\n batch_ppl = torch.FloatTensor(batch_ppl).to(self.device)\n batch_conf = self.model(batch_ppl)\n return batch_conf\n\n def __call__(self, batch_ppl):\n batch_predictions = self.get_predictions(batch_ppl)\n batch_predictions = batch_predictions[0] + batch_predictions[1]\n batch_predictions = batch_predictions.data.cpu()\n batch_predictions2 = (batch_predictions[:,0]).unsqueeze(1).numpy() #pre-merged logits\n return batch_predictions2\n \nclass MixedModelEnsembleWrapperTorch:\n def __init__(self, models, device):\n self.models = [model.to(device) for model in models]\n self.device = device\n \n def get_predictions(self, batch_ppl):\n batch_ppl = torch.FloatTensor(batch_ppl).to(self.device)\n batch_logit = torch.zeros(batch_ppl.shape[0])\n for i in range(len(self.models)):\n logits = self.models[i](batch_ppl)\n logits = (logits[0]+logits[1]).data.cpu()\n batch_logit += logits.narrow(1,0,1).squeeze()\n batch_logit /= len(self.models)\n return batch_logit\n\n def __call__(self, batch_ppl):\n batch_logit = self.get_predictions(batch_ppl)\n return (batch_logit).unsqueeze(1).numpy() \n\n\n\n\n\nclass BloodMixedEnsembleWrapperTorchLogit:\n def __init__(self, models1, device):\n self.models1 = [model.to(device) for model in models1]\n self.device = device\n \n def get_predictions(self, batch_ppl):\n batch_ppl = torch.FloatTensor(batch_ppl).to(self.device)\n batch_logit1 = torch.zeros(batch_ppl.shape[0])\n for i in range(len(self.models1)):\n logits = self.models1[i](batch_ppl)\n logits = (logits[0]+logits[1]).data.cpu()\n batch_logit1 += logits.narrow(1,0,1).squeeze()\n batch_logit1 /= len(self.models1)\n\n return batch_logit1\n\n def __call__(self, batch_ppl):\n batch_logit1 = self.get_predictions(batch_ppl)\n return (batch_logit1).unsqueeze(1).numpy() \n\nclass SKlearnEnsembleWrapperLogit:\n def __init__(self, models, merge_logits=True):\n self.models = models\n self.merge_logits = merge_logits\n \n\n def get_predictions(self, batch_ppl):\n batch_logit = np.zeros(batch_ppl.shape[0])\n for i in range(len(self.models)):\n ###logits = self.models[i].predict_proba(batch_ppl)[:,1]\n logits = self.models[i].predict(batch_ppl)\n batch_logit += logits\n batch_logit /= len(self.models)\n\n return batch_logit\n \n def __call__(self, batch_ppl):\n batch_predictions = self.get_predictions(batch_ppl)\n if self.merge_logits:\n return np.expand_dims(batch_predictions,axis=-1)\n else:\n return batch_predictions.numpy()\n\n\n\n\n\n\n\n\n\n\n\n\n\nimport numpy as np\n\ndef get_efficient_mask_indices(inst, baseline, target):\n invert = np.sum(1*inst) >= len(inst)//2\n if invert:\n context = target.copy()\n insertion_target = baseline\n mask_indices = np.argwhere(inst==False).flatten()\n else:\n context = baseline.copy()\n insertion_target = target\n mask_indices = np.argwhere(inst==True).flatten()\n return mask_indices, context, insertion_target\n\n\n\n\nclass BasicXformer:\n def __init__(self, target_ppl, baseline_ppl):\n self.target = target_ppl\n self.baseline = baseline_ppl\n self.num_features = len(self.target)\n\n def simple_xform(self, inst):\n mask_indices = np.argwhere(inst==True).flatten()\n id_list = list(self.baseline)\n for i in mask_indices:\n id_list[i] = self.target[i]\n return id_list\n \n def efficient_xform(self, inst):\n mask_indices, base, change = get_efficient_mask_indices(inst, self.baseline, self.target)\n for i in mask_indices:\n base[i] = change[i]\n return base\n\n def get_contrastive_validities(self):\n validities = {}\n for i in range(self.num_features):\n if self.target[i]==self.baseline[i]:\n validities[i] = False\n else:\n validities[i] = True\n return validities\n\n def __call__(self, inst):\n instance = self.efficient_xform(inst)\n return instance \n\n\ndef CustomGroupedXformer(num_grouped_features,num_full_features,feature_grouping_dictionary):\n \n class CustomizedGroupedXformer():\n def __init__(self, target_ppl, baseline_ppl):\n self.target = target_ppl\n self.baseline = baseline_ppl\n self.num_features = num_grouped_features\n self.num_full_features = num_full_features\n\n def group_masks(self, inst):\n new_inst = np.ones( self.num_full_features ).astype(bool)\n for grouped_feat in feature_grouping_dictionary:\n corresp_feats=feature_grouping_dictionary[grouped_feat] #all features corresponding to the group\n new_inst[corresp_feats] = inst[grouped_feat]\n return new_inst\n \n def simple_xform(self, inst):\n mask_indices = np.argwhere(inst==True).flatten()\n id_list = list(self.baseline)\n for i in mask_indices:\n id_list[i] = self.target[i]\n return id_list\n \n def efficient_xform(self, inst):\n inst = self.group_masks(inst)\n mask_indices, base, change = get_efficient_mask_indices(inst, self.baseline, self.target)\n for i in mask_indices:\n base[i] = change[i]\n return base\n\n def get_contrastive_validities(self):\n validities = {}\n for grouped_feat in feature_grouping_dictionary:\n corresp_feats=feature_grouping_dictionary[grouped_feat] #all features corresponding to the group\n if (self.target[corresp_feats]==self.baseline[corresp_feats]).all():\n validities[grouped_feat] = False\n else:\n validities[grouped_feat] = True\n return validities\n\n def __call__(self, inst):\n instance = self.efficient_xform(inst)\n return instance\n\n return CustomizedGroupedXformer\n\n \n", "repo_name": "EnouenJ/sparse-interaction-additive-networks", "sub_path": "code/basic_wrappers.py", "file_name": "basic_wrappers.py", "file_ext": "py", "file_size_in_byte": 7011, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "37", "api": [{"api_name": "torch.FloatTensor", "line_number": 11, "usage_type": "call"}, {"api_name": "torch.FloatTensor", "line_number": 26, "usage_type": "call"}, {"api_name": "torch.FloatTensor", "line_number": 43, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 44, "usage_type": "call"}, {"api_name": "torch.FloatTensor", "line_number": 66, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 67, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 87, "usage_type": "call"}, {"api_name": "numpy.expand_dims", "line_number": 99, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 118, "usage_type": "call"}, {"api_name": "numpy.argwhere", "line_number": 122, "usage_type": "call"}, {"api_name": "numpy.argwhere", "line_number": 126, "usage_type": "call"}, {"api_name": "numpy.argwhere", "line_number": 139, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 175, "usage_type": "call"}, {"api_name": "numpy.argwhere", "line_number": 182, "usage_type": "call"}]} +{"seq_id": "19431759355", "text": "\"\"\"Toyota Connected Services API.\"\"\"\r\n\r\nfrom datetime import date, datetime, timezone\r\nfrom uuid import uuid4\r\n\r\nfrom mytoyota.const import (\r\n VEHICLE_ASSOCIATION_ENDPOINT,\r\n VEHICLE_GLOBAL_REMOTE_ELECTRIC_STATUS_ENDPOINT,\r\n VEHICLE_GLOBAL_REMOTE_STATUS_ENDPOINT,\r\n VEHICLE_GUID_ENDPOINT,\r\n VEHICLE_HEALTH_STATUS_ENDPOINT,\r\n VEHICLE_LOCATION_ENDPOINT,\r\n VEHICLE_NOTIFICATION_HISTORY_ENDPOINT,\r\n VEHICLE_TELEMETRY_ENDPOINT,\r\n VEHICLE_TRIPS_ENDPOINT,\r\n)\r\nfrom mytoyota.controller import Controller\r\nfrom mytoyota.models.endpoints.electric import ElectricResponseModel\r\nfrom mytoyota.models.endpoints.location import LocationResponseModel\r\nfrom mytoyota.models.endpoints.notifications import NotificationResponseModel\r\nfrom mytoyota.models.endpoints.status import RemoteStatusResponseModel\r\nfrom mytoyota.models.endpoints.telemetry import TelemetryResponseModel\r\nfrom mytoyota.models.endpoints.trips import TripsResponseModel\r\nfrom mytoyota.models.endpoints.vehicle_guid import VehiclesResponseModel\r\nfrom mytoyota.models.endpoints.vehicle_health import VehicleHealthResponseModel\r\n\r\n\r\nclass Api:\r\n \"\"\"API Class. Allows access to available endpoints to retrieve the raw data.\"\"\"\r\n\r\n def __init__(self, controller: Controller) -> None: # noqa: D417\r\n \"\"\"Initialise the API.\r\n\r\n Initialise the API and set the Controller\r\n\r\n Parameters\r\n ----------\r\n controller: Controller: A controller class to managing communication\r\n \"\"\"\r\n self.controller = controller\r\n\r\n async def _request_and_parse(self, model, method: str, endpoint: str, **kwargs):\r\n \"\"\"Parse requests and responses.\"\"\"\r\n response = await self.controller.request_json(\r\n method=method, endpoint=endpoint, **kwargs\r\n )\r\n return model(**response)\r\n\r\n async def set_vehicle_alias_endpoint(self, alias: str, guid: str, vin: str):\r\n \"\"\"Set the alias for a vehicle.\"\"\"\r\n return await self.controller.request_raw(\r\n method=\"PUT\",\r\n endpoint=VEHICLE_ASSOCIATION_ENDPOINT,\r\n vin=vin,\r\n headers={\r\n \"datetime\": str(int(datetime.now(timezone.utc).timestamp() * 1000)),\r\n \"x-correlationid\": str(uuid4()),\r\n \"Content-Type\": \"application/json\",\r\n },\r\n body={\"guid\": guid, \"vin\": vin, \"nickName\": alias},\r\n )\r\n\r\n # TODO: Remove for now as it seems to have no effect. The App is sending it! # pylint: disable=W0511\r\n # async def post_wake_endpoint(self) -> None:\r\n # \"\"\"Send a wake request to the vehicle.\"\"\"\r\n # await self.controller.request_raw(\r\n # method=\"POST\", endpoint=\"/v2/global/remote/wake\"\r\n # )\r\n\r\n async def get_vehicles_endpoint(self) -> VehiclesResponseModel:\r\n \"\"\"Return list of vehicles registered with provider.\"\"\"\r\n return await self._request_and_parse(\r\n VehiclesResponseModel, \"GET\", VEHICLE_GUID_ENDPOINT\r\n )\r\n\r\n async def get_location_endpoint(\r\n self, vin: str\r\n ) -> LocationResponseModel: # noqa: D417\r\n \"\"\"Get the last known location of your car. Only updates when car is parked.\r\n\r\n Response includes Lat, Lon position. * If supported.\r\n\r\n Parameters\r\n ----------\r\n vin: str: The vehicles VIN\r\n \"\"\"\r\n return await self._request_and_parse(\r\n LocationResponseModel, \"GET\", VEHICLE_LOCATION_ENDPOINT, vin=vin\r\n )\r\n\r\n async def get_vehicle_health_status_endpoint(\r\n self, vin: str\r\n ) -> VehicleHealthResponseModel: # noqa: D417\r\n \"\"\"Get the latest health status.\r\n\r\n Response includes the quantity of engine oil and any dashboard warning lights. \\n\r\n * If supported.\r\n\r\n Parameters\r\n ----------\r\n vin: str: The vehicles VIN\r\n \"\"\"\r\n return await self._request_and_parse(\r\n VehicleHealthResponseModel, \"GET\", VEHICLE_HEALTH_STATUS_ENDPOINT, vin=vin\r\n )\r\n\r\n async def get_remote_status_endpoint(self, vin: str) -> RemoteStatusResponseModel:\r\n \"\"\"Get information about the vehicle.\"\"\"\r\n return await self._request_and_parse(\r\n RemoteStatusResponseModel,\r\n \"GET\",\r\n VEHICLE_GLOBAL_REMOTE_STATUS_ENDPOINT,\r\n vin=vin,\r\n )\r\n\r\n async def get_vehicle_electric_status_endpoint(\r\n self, vin: str\r\n ) -> ElectricResponseModel: # noqa: D417\r\n \"\"\"Get the latest electric status.\r\n\r\n Response includes current battery level, EV Range, EV Range with AC, \\n\r\n fuel level, fuel range and current charging status\r\n\r\n Parameters\r\n ----------\r\n vin: str: The vehicles VIN\r\n \"\"\"\r\n return await self._request_and_parse(\r\n ElectricResponseModel,\r\n \"GET\",\r\n VEHICLE_GLOBAL_REMOTE_ELECTRIC_STATUS_ENDPOINT,\r\n vin=vin,\r\n )\r\n\r\n async def get_telemetry_endpoint(\r\n self, vin: str\r\n ) -> TelemetryResponseModel: # noqa: D417\r\n \"\"\"Get the latest telemetry status.\r\n\r\n Response includes current fuel level, distance to empty and odometer\r\n\r\n Parameters\r\n ----------\r\n vin: str: The vehicles VIN\r\n \"\"\"\r\n return await self._request_and_parse(\r\n TelemetryResponseModel, \"GET\", VEHICLE_TELEMETRY_ENDPOINT, vin=vin\r\n )\r\n\r\n async def get_notification_endpoint(\r\n self, vin: str\r\n ) -> NotificationResponseModel: # noqa: D417\r\n \"\"\"Get all available notifications for the vehicle.\r\n\r\n A notification includes a message, notification date, read flag, date read.\r\n\r\n NOTE: Currently no way to mark notification as read or limit the response.\r\n\r\n Parameters\r\n ----------\r\n vin: str: The vehicles VIN\r\n \"\"\"\r\n return await self._request_and_parse(\r\n NotificationResponseModel,\r\n \"GET\",\r\n VEHICLE_NOTIFICATION_HISTORY_ENDPOINT,\r\n vin=vin,\r\n )\r\n\r\n async def get_trips_endpoint( # noqa: PLR0913, D417\r\n self,\r\n vin: str,\r\n from_date: date,\r\n to_date: date,\r\n route: bool = False,\r\n summary: bool = True,\r\n limit: int = 5,\r\n offset: int = 0,\r\n ) -> TripsResponseModel:\r\n \"\"\"Get list of trips.\r\n\r\n Retrieves a list of all trips between the given dates. \\n\r\n The default data(route = False, summary = False) provides\r\n a basic summary of each trip and includes Coaching message and electrical use.\r\n\r\n Parameters\r\n ----------\r\n vin: str: The vehicles VIN\r\n from_date: date: From date to include trips, inclusive. Cant be in the future.\r\n to_date: date: To date to include trips, inclusive. Cant be in the future.\r\n route: bool: If true returns the route of each trip as a list of coordinates.\r\n Suitable for drawing on a map.\r\n summary: bool: If true returns a summary of each month and day in the date range\r\n limit: int: Limit of number of trips to return in one request. Max 50.\r\n offset: int: Offset into trips to start the request.\r\n \"\"\"\r\n endpoint = VEHICLE_TRIPS_ENDPOINT.format(\r\n from_date=from_date,\r\n to_date=to_date,\r\n route=route,\r\n summary=summary,\r\n limit=limit,\r\n offset=offset,\r\n )\r\n return await self._request_and_parse(\r\n TripsResponseModel, \"GET\", endpoint, vin=vin\r\n )\r\n", "repo_name": "CM000n/mytoyota", "sub_path": "mytoyota/api.py", "file_name": "api.py", "file_ext": "py", "file_size_in_byte": 7695, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "github-code", "pt": "37", "api": [{"api_name": "mytoyota.controller.Controller", "line_number": 31, "usage_type": "name"}, {"api_name": "mytoyota.const.VEHICLE_ASSOCIATION_ENDPOINT", "line_number": 53, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 56, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 56, "usage_type": "name"}, {"api_name": "datetime.timezone.utc", "line_number": 56, "usage_type": "attribute"}, {"api_name": "datetime.timezone", "line_number": 56, "usage_type": "name"}, {"api_name": "uuid.uuid4", "line_number": 57, "usage_type": "call"}, {"api_name": "mytoyota.models.endpoints.vehicle_guid.VehiclesResponseModel", "line_number": 73, "usage_type": "argument"}, {"api_name": "mytoyota.const.VEHICLE_GUID_ENDPOINT", "line_number": 73, "usage_type": "argument"}, {"api_name": "mytoyota.models.endpoints.vehicle_guid.VehiclesResponseModel", "line_number": 70, "usage_type": "name"}, {"api_name": "mytoyota.models.endpoints.location.LocationResponseModel", "line_number": 88, "usage_type": "argument"}, {"api_name": "mytoyota.const.VEHICLE_LOCATION_ENDPOINT", "line_number": 88, "usage_type": "argument"}, {"api_name": "mytoyota.models.endpoints.location.LocationResponseModel", "line_number": 78, "usage_type": "name"}, {"api_name": "mytoyota.models.endpoints.vehicle_health.VehicleHealthResponseModel", "line_number": 104, "usage_type": "argument"}, {"api_name": "mytoyota.const.VEHICLE_HEALTH_STATUS_ENDPOINT", "line_number": 104, "usage_type": "argument"}, {"api_name": "mytoyota.models.endpoints.vehicle_health.VehicleHealthResponseModel", "line_number": 93, "usage_type": "name"}, {"api_name": "mytoyota.models.endpoints.status.RemoteStatusResponseModel", "line_number": 110, "usage_type": "argument"}, {"api_name": "mytoyota.const.VEHICLE_GLOBAL_REMOTE_STATUS_ENDPOINT", "line_number": 112, "usage_type": "argument"}, {"api_name": "mytoyota.models.endpoints.status.RemoteStatusResponseModel", "line_number": 107, "usage_type": "name"}, {"api_name": "mytoyota.models.endpoints.electric.ElectricResponseModel", "line_number": 129, "usage_type": "argument"}, {"api_name": "mytoyota.const.VEHICLE_GLOBAL_REMOTE_ELECTRIC_STATUS_ENDPOINT", "line_number": 131, "usage_type": "argument"}, {"api_name": "mytoyota.models.endpoints.electric.ElectricResponseModel", "line_number": 118, "usage_type": "name"}, {"api_name": "mytoyota.models.endpoints.telemetry.TelemetryResponseModel", "line_number": 147, "usage_type": "argument"}, {"api_name": "mytoyota.const.VEHICLE_TELEMETRY_ENDPOINT", "line_number": 147, "usage_type": "argument"}, {"api_name": "mytoyota.models.endpoints.telemetry.TelemetryResponseModel", "line_number": 137, "usage_type": "name"}, {"api_name": "mytoyota.models.endpoints.notifications.NotificationResponseModel", "line_number": 164, "usage_type": "argument"}, {"api_name": "mytoyota.const.VEHICLE_NOTIFICATION_HISTORY_ENDPOINT", "line_number": 166, "usage_type": "argument"}, {"api_name": "mytoyota.models.endpoints.notifications.NotificationResponseModel", "line_number": 152, "usage_type": "name"}, {"api_name": "datetime.date", "line_number": 173, "usage_type": "name"}, {"api_name": "datetime.date", "line_number": 174, "usage_type": "name"}, {"api_name": "mytoyota.const.VEHICLE_TRIPS_ENDPOINT.format", "line_number": 197, "usage_type": "call"}, {"api_name": "mytoyota.const.VEHICLE_TRIPS_ENDPOINT", "line_number": 197, "usage_type": "name"}, {"api_name": "mytoyota.models.endpoints.trips.TripsResponseModel", "line_number": 206, "usage_type": "argument"}, {"api_name": "mytoyota.models.endpoints.trips.TripsResponseModel", "line_number": 179, "usage_type": "name"}]} +{"seq_id": "36292343107", "text": "# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import migrations, models\nfrom django.conf import settings\nimport django.db.models.deletion\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('direccion', '0001_initial'),\n ('estadoderegistro', '0001_initial'),\n migrations.swappable_dependency(settings.AUTH_USER_MODEL),\n ]\n\n operations = [\n migrations.CreateModel(\n name='Ambiente',\n fields=[\n ('id', models.AutoField(serialize=False, verbose_name='ID', primary_key=True, auto_created=True)),\n ('ambiente', models.CharField(max_length=100, unique=True)),\n ('descripcion', models.TextField()),\n ('conteo_de_ambientes', models.BooleanField(default=False)),\n ],\n options={\n 'verbose_name': 'ambiente',\n 'ordering': ['ambiente'],\n 'verbose_name_plural': 'Ambientes',\n },\n ),\n migrations.CreateModel(\n name='AmbienteEstadoDeRegistro',\n fields=[\n ('id', models.AutoField(serialize=False, verbose_name='ID', primary_key=True, auto_created=True)),\n ('fecha', models.DateField(auto_now_add=True)),\n ('observacion', models.TextField(blank=True)),\n ('predefinido', models.BooleanField(default=None)),\n ('ambiente', models.ForeignKey(to='ambiente.Ambiente')),\n ('estado_de_registro', models.ForeignKey(to='estadoderegistro.EstadoDeRegistro')),\n ('usuario', models.ForeignKey(to=settings.AUTH_USER_MODEL)),\n ],\n options={\n 'verbose_name': 'Estado de registro de ambiente',\n 'ordering': ['ambiente', 'estado_de_registro'],\n 'verbose_name_plural': 'Estados de registro de ambiente',\n },\n ),\n migrations.CreateModel(\n name='AmbientePorTipoDeInmueble',\n fields=[\n ('id', models.AutoField(serialize=False, verbose_name='ID', primary_key=True, auto_created=True)),\n ('predeterminado', models.BooleanField(default=False)),\n ('ambiente', models.ForeignKey(on_delete=django.db.models.deletion.PROTECT, to='ambiente.Ambiente')),\n ('especificacion_de_inmueble', models.ForeignKey(on_delete=django.db.models.deletion.PROTECT, to='direccion.EspecificacionDeInmueble')),\n ],\n options={\n 'verbose_name': 'Ambiente por tipo inmueble',\n 'ordering': ['especificacion_de_inmueble', 'ambiente'],\n 'verbose_name_plural': 'Ambientes por tipos de inmueble',\n },\n ),\n ]\n", "repo_name": "yusnelvy/mtvmcotizacionv02", "sub_path": "ambiente/migrations/0001_initial.py", "file_name": "0001_initial.py", "file_ext": "py", "file_size_in_byte": 2776, "program_lang": "python", "lang": "es", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "django.db.migrations.Migration", "line_number": 9, "usage_type": "attribute"}, {"api_name": "django.db.migrations", "line_number": 9, "usage_type": "name"}, {"api_name": "django.db.migrations.swappable_dependency", "line_number": 14, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 14, "usage_type": "name"}, {"api_name": "django.conf.settings.AUTH_USER_MODEL", "line_number": 14, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 14, "usage_type": "name"}, {"api_name": "django.db.migrations.CreateModel", "line_number": 18, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 18, "usage_type": "name"}, {"api_name": "django.db.models.AutoField", "line_number": 21, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 21, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 22, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 22, "usage_type": "name"}, {"api_name": "django.db.models.TextField", "line_number": 23, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 23, "usage_type": "name"}, {"api_name": "django.db.models.BooleanField", "line_number": 24, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 24, "usage_type": "name"}, {"api_name": "django.db.migrations.CreateModel", "line_number": 32, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 32, "usage_type": "name"}, {"api_name": "django.db.models.AutoField", "line_number": 35, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 35, "usage_type": "name"}, {"api_name": "django.db.models.DateField", "line_number": 36, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 36, "usage_type": "name"}, {"api_name": "django.db.models.TextField", "line_number": 37, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 37, "usage_type": "name"}, {"api_name": "django.db.models.BooleanField", "line_number": 38, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 38, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 39, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 39, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 40, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 40, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 41, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 41, "usage_type": "name"}, {"api_name": "django.conf.settings.AUTH_USER_MODEL", "line_number": 41, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 41, "usage_type": "name"}, {"api_name": "django.db.migrations.CreateModel", "line_number": 49, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 49, "usage_type": "name"}, {"api_name": "django.db.models.AutoField", "line_number": 52, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 52, "usage_type": "name"}, {"api_name": "django.db.models.BooleanField", "line_number": 53, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 53, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 54, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 54, "usage_type": "name"}, {"api_name": "django.db.db", "line_number": 54, "usage_type": "attribute"}, {"api_name": "django.db", "line_number": 54, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 55, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 55, "usage_type": "name"}, {"api_name": "django.db.db", "line_number": 55, "usage_type": "attribute"}, {"api_name": "django.db", "line_number": 55, "usage_type": "name"}]} +{"seq_id": "30591275370", "text": "import pickle\r\n\r\nimport numpy as np\r\n\r\nimport torch\r\nimport torchvision as tv\r\nimport torch.nn as nn\r\nimport torch.nn.functional as F\r\nimport torch.optim as O\r\n\r\nimport cv2\r\n\r\nfrom nn_helpers import oneHotEncodeOneCol, y_mse, createLenet5\r\n\r\n\r\nclass Img2Obj():\r\n def __init__(self, batch_size=64, test_batch_size=1000, device=torch.device('cuda' if torch.cuda.is_available() else 'cpu')):\r\n self.batch_size = batch_size\r\n self.test_batch_size = test_batch_size\r\n self.device = device\r\n\r\n dataset = tv.datasets.CIFAR100\r\n train_dataset = dataset('./data', train=True, download=True,\r\n transform=tv.transforms.Compose([\r\n tv.transforms.ToTensor()\r\n ]))\r\n test_dataset = dataset('./data', train=False, download=True,\r\n transform=tv.transforms.Compose([\r\n tv.transforms.ToTensor()\r\n ]))\r\n\r\n self.train_loader = torch.utils.data.DataLoader(\r\n train_dataset, batch_size=self.batch_size, shuffle=True)\r\n self.train_test_loader = torch.utils.data.DataLoader(\r\n train_dataset, batch_size=self.test_batch_size, shuffle=True)\r\n\r\n # split train data in validation set too\r\n\r\n self.test_loader = torch.utils.data.DataLoader(\r\n test_dataset, batch_size=self.test_batch_size, shuffle=True)\r\n\r\n self.train_size = len(self.train_loader)\r\n self.test_size = len(self.test_loader)\r\n\r\n self.net = createLenet5(in_channels=3, classes=100).to(device)\r\n self.labels = pickle.load(open('./data/cifar-100-python/meta', 'rb'))['fine_label_names']\r\n\r\n def forward(self, x):\r\n y = None\r\n with torch.no_grad():\r\n y = self.net(x)\r\n \r\n return torch.argmax(self.net(x), dim=1)\r\n\r\n # img should be a tensor of shape (1, 3, 32, 32)\r\n def view(self, img, denormalize=False):\r\n y = self.forward(img)\r\n label = self.labels[y]\r\n img_n = img.numpy()[0].transpose(1, 2, 0)\r\n if denormalize:\r\n img_n = (img_n * 255)\r\n img_n = img_n.astype(np.uint8)\r\n img_n = cv2.cvtColor(img_n, cv2.COLOR_RGB2BGR)\r\n cv2.namedWindow(label, cv2.WINDOW_NORMAL)\r\n cv2.resizeWindow(label, 600, 600)\r\n cv2.imshow(label, img_n)\r\n cv2.waitKey(0)\r\n cv2.destroyAllWindows()\r\n \r\n def cam(self):\r\n cap = cv2.VideoCapture(0)\r\n while(True):\r\n ret, frame = cap.read()\r\n frame = cv2.flip(frame, 1)\r\n img = cv2.resize(frame, (32, 32))\r\n img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\r\n img = img.transpose(2, 0, 1)\r\n img = torch.tensor(img).float()\r\n img = img / 255\r\n label = self.labels[self.forward(img.view(1, *img.shape))]\r\n cv2.putText(frame, label, (40, 50), cv2.FONT_HERSHEY_SIMPLEX, 2, 255, 2)\r\n cv2.imshow(\"webcam\", frame)\r\n if cv2.waitKey(1) & 0xFF == ord('q'):\r\n break\r\n \r\n cap.release()\r\n cv2.destroyAllWindows()\r\n\r\n def train(self, epochs=50, validate_every=2000, save=False):\r\n lenet5_cifar100_dev = self.net\r\n opt = O.Adam(lenet5_cifar100_dev.parameters(), lr=0.001)\r\n criterion = nn.CrossEntropyLoss(reduction=\"mean\")\r\n train_cross_entropy = []\r\n train_accuracy = []\r\n validation_cross_entropy = []\r\n validation_accuracy = []\r\n\r\n best_model_accuracy = 0\r\n\r\n for epoch in range(epochs):\r\n n_correct = 0\r\n n_total = 0\r\n for i, batch in enumerate(self.train_loader):\r\n x, labels = batch\r\n x, labels = x.to(self.device), labels.to(self.device)\r\n N = x.shape[0]\r\n\r\n # training mode (for things like dropout)\r\n lenet5_cifar100_dev.train()\r\n\r\n # clear previous gradients\r\n opt.zero_grad()\r\n\r\n y_hat = lenet5_cifar100_dev(x)\r\n loss = criterion(y_hat, labels)\r\n loss.backward()\r\n opt.step()\r\n\r\n train_cross_entropy.append(loss)\r\n\r\n n_correct += (torch.argmax(y_hat, dim=1)\r\n == labels).sum().item()\r\n n_total += N\r\n\r\n # evaluation mode (e.g. adds dropped neurons back in)\r\n lenet5_cifar100_dev.eval()\r\n if i % validate_every == 0:\r\n n_val_correct = 0\r\n n_val_total = 0\r\n v_cross_entropy_sum = 0\r\n\r\n # don't calculate gradients here\r\n with torch.no_grad():\r\n for j, v_batch in enumerate(self.test_loader):\r\n v_x, v_labels = v_batch\r\n v_x, v_labels = v_x.to(\r\n self.device), v_labels.to(self.device)\r\n v_N = v_x.shape[0]\r\n\r\n v_y_hat = lenet5_cifar100_dev(v_x)\r\n v_loss = criterion(v_y_hat, v_labels)\r\n v_cross_entropy_sum += v_loss\r\n n_val_correct += (torch.argmax(v_y_hat,\r\n dim=1) == v_labels).sum().item()\r\n n_val_total += v_N\r\n\r\n print(\r\n f\"[epoch {epoch + 1}, iteration {i}] \\t accuracy: {n_val_correct / n_val_total} \\t cross entropy: {v_cross_entropy_sum / n_val_total}\")\r\n validation_accuracy.append(n_val_correct / n_val_total)\r\n validation_cross_entropy.append(\r\n v_cross_entropy_sum / n_val_total)\r\n if n_val_correct / n_val_total >= best_model_accuracy:\r\n best_model_accuracy = n_val_correct / n_val_total\r\n if save:\r\n print(\"saving\")\r\n torch.save(lenet5_cifar100_dev.state_dict(),\r\n './trained_models/lenet5_cifar100')\r\n\r\n print(\r\n f\"epoch {epoch + 1} accumulated train accuracy: {n_correct / n_total}\")\r\n train_accuracy.append(n_correct / n_total)\r\n\r\n return (train_cross_entropy, train_accuracy, validation_cross_entropy, validation_accuracy)\r\n", "repo_name": "parthsdoshi/nn-lenet5-cifar100", "sub_path": "img2obj.py", "file_name": "img2obj.py", "file_ext": "py", "file_size_in_byte": 6531, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "torch.device", "line_number": 17, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 17, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 17, "usage_type": "attribute"}, {"api_name": "torchvision.datasets", "line_number": 22, "usage_type": "attribute"}, {"api_name": "torchvision.transforms.Compose", "line_number": 24, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 24, "usage_type": "attribute"}, {"api_name": "torchvision.transforms.ToTensor", "line_number": 25, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 25, "usage_type": "attribute"}, {"api_name": "torchvision.transforms.Compose", "line_number": 28, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 28, "usage_type": "attribute"}, {"api_name": "torchvision.transforms.ToTensor", "line_number": 29, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 29, "usage_type": "attribute"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 32, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 32, "usage_type": "attribute"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 34, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 34, "usage_type": "attribute"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 39, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 39, "usage_type": "attribute"}, {"api_name": "nn_helpers.createLenet5", "line_number": 45, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 46, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 50, "usage_type": "call"}, {"api_name": "torch.argmax", "line_number": 53, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 62, "usage_type": "attribute"}, {"api_name": "cv2.cvtColor", "line_number": 63, "usage_type": "call"}, {"api_name": "cv2.COLOR_RGB2BGR", "line_number": 63, "usage_type": "attribute"}, {"api_name": "cv2.namedWindow", "line_number": 64, "usage_type": "call"}, {"api_name": "cv2.WINDOW_NORMAL", "line_number": 64, "usage_type": "attribute"}, {"api_name": "cv2.resizeWindow", "line_number": 65, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 66, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 67, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 68, "usage_type": "call"}, {"api_name": "cv2.VideoCapture", "line_number": 71, "usage_type": "call"}, {"api_name": "cv2.flip", "line_number": 74, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 75, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 76, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2RGB", "line_number": 76, "usage_type": "attribute"}, {"api_name": "torch.tensor", "line_number": 78, "usage_type": "call"}, {"api_name": "cv2.putText", "line_number": 81, "usage_type": "call"}, {"api_name": "cv2.FONT_HERSHEY_SIMPLEX", "line_number": 81, "usage_type": "attribute"}, {"api_name": "cv2.imshow", "line_number": 82, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 83, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 87, "usage_type": "call"}, {"api_name": "torch.optim.Adam", "line_number": 91, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 91, "usage_type": "name"}, {"api_name": "torch.nn.CrossEntropyLoss", "line_number": 92, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 92, "usage_type": "name"}, {"api_name": "torch.argmax", "line_number": 121, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 133, "usage_type": "call"}, {"api_name": "torch.argmax", "line_number": 143, "usage_type": "call"}, {"api_name": "torch.save", "line_number": 156, "usage_type": "call"}]} +{"seq_id": "7473974893", "text": "import logging\nimport os\nimport pathlib\n\nimport pandas as pd\n\nlogger = logging.getLogger(__name__)\n\nMODULE_PATH = pathlib.Path(__file__).parent.resolve()\n\nEXCEL_SHEET = \"Results\"\nROW_START = 16\nREGIONS = {\n \"NO_LENS\": {\"usecols\": \"B:D\"},\n \"LENS3_333\": {\"usecols\": \"F:H\"},\n \"LENS2_428\": {\"usecols\": \"J:L\"},\n \"LENS1_750\": {\"usecols\": \"N:P\"},\n}\n\n# Lens information\n# Radii in micron:\nxrt_lenses_radii = [750.0, 428.6, 333.3]\ntfs_lens_radii = [\n # 0.0,\n 500.0,\n 300.0,\n 250.0,\n 200.0,\n 125.0,\n 62.5,\n 50.0,\n 50.0,\n 50.0,\n]\n\n\n# Min effective radius is when all lenses are inserted:\nMIN_RADIUS = 1 / sum(1 / radius for radius in tfs_lens_radii)\n# Max radius is when the largest is inserted:\nMAX_RADIUS = max(tfs_lens_radii)\n\n# In these ranges, a transfocator lens MUST be inserted\nREQUIRES_LENS_RANGE = {\n 0: None,\n 3: (9.50e3, 11.11e3),\n 2: (8.28e3, 10.02e3),\n 1: (5.96e3, 8.02e3),\n}\n\nMIN_ENERGY = {\n 0: 0.0,\n 3: 9.50e3,\n 2: 8.28e3,\n 1: 5.96e3,\n}\n\n\ndef read_spreadsheet(spreadsheet=None):\n if spreadsheet is None:\n spreadsheet = SPREADSHEET\n\n for name, read_kw in REGIONS.items():\n df = pd.read_excel(\n spreadsheet,\n engine=\"openpyxl\",\n sheet_name=EXCEL_SHEET,\n skiprows=ROW_START - 1,\n header=None,\n **read_kw\n )\n df.columns = [\"energy\", \"trip_min\", \"trip_max\"]\n df.energy *= 1e3 # keV -> eV\n df = df.dropna()\n df = df.set_index(df.energy)\n df.loc[df.trip_max > 1e4, \"trip_max\"] = 1e4\n yield name, df\n\n\n# Configuration for reading the spreadsheet:\ntry:\n SPREADSHEET = pathlib.Path(os.environ[\"TRANSFOCATOR_SPREADSHEET\"])\nexcept KeyError:\n SPREADSHEET = MODULE_PATH / \"MFX_EnergyLensInterlock_Tables_Transposed.xlsx\"\n\nif not SPREADSHEET.exists():\n logger.error(\"Table not available (``TRANSFOCATOR_SPREADSHEET``): %s\", SPREADSHEET)\ndata = dict(read_spreadsheet())\n", "repo_name": "pcdshub/transfocate", "sub_path": "transfocate/table/info.py", "file_name": "info.py", "file_ext": "py", "file_size_in_byte": 1970, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "37", "api": [{"api_name": "logging.getLogger", "line_number": 7, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 9, "usage_type": "call"}, {"api_name": "pandas.read_excel", "line_number": 63, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 81, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 81, "usage_type": "attribute"}]} +{"seq_id": "12879497546", "text": "\"\"\"\nTests oversamplers with a few samples.\n\"\"\"\n\nimport pytest\nimport numpy as np\n\nimport smote_variants as sv\n\nfrom smote_variants.datasets import (load_1_dim,\n load_illustration_2_class,\n load_normal,\n load_same_num,\n load_some_min_some_maj,\n load_1_min_some_maj,\n load_2_min_some_maj,\n load_3_min_some_maj,\n load_4_min_some_maj,\n load_5_min_some_maj,\n load_1_min_1_maj,\n load_repeated,\n load_all_min_noise,\n load_separable,\n load_linearly_dependent,\n load_alternating,\n load_high_dim)\n\n# disabling smote-variants logging\nimport logging\nlogger = logging.getLogger('smote_variants')\nlogger.setLevel(logging.CRITICAL)\n\noversamplers = [sv.SMOTE_AMSR(topology='star'), sv.SMOTE_AMSR(topology='bus'), sv.SMOTE_AMSR(topology='mesh')]\n\n@pytest.mark.parametrize(\"smote_obj\", oversamplers)\ndef test_1_dim(smote_obj):\n \"\"\"\n Testing oversamplers with 1 minority sample.\n\n Args:\n smote_obj (obj): an oversampler obj.\n \"\"\"\n dataset = load_1_dim()\n\n X, y = smote_obj.sample(dataset['data'], dataset['target'])\n\n assert np.unique(y).shape[0] == 2\n assert X.shape[0] > 0\n\n@pytest.mark.parametrize(\"smote_obj\", oversamplers)\ndef test_1_min_some_maj(smote_obj):\n \"\"\"\n Testing oversamplers with 1 minority sample.\n\n Args:\n smote_obj (obj): an oversampler obj.\n \"\"\"\n dataset = load_1_min_some_maj()\n\n X, y = smote_obj.sample(dataset['data'], dataset['target'])\n\n assert np.unique(y).shape[0] == 2\n assert X.shape[0] > 0\n\n@pytest.mark.parametrize(\"smote_obj\", oversamplers)\ndef test_1_min_1_maj(smote_obj):\n \"\"\"\n Testing oversamplers with 1 minority and 1 majority sample.\n\n Args:\n smote_obj (obj): an oversampler obj.\n \"\"\"\n dataset = load_1_min_1_maj()\n\n X, y = smote_obj.sample(dataset['data'], dataset['target'])\n\n assert np.unique(y).shape[0] == 2\n assert X.shape[0] > 0\n\n@pytest.mark.parametrize(\"smote_obj\", oversamplers)\ndef test_repeated(smote_obj):\n \"\"\"\n Testing oversamplers with repeated samples.\n\n Args:\n smote_obj (obj): an oversampler obj.\n \"\"\"\n dataset = load_repeated()\n\n X, y = smote_obj.sample(dataset['data'], dataset['target'])\n\n assert np.unique(y).shape[0] == 2\n assert X.shape[0] > 0\n\n@pytest.mark.parametrize(\"smote_obj\", oversamplers)\ndef test_2_min_some_maj(smote_obj):\n \"\"\"\n Testing oversamplers with 2 minority samples.\n\n Args:\n smote_obj (obj): an oversampler obj.\n \"\"\"\n dataset = load_2_min_some_maj()\n\n X, y = smote_obj.sample(dataset['data'], dataset['target'])\n\n assert np.unique(y).shape[0] == 2\n assert X.shape[0] > 0\n\n@pytest.mark.parametrize(\"smote_obj\", oversamplers)\ndef test_3_min_some_maj(smote_obj):\n \"\"\"\n Testing oversamplers with 3 minority samples.\n\n Args:\n smote_obj (obj): an oversampler obj.\n \"\"\"\n dataset = load_3_min_some_maj()\n\n X, y = smote_obj.sample(dataset['data'], dataset['target'])\n\n assert np.unique(y).shape[0] == 2\n assert X.shape[0] > 0\n\n@pytest.mark.parametrize(\"smote_obj\", oversamplers)\ndef test_4_min_some_maj(smote_obj):\n \"\"\"\n Testing oversamplers with 4 minority samples.\n\n Args:\n smote_obj (obj): an oversampler obj.\n \"\"\"\n dataset = load_4_min_some_maj()\n\n X, y = smote_obj.sample(dataset['data'], dataset['target'])\n\n assert np.unique(y).shape[0] == 2\n assert X.shape[0] > 0\n\n@pytest.mark.parametrize(\"smote_obj\", oversamplers)\ndef test_5_min_some_maj(smote_obj):\n \"\"\"\n Testing oversamplers with 5 minority samples.\n\n Args:\n smote_obj (obj): an oversampler obj.\n \"\"\"\n dataset = load_5_min_some_maj()\n\n X, y = smote_obj.sample(dataset['data'], dataset['target'])\n\n assert np.unique(y).shape[0] == 2\n assert X.shape[0] > 0\n\n@pytest.mark.parametrize(\"smote_obj\", oversamplers)\ndef test_all_min_noise(smote_obj):\n \"\"\"\n Testing oversamplers with all minority samples being noise.\n\n Args:\n smote_obj (obj): an oversampler obj.\n \"\"\"\n dataset = load_all_min_noise()\n\n X, y = smote_obj.sample(dataset['data'], dataset['target'])\n\n assert np.unique(y).shape[0] == 2\n assert X.shape[0] > 0\n\n@pytest.mark.parametrize(\"smote_obj\", oversamplers)\ndef test_alternating(smote_obj):\n \"\"\"\n Testing oversamplers with alternating minority samples.\n\n Args:\n smote_obj (obj): an oversampler obj.\n \"\"\"\n dataset = load_alternating()\n\n X, y = smote_obj.sample(dataset['data'], dataset['target'])\n\n assert np.unique(y).shape[0] == 2\n assert X.shape[0] > 0\n\n@pytest.mark.parametrize(\"smote_obj\", oversamplers)\ndef test_high_dim(smote_obj):\n \"\"\"\n Testing an oversampler with high dimensionality data.\n\n Args:\n smote_obj (obj): an oversampler obj\n \"\"\"\n dataset = load_high_dim()\n\n X, y = smote_obj.sample(dataset['data'], dataset['target'])\n assert len(X) > 0\n assert X.shape[1] == smote_obj\\\n .preprocessing_transform(dataset['data']).shape[1]\n assert np.unique(y).shape[0] == 2\n assert X.shape[0] > 0\n\n@pytest.mark.parametrize(\"smote_obj\", oversamplers)\ndef test_illustration(smote_obj):\n \"\"\"\n Testing oversamplers with illustration data.\n\n Args:\n smote_obj (obj): an oversampler obj.\n \"\"\"\n dataset = load_illustration_2_class()\n\n X, y = smote_obj.sample(dataset['data'], dataset['target'])\n\n assert np.unique(y).shape[0] == 2\n assert X.shape[0] > 0\n\n@pytest.mark.parametrize(\"smote_obj\", oversamplers)\ndef test_linearly_dependent(smote_obj):\n \"\"\"\n Testing oversamplers with 2 minority samples.\n\n Args:\n smote_obj (obj): an oversampler obj.\n \"\"\"\n dataset = load_linearly_dependent()\n\n X, y = smote_obj.sample(dataset['data'], dataset['target'])\n\n assert np.unique(y).shape[0] == 2\n assert X.shape[0] > 0\n\n@pytest.mark.parametrize(\"smote_obj\", oversamplers)\ndef test_normal(smote_obj):\n \"\"\"\n Tests an oversmampler\n\n Args:\n smote_obj (obj): an oversampler obj\n \"\"\"\n dataset = load_normal()\n\n X, y = smote_obj.sample(dataset['data'], dataset['target'])\n\n assert np.unique(y).shape[0] == 2\n assert X.shape[0] > 0\n\n@pytest.mark.parametrize(\"smote_obj\", oversamplers)\ndef test_reproducibility(smote_obj):\n \"\"\"\n Tests the reproducibility of oversampling.\n\n Args:\n smote_obj (obj): an oversampling obj\n \"\"\"\n dataset = load_normal()\n\n X_normal = dataset['data'] # pylint: disable=invalid-name\n y_normal = dataset['target']\n\n X_orig = X_normal.copy()\n y_orig = y_normal.copy()\n\n X_a, y_a = smote_obj.__class__(random_state=5).sample(X_normal, y_normal)\n oversampler = smote_obj.__class__(random_state=5)\n X_b, y_b = oversampler.sample(X_normal, y_normal)\n X_c, y_c = smote_obj.__class__(**oversampler.get_params()).sample(X_normal, y_normal)\n\n assert np.array_equal(X_a, X_b)\n assert np.array_equal(X_b, X_c)\n assert np.array_equal(X_orig, X_normal)\n\n assert np.array_equal(y_a, y_b)\n assert np.array_equal(y_b, y_c)\n assert np.array_equal(y_orig, y_normal)\n\n@pytest.mark.parametrize(\"smote_obj\", oversamplers)\ndef test_same_num(smote_obj):\n \"\"\"\n Tests oversamplers with equalized data.\n\n Args:\n smote_obj (obj): an oversampling obj\n \"\"\"\n dataset = load_same_num()\n\n X, y = smote_obj.sample(dataset['data'], dataset['target'])\n\n assert np.unique(y).shape[0] == 2\n assert X.shape[0] > 0\n\n@pytest.mark.parametrize(\"smote_obj\", oversamplers)\ndef test_separable(smote_obj):\n \"\"\"\n Testing oversamplers with 3 minority samples.\n\n Args:\n smote_obj (obj): an oversampler obj.\n \"\"\"\n dataset = load_separable()\n\n X, y = smote_obj.sample(dataset['data'], dataset['target'])\n\n assert np.unique(y).shape[0] == 2\n assert X.shape[0] > 0\n\n@pytest.mark.parametrize(\"smote_obj\", oversamplers)\ndef test_some_min_some_maj(smote_obj):\n \"\"\"\n Tests an oversampler with only a few samples.\n\n Args:\n smote_obj (obj): the oversampler obj\n \"\"\"\n dataset = load_some_min_some_maj()\n\n X, y = smote_obj.sample(dataset['data'], dataset['target'])\n\n assert np.unique(y).shape[0] == 2\n assert X.shape[0] > 0\n\n@pytest.mark.parametrize(\"smote_obj\", oversamplers)\ndef test_parameters(smote_obj):\n \"\"\"\n Test the parameterization.\n\n Args:\n smote_obj (obj): an oversampling object\n \"\"\"\n random_state = np.random.RandomState(5)\n\n par_comb = smote_obj.__class__.parameter_combinations()\n\n original_parameters = random_state.choice(par_comb)\n oversampler = smote_obj.__class__(**original_parameters)\n parameters = oversampler.get_params()\n\n assert all(v == parameters[k] for k, v in original_parameters.items())\n", "repo_name": "analyticalmindsltd/smote_variants", "sub_path": "tests/oversampling/test_specific.py", "file_name": "test_specific.py", "file_ext": "py", "file_size_in_byte": 9222, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 553, "dataset": "github-code", "pt": "37", "api": [{"api_name": "logging.getLogger", "line_number": 30, "usage_type": "call"}, {"api_name": "logging.CRITICAL", "line_number": 31, "usage_type": "attribute"}, {"api_name": "smote_variants.SMOTE_AMSR", "line_number": 33, "usage_type": "call"}, {"api_name": "smote_variants.datasets.load_1_dim", "line_number": 43, "usage_type": "call"}, {"api_name": "numpy.unique", "line_number": 47, "usage_type": "call"}, {"api_name": "pytest.mark.parametrize", "line_number": 35, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 35, "usage_type": "attribute"}, {"api_name": "smote_variants.datasets.load_1_min_some_maj", "line_number": 58, "usage_type": "call"}, {"api_name": "numpy.unique", "line_number": 62, "usage_type": "call"}, {"api_name": "pytest.mark.parametrize", "line_number": 50, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 50, "usage_type": "attribute"}, {"api_name": "smote_variants.datasets.load_1_min_1_maj", "line_number": 73, "usage_type": "call"}, {"api_name": "numpy.unique", "line_number": 77, "usage_type": "call"}, {"api_name": "pytest.mark.parametrize", "line_number": 65, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 65, "usage_type": "attribute"}, {"api_name": "smote_variants.datasets.load_repeated", "line_number": 88, "usage_type": "call"}, {"api_name": "numpy.unique", "line_number": 92, "usage_type": "call"}, {"api_name": "pytest.mark.parametrize", "line_number": 80, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 80, "usage_type": "attribute"}, {"api_name": "smote_variants.datasets.load_2_min_some_maj", "line_number": 103, "usage_type": "call"}, {"api_name": "numpy.unique", "line_number": 107, "usage_type": "call"}, {"api_name": "pytest.mark.parametrize", "line_number": 95, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 95, "usage_type": "attribute"}, {"api_name": "smote_variants.datasets.load_3_min_some_maj", "line_number": 118, "usage_type": "call"}, {"api_name": "numpy.unique", "line_number": 122, "usage_type": "call"}, {"api_name": "pytest.mark.parametrize", "line_number": 110, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 110, "usage_type": "attribute"}, {"api_name": "smote_variants.datasets.load_4_min_some_maj", "line_number": 133, "usage_type": "call"}, {"api_name": "numpy.unique", "line_number": 137, "usage_type": "call"}, {"api_name": "pytest.mark.parametrize", "line_number": 125, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 125, "usage_type": "attribute"}, {"api_name": "smote_variants.datasets.load_5_min_some_maj", "line_number": 148, "usage_type": "call"}, {"api_name": "numpy.unique", "line_number": 152, "usage_type": "call"}, {"api_name": "pytest.mark.parametrize", "line_number": 140, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 140, "usage_type": "attribute"}, {"api_name": "smote_variants.datasets.load_all_min_noise", "line_number": 163, "usage_type": "call"}, {"api_name": "numpy.unique", "line_number": 167, "usage_type": "call"}, {"api_name": "pytest.mark.parametrize", "line_number": 155, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 155, "usage_type": "attribute"}, {"api_name": "smote_variants.datasets.load_alternating", "line_number": 178, "usage_type": "call"}, {"api_name": "numpy.unique", "line_number": 182, "usage_type": "call"}, {"api_name": "pytest.mark.parametrize", "line_number": 170, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 170, "usage_type": "attribute"}, {"api_name": "smote_variants.datasets.load_high_dim", "line_number": 193, "usage_type": "call"}, {"api_name": "numpy.unique", "line_number": 199, "usage_type": "call"}, {"api_name": "pytest.mark.parametrize", "line_number": 185, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 185, "usage_type": "attribute"}, {"api_name": "smote_variants.datasets.load_illustration_2_class", "line_number": 210, "usage_type": "call"}, {"api_name": "numpy.unique", "line_number": 214, "usage_type": "call"}, {"api_name": "pytest.mark.parametrize", "line_number": 202, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 202, "usage_type": "attribute"}, {"api_name": "smote_variants.datasets.load_linearly_dependent", "line_number": 225, "usage_type": "call"}, {"api_name": "numpy.unique", "line_number": 229, "usage_type": "call"}, {"api_name": "pytest.mark.parametrize", "line_number": 217, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 217, "usage_type": "attribute"}, {"api_name": "smote_variants.datasets.load_normal", "line_number": 240, "usage_type": "call"}, {"api_name": "numpy.unique", "line_number": 244, "usage_type": "call"}, {"api_name": "pytest.mark.parametrize", "line_number": 232, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 232, "usage_type": "attribute"}, {"api_name": "smote_variants.datasets.load_normal", "line_number": 255, "usage_type": "call"}, {"api_name": "numpy.array_equal", "line_number": 268, "usage_type": "call"}, {"api_name": "numpy.array_equal", "line_number": 269, "usage_type": "call"}, {"api_name": "numpy.array_equal", "line_number": 270, "usage_type": "call"}, {"api_name": "numpy.array_equal", "line_number": 272, "usage_type": "call"}, {"api_name": "numpy.array_equal", "line_number": 273, "usage_type": "call"}, {"api_name": "numpy.array_equal", "line_number": 274, "usage_type": "call"}, {"api_name": "pytest.mark.parametrize", "line_number": 247, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 247, "usage_type": "attribute"}, {"api_name": "smote_variants.datasets.load_same_num", "line_number": 284, "usage_type": "call"}, {"api_name": "numpy.unique", "line_number": 288, "usage_type": "call"}, {"api_name": "pytest.mark.parametrize", "line_number": 276, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 276, "usage_type": "attribute"}, {"api_name": "smote_variants.datasets.load_separable", "line_number": 299, "usage_type": "call"}, {"api_name": "numpy.unique", "line_number": 303, "usage_type": "call"}, {"api_name": "pytest.mark.parametrize", "line_number": 291, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 291, "usage_type": "attribute"}, {"api_name": "smote_variants.datasets.load_some_min_some_maj", "line_number": 314, "usage_type": "call"}, {"api_name": "numpy.unique", "line_number": 318, "usage_type": "call"}, {"api_name": "pytest.mark.parametrize", "line_number": 306, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 306, "usage_type": "attribute"}, {"api_name": "numpy.random.RandomState", "line_number": 329, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 329, "usage_type": "attribute"}, {"api_name": "pytest.mark.parametrize", "line_number": 321, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 321, "usage_type": "attribute"}]} +{"seq_id": "19859111491", "text": "import numpy as np\nimport cv2\nfrom sklearn.metrics import confusion_matrix, precision_score, recall_score, f1_score \nfrom sklearn.metrics.cluster import fowlkes_mallows_score\n\n#---load image as narray from file---#\ndef img_load(filepath):\n img = cv2.imread(filepath)\n img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n return img\n\ndef img_save(savepath, img):\n img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n cv2.imwrite(savepath, img)\n\n#---normalize img pixel val to [0, 1]---#\ndef img_normalize(img):\n val_range = img.max() - img.min()\n return (img - img.min()) / val_range\n\n#---load label from file---#\ndef label_load(filepath):\n f = open(filepath)\n line = f.readline()\n label = line\n while line:\n line = f.readline()\n if line:\n label += \"/\"\n label += line\n return label\n\n\n# calculate FMI global\n'''\n return: FMI global\n'''\ndef compute_FMscores_global(lab1, lab2):\n '''\n lab1: grand truth\n lab2: predict labels\n '''\n return fowlkes_mallows_score(lab1, lab2)\n\n# calculer FMI local/individual \ndef compute_cocluster_mat(labels):\n mat = np.zeros([labels.shape[0], labels.shape[0]])\n for i in range(labels.shape[0]):\n for j in range(i, labels.shape[0]):\n mat[i, j] = int(labels[i] == labels[j])\n mat[j, i] = mat[i, j]\n return mat\n'''\n FMI_arr: FMI local\n'''\ndef compute_FMscores_local(lab1, lab2):\n '''\n lab1: grand truth\n lab2: predict labels\n '''\n mat1 = compute_cocluster_mat(lab1)\n mat2 = compute_cocluster_mat(lab2)\n \n FP_arr = np.sum((mat1 - mat2 == -1), axis = 1)\n FN_arr = np.sum((mat1 - mat2 == 1), axis = 1)\n TP_arr = np.sum((mat1 + mat2 == 2), axis = 1) - 1\n\n FMI_arr = np.zeros(TP_arr.shape)\n for i in range(len(TP_arr)):\n if TP_arr[i] != 0:\n FMI_arr[i] = float(TP_arr[i] / float(np.sqrt((TP_arr[i] + FP_arr[i]) * (TP_arr[i] + FN_arr[i]))))\n else:\n FMI_arr[i] = 0\n\n return FMI_arr\n\n\n\n#---compute global F1 score---#\ndef compute_f1_global(gt_labels, predicts):\n return f1_score(gt_labels, predicts, average='macro')\n \n#---compute local F1 score---#\ndef compute_f1_local(gt_labels, predicts):\n f1_s = []\n label_set = np.unique(gt_labels)\n for label in label_set:\n label_indices = np.array(np.where(gt_labels == label)).flatten()\n label_per_class = gt_labels[label_indices]\n predict_per_class = predicts[label_indices]\n #print(label_per_class.shape)\n #print(label_per_class)\n #print(predict_per_class.shape)\n #print(predict_per_class)\n f1 = f1_score(label_per_class, predict_per_class, average='micro')\n f1_s.append(f1)\n return f1_s\n\n\ndef all_metrics_test(ground_truth, predict, nb_class, average='macro'):\n cm = confusion_matrix(ground_truth, predict)\n f1 = f1_score(ground_truth, predict, labels=np.arange(nb_class), average=average)\n ps = precision_score(ground_truth, predict, labels=np.arange(nb_class), average=average)\n rs = recall_score(ground_truth, predict, labels=np.arange(nb_class), average=average)\n return f1, cm, ps, rs\n\n\n\n\n\n# feature extraction\n# extract features for an image\n'''\n feature_maps: flattened (feature vector) \n'''\ndef get_img_feature(img, model, nb_layer_fix, num_layer_ex, transform, device=\"cuda\"):\n '''\n img: image(np array)\n model: net(mobilenet)\n num_layer: index of layer for feature extraction\n transform: preprocess\n device: \"cpu\"/\"cuda\"\n '''\n model.to(device)\n img_batch = transform(img).unsqueeze(0)\n input = img_batch.to(device)\n for i in range(num_layer_ex):\n if i < nb_layer_fix:\n layer = model.features[i]\n else:\n layer = model.train_features[i-nb_layer_fix]\n output = layer(input)\n input = output\n output = input\n feature_maps = output.squeeze(0).cpu().detach()\n return feature_maps.numpy().flatten()\n\n\n\n\n# feature extraction\n# extract features for an image\n'''\n feature_maps: flattened (feature vector) \n'''\ndef get_img_feature_score(img, model, num_layer_ex, transform, device=\"cuda\"):\n '''\n img: image(np array)\n model: net(mobilenet)\n num_layer: index of layer for feature extraction\n transform: preprocess\n device: \"cpu\"/\"cuda\"\n '''\n model.to(device)\n img_batch = transform(img).unsqueeze(0)\n input = img_batch.to(device)\n for i in range(num_layer_ex):\n layer = model.features[i]\n output = layer(input)\n input = output\n output = input\n feature_maps = output.squeeze(0).cpu().detach()\n return feature_maps.numpy().flatten()", "repo_name": "Oitron/View-selection", "sub_path": "tools.py", "file_name": "tools.py", "file_ext": "py", "file_size_in_byte": 4685, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "cv2.imread", "line_number": 8, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 9, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2RGB", "line_number": 9, "usage_type": "attribute"}, {"api_name": "cv2.cvtColor", "line_number": 13, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2RGB", "line_number": 13, "usage_type": "attribute"}, {"api_name": "cv2.imwrite", "line_number": 14, "usage_type": "call"}, {"api_name": "sklearn.metrics.cluster.fowlkes_mallows_score", "line_number": 43, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 47, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 64, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 65, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 66, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 68, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 71, "usage_type": "call"}, {"api_name": "sklearn.metrics.f1_score", "line_number": 81, "usage_type": "call"}, {"api_name": "numpy.unique", "line_number": 86, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 88, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 88, "usage_type": "call"}, {"api_name": "sklearn.metrics.f1_score", "line_number": 95, "usage_type": "call"}, {"api_name": "sklearn.metrics.confusion_matrix", "line_number": 101, "usage_type": "call"}, {"api_name": "sklearn.metrics.f1_score", "line_number": 102, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 102, "usage_type": "call"}, {"api_name": "sklearn.metrics.precision_score", "line_number": 103, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 103, "usage_type": "call"}, {"api_name": "sklearn.metrics.recall_score", "line_number": 104, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 104, "usage_type": "call"}]} +{"seq_id": "32234268197", "text": "import ccxt\r\nimport time\r\nimport os\r\nimport sys\r\n\r\nsys.path.insert(1, '../src')\r\nimport func_get\r\nimport func_update\r\nimport func_grid\r\n\r\n\r\ndef run_bot(config_system, config_params_path, last_loop_path, transfer_path, open_orders_df_path, transactions_df_path, error_log_df_path, cash_flow_df_path):\r\n bot_name = os.path.basename(os.getcwd())\r\n exchange = func_get.get_exchange(config_system)\r\n config_params = func_get.get_json(config_params_path)\r\n \r\n func_grid.clear_orders_grid('buy', exchange, bot_name, config_params, open_orders_df_path, transactions_df_path, error_log_df_path)\r\n func_grid.clear_orders_grid('sell', exchange, bot_name, config_params, open_orders_df_path, transactions_df_path, error_log_df_path)\r\n func_grid.print_report_grid(exchange, config_params, open_orders_df_path)\r\n \r\n cont_flag = func_grid.check_circuit_breaker(exchange, bot_name, config_system, config_params, last_loop_path, open_orders_df_path, transactions_df_path, error_log_df_path)\r\n\r\n if cont_flag:\r\n func_grid.open_buy_orders_grid(exchange, config_params, transfer_path, open_orders_df_path, transactions_df_path, error_log_df_path, cash_flow_df_path)\r\n\r\n end_date_flag, prev_date = func_get.check_end_date(cash_flow_df_path, transactions_df_path)\r\n\r\n if end_date_flag:\r\n func_grid.update_end_date_grid(prev_date, exchange, bot_name, config_system, config_params, config_params_path, last_loop_path, transfer_path, open_orders_df_path, transactions_df_path, error_log_df_path, cash_flow_df_path)\r\n\r\n func_update.update_timestamp(last_loop_path)\r\n\r\n\r\nif __name__ == '__main__':\r\n config_system_path = 'config_system.json'\r\n config_params_path = 'config_params.json'\r\n last_loop_path = 'last_loop.json'\r\n transfer_path = 'transfer.json'\r\n open_orders_df_path = 'open_orders.csv'\r\n transactions_df_path = 'transactions.csv'\r\n error_log_df_path = 'error_log.csv'\r\n cash_flow_df_path = 'cash_flow.csv'\r\n\r\n while True:\r\n config_system = func_get.get_json(config_system_path)\r\n idle_loop = config_system['idle_loop']\r\n \r\n if config_system['run_flag'] == 1:\r\n print(\"Start loop\")\r\n try:\r\n run_bot(config_system, config_params_path, last_loop_path, transfer_path, open_orders_df_path, transactions_df_path, error_log_df_path, cash_flow_df_path)\r\n except (ccxt.RequestTimeout, ccxt.NetworkError, ccxt.ExchangeError):\r\n func_update.append_error_log('ConnectionError', error_log_df_path)\r\n print(\"No connection: Skip the loop\")\r\n \r\n print(\"End loop\")\r\n print(f\"Wait {idle_loop} seconds\")\r\n else:\r\n print(\"Stop process\")\r\n \r\n time.sleep(idle_loop)", "repo_name": "neozan/puresed-bot", "sub_path": "bot_grid/run.py", "file_name": "run.py", "file_ext": "py", "file_size_in_byte": 2775, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "37", "api": [{"api_name": "sys.path.insert", "line_number": 6, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 6, "usage_type": "attribute"}, {"api_name": "os.path.basename", "line_number": 13, "usage_type": "call"}, {"api_name": "os.path", "line_number": 13, "usage_type": "attribute"}, {"api_name": "os.getcwd", "line_number": 13, "usage_type": "call"}, {"api_name": "func_get.get_exchange", "line_number": 14, "usage_type": "call"}, {"api_name": "func_get.get_json", "line_number": 15, "usage_type": "call"}, {"api_name": "func_grid.clear_orders_grid", "line_number": 17, "usage_type": "call"}, {"api_name": "func_grid.clear_orders_grid", "line_number": 18, "usage_type": "call"}, {"api_name": "func_grid.print_report_grid", "line_number": 19, "usage_type": "call"}, {"api_name": "func_grid.check_circuit_breaker", "line_number": 21, "usage_type": "call"}, {"api_name": "func_grid.open_buy_orders_grid", "line_number": 24, "usage_type": "call"}, {"api_name": "func_get.check_end_date", "line_number": 26, "usage_type": "call"}, {"api_name": "func_grid.update_end_date_grid", "line_number": 29, "usage_type": "call"}, {"api_name": "func_update.update_timestamp", "line_number": 31, "usage_type": "call"}, {"api_name": "func_get.get_json", "line_number": 45, "usage_type": "call"}, {"api_name": "ccxt.RequestTimeout", "line_number": 52, "usage_type": "attribute"}, {"api_name": "ccxt.NetworkError", "line_number": 52, "usage_type": "attribute"}, {"api_name": "ccxt.ExchangeError", "line_number": 52, "usage_type": "attribute"}, {"api_name": "func_update.append_error_log", "line_number": 53, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 61, "usage_type": "call"}]} +{"seq_id": "14304024710", "text": "\"\"\"\nThis module creates player ratings and sort the ratings into the appropriate\nteam files\n\"\"\"\nimport os.path\nimport json\n\nfrom simulator import rate_player\nfrom constant import PLAYER_RATING_PATH\nfrom constant import PLAYER_SEASON_PATH\nfrom constant import TEAM_DICT\nfrom constant import PLAYER_DICT\n\n\n# Main functions\ndef sort_player_into_team():\n \"\"\"Create directories and files and sort data into appropriate folders.\n \"\"\"\n # calculate the player ratings\n player_ratings = {}\n for player_id in PLAYER_DICT.keys():\n rating = rate_player.SinglePlayerRating(player_id)\n player_ratings[PLAYER_DICT[player_id]] = rating.get_rating()\n\n for team_abb in TEAM_DICT.values():\n sorted_dir = os.path.join(PLAYER_RATING_PATH, f'{team_abb}.json')\n if not os.path.exists(sorted_dir):\n # sort player ratings into teams directories\n sorted_player_ratings = []\n for index in PLAYER_DICT.keys():\n player_name = PLAYER_DICT[index]\n player_path = os.path.join(PLAYER_SEASON_PATH,\n f'{player_name}.json')\n with open(player_path, 'r') as player_file:\n file = json.load(player_file)\n\n # put all the player rating for the same team into a dictionary\n if file[-1][\"TEAM_ABBREVIATION\"] == team_abb and \\\n file[-1][\"SEASON_ID\"] != '2016-17':\n sorted_player_ratings.append(player_ratings[player_name])\n\n with open(sorted_dir, 'w') as outfile:\n json.dump(sorted_player_ratings, outfile)\n\n return True # used in test cases\n", "repo_name": "larryworm1127/nba_simulator", "sub_path": "stats_files/create_player_rating.py", "file_name": "create_player_rating.py", "file_ext": "py", "file_size_in_byte": 1684, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "37", "api": [{"api_name": "constant.PLAYER_DICT.keys", "line_number": 21, "usage_type": "call"}, {"api_name": "constant.PLAYER_DICT", "line_number": 21, "usage_type": "name"}, {"api_name": "simulator.rate_player.SinglePlayerRating", "line_number": 22, "usage_type": "call"}, {"api_name": "simulator.rate_player", "line_number": 22, "usage_type": "name"}, {"api_name": "constant.PLAYER_DICT", "line_number": 23, "usage_type": "name"}, {"api_name": "constant.TEAM_DICT.values", "line_number": 25, "usage_type": "call"}, {"api_name": "constant.TEAM_DICT", "line_number": 25, "usage_type": "name"}, {"api_name": "os.path.path.join", "line_number": 26, "usage_type": "call"}, {"api_name": "constant.PLAYER_RATING_PATH", "line_number": 26, "usage_type": "argument"}, {"api_name": "os.path.path", "line_number": 26, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 26, "usage_type": "name"}, {"api_name": "os.path.path.exists", "line_number": 27, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 27, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 27, "usage_type": "name"}, {"api_name": "constant.PLAYER_DICT.keys", "line_number": 30, "usage_type": "call"}, {"api_name": "constant.PLAYER_DICT", "line_number": 30, "usage_type": "name"}, {"api_name": "constant.PLAYER_DICT", "line_number": 31, "usage_type": "name"}, {"api_name": "os.path.path.join", "line_number": 32, "usage_type": "call"}, {"api_name": "constant.PLAYER_SEASON_PATH", "line_number": 32, "usage_type": "argument"}, {"api_name": "os.path.path", "line_number": 32, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 32, "usage_type": "name"}, {"api_name": "json.load", "line_number": 35, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 43, "usage_type": "call"}]} +{"seq_id": "24423745533", "text": "import _path_config\n\nimport sys\nimport csv\n\nfrom reviews.data import ReviewsCorpus\n\nfrom gensim.models import Doc2Vec\n\nif __name__ == '__main__':\n if len(sys.argv) != 3:\n print('Usage: python3 train_doc2vec.py sents.csv doc2vec.pickle')\n print('Reads sentences from sents.csv (as output by extract_review_sentences.py) and')\n print('trains a doc2vec model over them, writing it to doc2vec.pickle.')\n else:\n _, sents_fname, model_fname = sys.argv\n \n sentences = ReviewsCorpus(sents_fname)\n model = Doc2Vec(alpha=0.025, min_alpha=0.025) # use fixed learning rate\n model.build_vocab(sentences)\n for epoch in range(10):\n model.train(sentences)\n model.alpha -= 0.002 # decrease the learning rate\n model.min_alpha = model.alpha # fix the learning rate, no decay\n \n model.save(model_fname)\n", "repo_name": "cassidylaidlaw/reviews-summary", "sub_path": "scripts/train_doc2vec.py", "file_name": "train_doc2vec.py", "file_ext": "py", "file_size_in_byte": 906, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "sys.argv", "line_number": 11, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 16, "usage_type": "attribute"}, {"api_name": "reviews.data.ReviewsCorpus", "line_number": 18, "usage_type": "call"}, {"api_name": "gensim.models.Doc2Vec", "line_number": 19, "usage_type": "call"}]} +{"seq_id": "13948673177", "text": "#!/usr/bin/python\n# -*- coding:utf-8 -*-\n\nimport sys\nfrom PyQt5.QtWidgets import QApplication, QWidget, QPushButton, QToolTip, QMessageBox\nfrom PyQt5.QtGui import QFont\n\n\nclass FooWidget(QWidget):\n def __init__(self):\n super().__init__()\n self.initUI()\n\n def initUI(self):\n QToolTip.setFont(QFont('微软雅黑', 10))\n\n self.resize(500, 500)\n self.move(100, 100)\n self.setWindowTitle('foo')\n self.setFont(QFont('微软雅黑', 10))\n\n btn = QPushButton('click me', self)\n btn.move(100, 100)\n btn.setToolTip('hello PyQT')\n btn.clicked.connect(lambda: QMessageBox.information(self, 'msg box', 'hello pyqt5'))\n\n self.show()\n\n\nif __name__ == '__main__':\n app = QApplication(sys.argv)\n mainform = FooWidget()\n sys.exit(app.exec_())\n", "repo_name": "taccisum/py_learning", "sub_path": "learning/pyqt5/qtooltip.py", "file_name": "qtooltip.py", "file_ext": "py", "file_size_in_byte": 850, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "PyQt5.QtWidgets.QWidget", "line_number": 9, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QToolTip.setFont", "line_number": 15, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QToolTip", "line_number": 15, "usage_type": "name"}, {"api_name": "PyQt5.QtGui.QFont", "line_number": 15, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QFont", "line_number": 20, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QPushButton", "line_number": 22, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.information", "line_number": 25, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 25, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QApplication", "line_number": 31, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 31, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 33, "usage_type": "call"}]} +{"seq_id": "32049188215", "text": "from contas.models import Docente\nfrom django.db import models\nfrom django.conf import settings\n\n\nclass Discente(models.Model):\n usuario = models.OneToOneField(\n settings.AUTH_USER_MODEL, primary_key=True, on_delete=models.CASCADE)\n docente = models.ForeignKey(Docente, on_delete=models.SET_NULL, null=True)\n vinculo = models.CharField(max_length=25, blank=True, null=True)\n inicio_vinculo = models.DateField()\n setor = models.CharField(max_length=250, blank=True, null=True)\n departamento = models.CharField(max_length=250, blank=True, null=True)\n periodo_de_permanencia = models.DateField(null=True)\n\n def __str__(self):\n return self.usuario.get_full_name()\n", "repo_name": "gustavofisica/cme", "sub_path": "apps/contas/models/discente.py", "file_name": "discente.py", "file_ext": "py", "file_size_in_byte": 698, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "django.db.models.Model", "line_number": 6, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 6, "usage_type": "name"}, {"api_name": "django.db.models.OneToOneField", "line_number": 7, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 7, "usage_type": "name"}, {"api_name": "django.conf.settings.AUTH_USER_MODEL", "line_number": 8, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 8, "usage_type": "name"}, {"api_name": "django.db.models.CASCADE", "line_number": 8, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 8, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 9, "usage_type": "call"}, {"api_name": "contas.models.Docente", "line_number": 9, "usage_type": "argument"}, {"api_name": "django.db.models", "line_number": 9, "usage_type": "name"}, {"api_name": "django.db.models.SET_NULL", "line_number": 9, "usage_type": "attribute"}, {"api_name": "django.db.models.CharField", "line_number": 10, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 10, "usage_type": "name"}, {"api_name": "django.db.models.DateField", "line_number": 11, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 11, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 12, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 12, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 13, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 13, "usage_type": "name"}, {"api_name": "django.db.models.DateField", "line_number": 14, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 14, "usage_type": "name"}]} +{"seq_id": "13631126586", "text": "# coding:utf-8\n\"\"\"TourRecSys URL Configuration\n\nThe `urlpatterns` list routes URLs to views. For more information please see:\n https://docs.djangoproject.com/en/1.11/topics/http/urls/\nExamples:\nFunction views\n 1. Add an import: from my_app import views\n 2. Add a URL to urlpatterns: url(r'^$', views.home, name='home')\nClass-based views\n 1. Add an import: from other_app.views import Home\n 2. Add a URL to urlpatterns: url(r'^$', Home.as_view(), name='home')\nIncluding another URLconf\n 1. Import the include() function: from django.conf.urls import url, include\n 2. Add a URL to urlpatterns: url(r'^blog/', include('blog.urls'))\n\"\"\"\nfrom django.conf.urls import url\nfrom django.contrib import admin\nfrom tour import views\n\nurlpatterns = [\n url(r'^admin/', admin.site.urls),\n\n # 首页\n url(r'^$', views.init),\n\n # 详情\n url(r'^detail', views.detail),\n\n # login & register & logout\n url(r'^login', views.sign_in),\n url(r'^register', views.register),\n url(r'^logout', views.sign_out),\n\n # 搜索\n url(r'^search', views.search),\n\n # 收藏\n url(r'^collection', views.collection),\n\n # personal info\n url(r'^info', views.info),\n\n]\n", "repo_name": "wikizero/TourRecSys", "sub_path": "TourRecSys/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 1199, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "django.conf.urls.url", "line_number": 22, "usage_type": "call"}, {"api_name": "django.contrib.admin.site", "line_number": 22, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 22, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 25, "usage_type": "call"}, {"api_name": "tour.views.init", "line_number": 25, "usage_type": "attribute"}, {"api_name": "tour.views", "line_number": 25, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 28, "usage_type": "call"}, {"api_name": "tour.views.detail", "line_number": 28, "usage_type": "attribute"}, {"api_name": "tour.views", "line_number": 28, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 31, "usage_type": "call"}, {"api_name": "tour.views.sign_in", "line_number": 31, "usage_type": "attribute"}, {"api_name": "tour.views", "line_number": 31, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 32, "usage_type": "call"}, {"api_name": "tour.views.register", "line_number": 32, "usage_type": "attribute"}, {"api_name": "tour.views", "line_number": 32, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 33, "usage_type": "call"}, {"api_name": "tour.views.sign_out", "line_number": 33, "usage_type": "attribute"}, {"api_name": "tour.views", "line_number": 33, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 36, "usage_type": "call"}, {"api_name": "tour.views.search", "line_number": 36, "usage_type": "attribute"}, {"api_name": "tour.views", "line_number": 36, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 39, "usage_type": "call"}, {"api_name": "tour.views.collection", "line_number": 39, "usage_type": "attribute"}, {"api_name": "tour.views", "line_number": 39, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 42, "usage_type": "call"}, {"api_name": "tour.views.info", "line_number": 42, "usage_type": "attribute"}, {"api_name": "tour.views", "line_number": 42, "usage_type": "name"}]} +{"seq_id": "34882497968", "text": "import torch\nimport numpy as np\nimport segmentation_models_pytorch as smp\n\n\ndef unet_resnet(encoder):\n # ENCODER = 'se_resnext50_32x4d'\n ENCODER_WEIGHTS = 'imagenet'\n CLASSES = ['building']\n ACTIVATION = 'sigmoid' # could be None for logits or 'softmax2d' for multicalss segmentation\n DEVICE = 'cuda'\n\n # create segmentation model with pretrained encoder\n model = smp.Unet(\n encoder_name=encoder,\n encoder_weights='imagenet',\n classes=1,\n activation='sigmoid',\n )\n device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n model.to(device)\n preprocessing_fn = smp.encoders.get_preprocessing_fn(encoder, ENCODER_WEIGHTS)\n\n return model, preprocessing_fn", "repo_name": "wwymak/satellite-segmentation-e2e", "sub_path": "modelling/models/unets.py", "file_name": "unets.py", "file_ext": "py", "file_size_in_byte": 732, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "segmentation_models_pytorch.Unet", "line_number": 14, "usage_type": "call"}, {"api_name": "torch.device", "line_number": 20, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 20, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 20, "usage_type": "attribute"}, {"api_name": "segmentation_models_pytorch.encoders.get_preprocessing_fn", "line_number": 22, "usage_type": "call"}, {"api_name": "segmentation_models_pytorch.encoders", "line_number": 22, "usage_type": "attribute"}]} +{"seq_id": "26476344713", "text": "import argparse\nimport datetime\nimport json\nfrom logging import getLogger, StreamHandler, FileHandler, Formatter, INFO, DEBUG\nimport os\nfrom pathlib import Path\nimport random\nimport subprocess\nimport sys\nimport time\n\nfrom py4j.java_gateway import JavaGateway, GatewayParameters\nfrom rdkit import Chem, RDLogger\n\nfrom mcts_main import Mcts\nfrom mcts_modules import print_route, save_route\nfrom model_modules import load_model\nfrom utils import is_port_in_used, ReactionUtils, get_default_config\n\n\ndef get_parser():\n \"\"\" Parse arguments\n Args:\n Returns:\n argparse.Namespace\n \"\"\"\n parser = argparse.ArgumentParser(\n description='description',\n usage='usage'\n )\n parser.add_argument(\n \"-a\", \"--max_atom_num\", required=False, type=int,\n help=\"Max number of atoms in a molecule\"\n )\n parser.add_argument(\n \"-c\", \"--search_count\", required=False, type=int, default=100,\n help=\"the maximum number of iterations of MCTS\"\n )\n parser.add_argument(\n \"--config\", type=str,\n help=\"path to config file\"\n )\n parser.add_argument(\n \"-d\", \"--rollout_depth\", required=False, type=int,\n help=\"Rollout max depth count\"\n )\n parser.add_argument(\n \"--debug\", action=\"store_true\",\n help=\"Debug mode\"\n )\n parser.add_argument(\n \"-e\", \"--expansion_model\", required=False, type=str,\n help=\"Path to expansion model file\"\n )\n parser.add_argument(\n \"-er\", \"--expansion_rules\", required=False, type=str,\n help=\"Path to reaction rules for expansion\"\n )\n parser.add_argument(\n \"-f\", \"--descriptor\", required=False, type=str,\n help=\"Specify ECFP or GCN\"\n )\n parser.add_argument(\n '--gcn_expansion_config', required=False, type=str,\n help='Path to GCN expansion config file'\n )\n parser.add_argument(\n '--gcn_rollout_config', required=False, type=str,\n help='Path to GCN rollout config file'\n )\n parser.add_argument(\n \"-m\", \"--starting_material\", required=False, type=str,\n help=\"Path to starting materials file\"\n )\n parser.add_argument(\n \"-p\", \"--rollout_model\", required=False, type=str,\n help=\"Path to rollout model file\"\n )\n parser.add_argument(\n \"-pr\", \"--rollout_rules\", required=False, type=str,\n help=\"Path to reaction rules for playout\"\n )\n parser.add_argument(\n \"-r\", \"--save_result_dir\", type=str, default=\"result\",\n help=\"Path to a result directory\"\n )\n parser.add_argument(\n \"-t\", \"--target\", required=False, type=str,\n help=\"Path to target molecule file\"\n )\n parser.add_argument(\n \"-k\", \"--knowledge\", required=False, nargs=\"+\", default=[], type=str,\n choices=[\"cdscore\", \"rdscore\", \"asscore\", \"stscore\", \"all\"],\n help=\"choice chemical knowledges\"\n )\n parser.add_argument(\n \"--knowledge_weights\", required=False, nargs=4, default=[1., 1., 1., 1.], type=float,\n help=\"knowledge score's weights in selection. [cdscore, rdscore, asscore, stscore]\"\n )\n parser.add_argument(\n \"--save_tree\", required=False, action='store_true', default=False,\n help=\"save searched tree information\"\n )\n parser.add_argument(\n \"--sel_const\", required=False, default=3, type=int,\n help=\"constant value for selection\"\n )\n parser.add_argument(\n \"--expansion_num\", required=False, type=int, default=50,\n help=\"the number of expanded nodes during the expansion step\"\n )\n return parser.parse_args()\n\n\ndef get_logger(level, save_dir):\n # logger\n logger = getLogger(__name__)\n logger.setLevel(level)\n logger.propagate = False\n # formatter\n formatter = Formatter(\"%(asctime)s : %(levelname)s : %(message)s \")\n # handler\n fh = FileHandler(filename=os.path.join(save_dir, \"run.log\"), mode='w')\n fh.setLevel(level)\n fh.setFormatter(formatter)\n sh = StreamHandler()\n sh.setLevel(level)\n sh.setFormatter(formatter)\n #\n logger.addHandler(fh)\n logger.addHandler(sh)\n return logger\n\n\ndef main():\n args = get_parser()\n os.environ['CUDA_VISIBLE_DEVICES'] = \"\"\n\n # Setup config: Arguments take priority over config file\n config = get_default_config()\n if args.config is not None:\n with open(args.config, 'r') as f:\n config.update(json.load(f))\n config['max_atom_num'] = int(args.max_atom_num or config['max_atom_num'])\n config['search_count'] = int(args.search_count or config['search_count'])\n config['rollout_depth'] = int(args.rollout_depth or config['rollout_depth'])\n config['expansion_model'] = args.expansion_model or config['expansion_model']\n config['expansion_rules'] = args.expansion_rules or config['expansion_rules']\n config['rollout_model'] = args.rollout_model or config['rollout_model']\n config['rollout_rules'] = args.rollout_rules or config['rollout_rules']\n config['descriptor'] = args.descriptor or config['descriptor']\n config['gcn_expansion_config'] = args.gcn_expansion_config or config['gcn_expansion_config']\n config['gcn_rollout_config'] = args.gcn_rollout_config or config['gcn_rollout_config']\n config['starting_material'] = args.starting_material or config['starting_material']\n config['save_result_dir'] = args.save_result_dir or config['save_result_dir']\n config['target'] = args.target or config['target']\n config['knowledge'] = set(args.knowledge)\n config[\"knowledge_weights\"] = args.knowledge_weights\n config['save_tree'] = args.save_tree\n config['selection_constant'] = args.sel_const\n config['expansion_num'] = args.expansion_num\n\n # Create save directory\n now = datetime.datetime.now()\n name_stem = config[\"target\"].split('/')[-1].split('.')[0]\n config[\"save_result_dir\"] = os.path.join(config[\"save_result_dir\"], f\"{name_stem}_{now:%Y%m%d%H%M}\")\n os.makedirs(config[\"save_result_dir\"], exist_ok=True)\n\n # Save parameters\n with open(os.path.join(config[\"save_result_dir\"], \"parameters.json\"), 'w') as f:\n json.dump({k: repr(v) for k, v, in config.items()}, f, indent=2)\n\n # Setup logger\n level = DEBUG if args.debug else INFO\n logger = get_logger(level, config[\"save_result_dir\"])\n if not args.debug:\n RDLogger.DisableLog(\"rdApp.*\")\n\n # Setup JVM\n gateway_port = 25333 + random.randint(1, 3000)\n while is_port_in_used(gateway_port):\n gateway_port += 1\n proxy_port = gateway_port + 1\n while is_port_in_used(proxy_port):\n proxy_port += 1\n logger.info(f\"gateway port: {gateway_port} proxy port: {proxy_port}\\n\")\n\n cmd = f\"java CxnUtils {gateway_port} {config['rollout_rules']}\"\n subprocess.Popen(cmd.split(\" \"))\n time.sleep(3)\n gateway = JavaGateway(start_callback_server=True,\n python_proxy_port=proxy_port,\n gateway_parameters=GatewayParameters(port=gateway_port, auto_convert=True))\n logger.info(\"Start up java gateway\")\n\n try:\n # data preparation\n target_mol = Chem.MolFromMolFile(config['target'])\n if target_mol is None:\n logger.error(\"Can't read the input molecule file. Please check it.\")\n sys.exit(1)\n expansion_rules = ReactionUtils.get_reactions(config['expansion_rules'], config['save_result_dir'])\n rollout_rules = ReactionUtils.get_reactions(config['rollout_rules'], config['save_result_dir'])\n with open(config['starting_material'], 'r') as f:\n start_materials = set([s.strip() for s in f.readlines()])\n if config['descriptor'] == 'ECFP':\n expansion_model = load_model('expansion', config, class_num=len(expansion_rules))\n rollout_model = load_model('rollout', config, class_num=len(rollout_rules))\n elif config['descriptor'] == 'GCN':\n expansion_model = load_model('expansion', config, class_num=len(expansion_rules))\n rollout_model = load_model('rollout', config, class_num=len(rollout_rules))\n else:\n logger.error(\"set 'descriptor' to GCN or ECFP\")\n sys.exit(1)\n # main process\n mcts = Mcts(target_mol, expansion_rules, rollout_rules, start_materials, config)\n\n logger.info(f\"[INFO] knowledge type: {config['knowledge']}\")\n logger.info(\"[INFO] start search\")\n start = time.time()\n leaf_node, is_proven = mcts.search(expansion_model, rollout_model, logger, gateway=gateway)\n elapsed_time = time.time() - start\n logger.info(f\"[INFO] done in {elapsed_time:5f} s\")\n\n with open(os.path.join(config['save_result_dir'], \"time.txt\"), 'w') as f:\n f.write(f\"{elapsed_time}\")\n\n nodes = []\n if leaf_node is None:\n Path(os.path.join(config['save_result_dir'], \"not_proven\")).touch()\n logger.info(\"Can't apply any predicted reaction templates to the target compound.\")\n sys.exit()\n while leaf_node.parent_node is not None:\n nodes.append(leaf_node)\n leaf_node = leaf_node.parent_node\n else:\n nodes.append(leaf_node)\n print_route(nodes, is_proven, logger)\n save_route(nodes, config['save_result_dir'], is_proven, config[\"knowledge_weights\"])\n finally:\n gateway.shutdown()\n logger.info(\"Shutdown java gateway\")\n\n\nif __name__ == \"__main__\":\n main()\n", "repo_name": "clinfo/ReTReK", "sub_path": "run.py", "file_name": "run.py", "file_ext": "py", "file_size_in_byte": 9411, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 28, "dataset": "github-code", "pt": "37", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 27, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 117, "usage_type": "call"}, {"api_name": "logging.Formatter", "line_number": 121, "usage_type": "call"}, {"api_name": "logging.FileHandler", "line_number": 123, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 123, "usage_type": "call"}, {"api_name": "os.path", "line_number": 123, "usage_type": "attribute"}, {"api_name": "logging.StreamHandler", "line_number": 126, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 137, "usage_type": "attribute"}, {"api_name": "utils.get_default_config", "line_number": 140, "usage_type": "call"}, {"api_name": "json.load", "line_number": 143, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 164, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 164, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 166, "usage_type": "call"}, {"api_name": "os.path", "line_number": 166, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 167, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 170, "usage_type": "call"}, {"api_name": "os.path", "line_number": 170, "usage_type": "attribute"}, {"api_name": "json.dump", "line_number": 171, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 174, "usage_type": "name"}, {"api_name": "logging.INFO", "line_number": 174, "usage_type": "name"}, {"api_name": "rdkit.RDLogger.DisableLog", "line_number": 177, "usage_type": "call"}, {"api_name": "rdkit.RDLogger", "line_number": 177, "usage_type": "name"}, {"api_name": "random.randint", "line_number": 180, "usage_type": "call"}, {"api_name": "utils.is_port_in_used", "line_number": 181, "usage_type": "call"}, {"api_name": "utils.is_port_in_used", "line_number": 184, "usage_type": "call"}, {"api_name": "subprocess.Popen", "line_number": 189, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 190, "usage_type": "call"}, {"api_name": "py4j.java_gateway.JavaGateway", "line_number": 191, "usage_type": "call"}, {"api_name": "py4j.java_gateway.GatewayParameters", "line_number": 193, "usage_type": "call"}, {"api_name": "rdkit.Chem.MolFromMolFile", "line_number": 198, "usage_type": "call"}, {"api_name": "rdkit.Chem", "line_number": 198, "usage_type": "name"}, {"api_name": "sys.exit", "line_number": 201, "usage_type": "call"}, {"api_name": "utils.ReactionUtils.get_reactions", "line_number": 202, "usage_type": "call"}, {"api_name": "utils.ReactionUtils", "line_number": 202, "usage_type": "name"}, {"api_name": "utils.ReactionUtils.get_reactions", "line_number": 203, "usage_type": "call"}, {"api_name": "utils.ReactionUtils", "line_number": 203, "usage_type": "name"}, {"api_name": "model_modules.load_model", "line_number": 207, "usage_type": "call"}, {"api_name": "model_modules.load_model", "line_number": 208, "usage_type": "call"}, {"api_name": "model_modules.load_model", "line_number": 210, "usage_type": "call"}, {"api_name": "model_modules.load_model", "line_number": 211, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 214, "usage_type": "call"}, {"api_name": "mcts_main.Mcts", "line_number": 216, "usage_type": "call"}, {"api_name": "time.time", "line_number": 220, "usage_type": "call"}, {"api_name": "time.time", "line_number": 222, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 225, "usage_type": "call"}, {"api_name": "os.path", "line_number": 225, "usage_type": "attribute"}, {"api_name": "pathlib.Path", "line_number": 230, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 230, "usage_type": "call"}, {"api_name": "os.path", "line_number": 230, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 232, "usage_type": "call"}, {"api_name": "mcts_modules.print_route", "line_number": 238, "usage_type": "call"}, {"api_name": "mcts_modules.save_route", "line_number": 239, "usage_type": "call"}]} +{"seq_id": "22284523031", "text": "# -*- coding: utf-8 -*-\n\nfrom datetime import datetime, timedelta\nfrom json import JSONEncoder, JSONDecoder\n\nfrom report import basic_cashflows\nfrom report import basic_lcm_notifications\nfrom report import basic_listed_positions\nfrom report import basic_portfolios\nfrom report import basic_positions\nfrom report import basic_risks\nfrom report.eod import eod_client_valuation_report\nfrom report.intraday.intraday_daily_pnl_by_underlyer_report import intraday_daily_pnl_by_underlyer_report\nfrom report.intraday.intraday_portfolio_trades_report import intraday_portfolio_trades_report\nfrom report.intraday.intraday_position_report import intraday_position_report\nfrom report.intraday.intraday_expiring_position_report import intraday_expiring_position_report\nfrom report.intraday.intraday_risk_by_underlyer_report import intraday_risk_by_underlyer_report\nfrom market_data.real_time_market_data import update_market_data\nfrom utils import utils\nfrom config.bct_config import bct_password, bct_user\n\n# -------------------------------------------------------------------------------\n# Some Parameter Be Defined\nip = 'localhost'\nlogin_body = {\n 'userName': bct_user,\n 'password': bct_password\n}\n\nPE_DEFAULT_INTRADAY = 'DEFAULT_INTRADAY_CALENDARS'\n\nINTRADAY_BASIC_POSITIONS = 'intraday:basic:positions'\nINTRADAY_BASIC_EXPIRING = 'intraday:basic:expiring'\nINTRADAY_BASIC_POSITION_INDEX = 'intraday:basic:position_index'\nINTRADAY_BASIC_CASH_FLOW = 'intraday:basic:cashFlow'\nINTRADAY_BASIC_CASH_FLOW_TODAY = 'intraday:basic:cashFlowToday'\nINTRADAY_BASIC_RISKS = 'intraday:basic:risks'\nINTRADAY_BASIC_UNDELRYER_POSITION = 'intraday:basic:underlyerPosition'\nINTRADAY_BASIC_PORTFOLIO_TRADES = 'intraday:basic:portfolioTrades'\nTRADE_QUEUE = 'trade:queue'\nINTRADAY_NOTIFY = 'intraday:notify'\nTRADE_EXPIRING_QUEUE = 'tradeExpiring:queue'\nINTRADAY_CUSTOM_POSITION = 'intraday:custom:position'\nINTRADAY_CUSTOM_EXPIRING_POSITION = 'intraday:custom:expiringPosition'\nRISK_QUEUE = 'risk:queue'\nPNL_QUEUE = 'pnl:queue'\nPORTFOLIO_RISK_QUEUE = 'portfolioRisk:queue'\nPOSITION_REPORT = 'pos_rpt_default_close'\nHST_PNL_REPORT = 'hst_pnl_rpt_default_close'\n\n\n# -------------------------------------------------------------------------------\n# PythonOperator To Ready Position Data\ndef basic_position_run():\n headers = utils.login(ip, login_body)\n position, expiring, position_index = basic_positions.get_intraday_positions(ip, headers)\n position_index_result = JSONEncoder().encode(position_index)\n position_result = JSONEncoder().encode(position)\n expiring_result = JSONEncoder().encode(expiring)\n\n r = utils.get_redis_conn(ip)\n r.set(INTRADAY_BASIC_EXPIRING, str(expiring_result))\n r.set(INTRADAY_BASIC_POSITIONS, str(position_result))\n r.set(INTRADAY_BASIC_POSITION_INDEX, str(position_index_result))\n print('Basic Position Data Has Save To Redis')\n\n\n# -------------------------------------------------------------------------------\n# PythonOperator To Ready CashFlow Data\ndef basic_cash_flow_run():\n headers = utils.login(ip, login_body)\n cash_flow = basic_cashflows.get_cash_flow(ip, headers)\n cash_flow_result = JSONEncoder().encode(cash_flow)\n\n r = utils.get_redis_conn(ip)\n r.set(INTRADAY_BASIC_CASH_FLOW, str(cash_flow_result))\n print('Basic CashFlow Data Has Save To Redis')\n\n\n# -------------------------------------------------------------------------------\n# PythonOperator To Ready CashFlowToday Data\ndef basic_cash_flow_today_run():\n headers = utils.login(ip, login_body)\n cash_flow_today = basic_cashflows.get_cash_flows_today(ip, headers)\n cash_flow_today_result = JSONEncoder().encode(cash_flow_today)\n\n r = utils.get_redis_conn(ip)\n r.set(INTRADAY_BASIC_CASH_FLOW_TODAY, str(cash_flow_today_result))\n print('Basic CashFlowToday Data Has Save To Redis')\n\n\n# -------------------------------------------------------------------------------\n# PythonOperator To Ready Risk Data\ndef basic_risks_run():\n valuation_time = datetime.now()\n pricing_environment = PE_DEFAULT_INTRADAY\n headers = utils.login(ip, login_body)\n r = utils.get_redis_conn(ip)\n position_result = r.get(INTRADAY_BASIC_POSITIONS)\n positions = JSONDecoder().decode(bytes.decode(position_result))\n expiring_result = r.get(INTRADAY_BASIC_EXPIRING)\n expirings = JSONDecoder().decode(bytes.decode(expiring_result))\n risk = basic_risks.get_risks([positions, expirings], pricing_environment, valuation_time, ip, headers)[0]\n risk_result = JSONEncoder().encode(risk)\n\n r.set(INTRADAY_BASIC_RISKS, str(risk_result))\n print('Basic Risk Data Has Save To Redis')\n\n\n# -------------------------------------------------------------------------------\n# PythonOperator To Ready UnderlyerPosition Data\ndef basic_underlyer_position_run():\n headers = utils.login(ip, login_body)\n underlyer_position = basic_listed_positions.get_underlyer_positions(ip, headers, PE_DEFAULT_INTRADAY)\n underlyer_position_result = JSONEncoder().encode(underlyer_position)\n\n r = utils.get_redis_conn(ip)\n r.set(INTRADAY_BASIC_UNDELRYER_POSITION, str(underlyer_position_result))\n print('Basic UnderlyerPosition Data Has Save To Redis')\n\n\n# -------------------------------------------------------------------------------\n# PythonOperator To Generate RealTimePositionReport\ndef real_time_position_run():\n r = utils.get_redis_conn(ip)\n position_result = r.get(INTRADAY_BASIC_POSITIONS)\n position = JSONDecoder().decode(bytes.decode(position_result))\n\n risk_result = r.get(INTRADAY_BASIC_RISKS)\n risk = JSONDecoder().decode(bytes.decode(risk_result))\n\n cash_flow_result = r.get(INTRADAY_BASIC_CASH_FLOW)\n cash_flow = JSONDecoder().decode(bytes.decode(cash_flow_result))\n\n headers = utils.login(ip, login_body)\n reports = intraday_position_report(position, risk, cash_flow, PE_DEFAULT_INTRADAY, ip, headers)\n position_result = JSONEncoder().encode(reports)\n r.set(TRADE_QUEUE, str(position_result))\n r.publish(TRADE_QUEUE, str(position_result))\n r.set(INTRADAY_CUSTOM_POSITION, str(position_result))\n\n\n# -------------------------------------------------------------------------------\n# PythonOperator To Generate RealTimeExpiringPositionReport\ndef real_time_expiring_position_run():\n r = utils.get_redis_conn(ip)\n position_result = r.get(INTRADAY_BASIC_EXPIRING)\n position = JSONDecoder().decode(bytes.decode(position_result))\n\n risk_result = r.get(INTRADAY_BASIC_RISKS)\n risk = JSONDecoder().decode(bytes.decode(risk_result))\n\n cash_flow_result = r.get(INTRADAY_BASIC_CASH_FLOW)\n cash_flow = JSONDecoder().decode(bytes.decode(cash_flow_result))\n\n reports = intraday_expiring_position_report(position, risk, cash_flow)\n position_result = JSONEncoder().encode(reports)\n r.set(TRADE_EXPIRING_QUEUE, str(position_result))\n r.publish(TRADE_EXPIRING_QUEUE, str(position_result))\n r.set(INTRADAY_CUSTOM_EXPIRING_POSITION, str(position_result))\n\n\n# -------------------------------------------------------------------------------\n# PythonOperator To Generate EodRiskReport\ndef real_time_risk_run():\n r = utils.get_redis_conn(ip)\n position_result = r.get(INTRADAY_CUSTOM_POSITION)\n position = JSONDecoder().decode(bytes.decode(position_result))\n\n underlyer_position_result = r.get(INTRADAY_BASIC_UNDELRYER_POSITION)\n underlyer_position = JSONDecoder().decode(bytes.decode(underlyer_position_result))\n\n headers = utils.login(ip, login_body)\n pe_description = utils.get_pricing_env_description(PE_DEFAULT_INTRADAY, ip, headers)\n reports = intraday_risk_by_underlyer_report(position, underlyer_position, ip, headers, pe_description)\n\n risk_result = JSONEncoder().encode(reports)\n r.set(RISK_QUEUE, str(risk_result))\n r.publish(RISK_QUEUE, str(risk_result))\n\n\n# -------------------------------------------------------------------------------\n# PythonOperator To Generate EodPnlReport\ndef real_time_pnl_run():\n r = utils.get_redis_conn(ip)\n risk_result = r.get(INTRADAY_BASIC_RISKS)\n risk = JSONDecoder().decode(bytes.decode(risk_result))\n\n position_index_result = r.get(INTRADAY_BASIC_POSITION_INDEX)\n position_index = JSONDecoder().decode(bytes.decode(position_index_result))\n\n underlyer_position_result = r.get(INTRADAY_BASIC_UNDELRYER_POSITION)\n underlyer_position = JSONDecoder().decode(bytes.decode(underlyer_position_result))\n\n cash_flow_today_result = r.get(INTRADAY_BASIC_CASH_FLOW_TODAY)\n cash_flow_today = JSONDecoder().decode(bytes.decode(cash_flow_today_result))\n\n now_date = datetime.now().date()\n yst_date = now_date + timedelta(days=-1)\n yst_params = {\n 'reportName': POSITION_REPORT,\n 'valuationDate': str(yst_date)\n }\n headers = utils.login(ip, login_body)\n yst_position = utils.call_request(ip, 'report-service', 'rptLatestPositionReportByNameAndDate',\n yst_params, headers)['result']\n yst_params['reportName'] = HST_PNL_REPORT\n yst_historical_pnl = utils.call_request(ip, 'report-service', 'rptLatestPnlHstReportByNameAndDate',\n yst_params, headers)['result']\n pe_description = utils.get_pricing_env_description(PE_DEFAULT_INTRADAY, ip, headers)\n reports = intraday_daily_pnl_by_underlyer_report(\n risk, cash_flow_today, underlyer_position, position_index, yst_position, yst_historical_pnl, pe_description)\n pnl_result = JSONEncoder().encode(reports)\n r.set(PNL_QUEUE, str(pnl_result))\n r.publish(PNL_QUEUE, str(pnl_result))\n\n\ndef real_time_valuation_run():\n r = utils.get_redis_conn(ip)\n risk_result = r.get(INTRADAY_BASIC_RISKS)\n risk = JSONDecoder().decode(bytes.decode(risk_result))\n\n position_result = r.get(INTRADAY_BASIC_POSITIONS)\n position = JSONDecoder().decode(bytes.decode(position_result))\n\n cash_flow_result = r.get(INTRADAY_BASIC_CASH_FLOW)\n cash_flow = JSONDecoder().decode(bytes.decode(cash_flow_result))\n\n pnls = eod_client_valuation_report.option_total_pnl(position, risk, cash_flow)\n\n headers = utils.login(ip, login_body)\n client_valuations = eod_client_valuation_report.client_valuation(position, pnls, datetime.now(), ip, headers)\n eod_client_valuation_report.process_and_save_report(client_valuations, ip, headers)\n\n\ndef trade_notification_run():\n headers = utils.login(ip, login_body)\n basic_lcm_notifications.trade_notification_all(ip, headers)\n\n\ndef basic_portfolio_trades_run():\n headers = utils.login(ip, login_body)\n portfolio_trades = basic_portfolios.get_portfolio_trades(ip, headers)\n portfolio_trades_result = JSONEncoder().encode(portfolio_trades)\n\n r = utils.get_redis_conn(ip)\n r.set(INTRADAY_BASIC_PORTFOLIO_TRADES, str(portfolio_trades_result))\n print('Basic portfolioTrades Data Has Save To Redis')\n\n\ndef real_time_portfolio_trades_run():\n r = utils.get_redis_conn(ip)\n risk_result = r.get(INTRADAY_BASIC_RISKS)\n risk = JSONDecoder().decode(bytes.decode(risk_result))\n\n position_index_result = r.get(INTRADAY_BASIC_POSITION_INDEX)\n position_index = JSONDecoder().decode(bytes.decode(position_index_result))\n\n portfolio_trades_result = r.get(INTRADAY_BASIC_PORTFOLIO_TRADES)\n portfolio_trades = JSONDecoder().decode(bytes.decode(portfolio_trades_result))\n headers = utils.login(ip, login_body)\n pe_description = utils.get_pricing_env_description(PE_DEFAULT_INTRADAY, ip, headers)\n portfolio_report = intraday_portfolio_trades_report(risk, position_index, portfolio_trades, pe_description)\n portfolio_report_result = JSONEncoder().encode(portfolio_report)\n r.set(PORTFOLIO_RISK_QUEUE, str(portfolio_report_result))\n r.publish(PORTFOLIO_RISK_QUEUE, str(portfolio_report_result))\n\n\n# -------------------------------------------------------------------------------\n# PythonOperator To update market data\ndef real_time_market_data():\n update_market_data('intraday')\n\n\n# -------------------------------------------------------------------------------\n# Intraday report notification\ndef intraday_expiring_position_report_notifier():\n intraday_report_notifier(\"EXPIRING_POSITION\")\n\n\ndef intraday_pnl_report_notifier():\n intraday_report_notifier(\"PNL\")\n\n\ndef intraday_risk_report_notifier():\n intraday_report_notifier(\"RISK\")\n\n\ndef intraday_valuation_report_notifier():\n intraday_report_notifier(\"VALUATION\")\n\n\ndef intraday_portfolio_risk_report_notifier():\n intraday_report_notifier(\"PORTFOLIO_RISK\")\n\n\ndef intraday_report_notifier(notification_type):\n r = utils.get_redis_conn(ip)\n valuation_time = str(datetime.now().isoformat())\n msg = {\"reportType\": notification_type, \"valuationTime\": valuation_time}\n msg_json = JSONEncoder().encode(msg)\n r.set(INTRADAY_NOTIFY, str(msg_json))\n r.publish(INTRADAY_NOTIFY, str(msg_json))\n\n\nif __name__ == '__main__':\n basic_position_run()\n basic_cash_flow_run()\n basic_cash_flow_today_run()\n basic_risks_run()\n basic_underlyer_position_run()\n basic_portfolio_trades_run()\n real_time_position_run()\n real_time_expiring_position_run()\n real_time_risk_run()\n real_time_pnl_run()\n real_time_valuation_run()\n real_time_portfolio_trades_run()\n", "repo_name": "zhanrendong/jkzx1", "sub_path": "scripts/airflow/intraday.py", "file_name": "intraday.py", "file_ext": "py", "file_size_in_byte": 13185, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "config.bct_config.bct_user", "line_number": 26, "usage_type": "name"}, {"api_name": "config.bct_config.bct_password", "line_number": 27, "usage_type": "name"}, {"api_name": "utils.utils.login", "line_number": 55, "usage_type": "call"}, {"api_name": "utils.utils", "line_number": 55, "usage_type": "name"}, {"api_name": "report.basic_positions.get_intraday_positions", "line_number": 56, "usage_type": "call"}, {"api_name": "report.basic_positions", "line_number": 56, "usage_type": "name"}, {"api_name": "json.JSONEncoder", "line_number": 57, "usage_type": "call"}, {"api_name": "json.JSONEncoder", "line_number": 58, "usage_type": "call"}, {"api_name": "json.JSONEncoder", "line_number": 59, "usage_type": "call"}, {"api_name": "utils.utils.get_redis_conn", "line_number": 61, "usage_type": "call"}, {"api_name": "utils.utils", "line_number": 61, "usage_type": "name"}, {"api_name": "utils.utils.login", "line_number": 71, "usage_type": "call"}, {"api_name": "utils.utils", "line_number": 71, "usage_type": "name"}, {"api_name": "report.basic_cashflows.get_cash_flow", "line_number": 72, "usage_type": "call"}, {"api_name": "report.basic_cashflows", "line_number": 72, "usage_type": "name"}, {"api_name": "json.JSONEncoder", "line_number": 73, "usage_type": "call"}, {"api_name": "utils.utils.get_redis_conn", "line_number": 75, "usage_type": "call"}, {"api_name": "utils.utils", "line_number": 75, "usage_type": "name"}, {"api_name": "utils.utils.login", "line_number": 83, "usage_type": "call"}, {"api_name": "utils.utils", "line_number": 83, "usage_type": "name"}, {"api_name": "report.basic_cashflows.get_cash_flows_today", "line_number": 84, "usage_type": "call"}, {"api_name": "report.basic_cashflows", "line_number": 84, "usage_type": "name"}, {"api_name": "json.JSONEncoder", "line_number": 85, "usage_type": "call"}, {"api_name": "utils.utils.get_redis_conn", "line_number": 87, "usage_type": "call"}, {"api_name": "utils.utils", "line_number": 87, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 95, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 95, "usage_type": "name"}, {"api_name": "utils.utils.login", "line_number": 97, "usage_type": "call"}, {"api_name": "utils.utils", "line_number": 97, "usage_type": "name"}, {"api_name": "utils.utils.get_redis_conn", "line_number": 98, "usage_type": "call"}, {"api_name": "utils.utils", "line_number": 98, "usage_type": "name"}, {"api_name": "json.JSONDecoder", "line_number": 100, "usage_type": "call"}, {"api_name": "json.JSONDecoder", "line_number": 102, "usage_type": "call"}, {"api_name": "report.basic_risks.get_risks", "line_number": 103, "usage_type": "call"}, {"api_name": "report.basic_risks", "line_number": 103, "usage_type": "name"}, {"api_name": "json.JSONEncoder", "line_number": 104, "usage_type": "call"}, {"api_name": "utils.utils.login", "line_number": 113, "usage_type": "call"}, {"api_name": "utils.utils", "line_number": 113, "usage_type": "name"}, {"api_name": "report.basic_listed_positions.get_underlyer_positions", "line_number": 114, "usage_type": "call"}, {"api_name": "report.basic_listed_positions", "line_number": 114, "usage_type": "name"}, {"api_name": "json.JSONEncoder", "line_number": 115, "usage_type": "call"}, {"api_name": "utils.utils.get_redis_conn", "line_number": 117, "usage_type": "call"}, {"api_name": "utils.utils", "line_number": 117, "usage_type": "name"}, {"api_name": "utils.utils.get_redis_conn", "line_number": 125, "usage_type": "call"}, {"api_name": "utils.utils", "line_number": 125, "usage_type": "name"}, {"api_name": "json.JSONDecoder", "line_number": 127, "usage_type": "call"}, {"api_name": "json.JSONDecoder", "line_number": 130, "usage_type": "call"}, {"api_name": "json.JSONDecoder", "line_number": 133, "usage_type": "call"}, {"api_name": "utils.utils.login", "line_number": 135, "usage_type": "call"}, {"api_name": "utils.utils", "line_number": 135, "usage_type": "name"}, {"api_name": "report.intraday.intraday_position_report.intraday_position_report", "line_number": 136, "usage_type": "call"}, {"api_name": "json.JSONEncoder", "line_number": 137, "usage_type": "call"}, {"api_name": "utils.utils.get_redis_conn", "line_number": 146, "usage_type": "call"}, {"api_name": "utils.utils", "line_number": 146, "usage_type": "name"}, {"api_name": "json.JSONDecoder", "line_number": 148, "usage_type": "call"}, {"api_name": "json.JSONDecoder", "line_number": 151, "usage_type": "call"}, {"api_name": "json.JSONDecoder", "line_number": 154, "usage_type": "call"}, {"api_name": "report.intraday.intraday_expiring_position_report.intraday_expiring_position_report", "line_number": 156, "usage_type": "call"}, {"api_name": "json.JSONEncoder", "line_number": 157, "usage_type": "call"}, {"api_name": "utils.utils.get_redis_conn", "line_number": 166, "usage_type": "call"}, {"api_name": "utils.utils", "line_number": 166, "usage_type": "name"}, {"api_name": "json.JSONDecoder", "line_number": 168, "usage_type": "call"}, {"api_name": "json.JSONDecoder", "line_number": 171, "usage_type": "call"}, {"api_name": "utils.utils.login", "line_number": 173, "usage_type": "call"}, {"api_name": "utils.utils", "line_number": 173, "usage_type": "name"}, {"api_name": "utils.utils.get_pricing_env_description", "line_number": 174, "usage_type": "call"}, {"api_name": "utils.utils", "line_number": 174, "usage_type": "name"}, {"api_name": "report.intraday.intraday_risk_by_underlyer_report.intraday_risk_by_underlyer_report", "line_number": 175, "usage_type": "call"}, {"api_name": "json.JSONEncoder", "line_number": 177, "usage_type": "call"}, {"api_name": "utils.utils.get_redis_conn", "line_number": 185, "usage_type": "call"}, {"api_name": "utils.utils", "line_number": 185, "usage_type": "name"}, {"api_name": "json.JSONDecoder", "line_number": 187, "usage_type": "call"}, {"api_name": "json.JSONDecoder", "line_number": 190, "usage_type": "call"}, {"api_name": "json.JSONDecoder", "line_number": 193, "usage_type": "call"}, {"api_name": "json.JSONDecoder", "line_number": 196, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 198, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 198, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 199, "usage_type": "call"}, {"api_name": "utils.utils.login", "line_number": 204, "usage_type": "call"}, {"api_name": "utils.utils", "line_number": 204, "usage_type": "name"}, {"api_name": "utils.utils.call_request", "line_number": 205, "usage_type": "call"}, {"api_name": "utils.utils", "line_number": 205, "usage_type": "name"}, {"api_name": "utils.utils.call_request", "line_number": 208, "usage_type": "call"}, {"api_name": "utils.utils", "line_number": 208, "usage_type": "name"}, {"api_name": "utils.utils.get_pricing_env_description", "line_number": 210, "usage_type": "call"}, {"api_name": "utils.utils", "line_number": 210, "usage_type": "name"}, {"api_name": "report.intraday.intraday_daily_pnl_by_underlyer_report.intraday_daily_pnl_by_underlyer_report", "line_number": 211, "usage_type": "call"}, {"api_name": "json.JSONEncoder", "line_number": 213, "usage_type": "call"}, {"api_name": "utils.utils.get_redis_conn", "line_number": 219, "usage_type": "call"}, {"api_name": "utils.utils", "line_number": 219, "usage_type": "name"}, {"api_name": "json.JSONDecoder", "line_number": 221, "usage_type": "call"}, {"api_name": "json.JSONDecoder", "line_number": 224, "usage_type": "call"}, {"api_name": "json.JSONDecoder", "line_number": 227, "usage_type": "call"}, {"api_name": "report.eod.eod_client_valuation_report.option_total_pnl", "line_number": 229, "usage_type": "call"}, {"api_name": "report.eod.eod_client_valuation_report", "line_number": 229, "usage_type": "name"}, {"api_name": "utils.utils.login", "line_number": 231, "usage_type": "call"}, {"api_name": "utils.utils", "line_number": 231, "usage_type": "name"}, {"api_name": "report.eod.eod_client_valuation_report.client_valuation", "line_number": 232, "usage_type": "call"}, {"api_name": "report.eod.eod_client_valuation_report", "line_number": 232, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 232, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 232, "usage_type": "name"}, {"api_name": "report.eod.eod_client_valuation_report.process_and_save_report", "line_number": 233, "usage_type": "call"}, {"api_name": "report.eod.eod_client_valuation_report", "line_number": 233, "usage_type": "name"}, {"api_name": "utils.utils.login", "line_number": 237, "usage_type": "call"}, {"api_name": "utils.utils", "line_number": 237, "usage_type": "name"}, {"api_name": "report.basic_lcm_notifications.trade_notification_all", "line_number": 238, "usage_type": "call"}, {"api_name": "report.basic_lcm_notifications", "line_number": 238, "usage_type": "name"}, {"api_name": "utils.utils.login", "line_number": 242, "usage_type": "call"}, {"api_name": "utils.utils", "line_number": 242, "usage_type": "name"}, {"api_name": "report.basic_portfolios.get_portfolio_trades", "line_number": 243, "usage_type": "call"}, {"api_name": "report.basic_portfolios", "line_number": 243, "usage_type": "name"}, {"api_name": "json.JSONEncoder", "line_number": 244, "usage_type": "call"}, {"api_name": "utils.utils.get_redis_conn", "line_number": 246, "usage_type": "call"}, {"api_name": "utils.utils", "line_number": 246, "usage_type": "name"}, {"api_name": "utils.utils.get_redis_conn", "line_number": 252, "usage_type": "call"}, {"api_name": "utils.utils", "line_number": 252, "usage_type": "name"}, {"api_name": "json.JSONDecoder", "line_number": 254, "usage_type": "call"}, {"api_name": "json.JSONDecoder", "line_number": 257, "usage_type": "call"}, {"api_name": "json.JSONDecoder", "line_number": 260, "usage_type": "call"}, {"api_name": "utils.utils.login", "line_number": 261, "usage_type": "call"}, {"api_name": "utils.utils", "line_number": 261, "usage_type": "name"}, {"api_name": "utils.utils.get_pricing_env_description", "line_number": 262, "usage_type": "call"}, {"api_name": "utils.utils", "line_number": 262, "usage_type": "name"}, {"api_name": "report.intraday.intraday_portfolio_trades_report.intraday_portfolio_trades_report", "line_number": 263, "usage_type": "call"}, {"api_name": "json.JSONEncoder", "line_number": 264, "usage_type": "call"}, {"api_name": "market_data.real_time_market_data.update_market_data", "line_number": 272, "usage_type": "call"}, {"api_name": "utils.utils.get_redis_conn", "line_number": 298, "usage_type": "call"}, {"api_name": "utils.utils", "line_number": 298, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 299, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 299, "usage_type": "name"}, {"api_name": "json.JSONEncoder", "line_number": 301, "usage_type": "call"}]} +{"seq_id": "25020134829", "text": "\nfrom django.urls import path\nfrom .views import *\napp_name='web'\nurlpatterns = [\n path('home/' , home , name=\"home\" ),\n\n path('',index,name=\"index\"),\n\n path('detail/',detail,name='detail'),\n\n path('about/',about,name='about'),\n\n path('contact/',Contact, name='contact'),\n\n\n\n]\n", "repo_name": "mohammadT99/django_shop", "sub_path": "post/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 303, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "django.urls.path", "line_number": 6, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 8, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 10, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 12, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 14, "usage_type": "call"}]} +{"seq_id": "18677703776", "text": "\"\"\"\n SqueezeNext for ImageNet-1K, implemented in TensorFlow.\n Original paper: 'SqueezeNext: Hardware-Aware Neural Network Design,' https://arxiv.org/abs/1803.10615.\n\"\"\"\n\n__all__ = ['SqueezeNext', 'sqnxt23_w1', 'sqnxt23_w3d2', 'sqnxt23_w2', 'sqnxt23v5_w1', 'sqnxt23v5_w3d2', 'sqnxt23v5_w2']\n\nimport os\nimport tensorflow as tf\nfrom .common import maxpool2d, conv_block, conv1x1_block, conv7x7_block, is_channels_first, flatten\n\n\ndef sqnxt_unit(x,\n in_channels,\n out_channels,\n strides,\n training,\n data_format,\n name=\"sqnxt_unit\"):\n \"\"\"\n SqueezeNext unit.\n\n Parameters:\n ----------\n x : Tensor\n Input tensor.\n in_channels : int\n Number of input channels.\n out_channels : int\n Number of output channels.\n strides : int or tuple/list of 2 int\n Strides of the convolution.\n training : bool, or a TensorFlow boolean scalar tensor\n Whether to return the output in training mode or in inference mode.\n data_format : str\n The ordering of the dimensions in tensors.\n name : str, default 'sqnxt_unit'\n Block name.\n\n Returns:\n -------\n Tensor\n Resulted tensor.\n \"\"\"\n if strides == 2:\n reduction_den = 1\n resize_identity = True\n elif in_channels > out_channels:\n reduction_den = 4\n resize_identity = True\n else:\n reduction_den = 2\n resize_identity = False\n\n if resize_identity:\n identity = conv1x1_block(\n x=x,\n in_channels=in_channels,\n out_channels=out_channels,\n strides=strides,\n use_bias=True,\n training=training,\n data_format=data_format,\n name=name + \"/identity_conv\")\n else:\n identity = x\n\n x = conv1x1_block(\n x=x,\n in_channels=in_channels,\n out_channels=(in_channels // reduction_den),\n strides=strides,\n use_bias=True,\n training=training,\n data_format=data_format,\n name=name + \"/conv1\")\n x = conv1x1_block(\n x=x,\n in_channels=(in_channels // reduction_den),\n out_channels=(in_channels // (2 * reduction_den)),\n use_bias=True,\n training=training,\n data_format=data_format,\n name=name + \"/conv2\")\n x = conv_block(\n x=x,\n in_channels=(in_channels // (2 * reduction_den)),\n out_channels=(in_channels // reduction_den),\n kernel_size=(1, 3),\n strides=1,\n padding=(0, 1),\n use_bias=True,\n training=training,\n data_format=data_format,\n name=name + \"/conv3\")\n x = conv_block(\n x=x,\n in_channels=(in_channels // reduction_den),\n out_channels=(in_channels // reduction_den),\n kernel_size=(3, 1),\n strides=1,\n padding=(1, 0),\n use_bias=True,\n training=training,\n data_format=data_format,\n name=name + \"/conv4\")\n x = conv1x1_block(\n x=x,\n in_channels=(in_channels // reduction_den),\n out_channels=out_channels,\n use_bias=True,\n training=training,\n data_format=data_format,\n name=name + \"/conv5\")\n\n x = x + identity\n x = tf.nn.relu(x, name=name + \"/final_activ\")\n return x\n\n\ndef sqnxt_init_block(x,\n in_channels,\n out_channels,\n training,\n data_format,\n name=\"sqnxt_init_block\"):\n \"\"\"\n ResNet specific initial block.\n\n Parameters:\n ----------\n x : Tensor\n Input tensor.\n in_channels : int\n Number of input channels.\n out_channels : int\n Number of output channels.\n training : bool, or a TensorFlow boolean scalar tensor\n Whether to return the output in training mode or in inference mode.\n data_format : str\n The ordering of the dimensions in tensors.\n name : str, default 'sqnxt_init_block'\n Block name.\n\n Returns:\n -------\n Tensor\n Resulted tensor.\n \"\"\"\n x = conv7x7_block(\n x=x,\n in_channels=in_channels,\n out_channels=out_channels,\n strides=2,\n padding=1,\n use_bias=True,\n training=training,\n data_format=data_format,\n name=name + \"/conv\")\n x = maxpool2d(\n x=x,\n pool_size=3,\n strides=2,\n ceil_mode=True,\n data_format=data_format,\n name=name + \"/pool\")\n return x\n\n\nclass SqueezeNext(object):\n \"\"\"\n SqueezeNext model from 'SqueezeNext: Hardware-Aware Neural Network Design,' https://arxiv.org/abs/1803.10615.\n\n Parameters:\n ----------\n channels : list of list of int\n Number of output channels for each unit.\n init_block_channels : int\n Number of output channels for the initial unit.\n final_block_channels : int\n Number of output channels for the final block of the feature extractor.\n in_channels : int, default 3\n Number of input channels.\n in_size : tuple of two ints, default (224, 224)\n Spatial size of the expected input image.\n classes : int, default 1000\n Number of classification classes.\n data_format : str, default 'channels_last'\n The ordering of the dimensions in tensors.\n \"\"\"\n def __init__(self,\n channels,\n init_block_channels,\n final_block_channels,\n in_channels=3,\n in_size=(224, 224),\n classes=1000,\n data_format=\"channels_last\",\n **kwargs):\n super(SqueezeNext, self).__init__(**kwargs)\n assert (data_format in [\"channels_last\", \"channels_first\"])\n self.channels = channels\n self.init_block_channels = init_block_channels\n self.final_block_channels = final_block_channels\n self.in_channels = in_channels\n self.in_size = in_size\n self.classes = classes\n self.data_format = data_format\n\n def __call__(self,\n x,\n training=False):\n \"\"\"\n Build a model graph.\n\n Parameters:\n ----------\n x : Tensor\n Input tensor.\n training : bool, or a TensorFlow boolean scalar tensor, default False\n Whether to return the output in training mode or in inference mode.\n\n Returns:\n -------\n Tensor\n Resulted tensor.\n \"\"\"\n in_channels = self.in_channels\n x = sqnxt_init_block(\n x=x,\n in_channels=in_channels,\n out_channels=self.init_block_channels,\n training=training,\n data_format=self.data_format,\n name=\"features/init_block\")\n in_channels = self.init_block_channels\n for i, channels_per_stage in enumerate(self.channels):\n for j, out_channels in enumerate(channels_per_stage):\n strides = 2 if (j == 0) and (i != 0) else 1\n x = sqnxt_unit(\n x=x,\n in_channels=in_channels,\n out_channels=out_channels,\n strides=strides,\n training=training,\n data_format=self.data_format,\n name=\"features/stage{}/unit{}\".format(i + 1, j + 1))\n in_channels = out_channels\n x = conv1x1_block(\n x=x,\n in_channels=in_channels,\n out_channels=self.final_block_channels,\n use_bias=True,\n training=training,\n data_format=self.data_format,\n name=\"features/final_block\")\n x = tf.keras.layers.AveragePooling2D(\n pool_size=7,\n strides=1,\n data_format=self.data_format,\n name=\"features/final_pool\")(x)\n\n # x = tf.layers.flatten(x)\n x = flatten(\n x=x,\n data_format=self.data_format)\n x = tf.keras.layers.Dense(\n units=self.classes,\n name=\"output\")(x)\n\n return x\n\n\ndef get_squeezenext(version,\n width_scale,\n model_name=None,\n pretrained=False,\n root=os.path.join(\"~\", \".tensorflow\", \"models\"),\n **kwargs):\n \"\"\"\n Create SqueezeNext model with specific parameters.\n\n Parameters:\n ----------\n version : str\n Version of SqueezeNet ('23' or '23v5').\n width_scale : float\n Scale factor for width of layers.\n model_name : str or None, default None\n Model name for loading pretrained model.\n pretrained : bool, default False\n Whether to load the pretrained weights for model.\n root : str, default '~/.tensorflow/models'\n Location for keeping the model parameters.\n\n Returns:\n -------\n functor\n Functor for model graph creation with extra fields.\n \"\"\"\n\n init_block_channels = 64\n final_block_channels = 128\n channels_per_layers = [32, 64, 128, 256]\n\n if version == '23':\n layers = [6, 6, 8, 1]\n elif version == '23v5':\n layers = [2, 4, 14, 1]\n else:\n raise ValueError(\"Unsupported SqueezeNet version {}\".format(version))\n\n channels = [[ci] * li for (ci, li) in zip(channels_per_layers, layers)]\n\n if width_scale != 1:\n channels = [[int(cij * width_scale) for cij in ci] for ci in channels]\n init_block_channels = int(init_block_channels * width_scale)\n final_block_channels = int(final_block_channels * width_scale)\n\n net = SqueezeNext(\n channels=channels,\n init_block_channels=init_block_channels,\n final_block_channels=final_block_channels,\n **kwargs)\n\n if pretrained:\n if (model_name is None) or (not model_name):\n raise ValueError(\"Parameter `model_name` should be properly initialized for loading pretrained model.\")\n from .model_store import download_state_dict\n net.state_dict, net.file_path = download_state_dict(\n model_name=model_name,\n local_model_store_dir_path=root)\n else:\n net.state_dict = None\n net.file_path = None\n\n return net\n\n\ndef sqnxt23_w1(**kwargs):\n \"\"\"\n 1.0-SqNxt-23 model from 'SqueezeNext: Hardware-Aware Neural Network Design,' https://arxiv.org/abs/1803.10615.\n\n Parameters:\n ----------\n pretrained : bool, default False\n Whether to load the pretrained weights for model.\n root : str, default '~/.tensorflow/models'\n Location for keeping the model parameters.\n\n Returns:\n -------\n functor\n Functor for model graph creation with extra fields.\n \"\"\"\n return get_squeezenext(version=\"23\", width_scale=1.0, model_name=\"sqnxt23_w1\", **kwargs)\n\n\ndef sqnxt23_w3d2(**kwargs):\n \"\"\"\n 1.5-SqNxt-23 model from 'SqueezeNext: Hardware-Aware Neural Network Design,' https://arxiv.org/abs/1803.10615.\n\n Parameters:\n ----------\n pretrained : bool, default False\n Whether to load the pretrained weights for model.\n root : str, default '~/.tensorflow/models'\n Location for keeping the model parameters.\n\n Returns:\n -------\n functor\n Functor for model graph creation with extra fields.\n \"\"\"\n return get_squeezenext(version=\"23\", width_scale=1.5, model_name=\"sqnxt23_w3d2\", **kwargs)\n\n\ndef sqnxt23_w2(**kwargs):\n \"\"\"\n 2.0-SqNxt-23 model from 'SqueezeNext: Hardware-Aware Neural Network Design,' https://arxiv.org/abs/1803.10615.\n\n Parameters:\n ----------\n pretrained : bool, default False\n Whether to load the pretrained weights for model.\n root : str, default '~/.tensorflow/models'\n Location for keeping the model parameters.\n\n Returns:\n -------\n functor\n Functor for model graph creation with extra fields.\n \"\"\"\n return get_squeezenext(version=\"23\", width_scale=2.0, model_name=\"sqnxt23_w2\", **kwargs)\n\n\ndef sqnxt23v5_w1(**kwargs):\n \"\"\"\n 1.0-SqNxt-23v5 model from 'SqueezeNext: Hardware-Aware Neural Network Design,' https://arxiv.org/abs/1803.10615.\n\n Parameters:\n ----------\n pretrained : bool, default False\n Whether to load the pretrained weights for model.\n root : str, default '~/.tensorflow/models'\n Location for keeping the model parameters.\n\n Returns:\n -------\n functor\n Functor for model graph creation with extra fields.\n \"\"\"\n return get_squeezenext(version=\"23v5\", width_scale=1.0, model_name=\"sqnxt23v5_w1\", **kwargs)\n\n\ndef sqnxt23v5_w3d2(**kwargs):\n \"\"\"\n 1.5-SqNxt-23v5 model from 'SqueezeNext: Hardware-Aware Neural Network Design,' https://arxiv.org/abs/1803.10615.\n\n Parameters:\n ----------\n pretrained : bool, default False\n Whether to load the pretrained weights for model.\n root : str, default '~/.tensorflow/models'\n Location for keeping the model parameters.\n\n Returns:\n -------\n functor\n Functor for model graph creation with extra fields.\n \"\"\"\n return get_squeezenext(version=\"23v5\", width_scale=1.5, model_name=\"sqnxt23v5_w3d2\", **kwargs)\n\n\ndef sqnxt23v5_w2(**kwargs):\n \"\"\"\n 2.0-SqNxt-23v5 model from 'SqueezeNext: Hardware-Aware Neural Network Design,' https://arxiv.org/abs/1803.10615.\n\n Parameters:\n ----------\n pretrained : bool, default False\n Whether to load the pretrained weights for model.\n root : str, default '~/.tensorflow/models'\n Location for keeping the model parameters.\n\n Returns:\n -------\n functor\n Functor for model graph creation with extra fields.\n \"\"\"\n return get_squeezenext(version=\"23v5\", width_scale=2.0, model_name=\"sqnxt23v5_w2\", **kwargs)\n\n\ndef _test():\n import numpy as np\n\n data_format = \"channels_last\"\n pretrained = False\n\n models = [\n sqnxt23_w1,\n sqnxt23_w3d2,\n sqnxt23_w2,\n sqnxt23v5_w1,\n sqnxt23v5_w3d2,\n sqnxt23v5_w2,\n ]\n\n for model in models:\n\n net = model(pretrained=pretrained, data_format=data_format)\n x = tf.placeholder(\n dtype=tf.float32,\n shape=(None, 3, 224, 224) if is_channels_first(data_format) else (None, 224, 224, 3),\n name=\"xx\")\n y_net = net(x)\n\n weight_count = np.sum([np.prod(v.get_shape().as_list()) for v in tf.trainable_variables()])\n print(\"m={}, {}\".format(model.__name__, weight_count))\n assert (model != sqnxt23_w1 or weight_count == 724056)\n assert (model != sqnxt23_w3d2 or weight_count == 1511824)\n assert (model != sqnxt23_w2 or weight_count == 2583752)\n assert (model != sqnxt23v5_w1 or weight_count == 921816)\n assert (model != sqnxt23v5_w3d2 or weight_count == 1953616)\n assert (model != sqnxt23v5_w2 or weight_count == 3366344)\n\n with tf.Session() as sess:\n if pretrained:\n from .model_store import init_variables_from_state_dict\n init_variables_from_state_dict(sess=sess, state_dict=net.state_dict)\n else:\n sess.run(tf.global_variables_initializer())\n x_value = np.zeros((1, 3, 224, 224) if is_channels_first(data_format) else (1, 224, 224, 3), np.float32)\n y = sess.run(y_net, feed_dict={x: x_value})\n assert (y.shape == (1, 1000))\n tf.reset_default_graph()\n\n\nif __name__ == \"__main__\":\n _test()\n", "repo_name": "osmr/imgclsmob", "sub_path": "tensorflow_/tensorflowcv/models/squeezenext.py", "file_name": "squeezenext.py", "file_ext": "py", "file_size_in_byte": 15382, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2864, "dataset": "github-code", "pt": "37", "api": [{"api_name": "common.conv1x1_block", "line_number": 56, "usage_type": "call"}, {"api_name": "common.conv1x1_block", "line_number": 68, "usage_type": "call"}, {"api_name": "common.conv1x1_block", "line_number": 77, "usage_type": "call"}, {"api_name": "common.conv_block", "line_number": 85, "usage_type": "call"}, {"api_name": "common.conv_block", "line_number": 96, "usage_type": "call"}, {"api_name": "common.conv1x1_block", "line_number": 107, "usage_type": "call"}, {"api_name": "tensorflow.nn.relu", "line_number": 117, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 117, "usage_type": "attribute"}, {"api_name": "common.conv7x7_block", "line_number": 150, "usage_type": "call"}, {"api_name": "common.maxpool2d", "line_number": 160, "usage_type": "call"}, {"api_name": "common.conv1x1_block", "line_number": 249, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.AveragePooling2D", "line_number": 257, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 257, "usage_type": "attribute"}, {"api_name": "common.flatten", "line_number": 264, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Dense", "line_number": 267, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 267, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 278, "usage_type": "call"}, {"api_name": "os.path", "line_number": 278, "usage_type": "attribute"}, {"api_name": "model_store.download_state_dict", "line_number": 330, "usage_type": "call"}, {"api_name": "tensorflow.placeholder", "line_number": 472, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 473, "usage_type": "attribute"}, {"api_name": "common.is_channels_first", "line_number": 474, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 478, "usage_type": "call"}, {"api_name": "numpy.prod", "line_number": 478, "usage_type": "call"}, {"api_name": "tensorflow.trainable_variables", "line_number": 478, "usage_type": "call"}, {"api_name": "tensorflow.Session", "line_number": 487, "usage_type": "call"}, {"api_name": "model_store.init_variables_from_state_dict", "line_number": 490, "usage_type": "call"}, {"api_name": "tensorflow.global_variables_initializer", "line_number": 492, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 493, "usage_type": "call"}, {"api_name": "common.is_channels_first", "line_number": 493, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 493, "usage_type": "attribute"}, {"api_name": "tensorflow.reset_default_graph", "line_number": 496, "usage_type": "call"}]} +{"seq_id": "24912634356", "text": "from concurrent.futures import ThreadPoolExecutor\nfrom datetime import datetime\nfrom threading import Lock\n\nimport requests\nfrom yaml import safe_load\n\nwith open(\"config.yaml\") as f:\n config = safe_load(f)\nsources = config.get('sources') or {}\nhosts = sources.get('by_hosts') or []\ndomain_lists = sources.get('by_domains') or []\nother = sources.get('others') or []\nwhitelist = set(config.get('whitelist', []) or [])\n\n\ndef add_host(host):\n if host:\n host = host.strip(\". \\t\\n\").split(' ')[0]\n if host not in whitelist and not host.startswith('#'):\n data.add(host + \" CNAME .\")\n # data.add(\"*.\" + host)\n\n\ndef process_domains(content):\n for line in content.splitlines():\n line = line.strip()\n if line and not line.startswith('#'):\n add_host(line)\n\n\ndef process_hosts(content):\n for line in content.splitlines():\n line = line.strip()\n if line.startswith('127.0.0.1') or line.startswith('0.0.0.0'):\n chunks = line.split()\n if len(chunks) >= 2:\n ip, host = chunks[:2]\n add_host(host)\n\n\ndef download_file(url, ftype):\n for i in range(5):\n try:\n print(\"download\", url)\n resp = requests.get(url)\n resp.raise_for_status()\n with lock:\n if ftype == \"hosts\":\n process_hosts(resp.text)\n elif ftype == \"domains\":\n process_domains(resp.text)\n else:\n print(\"wrong ftype :\", ftype)\n\n except requests.RequestException as err:\n print(\"URL ERROR : \", err)\n\n\nif __name__ == '__main__':\n data = set()\n lock = Lock()\n list(map(add_host, other))\n with ThreadPoolExecutor(4) as executor:\n for file in set(hosts):\n executor.submit(download_file, file, \"hosts\")\n for file in set(domain_lists):\n executor.submit(download_file, file, 'domains')\n\n header = f\"\"\"\n$TTL 2w\n\n@ IN SOA localhost. root.localhost. (\n {datetime.now().strftime(\"%Y%m%d\")} ; serial\n 604800 ; refresh\n 14400 ; retry\n 1209600 ; expiry\n 345600 ; minimum\n)\n\n@ IN NS localhost.\n\n\"\"\"\n\n data = sorted(set(data))\n\n with open(config.get('zone_file', 'zone.txt'), \"w\") as zone:\n zone.write(header)\n zone.write('\\n'.join(data))\n zone.write('\\n')\n\n print(len(data), \"entries\")\n", "repo_name": "Raphhael/bindhole", "sub_path": "adblock/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 2434, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "yaml.safe_load", "line_number": 9, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 46, "usage_type": "call"}, {"api_name": "requests.RequestException", "line_number": 56, "usage_type": "attribute"}, {"api_name": "threading.Lock", "line_number": 62, "usage_type": "call"}, {"api_name": "concurrent.futures.ThreadPoolExecutor", "line_number": 64, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 74, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 74, "usage_type": "name"}]} +{"seq_id": "7814542282", "text": "import datetime\nfrom typing import List\nimport uuid\n\ndef getUUID():\n \"\"\"\n random uuid\n :return: hex with the format '8-4-4-4-12'\n \"\"\"\n uid = uuid.uuid4().hex\n return f\"{uid[:8]}-{uid[8:12]}-{uid[12:16]}-{uid[16:20]}-{uid[20:]}\"\n\ndef color(string, color: str) -> str:\n dic = {\n 'white': '\\033[30m',\n 'red': '\\033[31m',\n 'green': '\\033[32m',\n 'yellow': '\\033[33m',\n 'blue': '\\033[34m',\n 'purple': '\\033[35m',\n 'cyan': '\\033[36m',\n 'black': '\\033[37m'\n }\n return dic[color] + string + '\\033[0m'\n\n\ndef dictToString(cookies: dict) -> str:\n cookie = \"\"\n for i in cookies:\n cookie += f\"{i}={cookies[i]}; \"\n return cookie\n\n\ndef formatHistory(histories: List[dict]) -> str:\n string = f\"\\n====== Histories of <{histories[0]['conversation_id'] if len(histories) > 1 else ''}> ======\\n\"\n for i in histories:\n string += f\"({color('user', 'green') if i['is_user'] else color('Open-Assistant', 'blue')}): {i['text']}\\n\"\n string += \"\\n\"\n return string\n\n\ndef getTextFromInput(conversation_id, addition: str = \"\"):\n while 1:\n text = input(f\"{addition}({conversation_id}) > \")\n if not text:\n continue\n else:\n return text\n\n\ndef formatConversations(conversations: dict):\n string = \"* Conversations established:\\n\\n\"\n # for i in conversations:\n # string += f\"\t{i}. {conversations[i]}\\n\"\n cons = tuple(conversations.items())\n for i in range(len(cons)):\n string += f\"\t{i}. [{cons[i][0]}] - {cons[i][-1]}\\n\"\n # string += \"\\n\"\n return string\n\n\ndef getTime():\n return str(datetime.datetime.now())\n\n\ndef getIdByIndex(conversations: dict, index: int) -> str:\n cons = list(conversations.keys())\n if 0 <= index <= len(cons):\n return cons[index]\n raise Exception(\"Index out of bounds\")\n", "repo_name": "ogios/huggingchat-api", "sub_path": "hugchat_api/utils/__init__.py", "file_name": "__init__.py", "file_ext": "py", "file_size_in_byte": 1894, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 10, "dataset": "github-code", "pt": "37", "api": [{"api_name": "uuid.uuid4", "line_number": 10, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 34, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 63, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 63, "usage_type": "attribute"}]} +{"seq_id": "71484723947", "text": "import threading\nimport serial\nimport time\n\nbleSerial = serial.Serial(\"/dev/ttyS0\", baudrate=9600, timeout=1.0)\n\ngData = \"\"\n\ndef serial_thread():\n global gData\n while True:\n data = bleSerial.readline()\n data = data.decode()\n gData = data\n\ndef main():\n global gData\n try:\n while True:\n if gData.find(\"go\") >= 0:\n gData = \"\"\n print(\"ok go\")\n elif gData.find(\"back\") >= 0:\n gData = \"\"\n print(\"ok back\")\n elif gData.find(\"left\") >= 0:\n gData = \"\"\n print(\"ok left\")\n elif gData.find(\"right\") >= 0:\n gData = \"\"\n print(\"ok right\")\n elif gData.find(\"stop\") >= 0:\n gData = \"\"\n print(\"ok stop\")\n\n except KeyboardInterrupt:\n pass\n\nif __name__ == '__main__':\n task1 = threading.Thread(target = serial_thread)\n task1.start()\n main()\n bleSerial.close()\n \n\n", "repo_name": "Bae-hong-seob/Self-driving-robot", "sub_path": "AI_CAR_CODE/4_4.py", "file_name": "4_4.py", "file_ext": "py", "file_size_in_byte": 1012, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "37", "api": [{"api_name": "serial.Serial", "line_number": 5, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 40, "usage_type": "call"}]} +{"seq_id": "39494675037", "text": "import sys\nimport os\nimport importlib\n\n# path to the framework repository of the compiler\nsys.path.insert(0, os.path.dirname(\n os.path.realpath(__file__)) + '/../ebpf')\nrun_ebpf_test = importlib.import_module('run-ebpf-test')\n\narg_parser = run_ebpf_test.PARSER\n\nif __name__ == \"__main__\":\n # Parse options and process argv\n args, argv = arg_parser.parse_known_args()\n options = run_ebpf_test.Options()\n options.compiler = run_ebpf_test.check_path(args.compiler)\n options.p4filename = run_ebpf_test.check_path(args.p4filename)\n options.verbose = args.verbose\n options.replace = args.replace\n options.cleanupTmp = args.nocleanup\n options.target = args.target\n # Switch test directory based on path to run-ubpf-test.py\n options.testdir = os.path.dirname(os.path.realpath(__file__))\n options.extern = args.extern\n\n # All args after '--' are intended for the p4 compiler\n argv = argv[1:]\n # Run the test with the extracted options and modified argv\n result = run_ebpf_test.run_test(options, argv)\n sys.exit(result)\n", "repo_name": "Invincibleyc/P4B-Translator", "sub_path": "backends/ubpf/run-ubpf-test.py", "file_name": "run-ubpf-test.py", "file_ext": "py", "file_size_in_byte": 1064, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "37", "api": [{"api_name": "sys.path.insert", "line_number": 6, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 6, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 6, "usage_type": "call"}, {"api_name": "os.path", "line_number": 6, "usage_type": "attribute"}, {"api_name": "os.path.realpath", "line_number": 7, "usage_type": "call"}, {"api_name": "os.path", "line_number": 7, "usage_type": "attribute"}, {"api_name": "importlib.import_module", "line_number": 8, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 23, "usage_type": "call"}, {"api_name": "os.path", "line_number": 23, "usage_type": "attribute"}, {"api_name": "os.path.realpath", "line_number": 23, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 30, "usage_type": "call"}]} +{"seq_id": "28391977038", "text": "\"\"\"\nAll things related to url parsing.\n\nMost of the code here was modified from newspaper's module for cleaning urls.\nFrom: https://github.com/codelucas/newspaper/blob/master/newspaper/urls.py\n\"\"\"\n\nimport copy\nimport time\nfrom urlparse import (\n urlparse, urljoin, urlsplit, urlunsplit, parse_qs\n)\n\nimport tldextract\n\nfrom newslynx.lib.common import make_soup\nfrom newslynx.lib import network\nfrom newslynx.lib import meta\nfrom newslynx.lib import html\nfrom newslynx.util import uniq\nfrom newslynx.core import settings\nfrom newslynx.lib.regex import *\n\n# url chunks\nALLOWED_TYPES = [\n 'html', 'htm', 'md', 'rst', 'aspx', 'jsp', 'rhtml', 'cgi',\n 'xhtml', 'jhtml', 'asp'\n]\n\nGOOD_PATHS = [\n 'story', 'article', 'feature', 'featured', 'slides',\n 'slideshow', 'gallery', 'news', 'video', 'media',\n 'v', 'radio', 'press', 'blog', 'movies', \"project\",\n 'interactive', 'app'\n]\n\nBAD_CHUNKS = [\n 'careers', 'contact', 'about', 'faq', 'terms', 'privacy',\n 'advert', 'preferences', 'feedback', 'info', 'browse', 'howto',\n 'account', 'subscribe', 'donate', 'shop', 'admin', 'author', 'topic',\n 'comments'\n]\n\nBAD_DOMAINS = [\n 'amazon', 'doubleclick', 'twitter',\n 'facebook', 'pinterest', 'google',\n]\n\nVIDEO_DOMAINS = [\n 'youtube', 'vimeo', 'dailymotion', 'kewego'\n]\n\nURL_TAGS = ['a', 'embed', 'video', 'iframe']\n\nURL_ATTRS = ['href', 'src']\n\nMAX_LEN = 150\nMIN_LEN = 11\n\nIMG_FILETYPES = frozenset([\n 'png', 'jpg', 'jpeg', 'gif',\n 'bmp', 'webp', 'tiff', 'svg', 'ico'\n])\n\nREDIRECT_QUERY_PARAMS = ['url', 'u']\n\nKEEP_PARAMS = ('id', 'p', 'v', 'story_fbid')\n\n\ndef prepare(url, source=None, canonicalize=True, expand=True, keep_params=KEEP_PARAMS):\n \"\"\"\n Operations that unshorten a url, reconcile embeds,\n resolves redirects, strip parameters (with optional\n ones to keep), and then attempts to canonicalize the url\n by checking the page source's metadata.\n\n All urls that enter `merlynne` are first treated with this function.\n \"\"\"\n if not url or url == \"\":\n return None\n\n # encode.\n url = url.encode('utf-8', errors='ignore')\n\n # reconcile embeds:\n url = reconcile_embed(url)\n\n # reconcile redirects\n url = redirect_back(url, source)\n\n # check for non absolute urls.\n if source:\n source_domain = get_domain(source)\n\n # if the domain is in the source, attempt to absolutify it\n if source_domain in url:\n\n # check for non-absolute urls\n if not is_abs(url):\n url = urljoin(source, url)\n\n # check for missing scheme\n if not get_scheme(url):\n url = \"http://\" + url\n\n # check short urls\n if expand:\n if is_shortened(url):\n url = unshorten(url, attempts=1)\n\n # canonicalize\n if canonicalize:\n page_html = network.get(url)\n if page_html:\n soup = make_soup(page_html)\n _url = meta.canonical_url(soup)\n if _url:\n url = _url\n\n # if it got converted to None, return\n if not url:\n return None\n\n # remove arguments w/ optional parameters to keep.\n url = remove_args(url, keep_params)\n\n # remove index.html\n url = re_index_html.sub('', url)\n\n # always remove trailing slash\n if url.endswith('/'):\n url = url[:-1]\n return url\n\n\ndef join(base, path):\n \"\"\"\n Join two url elements.\n \"\"\"\n return urljoin(prepare(base, canonicalize=False, expand=False), path)\n\n\ndef unshorten(orig_url, **kw):\n \"\"\"\n Unshorten a url.\n \"\"\"\n if not orig_url:\n return None\n # set vars\n max_attempts = kw.get('max_attempts', 3)\n interval = kw.get('interval', 0.5)\n factor = kw.get('factor', 2)\n attempts = 0\n\n if not orig_url.startswith('http://'):\n orig_url = \"http://\" + orig_url\n u = copy.copy(orig_url)\n while attempts < max_attempts:\n u = _unshorten(u)\n attempts += 1\n # catch failures\n if not u:\n return orig_url\n # urls that probably aren't shortened.\n elif u == orig_url:\n return orig_url\n elif not is_shortened(u):\n return u\n interval *= factor\n time.sleep(interval)\n\n return u\n\n\n@network.retry(attempts=settings.NETWORK_MAX_RETRIES)\ndef shorten(url):\n \"\"\"\n Shorten a url on bitly, return it's new short url\n and global hash.\n \"\"\"\n from newslynx.core import bitly_api\n d = bitly_api.shorten(url)\n return {\n 'short_url': d.get('url'),\n 'global_hash': d.get('global_hash')\n }\n\n\ndef get_domain(url, **kw):\n \"\"\"\n Returns a url's domain, this method exists to\n encapsulate all url code into this file\n \"\"\"\n if url is None:\n return None\n\n # check for missing scheme\n if not get_scheme(url):\n url = \"http://\" + url\n\n domain = urlparse(url, **kw).netloc\n domain = re_www.sub('', domain)\n return domain\n\n\ndef get_simple_domain(url, **kw):\n \"\"\"\n Returns a standardized domain\n i.e.:\n get_simple_domain('http://publiceditor.blogs.nytimes.com/')\n >>> 'nytimes'\n \"\"\"\n if url is None:\n return None\n domain = get_domain(url)\n tld_dat = tldextract.extract(domain, **kw)\n return tld_dat.domain\n\n\ndef get_scheme(url, **kw):\n \"\"\"\n returns a url's scheme, this method exists to\n encapsulate all url code into this file\n \"\"\"\n if url is None:\n return None\n return urlparse(url, **kw).scheme\n\n\ndef get_path(url, **kw):\n \"\"\"\n returns a url's path, this method exists to\n encapsulate all url code into this file\n \"\"\"\n if url is None:\n return None\n\n # check for missing scheme\n if not get_scheme(url):\n url = \"http://\" + url\n\n return urlparse(url, **kw).path\n\n\ndef get_slug(url):\n \"\"\"\n turn a url into a slug, removing (index).html\n \"\"\"\n if url is None:\n return None\n\n # check for missing scheme\n if not get_scheme(url):\n url = \"http://\" + url\n\n url = get_path(url.decode('utf-8', 'ignore'))\n url = re_html.sub('', url).strip().lower()\n url = re_slug.sub(r'-', url)\n url = re_slug_end.sub('', url)\n\n if url.startswith('-'):\n url = url[1:]\n elif url.endswith('-'):\n url = url[-1]\n\n return url.strip()\n\n\ndef get_hash(url):\n \"\"\"\n turn a url into a unique md5 hash\n \"\"\"\n if url is None:\n return None\n\n # check for missing scheme\n if not get_scheme(url):\n url = \"http://\" + url\n\n url = re_http.sub('', url)\n url = re_www.sub('', url)\n url = re_html.sub('', url).strip()\n return hashlib.md5(url).hexdigest()\n\n\ndef get_path_hash(url, **kw):\n \"\"\"\n turn a url's path into a unique md5 hash\n \"\"\"\n if url is None:\n return None\n\n # check for missing scheme\n if not get_scheme(url):\n url = \"http://\" + url\n\n url = get_path(url, **kw)\n url = re_html.sub('', url)\n return hashlib.md5(url).hexdigest()\n\n\ndef get_query_string(url, **kw):\n \"\"\"\n Get the query string from a url.\n \"\"\"\n if url is None:\n return None\n\n # check for missing scheme\n if not get_scheme(url):\n url = \"http://\" + url\n\n return urlparse(url, **kw).query\n\n\ndef get_filetype(url, **kw):\n \"\"\"\n Input a URL and output the filetype of the file\n specified by the url. Returns None for no filetype.\n 'http://blahblah/images/car.jpg' -> 'jpg'\n 'http://yahoo.com' -> None\n \"\"\"\n if url is None:\n return None\n\n # check for missing scheme\n if not get_scheme(url):\n url = \"http://\" + url\n\n path = get_path(url, **kw)\n # Eliminate the trailing '/', we are extracting the file\n if path.endswith('/'):\n path = path[:-1]\n path_chunks = [x for x in path.split('/') if len(x) > 0]\n if not len(path_chunks):\n return None\n last_chunk = path_chunks[-1].split('.') # last chunk == file usually\n file_type = last_chunk[-1] if len(last_chunk) >= 2 else None\n return file_type or None\n\n\ndef is_article(url, pattern=None):\n \"\"\"\n Check if a url looks like it's an article.\n First, perform a few basic checks like making sure the format of the url\n is right, (scheme, domain, tld).\n Second, make sure that the url isn't some static resource, check the\n file type.\n Then, search of a YYYY/MM/DD pattern in the url. News sites\n love to use this pattern, this is a very safe bet.\n Separators can be [\\.-/_]. Years can be 2 or 4 digits, must\n have proper digits 1900-2099. Months and days can be\n ambiguous 2 digit numbers, one is even optional, some sites are\n liberal with their formatting also matches snippets of GET\n queries with keywords inside them. ex: asdf.php?topic_id=blahlbah\n We permit alphanumeric, _ and -.\n Our next check makes sure that a keyword is within one of the\n separators in a url (subdomain or early path separator).\n cnn.com/story/blah-blah-blah would pass due to \"story\".\n We filter out articles in this stage by aggressively checking to\n see if any resemblance of the source& domain's name or tld is\n present within the article title. If it is, that's bad. It must\n be a company link, like 'cnn is hiring new interns'.\n We also filter out articles with a subdomain or first degree path\n on a registered bad keyword.\n \"\"\"\n\n # optionally apply regex\n if pattern:\n pattern = compile_regex(pattern)\n if pattern.match(url):\n return True\n\n # 11 chars is shortest valid url length, eg: http://x.co\n if url is None or len(url) < 11:\n return False\n\n r1 = ('mailto:' in url) # TODO not sure if these rules are redundant\n r2 = ('http://' not in url) and ('https://' not in url)\n\n if r1 or r2:\n return False\n\n path = urlparse(url).path\n if not path:\n return None\n\n # input url is not in valid form (scheme, netloc, tld)\n if not path.startswith('/'):\n return False\n\n # the '/' which may exist at the end of the url provides us no information\n if path.endswith('/'):\n path = path[:-1]\n\n # '/story/cnn/blahblah/index.html' --> ['story', 'cnn', 'blahblah', 'index.html']\n path_chunks = [x for x in path.split('/') if len(x) > 0]\n\n # siphon out the file type. eg: .html, .htm, .md\n if len(path_chunks) > 0:\n file_type = get_filetype(url)\n\n # if the file type is a media type, reject instantly\n if file_type and file_type not in ALLOWED_TYPES:\n return False\n\n last_chunk = path_chunks[-1].split('.')\n # the file type is not of use to use anymore, remove from url\n if len(last_chunk) > 1:\n path_chunks[-1] = last_chunk[-2]\n\n # Index gives us no information\n if 'index' in path_chunks:\n path_chunks.remove('index')\n\n # extract the tld (top level domain)\n tld_dat = tldextract.extract(url)\n subd = tld_dat.subdomain\n tld = tld_dat.domain.lower()\n\n url_slug = path_chunks[-1] if path_chunks else u''\n\n if tld in BAD_DOMAINS:\n return False\n\n if len(path_chunks) == 0:\n dash_count, underscore_count = 0, 0\n else:\n dash_count = url_slug.count('-')\n underscore_count = url_slug.count('_')\n\n # If the url has a news slug title\n if url_slug and (dash_count > 4 or underscore_count > 4):\n\n if dash_count >= underscore_count:\n if tld not in [x.lower() for x in url_slug.split('-')]:\n return True\n\n if underscore_count > dash_count:\n if tld not in [x.lower() for x in url_slug.split('_')]:\n return True\n\n # There must be at least 2 subpaths\n if len(path_chunks) <= 1:\n return False\n\n # Check for subdomain & path red flags\n # Eg: http://cnn.com/careers.html or careers.cnn.com --> BAD\n for b in BAD_CHUNKS:\n if b in path_chunks or b == subd:\n return False\n\n match_date = re_url_date.search(url)\n\n # if we caught the verified date above, it's an article\n if match_date:\n return True\n\n for GOOD in GOOD_PATHS:\n if GOOD.lower() in [p.lower() for p in path_chunks]:\n return True\n\n return False\n\n\ndef is_video(url):\n \"\"\"\n A really stupid test for whether a url is a video.\n \"\"\"\n domain = get_domain(url)\n if not domain:\n return False\n for video_domain in VIDEO_DOMAINS:\n if video_domain in domain:\n return True\n return False\n\n\ndef is_internal(url, source_domain):\n \"\"\"\n determine interal vs. external urls.\n \"\"\"\n link_domain = get_domain(url)\n if source_domain in link_domain or link_domain in source_domain:\n return True\n return False\n\n\ndef is_image(url):\n \"\"\"\n determine if a url is an image.\n \"\"\"\n ext = get_filetype(url)\n if not ext:\n return False\n return ext in IMG_FILETYPES\n\n\n# SHORT DOMAINS #\n\ndef is_shortened(url, pattern=None):\n \"\"\"\n test url for short links.\n \"\"\"\n # pass in specific regexes\n if pattern:\n pattern = compile_regex(pattern)\n # only return if we match the custom domain, never fail\n # because of this\n if pattern.match(url):\n return True\n\n # test against known short domains\n domain = get_domain(url)\n if re_short_domains.search(domain):\n return True\n\n # test against bitly-ish short url pattern\n if re_short_url.search(url):\n return True\n\n return False\n\n\ndef is_abs(url):\n \"\"\"\n check if a url is absolute.\n \"\"\"\n return bool(get_domain(url))\n\n\ndef from_string(string, **kw):\n \"\"\"\n get urls from input string\n \"\"\"\n\n source = kw.get('source', None)\n exclude_images = kw.get('excl_img', True)\n\n if not string:\n return []\n\n raw_urls = re_url.findall(string)\n short_urls = [g[0].strip() for g in re_short_url_text.findall(string) if g]\n\n urls = []\n if source:\n for url in raw_urls:\n if not is_abs(url):\n url = urljoin(source, url)\n urls.append(url)\n else:\n urls = [u for u in raw_urls if is_valid(u)]\n\n # make sure short url regex doesn't create partial dupes.\n for u in short_urls:\n if any([r.startswith(u) or u == r for r in urls]):\n short_urls.remove(u)\n\n # combine\n urls += short_urls\n\n # remove images.\n if exclude_images:\n urls = [u for u in urls if not is_image(u)]\n\n # remove invalid urls\n urls = [u for u in urls if is_valid(u)]\n\n return uniq(urls)\n\n\ndef from_html(htmlstring, **kw):\n \"\"\"\n Extract urls from htmlstring, optionally reconciling\n relative urls + embeds + redirects.\n \"\"\"\n source = kw.get('source', None)\n exclude_images = kw.get('excl_img', True)\n\n if not htmlstring:\n return []\n final_urls = []\n if source:\n source_domain = get_domain(source)\n soup = make_soup(htmlstring)\n for tag in URL_TAGS:\n\n for el in soup.find_all(tag):\n\n for attr in URL_ATTRS:\n href = el.attrs.get(attr, None)\n\n if not href:\n continue\n url = reconcile_embed(href)\n\n if source:\n url = redirect_back(url, source_domain)\n if not is_abs(url):\n url = urljoin(source, url)\n\n if not is_valid(url):\n continue\n if exclude_images:\n if not is_image(url):\n final_urls.append(url)\n else:\n final_urls.append(url)\n return uniq(final_urls)\n\n\ndef from_any(html_or_string, **kw):\n \"\"\"\n Parse urls out of html or raw string.\n \"\"\"\n if not html_or_string:\n return []\n if html.is_html(html_or_string):\n return from_html(html_or_string, **kw)\n return from_string(html_or_string, **kw)\n\n\ndef remove_args(url, keep_params, frags=False):\n \"\"\"\n Remove all param arguments from a url.\n \"\"\"\n if not url:\n return None\n parsed = urlsplit(url)\n filtered_query = '&'.join(\n qry_item for qry_item in parsed.query.split('&')\n if qry_item.startswith(keep_params)\n )\n if frags:\n frag = parsed[4:]\n else:\n frag = ('',)\n\n return urlunsplit(parsed[:3] + (filtered_query,) + frag)\n\n\ndef redirect_back(url, source=None):\n \"\"\"\n Some sites like Pinterest have api's that cause news\n args to direct to their site with the real news url as a\n GET param. This method catches that and returns our param.\n \"\"\"\n domain = get_domain(url)\n query = get_query_string(url)\n\n if source:\n source_domain = get_domain(source)\n # If our url is even from a remotely similar domain or\n # sub domain, we don't need to redirect.\n if source_domain in domain or domain in source_domain:\n return url\n\n query_item = parse_qs(query)\n\n for k in REDIRECT_QUERY_PARAMS:\n if query_item.get(k):\n return query_item[k][0]\n return url\n\n\ndef get_query_param(url, param):\n \"\"\"\n Get the value of a query parameter\n from a url.\n \"\"\"\n p = urlparse(url)\n query_items = parse_qs(p.query)\n v = query_items.get(param)\n if not v or not len(v):\n return None\n return v[0]\n\n\ndef add_query_params(url, **kw):\n \"\"\"\n Add/update query strings to a url.\n \"\"\"\n p = urlparse(url)\n endpoint = \"{}://{}{}\".format(p.scheme, p.netloc, p.path)\n\n # allow for multiple query strings\n qs = [(k, v) for k, vv in parse_qs(p.query).items() for v in vv]\n\n # add in new query strings\n for k, v in kw.items():\n qs.append((k, str(v)))\n\n # format string\n qs = \"&\".join([\"{}={}\".format(q[0], q[1]) for q in qs])\n\n return \"{}?{}\".format(endpoint, qs)\n\n\ndef reconcile_embed(url):\n \"\"\"\n make an embedded movie url like this:\n //www.youtube.com/embed/vYNnPx8fZBs\n into a full url\n \"\"\"\n if not url:\n return None\n if url.startswith('//'):\n url = \"http:{}\".format(url)\n return url\n\n\ndef is_valid(url):\n \"\"\"\n method just for checking weird results from `_get_location` in `_unshorten`\n \"\"\"\n return MIN_LEN < len(url) < MAX_LEN and 'localhost' not in url and 'mailto:' not in url\n\n\ndef validate(url):\n \"\"\"\n Check if a url is valid (for form inputs).\n \"\"\"\n if not url:\n return False\n if re_url.search(url) is None:\n return False\n return is_valid(url)\n\n\ndef categorize_links(links, source_domain):\n \"\"\"\n Take in a list of links and categorize them into\n internal / external / articles / videos\n \"\"\"\n data = {\n 'external': [],\n 'internal': [],\n 'articles': {\n 'external': [],\n 'internal': [],\n },\n 'videos': [],\n 'shortened': []\n }\n\n for l in links:\n\n # check it it's internal / external\n internal = is_internal(l, source_domain)\n\n # is it shortened\n if is_shortened(l):\n data['shortened'].append(l)\n\n # is it an article\n elif is_article(l):\n if internal:\n data['articles']['internal'].append(l)\n else:\n data['articles']['external'].append(l)\n\n # is it a video\n elif is_video(l):\n data['videos'].append(l)\n\n # fallback on internal / external.\n elif internal:\n data['internal'].append(l)\n else:\n data['external'].append(l)\n\n return data\n\n\n# def _long_url(url):\n# \"\"\"\n# hit long url's api to unshorten a url\n# \"\"\"\n\n# r = requests.get(\n# 'http://api.longurl.org/v2/expand',\n# params={\n# 'url': url,\n# 'format': 'json'\n# }\n# )\n\n# if r.status_code == 200:\n# return r.json().get('long-url', url)\n\n# # DONT FAIL\n# return url\n\n\ndef _bypass_bitly_warning(url):\n \"\"\"\n Sometime bitly blocks unshorten attempts, this bypasses that.\n \"\"\"\n html_string = network.get(url)\n soup = make_soup(html_string)\n a = soup.find('a', {'id': 'clickthrough'})\n if a:\n return a.attrs.get('href')\n return url\n\n\n@network.retry(attempts=1)\ndef _unshorten(url, pattern=None):\n \"\"\"\n dual-method approach to unshortening a url\n \"\"\"\n orig_url = copy.copy(url)\n\n # method 1, get location\n url = network.get_location(url)\n\n if not is_valid(url):\n return None\n\n # check if there's a bitly warning.\n if re_bitly_warning.search(url):\n url = _bypass_bitly_warning(url)\n if not is_shortened(url, pattern=pattern):\n return url\n\n return url\n", "repo_name": "newslynx/newslynx-core", "sub_path": "newslynx/lib/url.py", "file_name": "url.py", "file_ext": "py", "file_size_in_byte": 20580, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 13, "dataset": "github-code", "pt": "37", "api": [{"api_name": "urlparse.urljoin", "line_number": 100, "usage_type": "call"}, {"api_name": "newslynx.lib.network.get", "line_number": 113, "usage_type": "call"}, {"api_name": "newslynx.lib.network", "line_number": 113, "usage_type": "name"}, {"api_name": "newslynx.lib.common.make_soup", "line_number": 115, "usage_type": "call"}, {"api_name": "newslynx.lib.meta.canonical_url", "line_number": 116, "usage_type": "call"}, {"api_name": "newslynx.lib.meta", "line_number": 116, "usage_type": "name"}, {"api_name": "urlparse.urljoin", "line_number": 140, "usage_type": "call"}, {"api_name": "copy.copy", "line_number": 157, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 170, "usage_type": "call"}, {"api_name": "newslynx.core.bitly_api.shorten", "line_number": 182, "usage_type": "call"}, {"api_name": "newslynx.core.bitly_api", "line_number": 182, "usage_type": "name"}, {"api_name": "newslynx.lib.network.retry", "line_number": 175, "usage_type": "call"}, {"api_name": "newslynx.lib.network", "line_number": 175, "usage_type": "name"}, {"api_name": "newslynx.core.settings.NETWORK_MAX_RETRIES", "line_number": 175, "usage_type": "attribute"}, {"api_name": "newslynx.core.settings", "line_number": 175, "usage_type": "name"}, {"api_name": "urlparse.urlparse", "line_number": 201, "usage_type": "call"}, {"api_name": "tldextract.extract", "line_number": 216, "usage_type": "call"}, {"api_name": "urlparse.urlparse", "line_number": 227, "usage_type": "call"}, {"api_name": "urlparse.urlparse", "line_number": 242, "usage_type": "call"}, {"api_name": "urlparse.urlparse", "line_number": 313, "usage_type": "call"}, {"api_name": "urlparse.urlparse", "line_number": 384, "usage_type": "call"}, {"api_name": "tldextract.extract", "line_number": 417, "usage_type": "call"}, {"api_name": "urlparse.urljoin", "line_number": 550, "usage_type": "call"}, {"api_name": "newslynx.util.uniq", "line_number": 570, "usage_type": "call"}, {"api_name": "newslynx.lib.common.make_soup", "line_number": 586, "usage_type": "call"}, {"api_name": "urlparse.urljoin", "line_number": 601, "usage_type": "call"}, {"api_name": "newslynx.util.uniq", "line_number": 610, "usage_type": "call"}, {"api_name": "newslynx.lib.html.is_html", "line_number": 619, "usage_type": "call"}, {"api_name": "newslynx.lib.html", "line_number": 619, "usage_type": "name"}, {"api_name": "urlparse.urlsplit", "line_number": 630, "usage_type": "call"}, {"api_name": "urlparse.urlunsplit", "line_number": 640, "usage_type": "call"}, {"api_name": "urlparse.parse_qs", "line_number": 659, "usage_type": "call"}, {"api_name": "urlparse.urlparse", "line_number": 672, "usage_type": "call"}, {"api_name": "urlparse.parse_qs", "line_number": 673, "usage_type": "call"}, {"api_name": "urlparse.urlparse", "line_number": 684, "usage_type": "call"}, {"api_name": "urlparse.parse_qs", "line_number": 688, "usage_type": "call"}, {"api_name": "newslynx.lib.network.get", "line_number": 800, "usage_type": "call"}, {"api_name": "newslynx.lib.network", "line_number": 800, "usage_type": "name"}, {"api_name": "newslynx.lib.common.make_soup", "line_number": 801, "usage_type": "call"}, {"api_name": "copy.copy", "line_number": 813, "usage_type": "call"}, {"api_name": "newslynx.lib.network.get_location", "line_number": 816, "usage_type": "call"}, {"api_name": "newslynx.lib.network", "line_number": 816, "usage_type": "name"}, {"api_name": "newslynx.lib.network.retry", "line_number": 808, "usage_type": "call"}, {"api_name": "newslynx.lib.network", "line_number": 808, "usage_type": "name"}]} +{"seq_id": "34497228097", "text": "import os\nfrom keras.models import Sequential\nfrom keras.layers import Dense\nfrom keras.layers import Flatten\nfrom keras.layers import Convolution2D\nfrom keras.layers import MaxPooling2D\nfrom keras.preprocessing.image import ImageDataGenerator\nfrom keras.models import load_model\nfrom keras.preprocessing.image import image\nimport numpy as np\n\nBASE = os.getcwd()\nTOTAL_DATASET = BASE + '/Datasets/TotalDatasets/'\nTRAINING_SET = BASE + '/Datasets/TraningSet/'\nTEST_SET = BASE + '/Datasets/TestSet/'\nTEMP = [TRAINING_SET, TEST_SET]\n# define train test split\nTOTAL_TRAINING_DATA = 0.8\n# define dir for saving model\nMODEL_DIR = BASE + '/Model/'\nNO_OF_EPOCH = 25\nTESTING_MODEL = TRAINING_SET + 'rock/2affjOmZChc9AXpR.png'\n\n\ndef main():\n make_framework()\n train_test_split()\n if check_for_model():\n classifier = load_model(MODEL_DIR + '/myModel')\n load(classifier)\n else:\n print(\"Do You Want To Run Neural Network \")\n decision = input(\"Press 'Y' If Yes\")\n if decision == 'Y':\n run()\n else:\n print(\"Exiting The Program\")\n\n\ndef get_avg_files(dir_name):\n file_counter = 0\n total_count = 0\n for dirs in os.listdir(dir_name):\n file_counter += len(os.listdir(str(dir_name + dirs)))\n total_count += 1\n return int(file_counter / total_count)\n\n\n# get the main dir where the images of 3 types are stored withhin TOTAL_DATASET for further classification\ndef get_dirs():\n types = os.listdir(TOTAL_DATASET)\n return types\n\n\n# Creating directories\ndef make_dirs(folder_name):\n try:\n os.makedirs(folder_name)\n except FileExistsError:\n print(\"The Folder Already Exists\", folder_name)\n\n\n# make overall folder structure for further classifications\ndef make_framework():\n make_dirs(TRAINING_SET)\n make_dirs(TEST_SET)\n make_dirs(MODEL_DIR)\n for dirname in TEMP:\n print(\"Creating Folder In\", dirname)\n for subdir in get_dirs():\n make_dirs(str(dirname + '/' + str(subdir)))\n\n\n# split images placed in totaldatset to train and test split\ndef train_test_split():\n print(\"Processing Images\")\n training = test = 0\n for dirs in get_dirs():\n total_data = len(os.listdir(str(TOTAL_DATASET + '/' + str(dirs))))\n print(\"Total Files in \", dirs, 'is', total_data)\n counter = 0\n CURRENT_DIR = ''\n for images in os.listdir(str(TOTAL_DATASET + '/' + str(dirs))):\n if counter <= (total_data * TOTAL_TRAINING_DATA):\n CURRENT_DIR = TRAINING_SET\n training += 1\n else:\n CURRENT_DIR = TEST_SET\n test += 1\n os.rename(TOTAL_DATASET + str(dirs) + '/' + str(images), CURRENT_DIR + str(dirs) + '/' + str(images))\n counter += 1\n print(\"Moving file\", images, 'to', CURRENT_DIR + '/' + str(dirs))\n\n\ndef run():\n # initializing the model\n classifier = Sequential()\n # adding convolution layer\n classifier.add(Convolution2D(64, 3, 3, input_shape=(150, 150, 3), activation='relu'))\n # adding max pooling layer\n classifier.add(MaxPooling2D(pool_size=(2, 2)))\n # adding another convolution layer\n classifier.add(Convolution2D(64, 3, 3, input_shape=(150, 150, 3), activation='relu'))\n # adding next pooling layer for convolution layer\n classifier.add(MaxPooling2D(pool_size=(2, 2)))\n # adding next convolution layer\n classifier.add(Convolution2D(64, 3, 3, input_shape=(150, 150, 3), activation='relu'))\n # adding one more pooling\n classifier.add(MaxPooling2D(pool_size=(2, 2)))\n # adding a flatten layer\n classifier.add(Flatten())\n # adding the last dense layer\n classifier.add(Dense(512, activation='relu'))\n # adding final layer for output\n classifier.add(Dense(3, activation='softmax'))\n # compiling the neural network\n classifier.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy'])\n # converting the images to preferred input size using keras\n train_datagen = ImageDataGenerator(\n rescale=1. / 255,\n shear_range=0.2,\n zoom_range=0.2,\n horizontal_flip=True)\n\n test_datagen = ImageDataGenerator(rescale=1. / 255)\n\n training_set = train_datagen.flow_from_directory(\n TRAINING_SET,\n target_size=(150, 150),\n batch_size=25,\n class_mode='categorical')\n\n test_set = test_datagen.flow_from_directory(\n TEST_SET,\n target_size=(150, 150),\n batch_size=25,\n class_mode='categorical')\n STEPS_IN_EPOCH_TRAINING = get_avg_files(TRAINING_SET)\n STEPS_IN_EPOCH_TEST = get_avg_files(TEST_SET)\n classifier.fit_generator(\n training_set,\n steps_per_epoch=STEPS_IN_EPOCH_TRAINING,\n epochs=NO_OF_EPOCH,\n validation_data=test_set,\n validation_steps=STEPS_IN_EPOCH_TEST)\n classifier.save(MODEL_DIR + 'myModel', overwrite=True)\n print(\"Model Trained And Saved At\", MODEL_DIR)\n\n\ndef load(classifier):\n test_image = image.load_img(TESTING_MODEL, target_size=(150, 150))\n test_image = image.img_to_array(test_image)\n test_image = np.expand_dims(test_image, axis=0)\n prediction = classifier.predict(test_image)\n # print(\"The Prediction is \", prediction)\n if prediction[0][0] == 1:\n print(\"Paper\")\n elif prediction[0][1] == 1:\n print(\"Rock\")\n elif prediction[0][2] == 1:\n print(\"Scissors\")\n\n\ndef check_for_model():\n return os.path.exists(MODEL_DIR + 'myModel')\n\n\nif __name__ == '__main__':\n main()\n", "repo_name": "dcostersabin/RockPaperSissor", "sub_path": "cnn.py", "file_name": "cnn.py", "file_ext": "py", "file_size_in_byte": 5530, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "os.getcwd", "line_number": 12, "usage_type": "call"}, {"api_name": "keras.models.load_model", "line_number": 29, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 43, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 44, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 51, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 58, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 79, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 83, "usage_type": "call"}, {"api_name": "os.rename", "line_number": 90, "usage_type": "call"}, {"api_name": "keras.models.Sequential", "line_number": 97, "usage_type": "call"}, {"api_name": "keras.layers.Convolution2D", "line_number": 99, "usage_type": "call"}, {"api_name": "keras.layers.MaxPooling2D", "line_number": 101, "usage_type": "call"}, {"api_name": "keras.layers.Convolution2D", "line_number": 103, "usage_type": "call"}, {"api_name": "keras.layers.MaxPooling2D", "line_number": 105, "usage_type": "call"}, {"api_name": "keras.layers.Convolution2D", "line_number": 107, "usage_type": "call"}, {"api_name": "keras.layers.MaxPooling2D", "line_number": 109, "usage_type": "call"}, {"api_name": "keras.layers.Flatten", "line_number": 111, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 113, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 115, "usage_type": "call"}, {"api_name": "keras.preprocessing.image.ImageDataGenerator", "line_number": 119, "usage_type": "call"}, {"api_name": "keras.preprocessing.image.ImageDataGenerator", "line_number": 125, "usage_type": "call"}, {"api_name": "keras.preprocessing.image.image.load_img", "line_number": 151, "usage_type": "call"}, {"api_name": "keras.preprocessing.image.image", "line_number": 151, "usage_type": "name"}, {"api_name": "keras.preprocessing.image.image.img_to_array", "line_number": 152, "usage_type": "call"}, {"api_name": "keras.preprocessing.image.image", "line_number": 152, "usage_type": "name"}, {"api_name": "numpy.expand_dims", "line_number": 153, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 165, "usage_type": "call"}, {"api_name": "os.path", "line_number": 165, "usage_type": "attribute"}]} +{"seq_id": "10308516668", "text": "import math\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom .attention import LayerNorm, Block\nfrom .conv import DWConv1d\n\n\ndef sinusoids(length, channels, max_timescale=10000):\n \"\"\"Returns sinusoids for positional embedding\"\"\"\n assert channels % 2 == 0\n scales = torch.arange(channels // 2) / (channels // 2 - 1)\n inv_timescales = torch.exp(-math.log(max_timescale) * scales)\n scaled_time = torch.arange(length)[:, None] * inv_timescales[None, :]\n return torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], dim=1)\n\n\nclass StridingAudioEncoder(nn.Module):\n def __init__(self, config):\n super().__init__()\n\n self.config = config\n\n conv = [nn.Conv1d(config.d_input, config.d_conv, kernel_size=3, stride=config.conv_strides[0], padding=1)]\n for stride in config.conv_strides[1:-1]:\n conv.append(DWConv1d(config.d_conv, config.d_conv, kernel_size=3, stride=stride, padding=1))\n conv.append(DWConv1d(config.d_conv, config.n_embd, kernel_size=3, stride=config.conv_strides[-1], padding=1))\n self.conv = nn.ModuleList(conv)\n\n assert config.rotary_emb_dim\n self.transformer = nn.ModuleDict(dict(\n drop = nn.Dropout(config.dropout),\n h = nn.ModuleList([Block(config) for _ in range(config.n_layer)]),\n ln_f = LayerNorm(config.n_embd, bias=config.bias),\n ))\n\n def subsampled_lengths(self, input_lengths):\n # https://github.com/vdumoulin/conv_arithmetic\n o = input_lengths\n for conv in self.conv:\n p, k, s = conv.padding[0], conv.kernel_size[0], conv.stride[0]\n o = o + 2 * p - k\n o = torch.floor(o / s + 1)\n return o.int()\n\n def forward(self, x, input_lengths, measure_entropy=False):\n x = x.mT\n for conv in self.conv:\n x = F.gelu(conv(x))\n x = x.mT\n\n _, t, c = x.size()\n pos = torch.arange(0, t, dtype=torch.long, device=x.device).unsqueeze(0) # shape (1, t)\n x = self.transformer.drop(x) # shape (b, t, c)\n\n for i, block in enumerate(self.transformer.h):\n x, _att_entropy, _present = block(x, past=None, measure_entropy=measure_entropy)\n x = self.transformer.ln_f(x)\n\n return x, self.subsampled_lengths(input_lengths), {}\n\n\nclass AudioEncoder(nn.Module):\n def __init__(self, config):\n super().__init__()\n\n self.config = config\n\n # whisper style convolutions\n self.conv_pre = nn.Conv1d(config.d_input, config.n_embd, kernel_size=3, stride=1, padding=1)\n self.conv_subsample = nn.Conv1d(config.n_embd, config.n_embd, kernel_size=3, stride=2, padding=1)\n\n if config.rotary_emb_dim:\n self.transformer = nn.ModuleDict(dict(\n drop = nn.Dropout(config.dropout),\n h = nn.ModuleList([Block(config) for _ in range(config.n_layer)]),\n ln_f = LayerNorm(config.n_embd, bias=config.bias),\n ))\n\n else:\n self.transformer = nn.ModuleDict(dict(\n wpe = nn.Embedding(config.block_size, config.n_embd),\n\n drop = nn.Dropout(config.dropout),\n h = nn.ModuleList([Block(config) for _ in range(config.n_layer)]),\n ln_f = LayerNorm(config.n_embd, bias=config.bias),\n ))\n\n self.transformer.wpe.weight.data = sinusoids(config.block_size, config.n_embd)\n self.transformer.wpe.requires_grad_(False)\n\n def subsampled_lengths(self, input_lengths):\n # https://github.com/vdumoulin/conv_arithmetic\n p, k, s = self.conv_subsample.padding[0], self.conv_subsample.kernel_size[0], self.conv_subsample.stride[0]\n o = input_lengths + 2 * p - k\n o = torch.floor(o / s + 1)\n return o.int()\n\n def forward(self, x, input_lengths, measure_entropy=False):\n x = x.mT\n x = F.gelu(self.conv_pre(x))\n x = F.gelu(self.conv_subsample(x))\n x = x.mT\n\n _, t, c = x.size()\n pos = torch.arange(0, t, dtype=torch.long, device=x.device).unsqueeze(0) # shape (1, t)\n if self.config.rotary_emb_dim:\n x = self.transformer.drop(x) # shape (b, t, c)\n else:\n pe = self.transformer.wpe(pos)\n x = self.transformer.drop(x + pe) # shape (b, t, c)\n\n for i, block in enumerate(self.transformer.h):\n x, _att_entropy, _present = block(x, past=None, measure_entropy=measure_entropy)\n x = self.transformer.ln_f(x)\n\n return x, self.subsampled_lengths(input_lengths), {}\n\n\nif __name__ == '__main__':\n from ha.init import AudioEncoderConfig\n config = AudioEncoderConfig()\n encoder = AudioEncoder(config)\n print(encoder(torch.randn(1, config.block_size, config.d_input)))\n", "repo_name": "proger/haloop", "sub_path": "ha/attention_audio.py", "file_name": "attention_audio.py", "file_ext": "py", "file_size_in_byte": 4794, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 10, "dataset": "github-code", "pt": "37", "api": [{"api_name": "torch.arange", "line_number": 13, "usage_type": "call"}, {"api_name": "torch.exp", "line_number": 14, "usage_type": "call"}, {"api_name": "math.log", "line_number": 14, "usage_type": "call"}, {"api_name": "torch.arange", "line_number": 15, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 16, "usage_type": "call"}, {"api_name": "torch.sin", "line_number": 16, "usage_type": "call"}, {"api_name": "torch.cos", "line_number": 16, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 19, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 19, "usage_type": "name"}, {"api_name": "torch.nn.Conv1d", "line_number": 25, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 25, "usage_type": "name"}, {"api_name": "conv.append", "line_number": 27, "usage_type": "call"}, {"api_name": "conv.DWConv1d", "line_number": 27, "usage_type": "call"}, {"api_name": "conv.append", "line_number": 28, "usage_type": "call"}, {"api_name": "conv.DWConv1d", "line_number": 28, "usage_type": "call"}, {"api_name": "torch.nn.ModuleList", "line_number": 29, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 29, "usage_type": "name"}, {"api_name": "torch.nn.ModuleDict", "line_number": 32, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 32, "usage_type": "name"}, {"api_name": "torch.nn.Dropout", "line_number": 33, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 33, "usage_type": "name"}, {"api_name": "torch.nn.ModuleList", "line_number": 34, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 34, "usage_type": "name"}, {"api_name": "attention.Block", "line_number": 34, "usage_type": "call"}, {"api_name": "attention.LayerNorm", "line_number": 35, "usage_type": "call"}, {"api_name": "conv.padding", "line_number": 42, "usage_type": "attribute"}, {"api_name": "conv.kernel_size", "line_number": 42, "usage_type": "attribute"}, {"api_name": "conv.stride", "line_number": 42, "usage_type": "attribute"}, {"api_name": "torch.floor", "line_number": 44, "usage_type": "call"}, {"api_name": "torch.nn.functional.gelu", "line_number": 50, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 50, "usage_type": "name"}, {"api_name": "torch.arange", "line_number": 54, "usage_type": "call"}, {"api_name": "torch.long", "line_number": 54, "usage_type": "attribute"}, {"api_name": "torch.nn.Module", "line_number": 64, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 64, "usage_type": "name"}, {"api_name": "torch.nn.Conv1d", "line_number": 71, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 71, "usage_type": "name"}, {"api_name": "torch.nn.Conv1d", "line_number": 72, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 72, "usage_type": "name"}, {"api_name": "torch.nn.ModuleDict", "line_number": 75, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 75, "usage_type": "name"}, {"api_name": "torch.nn.Dropout", "line_number": 76, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 76, "usage_type": "name"}, {"api_name": "torch.nn.ModuleList", "line_number": 77, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 77, "usage_type": "name"}, {"api_name": "attention.Block", "line_number": 77, "usage_type": "call"}, {"api_name": "attention.LayerNorm", "line_number": 78, "usage_type": "call"}, {"api_name": "torch.nn.ModuleDict", "line_number": 82, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 82, "usage_type": "name"}, {"api_name": "torch.nn.Embedding", "line_number": 83, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 83, "usage_type": "name"}, {"api_name": "torch.nn.Dropout", "line_number": 85, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 85, "usage_type": "name"}, {"api_name": "torch.nn.ModuleList", "line_number": 86, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 86, "usage_type": "name"}, {"api_name": "attention.Block", "line_number": 86, "usage_type": "call"}, {"api_name": "attention.LayerNorm", "line_number": 87, "usage_type": "call"}, {"api_name": "torch.floor", "line_number": 97, "usage_type": "call"}, {"api_name": "torch.nn.functional.gelu", "line_number": 102, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 102, "usage_type": "name"}, {"api_name": "torch.nn.functional.gelu", "line_number": 103, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 103, "usage_type": "name"}, {"api_name": "torch.arange", "line_number": 107, "usage_type": "call"}, {"api_name": "torch.long", "line_number": 107, "usage_type": "attribute"}, {"api_name": "ha.init.AudioEncoderConfig", "line_number": 123, "usage_type": "call"}, {"api_name": "torch.randn", "line_number": 125, "usage_type": "call"}]} +{"seq_id": "43370784742", "text": "import re\nimport datetime\n\n\nclass SpliterApp:\n data = []\n ip = None\n method = None\n response = None\n time = None\n date = None\n route_request = None\n response_length = 0\n other_information = None\n UserAgent = None\n\n def setData(self, data):\n self.data = data\n\n def convertTimeStamp(self, date_time):\n date_regex = r'^(\\d{2})/(\\w+)/(\\d{4}):(\\d{2}):(\\d{2}):(\\d{2}) (\\+\\d{4})$'\n date_matches = re.match(date_regex, date_time)\n day = date_matches.group(1)\n month = date_matches.group(2)\n year = date_matches.group(3)\n hour = date_matches.group(4)\n minute = date_matches.group(5)\n second = date_matches.group(6)\n date = f\"{year}-{month}-{day}\"\n time = f\"{hour}:{minute}:{second}\"\n date_string = f\"{date} {time}\"\n date_obj = datetime.datetime.strptime(\n date_string, '%Y-%b-%d %H:%M:%S')\n return [date_obj, time, date]\n\n def getInformation(self):\n regex = r'^([\\d\\.]+) (\\S+) (\\S+) \\[(.*?)\\] \"(.*?)\" (\\d+) (\\d+) \"(.*?)\" \"(.*?)\"$'\n\n matches = re.match(regex, self.data)\n if matches:\n ip_address = matches.group(1)\n user_name = matches.group(2)\n auth_user = matches.group(3)\n timestamp = matches.group(4)\n timestamp = self.convertTimeStamp(timestamp)\n request = matches.group(5)\n status_code = matches.group(6)\n response_size = matches.group(7)\n referrer = matches.group(8)\n user_agent = matches.group(9)\n res = {\n 'ip_address': ip_address,\n 'user_name': user_name,\n 'auth_user': auth_user,\n 'timestamp': timestamp[0],\n 'date': timestamp[2],\n 'time': timestamp[1],\n 'request': request,\n 'status_code': status_code,\n 'response_size': response_size,\n 'referrer': referrer,\n 'user_agent': user_agent,\n }\n return res\n", "repo_name": "localho3t/BT-Analyser", "sub_path": "app/spliter/spliter_app.py", "file_name": "spliter_app.py", "file_ext": "py", "file_size_in_byte": 2080, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "37", "api": [{"api_name": "re.match", "line_number": 22, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 32, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 32, "usage_type": "attribute"}, {"api_name": "re.match", "line_number": 39, "usage_type": "call"}]} +{"seq_id": "21901214325", "text": "import glob\nimport textract\nimport numpy as np\n\n\nclass ReadInterviewTranscripts:\n def __init__(self):\n pass\n\n def read_word_file_text(self, filename):\n print(filename)\n text = textract.process(filename)\n return text\n\n def extract_content_from_utterance(self, utterance):\n if '\\t' in utterance:\n utterance_split = utterance.split('\\t')\n utterance_split = [x for x in utterance_split if x.strip()]\n assert len(utterance_split) == 2, utterance_split\n return utterance_split[1]\n else:\n return None\n\n def __read_interviewee_sentences_from_word_file__(self, word_file):\n\n assert word_file.endswith('.docx') or word_file.endswith('.doc')\n # print('word_file', word_file)\n\n text = self.read_word_file_text(filename=word_file)\n text = text.strip()\n # print(text)\n if not isinstance(text, str):\n text = str(text, 'utf-8')\n assert isinstance(text, str)\n\n dialogue_utterances = text.split('\\n')\n # print('len(dialogue_utterances)', len(dialogue_utterances))\n\n sentences = []\n is_patient_else_therapist = None\n\n for curr_utterance in dialogue_utterances:\n curr_utterance = curr_utterance.strip()\n\n if not curr_utterance:\n continue\n\n # print(curr_utterance)\n\n content_from_utterance = None\n if curr_utterance.startswith('Primary Interviewer ') \\\n or curr_utterance.startswith('Primary Interviewer:') \\\n or curr_utterance.startswith('S1 '):\n is_patient_else_therapist = False\n elif curr_utterance.startswith('Subject ') \\\n or curr_utterance.startswith('Subject:') or curr_utterance.startswith('S2 '):\n is_patient_else_therapist = True\n content_from_utterance = self.extract_content_from_utterance(utterance=curr_utterance)\n # print('-' * 10)\n # print(curr_utterance)\n # print(content_from_utterance)\n else:\n # from previous iteration\n if (is_patient_else_therapist is not None) and is_patient_else_therapist:\n content_from_utterance = curr_utterance\n is_patient_else_therapist = None\n\n # print(is_patient_else_therapist)\n\n if content_from_utterance is not None:\n sentences.append(content_from_utterance)\n\n sentences = np.array(sentences)\n\n return sentences\n\n def read_sentence_sets_from_files_in_dir(\n self,\n # where your files are located\n dir_path,\n ):\n files_names_in_dir = glob.glob(dir_path+'/*.docx') + glob.glob(dir_path+'/*.doc')\n files_names_in_dir = [curr_file for curr_file in files_names_in_dir if '~$' not in curr_file]\n\n sentence_sets_from_files = []\n for curr_file in files_names_in_dir:\n sentences_from_curr_file = self.__read_interviewee_sentences_from_word_file__(curr_file)\n sentences_from_curr_file = ' '.join(sentences_from_curr_file)\n sentence_sets_from_files.append(sentences_from_curr_file)\n\n return sentence_sets_from_files\n", "repo_name": "sgarg87/language_in_psychiatry", "sub_path": "read_interview_transcripts.py", "file_name": "read_interview_transcripts.py", "file_ext": "py", "file_size_in_byte": 3299, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "37", "api": [{"api_name": "textract.process", "line_number": 12, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 73, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 82, "usage_type": "call"}]} +{"seq_id": "5128421256", "text": "from PIL import Image\r\nimport numpy as np\r\nimport cv2\r\nimport torch\r\nfrom torch.utils.data import Dataset\r\n\r\nimport matplotlib.pyplot as plt\r\n\r\nfrom utils import cvtColor, preprocess_input\r\n\r\n\r\nclass data_loader(Dataset):\r\n\r\n def __init__(self, annotation_lines, input_shape):\r\n super(data_loader, self).__init__()\r\n\r\n self.annotation_lines = annotation_lines\r\n self.length = len(annotation_lines)\r\n\r\n self.input_shape = input_shape\r\n\r\n def __len__(self):\r\n return self.length\r\n\r\n def __getitem__(self, index):\r\n\r\n index = index % self.length\r\n\r\n line = self.annotation_lines[index].split()\r\n\r\n image_ol = cvtColor(Image.open(line[0]).resize(self.input_shape, Image.BILINEAR))\r\n image_sd = cvtColor(Image.open(line[1]).resize(self.input_shape, Image.BILINEAR))\r\n\r\n image_ol = self.get_random_data(image_ol)\r\n image_sd = self.get_random_data(image_sd)\r\n\r\n image_ol = np.transpose(preprocess_input(image_ol), (2, 0, 1))\r\n image_sd = np.transpose(preprocess_input(image_sd), (2, 0, 1))\r\n\r\n gt = np.array(list(map(int, line[2].split(','))))\r\n\r\n return image_ol, image_sd, gt\r\n\r\n\r\n def get_random_data(self, image, hue=.1, sat=0.7, val=0.4):\r\n\r\n image_data = np.array(image, np.uint8)\r\n\r\n r = np.random.uniform(-1, 1, 3) * [hue, sat, val] + 1\r\n\r\n hue, sat, val = cv2.split(cv2.cvtColor(image_data, cv2.COLOR_RGB2HSV))\r\n dtype = image_data.dtype\r\n\r\n x = np.arange(0, 256, dtype=r.dtype)\r\n lut_hue = ((x * r[0]) % 180).astype(dtype)\r\n lut_sat = np.clip(x * r[1], 0, 255).astype(dtype)\r\n lut_val = np.clip(x * r[2], 0, 255).astype(dtype)\r\n\r\n image_data = cv2.merge((cv2.LUT(hue, lut_hue), cv2.LUT(sat, lut_sat), cv2.LUT(val, lut_val)))\r\n image_data = cv2.cvtColor(image_data, cv2.COLOR_HSV2RGB)\r\n\r\n return image_data\r\n\r\ndef DvX_dataset_collate(batch):\r\n\r\n img_ols, img_sds, gt_s = [], [], []\r\n\r\n for img_ol, img_sd, gt in batch:\r\n img_ols.append(img_ol)\r\n img_sds.append(img_sd)\r\n gt_s.append(gt)\r\n\r\n img_ols = torch.from_numpy(np.array(img_ols)).type(torch.FloatTensor)\r\n img_sds = torch.from_numpy(np.array(img_sds)).type(torch.FloatTensor)\r\n gt_s = torch.from_numpy(np.array(gt_s)).type(torch.FloatTensor)\r\n\r\n return img_ols, img_sds, gt_s", "repo_name": "Mbwslib/DvXray", "sub_path": "dataset.py", "file_name": "dataset.py", "file_ext": "py", "file_size_in_byte": 2367, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "torch.utils.data.Dataset", "line_number": 12, "usage_type": "name"}, {"api_name": "utils.cvtColor", "line_number": 31, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 31, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 31, "usage_type": "name"}, {"api_name": "PIL.Image.BILINEAR", "line_number": 31, "usage_type": "attribute"}, {"api_name": "utils.cvtColor", "line_number": 32, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 32, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 32, "usage_type": "name"}, {"api_name": "PIL.Image.BILINEAR", "line_number": 32, "usage_type": "attribute"}, {"api_name": "numpy.transpose", "line_number": 37, "usage_type": "call"}, {"api_name": "utils.preprocess_input", "line_number": 37, "usage_type": "call"}, {"api_name": "numpy.transpose", "line_number": 38, "usage_type": "call"}, {"api_name": "utils.preprocess_input", "line_number": 38, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 40, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 47, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 47, "usage_type": "attribute"}, {"api_name": "numpy.random.uniform", "line_number": 49, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 49, "usage_type": "attribute"}, {"api_name": "cv2.split", "line_number": 51, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 51, "usage_type": "call"}, {"api_name": "cv2.COLOR_RGB2HSV", "line_number": 51, "usage_type": "attribute"}, {"api_name": "numpy.arange", "line_number": 54, "usage_type": "call"}, {"api_name": "numpy.clip", "line_number": 56, "usage_type": "call"}, {"api_name": "numpy.clip", "line_number": 57, "usage_type": "call"}, {"api_name": "cv2.merge", "line_number": 59, "usage_type": "call"}, {"api_name": "cv2.LUT", "line_number": 59, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 60, "usage_type": "call"}, {"api_name": "cv2.COLOR_HSV2RGB", "line_number": 60, "usage_type": "attribute"}, {"api_name": "torch.from_numpy", "line_number": 73, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 73, "usage_type": "call"}, {"api_name": "torch.FloatTensor", "line_number": 73, "usage_type": "attribute"}, {"api_name": "torch.from_numpy", "line_number": 74, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 74, "usage_type": "call"}, {"api_name": "torch.FloatTensor", "line_number": 74, "usage_type": "attribute"}, {"api_name": "torch.from_numpy", "line_number": 75, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 75, "usage_type": "call"}, {"api_name": "torch.FloatTensor", "line_number": 75, "usage_type": "attribute"}]} +{"seq_id": "9803638570", "text": "import pytest\n\nimport translate\nimport translate.backend\nimport translate.exceptions\n\nmgr = None\nDEACTIVATE_WAS_CALLED = False\n\n\ndef setup_module():\n global mgr\n mgr = translate.backend.BackendManager({\n 'dummy': {'active': True},\n 'test_backend': {'foo': 'bar',\n 'preference': 1000}\n })\n\n\nclass TestBackendManager:\n\n def setup_class(self):\n self.mgr = mgr\n\n def test_default_loads(self):\n assert len(self.mgr.backends) != 0\n modules = [m.__module__ for m in self.mgr.backends]\n assert 'dummy' in modules\n\n def test_bad_backend(self):\n from tests.test_backends.bad_backend import BadBackend\n\n # it's a subclass, but doesn't implement all methods, so it's not an\n # instance\n assert issubclass(BadBackend, translate.backend.IBackend)\n\n # ABCMeta should stop this from being instantiated\n backend = None\n with pytest.raises(TypeError):\n backend = BadBackend()\n\n assert not isinstance(backend, translate.backend.IBackend)\n\n def test_load_extra(self):\n before = self.mgr.backends[:]\n self.mgr.load_backends('tests/test_backends')\n diff = set(self.mgr.backends) - set(before)\n\n assert 'bad_backend' not in [m.__module__ for m in self.mgr.backends]\n\n assert len(diff) != 0\n\n modules = [m.__module__ for m in list(diff)]\n\n assert 'test_backend' in modules\n\n def test_find_best(self):\n backend = self.mgr.find_best('en', 'en')\n\n assert backend.name == 'Test Backend'\n assert backend.preference == 1000\n\n def test_find_all(self):\n backends = self.mgr.find_all('en', 'en')\n\n assert 'Test Backend' in [b.name for b in backends]\n assert 'Dummy' in [b.name for b in backends]\n\n prefs = [b.preference for b in backends]\n assert sorted(prefs, reverse=True) == prefs\n\n def test_raise_bad_data(self):\n \"\"\"Make sure all backends fail on bad input\"\"\"\n\n for backend in self.mgr.backends:\n\n with pytest.raises(translate.exceptions.TranslationException):\n print(backend.name)\n backend.translate('foo', from_lang='this-is-no-language',\n to_lang='dont-even-pretend-this-is-a-lang')\n\n def test_shutdown(self):\n global DEACTIVATE_WAS_CALLED\n DEACTIVATE_WAS_CALLED = False\n\n self.mgr.shutdown()\n\n # This is set when shutdown is called.\n assert DEACTIVATE_WAS_CALLED\n", "repo_name": "erik/translate", "sub_path": "tests/backend_test.py", "file_name": "backend_test.py", "file_ext": "py", "file_size_in_byte": 2533, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "37", "api": [{"api_name": "translate.backend.BackendManager", "line_number": 13, "usage_type": "call"}, {"api_name": "translate.backend", "line_number": 13, "usage_type": "attribute"}, {"api_name": "tests.test_backends.bad_backend.BadBackend", "line_number": 35, "usage_type": "name"}, {"api_name": "translate.backend", "line_number": 35, "usage_type": "attribute"}, {"api_name": "pytest.raises", "line_number": 39, "usage_type": "call"}, {"api_name": "tests.test_backends.bad_backend.BadBackend", "line_number": 40, "usage_type": "call"}, {"api_name": "translate.backend", "line_number": 42, "usage_type": "attribute"}, {"api_name": "pytest.raises", "line_number": 77, "usage_type": "call"}, {"api_name": "translate.exceptions", "line_number": 77, "usage_type": "attribute"}]} +{"seq_id": "18840209435", "text": "\"\"\"Pynecone CLI to create, run, and deploy apps.\"\"\"\n\nimport os\nfrom pathlib import Path\n\nimport httpx\nimport typer\n\nfrom pynecone import constants, utils\nfrom pynecone.telemetry import pynecone_telemetry\n\n# Create the app.\ncli = typer.Typer()\n\n\n@cli.command()\ndef version():\n \"\"\"Get the Pynecone version.\"\"\"\n utils.console.print(constants.VERSION)\n\n\n@cli.command()\ndef init():\n \"\"\"Initialize a new Pynecone app in the current directory.\"\"\"\n app_name = utils.get_default_app_name()\n\n # Make sure they don't name the app \"pynecone\".\n if app_name == constants.MODULE_NAME:\n utils.console.print(\n f\"[red]The app directory cannot be named [bold]{constants.MODULE_NAME}.\"\n )\n raise typer.Exit()\n\n with utils.console.status(f\"[bold]Initializing {app_name}\"):\n # Set up the web directory.\n utils.install_bun()\n utils.initialize_web_directory()\n\n # Set up the app directory, only if the config doesn't exist.\n if not os.path.exists(constants.CONFIG_FILE):\n utils.create_config(app_name)\n utils.initialize_app_directory(app_name)\n utils.set_pynecone_project_hash()\n pynecone_telemetry(\"init\", utils.get_config().telemetry_enabled)\n else:\n utils.set_pynecone_project_hash()\n pynecone_telemetry(\"reinit\", utils.get_config().telemetry_enabled)\n\n # Initialize the .gitignore.\n utils.initialize_gitignore()\n # Finish initializing the app.\n utils.console.log(f\"[bold green]Finished Initializing: {app_name}\")\n\n\n@cli.command()\ndef run(\n env: constants.Env = typer.Option(\n constants.Env.DEV, help=\"The environment to run the app in.\"\n ),\n frontend: bool = typer.Option(\n True, \"--no-frontend\", help=\"Disable frontend execution.\"\n ),\n backend: bool = typer.Option(\n True, \"--no-backend\", help=\"Disable backend execution.\"\n ),\n loglevel: constants.LogLevel = typer.Option(\n constants.LogLevel.ERROR, help=\"The log level to use.\"\n ),\n port: str = typer.Option(None, help=\"Specify a different port.\"),\n):\n \"\"\"Run the app in the current directory.\"\"\"\n frontend_port = utils.get_config().port if port is None else port\n backend_port = utils.get_api_port()\n\n # If something is running on the ports, ask the user if they want to kill or change it.\n if utils.is_process_on_port(frontend_port):\n frontend_port = utils.change_or_terminate_port(frontend_port, \"frontend\")\n\n if utils.is_process_on_port(backend_port):\n backend_port = utils.change_or_terminate_port(backend_port, \"backend\")\n\n # Check that the app is initialized.\n if frontend and not utils.is_initialized():\n utils.console.print(\n \"[red]The app is not initialized. Run [bold]pc init[/bold] first.\"\n )\n raise typer.Exit()\n\n # Check that the template is up to date.\n if frontend and not utils.is_latest_template():\n utils.console.print(\n \"[red]The base app template has updated. Run [bold]pc init[/bold] again.\"\n )\n raise typer.Exit()\n\n # Get the app module.\n utils.console.rule(\"[bold]Starting Pynecone App\")\n app = utils.get_app()\n\n # Get the frontend and backend commands, based on the environment.\n frontend_cmd = backend_cmd = None\n if env == constants.Env.DEV:\n frontend_cmd, backend_cmd = utils.run_frontend, utils.run_backend\n if env == constants.Env.PROD:\n frontend_cmd, backend_cmd = utils.run_frontend_prod, utils.run_backend_prod\n assert frontend_cmd and backend_cmd, \"Invalid env\"\n\n # Post a telemetry event.\n pynecone_telemetry(f\"run-{env.value}\", utils.get_config().telemetry_enabled)\n\n # Run the frontend and backend.\n try:\n if frontend:\n frontend_cmd(app.app, Path.cwd(), frontend_port)\n if backend:\n backend_cmd(app.__name__, port=int(backend_port), loglevel=loglevel)\n finally:\n utils.kill_process_on_port(frontend_port)\n utils.kill_process_on_port(backend_port)\n\n\n@cli.command()\ndef deploy(dry_run: bool = typer.Option(False, help=\"Whether to run a dry run.\")):\n \"\"\"Deploy the app to the Pynecone hosting service.\"\"\"\n # Get the app config.\n config = utils.get_config()\n config.api_url = utils.get_production_backend_url()\n\n # Check if the deploy url is set.\n if config.pcdeploy_url is None:\n typer.echo(\"This feature is coming soon!\")\n return\n\n # Compile the app in production mode.\n typer.echo(\"Compiling production app\")\n app = utils.get_app().app\n utils.export_app(app, zip=True, deploy_url=config.deploy_url)\n\n # Exit early if this is a dry run.\n if dry_run:\n return\n\n # Deploy the app.\n data = {\"userId\": config.username, \"projectId\": config.app_name}\n original_response = httpx.get(config.pcdeploy_url, params=data)\n response = original_response.json()\n frontend = response[\"frontend_resources_url\"]\n backend = response[\"backend_resources_url\"]\n\n # Upload the frontend and backend.\n with open(constants.FRONTEND_ZIP, \"rb\") as f:\n httpx.put(frontend, data=f) # type: ignore\n\n with open(constants.BACKEND_ZIP, \"rb\") as f:\n httpx.put(backend, data=f) # type: ignore\n\n\n@cli.command()\ndef export(\n zipping: bool = typer.Option(\n True, \"--no-zip\", help=\"Disable zip for backend and frontend exports.\"\n ),\n frontend: bool = typer.Option(\n True, \"--backend-only\", help=\"Export only backend.\", show_default=False\n ),\n backend: bool = typer.Option(\n True, \"--frontend-only\", help=\"Export only frontend.\", show_default=False\n ),\n for_pc_deploy: bool = typer.Option(\n False,\n \"--for-pc-deploy\",\n help=\"Whether export the app for Pynecone Deploy Service.\",\n ),\n):\n \"\"\"Export the app to a zip file.\"\"\"\n config = utils.get_config()\n\n if for_pc_deploy:\n # Get the app config and modify the api_url base on username and app_name.\n config.api_url = utils.get_production_backend_url()\n\n # Compile the app in production mode and export it.\n utils.console.rule(\"[bold]Compiling production app and preparing for export.\")\n app = utils.get_app().app\n utils.export_app(\n app,\n backend=backend,\n frontend=frontend,\n zip=zipping,\n deploy_url=config.deploy_url,\n )\n\n # Post a telemetry event.\n pynecone_telemetry(\"export\", utils.get_config().telemetry_enabled)\n\n if zipping:\n utils.console.rule(\n \"\"\"Backend & Frontend compiled. See [green bold]backend.zip[/green bold] \n and [green bold]frontend.zip[/green bold].\"\"\"\n )\n else:\n utils.console.rule(\n \"\"\"Backend & Frontend compiled. See [green bold]app[/green bold] \n and [green bold].web/_static[/green bold] directories.\"\"\"\n )\n\n\nmain = cli\n\nif __name__ == \"__main__\":\n main()\n", "repo_name": "SergioCantera/PowerSieroBlog", "sub_path": "venv/Lib/site-packages/pynecone/pc.py", "file_name": "pc.py", "file_ext": "py", "file_size_in_byte": 6941, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "37", "api": [{"api_name": "typer.Typer", "line_number": 13, "usage_type": "call"}, {"api_name": "pynecone.utils.console.print", "line_number": 19, "usage_type": "call"}, {"api_name": "pynecone.utils.console", "line_number": 19, "usage_type": "attribute"}, {"api_name": "pynecone.utils", "line_number": 19, "usage_type": "name"}, {"api_name": "pynecone.constants.VERSION", "line_number": 19, "usage_type": "attribute"}, {"api_name": "pynecone.constants", "line_number": 19, "usage_type": "name"}, {"api_name": "pynecone.utils.get_default_app_name", "line_number": 25, "usage_type": "call"}, {"api_name": "pynecone.utils", "line_number": 25, "usage_type": "name"}, {"api_name": "pynecone.constants.MODULE_NAME", "line_number": 28, "usage_type": "attribute"}, {"api_name": "pynecone.constants", "line_number": 28, "usage_type": "name"}, {"api_name": "pynecone.utils.console.print", "line_number": 29, "usage_type": "call"}, {"api_name": "pynecone.utils.console", "line_number": 29, "usage_type": "attribute"}, {"api_name": "pynecone.utils", "line_number": 29, "usage_type": "name"}, {"api_name": "pynecone.constants.MODULE_NAME", "line_number": 30, "usage_type": "attribute"}, {"api_name": "pynecone.constants", "line_number": 30, "usage_type": "name"}, {"api_name": "typer.Exit", "line_number": 32, "usage_type": "call"}, {"api_name": "pynecone.utils.console.status", "line_number": 34, "usage_type": "call"}, {"api_name": "pynecone.utils.console", "line_number": 34, "usage_type": "attribute"}, {"api_name": "pynecone.utils", "line_number": 34, "usage_type": "name"}, {"api_name": "pynecone.utils.install_bun", "line_number": 36, "usage_type": "call"}, {"api_name": "pynecone.utils", "line_number": 36, "usage_type": "name"}, {"api_name": "pynecone.utils.initialize_web_directory", "line_number": 37, "usage_type": "call"}, {"api_name": "pynecone.utils", "line_number": 37, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 40, "usage_type": "call"}, {"api_name": "os.path", "line_number": 40, "usage_type": "attribute"}, {"api_name": "pynecone.constants.CONFIG_FILE", "line_number": 40, "usage_type": "attribute"}, {"api_name": "pynecone.constants", "line_number": 40, "usage_type": "name"}, {"api_name": "pynecone.utils.create_config", "line_number": 41, "usage_type": "call"}, {"api_name": "pynecone.utils", "line_number": 41, "usage_type": "name"}, {"api_name": "pynecone.utils.initialize_app_directory", "line_number": 42, "usage_type": "call"}, {"api_name": "pynecone.utils", "line_number": 42, "usage_type": "name"}, {"api_name": "pynecone.utils.set_pynecone_project_hash", "line_number": 43, "usage_type": "call"}, {"api_name": "pynecone.utils", "line_number": 43, "usage_type": "name"}, {"api_name": "pynecone.telemetry.pynecone_telemetry", "line_number": 44, "usage_type": "call"}, {"api_name": "pynecone.utils.get_config", "line_number": 44, "usage_type": "call"}, {"api_name": "pynecone.utils", "line_number": 44, "usage_type": "name"}, {"api_name": "pynecone.utils.set_pynecone_project_hash", "line_number": 46, "usage_type": "call"}, {"api_name": "pynecone.utils", "line_number": 46, "usage_type": "name"}, {"api_name": "pynecone.telemetry.pynecone_telemetry", "line_number": 47, "usage_type": "call"}, {"api_name": "pynecone.utils.get_config", "line_number": 47, "usage_type": "call"}, {"api_name": "pynecone.utils", "line_number": 47, "usage_type": "name"}, {"api_name": "pynecone.utils.initialize_gitignore", "line_number": 50, "usage_type": "call"}, {"api_name": "pynecone.utils", "line_number": 50, "usage_type": "name"}, {"api_name": "pynecone.utils.console.log", "line_number": 52, "usage_type": "call"}, {"api_name": "pynecone.utils.console", "line_number": 52, "usage_type": "attribute"}, {"api_name": "pynecone.utils", "line_number": 52, "usage_type": "name"}, {"api_name": "pynecone.constants.Env", "line_number": 57, "usage_type": "attribute"}, {"api_name": "pynecone.constants", "line_number": 57, "usage_type": "name"}, {"api_name": "pynecone.constants.LogLevel", "line_number": 66, "usage_type": "attribute"}, {"api_name": "pynecone.constants", "line_number": 66, "usage_type": "name"}, {"api_name": "typer.Option", "line_number": 57, "usage_type": "call"}, {"api_name": "pynecone.constants.Env", "line_number": 58, "usage_type": "attribute"}, {"api_name": "pynecone.constants", "line_number": 58, "usage_type": "name"}, {"api_name": "typer.Option", "line_number": 60, "usage_type": "call"}, {"api_name": "typer.Option", "line_number": 63, "usage_type": "call"}, {"api_name": "typer.Option", "line_number": 66, "usage_type": "call"}, {"api_name": "pynecone.constants.LogLevel", "line_number": 67, "usage_type": "attribute"}, {"api_name": "pynecone.constants", "line_number": 67, "usage_type": "name"}, {"api_name": "typer.Option", "line_number": 69, "usage_type": "call"}, {"api_name": "pynecone.utils.get_config", "line_number": 72, "usage_type": "call"}, {"api_name": "pynecone.utils", "line_number": 72, "usage_type": "name"}, {"api_name": "pynecone.utils.get_api_port", "line_number": 73, "usage_type": "call"}, {"api_name": "pynecone.utils", "line_number": 73, "usage_type": "name"}, {"api_name": "pynecone.utils.is_process_on_port", "line_number": 76, "usage_type": "call"}, {"api_name": "pynecone.utils", "line_number": 76, "usage_type": "name"}, {"api_name": "pynecone.utils.change_or_terminate_port", "line_number": 77, "usage_type": "call"}, {"api_name": "pynecone.utils", "line_number": 77, "usage_type": "name"}, {"api_name": "pynecone.utils.is_process_on_port", "line_number": 79, "usage_type": "call"}, {"api_name": "pynecone.utils", "line_number": 79, "usage_type": "name"}, {"api_name": "pynecone.utils.change_or_terminate_port", "line_number": 80, "usage_type": "call"}, {"api_name": "pynecone.utils", "line_number": 80, "usage_type": "name"}, {"api_name": "pynecone.utils.is_initialized", "line_number": 83, "usage_type": "call"}, {"api_name": "pynecone.utils", "line_number": 83, "usage_type": "name"}, {"api_name": "pynecone.utils.console.print", "line_number": 84, "usage_type": "call"}, {"api_name": "pynecone.utils.console", "line_number": 84, "usage_type": "attribute"}, {"api_name": "pynecone.utils", "line_number": 84, "usage_type": "name"}, {"api_name": "typer.Exit", "line_number": 87, "usage_type": "call"}, {"api_name": "pynecone.utils.is_latest_template", "line_number": 90, "usage_type": "call"}, {"api_name": "pynecone.utils", "line_number": 90, "usage_type": "name"}, {"api_name": "pynecone.utils.console.print", "line_number": 91, "usage_type": "call"}, {"api_name": "pynecone.utils.console", "line_number": 91, "usage_type": "attribute"}, {"api_name": "pynecone.utils", "line_number": 91, "usage_type": "name"}, {"api_name": "typer.Exit", "line_number": 94, "usage_type": "call"}, {"api_name": "pynecone.utils.console.rule", "line_number": 97, "usage_type": "call"}, {"api_name": "pynecone.utils.console", "line_number": 97, "usage_type": "attribute"}, {"api_name": "pynecone.utils", "line_number": 97, "usage_type": "name"}, {"api_name": "pynecone.utils.get_app", "line_number": 98, "usage_type": "call"}, {"api_name": "pynecone.utils", "line_number": 98, "usage_type": "name"}, {"api_name": "pynecone.constants.Env", "line_number": 102, "usage_type": "attribute"}, {"api_name": "pynecone.constants", "line_number": 102, "usage_type": "name"}, {"api_name": "pynecone.utils.run_frontend", "line_number": 103, "usage_type": "attribute"}, {"api_name": "pynecone.utils", "line_number": 103, "usage_type": "name"}, {"api_name": "pynecone.utils.run_backend", "line_number": 103, "usage_type": "attribute"}, {"api_name": "pynecone.constants.Env", "line_number": 104, "usage_type": "attribute"}, {"api_name": "pynecone.constants", "line_number": 104, "usage_type": "name"}, {"api_name": "pynecone.utils.run_frontend_prod", "line_number": 105, "usage_type": "attribute"}, {"api_name": "pynecone.utils", "line_number": 105, "usage_type": "name"}, {"api_name": "pynecone.utils.run_backend_prod", "line_number": 105, "usage_type": "attribute"}, {"api_name": "pynecone.telemetry.pynecone_telemetry", "line_number": 109, "usage_type": "call"}, {"api_name": "pynecone.utils.get_config", "line_number": 109, "usage_type": "call"}, {"api_name": "pynecone.utils", "line_number": 109, "usage_type": "name"}, {"api_name": "pathlib.Path.cwd", "line_number": 114, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 114, "usage_type": "name"}, {"api_name": "pynecone.utils.kill_process_on_port", "line_number": 118, "usage_type": "call"}, {"api_name": "pynecone.utils", "line_number": 118, "usage_type": "name"}, {"api_name": "pynecone.utils.kill_process_on_port", "line_number": 119, "usage_type": "call"}, {"api_name": "pynecone.utils", "line_number": 119, "usage_type": "name"}, {"api_name": "typer.Option", "line_number": 123, "usage_type": "call"}, {"api_name": "pynecone.utils.get_config", "line_number": 126, "usage_type": "call"}, {"api_name": "pynecone.utils", "line_number": 126, "usage_type": "name"}, {"api_name": "pynecone.utils.get_production_backend_url", "line_number": 127, "usage_type": "call"}, {"api_name": "pynecone.utils", "line_number": 127, "usage_type": "name"}, {"api_name": "typer.echo", "line_number": 131, "usage_type": "call"}, {"api_name": "typer.echo", "line_number": 135, "usage_type": "call"}, {"api_name": "pynecone.utils.get_app", "line_number": 136, "usage_type": "call"}, {"api_name": "pynecone.utils", "line_number": 136, "usage_type": "name"}, {"api_name": "pynecone.utils.export_app", "line_number": 137, "usage_type": "call"}, {"api_name": "pynecone.utils", "line_number": 137, "usage_type": "name"}, {"api_name": "httpx.get", "line_number": 145, "usage_type": "call"}, {"api_name": "pynecone.constants.FRONTEND_ZIP", "line_number": 151, "usage_type": "attribute"}, {"api_name": "pynecone.constants", "line_number": 151, "usage_type": "name"}, {"api_name": "httpx.put", "line_number": 152, "usage_type": "call"}, {"api_name": "pynecone.constants.BACKEND_ZIP", "line_number": 154, "usage_type": "attribute"}, {"api_name": "pynecone.constants", "line_number": 154, "usage_type": "name"}, {"api_name": "httpx.put", "line_number": 155, "usage_type": "call"}, {"api_name": "typer.Option", "line_number": 160, "usage_type": "call"}, {"api_name": "typer.Option", "line_number": 163, "usage_type": "call"}, {"api_name": "typer.Option", "line_number": 166, "usage_type": "call"}, {"api_name": "typer.Option", "line_number": 169, "usage_type": "call"}, {"api_name": "pynecone.utils.get_config", "line_number": 176, "usage_type": "call"}, {"api_name": "pynecone.utils", "line_number": 176, "usage_type": "name"}, {"api_name": "pynecone.utils.get_production_backend_url", "line_number": 180, "usage_type": "call"}, {"api_name": "pynecone.utils", "line_number": 180, "usage_type": "name"}, {"api_name": "pynecone.utils.console.rule", "line_number": 183, "usage_type": "call"}, {"api_name": "pynecone.utils.console", "line_number": 183, "usage_type": "attribute"}, {"api_name": "pynecone.utils", "line_number": 183, "usage_type": "name"}, {"api_name": "pynecone.utils.get_app", "line_number": 184, "usage_type": "call"}, {"api_name": "pynecone.utils", "line_number": 184, "usage_type": "name"}, {"api_name": "pynecone.utils.export_app", "line_number": 185, "usage_type": "call"}, {"api_name": "pynecone.utils", "line_number": 185, "usage_type": "name"}, {"api_name": "pynecone.telemetry.pynecone_telemetry", "line_number": 194, "usage_type": "call"}, {"api_name": "pynecone.utils.get_config", "line_number": 194, "usage_type": "call"}, {"api_name": "pynecone.utils", "line_number": 194, "usage_type": "name"}, {"api_name": "pynecone.utils.console.rule", "line_number": 197, "usage_type": "call"}, {"api_name": "pynecone.utils.console", "line_number": 197, "usage_type": "attribute"}, {"api_name": "pynecone.utils", "line_number": 197, "usage_type": "name"}, {"api_name": "pynecone.utils.console.rule", "line_number": 202, "usage_type": "call"}, {"api_name": "pynecone.utils.console", "line_number": 202, "usage_type": "attribute"}, {"api_name": "pynecone.utils", "line_number": 202, "usage_type": "name"}]} +{"seq_id": "13938063820", "text": "# -*- coding: utf-8 -*-\n# pylint:disable=invalid-name,too-few-public-methods,no-name-in-module,bad-continuation\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('info_internet_connection', '0001_initial'),\n ]\n\n operations = [\n migrations.AlterModelOptions(\n name='internet',\n options={'get_latest_by': 'timestamp'},\n ),\n migrations.AlterField(\n model_name='internet',\n name='connect_status',\n field=models.CharField(blank=True, max_length=12, null=True, choices=[(\n b'no_connect', b'no_connect'), (b'connecting', b'connecting_now'), (b'connected', b'connected')]),\n preserve_default=True,\n ),\n migrations.AlterField(\n model_name='internet',\n name='mode',\n field=models.CharField(blank=True, max_length=12, null=True, choices=[(\n b'off', b'off'), (b'2g', b'2g'), (b'3g', b'3g'), (b'4g', b'4g'), (b'sim_disabled', b'sim_disabled')]),\n preserve_default=True,\n ),\n migrations.AlterField(\n model_name='internet',\n name='sim',\n field=models.CharField(blank=True, max_length=12, null=True, choices=[(\n b'no_sim', b'No SIM installed'), (b'normal', b'normal'), (b'pin_locked', b'requires PIN'), (b'error', b'error')]),\n preserve_default=True,\n ),\n ]\n", "repo_name": "ojarva/home-info-display", "sub_path": "homedisplay/info_internet_connection/migrations/0002_auto_20141228_1047.py", "file_name": "0002_auto_20141228_1047.py", "file_ext": "py", "file_size_in_byte": 1510, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "37", "api": [{"api_name": "django.db.migrations.Migration", "line_number": 8, "usage_type": "attribute"}, {"api_name": "django.db.migrations", "line_number": 8, "usage_type": "name"}, {"api_name": "django.db.migrations.AlterModelOptions", "line_number": 15, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 15, "usage_type": "name"}, {"api_name": "django.db.migrations.AlterField", "line_number": 19, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 19, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 22, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 22, "usage_type": "name"}, {"api_name": "django.db.migrations.AlterField", "line_number": 26, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 26, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 29, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 29, "usage_type": "name"}, {"api_name": "django.db.migrations.AlterField", "line_number": 33, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 33, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 36, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 36, "usage_type": "name"}]} +{"seq_id": "1965548915", "text": "import matplotlib.pyplot as plt\nimport numpy as np\n\nfrom calendar import day_name\nfrom matplotlib.ticker import FormatStrFormatter\n\nplt.rcParams[\"font.sans-serif\"] = [\"SimHei\"]\n\nfig = plt.figure()\nax = fig.add_axes([0.2, 0.2, 0.7, 0.7])\n\nx = np.arange(1, 8)\ny = 2*x\n\nax.plot(x, y, ls=\"-\", lw=2, color=\"orange\", marker=\"o\", ms=20, mfc=\"c\", mec=\"b\")\n\n# RMB ticklabel\nax.yaxis.set_major_formatter(FormatStrFormatter(r\"$\\yen%.1f$\"))\t# 设置货币刻度标签\n# dayName ticklabel\nplt.xticks(x, day_name[0:7], rotation=20)\t\t\t\t\t\t# 设置时间序列刻度标签\n\nax.set_xlim(0, 8)\nax.set_ylim(0, 18)\n\nplt.show()", "repo_name": "liu-chunzhang/matplotlib_practice", "sub_path": "第2篇 精进/第5章 统计图形绘制进阶：图形样式/5.01 设置坐标轴的刻度样式/5.1.4 案例2——货币和时间序列样式的刻度标签/5.1.4.py", "file_name": "5.1.4.py", "file_ext": "py", "file_size_in_byte": 605, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "37", "api": [{"api_name": "matplotlib.pyplot.rcParams", "line_number": 7, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 7, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 9, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 9, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 12, "usage_type": "call"}, {"api_name": "matplotlib.ticker.FormatStrFormatter", "line_number": 18, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 20, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 20, "usage_type": "name"}, {"api_name": "calendar.day_name", "line_number": 20, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 25, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 25, "usage_type": "name"}]} +{"seq_id": "25929849693", "text": "# encoding: utf-8\n\nimport requests\nfrom lxml import etree\n\n\n# 1.将目标网站上的页面抓取下来\nBASE_DOMAIN = \"https://www.dy2018.com/\"\nHEADERS = {\n \"User-Agent\": \"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_13_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/70.0.3538.77 Safari/537.36\",\n \"Referer\": \"https://www.dy2018.com/html/gndy/dyzz/\"\n}\nURL = \"https://www.dy2018.com/html/gndy/dyzz/index.html\"\n\n\ndef get_detail_urls(url):\n resp = requests.get(url=url, headers=HEADERS)\n # print(resp.encoding) # ISO-8859-1\n # text = resp.content.decode(\"gbk\")\n text = resp.text\n html = etree.HTML(text)\n # tables = html.xpath(\"//table[@class='tbspan']\")\n detail_urls = html.xpath(\"//table[@class='tbspan']//a/@href\")\n # for table in tables:\n # print(\"========\")\n # print(etree.tostring(table, encoding=\"utf-8\").decode(\"utf-8\"))\n # ===============================\n # for detail_url in detail_urls:\n # detail_url = BASE_DOMAIN + detail_url\n # print(detail_url)\n # ===============================\n # def abc(url):\n # return BASE_DOMAIN + url\n # index = 0\n # for detail_url in detail_urls:\n # detail_url = abc(detail_url)\n # detail_urls[index] = detail_url\n # index += 1\n # ===============================\n # 下面这句等价于上面\n detail_urls = map(lambda url: BASE_DOMAIN + url, detail_urls)\n # print(detail_urls)\n return detail_urls\n\ndef parse_info(info, rule):\n return info.replace(rule, \"\").strip()\n\n\ndef parse_detail_page(url):\n resp = requests.get(url=url, headers=HEADERS)\n text = resp.content.decode(\"gbk\")\n html = etree.HTML(text)\n # //div[@class=\"title_all\"]/h1/text()\n title = html.xpath(\"//div[@class='title_all']/h1/text()\")[0]\n # //div[@id='Zoom']//img/@src\n zoomEle = html.xpath(\"//div[@id='Zoom']\")[0]\n # //img/@src\n imgs = zoomEle.xpath(\".//img/@src\")\n cover = imgs[0]\n screenshot = imgs[1]\n # //p[position()>1]\n infos = zoomEle.xpath(\".//p[position()>1]/text()\")\n year = \"\"\n country = \"\"\n category = \"\"\n douban_rating = \"\"\n duration = \"\"\n director = \"\"\n actors = list()\n profile = \"\"\n list_name = [\"◎年　　代\", \"◎产　　地\", \"◎类　　别\", \"◎豆瓣评分\", \"◎片　　长\", \"◎导　　演\", \"◎主　　演\", \"◎简　　介\"]\n for index, info in enumerate(infos):\n if info.startswith(list_name[0]):\n year = parse_info(info, list_name[0])\n elif info.startswith(list_name[1]):\n country = parse_info(info, list_name[1])\n elif info.startswith(list_name[2]):\n category = parse_info(info, list_name[2])\n elif info.startswith(list_name[3]):\n douban_rating = parse_info(info, list_name[3])\n elif info.startswith(list_name[4]):\n duration = parse_info(info, list_name[4])\n elif info.startswith(list_name[5]):\n director = parse_info(info, list_name[5])\n elif info.startswith(list_name[6]):\n actor = parse_info(info, list_name[6])\n actors.append(actor)\n for x in range(index + 1, len(infos)):\n if infos[x].startswith(list_name[7]):\n profile = parse_info(infos[x + 1], list_name[7])\n break\n actor = infos[x].strip()\n actors.append(actor)\n # //td[@bgcolor=\"#fdfddf\"]//a\n download_url = html.xpath(\"//td[@bgcolor='#fdfddf']//a/text()\")[0]\n # print(download_url)\n movie = {\n \"title\": title,\n \"cover\": cover,\n \"year\": year,\n \"country\": country,\n \"category\": category,\n \"douban_rating\": douban_rating,\n \"duration\": duration,\n \"director\": director,\n \"actors\": actors,\n \"profile\": profile,\n \"screenshot\": screenshot,\n \"download_url\": download_url\n }\n return movie\n\ndef spider():\n base_url = \"https://www.dy2018.com/html/gndy/dyzz/index.html\"\n base_url_other = \"https://www.dy2018.com/html/gndy/dyzz/index_{}.html\"\n movies = list()\n for x in range(1, 8):\n if x == 1:\n url = base_url\n else:\n url = base_url_other.format(x)\n detail_urls = get_detail_urls(url)\n for detail_url in detail_urls:\n # print(detail_url)\n movie = parse_detail_page(detail_url)\n movies.append(movie)\n print(movie)\n break\n\n# 2.将抓取下来的数据根据一定的规则进行提取\nmovies = list()\nif __name__ == \"__main__\":\n spider()\n\n\n", "repo_name": "kcshan/python_spider_study", "sub_path": "18.dytt8_spider.py", "file_name": "18.dytt8_spider.py", "file_ext": "py", "file_size_in_byte": 4571, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "37", "api": [{"api_name": "requests.get", "line_number": 17, "usage_type": "call"}, {"api_name": "lxml.etree.HTML", "line_number": 21, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 21, "usage_type": "name"}, {"api_name": "requests.get", "line_number": 50, "usage_type": "call"}, {"api_name": "lxml.etree.HTML", "line_number": 52, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 52, "usage_type": "name"}]} +{"seq_id": "32488642759", "text": "import os\nfrom argparse import ArgumentParser\nfrom typing import List\n\nimport pandas\nimport pandas as pd\nfrom tqdm import tqdm\n\nfrom tsa.cli.run import SubCommand\nfrom tsa.cli.tsa_cv import PREDICTORS\nfrom tsa.cli.tsa_ruleminer import TSARuleMinerSubCommand\n\nFeatureSelection = List[str]\n\n\nclass TSAEvalFsSubCommand(SubCommand):\n def __init__(self):\n super().__init__(\n \"tsa-eval-fs\",\n \"evaluate feature selection results\",\n expect_unknown_args=True,\n )\n\n def make_subparser(self, parser: ArgumentParser):\n parser.add_argument(\n \"-f\",\n \"--feature-files\",\n required=True,\n nargs=\"+\",\n help=\"input feature-selection file(s) (feature selection CSV)\",\n )\n parser.add_argument(\"--out\", \"-o\", required=True)\n parser.add_argument(\"--input\", help=\"Performance Data CSV\", required=True)\n parser.add_argument(\"--target\", default=\"f1_cfa\")\n parser.add_argument(\"--threshold\", default=0.8, type=float)\n parser.add_argument(\"--reverse-classes\", default=False, action=\"store_true\")\n parser.add_argument(\"--scenario-column\", default=\"scenario\")\n parser.add_argument(\n \"-p\",\n \"--predictor\",\n help=\"Name of the Predictor\",\n choices=PREDICTORS.keys(),\n default=\"DecisionTree\",\n )\n parser.add_argument(\n \"-q\",\n \"--query\",\n help=\"Query for filtering the feature selection set\",\n default=\"`gain.precision` > 0.01 and `mean.f1_score` > 0.5\",\n )\n parser.add_argument(\n \"--sort-by\",\n help=\"Sort feature selection set by ...\",\n default=\"mean.precision\",\n )\n parser.add_argument(\n \"--svgs\", required=False, default=False, action=\"store_true\"\n )\n parser.add_argument(\n \"--add-max-depth-column\",\n required=False,\n default=False,\n action=\"store_true\",\n help=\"If set, the max-depth for the decision tree in feature selection method is parsed from the file name.\",\n )\n\n def exec(self, args, parser, unknown_args):\n data, rules_miner = TSARuleMinerSubCommand.init_rulesminer(args, unknown_args)\n\n li = []\n for f in tqdm(args.feature_files):\n fs_results = pandas.read_csv(f, index_col=False)\n file_basename = os.path.basename(f)\n fs_results[\"file\"] = file_basename\n fs_results[\"key\"] = fs_results.apply(\n lambda r: self._get_row_key(r[\"features\"].split(\";\")), axis=1\n )\n fs_results.set_index(\"key\", inplace=True)\n fs_results[\"gain.precision\"] = fs_results.apply(\n lambda r: self._calc_gain(r, fs_results, variable=\"mean.precision\"),\n axis=1,\n )\n if args.add_max_depth_column:\n fs_results[\"max_depth\"] = self._parse_max_depth_decision_tree(\n file_basename\n )\n li.append(fs_results)\n\n fs_results = pd.concat(li, axis=0, ignore_index=True)\n\n fs_results = fs_results.query(args.query)\n fs_results.sort_values(by=\"mean.precision\", ascending=False, inplace=True)\n out_path = os.path.join(args.out, \"results.csv\")\n fs_results.to_csv(out_path)\n\n if args.svgs:\n for i, features in enumerate(fs_results[:5][\"features\"]):\n print(\"Precision Gain:\", fs_results.iloc[i][\"gain.precision\"])\n feature_set = features.split(\";\")\n print(feature_set)\n split = data.with_features(feature_set).get_split(\n args.target, [], args.threshold, args.reverse_classes\n )\n svg_path = os.path.join(args.out, f\"{i}.png\")\n rules_miner.extract_rules(split, svg_path)\n\n def _get_row_key(self, features):\n key = list(sorted(features))\n return \" \".join(key)\n\n def _calc_gain(self, row, fs_results, variable=\"mean.precision\"):\n features = row[\"features\"].split(\";\")\n last_features = features[:-1]\n if len(last_features) == 0:\n gain = row[variable]\n else:\n last_row = fs_results.loc[self._get_row_key(last_features)]\n gain = row[variable] - last_row[variable]\n return gain\n\n def _parse_max_depth_decision_tree(self, file_name: str):\n # expected file format is: \"*-depth=%{depth}rounds=*.csv\"\n return int(substring(file_name, \"-depth=\", \"rounds=\"))\n\n\ndef substring(s, before, after):\n return s[s.index(before) + len(before) : s.index(after)]\n", "repo_name": "dhelmr/master-thesis", "sub_path": "tsa/cli/tsa_eval_fs.py", "file_name": "tsa_eval_fs.py", "file_ext": "py", "file_size_in_byte": 4712, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "github-code", "pt": "37", "api": [{"api_name": "typing.List", "line_number": 13, "usage_type": "name"}, {"api_name": "tsa.cli.run.SubCommand", "line_number": 16, "usage_type": "name"}, {"api_name": "argparse.ArgumentParser", "line_number": 24, "usage_type": "name"}, {"api_name": "tsa.cli.tsa_cv.PREDICTORS.keys", "line_number": 42, "usage_type": "call"}, {"api_name": "tsa.cli.tsa_cv.PREDICTORS", "line_number": 42, "usage_type": "name"}, {"api_name": "tsa.cli.tsa_ruleminer.TSARuleMinerSubCommand.init_rulesminer", "line_number": 68, "usage_type": "call"}, {"api_name": "tsa.cli.tsa_ruleminer.TSARuleMinerSubCommand", "line_number": 68, "usage_type": "name"}, {"api_name": "tqdm.tqdm", "line_number": 71, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 72, "usage_type": "call"}, {"api_name": "os.path.basename", "line_number": 73, "usage_type": "call"}, {"api_name": "os.path", "line_number": 73, "usage_type": "attribute"}, {"api_name": "pandas.concat", "line_number": 89, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 93, "usage_type": "call"}, {"api_name": "os.path", "line_number": 93, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 104, "usage_type": "call"}, {"api_name": "os.path", "line_number": 104, "usage_type": "attribute"}]} +{"seq_id": "70278508909", "text": "import argparse\nimport decimal\nimport json\nimport logging\nimport os\nimport requests\nimport time\nfrom datetime import datetime\n\nimport web3\n\nfrom util import get_fleek_client\n\nlogging.basicConfig(level=logging.INFO)\nlogger = logging.getLogger()\n\nSTATS_FILE_PATH = os.environ[\"STATS_FILE_PATH\"]\n# IMPORTANT: Use checksummed addresses here\nSWAP_CONTRACT_ADDRESS = os.environ[\"SWAP_CONTRACT_ADDRESS\"]\nDEPLOYMENT_BLOCK = int(os.environ[\"DEPLOYMENT_BLOCK\"])\nADD_STATS_EVERY_N_BLOCK = int(os.environ[\"ADD_STATS_EVERY_N_BLOCK\"])\nHTTP_PROVIDER_URL = os.environ[\"HTTP_PROVIDER_URL\"]\nFLEEK_KEY_ID = os.environ[\"FLEEK_KEY_ID\"]\nFLEEK_KEY = os.environ[\"FLEEK_KEY\"]\nFLEEK_BUCKET = os.environ[\"FLEEK_BUCKET\"]\nINITIAL_VIRTUAL_PRICE = \"1000000000000000000\"\n\n# TODO npm run build before this is accessible\nSWAP_CONTRACT_ABI_PATH = \"Swap.json\"\nFLEEK_ENDPOINT = \"https://storageapi.fleek.co\"\n\n# Add timestamp, A, adminfee, swapfee in the future?\nBLOCK_NUMBER_IND = 0\nVIRTUAL_PRICE_IND = 1\nBTC_PRICE_IND = 2\n\n\ndef get_btc_price_at_timestamp_date(ts):\n dt = datetime.fromtimestamp(ts)\n logger.info(f\"Fetching price for timestamp: {ts} ({dt})\")\n try:\n end_ts = ts\n start_ts = ts - 60 * 60 * 2\n url = (\n f\"https://api.coingecko.com/api/v3/coins/bitcoin/market_chart/\"\n f\"range?vs_currency=usd&from={start_ts}&to={end_ts}\"\n )\n price_info = requests.get(url).json()\n prices = price_info[\"prices\"]\n price = int(decimal.Decimal(prices[-1][1]).quantize(decimal.Decimal(\"1\")))\n return price\n except Exception as e:\n\n logger.error(f\"Could not fetch btc price at {ts}: {e}\")\n\n\ndef get_existing_stats_file_content(fleek_aws_client):\n try:\n obj = fleek_aws_client.get_object(Bucket=FLEEK_BUCKET, Key=STATS_FILE_PATH)\n return json.loads(obj[\"Body\"].read().decode(\"utf-8\"))\n except fleek_aws_client.exceptions.NoSuchKey:\n logger.info(\"No existing file has been found.\")\n return []\n except Exception as e:\n logger.error(f\"Error reading existing file: {e}\")\n return None\n\n\ndef main(args):\n if args.dev:\n logger.info(\n \"IMPORTANT: Remember to delete the existing file from\"\n f\"{FLEEK_BUCKET} bucket when starting to test a fresh env.\"\n )\n logger.info(\n \"Running in dev mode, will generate stats as new blocks are\" \" mined.\"\n )\n try:\n f = open(SWAP_CONTRACT_ABI_PATH)\n swap_contract_artifact = json.loads(f.read())\n swap_contract_abi = swap_contract_artifact[\"abi\"]\n except Exception as e:\n logger.error(f\"Could not load swap contract ABI: {e}\")\n\n w3 = web3.Web3(web3.Web3.HTTPProvider(HTTP_PROVIDER_URL))\n fleek_aws_client = get_fleek_client(FLEEK_KEY_ID, FLEEK_KEY)\n\n stats_content = get_existing_stats_file_content(fleek_aws_client)\n if stats_content is None:\n logger.error(\n \"Error reading existing file. This can happen due to a\"\n \" Fleek outage. If it happens consistenly, delete the file \"\n \"from Fleek and wait for the script to regenerate the file \"\n \"from scratch.\"\n )\n return\n\n # Get the last block number\n if len(stats_content):\n last_block_num = stats_content[-1][BLOCK_NUMBER_IND]\n else:\n logger.info(\"Creating new file\")\n last_block_num = DEPLOYMENT_BLOCK\n\n swap = w3.eth.contract(abi=swap_contract_abi, address=SWAP_CONTRACT_ADDRESS)\n\n next_block_num = last_block_num + ADD_STATS_EVERY_N_BLOCK\n logger.info(\n f\"Next block number: {next_block_num}, current block: {w3.eth.blockNumber}\"\n )\n\n while True:\n\n # w3.eth.blockNumber gets updated dynamically\n while w3.eth.blockNumber > next_block_num:\n logger.info(f\"Fetching data for block: {next_block_num}\")\n\n # Virtual price has more digits than Number.MAX_SAFE_INTEGER.\n # When the pool initializes, the virtual price is returned as 0 but it's\n # actually effectively 1\n try:\n virtual_price = str(\n swap.functions.getVirtualPrice().call(\n block_identifier=next_block_num\n )\n or INITIAL_VIRTUAL_PRICE\n )\n except web3.exceptions.BadFunctionCallOutput as e:\n logger.error(\n f\"Error calling fn on block {next_block_num}. This is \"\n f\"likely due to the contract not having been deployed by\"\n f\" this block, moving to the next block: {e}\"\n )\n next_block_num += ADD_STATS_EVERY_N_BLOCK\n continue\n\n block_data = w3.eth.getBlock(next_block_num)\n btc_price = get_btc_price_at_timestamp_date(block_data.timestamp)\n if not btc_price:\n break\n\n stats_content.append([next_block_num, virtual_price, btc_price])\n\n # Rewrite the whole object every time, helps recover from where we left off,\n # if we're regenerating a lot of blocks and script stops due to provider\n # / rate limiting errors\n stats_bytes = json.dumps(stats_content, separators=(\",\", \":\")).encode(\n \"utf-8\"\n )\n while True:\n try:\n fleek_aws_client.put_object(\n Bucket=FLEEK_BUCKET, Key=STATS_FILE_PATH, Body=stats_bytes\n )\n logger.info(\n f\"Uploaded cumulative stats to Fleek (latest block: {next_block_num})\"\n )\n break\n except Exception as e:\n logger.error(f\"Error uploading stats file: {e}\")\n\n next_block_num += ADD_STATS_EVERY_N_BLOCK\n\n if not args.dev:\n break\n\n time.sleep(1)\n\n logger.info(\"Done for now.\")\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--dev\", action=\"store_true\")\n args = parser.parse_args()\n main(args)\n", "repo_name": "saddle-finance/saddle-pool-stats", "sub_path": "record_pool_stats.py", "file_name": "record_pool_stats.py", "file_ext": "py", "file_size_in_byte": 6099, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "37", "api": [{"api_name": "logging.basicConfig", "line_number": 14, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 14, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 15, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 17, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 19, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 20, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 21, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 22, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 23, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 24, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 25, "usage_type": "attribute"}, {"api_name": "datetime.datetime.fromtimestamp", "line_number": 39, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 39, "usage_type": "name"}, {"api_name": "requests.get", "line_number": 48, "usage_type": "call"}, {"api_name": "decimal.Decimal", "line_number": 50, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 60, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 80, "usage_type": "call"}, {"api_name": "web3.Web3", "line_number": 85, "usage_type": "call"}, {"api_name": "web3.Web3.HTTPProvider", "line_number": 85, "usage_type": "call"}, {"api_name": "util.get_fleek_client", "line_number": 86, "usage_type": "call"}, {"api_name": "web3.exceptions", "line_number": 128, "usage_type": "attribute"}, {"api_name": "json.dumps", "line_number": 147, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 167, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 173, "usage_type": "call"}]} +{"seq_id": "9052916215", "text": "#encoding:utf-8\n\"\"\"\n#百度m端挖词工具\n#无效关键词判断标准：\n# 1：没有搜索结果\n# 2：搜索结果没有10页，且第一页搜索结果没有包含5条关键词收录\n# 3：调整逻辑页数判断去掉，转为调用百度nlp接口判断当前关键词与所搜结果短文本的相似度\n\"\"\"\n\nimport os, sys, signal\nfrom selenium import webdriver\nfrom selenium.webdriver.chrome.options import Options\nfrom selenium.common.exceptions import TimeoutException, WebDriverException\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.support.ui import WebDriverWait\nfrom selenium.webdriver.support import expected_conditions as EC\nfrom aip import AipNlp\n#from sqlitehelper import dbHelper\n\n# api 授权信息\nAPP_ID = '16170203'\nAPP_KEY = 'TQRGlbD2wk9RiG7B48GmHXhV'\nSECRET_KEY = 'LM5F0BGMCnyRyoiQvMPr6ygoyPmq3OqB'\nnlp_client = AipNlp(APP_ID, APP_KEY, SECRET_KEY)\n\n\nclass MobileKeywords:\n \"\"\"\n 百度手机端关键词挖掘\n \"\"\"\n\n def __init__(self, tag_url, tag_kw, wait_seconds, f_kw, i_kw):\n \"\"\"\n 初始化\n :param tag_url:需要加载的网页地址\n :param wait_seconds:显示等待时间，默认为10S\n \"\"\"\n self.url = tag_url\n self.seconds = wait_seconds\n self.keywords = tag_kw\n self.dbHelper = None # 数据库辅助类\n self.retry_counter = 1 # 失败重试次数\n self.title_counter = 5 # 关键词在搜索结果中出现的次数\n self.kw_score = 6.1 # 关键词敏感度阀值\n self.page_keywords = 0 # 关键词搜索结果总页\n self.total_keywords = 0 # 总关键词记数\n self.file_save_path = \"\" # 关键词文件保存路径\n self.filter_keywords = f_kw # 过滤的关键词（关键词不得出现该列表中的任何词）\n self.include_keywords = i_kw # 包含关键词（关键词中必须包含该列表中的任何一个词）\n self.res_keywords = {\"valid\": False, \"sub_keywords\": []}\n self.browser = webdriver.Chrome(options=self.init_driver())\n self.wait = WebDriverWait(self.browser, wait_seconds)\n\n def init_save_info(self):\n \"\"\"\n 初始化关键词保存相关\n \"\"\"\n try:\n file_path = '/home/documents/seobaidu/baiduci/' #'/home/bbei/Project/anyCode/seoKeywords'\n if not os.path.exists(file_path):\n os.makedirs(file_path)\n file_name = os.path.join(file_path, u\"{}.txt\".format(self.keywords))\n self.file_save_path = file_name\n print(u\"-----------------------------------------------------\")\n print(u\"==关键词保存路径为：{}\".format(self.file_save_path))\n print(u\"-----------------------------------------------------\")\n except OSError as ex:\n print(u\"===关键词结果保存文件创建出错，请重试...\" + str(ex))\n os._exit(0)\n\n def init_driver(self):\n \"\"\"\n webdriver 配置项\n \"\"\"\n opt = Options()\n prefs = {\n 'profile.default_content_setting_values': {\n 'images': 2 # 禁止加载图片 1为开启\n #'javascript': 2 # 禁止运行js 1为开启\n }\n }\n opt.add_experimental_option(\"prefs\", prefs)\n opt.add_argument(\"blink-settings=imagesEnabled=false\") # 禁止加载图片\n opt.add_argument('--headless') # 无界面模式\n opt.add_argument('--disable-gpu') # 禁止使用硬件加速\n # 禁止使用插件\n opt.add_argument('--disable-extensions')\n # 针对selenium在centos7 server中的配置\n #解决DevToolsActivePort文件不存在的报错\n opt.add_argument('--no-sandbox')\n # 针对selenium在centos7 server中的配置\n opt.add_argument('--disable-dev-shm-usage')\n # 自定义请求头\n opt.add_argument(\"user-agent='Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:66.0) Gecko/20100101 Firefox/66.0'\")\n return opt\n\n def status_rest(self):\n \"\"\"\n 状态初始化，为下一个关键词准备\n \"\"\"\n self.title_counter = 5\n self.page_keywords = 0\n self.retry_counter = 1\n self.res_keywords = {\"valid\": False, \"sub_keywords\": []}\n\n def restart_driver(self):\n \"\"\"\n 当超时发生后，清理当前游览器，重新赋值\n \"\"\"\n self.wait = None\n self.browser.quit()\n self.browser = webdriver.Chrome(chrome_options=self.init_driver())\n self.wait = WebDriverWait(self.browser, self.seconds)\n self.restart_driver()\n\n def request_url(self, req_url, tipMsg):\n \"\"\"\n 请求给定的网站，超时进行默认次数的重试，成功则返回页面，反之返回None\n \"\"\"\n try:\n if self.retry_counter > 1:\n self.browser.quit()\n self.browser = webdriver.Chrome(chrome_options=self.init_driver())\n self.wait = WebDriverWait(self.browser, self.seconds)\n self.browser.get(req_url)\n return True\n except TimeoutException as ex:\n if self.retry_counter <= 1:\n print(u\"=== {}，正在尝试重试第 {} 次...\".format(tipMsg, self.retry_counter))\n self.retry_counter += 1\n self.request_url(req_url, tipMsg)\n else:\n self.retry_counter = 1\n return None\n\n def write_file(self, kw_data):\n \"\"\"\n 将关键词写入文件\n \"\"\"\n try:\n # 添加encoding='utf-8'避免出现UnicodeEncodeError错误\n with open(self.file_save_path, 'a+', encoding='utf-8') as f:\n f.writelines(kw_data)\n kw_data.clear() # 清空，为下次准备\n except UnicodeEncodeError as ex:\n print(u\"===关键词写入文件失败,继续下一个关键词...\")\n\n def ele_waiting(self, selector, selector_type, wait_type, retry):\n \"\"\"\n 等待页面元素加载完成\n :param selector: 页面元素的选择器\n :param selector_type: 页面元素选择器的种类\n :wait_type: 等待类型，是否出现？可以点击？...\n :retry: 等待��时，是否需要重试\n 找到则返回当前元素，反之为None\n \"\"\"\n cur_wait_type = EC.presence_of_element_located\n if wait_type == 'click':\n cur_wait_type = EC.element_to_be_clickable\n try:\n if selector_type == \"css\":\n ele = self.wait.until(cur_wait_type((By.CSS_SELECTOR, selector)))\n if selector_type == \"xpath\":\n ele = self.wait.until(cur_wait_type((By.XPATH, selector)))\n return ele\n except TimeoutException as ex:\n print(u\"Elements can't loaded {}\" + str(ex))\n if retry and self.retry_counter <= 1: # 默认重试1次，超过重试次数则直接返回结果\n self.retry_counter += 1\n self.is_valid_keywords()\n else:\n return None\n\n def ele_exist(self, selector, selector_type, is_single):\n \"\"\"\n 判断页面上某个元素是否存在\n :param selector: 元素选择器\n :selector_type: 选择其种类\n :is_single: 是否为单个元素\n 存在则返回当前元素，反之为None\n \"\"\"\n ele = None\n cur_selector_type = By.CSS_SELECTOR\n if selector_type == \"xpath\":\n cur_selector_type = By.XPATH\n\n try:\n if is_single:\n ele = self.browser.find_element(cur_selector_type, selector)\n else:\n ele = self.browser.find_elements(cur_selector_type, selector)\n except WebDriverException as ex:\n #print(u\"===元素未能发现...\" + selector)\n return ele\n return ele\n\n def is_valid_keywords(self):\n \"\"\"\n 判断当前关键词是否为有效关键词\n :param kw:等待检查的关键词\n 返回当前关键词有效标识；返回当前关键字所有相关词\n \"\"\"\n # 抓取目标网址\n tip_msg = \"打开百度首页超时\"\n if self.request_url(self.url, tip_msg) is None:\n print(u'=== %s 打开百度首页超时，继续下一个关键词!' % self.keywords)\n return self.res_keywords\n\n # 等待搜索框和搜索按钮加载完成\n try:\n txt_search = self.wait.until(EC.presence_of_element_located((By.CSS_SELECTOR, \"#index-kw\")))\n btn_search = self.wait.until(EC.element_to_be_clickable((By.CSS_SELECTOR, \"#index-bn\")))\n # 发送关键词，等待搜索结果\n txt_search.send_keys(self.keywords)\n #btn_search.click()\n #直接执行click无效，使用js脚本执行click\n self.browser.execute_script(\"arguments[0].click();\", btn_search)\n except WebDriverException as ex:\n print(u\"=== 页面元素拉取超时，放弃 %s，继续下一个！\" % self.keywords)\n #self.restart_driver() # 元素发生超时后，重启driver\n return self.res_keywords\n\n # 判断搜索结果是否为空\n page_result = self.ele_exist(\".se-noresult-tips\", \"css\", True)\n if page_result is not None:\n return self.res_keywords\n\n # 相关搜索词\n str_xpath = \".//a\"\n kw_relations = self.ele_exist(\".rw-list\", \"css\", True)\n if kw_relations is not None:\n links = kw_relations.find_elements_by_xpath(str_xpath)\n for lnk in links:\n self.res_keywords['sub_keywords'].append(lnk.text)\n\n # 判断关键词在首页出现次数\n str_xpath = \"/html/body/div[3]/div[2]/div[2]/div\"\n res_search = self.ele_exist(str_xpath, \"xpath\", False)\n if res_search is not None:\n for title in res_search:\n if u\"其他人还在搜\" in title.text: # 其他人还在搜 相关词\n kw_others = title.text.replace(\"其他人还在搜\", \"\").split(\"\\n\")\n for v in kw_others:\n if v.strip() == \"\":\n continue\n self.res_keywords['sub_keywords'].append(v.strip())\n continue\n if u\"相关搜索\" in title.text:\n continue\n if self.title_counter > 0: # 词意分析\n str_xpath = \"//div/article/header/div/a/h3\"\n res_title = self.ele_exist(str_xpath, \"xpath\", True)\n if res_title is None:\n continue\n try:\n res_nlp = nlp_client.simnet(self.keywords, res_title.text)\n except UnicodeEncodeError as ex:\n print(u\"=== {} 编码转换错误，词意分析出错，继续下个关键词...\".format(self.keywords))\n break\n except UnicodeDecodeError as ex:\n print(u\"=== {} 编码解码错误，词意分析出错，继续下个关键词...\".format(self.keywords))\n break\n except:\n print(u\"=== {} 接口调用出错，继续下个关键词...\".format(self.keywords))\n break\n if 'score' in res_nlp:\n if (res_nlp['score'] * 10) > self.kw_score:\n self.title_counter -= 1\n\n # 判断当前关键词搜索结果总页数是否大于10页\n str_xpath = \"/html/body/div[3]/div[2]/div[4]/div/a\"\n paging_url = self.ele_exist(str_xpath, \"xpath\", True)\n if paging_url is not None:\n paging_url = paging_url.get_attribute(\"href\").replace(\"pn=10\", \"pn=90\")\n tip_msg = \"拉取关键词翻页信息超时\"\n if self.request_url(paging_url, tip_msg) is None: # 拉取关键词翻页信息\n #self.update_msg(u\"<2>%s 翻页信息拉取失败，无效关键词，继续下一个词！\" % self.keywords)\n return self.res_keywords\n str_xpath = \"#page-controller > div > div.new-pagenav-right > a\"\n btn_next = self.ele_waiting(str_xpath, \"css\", True, False)\n # 注意：会出现没有１０页的搜索结果，但是页码任然为１０的情况，\n # 判断依据为，翻页到１０页，且有下一页的按钮\n if btn_next is not None:\n self.page_keywords = 10\n\n len_relation_kw = len(self.res_keywords[\"sub_keywords\"])\n if self.title_counter <= 0 and len_relation_kw > 0:\n self.res_keywords[\"valid\"] = True\n\n return self.res_keywords\n\n def searching(self):\n \"\"\"\n 关键词搜索，以广度为优先（bfs）\n \"\"\"\n queue = []\n queue_seen = set() # 保存已处理过的关键词\n sql_datas = [] # 插入数据库所需参数\n file_datas = [] # 插入文件所需数据\n queue.append(self.keywords)\n queue_seen.add(self.keywords)\n len_queue = len(queue)\n\n while len_queue > 0:\n self.keywords = queue.pop(0) # 取出第一个关键词\n\n # 判断当前关键词状态，并获取相关词\n r_keywords = self.is_valid_keywords()\n # 添加关键词到待处理队列\n for kw in r_keywords[\"sub_keywords\"]:\n is_past_keywords = False # 是否存在过滤词\n is_includ_keywords = False # 是否包含指定词\n # 关联词不能为空\n if kw.strip() == \"\":\n continue\n # 关键词存在过滤词不做处理\n for past_kw in self.filter_keywords:\n if past_kw in kw:\n is_past_keywords = True\n break\n # 关键词不包含指定词不做处理,包含词为空，则认为该关键词存在包含词\n if len(self.include_keywords) == 0:\n is_includ_keywords = True\n for inl_kw in self.include_keywords:\n if inl_kw in kw:\n is_includ_keywords = True\n break\n # 重复关键词，或者存在过滤词的关键词不做处理\n if is_past_keywords or not is_includ_keywords:\n #len_queue = len(queue) # 重新计算队列长度，避免无效的循环\n continue\n if kw not in queue_seen:\n queue.append(kw)\n queue_seen.add(kw)\n # 重新计算队列长度\n len_queue = len(queue)\n # 保存有效关键词\n if r_keywords[\"valid\"]:\n if self.keywords != \"\":\n self.total_keywords += 1\n #sql_datas.append((None, self.keywords))\n file_datas.append(self.keywords + \"\\n\")\n # 写入文件 2 个词一写，数据库暂时不考虑\n if self.total_keywords % 2 == 0:\n self.write_file(file_datas)\n # 打印提示信息，方便跟踪进度\n self.print_tips(self.total_keywords, len_queue)\n # 重置所有状态值，为下个关键词作准备\n self.status_rest()\n # 将剩余关键词写入文件\n self.write_file(file_datas)\n\n def print_tips(self, c_index, t_index):\n \"\"\"\n 打印提示消息\n \"\"\"\n is_valid_kw = u\"有效词\"\n if not self.res_keywords[\"valid\"]:\n is_valid_kw = u\"无效词\"\n print(u\"== ({} / {}) {}，相关词:{} 个,首页出现:{}次，结果是：{}。\".format(c_index, t_index, self.keywords,\n len(self.res_keywords[\"sub_keywords\"]),\n str(5 - self.title_counter), is_valid_kw))\n\n def shell_exit(self, signal_num, frame):\n \"\"\"\n 脚本被强制终止执行\n :param signal_num:参数名自定义，但是必须存在一个用于接受signal信号的参数\n :param frame:必须存在\n \"\"\"\n try:\n self.browser.quit() # 回收webdriver 资源\n print(u\"本次共采集关键词:{} 个, 文件保存路径为：{}\".format(self.total_keywords, self.file_save_path))\n os._exit(0)\n except:\n pass\n\n\nif __name__ == \"__main__\":\n seconds = 10\n url = \"https://m.baidu.com/\"\n\n #keywords,f_keywords,i_keywords 由.sh提供参数\n f_keywords, i_keywords = [], []\n keywords = sys.argv[1]\n if keywords.strip() == \"\":\n print(u\"主关键词不能为空,请重试...\")\n os._exit(0)\n if sys.argv[2].strip() != \"_fkw_\":\n if \",\" in sys.argv[2]:\n f_keywords = sys.argv[2].split(\",\")\n else:\n f_keywords.append(sys.argv[2])\n if sys.argv[3].strip() != \"_iKw_\":\n if \",\" in sys.argv[3]:\n i_keywords = sys.argv[3].split(\",\")\n else:\n i_keywords.append(sys.argv[3].strip())\n\n print(u\"====================================\")\n # print(u\"==初始化数据库\")\n # sqlhelper = dbHelper()\n # print(u\"==数据库初始完毕\")\n\n mk = MobileKeywords(url, keywords, seconds, f_keywords, i_keywords)\n mk.browser.set_page_load_timeout(seconds) # 页面超时时间为10S\n # mk.dbHelper = sqlhelper\n mk.init_save_info()\n # 加入信号处理模块，监测程序是否被强制终止(ctrl+c)\n signal.signal(signal.SIGINT, mk.shell_exit)\n print(u\"==开始采集关键词 %s\" % keywords)\n mk.searching()\n mk.browser.quit()\n print(u\"==关键词采集结束\")\n print(u\"====================================\")\n", "repo_name": "xXiaobei/anyCode", "sub_path": "seoKeywords/appMobile.py", "file_name": "appMobile.py", "file_ext": "py", "file_size_in_byte": 17955, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "aip.AipNlp", "line_number": 24, "usage_type": "call"}, {"api_name": "selenium.webdriver.Chrome", "line_number": 51, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 51, "usage_type": "name"}, {"api_name": "selenium.webdriver.support.ui.WebDriverWait", "line_number": 52, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 60, "usage_type": "call"}, {"api_name": "os.path", "line_number": 60, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 61, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 62, "usage_type": "call"}, {"api_name": "os.path", "line_number": 62, "usage_type": "attribute"}, {"api_name": "os._exit", "line_number": 69, "usage_type": "call"}, {"api_name": "selenium.webdriver.chrome.options.Options", "line_number": 75, "usage_type": "call"}, {"api_name": "selenium.webdriver.Chrome", "line_number": 112, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 112, "usage_type": "name"}, {"api_name": "selenium.webdriver.support.ui.WebDriverWait", "line_number": 113, "usage_type": "call"}, {"api_name": "selenium.webdriver.Chrome", "line_number": 123, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 123, "usage_type": "name"}, {"api_name": "selenium.webdriver.support.ui.WebDriverWait", "line_number": 124, "usage_type": "call"}, {"api_name": "selenium.common.exceptions.TimeoutException", "line_number": 127, "usage_type": "name"}, {"api_name": "selenium.webdriver.support.expected_conditions.presence_of_element_located", "line_number": 157, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.support.expected_conditions", "line_number": 157, "usage_type": "name"}, {"api_name": "selenium.webdriver.support.expected_conditions.element_to_be_clickable", "line_number": 159, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.support.expected_conditions", "line_number": 159, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.CSS_SELECTOR", "line_number": 162, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 162, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 164, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 164, "usage_type": "name"}, {"api_name": "selenium.common.exceptions.TimeoutException", "line_number": 166, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.CSS_SELECTOR", "line_number": 183, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 183, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 185, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 185, "usage_type": "name"}, {"api_name": "selenium.common.exceptions.WebDriverException", "line_number": 192, "usage_type": "name"}, {"api_name": "selenium.webdriver.support.expected_conditions.presence_of_element_located", "line_number": 211, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.expected_conditions", "line_number": 211, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.CSS_SELECTOR", "line_number": 211, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 211, "usage_type": "name"}, {"api_name": "selenium.webdriver.support.expected_conditions.element_to_be_clickable", "line_number": 212, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.expected_conditions", "line_number": 212, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.CSS_SELECTOR", "line_number": 212, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 212, "usage_type": "name"}, {"api_name": "selenium.common.exceptions.WebDriverException", "line_number": 218, "usage_type": "name"}, {"api_name": "os._exit", "line_number": 373, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 384, "usage_type": "attribute"}, {"api_name": "os._exit", "line_number": 387, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 388, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 389, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 390, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 392, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 393, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 394, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 395, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 397, "usage_type": "attribute"}, {"api_name": "signal.signal", "line_number": 409, "usage_type": "call"}, {"api_name": "signal.SIGINT", "line_number": 409, "usage_type": "attribute"}]} +{"seq_id": "10147348098", "text": "from flask import Flask, render_template,request\nimport mysql.connector\nimport database\napp = Flask(__name__)\n\n\n@app.route(\"/data\")\ndef get_data():\n while True:\n cur = database.mycursor\n cur.execute('SELECT MAX(id) FROM temperature')\n (last_id) = cur.fetchone()\n get_last_recond_id = (\"SELECT * FROM temperature WHERE id = %s\")\n cur.executemany(get_last_recond_id, (last_id,))\n data = cur.fetchall()\n return render_template('index.html', data=data)\n\n\n\nif __name__ == '__main__':\n app.run(debug=True)", "repo_name": "TarasBek/FullIotProject", "sub_path": "Frontend/front.py", "file_name": "front.py", "file_ext": "py", "file_size_in_byte": 555, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "flask.Flask", "line_number": 4, "usage_type": "call"}, {"api_name": "database.mycursor", "line_number": 10, "usage_type": "attribute"}, {"api_name": "flask.render_template", "line_number": 16, "usage_type": "call"}]} +{"seq_id": "30720427585", "text": "\r\n# %%\r\n\r\nimport random\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\nimport pandas as pd\r\nfrom tqdm import tqdm\r\n\r\nfrom IPython.core.interactiveshell import InteractiveShell\r\nInteractiveShell.ast_node_interactivity = \"all\"\r\npd.options.mode.chained_assignment = None\r\npd.set_option('display.max_columns', 200)\r\npd.set_option('display.max_rows', 200)\r\n\r\n\r\ndef make_day(teams, day):\r\n # https://en.wikipedia.org/wiki/Round-robin_tournament#Scheduling_algorithm\r\n\r\n day %= (len(teams) - 1)\r\n if day:\r\n teams = teams[:1] + teams[-day:] + teams[1:-day]\r\n half = len(teams) // 2\r\n return list(zip(teams[:half], teams[half:][::-1]))\r\n\r\n\r\ndef make_schedule(teams, n_weeks):\r\n assert not len(teams) % 2, \"Number of teams must be even!\"\r\n\r\n random.shuffle(teams)\r\n schedule = [make_day(teams, day) for day in range(n_weeks)]\r\n\r\n return schedule\r\n\r\n\r\ndef make_records(teams):\r\n prods = np.linspace(10, -10, len(teams))\r\n proj = {team: prods[team - 1] for team in teams}\r\n\r\n d1 = pd.DataFrame(0, index=teams, columns=['wins', 'losses'])\r\n d2 = pd.Series(proj).rename('proj')\r\n records = pd.concat([d2, d1], axis=1)\r\n return records\r\n\r\n\r\ndef run_week(schedule, records):\r\n for week in range(len(schedule)):\r\n games = schedule[week]\r\n for i, j in games:\r\n pi = records['proj'][i] + f_rdm()\r\n pj = records['proj'][j] + f_rdm()\r\n if pi > pj:\r\n records.loc[i, 'wins'] += 1\r\n records.loc[j, 'losses'] += 1\r\n else:\r\n records.loc[j, 'wins'] += 1\r\n records.loc[i, 'losses'] += 1\r\n\r\n\r\ndef run_season(n_weeks, n_teams, n_playoffs):\r\n teams = list(range(1, n_teams + 1))\r\n\r\n schedule = make_schedule(teams, n_weeks)\r\n\r\n records = make_records(teams)\r\n\r\n run_week(schedule, records)\r\n\r\n dsort = records.sort_values('wins', ascending=False)\r\n inwins = dsort.iloc[n_playoffs - 1]['wins']\r\n nout = dsort.iloc[n_playoffs:]['wins'].eq(inwins).sum()\r\n nin = dsort.iloc[:n_playoffs]['wins'].eq(inwins).sum()\r\n\r\n mostwins = records['wins'].max()\r\n leastwins = records['wins'].min()\r\n\r\n return [inwins, nin, nout, mostwins, leastwins]\r\n\r\n\r\n# %%\r\ndef f_rdm(scale=20): # default teams range from -10 to +10\r\n return np.random.normal(scale=scale, loc=0)\r\n\r\n\r\ndef main():\r\n n_weeks = 14\r\n n_teams = 14\r\n n_playoffs = 8\r\n\r\n n_sims = 500\r\n\r\n dresults = pd.DataFrame(\r\n columns=['nWins', 'nAbove', 'nBelow', 'mostWins', 'leastWins'])\r\n\r\n for sim in tqdm(range(n_sims)):\r\n output = run_season(n_weeks, n_teams, n_playoffs)\r\n dresults.loc[sim] = output\r\n\r\n dresults['odds'] = dresults['nBelow'] / dresults['nAbove']\r\n dresults['odds'] = dresults['nAbove'] / \\\r\n (dresults['nAbove'] + dresults['nBelow'])\r\n\r\n for i in np.sort(dresults['nWins'].unique()):\r\n omn = dresults.loc[dresults['nWins'].eq(i)]['odds'].mean()\r\n print(f'-- odds with {i} wins: {omn.round(2)}')\r\n\r\n dresults['mostWins'].value_counts(dropna=False).sort_index()\r\n\r\n return dresults\r\n\r\n\r\nif __name__ == '__main__':\r\n dresults = main()\r\n\r\n# %%\r\n", "repo_name": "KAValerio/FantasyFootballPlayoffChances", "sub_path": "FFplayoffRec.py", "file_name": "FFplayoffRec.py", "file_ext": "py", "file_size_in_byte": 3164, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "IPython.core.interactiveshell.InteractiveShell.ast_node_interactivity", "line_number": 11, "usage_type": "attribute"}, {"api_name": "IPython.core.interactiveshell.InteractiveShell", "line_number": 11, "usage_type": "name"}, {"api_name": "pandas.options", "line_number": 12, "usage_type": "attribute"}, {"api_name": "pandas.set_option", "line_number": 13, "usage_type": "call"}, {"api_name": "pandas.set_option", "line_number": 14, "usage_type": "call"}, {"api_name": "random.shuffle", "line_number": 30, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 37, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 40, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 41, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 42, "usage_type": "call"}, {"api_name": "numpy.random.normal", "line_number": 82, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 82, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 92, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 95, "usage_type": "call"}, {"api_name": "numpy.sort", "line_number": 103, "usage_type": "call"}]} +{"seq_id": "72257991786", "text": "import cv2 \nimport os\n\nroot_dir = './dataset'\nresized_dir = os.path.join(root_dir, 'resized_data') \naugmented_dir = os.path.join(root_dir, 'augmented_data')\n\ndef change_brightness_contrast(image, alpha, beta):\n \n # Aplicar a transformação linear para alterar brilho e contraste\n result = cv2.convertScaleAbs(image, alpha=alpha, beta=beta)\n return result\n\nfor filename in os.listdir(resized_dir):\n\n imageSelected2 = cv2.imread(os.path.join(resized_dir, filename))\n\n # Definir os parâmetros de brilho e contraste desejados\n alpha = 1.15 # Fator de contraste (1.0 significa sem alteração)\n beta = 30 # Fator de brilho\n\n # Alterar brilho e contraste\n modifiedImage = change_brightness_contrast(imageSelected2, alpha, beta)\n\n # Exibir as imagens original e alterada\n cv2.waitKey(0)\n cv2.destroyAllWindows()\n cv2.imwrite(os.path.join(augmented_dir, filename), modifiedImage)", "repo_name": "crisaoo/opuntia-stricta-howard-recognition", "sub_path": "augment.py", "file_name": "augment.py", "file_ext": "py", "file_size_in_byte": 921, "program_lang": "python", "lang": "pt", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "os.path.join", "line_number": 5, "usage_type": "call"}, {"api_name": "os.path", "line_number": 5, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 6, "usage_type": "call"}, {"api_name": "os.path", "line_number": 6, "usage_type": "attribute"}, {"api_name": "cv2.convertScaleAbs", "line_number": 11, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 14, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 16, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 16, "usage_type": "call"}, {"api_name": "os.path", "line_number": 16, "usage_type": "attribute"}, {"api_name": "cv2.waitKey", "line_number": 26, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 27, "usage_type": "call"}, {"api_name": "cv2.imwrite", "line_number": 28, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 28, "usage_type": "call"}, {"api_name": "os.path", "line_number": 28, "usage_type": "attribute"}]} +{"seq_id": "2417167637", "text": "import re\r\nfrom xml.etree.ElementTree import parse\r\n\r\n\r\nclass GetXmlTestcaseList:\r\n def ReadCaseFromXml(self, xmlfile):\r\n tree = parse(xmlfile)\r\n root = tree.getroot()\r\n # 逐行解析XML文件，将每行的内容存入list\r\n suite_list = []\r\n for node in root.getiterator('testsuite'):\r\n suite_dict = {}\r\n if node.tag == \"testsuite\":\r\n # print(node.attrib['name'])\r\n suite_dict['suite_name'] = GetXmlTestcaseList().removeInvalidCharacters(str(node.attrib['name']))\r\n case_list = []\r\n for child in node:\r\n if child.tag == \"node_order\":\r\n # print(child.text)\r\n suite_dict['suite_node_order'] = child.text\r\n continue\r\n if child.tag == \"details\":\r\n # print(child.text)\r\n suite_dict['suite_details'] = str(child.text)\r\n continue\r\n if child.tag == \"testcase\":\r\n case_dict = {}\r\n # print(child.attrib['name'])\r\n # print(child.attrib['internalid'])\r\n case_dict['case_name'] = child.attrib['name']\r\n case_dict['case_internalid'] = child.attrib['internalid']\r\n for grandChild in child:\r\n if grandChild.tag == \"node_order\":\r\n # print(grandChild.text)\r\n case_dict['case_node_order'] = grandChild.text\r\n continue\r\n if grandChild.tag == \"externalid\":\r\n # print(grandChild.text)\r\n case_dict['case_externalid'] = grandChild.text\r\n continue\r\n if grandChild.tag == \"summary\":\r\n # print(str(grandChild.text))\r\n case_dict['case_summary'] = GetXmlTestcaseList().removeHtmlTag(str(grandChild.text))\r\n continue\r\n if grandChild.tag == \"steps\":\r\n # print(str(grandChild.text))\r\n case_dict['case_steps'] = GetXmlTestcaseList().removeHtmlTag(str(grandChild.text))\r\n continue\r\n if grandChild.tag == \"expectedresults\":\r\n # print(str(grandChild.text))\r\n case_dict['case_expectedresults'] = GetXmlTestcaseList().removeHtmlTag(\r\n str(grandChild.text))\r\n continue\r\n case_list.append(case_dict)\r\n # print(case_list)\r\n suite_dict['testcase'] = case_list\r\n # print(suite_dict)\r\n if len(suite_dict) > 0:\r\n suite_list.append(suite_dict)\r\n # print(suite_list)\r\n return suite_list\r\n\r\n def removeHtmlTag(self, testcase):\r\n testcase = re.sub(r'<.*?>', \"\", str(testcase))\r\n testcase = testcase.replace('\\r', \"\")\r\n testcase = testcase.replace('“', \"\\\"\")\r\n testcase = testcase.replace('"', \"\\\"\")\r\n testcase = testcase.replace('”', \"\\\"\")\r\n testcase = testcase.replace(' ', \" \")\r\n testcase = testcase.replace('…', \"...\")\r\n testcase = testcase.replace('<', \"<\")\r\n testcase = testcase.replace('>', \">\")\r\n testcase = testcase.replace('&', \"&\")\r\n testcase = testcase.replace('‘', \"【\")\r\n testcase = testcase.replace('’', \"】\")\r\n testcase = testcase.replace('\\ufeff', \"\")\r\n return testcase\r\n\r\n def removeInvalidCharacters(self, param):\r\n invalidCharacters = ['*', ':', '/', '\\\\', '?', '[', ']']\r\n for i in range(len(invalidCharacters)):\r\n param = param.replace(invalidCharacters[i], ' ')\r\n return param\r\n\r\n\r\nif __name__ == \"__main__\":\r\n GetXmlTestcaseList().ReadCaseFromXml('all_testsuites.xml')\r\n", "repo_name": "ShiehLong/XmlConvert", "sub_path": "ConvertFileCore/GetXmlTestcaseList.py", "file_name": "GetXmlTestcaseList.py", "file_ext": "py", "file_size_in_byte": 4251, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "37", "api": [{"api_name": "xml.etree.ElementTree.parse", "line_number": 7, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 64, "usage_type": "call"}]} +{"seq_id": "2040355221", "text": "import cv2 as cv\nimport mediapipe as mp\nimport time \nimport opencv \nimport numpy as np\nclass shapeDetector():\n def __init__(self):\n pass\n def getContours(img):\n imgContour=img.copy()\n imgGray=cv.cvtColor(img,cv.COLOR_BGR2GRAY)\n imgBlur=cv.GaussianBlur(imgGray,(7,7),1)\n imgCanny=cv.Canny(imgBlur,50,50) \n \n contours,hierarchy=cv.findContours(imgCanny,cv.RETR_EXTERNAL,cv.CHAIN_APPROX_NONE)\n for cnt in contours:\n area=cv.contourArea(cnt)\n if area>500: \n cv.drawContours(imgContour,cnt,-1,(255,0,0),1 )\n peri=cv.arcLength(cnt,True)\n approx=cv.approxPolyDP(cnt,0.02*peri,True) #coordinates of vertex \n objCor=len(approx) #số cạnh \n x,y,w,h=cv.boundingRect(approx)\n \n if objCor==3:\n objectType='Tri'\n elif objCor==4:\n aspRatio=w/float(h)\n if aspRatio>0.95 and aspRatio<1.05: objectType='Square'\n else: objectType='rectangle'\n else:objectType='None'\n cv.rectangle(imgContour,(x,y),(x+w,y+h),(0,255,0),2)\n cv.putText(imgContour,objectType,(x+w//2,y+h//2),cv.FONT_HERSHEY_PLAIN,1,(0,0,0),1) \n return imgContour\ndef main():\n img=cv.imread('hinhhoc.jpg')\n #imgBlank=np.zeros_like(imgGray) \n \n imgContour=shapeDetector.getContours(img)\n #imgStack=opencv.stackImages([[imgGray,imgBlur,imgCannny],[imgBlank,imgBlank,imgBlank]],0.5)\n #imgStack=opencv.stackImages([img,imgContour],0.5)\n \n #cv.imshow('Detection',imgStack)\n cv.imshow('Detection',img)\n cv.imshow('Detection1',imgContour)\n cv.waitKey(0)\nif __name__ == \"__main__\":\n main()\n", "repo_name": "Shengpy/Open-CV", "sub_path": "test2/shape_Detection.py", "file_name": "shape_Detection.py", "file_ext": "py", "file_size_in_byte": 1829, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "cv2.cvtColor", "line_number": 11, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 11, "usage_type": "attribute"}, {"api_name": "cv2.GaussianBlur", "line_number": 12, "usage_type": "call"}, {"api_name": "cv2.Canny", "line_number": 13, "usage_type": "call"}, {"api_name": "cv2.findContours", "line_number": 15, "usage_type": "call"}, {"api_name": "cv2.RETR_EXTERNAL", "line_number": 15, "usage_type": "attribute"}, {"api_name": "cv2.CHAIN_APPROX_NONE", "line_number": 15, "usage_type": "attribute"}, {"api_name": "cv2.contourArea", "line_number": 17, "usage_type": "call"}, {"api_name": "cv2.drawContours", "line_number": 19, "usage_type": "call"}, {"api_name": "cv2.arcLength", "line_number": 20, "usage_type": "call"}, {"api_name": "cv2.approxPolyDP", "line_number": 21, "usage_type": "call"}, {"api_name": "cv2.boundingRect", "line_number": 23, "usage_type": "call"}, {"api_name": "cv2.rectangle", "line_number": 32, "usage_type": "call"}, {"api_name": "cv2.putText", "line_number": 33, "usage_type": "call"}, {"api_name": "cv2.FONT_HERSHEY_PLAIN", "line_number": 33, "usage_type": "attribute"}, {"api_name": "cv2.imread", "line_number": 36, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 44, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 45, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 46, "usage_type": "call"}]} +{"seq_id": "12773245241", "text": "from ELCA import transit, lc_fitter\r\n\r\nfrom bls import BLS\r\n\r\nimport matplotlib.pyplot as plt\r\nimport numpy as np\r\n\r\nclass fitData():\r\n def __init__(self):\r\n self.m=[]\r\n self.b=[]\r\n self.std=[]\r\n self.t=[]\r\n\r\n def save(self,t,b,m,std):\r\n self.t.append(t)\r\n self.b.append(b)\r\n self.m.append(m)\r\n self.std.append(std)\r\n def get(self,i):\r\n return self.t[i],self.b[i],self.m[i],self.std[i]\r\n\r\ndef phase_bin(time,flux,per,tmid=0,cadence=16,offset=0.25):\r\n '''\r\n Phase fold data and bin according to time cadence\r\n time - [days]\r\n flux - arbitrary unit\r\n per - period in [days]\r\n tmid - value in days\r\n cadence - spacing to bin data to [minutes] \r\n '''\r\n phase = ((time-tmid)/per + offset)%1\r\n\r\n sortidx = np.argsort(phase)\r\n sortflux = flux[sortidx]\r\n sortphase = phase[sortidx]\r\n\r\n cad = cadence/60./24/per # phase cadence according to kepler cadence\r\n pbins = np.arange(0,1+cad,cad) # phase bins\r\n bindata = np.zeros(pbins.shape[0]-1)\r\n for i in range(pbins.shape[0]-1):\r\n pidx = (sortphase > pbins[i]) & (sortphase < pbins[i+1])\r\n\r\n if pidx.sum() == 0 or np.isnan(sortflux[pidx]).all():\r\n bindata[i] = np.nan\r\n continue\r\n\r\n bindata[i] = np.nanmean(sortflux[pidx])\r\n\r\n phases = pbins[:-1]+np.diff(pbins)*0.5\r\n\r\n # remove nans\r\n #nonans = ~np.isnan(bindata)\r\n #return phases[nonans],bindata[nonans]\r\n return phases, bindata\r\n\r\ndef BLLS(t,f, fmodel, periods, q_range):\r\n\r\n # alloc data\r\n pdata = fitData()\r\n pdata.p = []; pdata.q = []\r\n for p in periods:\r\n\r\n # compute phase and sort data\r\n phase,data = phase_bin(t,f,p,min(t),cadence=2,offset=0.25)\r\n sidx = np.argsort(phase)\r\n sphase = phase[sidx]\r\n sdata = data[sidx]\r\n nonans = ~np.isnan(sdata)\r\n\r\n # loop through fractions of phase\r\n qdata = fitData()\r\n qdata.q = []\r\n for q in q_range:\r\n\r\n # construct model\r\n model = np.zeros(sdata.shape)\r\n model[sphase0).sum() * 0.2) ):\r\n smodel = np.roll(model,i) # replace with shift \r\n \r\n # pull out sub region of data around model to optimize linalg?\r\n # solve linear least squares to optimize transit pars\r\n A = np.vstack([np.ones(len(smodel[nonans])), smodel[nonans]]).T\r\n b, m = np.linalg.lstsq(A, sdata[nonans], rcond=None)[0]\r\n y = b + m*smodel[nonans]\r\n tdata.save(i, b, m, np.std(sdata[nonans]-y))\r\n\r\n # figure out why the amplitude is not close to my expected SNR\r\n # pick the best fit from t0\r\n snr = np.array(tdata.m)*-1 / np.array(tdata.std)\r\n qdata.save( *tdata.get(np.argmax(snr)) )\r\n qdata.q.append(q)\r\n \r\n # pick the best fit from transit duration\r\n snr = np.array(qdata.m)*-1 / np.array(qdata.std)\r\n pdata.save( *qdata.get(np.argmax(snr)) )\r\n pdata.q.append(q)\r\n pdata.p.append(p)\r\n \r\n # compute final snr for BLLS\r\n return pdata\r\n\r\nif __name__ == \"__main__\":\r\n\r\n t = np.linspace(0,20, int(20*24*60*0.5) )\r\n NOISE = 7.5e-4\r\n\r\n # randomly delete data \r\n t = np.random.choice(t, int(0.9*t.shape[0]) )\r\n t = np.sort(t)\r\n\r\n init = { 'rp':np.random.normal(0.05,0.01), # Rp/Rs\r\n 'ar':np.random.normal(15,0.1), # a/Rs\r\n 'per':np.random.normal(3.5,0.1), # period [days]\r\n 'inc':89.5, # Inclination [deg]\r\n 'u1': 0.3, 'u2': 0.1, # limb darkening (linear, quadratic)\r\n 'ecc':0, 'ome':0, # Eccentricity, Arg of periastron\r\n 'a0':1, 'a1':0, # Airmass extinction terms\r\n 'a2':0, 'tm':3.5*0.25 } # tm = Mid Transit time (Days)\r\n\r\n # only report params with bounds, all others will be fixed to initial value\r\n mybounds = {\r\n 'rp':[0,1],\r\n 'tm':[min(t),max(t)],\r\n 'a0':[-np.inf,np.inf],\r\n 'a1':[-np.inf,np.inf]\r\n }\r\n\r\n # GENERATE NOISY DATA\r\n tmodel = transit(time=t, values=init) \r\n data = tmodel + np.random.normal(0, NOISE, len(t))\r\n dataerr = np.random.normal(400e-6, 50e-6, len(t))\r\n\r\n # scale a transit model between 0 and 1\r\n stmodel = ((tmodel-tmodel.min() )/ max(tmodel-tmodel.min()))[:2000]\r\n intrans = -1*(stmodel[stmodel<1]-1)\r\n xp = np.linspace(0,1, len(intrans) )\r\n fmodel = lambda x : np.interp(x, xp, intrans)\r\n\r\n # search for periodic signals\r\n # compute the phase space to search \r\n pdata = BLLS(t,data, fmodel, np.linspace(2,10,250), np.linspace(0.05,0.15,50))\r\n snr = np.array(pdata.m)*-1 / np.array(pdata.std)\r\n\r\n # figure out BLS scaling to SNR \r\n bls = BLS(t, data, np.ones(dataerr.shape[0]), period_range=(2,10), q_range=(0.05, 0.15), nf=500, nbin=100)\r\n res = bls()\r\n periods = 1./bls.freqs\r\n\r\n fig = plt.figure()\r\n ax0 = plt.subplot2grid((1,3),(0,0), colspan=2)\r\n ax1 = plt.subplot2grid((1,3),(0,2))\r\n #ax2 = plt.subplot2grid((2,2),(1,1))\r\n\r\n ax0.plot(t,data,'k.',alpha=0.5)\r\n ax0.set_title(\"Test Data\")\r\n ax0.set_ylabel(\"Relative Flux\")\r\n ax0.set_xlabel(\"Time [day]\")\r\n ax1.plot(periods,res.sde,'k-',label='BLS (Kovacs 2002)')\r\n ax1.set_xlabel('Period [day]')\r\n #ax1.set_ylabel('SNE')\r\n ax1.set_title(\"Transit Periodogram\")\r\n ax1.plot(pdata.p,snr,label='Custom')\r\n ax1.set_xlabel(\"Period [day]\")\r\n ax1.set_ylabel(\"S/N\")\r\n ax1.plot([min(pdata.p),max(pdata.p)],[init['rp']**2/NOISE,init['rp']**2/NOISE],'k--',label='Truth')\r\n ax1.legend(loc='best')\r\n ax1.set_ylim([0,5])\r\n plt.tight_layout()\r\n plt.show()\r\n '''\r\n #dataerr = np.random.normal(400e-6, 50e-6, len(t))\r\n myfit = lc_fitter(t,data,\r\n dataerr=dataerr,\r\n init= init,\r\n bounds= mybounds,\r\n nested=True,\r\n loss='soft_l1'\r\n )\r\n for k in myfit.data['freekeys']:\r\n print( '{}: {:.6f} +- {:.6f}'.format(k,myfit.data['NS']['parameters'][k],myfit.data['NS']['errors'][k]) )\r\n\r\n myfit.plot_results(show=True,t='NS')\r\n myfit.plot_posteriors(show=True)\r\n '''", "repo_name": "pearsonkyle/Exoplanet-Transit-Periodogram", "sub_path": "bls_test.py", "file_name": "bls_test.py", "file_ext": "py", "file_size_in_byte": 6615, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "37", "api": [{"api_name": "numpy.argsort", "line_number": 34, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 39, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 40, "usage_type": "call"}, {"api_name": "numpy.isnan", "line_number": 44, "usage_type": "call"}, {"api_name": "numpy.nan", "line_number": 45, "usage_type": "attribute"}, {"api_name": "numpy.nanmean", "line_number": 48, "usage_type": "call"}, {"api_name": "numpy.diff", "line_number": 50, "usage_type": "call"}, {"api_name": "numpy.argsort", "line_number": 66, "usage_type": "call"}, {"api_name": "numpy.isnan", "line_number": 69, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 77, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 83, "usage_type": "call"}, {"api_name": "numpy.roll", "line_number": 84, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 88, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 88, "usage_type": "call"}, {"api_name": "numpy.linalg.lstsq", "line_number": 89, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 89, "usage_type": "attribute"}, {"api_name": "numpy.std", "line_number": 91, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 95, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 96, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 100, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 101, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 110, "usage_type": "call"}, {"api_name": "numpy.random.choice", "line_number": 114, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 114, "usage_type": "attribute"}, {"api_name": "numpy.sort", "line_number": 115, "usage_type": "call"}, {"api_name": "numpy.random.normal", "line_number": 117, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 117, "usage_type": "attribute"}, {"api_name": "numpy.random.normal", "line_number": 118, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 118, "usage_type": "attribute"}, {"api_name": "numpy.random.normal", "line_number": 119, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 119, "usage_type": "attribute"}, {"api_name": "numpy.inf", "line_number": 130, "usage_type": "attribute"}, {"api_name": "numpy.inf", "line_number": 131, "usage_type": "attribute"}, {"api_name": "ELCA.transit", "line_number": 135, "usage_type": "call"}, {"api_name": "numpy.random.normal", "line_number": 136, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 136, "usage_type": "attribute"}, {"api_name": "numpy.random.normal", "line_number": 137, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 137, "usage_type": "attribute"}, {"api_name": "numpy.linspace", "line_number": 142, "usage_type": "call"}, {"api_name": "numpy.interp", "line_number": 143, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 147, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 148, "usage_type": "call"}, {"api_name": "bls.BLS", "line_number": 151, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 151, "usage_type": "call"}, {"api_name": "bls.freqs", "line_number": 153, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 155, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 155, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot2grid", "line_number": 156, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 156, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot2grid", "line_number": 157, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 157, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tight_layout", "line_number": 174, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 174, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 175, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 175, "usage_type": "name"}]} +{"seq_id": "41648611832", "text": "\"\"\"\nThis class models the first dummy page needed by the framework to start.\nURL: None\nPlease do not modify or delete this page\n\"\"\"\nimport os, sys, time\nsys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))\nfrom .Mobile_Base_Page import Mobile_Base_Page\nfrom utils.Wrapit import Wrapit\nimport conf.locators_mobile_acclerate_conf as locators\nimport conf.mobile_app_conf as app_conf\n\nclass Net163Music_Mobile_Page(Mobile_Base_Page):\n app_conf = app_conf.app_net126Music\n netease_userAgreement = locators.netease_userAgreement\n netease_trial = locators.netease_trial\n netease_rand = locators.netease_rand\n netease_search = locators.netease_search\n netease_search_text = locators.netease_search_text\n netease_search_button = locators.netease_search_button\n netease_search_result1 = locators.netease_search_result1\n netease_error = locators.netease_error\n def start(self):\n \"Use this method to go to specific URL -- if needed\"\n print(\"start\")\n pass\n\n @Wrapit._screenshot\n def click_netease_userAgreement(self):\n try:\n # Click on real time price page button.\n result_flag = None\n if self.click_element(self.netease_userAgreement):\n result_flag = True\n else:\n result_flag = False \n self.conditional_write(result_flag,\n positive='click userAgreement',\n negative='Failed to click userAgreement',\n level='debug')\n except Exception as e:\n self.write(str(e))\n return result_flag\n\n @Wrapit._screenshot\n def click_netease_trial(self):\n try:\n # Click on real time price page button.\n result_flag = None\n if self.click_element(self.netease_trial):\n result_flag = True\n else:\n result_flag = False \n self.conditional_write(result_flag,\n positive='click trial',\n negative='Failed to click trial',\n level='debug')\n except Exception as e:\n self.write(str(e))\n return result_flag\n @Wrapit._screenshot\n def click_netease_rand(self):\n try:\n # Click on real time price page button.\n result_flag = None\n if self.click_element(self.netease_rand):\n result_flag = True\n else:\n result_flag = False \n self.conditional_write(result_flag,\n positive='click netease_rand',\n negative='Failed to click netease_rand',\n level='debug')\n except Exception as e:\n self.write(str(e))\n return result_flag\n\n @Wrapit._screenshot\n def click_netease_netease_search(self):\n try:\n # Click on real time price page button.\n result_flag = None\n if self.click_element(self.netease_search):\n result_flag = True\n else:\n result_flag = False \n self.conditional_write(result_flag,\n positive='click netease_search',\n negative='Failed to netease_search',\n level='debug')\n except Exception as e:\n self.write(str(e))\n return result_flag\n \n @Wrapit._screenshot\n def input_netease_search_text(self,text):\n try:\n # Click on real time price page button.\n result_flag = None\n if self.set_text(self.netease_search_text,text):\n result_flag = True\n else:\n result_flag = False \n self.conditional_write(result_flag,\n positive='input netease_search_text',\n negative='Failed to input netease_search_text',\n level='debug')\n except Exception as e:\n self.write(str(e))\n return result_flag\n\n @Wrapit._screenshot\n def click_netease_search_button(self):\n try:\n # Click on real time price page button.\n result_flag = None\n if self.click_element(self.netease_search_button):\n result_flag = True\n else:\n result_flag = False \n self.conditional_write(result_flag,\n positive='click netease_search_button',\n negative='Failed to netease_search_button',\n level='debug')\n except Exception as e:\n self.write(str(e))\n return result_flag\n\n @Wrapit._screenshot\n def click_netease_netease_search_result1(self):\n try:\n # Click on real time price page button.\n result_flag = None\n if self.click_element(self.netease_search_result1):\n result_flag = True\n else:\n result_flag = False \n self.conditional_write(result_flag,\n positive='click netease_search_result1',\n negative='Failed to netease_search_result1',\n level='debug')\n except Exception as e:\n self.write(str(e))\n return result_flag\n\n @Wrapit._screenshot\n def check_result_errror(self):\n try:\n # Click on real time price page button.\n result_flag = None\n if self.check_element_displayed(self.netease_error):\n result_flag = False\n else:\n result_flag = True \n self.conditional_write(result_flag,\n positive='check area limit',\n negative='Failed to check area limit',\n level='debug')\n except Exception as e:\n self.write(str(e))\n return result_flag ", "repo_name": "justtwo2/TestGen", "sub_path": "qxf2-page-object-model-master/page_objects/net163Music_mobile_page.py", "file_name": "net163Music_mobile_page.py", "file_ext": "py", "file_size_in_byte": 5744, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "sys.path.append", "line_number": 7, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 7, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 7, "usage_type": "call"}, {"api_name": "os.path", "line_number": 7, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 7, "usage_type": "call"}, {"api_name": "Mobile_Base_Page.Mobile_Base_Page", "line_number": 13, "usage_type": "name"}, {"api_name": "conf.mobile_app_conf", "line_number": 14, "usage_type": "name"}, {"api_name": "conf.mobile_app_conf.app_net126Music", "line_number": 14, "usage_type": "attribute"}, {"api_name": "conf.locators_mobile_acclerate_conf.netease_userAgreement", "line_number": 15, "usage_type": "attribute"}, {"api_name": "conf.locators_mobile_acclerate_conf", "line_number": 15, "usage_type": "name"}, {"api_name": "conf.locators_mobile_acclerate_conf.netease_trial", "line_number": 16, "usage_type": "attribute"}, {"api_name": "conf.locators_mobile_acclerate_conf", "line_number": 16, "usage_type": "name"}, {"api_name": "conf.locators_mobile_acclerate_conf.netease_rand", "line_number": 17, "usage_type": "attribute"}, {"api_name": "conf.locators_mobile_acclerate_conf", "line_number": 17, "usage_type": "name"}, {"api_name": "conf.locators_mobile_acclerate_conf.netease_search", "line_number": 18, "usage_type": "attribute"}, {"api_name": "conf.locators_mobile_acclerate_conf", "line_number": 18, "usage_type": "name"}, {"api_name": "conf.locators_mobile_acclerate_conf.netease_search_text", "line_number": 19, "usage_type": "attribute"}, {"api_name": "conf.locators_mobile_acclerate_conf", "line_number": 19, "usage_type": "name"}, {"api_name": "conf.locators_mobile_acclerate_conf.netease_search_button", "line_number": 20, "usage_type": "attribute"}, {"api_name": "conf.locators_mobile_acclerate_conf", "line_number": 20, "usage_type": "name"}, {"api_name": "conf.locators_mobile_acclerate_conf.netease_search_result1", "line_number": 21, "usage_type": "attribute"}, {"api_name": "conf.locators_mobile_acclerate_conf", "line_number": 21, "usage_type": "name"}, {"api_name": "conf.locators_mobile_acclerate_conf.netease_error", "line_number": 22, "usage_type": "attribute"}, {"api_name": "conf.locators_mobile_acclerate_conf", "line_number": 22, "usage_type": "name"}, {"api_name": "utils.Wrapit.Wrapit._screenshot", "line_number": 28, "usage_type": "attribute"}, {"api_name": "utils.Wrapit.Wrapit", "line_number": 28, "usage_type": "name"}, {"api_name": "utils.Wrapit.Wrapit._screenshot", "line_number": 45, "usage_type": "attribute"}, {"api_name": "utils.Wrapit.Wrapit", "line_number": 45, "usage_type": "name"}, {"api_name": "utils.Wrapit.Wrapit._screenshot", "line_number": 61, "usage_type": "attribute"}, {"api_name": "utils.Wrapit.Wrapit", "line_number": 61, "usage_type": "name"}, {"api_name": "utils.Wrapit.Wrapit._screenshot", "line_number": 78, "usage_type": "attribute"}, {"api_name": "utils.Wrapit.Wrapit", "line_number": 78, "usage_type": "name"}, {"api_name": "utils.Wrapit.Wrapit._screenshot", "line_number": 95, "usage_type": "attribute"}, {"api_name": "utils.Wrapit.Wrapit", "line_number": 95, "usage_type": "name"}, {"api_name": "utils.Wrapit.Wrapit._screenshot", "line_number": 112, "usage_type": "attribute"}, {"api_name": "utils.Wrapit.Wrapit", "line_number": 112, "usage_type": "name"}, {"api_name": "utils.Wrapit.Wrapit._screenshot", "line_number": 129, "usage_type": "attribute"}, {"api_name": "utils.Wrapit.Wrapit", "line_number": 129, "usage_type": "name"}, {"api_name": "utils.Wrapit.Wrapit._screenshot", "line_number": 146, "usage_type": "attribute"}, {"api_name": "utils.Wrapit.Wrapit", "line_number": 146, "usage_type": "name"}]} +{"seq_id": "42268169882", "text": "\"\"\"Executes pylint\"\"\"\nfrom pylint.lint import Run\ndef main():\n \"\"\"Executes pylint and validates score\"\"\"\n\n try:\n results = Run(['saltypie'], do_exit=False)\n except TypeError:\n results = Run(['saltypie'], exit=False)\n if results.linter.stats['global_note'] <= 9:\n exit('pylint score must be greater than 9')\n\nmain()\n", "repo_name": "wils0ns/saltypie", "sub_path": "tests/run_pylint.py", "file_name": "run_pylint.py", "file_ext": "py", "file_size_in_byte": 348, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "37", "api": [{"api_name": "pylint.lint.Run", "line_number": 7, "usage_type": "call"}, {"api_name": "pylint.lint.Run", "line_number": 9, "usage_type": "call"}]} +{"seq_id": "29716218371", "text": "import ConfigParser\nimport argparse\nimport datetime\nimport json\nimport logging\nimport os\nimport smtplib\nimport sys\nimport time\nimport utils.mail\n\nimport deploy_utils\n\nfrom optparse import make_option\nfrom os import path\n\nfrom django.conf import settings\nfrom django.core.management.base import BaseCommand, CommandError\nfrom django.utils import timezone\n\nfrom monitor.models import Status, Service, Cluster, Job, Task\n\nBOOL_METRIC_MAP = {\n \"tag.IsOutOfSync\": \"true\",\n \"tag.HAState\": \"active\",\n}\n\nSTATUS_FILE_PATH = 'cluster.status'\n# alert when cluster is not OK for ERROR_TIMES_FOR_ALERT\nERROR_TIMES_FOR_ALERT = 3\n\nlogger = logging.getLogger(__name__)\n\nclass CollectorConfig:\n class Service:\n def __init__(self, options, config, name):\n # Parse service config.\n self.name = name\n self.jobs = config.get(name, \"jobs\").split()\n self.clusters = {}\n for cluster_name in config.get(name, \"clusters\").split():\n args = argparse.Namespace()\n args.service = self.name\n args.cluster = cluster_name\n # Parse cluster config.\n self.clusters[cluster_name] = deploy_utils.get_service_config(args)\n self.metric_url = config.get(name, \"metric_url\")\n\n def __init__(self, args, options):\n # Parse collector config.\n config_path = os.path.join(deploy_utils.get_config_dir(), 'owl/collector.cfg')\n self.args = args\n self.options = options\n self.config = self.parse_config_file(config_path)\n self.services = {}\n for service_name in self.config.get(\"collector\", \"services\").split():\n self.services[service_name] = CollectorConfig.Service(\n options, self.config, service_name)\n self.period = self.config.getint(\"collector\", \"period\")\n\n def parse_config_file(self, config_path):\n config_parser = ConfigParser.SafeConfigParser()\n config_parser.optionxform = str\n logger.info(\"Parsing config file: %s\", config_path)\n if not config_parser.read(config_path):\n logger.critical(\"Can't parse config file: %s\", config_path)\n sys.exit(1)\n logger.info(\"Successfully parsed config file\")\n return config_parser\n\nclass StatusChecker:\n \"\"\"Check status of all active clusters and jobs, which are inferred from\n tasks' status.\"\"\"\n\n def __init__(self, collector_config, last_status, options, mailer):\n self.collector_config = collector_config\n self.last_status = last_status\n self.options = options\n self.alert_msg = ''\n self.mailer = mailer\n\n def get_latest_metric(self, task, group_name, metric_name):\n try:\n metric = json.loads(task.last_metrics)\n return metric[group_name][metric_name]\n except Exception as e:\n logger.warning(\"%r failed to get metric: %r\", task, e)\n return 0\n\n def is_namenode_active(self, task):\n try:\n metric = self.get_latest_metric(\n task, \"Hadoop:service=NameNode,name=FSNamesystem\", \"tag.HAState\")\n return bool(metric)\n except Exception as e:\n logger.warning(\"%r failed to get metric: %r\", task, e)\n return False\n\n def is_master_active(self, task):\n try:\n metric = self.get_latest_metric(\n task, \"hadoop:service=Master,name=Master\", \"IsActiveMaster\")\n return bool(metric)\n except Exception as e:\n logger.warning(\"%r failed to get metric: %r\", task, e)\n return False\n\n def check_hdfs_cluster_status(self, cluster):\n job = cluster.jobs[\"journalnode\"]\n if (job.running_tasks_count < 2 or\n job.running_tasks_count < (job.total_tasks_count / 2 + 1)):\n job.last_status = Status.ERROR\n job.last_message = \"Too few running journalnodes!\"\n\n job = cluster.jobs[\"namenode\"]\n if job.running_tasks_count < 1:\n job.last_status = Status.ERROR\n job.last_message = \"No running namenodes!\"\n else:\n active = 0\n for task in job.running_tasks.itervalues():\n if self.is_namenode_active(task):\n # update cluster entry\n cluster.entry = '%s:%d' % (task.host, task.port)\n cluster.version = self.get_latest_metric(task,\n 'Hadoop:service=NameNode,name=NameNodeInfo',\n 'Version')\n active += 1\n if active > 1:\n job.last_status = Status.ERROR\n job.last_message = \"Too many active namenodes!\"\n elif active < 1:\n job.last_status = Status.ERROR\n job.last_message = \"No active namenodes!\"\n elif job.running_tasks_count < 2:\n job.last_status = Status.WARN\n job.last_message = \"Less than 2 running namenodes, no HA guarantee\"\n\n job = cluster.jobs[\"datanode\"]\n if job.running_tasks_count < 3:\n job.last_status = Status.ERROR\n job.last_message = \"Too few running datanodes!\"\n cluster.last_status = max([job.last_status for job in cluster.jobs.itervalues()])\n\n def check_hbase_cluster_status(self, cluster):\n job = cluster.jobs[\"master\"]\n if job.running_tasks_count < 1:\n job.last_status = Status.ERROR\n job.last_message = \"No running masters!\"\n else:\n active = 0\n for task in job.running_tasks.itervalues():\n if self.is_master_active(task):\n # update cluster entry\n cluster.entry = '%s:%d' % (task.host, task.port)\n version = self.get_latest_metric(task,\n 'hadoop:service=HBase,name=Info',\n 'version')\n revision = self.get_latest_metric(task,\n 'hadoop:service=HBase,name=Info',\n 'revision')\n cluster.version = '%s, r%s' % (version, revision)\n active += 1\n if active > 1:\n job.last_status = Status.ERROR\n job.last_message = \"Too many active masters!\"\n elif active < 1:\n job.last_status = Status.ERROR\n job.last_message = \"No active masters!\"\n elif job.running_tasks_count < 2:\n # TODO: Now it always reports warning as backup master doesn't run a http\n # server before it acquires zk lock. Comment this out and would change\n # master's startup workflow.\n #job.last_status = Status.WARN\n #job.last_message = \"Less than 2 running masters, no HA guarantee\"\n pass\n\n job = cluster.jobs[\"regionserver\"]\n if job.running_tasks_count < 3:\n job.last_status = Status.ERROR\n job.last_message = \"Too few running regionservers!\"\n cluster.last_status = max([job.last_status for job in cluster.jobs.itervalues()])\n\n def check_yarn_cluster_status(self, cluster):\n job = cluster.jobs[\"resourcemanager\"]\n for task in job.running_tasks.itervalues():\n # update cluster entry\n cluster.entry = '%s:%d' % (task.host, task.port)\n if job.running_tasks_count < 1:\n job.last_status = Status.ERROR\n job.last_message = \"No running resourcemanager!\"\n\n job = cluster.jobs[\"proxyserver\"]\n if job.running_tasks_count < 1:\n job.last_status = Status.ERROR\n job.last_message = \"No running proxyserver!\"\n\n job = cluster.jobs[\"nodemanager\"]\n if job.running_tasks_count < 3:\n job.last_status = Status.ERROR\n job.last_message = \"Too few running nodemanager!\"\n cluster.last_status = max([job.last_status for job in cluster.jobs.itervalues()])\n\n job = cluster.jobs[\"historyserver\"]\n if job.running_tasks_count < 1:\n job.last_status = Status.ERROR\n job.last_message = \"Too few running historyserver!\"\n cluster.last_status = max([job.last_status for job in cluster.jobs.itervalues()])\n\n def check_impala_cluster_status(self, cluster):\n job = cluster.jobs[\"statestored\"]\n for task in job.running_tasks.itervalues():\n # update cluster entry\n cluster.entry = '%s:%d' % (task.host, task.port)\n if job.running_tasks_count < 1:\n job.last_status = Status.ERROR\n job.last_message = \"No running statestored!\"\n\n job = cluster.jobs[\"impalad\"]\n if job.running_tasks_count < 3:\n job.last_status = Status.ERROR\n job.last_message = \"Too few running impalad!\"\n cluster.last_status = max([job.last_status for job in cluster.jobs.itervalues()])\n\n def check_cluster_status(self, cluster):\n cluster.jobs = {}\n cluster.last_status = Status.OK\n cluster.last_message = \"\"\n\n for job in cluster.job_set.all():\n job.running_tasks = {}\n job.tasks = {}\n job.last_status = Status.OK\n job.last_message = \"\"\n job.running_tasks_count = 0\n job.total_tasks_count = 0\n for task in job.task_set.filter(active=True):\n if task.health:\n job.running_tasks[task.id] = task\n job.running_tasks_count += 1\n job.total_tasks_count += 1\n cluster.jobs[job.name] = job\n\n service_handler = {\n \"hdfs\": self.check_hdfs_cluster_status,\n \"hbase\": self.check_hbase_cluster_status,\n \"yarn\": self.check_yarn_cluster_status,\n \"impala\": self.check_impala_cluster_status,\n }\n service_handler[cluster.service.name](cluster)\n self.handle_status_result(cluster)\n\n def handle_status_result(self, cluster):\n # last_status store cluster_name->(status, status_times)\n (cluster_status, status_times) = self.last_status.setdefault(str(cluster), (Status.OK, 0))\n need_send_alert = False\n\n if cluster.last_status != cluster_status:\n self.last_status[str(cluster)] = (cluster.last_status, 1)\n if cluster.last_status == Status.OK and status_times >= ERROR_TIMES_FOR_ALERT:\n # send alert when cluster changed to from PROBLEM(alerted) to OK\n need_send_alert = True\n else:\n self.last_status[str(cluster)] = (cluster.last_status, status_times+1)\n # send alert when cluster in PROBLEM stutus reached ERROR_TIMES_FOR_ALERT times\n if cluster.last_status != Status.OK and status_times + 1 == ERROR_TIMES_FOR_ALERT:\n need_send_alert = True\n\n if need_send_alert:\n self.alert_msg += '[%s]Cluster[%s]\\n' \\\n % ('OK' if cluster.last_status == Status.OK else 'PROBLEM',\n cluster)\n for job in cluster.jobs.itervalues():\n if job.last_status != Status.OK:\n self.alert_msg += 'Job[%s] not healthy: %s\\n' % (job.name, job.last_message)\n self.alert_msg += '******\\n'\n\n\n def check_status(self):\n self.alert_msg = ''\n logger.info(\"checking clusters status\")\n\n self.start_time = time.time()\n for cluster in Cluster.objects.filter(active=True).all():\n self.check_cluster_status(cluster)\n logger.info(\"spent %f seconds for updating clusters status\",\n time.time() - self.start_time)\n if self.alert_msg:\n logger.warn('alert msg: %r' % self.alert_msg)\n self.mailer.send_email(subject = 'OWL cluster alert',\n content = self.alert_msg,\n to_email = self.options['to_email'])\n json.dump(self.last_status, open(STATUS_FILE_PATH, 'w'))\n\nclass Command(BaseCommand):\n args = ''\n help = \"Run the background collector to fetch metrics from /jmx on each server.\"\n\n option_list = BaseCommand.option_list + (\n make_option(\n \"--to_email\",\n help=\"Email address to\"),\n make_option(\n \"--period\",\n default=60,\n help=\"Check period\"),\n )\n\n def handle(self, *args, **options):\n self.args = args\n self.options = options\n self.mailer = utils.mail.Mailer(options)\n\n self.stdout.write(\"args: %r\\n\" % (args, ))\n self.stdout.write(\"options: %r\\n\" % options)\n\n self.collector_config = CollectorConfig(self.args, self.options)\n\n self.last_status = {}\n try:\n self.last_status = json.load(open(STATUS_FILE_PATH, 'r'))\n except Exception as e:\n logger.warning('Failed to load status file: %r', e)\n\n status_checker = StatusChecker(self.collector_config,\n self.last_status,\n self.options,\n self.mailer)\n\n while True:\n try:\n status_checker.check_status()\n except Exception as e:\n logger.warning('OWL cluster checker error: %r', e)\n # send alert email when program got error\n admin_email = ''\n try:\n admin_email = settings.ADMINS[0][1]\n except:\n pass\n self.mailer.send_email(subject = 'OWL cluster check error',\n content = repr(e),\n to_email = admin_email,\n )\n time.sleep(int(self.options['period']))\n", "repo_name": "XiaoMi/minos", "sub_path": "owl/alert/management/commands/alert.py", "file_name": "alert.py", "file_ext": "py", "file_size_in_byte": 12499, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 520, "dataset": "github-code", "pt": "37", "api": [{"api_name": "logging.getLogger", "line_number": 32, "usage_type": "call"}, {"api_name": "argparse.Namespace", "line_number": 42, "usage_type": "call"}, {"api_name": "deploy_utils.get_service_config", "line_number": 46, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 51, "usage_type": "call"}, {"api_name": "os.path", "line_number": 51, "usage_type": "attribute"}, {"api_name": "deploy_utils.get_config_dir", "line_number": 51, "usage_type": "call"}, {"api_name": "ConfigParser.SafeConfigParser", "line_number": 62, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 67, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 84, "usage_type": "call"}, {"api_name": "monitor.models.Status.ERROR", "line_number": 112, "usage_type": "attribute"}, {"api_name": "monitor.models.Status", "line_number": 112, "usage_type": "name"}, {"api_name": "monitor.models.Status.ERROR", "line_number": 117, "usage_type": "attribute"}, {"api_name": "monitor.models.Status", "line_number": 117, "usage_type": "name"}, {"api_name": "monitor.models.Status.ERROR", "line_number": 130, "usage_type": "attribute"}, {"api_name": "monitor.models.Status", "line_number": 130, "usage_type": "name"}, {"api_name": "monitor.models.Status.ERROR", "line_number": 133, "usage_type": "attribute"}, {"api_name": "monitor.models.Status", "line_number": 133, "usage_type": "name"}, {"api_name": "monitor.models.Status.WARN", "line_number": 136, "usage_type": "attribute"}, {"api_name": "monitor.models.Status", "line_number": 136, "usage_type": "name"}, {"api_name": "monitor.models.Status.ERROR", "line_number": 141, "usage_type": "attribute"}, {"api_name": "monitor.models.Status", "line_number": 141, "usage_type": "name"}, {"api_name": "monitor.models.Status.ERROR", "line_number": 148, "usage_type": "attribute"}, {"api_name": "monitor.models.Status", "line_number": 148, "usage_type": "name"}, {"api_name": "monitor.models.Status.ERROR", "line_number": 165, "usage_type": "attribute"}, {"api_name": "monitor.models.Status", "line_number": 165, "usage_type": "name"}, {"api_name": "monitor.models.Status.ERROR", "line_number": 168, "usage_type": "attribute"}, {"api_name": "monitor.models.Status", "line_number": 168, "usage_type": "name"}, {"api_name": "monitor.models.Status.ERROR", "line_number": 180, "usage_type": "attribute"}, {"api_name": "monitor.models.Status", "line_number": 180, "usage_type": "name"}, {"api_name": "monitor.models.Status.ERROR", "line_number": 190, "usage_type": "attribute"}, {"api_name": "monitor.models.Status", "line_number": 190, "usage_type": "name"}, {"api_name": "monitor.models.Status.ERROR", "line_number": 195, "usage_type": "attribute"}, {"api_name": "monitor.models.Status", "line_number": 195, "usage_type": "name"}, {"api_name": "monitor.models.Status.ERROR", "line_number": 200, "usage_type": "attribute"}, {"api_name": "monitor.models.Status", "line_number": 200, "usage_type": "name"}, {"api_name": "monitor.models.Status.ERROR", "line_number": 206, "usage_type": "attribute"}, {"api_name": "monitor.models.Status", "line_number": 206, "usage_type": "name"}, {"api_name": "monitor.models.Status.ERROR", "line_number": 216, "usage_type": "attribute"}, {"api_name": "monitor.models.Status", "line_number": 216, "usage_type": "name"}, {"api_name": "monitor.models.Status.ERROR", "line_number": 221, "usage_type": "attribute"}, {"api_name": "monitor.models.Status", "line_number": 221, "usage_type": "name"}, {"api_name": "monitor.models.Status.OK", "line_number": 227, "usage_type": "attribute"}, {"api_name": "monitor.models.Status", "line_number": 227, "usage_type": "name"}, {"api_name": "monitor.models.Status.OK", "line_number": 233, "usage_type": "attribute"}, {"api_name": "monitor.models.Status", "line_number": 233, "usage_type": "name"}, {"api_name": "monitor.models.Status.OK", "line_number": 255, "usage_type": "attribute"}, {"api_name": "monitor.models.Status", "line_number": 255, "usage_type": "name"}, {"api_name": "monitor.models.Status.OK", "line_number": 260, "usage_type": "attribute"}, {"api_name": "monitor.models.Status", "line_number": 260, "usage_type": "name"}, {"api_name": "monitor.models.Status.OK", "line_number": 266, "usage_type": "attribute"}, {"api_name": "monitor.models.Status", "line_number": 266, "usage_type": "name"}, {"api_name": "monitor.models.Status.OK", "line_number": 271, "usage_type": "attribute"}, {"api_name": "monitor.models.Status", "line_number": 271, "usage_type": "name"}, {"api_name": "monitor.models.Status.OK", "line_number": 274, "usage_type": "attribute"}, {"api_name": "monitor.models.Status", "line_number": 274, "usage_type": "name"}, {"api_name": "time.time", "line_number": 283, "usage_type": "call"}, {"api_name": "monitor.models.Cluster.objects.filter", "line_number": 284, "usage_type": "call"}, {"api_name": "monitor.models.Cluster.objects", "line_number": 284, "usage_type": "attribute"}, {"api_name": "monitor.models.Cluster", "line_number": 284, "usage_type": "name"}, {"api_name": "time.time", "line_number": 287, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 293, "usage_type": "call"}, {"api_name": "django.core.management.base.BaseCommand", "line_number": 295, "usage_type": "name"}, {"api_name": "django.core.management.base.BaseCommand.option_list", "line_number": 299, "usage_type": "attribute"}, {"api_name": "django.core.management.base.BaseCommand", "line_number": 299, "usage_type": "name"}, {"api_name": "optparse.make_option", "line_number": 300, "usage_type": "call"}, {"api_name": "optparse.make_option", "line_number": 303, "usage_type": "call"}, {"api_name": "utils.mail.mail.Mailer", "line_number": 312, "usage_type": "call"}, {"api_name": "utils.mail.mail", "line_number": 312, "usage_type": "attribute"}, {"api_name": "utils.mail", "line_number": 312, "usage_type": "name"}, {"api_name": "json.load", "line_number": 321, "usage_type": "call"}, {"api_name": "django.conf.settings.ADMINS", "line_number": 338, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 338, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 345, "usage_type": "call"}]} +{"seq_id": "14554436970", "text": "# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import migrations, models\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('gallery', '0001_initial'),\n ]\n\n operations = [\n migrations.AddField(\n model_name='item',\n name='comment',\n field=models.TextField(blank=True),\n ),\n migrations.AddField(\n model_name='item',\n name='credit',\n field=models.CharField(max_length=200, blank=True),\n ),\n migrations.AddField(\n model_name='item',\n name='year',\n field=models.PositiveSmallIntegerField(null=True, blank=True),\n ),\n migrations.AlterField(\n model_name='item',\n name='title',\n field=models.CharField(max_length=200, blank=True),\n ),\n ]\n", "repo_name": "comzeradd/athensreport.org", "sub_path": "athensreport/gallery/migrations/0002_auto_20151205_1247.py", "file_name": "0002_auto_20151205_1247.py", "file_ext": "py", "file_size_in_byte": 881, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "django.db.migrations.Migration", "line_number": 7, "usage_type": "attribute"}, {"api_name": "django.db.migrations", "line_number": 7, "usage_type": "name"}, {"api_name": "django.db.migrations.AddField", "line_number": 14, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 14, "usage_type": "name"}, {"api_name": "django.db.models.TextField", "line_number": 17, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 17, "usage_type": "name"}, {"api_name": "django.db.migrations.AddField", "line_number": 19, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 19, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 22, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 22, "usage_type": "name"}, {"api_name": "django.db.migrations.AddField", "line_number": 24, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 24, "usage_type": "name"}, {"api_name": "django.db.models.PositiveSmallIntegerField", "line_number": 27, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 27, "usage_type": "name"}, {"api_name": "django.db.migrations.AlterField", "line_number": 29, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 29, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 32, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 32, "usage_type": "name"}]} +{"seq_id": "74651917227", "text": "#!/usr/bin/python3\nfrom sqlalchemy import create_engine\nfrom sqlalchemy.ext.declarative import declarative_base\nfrom sqlalchemy import Column, Integer, String, Sequence\nfrom sqlalchemy.orm import sessionmaker\n\n#creating an instance of create_engine\nengine = create_engine('sqlite:///:memory:', echo=True)\n\n#an instance of declarative base\nBase = declarative_base()\n\nclass User(Base):\n __tablename__ = 'users'\n\n id = Column(Integer, Sequence('user_id_seq'), primary_key=True)\n name = Column(String)\n fullname = Column(String)\n nickname = Column(String)\n\n def __repr__(self):\n return f\"\"\n\n#Creating an instance of a mapped class\n#int_user = User(name='joshua', fullname='Lenge Joshua', nickname='HendrixxSdiddy')\n#print(int_user.name)\n#print(int_user.fullname)\n#print(str(int_user.id))\n\n\"\"\"\n#Schema\nUser.__table__\nTable('users', MetaData(bind=None),\n Column('id', Integer(), table=, primary_key=True, nullable=False),\n Column('name', String(), table=),\n Column('fullname', String(), table=),\n Column('nickname', String(), table=, schema=None)\n\"\"\"\n\n#Creatin a session\nSession = sessionmaker(bind=engine)\nSession = sessionmaker()\nSession.configure(bind=engine)\n\n#for any conversation with the database, Session is instantiated\nsession = Session()\n\n#Adding and Updating objects\nint_user1 = User(name='lenge', fullname='lege danladi', nickname='Hendrixx')\nsession.add(int_user1)\n\nour_user = session.query(User).filter_by(name='lenge').first()\n\n#print(our_user)\n#print(int_user = our_user)\n\n#Adding more User objects using add_all()\nsession.add_all([\n User(name='Dan', fullname='Lenge Josh', nickname='Hendrixx'),\n User(name='Regan', fullname='regan', nickname='twista'),\n User(name='Josh', fullname='Josh lenge', nickname='Kim'),])\n\n#Check for modification if made to any object of User\n#print(session.dirty)\n\n#Check for new added Updates\n#print(session.new)\n\n#Commit all pending transactions to the database\nsession.commit()\n\n", "repo_name": "hendrixxD/alx-higher_level_programming", "sub_path": "0x0F-python-object_relational_mapping/alchemy_into.py", "file_name": "alchemy_into.py", "file_ext": "py", "file_size_in_byte": 2089, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "sqlalchemy.create_engine", "line_number": 8, "usage_type": "call"}, {"api_name": "sqlalchemy.ext.declarative.declarative_base", "line_number": 11, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 16, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 16, "usage_type": "argument"}, {"api_name": "sqlalchemy.Sequence", "line_number": 16, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 17, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 17, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 18, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 18, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 19, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 19, "usage_type": "argument"}, {"api_name": "sqlalchemy.orm.sessionmaker", "line_number": 41, "usage_type": "call"}, {"api_name": "sqlalchemy.orm.sessionmaker", "line_number": 42, "usage_type": "call"}]} +{"seq_id": "42005300386", "text": "# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Sun Jan 9 22:30:33 2022\r\n\r\n@author: Administrator\r\n\"\"\"\r\n\r\n# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Sun Jan 2 22:27:27 2022\r\n\r\n@author: Administrator\r\n\"\"\"\r\n\r\nimport numpy as np\r\nfrom scipy.signal import correlate2d,firwin,filtfilt\r\nfrom skimage.util import view_as_windows\r\nimport os\r\nfrom scipy.io import wavfile\r\n#import pyworld\r\nfrom scipy.signal import resample\r\n#import low_cut_filter\r\nclass WSOLA(object):\r\n def __init__(self,fs,speech_rate,shiftms=10):\r\n self.fs = fs\r\n self.speech_rate = speech_rate\r\n self.shiftms = shiftms\r\n self.sl = int(self.fs * self.shiftms/1000)\r\n self.fl = self.sl *2\r\n self.epstep = int(self.sl *self.speech_rate)\r\n self.win = np.hanning(self.fl)\r\n def duration_modification(self,x):#x为输入的语音信号\r\n wlen = len(x)\r\n wsolaed = np.zeros(int(wlen/self.speech_rate),dtype = 'd')\r\n sp = self.sl * 2\r\n rp = sp + self.sl\r\n ep = sp + self.epstep\r\n outp = self.sl\r\n wsolaed[:outp] = x[:outp]\r\n \r\n while wlen > ep + self.fl:\r\n ref = x[rp - self.sl:rp + self.sl]\r\n buff = x[ep - self.fl:ep + self.fl]\r\n delta = self._search_minimum_distance(ref,buff)\r\n epd = ep + delta\r\n spdata = x[sp:sp+self.sl] * self.win[self.sl:]\r\n epdata = x[epd - self.sl : epd] * self.win[:self.sl]\r\n if len(spdata) == len(wsolaed[outp:outp + self.sl]):\r\n wsolaed[outp:outp + self.sl] = spdata + epdata\r\n else:\r\n wsolaed_len = len(wsolaed[outp:outp + self.sl])\r\n wsolaed[outp:outp + self.sl] = spdata[:wsolaed_len] + epdata[:wsolaed_len]\r\n outp += self.sl\r\n sp = epd\r\n rp = sp + self.sl\r\n ep += self.epstep\r\n return wsolaed\r\n \r\n def _search_minimum_distance(self,ref,buff):\r\n if len(ref) < self.fl:\r\n ref = np.r_[ref,np.zeros(self.fl - len(ref))]\r\n \r\n buffmat = view_as_windows(buff,self.fl) * self.win\r\n refwin = np.array(ref * self.win).reshape(1,self.fl)\r\n corr = correlate2d(buffmat,refwin,mode='valid')\r\n \r\n return np.argmax(corr) - self.sl\r\n'''\r\n#高频修复\r\ndef high_frenquency_completion(x,transformed,f0rate,par):\r\n x = np.array(x,dtype = np.float)\r\n f0,time_axis = pyworld.harvest(x,par['fs'],f0_floor=par['shiftms'],f0_ceil=par['maxf0'],frame_period=par['shiftms'])\r\n spc = pyworld.cheaptrick(x,f0,time_axis,par['fs'],fft_size=par['fftl'])\r\n ap = pyworld.d4c(x,f0,time_axis,par['fs'],fft_size=par['fftl'])\r\n \r\n uf0 = np.zeros(len(f0))\r\n unvoice_anasyn = pyworld.synthesize(uf0,spc,ap,par['fs'],frame_period=par['shiftms'])\r\n \r\n fil = firwin(255,f0rate,pass_zero=False)\r\n HPFed_unvoice_anasyn = filtfilt(fil,1,unvoice_anasyn)\r\n \r\n if len(HPFed_unvoice_anasyn) > len(transformed):\r\n return transformed + HPFed_unvoice_anasyn[:len(transformed)]\r\n else:\r\n transformed[:len(HPFed_unvoice_anasyn)] += HPFed_unvoice_anasyn\r\n return transformed\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\ndef transform_f0(x,f0rate,config):\r\n if f0rate < 1.0:\r\n completion = True\r\n else: \r\n completion = False\r\n \r\n fs = config[\"fs\"]\r\n #x = low_cut_filter(x,fs,cutoff=70)\r\n \r\n wsola = WSOLA(fs,speech_rate = f0rate,shiftms = 10)\r\n wsolaed = wsola.duration_modification(x)\r\n \r\n xlen = len(x)\r\n transformed = resample(wsolaed,xlen)#,random_state=xlen\r\n \r\n if completion:\r\n transformed = high_frenquency_completion(x,transformed,f0rate,config)\r\n \r\n return transformed\r\n\r\n'''\r\n\r\n \r\nif __name__==\"__main__\":\r\n #config = config_all[\"Feature\"]\r\n\r\n\r\n\r\n cleandir = \"G:/语音测试程序/代码/md_clean\"\r\n for cleanwav in os.listdir(cleandir):\r\n \r\n fs,x = wavfile.read(cleandir+'/'+cleanwav)\r\n x = np.array(x,dtype = np.float)\r\n wsola_long=WSOLA(fs,speech_rate=1/1.25,shiftms=10)\r\n wsolaed_long = wsola_long.duration_modification(x)\r\n \r\n wsola_short = WSOLA(fs,speech_rate = 1/0.75,shiftms = 10)\r\n wsolaed_short = wsola_short.duration_modification(x)\r\n \r\n \r\n speedwav1 = \"G:/语音测试程序/代码/speed1.25/\"\r\n speedlong = speedwav1 + cleanwav[:-4]+\"_\"+\"_long\"+\".wav\"\r\n speedwav2 = \"G:/语音测试程序/代码/speed0.75/\"\r\n speedshort = speedwav2 + cleanwav[:-4]+\"_\"+\"_short\"+\".wav\"\r\n wavfile.write(speedlong,fs,wsolaed_long.astype(np.int16))\r\n wavfile.write(speedshort,fs,wsolaed_short.astype(np.int16))\r\n'''\r\n \r\n \r\n fs,x = wavfile.read(\"G:/王斐斐/vcc/demo_clean/A2_23.wav\")\r\n x = np.array(x,dtype = np.float)\r\n wsola_long=WSOLA(fs,speech_rate=1/1.25,shiftms=10)\r\n wsolaed_long = wsola_long.duration_modification(x)pi\r\n \r\n wsola_short = WSOLA(fs,speech_rate = 1/0.75,shiftms = 10)\r\n wsolaed_short = wsola_short.duration_modification(x)\r\n \r\n wavfile.write(\"wsola_long.wav\",fs,wsolaed_long.astype(np.int16))\r\n wavfile.write(\"wsola_short.wav\",fs,wsolaed_short.astype(np.int16))\r\n \r\n'''\r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n ", "repo_name": "SIYUAN9446/SR-MT", "sub_path": "code/MRs/speed.py", "file_name": "speed.py", "file_ext": "py", "file_size_in_byte": 5285, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "numpy.hanning", "line_number": 31, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 34, "usage_type": "call"}, {"api_name": "numpy.r_", "line_number": 61, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 61, "usage_type": "call"}, {"api_name": "skimage.util.view_as_windows", "line_number": 63, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 64, "usage_type": "call"}, {"api_name": "scipy.signal.correlate2d", "line_number": 65, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 67, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 123, "usage_type": "call"}, {"api_name": "scipy.io.wavfile.read", "line_number": 125, "usage_type": "call"}, {"api_name": "scipy.io.wavfile", "line_number": 125, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 126, "usage_type": "call"}, {"api_name": "numpy.float", "line_number": 126, "usage_type": "attribute"}, {"api_name": "scipy.io.wavfile.write", "line_number": 138, "usage_type": "call"}, {"api_name": "scipy.io.wavfile", "line_number": 138, "usage_type": "name"}, {"api_name": "numpy.int16", "line_number": 138, "usage_type": "attribute"}, {"api_name": "scipy.io.wavfile.write", "line_number": 139, "usage_type": "call"}, {"api_name": "scipy.io.wavfile", "line_number": 139, "usage_type": "name"}, {"api_name": "numpy.int16", "line_number": 139, "usage_type": "attribute"}]} +{"seq_id": "31969965025", "text": "import os\nfrom typing import Dict, List, Tuple\n\nimport distributed\nimport pandas as pd\nfrom dask import delayed\nfrom dask.delayed import Delayed\n\nfrom experiments.dask_utils.computations import compute_delayed_functions\n\n\nfrom experiments.dask_utils.dask_initialization import reconnect_client_to_ssh_cluster\nfrom experiments.decision_tree_rule_learning.relative_file_naming import \\\n get_tree_derived_rules_rel_file_name_without_extension\nfrom experiments.file_naming.classifier_naming import SingleTargetClassifierIndicator\n\nfrom experiments.arcbench_data_preparation.reworked_one_hot_encoding import get_original_data_fold_abs_file_name, \\\n TrainTestEnum\n\nfrom experiments.utils.experiment_logging import create_logger, close_logger\nfrom experiments.file_naming.classifier_naming import get_tree_based_mids_dir, \\\n get_tree_based_mids_clf_abs_file_name\nfrom experiments.e2_multi_directional_model_comparison.file_naming.evaluation_naming import (\n get_tree_based_mids_target_attr_to_score_info_abs_file_name,\n get_tree_based_mids_interpret_stats_abs_file_name)\n\nfrom mdrsl.rule_models.multi_target_rule_set_clf_utils.rule_combining_strategy import (\n WeightedVotingRuleCombinator, RuleCombiningStrategy)\nfrom mdrsl.rule_models.mids.io_mids import (\n load_mids_classifier, store_mids_target_attr_to_score_info, store_mids_interpret_stats)\nfrom mdrsl.evaluation.predictive_performance_metrics import ScoreInfo\nfrom mdrsl.rule_models.mids.model_evaluation.mids_interpretability_metrics import MIDSInterpretabilityStatistics, \\\n MIDSInterpretabilityStatisticsCalculator\nfrom mdrsl.rule_models.mids.model_evaluation.scoring_mids import score_MIDS_on_its_targets_without_nans\nfrom mdrsl.rule_models.mids.mids_classifier import MIDSClassifier\nfrom mdrsl.rule_models.mids.mids_ruleset import MIDSRuleSet\nfrom mdrsl.rule_models.mids.model_fitting.mids_with_value_reuse import MIDSValueReuse\n\nTargetAttr = str\n\n\ndef evaluate_mids_model_for_dataset_fold_target_attribute(\n dataset_name: str,\n fold_i: int,\n classifier_indicator: SingleTargetClassifierIndicator,\n nb_of_trees_per_model: int,\n nb_of_original_targets_to_predict: int,\n min_support: float,\n max_depth: int\n):\n logger = create_logger(\n logger_name=f'evaluate_mids_model_tree_derived_' + get_tree_derived_rules_rel_file_name_without_extension(\n dataset_name=dataset_name, fold_i=fold_i, classifier_indicator=classifier_indicator,\n nb_of_trees_per_model=nb_of_trees_per_model,\n nb_of_original_targets_to_predict=nb_of_original_targets_to_predict,\n min_support=min_support, max_depth=max_depth),\n log_file_name=os.path.join(get_tree_based_mids_dir(),\n get_tree_derived_rules_rel_file_name_without_extension(\n dataset_name=dataset_name, fold_i=fold_i,\n classifier_indicator=classifier_indicator,\n nb_of_trees_per_model=nb_of_trees_per_model,\n nb_of_original_targets_to_predict=nb_of_original_targets_to_predict,\n min_support=min_support, max_depth=max_depth)\n + '_model_evaluation_tree_derived_rules.log')\n )\n\n # --- load test data ----------------------------------------------------------------------------------------------\n # read in original (discretized) training data\n original_test_data_fold_abs_file_name = get_original_data_fold_abs_file_name(dataset_name, fold_i,\n TrainTestEnum.test)\n df_test_original_column_order = pd.read_csv(original_test_data_fold_abs_file_name,\n delimiter=',')\n\n # --- load classifier ---------------------------------------------------------------------------------------------\n tree_based_mids_classifier_abs_file_name = get_tree_based_mids_clf_abs_file_name(\n dataset_name=dataset_name, fold_i=fold_i,\n classifier_indicator=classifier_indicator, nb_of_trees_per_model=nb_of_trees_per_model,\n nb_of_original_targets_to_predict=nb_of_original_targets_to_predict,\n min_support=min_support, max_depth=max_depth\n )\n\n # mids_classifier_abs_file_name = get_mids_clf_abs_file_name(dataset_name, fold_i)\n logger.info(f\"start loading MIDS model from {tree_based_mids_classifier_abs_file_name}\")\n mids_classifier: MIDSClassifier = load_mids_classifier(tree_based_mids_classifier_abs_file_name)\n logger.info(\"finished loading MIDS model\")\n logger.info(mids_classifier)\n reconstructed_mids = MIDSValueReuse()\n reconstructed_mids.classifier = mids_classifier\n\n mids_classifier.rule_combination_strategy = RuleCombiningStrategy.WEIGHTED_VOTE\n mids_classifier.rule_combinator = WeightedVotingRuleCombinator()\n\n # --- Evaluate and store interpretability statistics --------------------------------------------------------------\n filter_nans: bool = True\n target_attr_to_score_info_map: Dict[str, ScoreInfo] = score_MIDS_on_its_targets_without_nans(\n reconstructed_mids, df_test_original_column_order, filter_nans=filter_nans)\n logger.info(\"Evaluated MIDS classifier on predictive performance\")\n target_attrs: List[TargetAttr] = mids_classifier.target_attrs\n for target_attr in target_attrs:\n target_attr_score_info: ScoreInfo = target_attr_to_score_info_map[target_attr]\n logger.info(f\"\\t{target_attr}:\\n {target_attr_score_info.to_str(' ')}\")\n logger.info(\"\\t---\")\n\n # mids_target_attr_to_score_info_abs_file_name: str = get_mids_target_attr_to_score_info_abs_file_name(\n # dataset_name, fold_i)\n\n tree_based_mids_target_attr_to_score_info_abs_file_name: str = \\\n get_tree_based_mids_target_attr_to_score_info_abs_file_name(\n dataset_name=dataset_name, fold_i=fold_i,\n classifier_indicator=classifier_indicator, nb_of_trees_per_model=nb_of_trees_per_model,\n nb_of_original_targets_to_predict=nb_of_original_targets_to_predict,\n min_support=min_support, max_depth=max_depth\n )\n\n store_mids_target_attr_to_score_info(tree_based_mids_target_attr_to_score_info_abs_file_name,\n target_attr_to_score_info_map)\n logger.info(f\"Wrote MIDS Dict[TargetAttr, ScoreInfo] to {tree_based_mids_target_attr_to_score_info_abs_file_name}\")\n\n # --- Evaluate and store interpretability statistics --------------------------------------------------------------\n interpret_stats: MIDSInterpretabilityStatistics \\\n = MIDSInterpretabilityStatisticsCalculator.calculate_ruleset_statistics(\n MIDSRuleSet(mids_classifier.rules), df_test_original_column_order, target_attributes=target_attrs)\n logger.info(\"Evaluated MIDS classifier on interpretability\")\n logger.info(interpret_stats.to_str(\"\\n\"))\n\n # mids_interpret_stats_abs_file_name: str = get_mids_interpret_stats_abs_file_name(\n # dataset_name, fold_i)\n tree_based_mids_interpret_stats_abs_file_name: str = get_tree_based_mids_interpret_stats_abs_file_name(\n dataset_name=dataset_name, fold_i=fold_i,\n classifier_indicator=classifier_indicator, nb_of_trees_per_model=nb_of_trees_per_model,\n nb_of_original_targets_to_predict=nb_of_original_targets_to_predict,\n min_support=min_support, max_depth=max_depth\n )\n store_mids_interpret_stats(tree_based_mids_interpret_stats_abs_file_name, interpret_stats)\n logger.info(f\"Wrote MIDSInterpretabilityStatistics to {tree_based_mids_interpret_stats_abs_file_name}\")\n logger.info(\"---\")\n\n close_logger(logger)\n\n\ndef main():\n from experiments.arcbench_data_preparation.dataset_info import datasets\n datasets = [dict(filename=\"iris\", targetvariablename=\"class\", numerical=True)]\n from experiments.dask_utils.dask_initialization import scheduler_host_name\n scheduler_host: str = scheduler_host_name\n list_of_computations: List[Tuple[Delayed, Dict]] = []\n\n nb_of_folds: int = 10\n classifier_indicator = SingleTargetClassifierIndicator.random_forest\n nb_of_original_targets_to_predict: int = 2\n\n nb_of_trees_per_model_list: List[int] = [5, 10]\n min_support: float = 0.1 # min_samples_leaf must be at least 1 or in (0, 0.5], got 0\n\n max_depth: int = 7 - nb_of_original_targets_to_predict\n\n use_dask = False\n if use_dask:\n client = reconnect_client_to_ssh_cluster(scheduler_host)\n\n for dataset_info in datasets:\n dataset_name = dataset_info['filename']\n\n for fold_i in range(nb_of_folds):\n\n for nb_of_trees_per_model in nb_of_trees_per_model_list:\n\n if use_dask:\n func_args = dict(\n dataset_name=dataset_name,\n fold_i=fold_i,\n classifier_indicator=classifier_indicator,\n nb_of_trees_per_model=nb_of_trees_per_model,\n nb_of_original_targets_to_predict=nb_of_original_targets_to_predict,\n min_support=min_support,\n max_depth=max_depth\n )\n\n delayed_func = \\\n delayed(evaluate_mids_model_for_dataset_fold_target_attribute)(\n **func_args\n )\n list_of_computations.append((delayed_func, func_args))\n else:\n evaluate_mids_model_for_dataset_fold_target_attribute(\n dataset_name=dataset_name,\n fold_i=fold_i,\n classifier_indicator=classifier_indicator,\n nb_of_trees_per_model=nb_of_trees_per_model,\n nb_of_original_targets_to_predict=nb_of_original_targets_to_predict,\n min_support=min_support,\n max_depth=max_depth\n )\n\n if use_dask:\n log_file_dir: str = get_tree_based_mids_dir()\n\n logger_name: str = 'model_evaluation_tree_derived_rules_ERROR_LOGGER'\n logger_file_name: str = os.path.join(\n log_file_dir,\n f'ERROR_LOG_model_evaluation_tree_derived_rules.log'\n )\n\n compute_delayed_functions(\n list_of_computations=list_of_computations,\n client=client,\n nb_of_retries_if_erred=5,\n error_logger_name=logger_name,\n error_logger_file_name=logger_file_name\n )\n if use_dask:\n nb_of_retries_if_erred = 2\n print(\"start compute\")\n print(list_of_computations)\n distributed.wait(client.compute(list_of_computations, retries=nb_of_retries_if_erred))\n print(\"end compute\")\n # distributed.wait(list_of_computations)\n\n\nif __name__ == '__main__':\n main()\n", "repo_name": "joschout/Multi-Directional-Rule-Set-Learning", "sub_path": "experiments/e2_multi_directional_model_comparison/model_evaluation/mids_tree_based_model_evaluation.py", "file_name": "mids_tree_based_model_evaluation.py", "file_ext": "py", "file_size_in_byte": 10995, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "37", "api": [{"api_name": "experiments.file_naming.classifier_naming.SingleTargetClassifierIndicator", "line_number": 45, "usage_type": "name"}, {"api_name": "experiments.utils.experiment_logging.create_logger", "line_number": 51, "usage_type": "call"}, {"api_name": "experiments.decision_tree_rule_learning.relative_file_naming.get_tree_derived_rules_rel_file_name_without_extension", "line_number": 52, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 57, "usage_type": "call"}, {"api_name": "os.path", "line_number": 57, "usage_type": "attribute"}, {"api_name": "experiments.file_naming.classifier_naming.get_tree_based_mids_dir", "line_number": 57, "usage_type": "call"}, {"api_name": "experiments.decision_tree_rule_learning.relative_file_naming.get_tree_derived_rules_rel_file_name_without_extension", "line_number": 58, "usage_type": "call"}, {"api_name": "experiments.arcbench_data_preparation.reworked_one_hot_encoding.get_original_data_fold_abs_file_name", "line_number": 69, "usage_type": "call"}, {"api_name": "experiments.arcbench_data_preparation.reworked_one_hot_encoding.TrainTestEnum.test", "line_number": 70, "usage_type": "attribute"}, {"api_name": "experiments.arcbench_data_preparation.reworked_one_hot_encoding.TrainTestEnum", "line_number": 70, "usage_type": "name"}, {"api_name": "pandas.read_csv", "line_number": 71, "usage_type": "call"}, {"api_name": "experiments.file_naming.classifier_naming.get_tree_based_mids_clf_abs_file_name", "line_number": 75, "usage_type": "call"}, {"api_name": "mdrsl.rule_models.mids.mids_classifier.MIDSClassifier", "line_number": 84, "usage_type": "name"}, {"api_name": "mdrsl.rule_models.mids.io_mids.load_mids_classifier", "line_number": 84, "usage_type": "call"}, {"api_name": "mdrsl.rule_models.mids.model_fitting.mids_with_value_reuse.MIDSValueReuse", "line_number": 87, "usage_type": "call"}, {"api_name": "mdrsl.rule_models.multi_target_rule_set_clf_utils.rule_combining_strategy.RuleCombiningStrategy.WEIGHTED_VOTE", "line_number": 90, "usage_type": "attribute"}, {"api_name": "mdrsl.rule_models.multi_target_rule_set_clf_utils.rule_combining_strategy.RuleCombiningStrategy", "line_number": 90, "usage_type": "name"}, {"api_name": "mdrsl.rule_models.multi_target_rule_set_clf_utils.rule_combining_strategy.WeightedVotingRuleCombinator", "line_number": 91, "usage_type": "call"}, {"api_name": "typing.Dict", "line_number": 95, "usage_type": "name"}, {"api_name": "mdrsl.evaluation.predictive_performance_metrics.ScoreInfo", "line_number": 95, "usage_type": "name"}, {"api_name": "mdrsl.rule_models.mids.model_evaluation.scoring_mids.score_MIDS_on_its_targets_without_nans", "line_number": 95, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 98, "usage_type": "name"}, {"api_name": "mdrsl.evaluation.predictive_performance_metrics.ScoreInfo", "line_number": 100, "usage_type": "name"}, {"api_name": "experiments.e2_multi_directional_model_comparison.file_naming.evaluation_naming.get_tree_based_mids_target_attr_to_score_info_abs_file_name", "line_number": 108, "usage_type": "call"}, {"api_name": "mdrsl.rule_models.mids.io_mids.store_mids_target_attr_to_score_info", "line_number": 115, "usage_type": "call"}, {"api_name": "mdrsl.rule_models.mids.model_evaluation.mids_interpretability_metrics.MIDSInterpretabilityStatistics", "line_number": 120, "usage_type": "name"}, {"api_name": "mdrsl.rule_models.mids.model_evaluation.mids_interpretability_metrics.MIDSInterpretabilityStatisticsCalculator.calculate_ruleset_statistics", "line_number": 121, "usage_type": "call"}, {"api_name": "mdrsl.rule_models.mids.model_evaluation.mids_interpretability_metrics.MIDSInterpretabilityStatisticsCalculator", "line_number": 121, "usage_type": "name"}, {"api_name": "mdrsl.rule_models.mids.mids_ruleset.MIDSRuleSet", "line_number": 122, "usage_type": "call"}, {"api_name": "experiments.e2_multi_directional_model_comparison.file_naming.evaluation_naming.get_tree_based_mids_interpret_stats_abs_file_name", "line_number": 128, "usage_type": "call"}, {"api_name": "mdrsl.rule_models.mids.io_mids.store_mids_interpret_stats", "line_number": 134, "usage_type": "call"}, {"api_name": "experiments.utils.experiment_logging.close_logger", "line_number": 138, "usage_type": "call"}, {"api_name": "experiments.arcbench_data_preparation.dataset_info.datasets", "line_number": 143, "usage_type": "name"}, {"api_name": "experiments.dask_utils.dask_initialization.scheduler_host_name", "line_number": 145, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 146, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 146, "usage_type": "name"}, {"api_name": "dask.delayed.Delayed", "line_number": 146, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 146, "usage_type": "name"}, {"api_name": "experiments.file_naming.classifier_naming.SingleTargetClassifierIndicator.random_forest", "line_number": 149, "usage_type": "attribute"}, {"api_name": "experiments.file_naming.classifier_naming.SingleTargetClassifierIndicator", "line_number": 149, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 152, "usage_type": "name"}, {"api_name": "experiments.dask_utils.dask_initialization.reconnect_client_to_ssh_cluster", "line_number": 159, "usage_type": "call"}, {"api_name": "experiments.arcbench_data_preparation.dataset_info.datasets", "line_number": 161, "usage_type": "name"}, {"api_name": "dask.delayed", "line_number": 180, "usage_type": "call"}, {"api_name": "experiments.file_naming.classifier_naming.get_tree_based_mids_dir", "line_number": 196, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 199, "usage_type": "call"}, {"api_name": "os.path", "line_number": 199, "usage_type": "attribute"}, {"api_name": "experiments.dask_utils.computations.compute_delayed_functions", "line_number": 204, "usage_type": "call"}, {"api_name": "distributed.wait", "line_number": 215, "usage_type": "call"}]} +{"seq_id": "71270291629", "text": "import os\nimport sys\nimport logging\nimport atexit\nimport traceback\nimport threading\nimport functools\nimport time\nimport io\n\nFILE_TYPES = (io.IOBase,)\n\nlogger = logging.getLogger(__name__)\nlogger.addHandler(logging.NullHandler())\n\n# These are default test results and their values. This should be reset to 0\n# after particular test is finished.\n\nFILE = 99\nERROR = 50\nFAIL = 40\nPASS = 30\nINFO = 20\nDEBUG = 10\nNONE = 0\n\n# used in cleanup function\n_PID = os.getpid()\n\n\ndef result_to_name(result):\n \"\"\"\n Translate reporter result to string.\n \"\"\"\n mapping = {\n ERROR: \"ERROR\",\n FAIL: \"FAIL\",\n PASS: \"PASS\",\n FILE: \"FILE\",\n INFO: \"INFO\",\n DEBUG: \"DEBUG\",\n NONE: \"NONE\",\n }\n\n return mapping[result]\n\n\ndef dumb_synchronized(method):\n @functools.wraps(method)\n def wrapped(self, *args, **kwargs):\n with self._lock:\n return method(self, *args, **kwargs)\n\n return wrapped\n\n\ndef dump_tb(tb):\n \"\"\"\n Routine for reporting tracebacks.\n \"\"\"\n import tempfile\n import pickle\n\n def dump_tr(obj):\n \"\"\"\n Return pickleable objects or their repr().\n \"\"\"\n try:\n pickle.dumps(obj)\n return obj\n except (TypeError, AttributeError, pickle.PicklingError):\n return repr(obj)\n\n dump = []\n\n while tb is not None:\n entry = {}\n frame = tb.tb_frame\n entry[\"stack\"] = traceback.format_stack(frame)[-1]\n entry[\"locals\"] = {\n key: dump_tr(val)\n for key, val in frame.f_locals.items()\n }\n dump.append(entry)\n tb = tb.tb_next\n\n df = tempfile.TemporaryFile()\n pickle.dump(dump, df)\n\n return df\n\n\n@atexit.register\ndef cleanup():\n \"\"\"\n Cleanup method.\n \"\"\"\n import io\n\n logger.info(\"Reporter: calling cleanup()\")\n\n reporter = Reporter.get_reporter()\n\n tb = getattr(sys, \"last_traceback\", None)\n vl = getattr(sys, \"last_value\", None)\n\n if tb is not None:\n tb_msg = repr(vl) + '\\n' + \"\".join(traceback.format_tb(tb))\n fo = io.StringIO(tb_msg)\n reporter.send_file(fo, \"traceback.log\")\n\n dump = dump_tb(tb)\n reporter.send_file(dump, \"traceback.dump\")\n\n# when somebody uses fork() and the process ends, don't do anything,\n# since this is handled by parent process\n if _PID != os.getpid():\n return\n reporter.test_end(clean_end=False)\n\n\ndef make_text(smth):\n \"\"\"\n Helper function to coerce UTF-8 bytes into str\n \"\"\"\n if isinstance(smth, bytes):\n return smth.decode('utf8')\n return smth\n\n\ndef make_bytes(smth):\n \"\"\"\n Helper function to coerce str into UTF-8 bytes\n \"\"\"\n if isinstance(smth, str):\n return smth.encode('utf8')\n return smth\n\n\nclass HandlerError(Exception):\n \"\"\"\n Generic exception for handlers.\n \"\"\"\n pass\n\n\nclass ReportRecord(object):\n \"\"\"\n ReportRecord instance represents and evet being logged.\n \"\"\"\n\n def __init__(self,\n result,\n msg=None,\n logfile=None,\n logname=None,\n flags=None):\n super(ReportRecord, self).__init__()\n self.timestamp = time.time()\n self.result = result\n self.msg = msg\n self.logfile = logfile\n self.logname = logname\n if flags is None:\n flags = {}\n self.flags = flags\n\n def __str__(self):\n return str({\n \"timestamp\": self.timestamp,\n \"result\": self.result,\n \"msg\": self.msg,\n \"logfile\": self.logfile,\n \"logname\": self.logname,\n \"flags\": self.flags,\n })\n\n\nclass Reporter(object):\n \"\"\"\n Class handling handlers for reporting. Implementing basic API.\n \"\"\"\n\n _instance = None\n _instance_lock = threading.RLock()\n\n @staticmethod\n def get_reporter():\n \"\"\"\n Return an instance of this class. The instance is the same with each\n call thus cereating a singleton.\n \"\"\"\n with Reporter._instance_lock:\n if Reporter._instance is None:\n logger.info(\"Reporter: creating new instance.\")\n Reporter._instance = Reporter()\n\n return Reporter._instance\n\n @staticmethod\n def drop_reporter():\n \"\"\"\n Delete the Reporter singleton.\n \"\"\"\n with Reporter._instance_lock:\n Reporter._instance = None\n\n def __init__(self):\n super(Reporter, self).__init__()\n self.overall_result = NONE\n self.handlers = []\n self.finished = False\n self._lock = threading.RLock()\n\n def __del__(self):\n try:\n logger.debug(\"Reporter: calling __del__\")\n # It may happen that we get \"NameError: name 'open' is not defined\" even though\n # the 'open' builtin function should still exist at this point.\n # See https://stackoverflow.com/questions/64679139 and RTT-4820.\n # \"pass\" is used as we can't log the error reasonably anyway\n except NameError:\n pass\n if not self.finished:\n self.test_end()\n\n @dumb_synchronized\n def test_end(self, clean_end=True):\n \"\"\"\n Flush all results and clean up.\n \"\"\"\n if self.finished:\n return self.overall_result\n\n # Catch \"NameError\" exception for the same reason as in __del__\n try:\n logger.info(\"Reporter: calling test_end\")\n except NameError:\n pass\n if not clean_end:\n self.log_error(\"Test ended unexpectedly.\")\n\n self.finished = True\n for handler in self.handlers:\n handler.close()\n\n self.handlers = []\n return self.overall_result\n\n @dumb_synchronized\n def log(self, result, msg, flags=None):\n \"\"\"\n Log a message with specific level.\n \"\"\"\n if FILE > result >= PASS:\n self.overall_result = max(self.overall_result, result)\n self._log(result, msg, flags=flags)\n\n @dumb_synchronized\n def log_error(self, msg, flags=None):\n \"\"\"\n Log an ERROR message.\n \"\"\"\n self.overall_result = max(self.overall_result, ERROR)\n self._log(ERROR, msg, flags=flags)\n\n @dumb_synchronized\n def log_fail(self, msg, flags=None):\n \"\"\"\n Log a FAIL message.\n \"\"\"\n self.overall_result = max(self.overall_result, FAIL)\n self._log(FAIL, msg, flags=flags)\n\n @dumb_synchronized\n def log_pass(self, msg, flags=None):\n \"\"\"\n Log a PASS message.\n \"\"\"\n self.overall_result = max(self.overall_result, PASS)\n self._log(PASS, msg, flags=flags)\n\n @dumb_synchronized\n def log_info(self, msg, flags=None):\n \"\"\"\n Log INFO message.\n \"\"\"\n self._log(INFO, msg, flags=flags)\n\n @dumb_synchronized\n def log_debug(self, msg, flags=None):\n \"\"\"\n Log DEBUG message.\n \"\"\"\n self._log(DEBUG, msg, flags=flags)\n\n @dumb_synchronized\n def send_file(self, logfile, logname=None, msg=None, flags=None):\n \"\"\"\n Send log file.\n \"\"\"\n logger.debug(\"Reporter: calling send_file(%s, %s, %s, %s)\", logfile,\n logname, msg, flags)\n self._log(FILE, msg=msg, logfile=logfile, logname=logname, flags=flags)\n\n def _log(self, result, msg, **kwargs):\n \"\"\"\n Low level logging routine.\n \"\"\"\n record = ReportRecord(result, msg, **kwargs)\n logger.info(\"Reporter: calling _log with record: %s\", record)\n\n self.call_handlers(record)\n\n @dumb_synchronized\n def add_handler(self, handler):\n \"\"\"\n Add the specified handler to this reporter.\n \"\"\"\n logger.info(\"Reporter: calling add_handler with %s\", handler)\n if self.finished:\n raise Exception(\"Cannot add handler if the test ended.\")\n if handler not in self.handlers:\n self.handlers.append(handler)\n\n @dumb_synchronized\n def remove_handler(self, handler):\n \"\"\"\n Remove the specified handler from this reporter.\n \"\"\"\n logger.info(\"Reporter: calling remove_handler with %s\", handler)\n if handler in self.handlers:\n self.handlers.remove(handler)\n\n def call_handlers(self, record):\n \"\"\"\n Pass the record to all registered handlers.\n \"\"\"\n logger.debug(\"Reporter: calling call_handlers on %s\", self.handlers)\n for handler in self.handlers:\n handler.emit(record)\n\n\nclass Handler(object):\n \"\"\"\n Class defining the Handler API.\n \"\"\"\n\n def __init__(self, result_level=INFO, process_logs=True):\n super(Handler, self).__init__()\n self._result_level = result_level\n self._process_logs = process_logs\n\n @property\n def result_level(self):\n \"\"\"\n Result level getter.\n \"\"\"\n return self._result_level\n\n @result_level.setter\n def result_level(self, result_level):\n \"\"\"\n Result level setter.\n \"\"\"\n self._result_level = result_level\n\n @property\n def process_logs(self):\n \"\"\"\n Getter for process_logs.\n \"\"\"\n return self._process_logs\n\n @process_logs.setter\n def process_logs(self, value):\n \"\"\"\n Set process_logs.\n \"\"\"\n self._process_logs = value\n\n def emit(self, record):\n \"\"\"\n Decides if we are reporting a result or a file and executes the correct\n routine.\n \"\"\"\n # Check default level and other tocnditions.\n if record.result < self.result_level:\n return\n\n if (record.result == FILE) and not self.process_logs:\n return\n\n if (record.result == FILE) and (record.logfile is not None):\n return self._emit_file(record)\n\n if record.msg is None:\n return\n\n self._emit_log(record)\n\n def _emit_log(self, record):\n \"\"\"\n Method taking care of handling messages.\n \"\"\"\n raise NotImplementedError\n\n def _emit_file(self, record):\n \"\"\"\n Method taking care of handling file records.\n \"\"\"\n raise NotImplementedError\n\n def close(self):\n \"\"\"\n This method should flush all outstanding results and logs.\n \"\"\"\n raise NotImplementedError\n", "repo_name": "rhinstaller/teres", "sub_path": "teres/__init__.py", "file_name": "__init__.py", "file_ext": "py", "file_size_in_byte": 10383, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "37", "api": [{"api_name": "io.IOBase", "line_number": 11, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 13, "usage_type": "call"}, {"api_name": "logging.NullHandler", "line_number": 14, "usage_type": "call"}, {"api_name": "os.getpid", "line_number": 28, "usage_type": "call"}, {"api_name": "functools.wraps", "line_number": 49, "usage_type": "call"}, {"api_name": "pickle.dumps", "line_number": 69, "usage_type": "call"}, {"api_name": "pickle.PicklingError", "line_number": 71, "usage_type": "attribute"}, {"api_name": "traceback.format_stack", "line_number": 79, "usage_type": "call"}, {"api_name": "tempfile.TemporaryFile", "line_number": 87, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 88, "usage_type": "call"}, {"api_name": "traceback.format_tb", "line_number": 108, "usage_type": "call"}, {"api_name": "io.StringIO", "line_number": 109, "usage_type": "call"}, {"api_name": "os.getpid", "line_number": 117, "usage_type": "call"}, {"api_name": "atexit.register", "line_number": 93, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 159, "usage_type": "call"}, {"api_name": "threading.RLock", "line_number": 185, "usage_type": "call"}, {"api_name": "threading.RLock", "line_number": 213, "usage_type": "call"}]} +{"seq_id": "29700202052", "text": "'''\nTenho que fazer um código em que o OUTPUT seja o primeiro número\ninteiro positivo faltante em uma sequencia\n\nPor exemplo, na seguinte lista, o output seria o valor 2\nnumeros = [1, 3, 10, -2, -14, 20]\n'''\nfrom typing import List\nfrom operator import le, gt, ne\n\n\ndef app(values: List[int]):\n \n if le(sum(values), 0):\n return 1\n \n if not values:\n return 1\n \n else:\n filtered_values = list(filter(lambda value: gt(value, 0), sorted(values)))\n r = range(filtered_values[0], filtered_values[-1])\n \n for R, value in zip(r, filtered_values):\n if ne(R, value):\n return R\n \n return filtered_values[-1] + 1\n\n \n ", "repo_name": "benilton02/tdd_python", "sub_path": "first_positive/app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 712, "program_lang": "python", "lang": "pt", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "typing.List", "line_number": 12, "usage_type": "name"}, {"api_name": "operator.le", "line_number": 14, "usage_type": "call"}, {"api_name": "operator.gt", "line_number": 21, "usage_type": "call"}, {"api_name": "operator.ne", "line_number": 25, "usage_type": "call"}]} +{"seq_id": "26997432614", "text": "import matplotlib.pyplot\r\nimport openpyxl as xl\r\nimport matplotlib.pyplot as plt\r\nimport numpy as np\r\n\r\n\r\n# returns a ordered list from a colum ignoring the first element (colum name)\r\ndef get_index(column, ws):\r\n col = []\r\n for i in range(2, len(ws[column])):\r\n col.append(float(ws.cell(row=i, column=(ord(column) - 64)).value))\r\n col.sort()\r\n return col\r\n\r\n\r\n# the object that will containg all the data in a season\r\nclass Season:\r\n def __init__(self, workbook):\r\n self.workbook = workbook\r\n print(\"loading workbook: \" + workbook)\r\n wb = xl.load_workbook(filename='workbooks\\\\' + workbook + \".xlsx\") # load workbook from file\r\n ws = wb.active\r\n\r\n # define all the indexes into ordered vectors\r\n self.FG = get_index('E', ws)\r\n self.FG_attempted = get_index('F', ws)\r\n self.FG_rate = get_index('G', ws)\r\n self.ThreeP = get_index('H', ws)\r\n self.ThreeP_attempted = get_index('I', ws)\r\n self.ThreeP_rate = get_index('J', ws)\r\n self.TwoP = get_index('K', ws)\r\n self.TwoP_attempted = get_index('L', ws)\r\n self.TwoP_rate = get_index('M', ws)\r\n self.FT = get_index('N', ws)\r\n self.FT_attempted = get_index('O', ws)\r\n self.FT_rate = get_index('P', ws)\r\n self.OR = get_index('Q', ws)\r\n self.DR = get_index('R', ws)\r\n self.AST = get_index('T', ws)\r\n self.STL = get_index('U', ws)\r\n self.BLK = get_index('V', ws)\r\n self.TOV = get_index('W', ws)\r\n self.PF = get_index('X', ws)\r\n self.PTS = get_index('Y', ws)\r\n\r\n wb.close() # close the workbook\r\n\r\n\r\n# creating a array with all the seasons #\r\nyear = 1980\r\nseasons = []\r\nwhile year < 2023:\r\n seasons.append(Season(str(year)))\r\n year = year + 1\r\n\r\n# creating all teams average arrays\r\nFG_avg = []\r\nFG_attempted_avg = []\r\nFG_rate_avg = []\r\nThreeP_avg = []\r\nThreeP_attempted_avg = []\r\nThreeP_rate_avg = []\r\nTwoP_avg = []\r\nTwoP_attempted_avg = []\r\nTwoP_rate_avg = []\r\nFT_avg = []\r\nFT_attempted_avg = []\r\nFT_rate_avg = []\r\nOR_avg = []\r\nDR_avg = []\r\nAST_avg = []\r\nSTL_avg = []\r\nBLK_avg = []\r\nTOV_avg = []\r\nPF_avg = []\r\nPTS_avg = []\r\n\r\nfor i in seasons:\r\n FG_avg.append(sum(i.FG) / len(i.FG))\r\n FG_attempted_avg.append(sum(i.FG_attempted) / len(i.FG_attempted))\r\n FG_rate_avg.append(sum(i.FG_rate) / len(i.FG_rate))\r\n ThreeP_avg.append(sum(i.ThreeP) / len(i.ThreeP))\r\n ThreeP_attempted_avg.append(sum(i.ThreeP_attempted) / len(i.ThreeP_attempted))\r\n ThreeP_rate_avg.append(sum(i.ThreeP_rate) / len(i.ThreeP_rate))\r\n TwoP_avg.append(sum(i.TwoP) / len(i.TwoP))\r\n TwoP_attempted_avg.append(sum(i.TwoP_attempted) / len(i.TwoP_attempted))\r\n TwoP_rate_avg.append(sum(i.TwoP_rate) / len(i.TwoP_rate))\r\n FT_avg.append(sum(i.FT) / len(i.FT))\r\n FT_attempted_avg.append(sum(i.FT_attempted) / len(i.FT_attempted))\r\n FT_rate_avg.append(sum(i.FT_rate) / len(i.FT_rate))\r\n OR_avg.append(sum(i.OR) / len(i.OR))\r\n DR_avg.append(sum(i.DR) / len(i.DR))\r\n AST_avg.append(sum(i.AST) / len(i.AST))\r\n STL_avg.append(sum(i.STL) / len(i.STL))\r\n BLK_avg.append(sum(i.BLK) / len(i.BLK))\r\n TOV_avg.append(sum(i.TOV) / len(i.TOV))\r\n PF_avg.append(sum(i.PF) / len(i.PF))\r\n PTS_avg.append(sum(i.PTS) / len(i.PTS))\r\n\r\n# making the avg plots\r\n\r\n# making the array with personalized variables\r\ntitle = [\"field goals made\", \"field goals attempted\", \"field goal percentage\", \"three-pointers made\", \"three-pointers attempted\", \"three-point percentage\", \"two-pointers made\", \"two-pointers attempted\", \"two-point percentage\", \"free throws made\", \"free throws attempted\", \"free throw percentage\", \"offensive rebounds\", \"defensive rebounds\", \"assists\", \"steals\", \"blocks\", \"turnovers\", \"personal fouls\", \"points\"]\r\ndata = [FG_avg, FG_attempted_avg, FG_rate_avg, ThreeP_avg, ThreeP_attempted_avg, ThreeP_rate_avg, TwoP_avg, TwoP_attempted_avg, TwoP_rate_avg, FT_avg, FT_attempted_avg, FT_rate_avg, OR_avg, DR_avg, AST_avg, STL_avg, BLK_avg, TOV_avg, PF_avg, PTS_avg]\r\n\r\n# plot\r\nfor a in range(0, len(data)):\r\n fig, ax = plt.subplots()\r\n ax.set_title(\"NBA average \" + title[a] + \" per game\")\r\n ax.set_xlabel(\"end season year\")\r\n ax.set_ylabel(title[a] + \"per game average\")\r\n ax.plot(range(1980, 2023), data[a], \"r+\")\r\n ax.plot(range(1980, 2023), data[a], linewidth=0.7)\r\n plt.grid()\r\n plt.xticks(np.arange(1980, 2024, 6))\r\n\r\nplt.show()\r\n", "repo_name": "rgherson/NBA_Analysis", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 4422, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "openpyxl.load_workbook", "line_number": 21, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 108, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 108, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 114, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 114, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 115, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 115, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 115, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 117, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 117, "usage_type": "name"}]} +{"seq_id": "26984418464", "text": "from datetime import date\nfrom mock import patch\nimport unittest\n\nimport fincalendar.holidays\nfrom fincalendar.holidays import holidays_set\n\n\nDUMMY_OVERRIDES = {\"SGP\": {\"add\": {date(2015, 5, 27), date(2016, 5, 27)},\n \"subtract\": {date(2015, 12, 25)}\n },\n \"HKG\": {\"add\": {date(2015, 5, 27), date(2016, 5, 27)}\n },\n \"GBR\": {\"subtract\": {date(2015, 12, 25)}\n }\n }\n\nclass HolidaysTest(unittest.TestCase):\n\n def setUp(self):\n pass\n\n @patch(\"fincalendar.holidays.OVERRIDES\", DUMMY_OVERRIDES)\n def test_Overrides(self):\n holidays = holidays_set(alpha_3_country_code='SGP', year=2015)\n self.assertIn(date(2015, 5, 27), holidays)\n\n @patch(\"fincalendar.holidays.OVERRIDES\", DUMMY_OVERRIDES)\n def test_NoOverrides(self):\n holidays = holidays_set(alpha_3_country_code='MYS', year=2015)\n self.assertIn(date(2015, 12, 25), holidays)\n\n @patch(\"fincalendar.holidays.OVERRIDES\", DUMMY_OVERRIDES)\n def test_MissingCountryCode(self):\n # The default calendar in workalendar has the 1st Jan as a holiday\n holidays = holidays_set(alpha_3_country_code='XXX', year=2015)\n self.assertEqual(holidays, {date(2015, 1, 1)})\n\n @patch(\"fincalendar.holidays.OVERRIDES\", DUMMY_OVERRIDES)\n def test_OverridesOnlyAdd(self):\n holidays = holidays_set(alpha_3_country_code='HKG', year=2015)\n self.assertIn(date(2015, 5, 27), holidays)\n\n @patch(\"fincalendar.holidays.OVERRIDES\", DUMMY_OVERRIDES)\n def test_OverridesOnlySubtract(self):\n holidays = holidays_set(alpha_3_country_code='GBR', year=2015)\n self.assertNotIn(date(2015, 12, 25), holidays)\n\nif __name__ == '__main__':\n unittest.main()\n", "repo_name": "amaas-fintech/fincalendar", "sub_path": "tests/holidays.py", "file_name": "holidays.py", "file_ext": "py", "file_size_in_byte": 1827, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "datetime.date", "line_number": 9, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 10, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 12, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 14, "usage_type": "call"}, {"api_name": "unittest.TestCase", "line_number": 18, "usage_type": "attribute"}, {"api_name": "fincalendar.holidays.holidays_set", "line_number": 25, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 26, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 23, "usage_type": "call"}, {"api_name": "fincalendar.holidays.holidays_set", "line_number": 30, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 31, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 28, "usage_type": "call"}, {"api_name": "fincalendar.holidays.holidays_set", "line_number": 36, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 37, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 33, "usage_type": "call"}, {"api_name": "fincalendar.holidays.holidays_set", "line_number": 41, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 42, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 39, "usage_type": "call"}, {"api_name": "fincalendar.holidays.holidays_set", "line_number": 46, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 47, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 44, "usage_type": "call"}, {"api_name": "unittest.main", "line_number": 50, "usage_type": "call"}]} +{"seq_id": "21126870929", "text": "import torch\nfrom pathlib import Path\nfrom torch.nn import functional as F\nfrom torch import nn\nfrom typing import Union\nimport clip\nfrom torch import Tensor\nfrom torchvision import transforms\nfrom torchvision.transforms import functional as TF\nfrom PIL import Image\n\n\nclass MakeCutouts(nn.Module):\n def __init__(self, cut_size, cutn, cut_pow=1.0):\n super().__init__()\n self.cut_size = cut_size\n self.cutn = cutn\n self.cut_pow = cut_pow\n\n def forward(self, input):\n sideY, sideX = input.shape[2:4]\n max_size = min(sideX, sideY)\n min_size = min(sideX, sideY, self.cut_size)\n cutouts = []\n for _ in range(self.cutn):\n size = int(torch.rand([]) ** self.cut_pow * (max_size - min_size) + min_size)\n offsetx = torch.randint(0, sideX - size + 1, ())\n offsety = torch.randint(0, sideY - size + 1, ())\n cutout = input[:, :, offsety : offsety + size, offsetx : offsetx + size]\n cutouts.append(F.adaptive_avg_pool2d(cutout, self.cut_size))\n return torch.cat(cutouts)\n\n\nclass CLIP:\n def __init__(self, device: str, cutn=16) -> None:\n self.device = device\n model, _ = clip.load(\"ViT-B/32\", device=self.device, jit=False)\n self.model = model.eval().requires_grad_(False)\n self.size = self.model.visual.input_resolution\n self.normalize = transforms.Normalize(\n mean=[0.48145466, 0.4578275, 0.40821073], std=[0.26862954, 0.26130258, 0.27577711]\n )\n self.make_cutouts = MakeCutouts(self.size, cutn)\n\n def parse_prompt(string: str) -> Union[str, Path]:\n return Path(string) if Path(string).exists() else string\n\n def prompts2embeddings(self, prompts: list[str]) -> Tensor:\n embeddings = []\n for prompt in prompts:\n if Path(prompt).exists():\n embeddings.append(self.img2embedding(prompt))\n pass\n else:\n embeddings.append(self.txt2embedding(prompt))\n embedding = torch.cat(embeddings).float().to(self.device)\n return embedding\n\n def txt2embedding(self, text: str) -> Tensor:\n text = clip.tokenize(text).to(self.device)\n return self.model.encode_text(text).detach().clone().cpu()\n\n def img2embedding(self, path: Path) -> Tensor:\n img = Image.open(path).convert(\"RGB\")\n img = TF.resize(img, min(side_x, side_y, *img.size), transforms.InterpolationMode.LANCZOS)\n batch = self.make_cutouts(TF.to_tensor(img).unsqueeze(0).to(self.device))\n return self.model.encode_image(self.normalize(batch)).float()\n", "repo_name": "morris-frank/latents", "sub_path": "latents/_clip.py", "file_name": "_clip.py", "file_ext": "py", "file_size_in_byte": 2628, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "torch.nn.Module", "line_number": 13, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 13, "usage_type": "name"}, {"api_name": "torch.rand", "line_number": 26, "usage_type": "call"}, {"api_name": "torch.randint", "line_number": 27, "usage_type": "call"}, {"api_name": "torch.randint", "line_number": 28, "usage_type": "call"}, {"api_name": "torch.nn.functional.adaptive_avg_pool2d", "line_number": 30, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 30, "usage_type": "name"}, {"api_name": "torch.cat", "line_number": 31, "usage_type": "call"}, {"api_name": "clip.load", "line_number": 37, "usage_type": "call"}, {"api_name": "torchvision.transforms.Normalize", "line_number": 40, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 40, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 46, "usage_type": "call"}, {"api_name": "typing.Union", "line_number": 45, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 45, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 51, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 56, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 48, "usage_type": "name"}, {"api_name": "clip.tokenize", "line_number": 60, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 59, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 63, "usage_type": "name"}, {"api_name": "PIL.Image.open", "line_number": 64, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 64, "usage_type": "name"}, {"api_name": "torchvision.transforms.functional.resize", "line_number": 65, "usage_type": "call"}, {"api_name": "torchvision.transforms.functional", "line_number": 65, "usage_type": "name"}, {"api_name": "torchvision.transforms.InterpolationMode", "line_number": 65, "usage_type": "attribute"}, {"api_name": "torchvision.transforms", "line_number": 65, "usage_type": "name"}, {"api_name": "torchvision.transforms.functional.to_tensor", "line_number": 66, "usage_type": "call"}, {"api_name": "torchvision.transforms.functional", "line_number": 66, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 63, "usage_type": "name"}]} +{"seq_id": "10308551638", "text": "import argparse\nfrom pathlib import Path\nimport pandas as pd\nimport sys\nfrom uk.subprocess import run, check_output\n\nparser = argparse.ArgumentParser(description=\"\"\"Simulate dataset queries.\n\nGiven a dataset with multiple labels per utterance,\na list of gradient norms and losses for each label-utterance pair,\ncompute the expected gradient length (EGL) for each utterance.\n\nEGL(x) = \\sum_y P(y|x) ||\\grad P(y|x)||**2\n\nBased on highest EGL values, select a batch utterances to query.\n\nThen, fulfill the query by reading true labels from the oracle dataset\nand the rest of the labels from the original dataset.\n\"\"\")\nparser.add_argument('--oracle', type=Path, default=Path('data/corrupted-librispeech/train-clean-100.ref.txt.piece'),\n help='dataset with true labels')\nparser.add_argument('--query-size', type=int, default=2196,\n help='number of utterances to query')\nparser.add_argument('--prev', type=Path, default=Path('exp/active/egl/03'),\n help='experiment directory')\nparser.add_argument('--exp', type=Path, default=Path('exp/active/egl/04'),\n help='experiment directory')\nparser.add_argument('--log', type=Path, required=True,\n help='log of the training run')\n\ndef read_text(filename: Path):\n with open(filename) as f:\n return pd.DataFrame([line.strip().split(maxsplit=1) for line in f], columns=['media_filename', 'text'])\n\ndef read_grads(filename: Path):\n return pd.read_csv(filename, sep='\\t', header=None, names=['stub', 'dataset_index', 'grad_norm', 'loss'])\n\ndef training_log_to_dataset(training_log_filename: Path):\n \"reads output of hac using heuristics to extract the dataset\"\n train_hypotheses = []\n with open(training_log_filename) as f:\n decoding_train = False\n for line in f:\n if decoding_train and line.startswith('12') and 'hyp' in line:\n epoch, dataset_index, hypN, text = line.strip().split('\\t')\n assert epoch == \"12\" and hypN.startswith('hyp'), f\"epoch={epoch}, hypN={hypN}\"\n train_hypotheses.append((int(dataset_index), text))\n elif line.startswith('valid [12'):\n decoding_train = True\n continue\n df = pd.DataFrame(train_hypotheses, columns=['dataset_index', 'hyp_text'])\n df.sort_values(by='dataset_index', ascending=True, inplace=True)\n return df.set_index('dataset_index')\n\n\nif __name__ == '__main__':\n args = parser.parse_args()\n\n oracle = read_text(args.oracle)\n corrupted = read_text(args.prev / 'corrupted.txt.piece')\n\n train_hypotheses = training_log_to_dataset(args.log)\n grad_norms_dataset = train_hypotheses.join(corrupted)\n grad_norms_dataset[['media_filename', 'hyp_text']].to_csv(args.exp / 'hyp.txt.piece', sep='\\t', header=False, index=False)\n\n if not (args.exp / 'grads.txt').exists():\n print('computing gradient norms', file=sys.stderr)\n check_output(' '.join([\n 'bash -c \"hac',\n f'--grad-norms fbank:{args.exp / \"hyp.txt.piece\"}',\n '--device cuda:1',\n '--init', str(args.exp / 'last.pt'),\n '--vocab exp/libribpe.vocab --compile >', str(args.exp / 'grads.txt'),\n '\"'\n ]), shell=True).strip().decode('utf-8')\n\n grad_norms_result = read_grads(args.exp / 'grads.txt')\n\n # Compute log-space EGL for each utterance\n grad_norms = pd.concat([\n grad_norms_dataset.reset_index(),\n grad_norms_result\n ], axis=1)\n\n import numpy as np\n from scipy.special import logsumexp\n #\n # \\log \\sum_y ||\\grad P(y|x)||**2 P(y|x) \n # = \\log \\sum_y exp(\\log ||\\grad P(y|x)||**2 - NLL(y|x))\n #\n grad_norms['product'] = np.log((grad_norms['grad_norm'] ** 2)) - grad_norms['loss']\n\n egl = grad_norms.groupby('media_filename')['product'].apply(logsumexp)\n egl.sort_values(ascending=False, inplace=True)\n\n egl.to_csv(args.exp / 'egl', sep='\\t', header=False)\n print('writing utterance scores to', args.exp / 'egl', file=sys.stderr)\n\n query = egl[:args.query_size]\n\n # Read true labels for the query from the oracle dataset\n oracle_query = oracle[oracle['media_filename'].isin(query.index)]\n # Concat clean.txt.piece from previous experiments\n oracle_query = pd.concat([read_text(args.prev / 'clean.txt.piece'), oracle_query])\n\n print('querying', len(query), 'clean utterances')\n oracle_query.to_csv(args.exp / 'clean.txt.piece', sep='\\t', header=False, index=False)\n print('writing ', args.exp / 'clean.txt.piece', file=sys.stderr)\n\n # Read the rest of the labels from the original dataset\n corrupted_rest = corrupted[~corrupted['media_filename'].isin(query.index)]\n corrupted_rest.to_csv(args.exp / 'corrupted.txt.piece', sep='\\t', header=False, index=False)\n\n print('writing combined dataset', file=sys.stderr)\n combined_train = pd.concat([oracle_query, corrupted_rest])\n combined_train.to_csv(args.exp / 'combined_train.txt.piece', sep='\\t', header=False, index=False)\n\n next_exp = args.exp.parent / f'{int(args.exp.name) + 1:02}'\n prefixes = ['mask:fbank:speed:', 'mask:fbank:speed:randpairs:']\n run([\n 'hac',\n '--train', ','.join([prefix + str(args.exp / 'combined_train.txt.piece') for prefix in prefixes]),\n '--eval', 'fbank:data/corrupted-librispeech/dev-clean.txt.piece',\n '--test-attempts', '20',\n '--test', f'fbank:{args.exp}/corrupted.txt.piece'\n ] + '--num-epochs 13 --num-workers 16 --lr_decay_iters 15835 --lr_schedule linear --warmup_iters 3000 --device cuda:1 --batch-size 48 --lr 0.0006 --min_lr 0 --eval-batch-size 1024 --compile --vocab exp/libribpe.vocab --weight_decay 0.1'.split() + [\n f'--exp', f'{next_exp}',\n ])\n print(\n 'python -m ha.query_sim',\n '--oracle', args.oracle,\n '--prev', args.exp,\n '--exp', next_exp,\n '--log', '???',\n )\n", "repo_name": "proger/haloop", "sub_path": "ha/query_sim.py", "file_name": "query_sim.py", "file_ext": "py", "file_size_in_byte": 5923, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 10, "dataset": "github-code", "pt": "37", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 7, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 20, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 24, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 26, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 28, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 31, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 33, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 35, "usage_type": "name"}, {"api_name": "pandas.read_csv", "line_number": 36, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 38, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 51, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 67, "usage_type": "attribute"}, {"api_name": "uk.subprocess.check_output", "line_number": 68, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 80, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 91, "usage_type": "call"}, {"api_name": "scipy.special.logsumexp", "line_number": 93, "usage_type": "argument"}, {"api_name": "sys.stderr", "line_number": 97, "usage_type": "attribute"}, {"api_name": "pandas.concat", "line_number": 104, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 108, "usage_type": "attribute"}, {"api_name": "sys.stderr", "line_number": 114, "usage_type": "attribute"}, {"api_name": "pandas.concat", "line_number": 115, "usage_type": "call"}, {"api_name": "uk.subprocess.run", "line_number": 120, "usage_type": "call"}]} +{"seq_id": "6259485715", "text": "\n\nfrom crispy_forms.bootstrap import (Accordion, AccordionGroup, FieldWithButtons,\n StrictButton, Tab)\nfrom crispy_forms.layout import Field, HTML, Layout, Submit\nfrom crispy_forms.helper import FormHelper\nfrom django.utils.translation import ugettext_lazy as _\nfrom haystack.forms import ModelSearchForm\n\n\nclass SearchForm(ModelSearchForm):\n\n def _wrap_all(self):\n # stylung\n self.helper.filter(\n str, max_level=4).wrap(\n Field, css_class=\"form-control\")\n\n def __init__(self, *args, **kwargs):\n super(SearchForm, self).__init__(*args, **kwargs)\n self.helper = FormHelper(self)\n self.helper.form_tag = False\n self.helper.form_show_labels = False\n self.helper.layout = Layout(\n FieldWithButtons('q', Submit('submit', _(\"Search...\")), css_class=\"col-xs-6 col-md-offset-3\")\n )\n", "repo_name": "django-leonardo/django-leonardo", "sub_path": "leonardo/module/search/forms.py", "file_name": "forms.py", "file_ext": "py", "file_size_in_byte": 904, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 97, "dataset": "github-code", "pt": "37", "api": [{"api_name": "haystack.forms.ModelSearchForm", "line_number": 11, "usage_type": "name"}, {"api_name": "crispy_forms.layout.Field", "line_number": 17, "usage_type": "argument"}, {"api_name": "crispy_forms.helper.FormHelper", "line_number": 21, "usage_type": "call"}, {"api_name": "crispy_forms.layout.Layout", "line_number": 24, "usage_type": "call"}, {"api_name": "crispy_forms.bootstrap.FieldWithButtons", "line_number": 25, "usage_type": "call"}, {"api_name": "crispy_forms.layout.Submit", "line_number": 25, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 25, "usage_type": "call"}]} +{"seq_id": "72404335147", "text": "import argparse\nimport commonFunctions as cf\n\ndef run(start_date, end_date, folder_path):\n\n range_file_list = [x for x in cf.getAllfiles(folder_path) if int(x)>=start_date and int(x)<= end_date]\n\n with open(str(start_date)+'_'+str(end_date),'a') as fout:\n for file in range_file_list:\n with open(folder_path+file,'r') as file_out:\n for line in file_out:\n fout.write(line)\n\n\nif __name__=='__main__':\n parser = argparse.ArgumentParser(description='for creating splits')\n\n parser.add_argument('--start_date', type=int,\n help='start date of the time frame')\n parser.add_argument('--end_date', type=int,\n help='inclusive end date of the time frame')\n\n\n parser.add_argument('--folder_path', type=str,\n help='path of where file are stored', default='/media/gaurav/Elements/Thesis/data/mappedData/correct_yer_match/mapped_data/')\n args = parser.parse_args()\n\n run(args.start_date,args.end_date,args.folder_path)", "repo_name": "grv1207/Exploring-Diachronic-Changes-Medical-Knowledge", "sub_path": "Data_Pre_Processing/create_time_frame.py", "file_name": "create_time_frame.py", "file_ext": "py", "file_size_in_byte": 1048, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "commonFunctions.getAllfiles", "line_number": 6, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 16, "usage_type": "call"}]} +{"seq_id": "39410860110", "text": "import sys\r\nimport argparse\r\nfrom tqdm import tqdm \r\nimport numpy as np\r\nimport math\r\nfrom collections import Counter\r\n\r\ntoken = {'2': '', '3': '' , '4': '', '5': '', '6' : '',\r\n '7': '', '8':'', '9' : '', '10': '', '11': '', '12' : ''}\r\ninv_map = {v: int(k) for k, v in token.items()}\r\n\r\ndef read_file(filename):\r\n data = []\r\n with open(filename) as f:\r\n for line in f:\r\n data.append(line.strip())\r\n return data\r\n\r\ndef parse_ops_file(filename, gold_grade_file=None, by=\"diff\"):\r\n if gold_grade_file is not None:\r\n codes = Counter()\r\n with open(gold_grade_file) as f:\r\n grades_data = [x.split(\"\\t\") for x in read_file(gold_grade_file)]\r\n src_grades, tgt_grades = zip(*grades_data)\r\n\r\n\r\n ops_data = read_file(filename)\r\n \r\n num_repos = []\r\n num_del = []\r\n num_ins = []\r\n for line in ops_data:\r\n r, d, i = map(int, line.strip()[1:-1].split(\", \"))\r\n num_repos.append(int(r))\r\n num_del.append(int(d))\r\n num_ins.append(int(i))\r\n if gold_grade_file is not None:\r\n sg = inv_map[src_grades[i]]\r\n tg = inv_map[tgt_grades[i]]\r\n if by==\"source\":\r\n grade_diff = sg\r\n elif by==\"target\":\r\n grade_diff = tg\r\n elif by==\"diff\":\r\n grade_diff = sg-tg\r\n\r\n if grade_diff in codes: \r\n codes[grade_diff].append([r, d, i])\r\n else:\r\n codes[grade_diff] = [[r, d, i]]\r\n\r\n if gold_grade_file is not None:\r\n avg_codes = {}\r\n for k in codes:\r\n avg_codes[k] = np.mean(np.array(codes[k]), axis=0)\r\n print(\"Grade %d, Repos: %f, Del: %f, Ins: %f\" % (k, np.mean(avg_codes[k][0]), np.mean(avg_codes[k][1]), np.mean(avg_codes[k][2])))\r\n\r\n\r\n print(\"Repos: %f, Del: %f, Ins: %f\" % (np.mean(num_repos), np.mean(num_del), np.mean(num_ins)))\r\n\r\n\r\ndef main():\r\n arg_parser = argparse.ArgumentParser(description='Compute ARI adjacency accuracy')\r\n arg_parser.add_argument('--ops_file', type=str, default=None)\r\n # Grades tab separated for source and target\r\n arg_parser.add_argument('--grade_file', type=str, default=None)\r\n arg_parser.add_argument('--by', type=str, default=\"diff\")\r\n \r\n args = arg_parser.parse_args()\r\n parse_ops_file(args.ops_file, args.grade_file, args.by)\r\n\r\nif __name__ == '__main__':\r\n main() ", "repo_name": "sweta20/EditingCL", "sub_path": "readability/ops_count.py", "file_name": "ops_count.py", "file_ext": "py", "file_size_in_byte": 2486, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "37", "api": [{"api_name": "collections.Counter", "line_number": 21, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 55, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 55, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 56, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 59, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 63, "usage_type": "call"}]} +{"seq_id": "23604118668", "text": "from models import barlowBYOL, LinearEvaluationCallback\nfrom dataset import CustomImageDataset\n\nimport torchvision.transforms as transforms\nfrom torchvision.datasets import CIFAR10\nfrom torch.utils.data import DataLoader, random_split\nfrom pytorch_lightning import Trainer\nfrom pytorch_lightning.callbacks import ModelCheckpoint\n\nimport torch.nn as nn\n\nfrom torchvision.models import resnet18\n\nfrom pytorch_lightning.loggers import TensorBoardLogger\nimport matplotlib.pyplot as plt\n\n\ndef main():\n\n transform = transforms.Compose([\n # transforms.Resize((128, 256)),\n transforms.ToTensor(),\n transforms.Normalize([0.5], [0.5])\n ])\n \n root_dir = './mel_spectrogram'\n batch_size = 16\n\n # Create the custom dataset\n dataset = CustomImageDataset(root_dir, transform=transform)\n\n # Split the dataset into train and validation sets (optional)\n train_size = int(0.8 * len(dataset))\n valid_size = len(dataset) - train_size\n train_dataset, val_dataset = random_split(dataset, [train_size, valid_size])\n\n # Create DataLoaders for the train and validation sets\n train_dataloader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True, num_workers=4, pin_memory=True)\n val_dataloader = DataLoader(val_dataset, batch_size=batch_size, shuffle=False, num_workers=4, pin_memory=True)\n\n # Load the CIFAR-10 dataset\n # train_dataset = CIFAR10(root='./data', train=True, download=True, transform=transform)\n # val_dataset = CIFAR10(root='./data', train=False, download=True, transform=transform)\n # train_dataloader = DataLoader(train_dataset, batch_size=256, shuffle=True, num_workers=4, pin_memory=True)\n # val_dataloader = DataLoader(val_dataset, batch_size=256, shuffle=False, num_workers=4, pin_memory=True)\n\n encoder = resnet18()\n encoder.conv1 = nn.Conv2d(1, 64, kernel_size=3, stride=1, padding=1, bias=False)\n encoder.maxpool = nn.MaxPool2d(kernel_size=1, stride=1)\n encoder.fc = nn.Identity()\n\n # Load the barlowBYOL model and train it\n\n logger = TensorBoardLogger(\"logs\", name=\"Barlow_BYOL\")\n \n barlow_byol = barlowBYOL(encoder=encoder, image_size=(216, 128), lr=3e-4, tau=0.99, encoder_out_dim=512)\n\n linear_evaluation = LinearEvaluationCallback(encoder_output_dim=512, num_classes=10)\n checkpoint_callback = ModelCheckpoint(every_n_epochs=100, save_top_k=-1, save_last=True)\n\n barlow_byol_trainer = Trainer(\n devices=1,\n accelerator='gpu',\n max_epochs=500,\n callbacks=[linear_evaluation, checkpoint_callback],\n logger=logger,\n log_every_n_steps=1\n )\n barlow_byol_trainer.fit(barlow_byol, train_dataloader, val_dataloader)\n\nif __name__ == '__main__':\n main()", "repo_name": "VebjornBerstad/Barlow-twins-BYOL", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 2728, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "torchvision.transforms.Compose", "line_number": 20, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 20, "usage_type": "name"}, {"api_name": "torchvision.transforms.ToTensor", "line_number": 22, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 22, "usage_type": "name"}, {"api_name": "torchvision.transforms.Normalize", "line_number": 23, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 23, "usage_type": "name"}, {"api_name": "dataset.CustomImageDataset", "line_number": 30, "usage_type": "call"}, {"api_name": "torch.utils.data.random_split", "line_number": 35, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 38, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 39, "usage_type": "call"}, {"api_name": "torchvision.models.resnet18", "line_number": 47, "usage_type": "call"}, {"api_name": "torch.nn.Conv2d", "line_number": 48, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 48, "usage_type": "name"}, {"api_name": "torch.nn.MaxPool2d", "line_number": 49, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 49, "usage_type": "name"}, {"api_name": "torch.nn.Identity", "line_number": 50, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 50, "usage_type": "name"}, {"api_name": "pytorch_lightning.loggers.TensorBoardLogger", "line_number": 54, "usage_type": "call"}, {"api_name": "models.barlowBYOL", "line_number": 56, "usage_type": "call"}, {"api_name": "models.LinearEvaluationCallback", "line_number": 58, "usage_type": "call"}, {"api_name": "pytorch_lightning.callbacks.ModelCheckpoint", "line_number": 59, "usage_type": "call"}, {"api_name": "pytorch_lightning.Trainer", "line_number": 61, "usage_type": "call"}]} +{"seq_id": "71482140907", "text": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nimport os\nimport re\n\nfrom distribute_setup import use_setuptools; use_setuptools()\nfrom setuptools import setup, find_packages\n\n# Refer to files relative to the directory containing setup.py\nrel_file = lambda *args: os.path.join(os.path.dirname(os.path.abspath(__file__)), *args)\n\ndef get_version():\n return open(rel_file('VERSION')).read().strip()\n\ndef get_requirements():\n reqs = open(rel_file('REQUIREMENTS')).read().splitlines()\n return filter(lambda line: line[:1].isalnum(), reqs)\n\nsetup(\n name = 'pistachio',\n version = get_version(),\n author = \"Zachary Voase\",\n author_email = \"z@zacharyvoase.com\",\n url = 'http://zacharyvoase.github.com/pistachio/',\n description = \"An experimental Mustache implementation in Python.\",\n packages = find_packages(where='src'),\n package_dir = {'': 'src'},\n install_requires = get_requirements(),\n)\n", "repo_name": "zacharyvoase/pistachio", "sub_path": "setup.py", "file_name": "setup.py", "file_ext": "py", "file_size_in_byte": 987, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "37", "api": [{"api_name": "distribute_setup.use_setuptools", "line_number": 7, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 11, "usage_type": "call"}, {"api_name": "os.path", "line_number": 11, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 11, "usage_type": "call"}, {"api_name": "os.path.abspath", "line_number": 11, "usage_type": "call"}, {"api_name": "setuptools.setup", "line_number": 20, "usage_type": "call"}, {"api_name": "setuptools.find_packages", "line_number": 27, "usage_type": "call"}]} +{"seq_id": "19649673569", "text": "#!/usr/bin/env python3\n\nimport sys\nimport time\n\nimport os.path\nimport signal\nimport subprocess\nfrom task_maker.config import Config\nfrom task_maker.task_maker_frontend import Frontend\nfrom typing import List\n\nSERVER_SPAWN_TIME = 1\nMAX_SPAWN_ATTEMPT = 3\n\n\ndef get_task_maker_path():\n \"\"\"\n Get the path of the cpp executable\n \"\"\"\n task_maker = os.path.dirname(__file__)\n task_maker = os.path.join(task_maker, \"bin\", \"task-maker\")\n return os.path.abspath(task_maker)\n\n\ndef spawn_backend(type: str, args: List[str], daemonize: bool):\n \"\"\"\n Spawn a backend service, eventually daemonizing it\n \"\"\"\n task_maker = get_task_maker_path()\n if daemonize:\n args.append(\"--daemon\")\n streams = subprocess.DEVNULL\n else:\n streams = None\n subprocess.run(\n [task_maker, type] + args,\n stdin=streams,\n stdout=streams,\n stderr=streams)\n\n\ndef spawn_server(config: Config):\n \"\"\"\n Spawn the server, passing its arguments from the config\n \"\"\"\n args = []\n if config.server_logfile is not None:\n args += [\"--logfile\", config.server_logfile]\n if config.server_pidfile is not None:\n args += [\"--pidfile\", config.server_pidfile]\n if config.storedir is not None:\n args += [\"--store-dir\", config.storedir]\n if config.tempdir is not None:\n args += [\"--temp-dir\", config.tempdir]\n if config.cache_size is not None:\n args += [\"--cache-size\", str(config.cache_size)]\n if config.server_address is not None:\n args += [\"--address\", config.server_address]\n if config.server_port is not None:\n args += [\"--port\", str(config.server_port)]\n if config.server_verbose:\n args += [\"--verbose\"]\n spawn_backend(\"server\", args, not config.run_server)\n\n\ndef spawn_worker(config: Config):\n \"\"\"\n Spawn the worker, passing its arguments from the config\n \"\"\"\n args = []\n if config.worker_logfile is not None:\n args += [\"--logfile\", config.worker_logfile]\n if config.worker_pidfile is not None:\n args += [\"--pidfile\", config.worker_pidfile]\n if config.storedir is not None:\n args += [\"--store-dir\", config.storedir]\n if config.tempdir is not None:\n args += [\"--temp-dir\", config.tempdir]\n if config.cache_size is not None:\n args += [\"--cache-size\", str(config.cache_size)]\n if config.worker_keep_sandboxes:\n args += [\"--keep_sandboxes\"]\n if config.worker_name is not None:\n args += [\"--name\", config.worker_name]\n if config.worker_num_cores is not None:\n args += [\"--num-cores\", str(config.worker_num_cores)]\n if config.worker_port is not None:\n args += [\"--port\", str(config.worker_port)]\n if config.worker_address is not None:\n args += [\"--server\", config.worker_address]\n if config.worker_pending_requests is not None:\n args += [\"--pending-requests\", str(config.worker_pending_requests)]\n if config.worker_verbose:\n args += [\"--verbose\"]\n spawn_backend(\"worker\", args, not config.run_worker)\n\n\ndef get_frontend(config: Config) -> Frontend:\n \"\"\"\n Run the frontend module connecting to the server and eventually spawning it\n if needed.\n \"\"\"\n try:\n return Frontend(config.host, config.port)\n except:\n if config.no_spawn:\n raise RuntimeError(\n \"Cannot connect to the server and spawning is forbidden\")\n spawn_server(config)\n print(\n \"Spawning server and workers\", end=\"\", flush=True, file=sys.stderr)\n for _ in range(3):\n print(\".\", end=\"\", flush=True, file=sys.stderr)\n time.sleep(SERVER_SPAWN_TIME / 3)\n print(file=sys.stderr)\n spawn_worker(config)\n for t in range(MAX_SPAWN_ATTEMPT):\n try:\n return Frontend(config.host, config.port)\n except:\n print(\"Attempt {} failed\".format(t + 1), file=sys.stderr)\n time.sleep(1)\n raise RuntimeError(\"Failed to spawn the server\")\n\n\ndef stop():\n proc = subprocess.run([\"ps\", \"ax\", \"-o\", \"pid,cmd\"],\n stdout=subprocess.PIPE)\n path = get_task_maker_path()\n running = [p.split()[:2] for p in proc.stdout.decode().splitlines()]\n pids = [int(pid) for pid, proc in running if proc == path]\n for pid in pids:\n print(\"Sending SIGTERM to pid %d\" % pid, file=sys.stderr)\n os.kill(pid, signal.SIGTERM)\n", "repo_name": "algorithm-ninja/task-maker", "sub_path": "python/manager.py", "file_name": "manager.py", "file_ext": "py", "file_size_in_byte": 4452, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 7, "dataset": "github-code", "pt": "37", "api": [{"api_name": "task_maker.config", "line_number": 21, "usage_type": "name"}, {"api_name": "os.path.path.dirname", "line_number": 21, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 21, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 21, "usage_type": "name"}, {"api_name": "task_maker.config", "line_number": 22, "usage_type": "name"}, {"api_name": "os.path.path.join", "line_number": 22, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 22, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 22, "usage_type": "name"}, {"api_name": "os.path.path.abspath", "line_number": 23, "usage_type": "call"}, {"api_name": "task_maker.config", "line_number": 23, "usage_type": "argument"}, {"api_name": "os.path.path", "line_number": 23, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 23, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 26, "usage_type": "name"}, {"api_name": "task_maker.config", "line_number": 30, "usage_type": "name"}, {"api_name": "subprocess.DEVNULL", "line_number": 33, "usage_type": "attribute"}, {"api_name": "subprocess.run", "line_number": 36, "usage_type": "call"}, {"api_name": "task_maker.config", "line_number": 37, "usage_type": "name"}, {"api_name": "task_maker.config.Config", "line_number": 43, "usage_type": "name"}, {"api_name": "task_maker.config.Config", "line_number": 67, "usage_type": "name"}, {"api_name": "task_maker.config.Config", "line_number": 99, "usage_type": "name"}, {"api_name": "task_maker.task_maker_frontend.Frontend", "line_number": 105, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 112, "usage_type": "attribute"}, {"api_name": "sys.stderr", "line_number": 114, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 115, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 116, "usage_type": "attribute"}, {"api_name": "task_maker.task_maker_frontend.Frontend", "line_number": 120, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 122, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 123, "usage_type": "call"}, {"api_name": "task_maker.task_maker_frontend.Frontend", "line_number": 99, "usage_type": "name"}, {"api_name": "subprocess.run", "line_number": 128, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 129, "usage_type": "attribute"}, {"api_name": "sys.stderr", "line_number": 134, "usage_type": "attribute"}, {"api_name": "os.path.kill", "line_number": 135, "usage_type": "call"}, {"api_name": "os.path", "line_number": 135, "usage_type": "name"}, {"api_name": "signal.SIGTERM", "line_number": 135, "usage_type": "attribute"}]} +{"seq_id": "25627356596", "text": "import requests,json\n\nbaseUrl = 'http://rsense-dev.cs.uml.edu/api/v1/projects/';\n\ndef projectGetRequest(projectID):\n \n urlProject = baseUrl+projectID+'?recur=true';\n\n data = requests.get(urlProject)\n\n return data\n\ndef getDatasetLocation(datasetName,parsedResponseProject):\n\n for i in range(0,parsedResponseProject.json()['dataSetCount']):\n\n if parsedResponseProject.json()['dataSets'][i]['name'] == datasetName:\n datasetLocation = i\n datasetID = parsedResponseProject.json()['dataSets'][i]['id']\n return datasetLocation\n\n return 'Dataset not found'\n\ndef getFieldID(fieldName,parsedResponseProject):\n\n for i in range(0,parsedResponseProject.json()['fieldCount']):\n\n if parsedResponseProject.json()['fields'][i]['name'] == fieldName:\n fieldID = parsedResponseProject.json()['fields'][i]['id']\n return fieldID;\n \n return \"Field Not Found\" \n \ndef getDatasetFieldData(projectID,datasetName,fieldName):\n\n values = []\n\n parsedResponseProject = projectGetRequest(projectID)\n\n datasetLocation = getDatasetLocation(datasetName,parsedResponseProject)\n\n fieldID = getFieldID(fieldName,parsedResponseProject)\n\n fieldID = str(fieldID)\n\n for i in range(0,parsedResponseProject.json()['dataSets'][datasetLocation]['datapointCount']):\n values.append(parsedResponseProject.json()['dataSets'][datasetLocation]['data'][i][fieldID]) \n\n return values\n\ndef postDataset(projectID,contributionKey,fieldName,datasetName,contributorName,fieldData):\n \n parsedResponseProject = projectGetRequest(projectID)\n fieldID = getFieldID(fieldName,parsedResponseProject)\n url = baseUrl+projectID+'/jsonDataUpload'\n\n payload = {\n 'title': datasetName, \n 'contribution_key': contributionKey, \n 'contributor_name': contributorName,\n 'data':\n {\n fieldID: fieldData\n }\n }\n headers = {'content-type': 'application/json'}\n\n r = requests.post(url, data=json.dumps(payload), headers=headers)\n", "repo_name": "engaging-computing/Teaching", "sub_path": "ExampleCode/Python/LibraryDiceApp/IsenseModule.py", "file_name": "IsenseModule.py", "file_ext": "py", "file_size_in_byte": 2113, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "37", "api": [{"api_name": "requests.get", "line_number": 9, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 68, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 68, "usage_type": "call"}]} +{"seq_id": "39490860617", "text": "import click\nimport numpy as np\nimport openai\nfrom sentence_transformers import SentenceTransformer\nfrom openai.embeddings_utils import get_embedding\n\nclass BaseTextMetric:\n \"\"\"\n Base class for text metrics that involve extracting embeddings using a model and then calculating a metric.\n \"\"\"\n\n def __init__(self, model, tokenizer, device):\n self.model = model\n self.tokenizer = tokenizer\n self.device = device\n\n def get_embeddings(self, texts, *args, **kwargs):\n \"\"\"\n Extract embeddings from a text using the model and tokenizer.\n \"\"\"\n if isinstance(self.model, SentenceTransformer):\n return self.model.encode(texts, show_progress_bar=False, convert_to_numpy=True ,device=self.device)\n elif self.model == \"precomputed\" and 'path' in kwargs:\n return np.load(kwargs['path'], allow_pickle=True)\n elif isinstance(self.model, str):\n if isinstance(texts, list):\n click.warning(f\"Warning: {self.model} only supports one text at a time.\")\n click.warning(\"Using the first text in the list.\")\n texts = texts[0]\n texts = texts.replace(\"\\n\", \" \")\n response = openai.Embedding.create(input=texts, engine=self.model)['data'][0]['embedding']\n return np.array(response).reshape(1, -1)\n else:\n raise NotImplementedError\n \n def get_metric(self, texts1, texts2=None, labels=None):\n \"\"\"\n Calculate the metric between two texts.\n \"\"\"\n raise NotImplementedError\n", "repo_name": "imartinf/multimemo", "sub_path": "src/metrics/base_text_metric.py", "file_name": "base_text_metric.py", "file_ext": "py", "file_size_in_byte": 1582, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "sentence_transformers.SentenceTransformer", "line_number": 21, "usage_type": "argument"}, {"api_name": "numpy.load", "line_number": 24, "usage_type": "call"}, {"api_name": "click.warning", "line_number": 27, "usage_type": "call"}, {"api_name": "click.warning", "line_number": 28, "usage_type": "call"}, {"api_name": "openai.Embedding.create", "line_number": 31, "usage_type": "call"}, {"api_name": "openai.Embedding", "line_number": 31, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 32, "usage_type": "call"}]} +{"seq_id": "24789475401", "text": "\"\"\"Unit tests for functions in metrics.py.\"\"\"\n\nfrom absl.testing import absltest\nfrom absl.testing import parameterized\nimport jax.numpy as jnp\nfrom scenic.projects.svvit import metrics\n\n\nclass MetricsTest(parameterized.TestCase):\n\n def setUp(self):\n self.one_hot_targets = jnp.array([[1, 0, 0], [0, 0, 1], [0, 1, 0],\n [0, 1, 0]])\n self.logits = jnp.array([[0.41, 0.39, 0.2], [0.4, 0.6, 0], [0.5, 0.1, 0.4],\n [0.3, 0.5, 0.2]])\n super().setUp()\n\n def test_truvari_presicion(self):\n m = metrics.truvari_precision(self.logits, self.one_hot_targets) # pytype: disable=wrong-arg-types # jnp-type\n self.assertAlmostEqual(m['truvari_precision'], 0.5, places=5)\n\n def test_truvari_recall(self):\n m = metrics.truvari_recall(self.logits, self.one_hot_targets) # pytype: disable=wrong-arg-types # jnp-type\n self.assertAlmostEqual(m['truvari_recall'], 1.0 / 3.0, places=5)\n\n def test_truvari_presicion_events(self):\n m = metrics.truvari_precision_events(self.logits, self.one_hot_targets) # pytype: disable=wrong-arg-types # jnp-type\n self.assertAlmostEqual(m['truvari_precision_events'], 1.0, places=5)\n\n def test_truvari_recall_events(self):\n m = metrics.truvari_recall_events(self.logits, self.one_hot_targets) # pytype: disable=wrong-arg-types # jnp-type\n self.assertAlmostEqual(m['truvari_recall_events'], 2.0 / 3.0, places=5)\n\n\nif __name__ == '__main__':\n absltest.main()\n", "repo_name": "google-research/scenic", "sub_path": "scenic/projects/svvit/tests/metrics_test.py", "file_name": "metrics_test.py", "file_ext": "py", "file_size_in_byte": 1478, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2619, "dataset": "github-code", "pt": "37", "api": [{"api_name": "absl.testing.parameterized.TestCase", "line_number": 9, "usage_type": "attribute"}, {"api_name": "absl.testing.parameterized", "line_number": 9, "usage_type": "name"}, {"api_name": "jax.numpy.array", "line_number": 12, "usage_type": "call"}, {"api_name": "jax.numpy", "line_number": 12, "usage_type": "name"}, {"api_name": "jax.numpy.array", "line_number": 14, "usage_type": "call"}, {"api_name": "jax.numpy", "line_number": 14, "usage_type": "name"}, {"api_name": "scenic.projects.svvit.metrics.truvari_precision", "line_number": 19, "usage_type": "call"}, {"api_name": "scenic.projects.svvit.metrics", "line_number": 19, "usage_type": "name"}, {"api_name": "scenic.projects.svvit.metrics.truvari_recall", "line_number": 23, "usage_type": "call"}, {"api_name": "scenic.projects.svvit.metrics", "line_number": 23, "usage_type": "name"}, {"api_name": "scenic.projects.svvit.metrics.truvari_precision_events", "line_number": 27, "usage_type": "call"}, {"api_name": "scenic.projects.svvit.metrics", "line_number": 27, "usage_type": "name"}, {"api_name": "scenic.projects.svvit.metrics.truvari_recall_events", "line_number": 31, "usage_type": "call"}, {"api_name": "scenic.projects.svvit.metrics", "line_number": 31, "usage_type": "name"}, {"api_name": "absl.testing.absltest.main", "line_number": 36, "usage_type": "call"}, {"api_name": "absl.testing.absltest", "line_number": 36, "usage_type": "name"}]} +{"seq_id": "32655368955", "text": "import random\nimport tensorflow as tf\nfrom keras import backend\nfrom keras.utils import data_utils\nfrom keras.utils import layer_utils\nfrom keras.engine import data_adapter\nfrom keras import layers\nimport numpy as np\nfrom matplotlib import cm\n\n\ndef colorize_img(value, vmin=None, vmax=None, cmap='jet'):\n \"\"\"\n A utility function for TensorFlow that maps a grayscale image to\n a matplotlib colormap for use with TensorBoard image summaries.\n By default it will normalize the input value to the range 0..1\n before mapping to a grayscale colormap.\n Arguments:\n - value: 4D Tensor of shape [batch_size,height, width,1]\n - vmin: the minimum value of the range used for normalization. (Default: value minimum)\n - vmax: the maximum value of the range used for normalization. (Default: value maximum)\n - cmap: a valid cmap named for use with matplotlib's 'get_cmap'.(Default: 'gray')\n \n Returns a 3D tensor of shape [batch_size,height, width,3].\n \"\"\"\n # normalize\n vmin = tf.reduce_min(value) if vmin is None else vmin\n vmax = tf.reduce_max(value) if vmax is None else vmax\n value = (value - vmin) / (vmax - vmin) # vmin..vmax\n\n # quantize\n indices = tf.cast(tf.round(value[:, :, :, 0]*255), dtype=tf.int32)\n\n # gather\n color_map = cm.get_cmap(cmap)\n colors = color_map(np.arange(256))[:, :3]\n colors = tf.constant(colors, dtype=tf.float32)\n value = tf.gather(colors, indices)\n return value\n\n\n# https://github.com/philferriere/tfoptflow/blob/bdc7a72e78008d1cd6db46e4667dffc2bab1fe9e/tfoptflow/core_costvol.py\ndef _cost_volume_block(c1, warp, search_range=2):\n \"\"\"Build cost volume for associating a pixel from the\n left image with its corresponding pixels in the right image.\n Args:\n c1: Level of the feature pyramid of the left image\n warp: Warped level of the feature pyramid of the right image\n search_range: Search range (maximum displacement)\n \"\"\"\n padded_lvl = tf.pad(warp, [[0, 0], [0, 0], [search_range, search_range], [0, 0]])\n width = c1.shape[2]\n max_offset = search_range * 2 + 1\n\n cost_vol = []\n for i in range(0, max_offset):\n slice = tf.slice(padded_lvl, [0, 0, i, 0], [-1, -1, width, -1])\n cost = tf.reduce_mean(c1 * slice, axis=3, keepdims=True)\n cost_vol.append(cost)\n\n cost_vol = tf.concat(cost_vol, axis=3)\n cost_curve = tf.concat([c1, cost_vol], axis=3)\n\n return cost_curve\n\ndef clip(x, clip_value_min, clip_value_max):\n clipped = tf.cast(x < clip_value_min, x.dtype) * clip_value_min + tf.cast(x >= clip_value_min, x.dtype) * x\n clipped = tf.cast(clipped > clip_value_max, x.dtype) * clip_value_max + tf.cast(clipped <= clip_value_max,\n x.dtype) * clipped\n return clipped\n\ndef bilinear_sampler(imgs, coords):\n \"\"\"\n Construct a new image by bilinear sampling from the input image.\n Points falling outside the source image boundary have value 0.\n Args:\n imgs: source image to be sampled from [batch, height_s, width_s, channels]\n coords: coordinates of source pixels to sample from [batch, height_t,width_t, 2].\n height_t/width_t correspond to the dimensions of the output image\n (don't need to be the same as height_s/width_s). The two channels\n correspond to x and y coordinates respectively.\n Returns:\n A new sampled image [batch, height_t, width_t, channels]\n \"\"\"\n\n def _repeat(x, n_repeats):\n rep = tf.transpose(\n tf.expand_dims(tf.ones(shape=tf.stack([\n n_repeats,\n ])), 1), [1, 0])\n rep = tf.cast(rep, 'float32')\n x = tf.matmul(tf.reshape(x, (-1, 1)), rep)\n return tf.reshape(x, [-1])\n\n coords_x, coords_y = tf.split(coords, [1, 1], axis=3)\n inp_size = tf.shape(imgs)\n coord_size = tf.shape(coords)\n out_size = [coord_size[0], coord_size[1], coord_size[2], inp_size[3]]\n\n coords_x = tf.cast(coords_x, 'float32')\n coords_y = tf.cast(coords_y, 'float32')\n\n # x0 = tf.floor(coords_x)\n x0 = tf.cast(coords_x + 2.0, 'int32')\n x0 = tf.cast(x0 - 2, 'float32')\n x1 = x0 + 1\n\n # y0 = tf.floor(coords_y)\n y0 = tf.cast(coords_y + 2.0, 'int32')\n y0 = tf.cast(y0 - 2, 'float32')\n y1 = y0 + 1\n\n y_max = tf.cast(inp_size[1] - 1, 'float32')\n x_max = tf.cast(inp_size[2] - 1, 'float32')\n zero = tf.zeros([1], dtype='float32')\n\n wt_x0 = x1 - coords_x\n wt_x1 = coords_x - x0\n wt_y0 = y1 - coords_y\n wt_y1 = coords_y - y0\n\n x0_safe = clip(x0, zero[0], x_max)\n y0_safe = clip(y0, zero[0], y_max)\n x1_safe = clip(x1, zero[0], x_max)\n y1_safe = clip(y1, zero[0], y_max)\n\n ## indices in the flat image to sample from\n dim2 = tf.cast(inp_size[2], 'float32')\n dim1 = tf.cast(inp_size[2] * inp_size[1], 'float32')\n base = tf.reshape(\n _repeat(tf.cast(tf.range(coord_size[0]), 'float32') * dim1, coord_size[1] * coord_size[2]),\n [out_size[0], out_size[1], out_size[2], 1]\n )\n\n base_y0 = base + y0_safe * dim2\n base_y1 = base + y1_safe * dim2\n idx00 = x0_safe + base_y0\n idx01 = x0_safe + base_y1\n idx10 = x1_safe + base_y0\n idx11 = x1_safe + base_y1\n\n ## sample from imgs\n imgs_flat = tf.reshape(imgs, [-1, inp_size[3]])\n imgs_flat = tf.cast(imgs_flat, 'float32')\n im00 = tf.reshape(tf.gather(imgs_flat, tf.cast(idx00, 'int32')), out_size)\n im01 = tf.reshape(tf.gather(imgs_flat, tf.cast(idx01, 'int32')), out_size)\n im10 = tf.reshape(tf.gather(imgs_flat, tf.cast(idx10, 'int32')), out_size)\n im11 = tf.reshape(tf.gather(imgs_flat, tf.cast(idx11, 'int32')), out_size)\n\n w00 = wt_x0 * wt_y0\n w01 = wt_x0 * wt_y1\n w10 = wt_x1 * wt_y0\n w11 = wt_x1 * wt_y1\n\n # output = tf.add_n([\n # w00 * im00, w01 * im01,\n # w10 * im10, w11 * im11\n # ])\n output = tf.add(tf.add(tf.multiply(w00, im00), tf.multiply(w01, im01)),\n tf.add(tf.multiply(w10, im10), tf.multiply(w11, im11)))\n\n return output\n\n\ndef _warp_image_block(img, flow):\n \"\"\"\n Given an image and a flow generate the warped image,\n for stereo img is the right image, flow is the disparity aligned with left.\n img: image that needs to be warped\n flow: Generic optical flow or disparity\n \"\"\"\n\n def build_coords(immy):\n max_height = 2048\n max_width = 2048\n pixel_coords = np.ones((1, max_height, max_width, 2))\n\n # build pixel coordinates and their disparity\n for i in range(0, max_height):\n for j in range(0, max_width):\n pixel_coords[0][i][j][0] = j\n pixel_coords[0][i][j][1] = i\n\n pixel_coords = tf.constant(pixel_coords, tf.float32)\n real_height = tf.shape(immy)[1]\n real_width = tf.shape(immy)[2]\n real_pixel_coord = pixel_coords[:, 0:real_height, 0:real_width, :]\n immy = tf.concat([immy, tf.zeros_like(immy)], axis=-1)\n output = real_pixel_coord - immy\n\n return output\n\n coords = build_coords(flow)\n warped = bilinear_sampler(img, coords)\n return warped\n\n\ndef _refinement_block(input, disp, output_shape):\n \"\"\"\n Final Layer in MADNet.\n Calculates the reprojection loss if training=True.\n Args:\n input: left_F2 tensor\n disp: D2 disparity from M2 module\n final_left: full resolution RGB left image\n final_right: full resolution RGB right image\n Returns:\n Full resolution disparity in float32 normalized 0-1\n \"\"\"\n layer_kwargs = {\n \"kernel_size\": (3, 3),\n \"padding\": \"same\",\n \"activation\": tf.keras.layers.Activation(tf.nn.leaky_relu, dtype=tf.float32, name=\"leaky_relu\"),\n \"use_bias\": True\n }\n context1 = tf.keras.layers.Conv2D(filters=128, dilation_rate=1, name=\"context1\", **layer_kwargs)\n context2 = tf.keras.layers.Conv2D(filters=128, dilation_rate=2, name=\"context2\", **layer_kwargs)\n context3 = tf.keras.layers.Conv2D(filters=128, dilation_rate=4, name=\"context3\", **layer_kwargs)\n context4 = tf.keras.layers.Conv2D(filters=96, dilation_rate=8, name=\"context4\", **layer_kwargs)\n context5 = tf.keras.layers.Conv2D(filters=64, dilation_rate=16, name=\"context5\", **layer_kwargs)\n context6 = tf.keras.layers.Conv2D(filters=32, dilation_rate=1, name=\"context6\", **layer_kwargs)\n context7 = tf.keras.layers.Conv2D(\n filters=1,\n kernel_size=(3, 3),\n dilation_rate=1,\n padding=\"same\",\n activation=\"linear\",\n use_bias=True,\n name=\"context7\"\n )\n\n volume = tf.keras.layers.concatenate([input, disp], axis=-1)\n x = context1(volume)\n x = context2(x)\n x = context3(x)\n x = context4(x)\n x = context5(x)\n x = context6(x)\n x = context7(x)\n\n context_disp = tf.keras.layers.add([disp, x])\n final_disparity = tf.image.resize(\n images=context_disp,\n name=\"final_disparity\",\n size=(output_shape[0], output_shape[1]),\n method='bilinear'\n )\n return final_disparity\n\n\ndef _stereo_estimator_block(name, costs, upsampled_disp=None):\n \"\"\"\n This is the stereo estimation network at resolution n.\n It uses the costs (from the pixel difference between the warped right image \n and the left image) combined with the upsampled disparity from the previous\n layer (when the layer is not the last layer).\n\n The output is predicted disparity for the network at resolution n.\n \"\"\"\n layer_kwargs = {\n \"kernel_size\": (3, 3),\n \"strides\": 1,\n \"padding\": \"same\",\n \"activation\": tf.keras.layers.Activation(tf.nn.leaky_relu, dtype=tf.float32, name=\"leaky_relu\"),\n \"use_bias\": True\n }\n disp1 = tf.keras.layers.Conv2D(filters=128, name=f\"{name}_disp1\", **layer_kwargs)\n disp2 = tf.keras.layers.Conv2D(filters=128, name=f\"{name}_disp2\", **layer_kwargs)\n disp3 = tf.keras.layers.Conv2D(filters=96, name=f\"{name}_disp3\", **layer_kwargs)\n disp4 = tf.keras.layers.Conv2D(filters=64, name=f\"{name}_disp4\", **layer_kwargs)\n disp5 = tf.keras.layers.Conv2D(filters=32, name=f\"{name}_disp5\", **layer_kwargs)\n disp6 = tf.keras.layers.Conv2D(\n filters=1,\n kernel_size=(3, 3),\n strides=1,\n padding=\"same\",\n activation=\"linear\",\n use_bias=True,\n name=f\"{name}_disp6\"\n )\n\n if upsampled_disp is not None:\n volume = tf.keras.layers.concatenate([costs, upsampled_disp], axis=-1)\n else:\n volume = costs\n\n x = disp1(volume)\n x = disp2(x)\n x = disp3(x)\n x = disp4(x)\n x = disp5(x)\n x = disp6(x)\n return x\n\n\ndef ModuleM(layer, search_range=2):\n \"\"\"\n Module MX is a sub-module of MADNet, which can be trained individually for \n online adaptation using the MAD (Modular ADaptaion) method.\n \"\"\"\n\n def _block(inputs):\n # Check if layer is the bottom of the pyramid\n if len(inputs) == 3:\n left, right, prev_disp = inputs\n mod_height, mod_width = left.shape[1], left.shape[2]\n # Upsample disparity from previous layer\n upsampled_disp = tf.image.resize(\n images=prev_disp,\n name=f\"upsampled_disp_{layer}\",\n size=(mod_height, mod_width),\n method='bilinear'\n )\n # Warp the right image into the left using upsampled disparity\n warped_left = _warp_image_block(right, upsampled_disp)\n else:\n left, right = inputs\n # No previous disparity exits, so use right image instead of warped left\n warped_left = right\n\n costs = _cost_volume_block(left, warped_left, search_range)\n\n # Get the disparity using cost volume between left and warped left images\n if len(inputs) == 3:\n module_disparity = _stereo_estimator_block(f\"volume_filtering_{layer}\", costs, upsampled_disp)\n else:\n module_disparity = _stereo_estimator_block(f\"volume_filtering_{layer}\", costs)\n\n return module_disparity\n\n return _block\n\n@tf.function\ndef _custom_train_step(self, data):\n \"\"\"\n This is a monkey patch for the standard keras train_step.\n\n This patch adds the following training features:\n 1. Training without groundtruth disparity. (self-supervised training)\n 2. Tensorboard summaries.\n 3. Loss is reduced for batch sizes larger than 1.\n \"\"\"\n # Left, right image inputs and groundtruth target disparity\n inputs, gt, sample_weight = data_adapter.unpack_x_y_sample_weight(data)\n\n left_input = inputs[\"left_input\"]\n right_input = inputs[\"right_input\"]\n\n with tf.GradientTape(persistent=False) as tape:\n # Forward pass\n final_disparity = self(inputs=inputs, training=True)\n # Calculate loss\n if gt is None:\n # Warp the right image into the left using final disparity\n warped_left = _warp_image_block(right_input, final_disparity)\n loss = self.compiled_loss(left_input, warped_left, sample_weight, regularization_losses=self.losses)\n else:\n loss = self.compiled_loss(gt, final_disparity, sample_weight, regularization_losses=self.losses)\n # Perform reduction on the loss\n # Note: displayed loss will be sum of all batch losses, but backprop will use the reduced loss\n batch_size = tf.shape(left_input)[0]\n reduced_loss = loss / tf.cast(batch_size, dtype=tf.float32)\n\n # Run backwards pass.\n self.optimizer.minimize(reduced_loss, self.trainable_variables, tape=tape)\n\n return_metrics = {}\n self.compiled_metrics.reset_state()\n if gt is not None:\n self.compiled_metrics.update_state(gt, final_disparity, sample_weight)\n # Collect metrics to return\n for metric in self.metrics:\n result = metric.result()\n if isinstance(result, dict):\n return_metrics.update(result)\n else:\n return_metrics[metric.name] = result\n return return_metrics\n\n\ndef _custom_test_step(predict_func):\n\n @tf.function\n def _test_step_block(self, data):\n x, y, sample_weight = data_adapter.unpack_x_y_sample_weight(data)\n y_pred = predict_func(self, data)\n # Updates stateful loss metrics.\n return_metrics = {}\n self.compiled_metrics.reset_state()\n self.compiled_metrics.update_state(y, y_pred, sample_weight)\n # Collect metrics to return\n for metric in self.metrics:\n result = metric.result()\n if isinstance(result, dict):\n return_metrics.update(result)\n else:\n return_metrics[metric.name] = result\n return return_metrics\n return _test_step_block\n\n\ndef _custom_predict_step(num_adapt, mad_type):\n \"\"\"\n This is a monkey patch for the standard keras predict_step.\n\n A Closure is utilised to enable the different inferencing modes shown below.\n This patch adds the following inferencing options:\n 1. Full adaptation while inferencing. (self-supervised learning)\n 2. or MAD adapation while inferencing. With options to adapt\n between 1-5 modules. (also self-supervised learning, but slower learning)\n \"\"\"\n # Full backprop on all layers\n if num_adapt == 6:\n @tf.function\n def _predict_step_block(self, data):\n inputs, _, _ = data_adapter.unpack_x_y_sample_weight(data)\n\n left_input = inputs[\"left_input\"]\n right_input = inputs[\"right_input\"]\n\n with tf.GradientTape(persistent=False) as tape:\n # Forward pass\n final_disparity = self(inputs=inputs, training=True)\n # Calculate loss\n # Warp the right image into the left using final disparity\n warped_left = _warp_image_block(right_input, final_disparity)\n loss = self.compiled_loss(left_input, warped_left)\n\n # Perform reduction on the loss\n # Note: displayed loss will be sum of all batch losses, but backprop will use the reduced loss\n batch_size = tf.shape(left_input)[0]\n reduced_loss = loss / tf.cast(batch_size, dtype=tf.float32)\n\n # Run backwards pass.\n self.optimizer.minimize(reduced_loss, self.trainable_variables, tape=tape)\n return final_disparity\n # MAD adaptation\n else:\n @tf.function\n def _predict_step_block(self, data):\n module_layers = [\n [\"conv1\", \"conv2\",\n \"context1\", \"context2\", \"context3\", \"context4\", \"context5\", \"context6\", \"context7\"],\n [\"conv3\", \"conv4\",\n \"volume_filtering_2_disp1\", \"volume_filtering_2_disp2\", \"volume_filtering_2_disp3\",\n \"volume_filtering_2_disp4\", \"volume_filtering_2_disp5\", \"volume_filtering_2_disp6\"],\n [\"conv5\", \"conv6\",\n \"volume_filtering_3_disp1\", \"volume_filtering_3_disp2\", \"volume_filtering_3_disp3\",\n \"volume_filtering_3_disp4\", \"volume_filtering_3_disp5\", \"volume_filtering_3_disp6\"],\n [\"conv7\", \"conv8\",\n \"volume_filtering_4_disp1\", \"volume_filtering_4_disp2\", \"volume_filtering_4_disp3\",\n \"volume_filtering_4_disp4\", \"volume_filtering_4_disp5\", \"volume_filtering_4_disp6\"],\n [\"conv9\", \"conv10\",\n \"volume_filtering_5_disp1\", \"volume_filtering_5_disp2\", \"volume_filtering_5_disp3\",\n \"volume_filtering_5_disp4\", \"volume_filtering_5_disp5\", \"volume_filtering_5_disp6\"],\n [\"conv11\", \"conv12\",\n \"volume_filtering_6_disp1\", \"volume_filtering_6_disp2\", \"volume_filtering_6_disp3\",\n \"volume_filtering_6_disp4\", \"volume_filtering_6_disp5\", \"volume_filtering_6_disp6\"],\n ]\n inputs, _, _ = data_adapter.unpack_x_y_sample_weight(data)\n\n left_input = inputs[\"left_input\"]\n right_input = inputs[\"right_input\"]\n\n with tf.GradientTape(persistent=True) as tape:\n # Forward pass\n final_disparity = self(inputs=inputs, training=True)\n # Calculate loss\n # Warp the right image into the left using final disparity\n warped_left = _warp_image_block(right_input, final_disparity)\n loss = self.compiled_loss(left_input, warped_left)\n\n # Perform reduction on the loss\n # Note: displayed loss will be sum of all batch losses, but backprop will use the reduced loss\n batch_size = tf.shape(left_input)[0]\n reduced_loss = loss / tf.cast(batch_size, dtype=tf.float32)\n\n # Run backwards pass.\n if mad_type == \"random\":\n # adapt_modules = random.sample(list(module_layers_dict.keys()), num_adapt)\n adapt_modules = random.sample(range(6), num_adapt)\n elif mad_type == \"sequential\":\n adapt_modules = []\n for i in range(num_adapt):\n new_id = i + self.last_adapt\n if new_id > 5:\n new_id = new_id % 6\n adapt_modules.append(new_id)\n self.last_adapt.assign(new_id)\n\n all_vars = [[], [], [], [], [], []]\n for i in range(6):\n for layer in module_layers[i]:\n model_layer = self.get_layer(layer)\n layer_vars = model_layer.trainable_variables\n for var in layer_vars:\n all_vars[i].append(var)\n\n # Graph tracing requires all variables to be created on the first pass,\n # so performing full mad on first pass\n if self.first_adapt_pass:\n adapt_modules = range(6)\n self.first_adapt_pass = False\n\n if mad_type == \"random\":\n # this adaptation method is faster but doesn't work with sequential\n for i in range(6):\n if i in adapt_modules:\n self.optimizer.minimize(reduced_loss, all_vars[i], tape=tape)\n elif mad_type == \"sequential\":\n def do_nothing(loss, vars, tape):\n # function that mimics the inputs and outputs of the optimize function\n return tf.constant([True, True, True, True, True, True])\n\n for i in range(6):\n tf.cond(\n tf.reduce_any(tf.equal(i, adapt_modules)),\n true_fn=lambda: self.optimizer.minimize(reduced_loss, all_vars[i], tape=tape),\n false_fn=lambda: do_nothing(reduced_loss, all_vars[i], tape=tape)\n )\n\n return final_disparity\n\n return _predict_step_block\n\n\ndef MADNet(input_shape=None,\n weights=None,\n input_tensor=None,\n num_adapt_modules=0,\n mad_mode=\"random\",\n search_range=2\n ):\n pretrained_weights = {\"synthetic\", \"kitti\", \"tf1_conversion_synthetic\", \"tf1_conversion_kitti\"}\n f\"\"\"\n Instantiates the MADNet architecture\n\n Reference:\n - [MADNet: Real-time self-adaptive deep stereo](\n https://arxiv.org/abs/1810.05424) (CVPR 2019)\n\n Args:\n input_shape: Optional shape tuple, to be specified if you would\n like to use a model with an input image resolution that is not\n (480, 640, 3).\n It should have exactly 3 inputs channels (480, 640, 3).\n You can also omit this option if you would like\n to infer input_shape from an input_tensor.\n If you choose to include both input_tensor and input_shape then\n input_shape will be used if they match, if the shapes\n do not match then we will throw an error.\n weights: String, one of `None` (random initialization),\n or one of the following pretrained weights: {pretrained_weights},\n or the path to the weights file to be loaded.\n input_tensor: Optional Keras tensor (i.e. output of `layers.Input()`)\n to use as image input for the model.\n num_adapt_modules: Integer, number of modules to perform adaptation on while inferencing.\n For standard inferencing, use num_adapt_modules=0,\n MAD is num_adapt_modules=2-5,\n Full backprop is num_adapt_modules=6.\n Note: This is for inferencing only, so doesnt affect training.\n If you would like to change the inferencing mode you will need to\n instantiate the model again with the new num_adapt_modules value.\n mad_mode: String, one of \"random\" or \"sequential\"\n This is only needed for MAD adaptation with num_adapt_modules in 1-5.\n \"random\", selects the modules to adapt randomly.\n \"sequential\", selects the modules to adapt sequentially. \n search_range: maximum search displacement for the cost volume\n\n Returns:\n A `keras.Model` instance.\n \"\"\"\n if not (weights is None or\n weights in pretrained_weights or\n tf.io.gfile.exists(weights) or\n tf.io.gfile.exists(weights + \".index\")):\n raise ValueError('The `weights` argument should be either '\n '`None` (random initialization), '\n f'one of the following pretrained weights: {pretrained_weights}, '\n 'or the path to the weights file to be loaded. \\n'\n f'Received `weights={weights}`')\n # Determine proper input shape and default size.\n # If both input_shape and input_tensor are used, they should match\n if input_shape is not None and input_tensor is not None:\n try:\n is_input_t_tensor = backend.is_keras_tensor(input_tensor)\n except ValueError:\n try:\n is_input_t_tensor = backend.is_keras_tensor(\n layer_utils.get_source_inputs(input_tensor))\n except ValueError:\n raise ValueError(\n f'input_tensor: {input_tensor}'\n 'is not type input_tensor. '\n f'Received `type(input_tensor)={type(input_tensor)}`'\n )\n if is_input_t_tensor:\n if backend.image_data_format() == 'channels_first':\n raise ValueError('Detected input_tensor in channels_first mode '\n 'please ensure channels are last`; '\n 'Received `input_tensor.shape='\n f'{input_tensor.shape}')\n else:\n if backend.int_shape(input_tensor)[2] != input_shape[1]:\n raise ValueError(\n 'input_tensor.shape[2] must equal input_shape[1]; '\n 'Received `input_tensor.shape='\n f'{input_tensor.shape}`, '\n f'`input_shape={input_shape}`')\n else:\n raise ValueError('input_tensor is not a Keras tensor; '\n f'Received `input_tensor={input_tensor}`')\n\n\n default_shape = (480, 640, 3)\n # If input_shape is None, infer shape from input_tensor.\n if input_shape is None and input_tensor is not None:\n\n try:\n backend.is_keras_tensor(input_tensor)\n except ValueError:\n raise ValueError('input_tensor must be a valid Keras tensor type; '\n f'Received {input_tensor} of type {type(input_tensor)}')\n\n if input_shape is None and not backend.is_keras_tensor(input_tensor):\n input_shape = default_shape\n elif input_shape is None and backend.is_keras_tensor(input_tensor):\n if backend.image_data_format() == 'channels_first':\n raise ValueError('Detected input_tensor in channels_first mode '\n 'please ensure channels are last`; '\n 'Received `input_tensor.shape='\n f'{input_tensor.shape}')\n else:\n input_shape = (backend.int_shape(input_tensor)[1],\n backend.int_shape(input_tensor)[2],\n 3)\n\n # If input_shape is None and no input_tensor\n elif input_shape is None:\n input_shape = default_shape\n\n # If input_shape is not None, assume default size.\n else:\n if backend.image_data_format() == 'channels_first':\n raise ValueError('Detected input_tensor in channels_first mode '\n 'please ensure channels are last`; '\n 'Received `input_tensor.shape='\n f'{input_tensor.shape}')\n\n if type(num_adapt_modules) is not int or num_adapt_modules < 0 or num_adapt_modules > 6:\n raise ValueError(\"num_adapt_modules needs to be an integer from 0-6.\"\n f\"\\nDetected num_adapt_modules value: {num_adapt_modules},\"\n f\"and data type: {type(num_adapt_modules)}\")\n\n if type(search_range) is not int or search_range < 1 or search_range > 10:\n raise ValueError(\"search_range needs to be an integer from 1-10.\"\n f\"\\nDetected search_range value: {search_range},\"\n f\"and data type: {type(search_range)}\")\n\n # left and right image inputs are set to the same resolution\n left_input = layers.Input(shape=input_shape, name=\"left_input\")\n right_input = layers.Input(shape=input_shape, name=\"right_input\")\n\n # Initializing the layers\n layer_kwargs = {\n \"kernel_size\": (3, 3),\n \"padding\": \"same\",\n \"activation\": tf.keras.layers.Activation(tf.nn.leaky_relu, dtype=tf.float32, name=\"leaky_relu\"),\n \"use_bias\": True\n }\n # Image feature pyramid (feature extractor)\n # F1\n conv1 = tf.keras.layers.Conv2D(\n filters=16,\n strides=2,\n name=\"conv1\",\n input_shape=(input_shape[0], input_shape[1], input_shape[2], ),\n **layer_kwargs)\n conv2 = tf.keras.layers.Conv2D(filters=16, strides=1, name=\"conv2\", **layer_kwargs)\n # F2\n conv3 = tf.keras.layers.Conv2D(filters=32, strides=2, name=\"conv3\", **layer_kwargs)\n conv4 = tf.keras.layers.Conv2D(filters=32, strides=1, name=\"conv4\", **layer_kwargs)\n # F3\n conv5 = tf.keras.layers.Conv2D(filters=64, strides=2, name=\"conv5\", **layer_kwargs)\n conv6 = tf.keras.layers.Conv2D(filters=64, strides=1, name=\"conv6\", **layer_kwargs)\n # F4\n conv7 = tf.keras.layers.Conv2D(filters=96, strides=2, name=\"conv7\", **layer_kwargs)\n conv8 = tf.keras.layers.Conv2D(filters=96, strides=1, name=\"conv8\", **layer_kwargs)\n # F5\n conv9 = tf.keras.layers.Conv2D(filters=128, strides=2, name=\"conv9\", **layer_kwargs)\n conv10 = tf.keras.layers.Conv2D(filters=128, strides=1, name=\"conv10\", **layer_kwargs)\n # F6\n conv11 = tf.keras.layers.Conv2D(filters=192, strides=2, name=\"conv11\", **layer_kwargs)\n conv12 = tf.keras.layers.Conv2D(filters=192, strides=1, name=\"conv12\", **layer_kwargs)\n\n #############################SCALE 6#################################\n M6 = ModuleM(layer=\"6\", search_range=search_range)\n ############################SCALE 5###################################\n M5 = ModuleM(layer=\"5\", search_range=search_range)\n ############################SCALE 4###################################\n M4 = ModuleM(layer=\"4\", search_range=search_range)\n ############################SCALE 3###################################\n M3 = ModuleM(layer=\"3\", search_range=search_range)\n ############################SCALE 2###################################\n M2 = ModuleM(layer=\"2\", search_range=search_range)\n\n #######################PYRAMID FEATURES###############################\n # Left image feature pyramid (feature extractor)\n # F1\n left_pyramid = conv1(left_input)\n left_F1 = conv2(left_pyramid)\n # F2\n left_pyramid = conv3(left_F1)\n left_F2 = conv4(left_pyramid)\n # F3\n left_pyramid = conv5(left_F2)\n left_F3 = conv6(left_pyramid)\n # F4\n left_pyramid = conv7(left_F3)\n left_F4 = conv8(left_pyramid)\n # F5\n left_pyramid = conv9(left_F4)\n left_F5 = conv10(left_pyramid)\n # F6\n left_pyramid = conv11(left_F5)\n left_F6 = conv12(left_pyramid)\n\n # Right image feature pyramid (feature extractor)\n # F1\n right_pyramid = conv1(right_input)\n right_F1 = conv2(right_pyramid)\n # F2\n right_pyramid = conv3(right_F1)\n right_F2 = conv4(right_pyramid)\n # F3\n right_pyramid = conv5(right_F2)\n right_F3 = conv6(right_pyramid)\n # F4\n right_pyramid = conv7(right_F3)\n right_F4 = conv8(right_pyramid)\n # F5\n right_pyramid = conv9(right_F4)\n right_F5 = conv10(right_pyramid)\n # F6\n right_pyramid = conv11(right_F5)\n right_F6 = conv12(right_pyramid)\n\n #############################SCALE 6#################################\n D6 = M6([left_F6, right_F6])\n ############################SCALE 5###################################\n D5 = M5([left_F5, right_F5, D6])\n ############################SCALE 4###################################\n D4 = M4([left_F4, right_F4, D5])\n ############################SCALE 3###################################\n D3 = M3([left_F3, right_F3, D4])\n ############################SCALE 2###################################\n D2 = M2([left_F2, right_F2, D3])\n ############################REFINEMENT################################\n final_disparity = _refinement_block(left_F2, D2, input_shape)\n\n # Monkey patch the train_step to use custom training\n tf.keras.Model.train_step = _custom_train_step\n # Only need to monkey patch the predict_step if doing adaptation\n if num_adapt_modules != 0:\n tf.keras.Model.last_adapt = tf.Variable(6)\n tf.keras.Model.first_adapt_pass = True\n tf.keras.Model.predict_step = _custom_predict_step(num_adapt_modules, mad_mode)\n tf.keras.Model.test_step = _custom_test_step(tf.keras.Model.predict_step)\n\n model = tf.keras.Model(inputs={\n \"left_input\": left_input,\n \"right_input\": right_input\n },\n outputs=final_disparity,\n name=\"MADNet\")\n\n if weights in pretrained_weights:\n pretrained_models_url = \"https://huggingface.co/ChristianOrr/madnet_keras/resolve/main/\"\n model_name = \"madnet_\" + weights + \".h5\"\n weight_path = pretrained_models_url + weights + \".h5\"\n weights_path = data_utils.get_file(model_name, weight_path, cache_subdir='models')\n model.load_weights(weights_path)\n elif weights is not None:\n model.load_weights(weights)\n\n return model\n", "repo_name": "liujiaxing7/madnet-deep-stereo-with-keras", "sub_path": "madnet.py", "file_name": "madnet.py", "file_ext": "py", "file_size_in_byte": 32879, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "tensorflow.reduce_min", "line_number": 27, "usage_type": "call"}, {"api_name": "tensorflow.reduce_max", "line_number": 28, "usage_type": "call"}, {"api_name": "tensorflow.cast", "line_number": 32, "usage_type": "call"}, {"api_name": "tensorflow.round", "line_number": 32, "usage_type": "call"}, {"api_name": "tensorflow.int32", "line_number": 32, "usage_type": "attribute"}, {"api_name": "matplotlib.cm.get_cmap", "line_number": 35, "usage_type": "call"}, {"api_name": "matplotlib.cm", "line_number": 35, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 36, "usage_type": "call"}, {"api_name": "tensorflow.constant", "line_number": 37, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 37, "usage_type": "attribute"}, {"api_name": "tensorflow.gather", "line_number": 38, "usage_type": "call"}, {"api_name": "tensorflow.pad", "line_number": 51, "usage_type": "call"}, {"api_name": "tensorflow.slice", "line_number": 57, "usage_type": "call"}, {"api_name": "tensorflow.reduce_mean", "line_number": 58, "usage_type": "call"}, {"api_name": "tensorflow.concat", "line_number": 61, "usage_type": "call"}, {"api_name": "tensorflow.concat", "line_number": 62, "usage_type": "call"}, {"api_name": "tensorflow.cast", "line_number": 67, "usage_type": "call"}, {"api_name": "tensorflow.cast", "line_number": 68, "usage_type": "call"}, {"api_name": "tensorflow.transpose", "line_number": 87, "usage_type": "call"}, {"api_name": "tensorflow.expand_dims", "line_number": 88, "usage_type": "call"}, {"api_name": "tensorflow.ones", "line_number": 88, "usage_type": "call"}, {"api_name": "tensorflow.stack", "line_number": 88, "usage_type": "call"}, {"api_name": "tensorflow.cast", "line_number": 91, "usage_type": "call"}, {"api_name": "tensorflow.matmul", "line_number": 92, "usage_type": "call"}, {"api_name": "tensorflow.reshape", "line_number": 92, "usage_type": "call"}, {"api_name": "tensorflow.reshape", "line_number": 93, "usage_type": "call"}, {"api_name": "tensorflow.split", "line_number": 95, "usage_type": "call"}, {"api_name": "tensorflow.shape", "line_number": 96, "usage_type": "call"}, {"api_name": "tensorflow.shape", "line_number": 97, "usage_type": "call"}, {"api_name": "tensorflow.cast", "line_number": 100, "usage_type": "call"}, {"api_name": "tensorflow.cast", "line_number": 101, "usage_type": "call"}, {"api_name": "tensorflow.cast", "line_number": 104, "usage_type": "call"}, {"api_name": "tensorflow.cast", "line_number": 105, "usage_type": "call"}, {"api_name": "tensorflow.cast", "line_number": 109, "usage_type": "call"}, {"api_name": "tensorflow.cast", "line_number": 110, "usage_type": "call"}, {"api_name": "tensorflow.cast", "line_number": 113, "usage_type": "call"}, {"api_name": "tensorflow.cast", "line_number": 114, "usage_type": "call"}, {"api_name": "tensorflow.zeros", "line_number": 115, "usage_type": "call"}, {"api_name": "tensorflow.cast", "line_number": 128, "usage_type": "call"}, {"api_name": "tensorflow.cast", "line_number": 129, "usage_type": "call"}, {"api_name": "tensorflow.reshape", "line_number": 130, "usage_type": "call"}, {"api_name": "tensorflow.cast", "line_number": 131, "usage_type": "call"}, {"api_name": "tensorflow.range", "line_number": 131, "usage_type": "call"}, {"api_name": "tensorflow.reshape", "line_number": 143, "usage_type": "call"}, {"api_name": "tensorflow.cast", "line_number": 144, "usage_type": "call"}, {"api_name": "tensorflow.reshape", "line_number": 145, "usage_type": "call"}, {"api_name": "tensorflow.gather", "line_number": 145, "usage_type": "call"}, {"api_name": "tensorflow.cast", "line_number": 145, "usage_type": "call"}, {"api_name": "tensorflow.reshape", "line_number": 146, "usage_type": "call"}, {"api_name": "tensorflow.gather", "line_number": 146, "usage_type": "call"}, {"api_name": "tensorflow.cast", "line_number": 146, "usage_type": "call"}, {"api_name": "tensorflow.reshape", "line_number": 147, "usage_type": "call"}, {"api_name": "tensorflow.gather", "line_number": 147, "usage_type": "call"}, {"api_name": "tensorflow.cast", "line_number": 147, "usage_type": "call"}, {"api_name": "tensorflow.reshape", "line_number": 148, "usage_type": "call"}, {"api_name": "tensorflow.gather", "line_number": 148, "usage_type": "call"}, {"api_name": "tensorflow.cast", "line_number": 148, "usage_type": "call"}, {"api_name": "tensorflow.add", "line_number": 159, "usage_type": "call"}, {"api_name": "tensorflow.multiply", "line_number": 159, "usage_type": "call"}, {"api_name": "tensorflow.add", "line_number": 160, "usage_type": "call"}, {"api_name": "tensorflow.multiply", "line_number": 160, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 176, "usage_type": "call"}, {"api_name": "tensorflow.constant", "line_number": 184, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 184, "usage_type": "attribute"}, {"api_name": "tensorflow.shape", "line_number": 185, "usage_type": "call"}, {"api_name": "tensorflow.shape", "line_number": 186, "usage_type": "call"}, {"api_name": "tensorflow.concat", "line_number": 188, "usage_type": "call"}, {"api_name": "tensorflow.zeros_like", "line_number": 188, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Activation", "line_number": 213, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 213, "usage_type": "attribute"}, {"api_name": "tensorflow.nn", "line_number": 213, "usage_type": "attribute"}, {"api_name": "tensorflow.float32", "line_number": 213, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.layers.Conv2D", "line_number": 216, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 216, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.layers.Conv2D", "line_number": 217, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 217, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.layers.Conv2D", "line_number": 218, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 218, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.layers.Conv2D", "line_number": 219, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 219, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.layers.Conv2D", "line_number": 220, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 220, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.layers.Conv2D", "line_number": 221, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 221, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.layers.Conv2D", "line_number": 222, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 222, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.layers.concatenate", "line_number": 232, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 232, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.layers.add", "line_number": 241, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 241, "usage_type": "attribute"}, {"api_name": "tensorflow.image.resize", "line_number": 242, "usage_type": "call"}, {"api_name": "tensorflow.image", "line_number": 242, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.layers.Activation", "line_number": 264, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 264, "usage_type": "attribute"}, {"api_name": "tensorflow.nn", "line_number": 264, "usage_type": "attribute"}, {"api_name": "tensorflow.float32", "line_number": 264, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.layers.Conv2D", "line_number": 267, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 267, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.layers.Conv2D", "line_number": 268, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 268, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.layers.Conv2D", "line_number": 269, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 269, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.layers.Conv2D", "line_number": 270, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 270, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.layers.Conv2D", "line_number": 271, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 271, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.layers.Conv2D", "line_number": 272, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 272, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.layers.concatenate", "line_number": 283, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 283, "usage_type": "attribute"}, {"api_name": "tensorflow.image.resize", "line_number": 308, "usage_type": "call"}, {"api_name": "tensorflow.image", "line_number": 308, "usage_type": "attribute"}, {"api_name": "keras.engine.data_adapter.unpack_x_y_sample_weight", "line_number": 344, "usage_type": "call"}, {"api_name": "keras.engine.data_adapter", "line_number": 344, "usage_type": "name"}, {"api_name": "tensorflow.GradientTape", "line_number": 349, "usage_type": "call"}, {"api_name": "tensorflow.shape", "line_number": 361, "usage_type": "call"}, {"api_name": "tensorflow.cast", "line_number": 362, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 362, "usage_type": "attribute"}, {"api_name": "tensorflow.function", "line_number": 333, "usage_type": "attribute"}, {"api_name": "keras.engine.data_adapter.unpack_x_y_sample_weight", "line_number": 385, "usage_type": "call"}, {"api_name": "keras.engine.data_adapter", "line_number": 385, "usage_type": "name"}, {"api_name": "tensorflow.function", "line_number": 383, "usage_type": "attribute"}, {"api_name": "keras.engine.data_adapter.unpack_x_y_sample_weight", "line_number": 416, "usage_type": "call"}, {"api_name": "keras.engine.data_adapter", "line_number": 416, "usage_type": "name"}, {"api_name": "tensorflow.GradientTape", "line_number": 421, "usage_type": "call"}, {"api_name": "tensorflow.shape", "line_number": 431, "usage_type": "call"}, {"api_name": "tensorflow.cast", "line_number": 432, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 432, "usage_type": "attribute"}, {"api_name": "tensorflow.function", "line_number": 414, "usage_type": "attribute"}, {"api_name": "keras.engine.data_adapter.unpack_x_y_sample_weight", "line_number": 460, "usage_type": "call"}, {"api_name": "keras.engine.data_adapter", "line_number": 460, "usage_type": "name"}, {"api_name": "tensorflow.GradientTape", "line_number": 465, "usage_type": "call"}, {"api_name": "tensorflow.shape", "line_number": 475, "usage_type": "call"}, {"api_name": "tensorflow.cast", "line_number": 476, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 476, "usage_type": "attribute"}, {"api_name": "random.sample", "line_number": 481, "usage_type": "call"}, {"api_name": "tensorflow.constant", "line_number": 513, "usage_type": "call"}, {"api_name": "tensorflow.cond", "line_number": 516, "usage_type": "call"}, {"api_name": "tensorflow.reduce_any", "line_number": 517, "usage_type": "call"}, {"api_name": "tensorflow.equal", "line_number": 517, "usage_type": "call"}, {"api_name": "tensorflow.function", "line_number": 439, "usage_type": "attribute"}, {"api_name": "tensorflow.io.gfile.exists", "line_number": 575, "usage_type": "call"}, {"api_name": "tensorflow.io", "line_number": 575, "usage_type": "attribute"}, {"api_name": "tensorflow.io.gfile.exists", "line_number": 576, "usage_type": "call"}, {"api_name": "tensorflow.io", "line_number": 576, "usage_type": "attribute"}, {"api_name": "keras.backend.is_keras_tensor", "line_number": 586, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 586, "usage_type": "name"}, {"api_name": "keras.backend.is_keras_tensor", "line_number": 589, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 589, "usage_type": "name"}, {"api_name": "keras.utils.layer_utils.get_source_inputs", "line_number": 590, "usage_type": "call"}, {"api_name": "keras.utils.layer_utils", "line_number": 590, "usage_type": "name"}, {"api_name": "keras.backend.image_data_format", "line_number": 598, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 598, "usage_type": "name"}, {"api_name": "keras.backend.int_shape", "line_number": 604, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 604, "usage_type": "name"}, {"api_name": "keras.backend.is_keras_tensor", "line_number": 620, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 620, "usage_type": "name"}, {"api_name": "keras.backend.is_keras_tensor", "line_number": 625, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 625, "usage_type": "name"}, {"api_name": "keras.backend.is_keras_tensor", "line_number": 627, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 627, "usage_type": "name"}, {"api_name": "keras.backend.image_data_format", "line_number": 628, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 628, "usage_type": "name"}, {"api_name": "keras.backend.int_shape", "line_number": 634, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 634, "usage_type": "name"}, {"api_name": "keras.backend.int_shape", "line_number": 635, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 635, "usage_type": "name"}, {"api_name": "keras.backend.image_data_format", "line_number": 644, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 644, "usage_type": "name"}, {"api_name": "keras.layers.Input", "line_number": 661, "usage_type": "call"}, {"api_name": "keras.layers", "line_number": 661, "usage_type": "name"}, {"api_name": "keras.layers.Input", "line_number": 662, "usage_type": "call"}, {"api_name": "keras.layers", "line_number": 662, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.Activation", "line_number": 668, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 668, "usage_type": "attribute"}, {"api_name": "tensorflow.nn", "line_number": 668, "usage_type": "attribute"}, {"api_name": "tensorflow.float32", "line_number": 668, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.layers.Conv2D", "line_number": 673, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 673, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.layers.Conv2D", "line_number": 679, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 679, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.layers.Conv2D", "line_number": 681, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 681, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.layers.Conv2D", "line_number": 682, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 682, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.layers.Conv2D", "line_number": 684, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 684, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.layers.Conv2D", "line_number": 685, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 685, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.layers.Conv2D", "line_number": 687, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 687, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.layers.Conv2D", "line_number": 688, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 688, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.layers.Conv2D", "line_number": 690, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 690, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.layers.Conv2D", "line_number": 691, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 691, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.layers.Conv2D", "line_number": 693, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 693, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.layers.Conv2D", "line_number": 694, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 694, "usage_type": "attribute"}, {"api_name": "tensorflow.keras", "line_number": 762, "usage_type": "attribute"}, {"api_name": "tensorflow.keras", "line_number": 765, "usage_type": "attribute"}, {"api_name": "tensorflow.Variable", "line_number": 765, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 766, "usage_type": "attribute"}, {"api_name": "tensorflow.keras", "line_number": 767, "usage_type": "attribute"}, {"api_name": "tensorflow.keras", "line_number": 768, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.Model", "line_number": 770, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 770, "usage_type": "attribute"}, {"api_name": "keras.utils.data_utils.get_file", "line_number": 781, "usage_type": "call"}, {"api_name": "keras.utils.data_utils", "line_number": 781, "usage_type": "name"}]} +{"seq_id": "30442604459", "text": "import os\r\nimport cv2 as cv\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\nimport pdb\r\n\r\nfrom cod.add_pieces_mosaic import *\r\nfrom cod.parameters import *\r\nfrom glob import glob\r\n\r\n\r\ndef load_pieces(params: Parameters):\r\n # citeste toate cele N piese folosite la mozaic din directorul corespunzator\r\n # toate cele N imagini au aceeasi dimensiune H x W x C, unde:\r\n # H = inaltime, W = latime, C = nr canale (C=1 gri, C=3 color)\r\n # functia intoarce pieseMozaic = matrice N x H x W x C in params\r\n # pieseMoziac[i, :, :, :] reprezinta piesa numarul i\r\n\r\n images = np.array([np.stack([cv.imread(img_path, cv.IMREAD_GRAYSCALE) for _ in range(3)],\r\n axis=-1) if params.grayscale_flag else cv.imread(img_path)\r\n for img_path in glob(params.small_images_dir + '*.png')])\r\n\r\n # citeste imaginile din director\r\n\r\n # if params.show_small_images:\r\n # for i in range(10):\r\n # for j in range(10):\r\n # plt.subplot(10, 10, i * 10 + j + 1)\r\n # # OpenCV reads images in BGR format, matplotlib reads images in RBG format\r\n # im = images[i * 10 + j].copy()\r\n # # BGR to RGB, swap the channels\r\n # im = im[:, :, [2, 1, 0]]\r\n # plt.imshow(im)\r\n # plt.show()\r\n\r\n params.small_images = images\r\n\r\n\r\ndef compute_dimensions(params: Parameters):\r\n # calculeaza dimensiunile mozaicului\r\n # obtine si imaginea de referinta redimensionata avand aceleasi dimensiuni\r\n # ca mozaicul\r\n\r\n # completati codul\r\n # calculeaza automat numarul de piese pe verticala\r\n h, w = params.image.shape[:2]\r\n small_img_h, small_img_w = params.small_images[0].shape[:2]\r\n ratio = w / h\r\n\r\n # redimensioneaza imaginea\r\n new_w = small_img_w * params.num_pieces_horizontal\r\n new_h = int(new_w // ratio)\r\n\r\n params.num_pieces_vertical = int(new_h // small_img_h)\r\n\r\n params.image_resized = cv.resize(params.image, (new_w, new_h))\r\n\r\n\r\ndef build_mosaic(params: Parameters):\r\n # incarcam imaginile din care vom forma mozaicul\r\n load_pieces(params)\r\n # calculeaza dimensiunea mozaicului\r\n compute_dimensions(params)\r\n\r\n img_mosaic = None\r\n if params.layout == 'caroiaj':\r\n if params.hexagon is True:\r\n img_mosaic = add_pieces_hexagon(params)\r\n else:\r\n img_mosaic = add_pieces_grid(params)\r\n elif params.layout == 'aleator':\r\n img_mosaic = add_pieces_random(params)\r\n else:\r\n print('Wrong option!')\r\n exit(-1)\r\n\r\n return img_mosaic\r\n", "repo_name": "Sergiu154/Mosaic-Generator", "sub_path": "cod/build_mosaic.py", "file_name": "build_mosaic.py", "file_ext": "py", "file_size_in_byte": 2603, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "numpy.array", "line_number": 19, "usage_type": "call"}, {"api_name": "numpy.stack", "line_number": 19, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 19, "usage_type": "call"}, {"api_name": "cv2.IMREAD_GRAYSCALE", "line_number": 19, "usage_type": "attribute"}, {"api_name": "cv2.imread", "line_number": 20, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 21, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 56, "usage_type": "call"}]} +{"seq_id": "10740791596", "text": "from fastapi import APIRouter, HTTPException, Query, Request\nfrom controllers import list_products, create_order, list_orders, get_order, update_product\nfrom models import Product, Order, New_Quantity\n\n# Create an instance of APIRouter for defining routes\nrouter = APIRouter()\n\n# Define an endpoint for the home page\n@router.get(\"/\")\nasync def home_page():\n \"\"\"\n Endpoint to return a simple greeting message.\n\n Returns:\n str: A greeting message.\n \"\"\"\n return \"HELLO\"\n\n# Define an endpoint to get a list of products\n@router.get(\"/products\")\nasync def get_products():\n \"\"\"\n Endpoint to retrieve a list of products.\n\n Returns:\n list: A list of product data.\n \"\"\"\n return list_products()\n\n# Define an endpoint to create an order\n@router.post(\"/order\")\nasync def post_order(order: Order):\n \"\"\"\n Endpoint to create an order.\n\n Args:\n order (Order): The order data to create.\n\n Returns:\n dict: A response containing the order_id.\n \"\"\"\n return {\"order_id\": create_order(order)}\n\n# Define an endpoint to retrieve a list of orders with optional pagination parameters\n@router.get(\"/orders\")\nasync def get_orders(\n limit: int = Query(10, description=\"Number of items to return\"),\n offset: int = Query(0, description=\"Offset for pagination\")\n):\n \"\"\"\n Endpoint to retrieve a list of orders with optional pagination.\n\n Args:\n limit (int, optional): Number of items to return per page.\n offset (int, optional): Offset for pagination.\n\n Returns:\n dict: A response containing total_orders and a list of orders.\n \"\"\"\n return list_orders(limit, offset)\n\n# Define an endpoint to retrieve a single order by order_id\n@router.get(\"/order/{order_id}\")\nasync def get_single_order(order_id: str):\n \"\"\"\n Endpoint to retrieve a single order by order_id.\n\n Args:\n order_id (str): The ID of the order to retrieve.\n\n Returns:\n Order: The order data.\n \n Raises:\n HTTPException: If the order is not found.\n \"\"\"\n order = get_order(order_id)\n if order is None:\n raise HTTPException(status_code=404, detail=\"Order not found\")\n return order\n\n# Define an endpoint to update the quantity of a product by product_id\n@router.put(\"/product/{product_id}\")\nasync def put_product(product_id: str, new_quantity: New_Quantity):\n \"\"\"\n Endpoint to update the quantity of a product by product_id.\n\n Args:\n product_id (str): The ID of the product to update.\n new_quantity (New_Quantity): The new quantity to set for the product.\n\n Returns:\n dict: A response message indicating the update status.\n \n Raises:\n HTTPException: If the product is not found.\n \"\"\"\n if not update_product(product_id, new_quantity):\n raise HTTPException(status_code=404, detail=\"Product not found\")\n return {\"message\": \"Product updated successfully\"}\n", "repo_name": "Bkmakwana2002/E-commerce-fastapi", "sub_path": "routers.py", "file_name": "routers.py", "file_ext": "py", "file_size_in_byte": 2914, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "fastapi.APIRouter", "line_number": 6, "usage_type": "call"}, {"api_name": "controllers.list_products", "line_number": 28, "usage_type": "call"}, {"api_name": "models.Order", "line_number": 32, "usage_type": "name"}, {"api_name": "controllers.create_order", "line_number": 42, "usage_type": "call"}, {"api_name": "fastapi.Query", "line_number": 47, "usage_type": "call"}, {"api_name": "fastapi.Query", "line_number": 48, "usage_type": "call"}, {"api_name": "controllers.list_orders", "line_number": 60, "usage_type": "call"}, {"api_name": "controllers.get_order", "line_number": 77, "usage_type": "call"}, {"api_name": "fastapi.HTTPException", "line_number": 79, "usage_type": "call"}, {"api_name": "models.New_Quantity", "line_number": 84, "usage_type": "name"}, {"api_name": "controllers.update_product", "line_number": 98, "usage_type": "call"}, {"api_name": "fastapi.HTTPException", "line_number": 99, "usage_type": "call"}]} +{"seq_id": "19388996335", "text": "\"\"\" Render gltf files via Blender Software \"\"\"\nimport argparse\nimport os\nimport time\nimport bpy\nimport mathutils\nimport json\n\nimport builtins as __builtin__\n\n#########################################\n\n# PRINT TO SYSTEM CONSOLE\n\n#########################################\n\n\ndef console_print(*args, **kwargs) -> None:\n \"\"\"Prints stuff to the console outside of blender. (to your terminal basically)\"\"\"\n for a in bpy.context.screen.areas:\n if a.type == \"CONSOLE\":\n c = {}\n c[\"area\"] = a\n c[\"space_data\"] = a.spaces.active\n c[\"region\"] = a.regions[-1]\n c[\"window\"] = bpy.context.window\n c[\"screen\"] = bpy.context.screen\n s = \" \".join([str(arg) for arg in args])\n for line in s.split(\"\\n\"):\n bpy.ops.console.scrollback_append(c, text=line)\n\n\ndef print(*args, **kwargs) -> None:\n \"\"\"Override pythons print function to pass args to internal console_print.\"\"\"\n console_print(*args, **kwargs) # to Python Console\n __builtin__.print(*args, **kwargs) # to System Console\n\n\n#########################################\n\n# MATERIALS\n\n#########################################\n\n\ndef remove_vertex_colors(obj: bpy.types.Object) -> None:\n \"\"\"Removes the Vertex Colors from the given object.\n\n Args:\n mat (bpy.types.Object): Blender object to remove the Vertex Colors from.\n \"\"\"\n vertex_colors = obj.data.vertex_colors\n while vertex_colors:\n print(vertex_colors[0])\n vertex_colors.remove(vertex_colors[0])\n\n\ndef set_object_material_basecolor(obj: bpy.types.Object, color) -> None:\n \"\"\"Set the base color in the Principled BSDF node for the material of the given object.\n\n Args:\n mat (bpy.types.Material)\n \"\"\"\n mat = obj.data.materials[0]\n # Remove Texture input from base color and set a color\n if mat.node_tree.nodes[\"Principled BSDF\"].inputs[0].links:\n base_color_link = mat.node_tree.nodes[\"Principled BSDF\"].inputs[0].links[0]\n mat.node_tree.links.remove(base_color_link)\n mat.node_tree.nodes[\"Principled BSDF\"].inputs[\"Base Color\"].default_value = color\n\n\ndef import_materials_from_blend(file_path) -> list:\n \"\"\"Loads materials from .blend files and replaces all materials with those in the target blend file.\n\n Args:\n file_path (str): The path to the .blend file containing materials\n \"\"\"\n materials = None\n with bpy.data.libraries.load(file_path, link=False) as (data_from, data_to):\n materials = data_from.materials\n data_to.materials = data_from.materials\n return materials\n\n\ndef get_bpy_materials(materials_dir: str) -> dict:\n \"\"\"Returns a dictionary of blender materials read from the materials directory.\n\n The materials_dir should contain .blend files that contain exactly one material.\n The name of the .blend file should match the material name in that file.\n\n Args:\n materials_dir (str): Path to directory containing .blend files that contain a material.\n \"\"\"\n bpy_materials = {}\n for material_fn in os.listdir(material_dir):\n if material_fn.endswith(\".blend\"):\n bpy_materials[material_fn] = import_materials_from_blend(f\"{materials_dir}/{material_fn}\")[0]\n return bpy_materials\n\n\ndef apply_material(ob: bpy.types.Object, mat: bpy.types.Material) -> bpy.types.Material:\n \"\"\"Apply material to given ob by material id\n\n Args:\n ob: object in scene to apply material to\n material_id: name of material in scene to apply\n \"\"\"\n # remove former materials from object\n if ob.data.materials:\n ob.data.materials.clear()\n # Add new material to object\n ob.data.materials.append(mat)\n\n return mat\n\n\ndef apply_materials(scene: bpy.types.Scene, rcfg_part: dict, bpy_materials: dict) -> None:\n \"\"\"Applies blender materials to all objects in the current scene.\n\n Args:\n scene (bpy.types.Scene): The blender scene.\n rcfg_part (dict): Machine part definition. Includes single_parts withmaterial definitions.\n bpy_materials (dict): Material dictionary that maps material names to actual blender materials.\n \"\"\"\n for bpy_obj in scene.objects:\n # Reset obj color for all mesh objects\n if bpy_obj.type == \"MESH\":\n remove_vertex_colors(bpy_obj)\n # Check for obj references in render config\n for rcfg_single_part in rcfg_part[\"single_parts\"]:\n if bpy_obj.name.startswith(rcfg_single_part[\"id\"]):\n # Do nothing if no material defined\n if rcfg_single_part[\"material\"] in [\"none\", None]:\n print(f\"Apply material: {bpy_obj.name}: {rcfg_single_part['material']}\")\n continue\n # Apply material to mesh obj\n print(f\"Apply material: {bpy_obj.name}: {rcfg_single_part['material']}\")\n apply_material(bpy_obj, bpy_materials[rcfg_single_part[\"material\"]])\n break\n\n\n#########################################\n\n# RENDER\n\n#########################################\n\n\ndef add_image_to_blender(file_path: str) -> bpy.types.Image:\n \"\"\"Add image to .blend file\n\n Args:\n file_path (str): path to image file\n\n Returns:\n bpy.types.Image: created image node\n \"\"\"\n return bpy.data.images.load(file_path, check_existing=True)\n\n\ndef add_hdri_map(file_path: str) -> tuple:\n \"\"\"Add hdri map to .blend\n\n Args:\n file_path (str): path to image file\n\n Returns:\n list(bpy.types.Material, bpy.types.EnvironmentTexture): [description]\n \"\"\"\n # Get the environment node tree of the current scene\n node_tree = bpy.context.scene.world.node_tree\n tree_nodes = node_tree.nodes\n # Clear all nodes\n tree_nodes.clear()\n # Add Background node\n node_background = tree_nodes.new(type=\"ShaderNodeBackground\")\n # Add Environment Texture node\n node_environment = tree_nodes.new(\"ShaderNodeTexEnvironment\")\n # Load and assign the image to the node property\n node_environment.image = bpy.data.images.load(file_path) # Relative path\n node_environment.location = -300, 0\n # Add Output node\n node_output = tree_nodes.new(type=\"ShaderNodeOutputWorld\")\n node_output.location = 200, 0\n # Link all nodes\n links = node_tree.links\n link = links.new(node_environment.outputs[\"Color\"], node_background.inputs[\"Color\"])\n link = links.new(node_background.outputs[\"Background\"], node_output.inputs[\"Surface\"])\n return node_background, node_environment\n\n\ndef translate_objects_by(objects: list, translate_by: mathutils.Vector) -> None:\n \"\"\"Translate objects by given vector\n\n Args:\n objects (list): objects to translate\n translate_by (mathutils.Vector): vector to translate by\n \"\"\"\n for ob in objects:\n ob.location += translate_by\n\n\ndef new_empty_scene() -> None:\n \"\"\"Open new empty scene.\"\"\"\n bpy.ops.wm.read_homefile(use_empty=True)\n\n\ndef objs_set_hide_render(objs: list[bpy.types.Object], hide_render: bool) -> None:\n \"\"\"Hide/show given objects in render.\n\n Args:\n objs (list[bpy.types.Object]): Objects to hide/show in render.\n hide_render (bool): Defines whether to hide (True) or not hide (False) objects in render.\n \"\"\"\n for obj in objs:\n obj.hide_render = hide_render\n\n\ndef get_compositor_depthmap_node_tree():\n \"\"\"Returns a Blender Compositor node tree that renders a normalized depth map.\"\"\"\n bpy.context.scene.use_nodes = True\n bpy.context.scene.render.use_compositing = True\n bpy.context.scene.view_layers[\"ViewLayer\"].use_pass_z = True\n tree = bpy.context.scene.node_tree\n links = tree.links\n # clear default nodes\n for n in tree.nodes:\n tree.nodes.remove(n)\n # create input render layer node\n map = tree.nodes.new(type=\"CompositorNodeMapValue\")\n map.size = [1.0]\n map.use_min = True\n map.min = [0]\n map.use_max = True\n map.max = [255]\n rl = tree.nodes.new(\"CompositorNodeRLayers\")\n normalize = tree.nodes.new(\"CompositorNodeNormalize\")\n invert = tree.nodes.new(\"CompositorNodeInvert\")\n links.new(rl.outputs[2], normalize.inputs[0])\n links.new(normalize.outputs[0], map.inputs[0])\n links.new(map.outputs[0], invert.inputs[1])\n\n # Depth map as 1-Channel PNG\n depth_file_output_png = tree.nodes.new(type=\"CompositorNodeOutputFile\")\n depth_file_output_png.format.color_mode = \"BW\"\n depth_file_output_png.format.file_format = \"PNG\"\n tree.links.new(invert.outputs[0], depth_file_output_png.inputs[0])\n # Depth map as OPEN_EXR\n depth_file_output_exr = tree.nodes.new(type=\"CompositorNodeOutputFile\")\n depth_file_output_exr.format.file_format = \"OPEN_EXR\"\n tree.links.new(rl.outputs[2], depth_file_output_exr.inputs[0])\n\n return tree, depth_file_output_png, depth_file_output_exr\n\n\ndef setup_gpu_cycles() -> None:\n \"\"\"Applies setup for GPU usage while rendering with Cycles render engine.\"\"\"\n # Render settings CYCLES GPU rendering\n # Set the device_type\n bpy.context.preferences.addons[\"cycles\"].preferences.compute_device_type = \"CUDA\"\n # Set the device and feature set\n bpy.context.scene.cycles.device = \"GPU\"\n # get_devices() to let Blender detects GPU device\n bpy.context.preferences.addons[\"cycles\"].preferences.get_devices()\n for d in bpy.context.preferences.addons[\"cycles\"].preferences.devices:\n for k in d.keys():\n print(f\"{k}: {d[k]}\")\n print(\"---\")\n d[\"use\"] = 1\n if d[\"type\"] == 0: # type 0 -> CPU\n d[\"use\"] = 0\n\n\ndef apply_render_settings(\n engine: str = \"CYCLES\",\n device: str = \"GPU\",\n res_x: int = 256,\n res_y: int = 256,\n out_format: str = \"PNG\",\n out_quality: int = 100,\n) -> None:\n \"\"\"asd\n\n Args:\n engine (str): The render engine.\n device (str): The render device. One of [\"GPU\", \"CPU\"]\n res_x (int): Render image resolution width.\n res_y (int): Render image resolution height.\n out_format (str): Image output format. One of [\"PNG\", \"JPG\"]\n out_quality (int): Output quality in percent. Integer Range [0, 100]\n \"\"\"\n scene = bpy.context.scene\n\n scene.render.engine = engine\n scene.render.resolution_x = res_x\n scene.render.resolution_y = res_y\n scene.render.film_transparent = True\n scene.render.image_settings.quality = out_quality\n scene.render.image_settings.file_format = out_format\n\n if engine.lower() == \"cycles\":\n scene.cycles.seed = 0\n scene.cycles.feature_set = \"SUPPORTED\"\n\n scene.cycles.samples = 4096\n scene.cycles.use_adaptive_sampling = True\n scene.cycles.adaptive_threshold = 0.01\n scene.cycles.time_limit = 0\n\n scene.cycles.use_denoising = True\n scene.cycles.denoiser = \"OPENIMAGEDENOISE\"\n\n scene.cycles.denoising_input_passes = \"RGB_ALBEDO_NORMAL\"\n scene.cycles.min_light_bounces = 0\n scene.cycles.min_transparent_bounces = 0\n scene.cycles.light_sampling_threshold = 0.01\n\n scene.cycles.max_bounces = 12\n scene.cycles.diffuse_bounces = 4\n scene.cycles.glossy_bounces = 4\n scene.cycles.transmission_bounces = 12\n scene.cycles.volume_bounces = 0\n scene.cycles.transparent_max_bounces = 8\n scene.cycles.sample_clamp_direct = 0\n scene.cycles.sample_clamp_indirect = 10\n scene.cycles.blur_glossy = 1\n\n scene.render.use_persistent_data = True\n\n if engine.lower() == \"cycles\" and device.lower() == \"gpu\":\n setup_gpu_cycles()\n\n\ndef load_gltf(file_path) -> None:\n \"\"\"Loads gltf file into active scene.\n\n Args:\n file_path (str): Path the the gltf file.\n \"\"\"\n bpy.ops.import_scene.gltf(filepath=file_path)\n\n bpy_world = bpy.context.scene.world\n if bpy_world is None:\n # create a new world\n new_world = bpy.data.worlds.new(\"World\")\n new_world.use_nodes = True\n bpy.context.scene.world = new_world\n\n\ndef export_render_settings(out_path: str) -> None:\n \"\"\"Exports the current render settings as json. file.\n\n Args:\n out_path (str): The path of the exported json file.\n \"\"\"\n\n render_settings = {\n \"engine\": bpy.context.scene.render.engine,\n \"resolution_x\": bpy.context.scene.render.resolution_x,\n \"resolution_y\": bpy.context.scene.render.resolution_y,\n \"film_transparent\": bpy.context.scene.render.film_transparent,\n \"quality\": bpy.context.scene.render.image_settings.quality,\n \"file_format\": bpy.context.scene.render.image_settings.file_format,\n \"cycles\": {\n \"seed\": bpy.context.scene.cycles.seed,\n \"feature_set\": bpy.context.scene.cycles.feature_set,\n \"samples\": bpy.context.scene.cycles.samples,\n \"use_adaptive_sampling\": bpy.context.scene.cycles.use_adaptive_sampling,\n \"adaptive_threshold\": bpy.context.scene.cycles.adaptive_threshold,\n \"time_limit\": bpy.context.scene.cycles.time_limit,\n \"use_denoising\": bpy.context.scene.cycles.use_denoising,\n \"denoiser\": bpy.context.scene.cycles.denoiser,\n \"denoising_input_passes\": bpy.context.scene.cycles.denoising_input_passes,\n \"min_light_bounces\": bpy.context.scene.cycles.min_light_bounces,\n \"min_transparent_bounces\": bpy.context.scene.cycles.min_transparent_bounces,\n \"light_sampling_threshold\": bpy.context.scene.cycles.light_sampling_threshold,\n \"max_bounces\": bpy.context.scene.cycles.max_bounces,\n \"diffuse_bounces\": bpy.context.scene.cycles.diffuse_bounces,\n \"glossy_bounces\": bpy.context.scene.cycles.glossy_bounces,\n \"transmission_bounces\": bpy.context.scene.cycles.transmission_bounces,\n \"volume_bounces\": bpy.context.scene.cycles.volume_bounces,\n \"transparent_max_bounces\": bpy.context.scene.cycles.transparent_max_bounces,\n \"sample_clamp_direct\": bpy.context.scene.cycles.sample_clamp_direct,\n \"sample_clamp_indirect\": bpy.context.scene.cycles.sample_clamp_indirect,\n \"blur_glossy\": bpy.context.scene.cycles.blur_glossy,\n \"use_persistent_data\": bpy.context.scene.render.use_persistent_data,\n },\n }\n with open(out_path, \"w\") as outfile:\n json.dump(render_settings, outfile)\n\n\ndef render(\n scene: bpy.types.Scene,\n rcfg_part: dict,\n part_id: str,\n envmap_dir: str,\n out_dir: str,\n) -> None:\n \"\"\"Renders the given rcfg_part as defined in it's render_setups.\n\n Parses render_setups for the given rcfg_part and activates defined scene components\n for each specific render setup.\n\n Args:\n scene (bpy.types.Scene): The scene to render from.\n rcfg_part (dict): Machine part definition. Includes single_parts withmaterial definitions.\n part_id (str): Id of the part to render.\n envmap_dir (str): Directory containing envmap files.\n out_dir (str): Output directory.\n\n \"\"\"\n # Load render setups\n render_setups = rcfg_part[\"scene\"][\"render_setups\"]\n # Get cameras from gltf scene\n cameras = [obj for obj in scene.objects if obj.type == \"CAMERA\"]\n # Get lights from gltf scene\n lights = [obj for obj in scene.objects if obj.type == \"LIGHT\"]\n\n # Hide all lights\n objs_set_hide_render(lights, True)\n\n # DEPTH MAP RENDER SETUP\n depthmap_node_tree, depth_file_output_png, depth_file_output_exr = get_compositor_depthmap_node_tree()\n\n bpy.ops.object.select_by_type(extend=False, type=\"MESH\")\n\n # scale all objects so largest dimension out of all objects equals 1\n # 1. Add selected objects to empty parent object\n parent_obj = bpy.data.objects.new(\"Empty\", None)\n for obj in bpy.context.selected_objects:\n obj.parent = parent_obj\n # 2. Rescale mesh objects so largest dimension out of all objects equals 1\n max_xdim, max_ydim, max_zdim = 0, 0, 0\n for obj in parent_obj.children:\n max_xdim = obj.dimensions.x if obj.dimensions.x > max_xdim else max_xdim\n max_ydim = obj.dimensions.y if obj.dimensions.y > max_ydim else max_ydim\n max_zdim = obj.dimensions.z if obj.dimensions.z > max_zdim else max_zdim\n max_dim = max(max_xdim, max_ydim, max_zdim)\n parent_obj.scale = (1 / max_dim, 1 / max_dim, 1 / max_dim)\n\n # Render Loop\n for i, render_setup in enumerate(render_setups):\n # CAMERA: load, add to scene, zoom to object\n render_camera = cameras[render_setup[\"camera_i\"]]\n scene.camera = render_camera\n bpy.ops.view3d.camera_to_view_selected()\n # Zoom in/out from 100% ?\n # translate_objects_by([cam], mathutils.Vector((0, 0, 0.5)))\n\n # LIGHTS: load, unhide\n render_lights = [lights[light_i] for light_i in render_setup[\"lights_i\"]]\n objs_set_hide_render(render_lights, False)\n\n # ENVMAPS: load, add to blender, use as hdri envmap\n render_envmap_fn = f\"{envmap_dir}/{render_setup['envmap_fname']}\"\n add_image_to_blender(render_envmap_fn)\n add_hdri_map(render_envmap_fn)\n\n # RENDER\n scene.render.filepath = f\"{out_dir}/render/rgb/{part_id}/{part_id}_{i:03d}\"\n\n # Set up rendering of depth map files\n depth_file_output_png.base_path = f\"{out_dir}/render/depth_png/{part_id}\"\n depth_file_output_png.file_slots[0].path = f\"{part_id}_{i:03d}_depth\"\n\n depth_file_output_exr.base_path = f\"{out_dir}/render/depth_exr/{part_id}\"\n depth_file_output_exr.file_slots[0].path = f\"{part_id}_{i:03d}_depth\"\n\n bpy.ops.render.render(write_still=True)\n\n ## fix depth map filename by removing frame number\n os.rename(\n f\"{depth_file_output_png.base_path}/{depth_file_output_png.file_slots[0].path}0001.png\",\n f\"{depth_file_output_png.base_path}/{depth_file_output_png.file_slots[0].path}.png\",\n )\n os.rename(\n f\"{depth_file_output_exr.base_path}/{depth_file_output_exr.file_slots[0].path}0001.exr\",\n f\"{depth_file_output_exr.base_path}/{depth_file_output_exr.file_slots[0].path}.exr\",\n )\n\n ## CLEANUP\n # Hide lights again after rendered\n objs_set_hide_render(render_lights, True)\n\n\ndef get_args():\n \"\"\"Returns script arguments as python variables.\"\"\"\n parser = argparse.ArgumentParser()\n # Only consider script args, ignore blender args\n _, all_arguments = parser.parse_known_args()\n double_dash_index = all_arguments.index(\"--\")\n script_args = all_arguments[double_dash_index + 1 :]\n\n parser.add_argument(\n \"--gltf_dir\",\n help=\"Directory with gltf files.\",\n type=str,\n required=True,\n )\n parser.add_argument(\n \"--material_dir\",\n help=\"Data directory for materials.\",\n type=str,\n default=None,\n )\n parser.add_argument(\n \"--envmap_dir\",\n help=\"Data directory for envmaps.\",\n type=str,\n required=True,\n )\n parser.add_argument(\n \"--rcfg_file\",\n help=\"Render configuration file.\",\n type=str,\n required=True,\n )\n parser.add_argument(\n \"--out_dir\",\n help=\"Directory to save the rendered images in.\",\n type=str,\n required=True,\n )\n parser.add_argument(\n \"--res_x\",\n help=\"Pixel Resolution in X direction.\",\n default=256,\n type=int,\n )\n parser.add_argument(\n \"--res_y\",\n help=\"Pixel Resolution in Y direction.\",\n default=256,\n type=int,\n )\n parser.add_argument(\n \"--out_quality\",\n help=\"The output quality [0, 100].\",\n default=100,\n type=int,\n metavar=\"[0, 100]\",\n choices=range(0, 101),\n )\n parser.add_argument(\n \"--out_format\",\n help=\"Output image format\",\n default=\"PNG\",\n type=str,\n choices=[\"JPEG\", \"PNG\"],\n )\n parser.add_argument(\n \"--engine\",\n help=\"Rendering engine\",\n default=\"CYCLES\",\n type=str,\n )\n parser.add_argument(\n \"--device\",\n help=\"The device used for rendering\",\n default=\"GPU\",\n type=str,\n )\n\n args, _ = parser.parse_known_args(script_args)\n return args\n\n\nif __name__ == \"__main__\":\n tstart = time.time()\n args = get_args()\n print(f\"Running Rendering with args:\\n{args}\")\n\n gltf_dir = args.gltf_dir\n material_dir = args.material_dir\n envmap_dir = args.envmap_dir\n rcfg_file = args.rcfg_file\n out_dir = args.out_dir\n res_x = args.res_x\n res_y = args.res_y\n out_format = args.out_format\n out_quality = args.out_quality\n engine = args.engine\n device = args.device\n\n # Load RCFG data\n with open(rcfg_file, \"r\") as rcfg_json:\n rcfg_data = json.load(rcfg_json)\n\n sorted_input_files = sorted(os.listdir(gltf_dir), key=lambda x: x.split(\"_\")[0])\n\n for glb_fname in sorted_input_files:\n if not glb_fname.endswith(\".glb\"):\n continue\n new_empty_scene()\n load_gltf(os.path.join(gltf_dir, glb_fname))\n part_id = glb_fname[:-4] # Remove .glb from glb filename\n scene = bpy.context.scene\n for part in rcfg_data[\"parts\"]:\n if part[\"id\"] == part_id:\n rcfg_part = part\n break\n\n if material_dir:\n bpy_materials = get_bpy_materials(material_dir)\n apply_materials(\n scene,\n rcfg_part,\n bpy_materials,\n )\n apply_render_settings(\n device=device,\n engine=engine,\n res_x=res_x,\n res_y=res_y,\n out_format=out_format,\n out_quality=out_quality,\n )\n render(\n scene,\n rcfg_part=rcfg_part,\n part_id=part_id,\n envmap_dir=envmap_dir,\n out_dir=out_dir,\n )\n\n # Export detailed render settings\n export_render_settings(out_path=f\"{out_dir}/render_settings.json\")\n tend = time.time() - tstart\n print(f\"Rendered {len(os.listdir(gltf_dir))} imgs in {tend} seconds\")\n", "repo_name": "dennisritter/synthnet-render-pipeline", "sub_path": "bpy_modules/render.py", "file_name": "render.py", "file_ext": "py", "file_size_in_byte": 22113, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "bpy.context", "line_number": 20, "usage_type": "attribute"}, {"api_name": "bpy.context", "line_number": 26, "usage_type": "attribute"}, {"api_name": "bpy.context", "line_number": 27, "usage_type": "attribute"}, {"api_name": "bpy.ops.console.scrollback_append", "line_number": 30, "usage_type": "call"}, {"api_name": "bpy.ops", "line_number": 30, "usage_type": "attribute"}, {"api_name": "builtins.print", "line_number": 36, "usage_type": "call"}, {"api_name": "bpy.types", "line_number": 46, "usage_type": "attribute"}, {"api_name": "bpy.types", "line_number": 58, "usage_type": "attribute"}, {"api_name": "bpy.data.libraries.load", "line_number": 79, "usage_type": "call"}, {"api_name": "bpy.data", "line_number": 79, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 95, "usage_type": "call"}, {"api_name": "bpy.types", "line_number": 101, "usage_type": "attribute"}, {"api_name": "bpy.types", "line_number": 117, "usage_type": "attribute"}, {"api_name": "bpy.data.images.load", "line_number": 158, "usage_type": "call"}, {"api_name": "bpy.data", "line_number": 158, "usage_type": "attribute"}, {"api_name": "bpy.types", "line_number": 149, "usage_type": "attribute"}, {"api_name": "bpy.context", "line_number": 171, "usage_type": "attribute"}, {"api_name": "bpy.data.images.load", "line_number": 180, "usage_type": "call"}, {"api_name": "bpy.data", "line_number": 180, "usage_type": "attribute"}, {"api_name": "mathutils.Vector", "line_number": 192, "usage_type": "attribute"}, {"api_name": "bpy.ops.wm.read_homefile", "line_number": 205, "usage_type": "call"}, {"api_name": "bpy.ops", "line_number": 205, "usage_type": "attribute"}, {"api_name": "bpy.types", "line_number": 208, "usage_type": "attribute"}, {"api_name": "bpy.context", "line_number": 221, "usage_type": "attribute"}, {"api_name": "bpy.context", "line_number": 222, "usage_type": "attribute"}, {"api_name": "bpy.context", "line_number": 223, "usage_type": "attribute"}, {"api_name": "bpy.context", "line_number": 224, "usage_type": "attribute"}, {"api_name": "bpy.context", "line_number": 260, "usage_type": "attribute"}, {"api_name": "bpy.context", "line_number": 262, "usage_type": "attribute"}, {"api_name": "bpy.context", "line_number": 264, "usage_type": "attribute"}, {"api_name": "bpy.context", "line_number": 265, "usage_type": "attribute"}, {"api_name": "bpy.context", "line_number": 292, "usage_type": "attribute"}, {"api_name": "bpy.ops.import_scene.gltf", "line_number": 340, "usage_type": "call"}, {"api_name": "bpy.ops", "line_number": 340, "usage_type": "attribute"}, {"api_name": "bpy.context", "line_number": 342, "usage_type": "attribute"}, {"api_name": "bpy.data.worlds.new", "line_number": 345, "usage_type": "call"}, {"api_name": "bpy.data", "line_number": 345, "usage_type": "attribute"}, {"api_name": "bpy.context", "line_number": 347, "usage_type": "attribute"}, {"api_name": "bpy.context", "line_number": 358, "usage_type": "attribute"}, {"api_name": "bpy.context", "line_number": 359, "usage_type": "attribute"}, {"api_name": "bpy.context", "line_number": 360, "usage_type": "attribute"}, {"api_name": "bpy.context", "line_number": 361, "usage_type": "attribute"}, {"api_name": "bpy.context", "line_number": 362, "usage_type": "attribute"}, {"api_name": "bpy.context", "line_number": 363, "usage_type": "attribute"}, {"api_name": "bpy.context", "line_number": 365, "usage_type": "attribute"}, {"api_name": "bpy.context", "line_number": 366, "usage_type": "attribute"}, {"api_name": "bpy.context", "line_number": 367, "usage_type": "attribute"}, {"api_name": "bpy.context", "line_number": 368, "usage_type": "attribute"}, {"api_name": "bpy.context", "line_number": 369, "usage_type": "attribute"}, {"api_name": "bpy.context", "line_number": 370, "usage_type": "attribute"}, {"api_name": "bpy.context", "line_number": 371, "usage_type": "attribute"}, {"api_name": "bpy.context", "line_number": 372, "usage_type": "attribute"}, {"api_name": "bpy.context", "line_number": 373, "usage_type": "attribute"}, {"api_name": "bpy.context", "line_number": 374, "usage_type": "attribute"}, {"api_name": "bpy.context", "line_number": 375, "usage_type": "attribute"}, {"api_name": "bpy.context", "line_number": 376, "usage_type": "attribute"}, {"api_name": "bpy.context", "line_number": 377, "usage_type": "attribute"}, {"api_name": "bpy.context", "line_number": 378, "usage_type": "attribute"}, {"api_name": "bpy.context", "line_number": 379, "usage_type": "attribute"}, {"api_name": "bpy.context", "line_number": 380, "usage_type": "attribute"}, {"api_name": "bpy.context", "line_number": 381, "usage_type": "attribute"}, {"api_name": "bpy.context", "line_number": 382, "usage_type": "attribute"}, {"api_name": "bpy.context", "line_number": 383, "usage_type": "attribute"}, {"api_name": "bpy.context", "line_number": 384, "usage_type": "attribute"}, {"api_name": "bpy.context", "line_number": 385, "usage_type": "attribute"}, {"api_name": "bpy.context", "line_number": 386, "usage_type": "attribute"}, {"api_name": "json.dump", "line_number": 390, "usage_type": "call"}, {"api_name": "bpy.types", "line_number": 394, "usage_type": "attribute"}, {"api_name": "bpy.ops.object.select_by_type", "line_number": 426, "usage_type": "call"}, {"api_name": "bpy.ops", "line_number": 426, "usage_type": "attribute"}, {"api_name": "bpy.data.objects.new", "line_number": 430, "usage_type": "call"}, {"api_name": "bpy.data", "line_number": 430, "usage_type": "attribute"}, {"api_name": "bpy.context", "line_number": 431, "usage_type": "attribute"}, {"api_name": "bpy.ops.view3d.camera_to_view_selected", "line_number": 447, "usage_type": "call"}, {"api_name": "bpy.ops", "line_number": 447, "usage_type": "attribute"}, {"api_name": "bpy.ops.render.render", "line_number": 470, "usage_type": "call"}, {"api_name": "bpy.ops", "line_number": 470, "usage_type": "attribute"}, {"api_name": "os.rename", "line_number": 473, "usage_type": "call"}, {"api_name": "os.rename", "line_number": 477, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 489, "usage_type": "call"}, {"api_name": "time.time", "line_number": 570, "usage_type": "call"}, {"api_name": "json.load", "line_number": 588, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 590, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 596, "usage_type": "call"}, {"api_name": "os.path", "line_number": 596, "usage_type": "attribute"}, {"api_name": "bpy.context", "line_number": 598, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 629, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 630, "usage_type": "call"}]} +{"seq_id": "37966626005", "text": "from django.contrib.auth.decorators import login_required\nfrom django.shortcuts import render,get_object_or_404\nfrom django.http import HttpResponse, JsonResponse\nfrom django.contrib.auth.decorators import login_required\nimport datetime\n\nfrom exam.models import Subject\nfrom exam.models import Test\nfrom exam.models import SCFU_TotalTime\n\n\n@login_required(login_url='user_login')\ndef getAllSubjectList(request):\n if (request.method == \"GET\"):\n subjectList = Subject.objects.values('subject_id','subject_name','mcq_total_test','essay_total_test')\n total_timeList = SCFU_TotalTime.objects.values('test_time')\n return JsonResponse({'results': list(subjectList), 'total_timeList':list(total_timeList)})\n else:\n return HttpResponse(\"Problem\")\n\n@login_required\ndef testInfoInsert(request):\n registered = False\n if(request.method==\"POST\"):\n subject_id = request.POST.get('subjectList')\n test_type = request.POST.get('testTypeList')\n test_id = request.POST.get('testIDList')\n test_name = request.POST.get('testName')\n test_total_questions = request.POST.get('testTotalQues')\n total_marks = request.POST.get('totalMarks')\n total_time = request.POST.get('totalTime')\n mode = request.POST.get('mode')\n\n if(test_id and mode == '1'):\n Test.objects.filter(test_id=test_id).update(test_name=test_name, test_totaltimes=total_time)\n return JsonResponse({'status': 1})\n elif(subject_id and test_type and test_id and test_name and test_total_questions and total_marks and total_time):\n test = Test.objects.create(\n subject_id=Subject.objects.get(pk=subject_id), test_type=test_type, test_id=test_id,\n test_name=test_name,\n test_total_questions=test_total_questions, test_totalmarks=total_marks, test_totaltimes=total_time,\n approver=\"Null\",\n datetime=datetime.datetime.now()\n )\n test.save()\n registered = True\n return JsonResponse({'status': 2})\n else:\n print(\"No Data\")\n return JsonResponse({'status': 3})\n\n else:\n return HttpResponse(\"Problem\")\n\n return render(request,'exam/onlineExam.html',{'registered':registered})\n\n@login_required\ndef testAvailableCheck(request):\n if (request.method == \"GET\"):\n test_id = request.GET.get('test_id')\n testAvailableList=Test.objects.values('test_id','test_name','test_totalmarks','test_type','test_total_questions','subject_id', 'test_totaltimes').filter(test_id=test_id)\n if(testAvailableList):\n return JsonResponse({'results': list(testAvailableList)})\n else:\n return JsonResponse({'results': '2'})\n else:\n return HttpResponse(\"Problem\")\n\n@login_required\ndef testNameCheck(request):\n if (request.method == \"GET\"):\n test_id = request.GET.get('test_id')\n testName = request.GET.get('testName')\n isTestAvailable=Test.objects.filter(test_name=testName).exclude(test_id=test_id)\n if(isTestAvailable):\n return JsonResponse(\"This name has already been used!\", safe=False)\n else:\n return JsonResponse(\"true\", safe=False)\n else:\n return HttpResponse(\"Problem\")\n", "repo_name": "nafi-pantha/SupportInCareer", "sub_path": "online_exam/exam/views/TestView.py", "file_name": "TestView.py", "file_ext": "py", "file_size_in_byte": 3311, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "exam.models.Subject.objects.values", "line_number": 15, "usage_type": "call"}, {"api_name": "exam.models.Subject.objects", "line_number": 15, "usage_type": "attribute"}, {"api_name": "exam.models.Subject", "line_number": 15, "usage_type": "name"}, {"api_name": "exam.models.SCFU_TotalTime.objects.values", "line_number": 16, "usage_type": "call"}, {"api_name": "exam.models.SCFU_TotalTime.objects", "line_number": 16, "usage_type": "attribute"}, {"api_name": "exam.models.SCFU_TotalTime", "line_number": 16, "usage_type": "name"}, {"api_name": "django.http.JsonResponse", "line_number": 17, "usage_type": "call"}, {"api_name": "django.http.HttpResponse", "line_number": 19, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 12, "usage_type": "call"}, {"api_name": "exam.models.Test.objects.filter", "line_number": 35, "usage_type": "call"}, {"api_name": "exam.models.Test.objects", "line_number": 35, "usage_type": "attribute"}, {"api_name": "exam.models.Test", "line_number": 35, "usage_type": "name"}, {"api_name": "django.http.JsonResponse", "line_number": 36, "usage_type": "call"}, {"api_name": "exam.models.Test.objects.create", "line_number": 38, "usage_type": "call"}, {"api_name": "exam.models.Test.objects", "line_number": 38, "usage_type": "attribute"}, {"api_name": "exam.models.Test", "line_number": 38, "usage_type": "name"}, {"api_name": "exam.models.Subject.objects.get", "line_number": 39, "usage_type": "call"}, {"api_name": "exam.models.Subject.objects", "line_number": 39, "usage_type": "attribute"}, {"api_name": "exam.models.Subject", "line_number": 39, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 43, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 43, "usage_type": "attribute"}, {"api_name": "django.http.JsonResponse", "line_number": 47, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 50, "usage_type": "call"}, {"api_name": "django.http.HttpResponse", "line_number": 53, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 55, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 21, "usage_type": "name"}, {"api_name": "exam.models.Test.objects.values", "line_number": 61, "usage_type": "call"}, {"api_name": "exam.models.Test.objects", "line_number": 61, "usage_type": "attribute"}, {"api_name": "exam.models.Test", "line_number": 61, "usage_type": "name"}, {"api_name": "django.http.JsonResponse", "line_number": 63, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 65, "usage_type": "call"}, {"api_name": "django.http.HttpResponse", "line_number": 67, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 57, "usage_type": "name"}, {"api_name": "exam.models.Test.objects.filter", "line_number": 74, "usage_type": "call"}, {"api_name": "exam.models.Test.objects", "line_number": 74, "usage_type": "attribute"}, {"api_name": "exam.models.Test", "line_number": 74, "usage_type": "name"}, {"api_name": "django.http.JsonResponse", "line_number": 76, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 78, "usage_type": "call"}, {"api_name": "django.http.HttpResponse", "line_number": 80, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 69, "usage_type": "name"}]} +{"seq_id": "32731318419", "text": "from sklearn import datasets, model_selection\nfrom sklearn.tree import DecisionTreeClassifier\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n\ndef load_data():\n iris = datasets.load_iris() # scikit-learn 自带的 iris 数据集\n X_train = iris.data\n y_train = iris.target\n return model_selection.train_test_split(X_train, y_train, test_size=0.25, random_state=0, stratify=y_train)\n\n\nmaxdepth = 40\n\nX_train, X_test, y_train, y_test = load_data()\ndepths = np.arange(1, maxdepth)\ntraining_scores = []\ntesting_scores = []\nfor depth in depths:\n clf = DecisionTreeClassifier(max_depth=depth)\n clf.fit(X_train, y_train)\n training_scores.append(clf.score(X_train, y_train))\n testing_scores.append(clf.score(X_test, y_test))\n\nfig = plt.figure()\nax = fig.add_subplot(1, 1, 1)\nax.plot(depths, training_scores, label=\"traing score\", marker='o')\nax.plot(depths, testing_scores, label=\"testing score\", marker='*')\nax.set_xlabel(\"maxdepth\")\nax.set_ylabel(\"score\")\nax.set_title(\"Decision Tree Classification\")\nax.legend(framealpha=0.5, loc='best')\nplt.show()\n", "repo_name": "xzhan99/machine_learning_project", "sub_path": "assignment2/tree_sample.py", "file_name": "tree_sample.py", "file_ext": "py", "file_size_in_byte": 1073, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "37", "api": [{"api_name": "sklearn.datasets.load_iris", "line_number": 8, "usage_type": "call"}, {"api_name": "sklearn.datasets", "line_number": 8, "usage_type": "name"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 11, "usage_type": "call"}, {"api_name": "sklearn.model_selection", "line_number": 11, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 17, "usage_type": "call"}, {"api_name": "sklearn.tree.DecisionTreeClassifier", "line_number": 21, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 26, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 26, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 34, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 34, "usage_type": "name"}]} +{"seq_id": "766155927", "text": "\"\"\"\n\"\"\"\nimport typing\nfrom collections import OrderedDict\n\nimport h5py\nimport numpy as np\n\nfrom ..constants import (\n BURSTSHAPE_PNAMES,\n FBULGE_PNAMES,\n MAH_PNAMES,\n MS_PNAMES,\n Q_PNAMES,\n)\n\nALL_DIFFSKY_PNAMES = []\nALL_DIFFSKY_PNAMES.extend(MAH_PNAMES)\nALL_DIFFSKY_PNAMES.extend(MS_PNAMES)\nALL_DIFFSKY_PNAMES.extend(Q_PNAMES)\nALL_DIFFSKY_PNAMES.extend(FBULGE_PNAMES)\nALL_DIFFSKY_PNAMES.extend(BURSTSHAPE_PNAMES)\nALL_DIFFSKY_PNAMES.extend([\"fknot\", \"fburst\", \"redshift\"])\n\n\nclass DiffskyParams(typing.NamedTuple):\n \"\"\"NamedTuple storing parameters of a Diffsky galaxy\"\"\"\n\n mah_params: np.float32\n ms_params: np.float32\n q_params: np.float32\n fburst: np.float32\n burstshape_params: np.float32\n fbulge_params: np.float32\n fknot: np.float32\n\n\ndef load_healpixel(fn, patlist=None):\n \"\"\"Load a Diffsky healpixel from hdf5, concatenating data stored by snapshot\n\n Parameters\n ----------\n fn : string\n Path to the hdf5 file storing the healpixel\n\n patlist : list of strings, optional\n List of column name patterns used to retrieve extra columns from the healpixel.\n For example, to select all columns related to LSST photometry,\n setting patlist=('LSST', ) will retrieve all columns in which\n 'LSST' appears somewhere in the column name.\n Default behavior is to return all available columns.\n Note that all the Diffsky model parameters are always returned.\n\n Returns\n -------\n data : OrderedDict\n\n metadata : OrderedDict\n\n Notes\n -----\n This standalone function can be used to load diffsky data from disk.\n Each healpixel of lsstdesc-diffsky data is stored on disk such that galaxies\n at different simulation snapshots are partitioned into separate hdf5 datasets.\n The load_diffsky_healpixel function concatenates all these separate datasets\n into flat ndarrays.\n\n DESC members working at NERSC can instead use the GCR:\n https://github.com/yymao/generic-catalog-reader\n\n For example usage of the GCR with diffsky,\n see lsstdesc-diffsky/notebooks/demo_load_catalog.ipynb\n\n \"\"\"\n data_collection, metadata = collect_healpixel_data(fn, patlist=patlist)\n data = _flatten_data_collection(data_collection)\n return data, metadata\n\n\ndef _get_extra_colnames(all_keys, patlist):\n if patlist is None:\n return all_keys\n else:\n extra_colnames = []\n for pat in patlist:\n extra_colnames.extend([key for key in all_keys if pat in key])\n return extra_colnames\n\n\ndef load_diffsky_params(cat):\n \"\"\"Load the collection of parameters that determine the SEDs of Diffsky galaxies.\n Results are returned as ndarrays of the shapes expected by the convenience functions\n used to compute SEDs and photometry of diffsky galaxies.\n\n Parameters\n ----------\n fn : string\n Path to the hdf5 file storing the healpixel\n\n Returns\n -------\n DiffskyParams : NamedTuple with the following entries\n\n mah_params : ndarray, shape (ngals, 4)\n Diffmah params specifying the mass assembly of the dark matter halo\n diffmah_params = (logm0, logtc, early_index, late_index)\n\n ms_params : ndarray, shape (ngals, 5)\n Diffstar params for the star-formation effiency\n and gas consumption timescale\n ms_params = (lgmcrit, lgy_at_mcrit, indx_lo, indx_hi, tau_dep)\n\n q_params : ndarray, shape (ngals, 4)\n Diffstar quenching params, (lg_qt, qlglgdt, lg_drop, lg_rejuv)\n\n fburst : ndarray, shape (ngals, )\n Fraction of stellar mass formed in a recent burst\n\n burstshape_params : ndarray, shape (ngals, 2)\n Parameters controlling the distribution of stellar ages in the recent burst\n\n fbulge_params : ndarray, shape (ngals, 2)\n Parameters controlling the disk/bulge decomposition\n\n fknot : ndarray, shape (ngals, )\n Fraction of disk mass located in bursty star-forming knots\n\n \"\"\"\n mah_params = np.vstack([cat[key] for key in MAH_PNAMES]).T\n ms_params = np.vstack([cat[key] for key in MS_PNAMES]).T\n q_params = np.vstack([cat[key] for key in Q_PNAMES]).T\n fburst = np.array(cat[\"fburst\"])\n burstshape_params = np.vstack([cat[key] for key in BURSTSHAPE_PNAMES]).T\n fbulge_params = np.vstack([cat[key] for key in FBULGE_PNAMES]).T\n fknot = np.array(cat[\"fknot\"])\n return DiffskyParams(\n mah_params, ms_params, q_params, fburst, burstshape_params, fbulge_params, fknot\n )\n\n\ndef collect_healpixel_data(fn, patlist):\n with h5py.File(fn, \"r\") as hdf:\n metadataset = hdf[\"metaData\"]\n metadata = OrderedDict()\n for key in metadataset.keys():\n metadata[key] = metadataset[key][...]\n\n _snaplist = [key for key in hdf.keys() if key != \"metaData\"]\n snapnums = sorted([int(key) for key in _snaplist])[::-1]\n snapkey0 = str(snapnums[0])\n dataset = hdf[snapkey0]\n all_keys = list(dataset.keys())\n extra_colnames = _get_extra_colnames(all_keys, patlist)\n desired_dataset_colnames = ALL_DIFFSKY_PNAMES.copy()\n desired_dataset_colnames.extend(extra_colnames)\n\n data_collection = OrderedDict()\n for snapnum in snapnums:\n snapkey = str(snapnum)\n dataset = hdf[snapkey]\n d = OrderedDict()\n\n for key in desired_dataset_colnames:\n d[key] = dataset[key][...]\n\n data_collection[snapkey] = d\n\n return data_collection, metadata\n\n\ndef _flatten_data_collection(data_collection):\n snapkeys = list(data_collection.keys())\n data_colnames = list(data_collection[snapkeys[0]].keys())\n exkey = data_colnames[0]\n ngals = [c[exkey].shape[0] for c in data_collection.values()]\n ngal_tot = sum(ngals)\n\n scalar_dtypes = [data_collection[snapkeys[0]][key].dtype for key in data_colnames]\n scalar_ndarrays = [np.zeros(ngal_tot, dtype=dt) for dt in scalar_dtypes]\n snapshot_ndarray = np.zeros(ngal_tot, dtype=np.int64)\n\n ifirst = 0\n for i, (snapkey, dataset) in enumerate(data_collection.items()):\n ngals_snap = ngals[i]\n ilast = ifirst + ngals_snap\n\n snapshot_ndarray[ifirst:ilast] = int(snapkey)\n for ndarray, key in zip(scalar_ndarrays, data_colnames):\n ndarray[ifirst:ilast] = dataset[key]\n\n ifirst = ilast\n\n mockout = OrderedDict()\n mockout[\"snapnum\"] = snapshot_ndarray\n for ndarray, key in zip(scalar_ndarrays, data_colnames):\n mockout[key] = ndarray\n\n return mockout\n", "repo_name": "LSSTDESC/lsstdesc-diffsky", "sub_path": "lsstdesc_diffsky/io_utils/load_diffsky_healpixel.py", "file_name": "load_diffsky_healpixel.py", "file_ext": "py", "file_size_in_byte": 6575, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "37", "api": [{"api_name": "constants.MAH_PNAMES", "line_number": 18, "usage_type": "argument"}, {"api_name": "constants.MS_PNAMES", "line_number": 19, "usage_type": "argument"}, {"api_name": "constants.Q_PNAMES", "line_number": 20, "usage_type": "argument"}, {"api_name": "constants.FBULGE_PNAMES", "line_number": 21, "usage_type": "argument"}, {"api_name": "constants.BURSTSHAPE_PNAMES", "line_number": 22, "usage_type": "argument"}, {"api_name": "typing.NamedTuple", "line_number": 26, "usage_type": "attribute"}, {"api_name": "numpy.float32", "line_number": 29, "usage_type": "attribute"}, {"api_name": "numpy.float32", "line_number": 30, "usage_type": "attribute"}, {"api_name": "numpy.float32", "line_number": 31, "usage_type": "attribute"}, {"api_name": "numpy.float32", "line_number": 32, "usage_type": "attribute"}, {"api_name": "numpy.float32", "line_number": 33, "usage_type": "attribute"}, {"api_name": "numpy.float32", "line_number": 34, "usage_type": "attribute"}, {"api_name": "numpy.float32", "line_number": 35, "usage_type": "attribute"}, {"api_name": "numpy.vstack", "line_number": 129, "usage_type": "call"}, {"api_name": "constants.MAH_PNAMES", "line_number": 129, "usage_type": "name"}, {"api_name": "numpy.vstack", "line_number": 130, "usage_type": "call"}, {"api_name": "constants.MS_PNAMES", "line_number": 130, "usage_type": "name"}, {"api_name": "numpy.vstack", "line_number": 131, "usage_type": "call"}, {"api_name": "constants.Q_PNAMES", "line_number": 131, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 132, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 133, "usage_type": "call"}, {"api_name": "constants.BURSTSHAPE_PNAMES", "line_number": 133, "usage_type": "name"}, {"api_name": "numpy.vstack", "line_number": 134, "usage_type": "call"}, {"api_name": "constants.FBULGE_PNAMES", "line_number": 134, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 135, "usage_type": "call"}, {"api_name": "h5py.File", "line_number": 142, "usage_type": "call"}, {"api_name": "collections.OrderedDict", "line_number": 144, "usage_type": "call"}, {"api_name": "collections.OrderedDict", "line_number": 157, "usage_type": "call"}, {"api_name": "collections.OrderedDict", "line_number": 161, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 179, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 180, "usage_type": "call"}, {"api_name": "numpy.int64", "line_number": 180, "usage_type": "attribute"}, {"api_name": "collections.OrderedDict", "line_number": 193, "usage_type": "call"}]} +{"seq_id": "10648607916", "text": "import json\n\nfrom PySide2 import QtWidgets, QtCore, QtGui\nfrom PySide2.QtCore import Qt, QSize, QEvent\nfrom PySide2.QtGui import QFont, QPixmap, QIcon\nfrom PySide2.QtWidgets import QListWidgetItem, QLabel, QDesktopWidget\n\nfrom config.setting import Setting\nfrom interface.ui_book_info import Ui_BookInfo\nfrom qt_owner import QtOwner\nfrom server import req, Log, config\nfrom util.status import Status\nfrom task.qt_task import QtTaskBase\nfrom tools.book import BookMgr\nfrom tools.str import Str\n\n\nclass BookInfoView(QtWidgets.QWidget, Ui_BookInfo, QtTaskBase):\n def __init__(self):\n super(self.__class__, self).__init__()\n Ui_BookInfo.__init__(self)\n QtTaskBase.__init__(self)\n self.setupUi(self)\n self.bookId = \"\"\n self.token = \"\"\n self.site = \"\"\n self.url = \"\"\n self.path = \"\"\n self.bookName = \"\"\n self.lastEpsId = -1\n self.pictureData = None\n\n self.picture.installEventFilter(self)\n self.title.setWordWrap(True)\n self.title.setTextInteractionFlags(Qt.TextSelectableByMouse)\n # self.epsListWidget.setFlow(self.epsListWidget.LeftToRight)\n self.epsListWidget.setWrapping(True)\n self.epsListWidget.wheelMode = 1\n self.epsListWidget.setViewMode(self.epsListWidget.ViewMode.ListMode)\n self.epsListWidget.setFlow(self.epsListWidget.Flow.TopToBottom)\n self.epsListWidget.setFrameShape(self.epsListWidget.NoFrame)\n self.epsListWidget.setResizeMode(self.epsListWidget.Adjust)\n\n self.commentButton.clicked.connect(self.OpenComment)\n\n data = str(QtOwner().GetFileData(\":/json/translate.json\"), encoding=\"utf-8\")\n self.tags = json.loads(data)\n\n self.epsListWidget.itemClicked.connect(self.ClickTagsItem)\n self.nameToTag = {}\n\n def Clear(self):\n self.ClearTask()\n self.epsListWidget.clear()\n self.nameToTag.clear()\n\n def SwitchCurrent(self, **kwargs):\n bookId = kwargs.get(\"bookId\")\n token = kwargs.get(\"token\", \"\")\n site = kwargs.get(\"site\", \"\")\n if not bookId:\n return\n\n self.OpenBook(bookId, token, site)\n\n def OpenBook(self, bookId, token=\"\", site=\"\"):\n self.bookId = bookId\n self.site = site\n if not self.site:\n self.site = config.CurSite\n self.Clear()\n QtOwner().ShowLoading()\n QtOwner().SetDirty()\n self.AddHttpTask(req.BookInfoReq(bookId, token=token, site=self.site), self.OpenBookBack)\n\n def OpenBookBack(self, data):\n QtOwner().CloseLoading()\n st = data.get(\"st\")\n if st == Status.Ok:\n maxPages = data.get(\"maxPages\")\n # self.listWidget.UpdatePage(1, maxPages)\n # self.listWidget.UpdateState()\n self.epsListWidget.clear()\n info = BookMgr().GetBookBySite(self.bookId, self.site)\n self.title.setText(info.baseInfo.title)\n self.bookName = info.baseInfo.title\n self.token = info.baseInfo.token\n self.picture.setText(Str.GetStr(Str.LoadingPicture))\n self.url = info.baseInfo.imgUrl\n self.path = \"\"\n self.idLabel.setText(self.bookId)\n self.updateTick.setText(info.pageInfo.posted)\n self.favoriteLabel.setText(str(info.pageInfo.favorites))\n self.pageLabel.setText(str(info.pageInfo.pages))\n self.lanLabel.setText(info.pageInfo.language)\n self.categoryList.AddItem(info.baseInfo.category)\n self.commentButton.setText(\"({})\".format(len(info.pageInfo.comment)))\n for tag in info.baseInfo.tags:\n tagData = tag.split(\":\")\n if len(tagData) >= 2:\n tagName = tagData[0]\n if not tagName:\n tag = \"misc\" + tag\n\n label = QLabel(tag)\n label.setAlignment(Qt.AlignCenter)\n label.setStyleSheet(\"color: rgb(196, 95, 125);\")\n font = QFont()\n font.setPointSize(12)\n font.setBold(True)\n label.setFont(font)\n\n item = QListWidgetItem(self.epsListWidget)\n item.setSizeHint(label.sizeHint() + QSize(20, 20))\n\n tagData = tag.split(\":\")\n if Setting.Language.autoValue != 3:\n if len(tagData) >= 2:\n tagName = tagData[0]\n if tagName in self.tags:\n if tagData[1] in self.tags.get(tagName, {}).get(\"data\"):\n tagInfo = self.tags.get(tagName, {}).get(\"data\", {}).get(tagData[1], {})\n label.setText(self.tags.get(tagName, {}).get(\"name\", \"\") + \":\" + tagInfo.get(\"dest\", \"\"))\n item.setToolTip(tagInfo.get('description', \"\"))\n\n # item.setToolTip(epsInfo.title)\n self.epsListWidget.setItemWidget(item, label)\n self.nameToTag[label.text()] = tag\n\n if config.IsLoadingPicture:\n self.AddDownloadTask(self.url, \"{}/{}_{}_cover\".format(config.CurSite, self.bookId, self.token), completeCallBack=self.UpdatePicture)\n\n else:\n msg = data.get(\"msg\")\n if msg:\n QtOwner().ShowError(msg)\n else:\n QtOwner().ShowError(Str.GetStr(st))\n return\n\n def UpdatePicture(self, data, status):\n if status == Status.Ok:\n self.pictureData = data\n pic = QtGui.QPixmap()\n pic.loadFromData(data)\n pic.scaled(self.picture.size(), QtCore.Qt.KeepAspectRatio)\n self.picture.setPixmap(pic)\n # self.picture.setScaledContents(True)\n self.update()\n else:\n self.picture.setText(Str.GetStr(Str.LoadingFail))\n return\n\n def LoadNextPage(self):\n return\n\n def StartRead(self):\n QtOwner().OpenReadView(self.bookId, self.title.text(), -1)\n return\n\n def LoadHistory(self):\n return\n\n def ClickTagsItem(self, item):\n widget = self.epsListWidget.itemWidget(item)\n text = self.nameToTag.get(widget.text())\n data = text.split(\"|\")\n if data[0]:\n text = data[0]\n text = text.strip(\"|\").strip(\"$\")\n text2 = text.split(\":\")\n if len(text2) >= 2:\n newText = text2[0] + \":\\\"\" + text2[1] + \"$\\\"\"\n else:\n newText = text+\"$\"\n\n QtOwner().OpenSearch(newText)\n return\n\n def OpenComment(self):\n QtOwner().OpenComment(self.bookId, self.site)\n return\n\n def eventFilter(self, obj, event):\n if event.type() == QEvent.MouseButtonPress:\n if event.button() == Qt.LeftButton:\n if self.pictureData:\n QtOwner().OpenWaifu2xTool(self.pictureData)\n return True\n else:\n return False\n else:\n return super(self.__class__, self).eventFilter(obj, event)\n\n def AddFavorite(self):\n if not config.CurLoginName:\n QtOwner().ShowError(Str.GetStr(Str.NotLogin))\n return\n QtOwner().OpenFavoriteInfo(self.bookId, self.bookName)\n return\n\n def AddDownload(self):\n bookId = self.bookId\n info = BookMgr().GetBookBySite(bookId, self.site)\n QtOwner().downloadView.AddDownload(bookId, info.baseInfo.token, config.CurSite)\n QtOwner().ShowMsg(Str.GetStr(Str.AddDownload))", "repo_name": "a023e/ehentai-qt", "sub_path": "src/view/info/book_info_view.py", "file_name": "book_info_view.py", "file_ext": "py", "file_size_in_byte": 7537, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "github-code", "pt": "37", "api": [{"api_name": "PySide2.QtWidgets.QWidget", "line_number": 18, "usage_type": "attribute"}, {"api_name": "PySide2.QtWidgets", "line_number": 18, "usage_type": "name"}, {"api_name": "interface.ui_book_info.Ui_BookInfo", "line_number": 18, "usage_type": "name"}, {"api_name": "task.qt_task.QtTaskBase", "line_number": 18, "usage_type": "name"}, {"api_name": "interface.ui_book_info.Ui_BookInfo.__init__", "line_number": 21, "usage_type": "call"}, {"api_name": "interface.ui_book_info.Ui_BookInfo", "line_number": 21, "usage_type": "name"}, {"api_name": "task.qt_task.QtTaskBase.__init__", "line_number": 22, "usage_type": "call"}, {"api_name": "task.qt_task.QtTaskBase", "line_number": 22, "usage_type": "name"}, {"api_name": "PySide2.QtCore.Qt.TextSelectableByMouse", "line_number": 35, "usage_type": "attribute"}, {"api_name": "PySide2.QtCore.Qt", "line_number": 35, "usage_type": "name"}, {"api_name": "qt_owner.QtOwner", "line_number": 46, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 47, "usage_type": "call"}, {"api_name": "server.config.CurSite", "line_number": 70, "usage_type": "attribute"}, {"api_name": "server.config", "line_number": 70, "usage_type": "name"}, {"api_name": "qt_owner.QtOwner", "line_number": 72, "usage_type": "call"}, {"api_name": "qt_owner.QtOwner", "line_number": 73, "usage_type": "call"}, {"api_name": "server.req.BookInfoReq", "line_number": 74, "usage_type": "call"}, {"api_name": "server.req", "line_number": 74, "usage_type": "name"}, {"api_name": "qt_owner.QtOwner", "line_number": 77, "usage_type": "call"}, {"api_name": "util.status.Status.Ok", "line_number": 79, "usage_type": "attribute"}, {"api_name": "util.status.Status", "line_number": 79, "usage_type": "name"}, {"api_name": "tools.book.BookMgr", "line_number": 84, "usage_type": "call"}, {"api_name": "tools.str.Str.GetStr", "line_number": 88, "usage_type": "call"}, {"api_name": "tools.str.Str", "line_number": 88, "usage_type": "name"}, {"api_name": "tools.str.Str.LoadingPicture", "line_number": 88, "usage_type": "attribute"}, {"api_name": "PySide2.QtWidgets.QLabel", "line_number": 105, "usage_type": "call"}, {"api_name": "PySide2.QtCore.Qt.AlignCenter", "line_number": 106, "usage_type": "attribute"}, {"api_name": "PySide2.QtCore.Qt", "line_number": 106, "usage_type": "name"}, {"api_name": "PySide2.QtGui.QFont", "line_number": 108, "usage_type": "call"}, {"api_name": "PySide2.QtWidgets.QListWidgetItem", "line_number": 113, "usage_type": "call"}, {"api_name": "PySide2.QtCore.QSize", "line_number": 114, "usage_type": "call"}, {"api_name": "config.setting.Setting.Language", "line_number": 117, "usage_type": "attribute"}, {"api_name": "config.setting.Setting", "line_number": 117, "usage_type": "name"}, {"api_name": "server.config.IsLoadingPicture", "line_number": 130, "usage_type": "attribute"}, {"api_name": "server.config", "line_number": 130, "usage_type": "name"}, {"api_name": "server.config.CurSite", "line_number": 131, "usage_type": "attribute"}, {"api_name": "server.config", "line_number": 131, "usage_type": "name"}, {"api_name": "qt_owner.QtOwner", "line_number": 136, "usage_type": "call"}, {"api_name": "qt_owner.QtOwner", "line_number": 138, "usage_type": "call"}, {"api_name": "tools.str.Str.GetStr", "line_number": 138, "usage_type": "call"}, {"api_name": "tools.str.Str", "line_number": 138, "usage_type": "name"}, {"api_name": "util.status.Status.Ok", "line_number": 142, "usage_type": "attribute"}, {"api_name": "util.status.Status", "line_number": 142, "usage_type": "name"}, {"api_name": "PySide2.QtGui.QPixmap", "line_number": 144, "usage_type": "call"}, {"api_name": "PySide2.QtGui", "line_number": 144, "usage_type": "name"}, {"api_name": "PySide2.QtCore.Qt", "line_number": 146, "usage_type": "attribute"}, {"api_name": "PySide2.QtCore", "line_number": 146, "usage_type": "name"}, {"api_name": "tools.str.Str.GetStr", "line_number": 151, "usage_type": "call"}, {"api_name": "tools.str.Str", "line_number": 151, "usage_type": "name"}, {"api_name": "tools.str.Str.LoadingFail", "line_number": 151, "usage_type": "attribute"}, {"api_name": "qt_owner.QtOwner", "line_number": 158, "usage_type": "call"}, {"api_name": "qt_owner.QtOwner", "line_number": 177, "usage_type": "call"}, {"api_name": "qt_owner.QtOwner", "line_number": 181, "usage_type": "call"}, {"api_name": "PySide2.QtCore.QEvent.MouseButtonPress", "line_number": 185, "usage_type": "attribute"}, {"api_name": "PySide2.QtCore.QEvent", "line_number": 185, "usage_type": "name"}, {"api_name": "PySide2.QtCore.Qt.LeftButton", "line_number": 186, "usage_type": "attribute"}, {"api_name": "PySide2.QtCore.Qt", "line_number": 186, "usage_type": "name"}, {"api_name": "qt_owner.QtOwner", "line_number": 188, "usage_type": "call"}, {"api_name": "server.config.CurLoginName", "line_number": 196, "usage_type": "attribute"}, {"api_name": "server.config", "line_number": 196, "usage_type": "name"}, {"api_name": "qt_owner.QtOwner", "line_number": 197, "usage_type": "call"}, {"api_name": "tools.str.Str.GetStr", "line_number": 197, "usage_type": "call"}, {"api_name": "tools.str.Str", "line_number": 197, "usage_type": "name"}, {"api_name": "tools.str.Str.NotLogin", "line_number": 197, "usage_type": "attribute"}, {"api_name": "qt_owner.QtOwner", "line_number": 199, "usage_type": "call"}, {"api_name": "tools.book.BookMgr", "line_number": 204, "usage_type": "call"}, {"api_name": "qt_owner.QtOwner", "line_number": 205, "usage_type": "call"}, {"api_name": "server.config.CurSite", "line_number": 205, "usage_type": "attribute"}, {"api_name": "server.config", "line_number": 205, "usage_type": "name"}, {"api_name": "qt_owner.QtOwner", "line_number": 206, "usage_type": "call"}, {"api_name": "tools.str.Str.GetStr", "line_number": 206, "usage_type": "call"}, {"api_name": "tools.str.Str", "line_number": 206, "usage_type": "name"}, {"api_name": "tools.str.Str.AddDownload", "line_number": 206, "usage_type": "attribute"}]} +{"seq_id": "5174089545", "text": "'''Example of how to load dicom files.\n\nRequirements\n------------\n- python3 (I haven't tested python2.7)\n- numpy\n- matplotlib\n- requests\n- pydicom\n- nibabel\n- Internet connection\n\nNotes\n-----\nThis example uses two methods from two different libraries:\n- pydicom\n- nibabel\n\nThese are the easiest ways I know of, could be better ways. Docs for\nboth of these libraries are easy use, go take a look!\n\nI will mention the warning on nibabel's dicom reader utilities --\nthey say they are still \"highly experimental\" -- so probably stick\nwith pydicom unless you need nifti for any pre-/postprocessing.\n'''\n\nimport shutil\nimport os\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport requests\nimport pydicom\nfrom nibabel.nicom.dicomwrappers import wrapper_from_file\n\nif __name__ == '__main__':\n\n ROOT_DIR = os.path.dirname(os.path.abspath(__file__))\n TEST_DATA_HOST = 'https://birly.groups.et.byu.net/'\n\n # First grab the dicom. It could be any dicom, but for this example we'll\n # grab on a server somewhere deep in the heart of Utah...\n web_path = 'mr_utils/test_data/examples/load_data/'\n file = 'IM-0013-0148.dcm'\n filename = '%s/%s' % (ROOT_DIR, file)\n print('Looking for %s...' % filename)\n if not os.path.isfile(filename):\n # If the dicom file doesn't exist locally, then download it\n print('Couldn\\'t find it, looking on the server!')\n url = '%s/%s/%s' % (TEST_DATA_HOST, web_path, file)\n try:\n with requests.get(url, stream=True) as r:\n print('Starting download...')\n with open(filename, 'wb') as f:\n shutil.copyfileobj(r.raw, f)\n print('Done downloading file!')\n except OSError:\n raise OSError('Whoops! Network problems, try again!')\n print('%s exists!' % filename)\n\n # Now we have a dicom we can work with. Let's try loading it with\n # pydicom, the obvious choice\n pydicom_dataset = pydicom.dcmread(filename)\n im0 = pydicom_dataset.pixel_array\n\n # prove it's numpy array\n assert isinstance(im0, np.ndarray), 'I should be a numpy array!'\n\n # Let's visually inspect it -- looks good!\n plt.imshow(im0, cmap='gray')\n plt.title('Look at me! I\\'m a numpy array!')\n plt.show()\n\n # Now let's try it a little different -- use nibabel!\n nibabel_dataset = wrapper_from_file(filename)\n im1 = nibabel_dataset.get_pixel_array()\n\n # Prove it's a numpy array\n assert isinstance(im1, np.ndarray), 'I should be a numpy array!'\n\n # Again, take a look:\n plt.imshow(im1, cmap='gray')\n plt.title('Look at me! I came from a nifti library!')\n plt.show()\n\n # Prove that we get the same data either way:\n assert np.all(im0 == im1), (\n 'I should have loaded the same data both times!')\n", "repo_name": "mckib2/mr_utils", "sub_path": "examples/load_data/load_dicom.py", "file_name": "load_dicom.py", "file_ext": "py", "file_size_in_byte": 2788, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 9, "dataset": "github-code", "pt": "37", "api": [{"api_name": "os.path.dirname", "line_number": 38, "usage_type": "call"}, {"api_name": "os.path", "line_number": 38, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 38, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 47, "usage_type": "call"}, {"api_name": "os.path", "line_number": 47, "usage_type": "attribute"}, {"api_name": "requests.get", "line_number": 52, "usage_type": "call"}, {"api_name": "shutil.copyfileobj", "line_number": 55, "usage_type": "call"}, {"api_name": "pydicom.dcmread", "line_number": 63, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 67, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 70, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 70, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 71, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 71, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 72, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 72, "usage_type": "name"}, {"api_name": "nibabel.nicom.dicomwrappers.wrapper_from_file", "line_number": 75, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 79, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 82, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 82, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 83, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 83, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 84, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 84, "usage_type": "name"}, {"api_name": "numpy.all", "line_number": 87, "usage_type": "call"}]} +{"seq_id": "23023837317", "text": "from datetime import datetime\nimport jdatetime\n\n\n# Taking Inputs from User\nyear, month, day = input(\"Please Enter your Birth date\\\nin format yyyy/mm/dd: \").split(\"/\")\nyear, month, day = int(year), int(month), int(day)\nprint(\"\\n************************************************\\n\")\n\n\n# Age in Seconds\norg = datetime(year, month, day)\nnow = datetime.now()\ndelta1 = (now - org).total_seconds()\nprint(f\"You are {delta1} seconds old\")\nprint(\"\\n************************************************\\n\")\n\n\n# Minutes and days remaining to next birthday\nif (month <= now.month) and (day > now.day):\n delta = datetime(now.year, org.month, org.day) - now\n d_days, d_mins = delta.days + 1, delta.total_seconds() / 60\nelse:\n delta = datetime(now.year + 1, org.month, org.day) - now\n d_days, d_mins = delta.days + 1, delta.total_seconds() / 60\nprint(f\"{d_days} days and {d_mins} minutes remaining to your next birthday.\")\nprint(\"\\n************************************************\\n\")\n\n\n# Converting birth date to Jalali format\njalali = jdatetime.date.fromgregorian(day=day, month=month, year=year)\nprint(f\"Your Birthdate in Jalali format is {str(jalali)}\")", "repo_name": "hosseinfaraji79/Daneshkar", "sub_path": "HW3/HW3_04.py", "file_name": "HW3_04.py", "file_ext": "py", "file_size_in_byte": 1148, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "datetime.datetime", "line_number": 13, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 14, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 14, "usage_type": "name"}, {"api_name": "datetime.datetime", "line_number": 22, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 25, "usage_type": "call"}, {"api_name": "jdatetime.date.fromgregorian", "line_number": 32, "usage_type": "call"}, {"api_name": "jdatetime.date", "line_number": 32, "usage_type": "attribute"}]} +{"seq_id": "17869057499", "text": "from django.db import models\n\n\n# Create your models here.\nclass Author(models.Model):\n \"\"\"\n Модель Автора\n \"\"\"\n first_name = models.CharField(\n max_length=100,\n verbose_name='Имя'\n )\n last_name = models.CharField(\n max_length=100,\n verbose_name='Фамилия'\n )\n birthday = models.DateField(\n null=True,\n blank=True,\n verbose_name='Дата рождения'\n )\n \n def __str__(self):\n return f'{self.last_name} {self.first_name} ({self.birthday})'\n\n class Meta:\n \"\"\"\n Мета-класс Автора\n \"\"\"\n verbose_name = 'автор'\n verbose_name_plural = 'авторы'\n db_table = 'authors'\n ordering = ['last_name', 'first_name', 'birthday']\n unique_together = ['first_name', 'last_name', 'birthday']\n", "repo_name": "VolodinAS/skillbox-python-django-practice", "sub_path": "13_IntroductionToDjangoRESTFramework/___project_library___/app_author/models.py", "file_name": "models.py", "file_ext": "py", "file_size_in_byte": 872, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "django.db.models.Model", "line_number": 5, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 5, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 9, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 9, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 13, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 13, "usage_type": "name"}, {"api_name": "django.db.models.DateField", "line_number": 17, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 17, "usage_type": "name"}]} +{"seq_id": "1691370663", "text": "#!/usr/bin/env python\nimport sys\n\nimport click\nfrom humanize import naturalsize\nfrom utz import process, singleton\n\n\ndef stderr(msg=''):\n sys.stderr.write(msg)\n sys.stderr.write('\\n')\n\n\n@click.command()\n@click.option('-h', '--human-readable', is_flag=True)\n@click.argument('filename')\ndef main(human_readable, filename):\n cmd = [\n 'ffprobe',\n '-v', 'quiet',\n '-select_streams', 'v:0',\n *(['-sexagesimal'] if human_readable else []),\n '-show_entries', 'format=filename,bit_rate,duration',\n '-show_entries', 'stream=width,height',\n '-of', 'json',\n '-i', filename,\n ]\n o = process.json(*cmd, log=stderr)\n stream = singleton(o['streams'], dedupe=False)\n format = o['format']\n width = stream['width']\n height = stream['height']\n duration = format['duration']\n bit_rate = format['bit_rate']\n if human_readable:\n bit_rate = naturalsize(bit_rate)\n duration = f'{duration}s'\n else:\n bit_rate = f'{bit_rate}b'\n print(f'{filename}: {width}x{height}, duration: {duration}, {bit_rate}ps')\n\n\nif __name__ == '__main__':\n main()\n", "repo_name": "ryan-williams/av-helpers", "sub_path": "video-stats.py", "file_name": "video-stats.py", "file_ext": "py", "file_size_in_byte": 1135, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "sys.stderr.write", "line_number": 10, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 10, "usage_type": "attribute"}, {"api_name": "sys.stderr.write", "line_number": 11, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 11, "usage_type": "attribute"}, {"api_name": "utz.process.json", "line_number": 28, "usage_type": "call"}, {"api_name": "utz.process", "line_number": 28, "usage_type": "name"}, {"api_name": "utz.singleton", "line_number": 29, "usage_type": "call"}, {"api_name": "humanize.naturalsize", "line_number": 36, "usage_type": "call"}, {"api_name": "click.command", "line_number": 14, "usage_type": "call"}, {"api_name": "click.option", "line_number": 15, "usage_type": "call"}, {"api_name": "click.argument", "line_number": 16, "usage_type": "call"}]} +{"seq_id": "31045196828", "text": "import random\nimport numpy as np\nimport altair as alt\nimport pandas as pd\nimport matplotlib.colors as mcolors\nfrom pyxem.signals.electron_diffraction2d import ElectronDiffraction2D\nfrom .clustering import PixelSegmenter\n\nimport os\nfrom typing import List, Dict\nimport hyperspy.api as hs\nfrom matplotlib import pyplot as plt\nimport matplotlib as mpl\nfrom matplotlib import cm\nimport seaborn as sns\nimport plotly.graph_objects as go\nimport ipywidgets as widgets\nfrom ipywidgets import Layout\nfrom IPython.display import display\n\n\ndef _plot_latent(dataset: ElectronDiffraction2D, latent:np.ndarray, ratio_to_be_shown:float=1.0):\n\n dp_size = dataset.data.shape[:2]\n x_id, y_id = np.meshgrid(range(dp_size[0]), range(dp_size[1]))\n x_id = x_id.ravel().reshape(-1, 1)\n y_id = y_id.ravel().reshape(-1, 1)\n z_id = dataset.data.reshape(dp_size[0],dp_size[1],-1).sum(axis=2).reshape(-1, 1)\n z_id = z_id / z_id.max()\n\n combined = np.concatenate(\n [\n x_id,\n y_id,\n z_id,\n latent,\n # dataset.data.reshape(-1, dataset.data.shape[-1]).round(2)\n ],\n axis=1,\n )\n\n if ratio_to_be_shown==1:\n sampled_combined = combined\n else:\n sampled_combined = random.choices(\n combined, k=int(latent.shape[0] // (ratio_to_be_shown ** -1))\n )\n sampled_combined = np.array(sampled_combined)\n\n source = pd.DataFrame(\n sampled_combined,\n columns=[\"x_id\", \"y_id\", \"z_id\", \"x\", \"y\"],\n index=pd.RangeIndex(0, sampled_combined.shape[0], name=\"pixel\"),\n ) \n\n # Plotting using Altair\n alt.data_transformers.disable_max_rows()\n\n # Brush\n brush = alt.selection(type=\"interval\")\n\n interaction = alt.selection(\n type=\"interval\",\n bind=\"scales\",\n on=\"[mousedown[event.shiftKey], mouseup] > mousemove\",\n translate=\"[mousedown[event.shiftKey], mouseup] > mousemove!\",\n zoom=\"wheel![event.shiftKey]\",\n )\n\n # Points\n points = (\n alt.Chart(source)\n .mark_circle(size=4.0,color='red')\n .encode(\n x=\"x:Q\",\n y=\"y:Q\", # use min extent to stabilize axis title placement\n # color=alt.Color( scale=alt.Scale(scheme=\"black\")),\n # color=alt.Color(\n # \"Cluster_id:N\", scale=alt.Scale(domain=domain, range=range_)\n # ),\n color=alt.condition(brush, alt.value('red'), alt.value('grey')),\n opacity=alt.condition(brush, alt.value(0.5), alt.value(0.5)),\n tooltip=[\n alt.Tooltip(\"x:Q\", format=\",.2f\"),\n alt.Tooltip(\"y:Q\", format=\",.2f\"),\n ],\n )\n .properties(width=450, height=450)\n .properties(title=alt.TitleParams(text=\"Latent space\"))\n .add_selection(\n brush,\n interaction\n )\n )\n\n # Heatmap\n intensity_df = pd.DataFrame(\n {\"x_intensity\": x_id.ravel(), \"y_intensity\": y_id.ravel(), \"z_intensity\": z_id.ravel()}\n )\n intensity = (\n alt.Chart(intensity_df)\n .mark_circle(size=6)\n .encode(\n x=alt.X(\"x_intensity:O\", axis=None),\n y=alt.Y(\"y_intensity:O\", axis=None),\n color=alt.Color(\n \"z_intensity:Q\", scale=alt.Scale(scheme=\"greys\", domain=[1.0, 0.0])\n ),\n )\n .properties(width=dp_size[0]*2, height=dp_size[1]*2)\n )\n heatmap = (\n alt.Chart(source)\n .mark_circle(size=5.0,color='red')\n .encode(\n x=alt.X(\"x_id:O\", axis=None),\n y=alt.Y(\"y_id:O\", axis=None),\n # color=alt.Color(\n # \"z_bse:Q\", scale=alt.Scale(scheme=\"Blues\", domain=[0.0, 1.0])\n # ),\n opacity=alt.condition(brush, alt.value(0.6), alt.value(0)),\n )\n .properties(width=dp_size[0]*2, height=dp_size[1]*2)\n .add_selection(brush)\n )\n\n heatmap_intensity = intensity + heatmap\n\n final_widgets = [points, heatmap_intensity]\n\n # Build chart\n chart = (\n alt.hconcat(*final_widgets)\n .resolve_legend(color=\"independent\")\n .configure_view(strokeWidth=0)\n )\n\n return chart\n\ndef view_bic(\n latent: np.ndarray,\n n_components: int = 20,\n model: str = \"BayesianGaussianMixture\",\n model_args: Dict = {\"random_state\": 6},\n):\n bic_list = PixelSegmenter.bic(latent, n_components, model, model_args)\n fig = go.Figure(\n data=go.Scatter(\n x=np.arange(1, n_components + 1, dtype=int),\n y=bic_list,\n mode=\"lines+markers\",\n ),\n layout=go.Layout(\n title=\"\",\n title_x=0.5,\n xaxis_title=\"Number of component\",\n yaxis_title=\"BIC\",\n width=800,\n height=600,\n ),\n )\n\n fig.update_layout(showlegend=False)\n fig.update_layout(template=\"simple_white\")\n fig.update_traces(marker_size=12)\n fig.show()\n save_csv(pd.DataFrame(data={\"bic\": bic_list}))\n\ndef save_csv(df):\n text = widgets.Text(\n value=\"file_name.csv\",\n placeholder=\"Type something\",\n description=\"Save as:\",\n disabled=False,\n continuous_update=True,\n )\n\n button = widgets.Button(description=\"Save\")\n out = widgets.Output()\n\n def save_to(_):\n out.clear_output()\n with out:\n df.to_csv(text.value)\n print(\"save the csv to\", text.value)\n\n button.on_click(save_to)\n all_widgets = widgets.HBox([text, button])\n display(all_widgets)\n display(out)\n\ndef save_fig(fig):\n file_name = widgets.Text(\n value=\"figure_name.tif\",\n placeholder=\"Type something\",\n description=\"Save as:\",\n disabled=False,\n continuous_update=True,\n layout=Layout(width=\"auto\"),\n )\n folder_name = widgets.Text(\n value=\"results\",\n placeholder=\"Type something\",\n description=\"Folder name:\",\n disabled=False,\n continuous_update=True,\n layout=Layout(width=\"auto\"),\n )\n dpi = widgets.BoundedIntText(\n value=\"96\",\n min=0,\n max=300,\n step=1,\n description=\"Set dpi:\",\n disabled=False,\n continuous_update=True,\n layout=Layout(width=\"auto\"),\n )\n pad = widgets.BoundedFloatText(\n value=\"0.01\",\n min=0.0,\n description=\"Set pad:\",\n disabled=False,\n continuous_update=True,\n layout=Layout(width=\"auto\"),\n )\n button = widgets.Button(description=\"Save\")\n out = widgets.Output()\n\n def save_to(_):\n out.clear_output()\n with out:\n if not os.path.isdir(folder_name.value):\n os.mkdir(folder_name.value)\n if isinstance(fig, mpl.figure.Figure):\n save_path = os.path.join(folder_name.value, file_name.value)\n fig.savefig(\n save_path, dpi=dpi.value, bbox_inches=\"tight\", pad_inches=pad.value\n )\n print(\"save figure to\", file_name.value)\n else:\n initial_file_name = file_name.value.split(\".\")\n folder_for_fig = os.path.join(folder_name.value, initial_file_name[0])\n if not os.path.isdir(folder_for_fig):\n os.mkdir(folder_for_fig)\n for i, single_fig in enumerate(fig):\n save_path = os.path.join(\n folder_for_fig,\n f\"{initial_file_name[0]}_{i:02}.{initial_file_name[1]}\",\n )\n single_fig.savefig(\n save_path,\n dpi=dpi.value,\n bbox_inches=\"tight\",\n pad_inches=pad.value,\n )\n print(\"save all figure to folder:\", folder_for_fig)\n\n button.on_click(save_to)\n all_widgets = widgets.HBox(\n [folder_name, file_name, dpi, pad, button], layout=Layout(width=\"auto\")\n )\n display(all_widgets)\n display(out)\n", "repo_name": "poyentung/autoencoder", "sub_path": "dimension_reduction/visualisation.py", "file_name": "visualisation.py", "file_ext": "py", "file_size_in_byte": 8024, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "pyxem.signals.electron_diffraction2d.ElectronDiffraction2D", "line_number": 22, "usage_type": "name"}, {"api_name": "numpy.ndarray", "line_number": 22, "usage_type": "attribute"}, {"api_name": "numpy.meshgrid", "line_number": 25, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 31, "usage_type": "call"}, {"api_name": "random.choices", "line_number": 45, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 48, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 50, "usage_type": "call"}, {"api_name": "pandas.RangeIndex", "line_number": 53, "usage_type": "call"}, {"api_name": "altair.data_transformers.disable_max_rows", "line_number": 57, "usage_type": "call"}, {"api_name": "altair.data_transformers", "line_number": 57, "usage_type": "attribute"}, {"api_name": "altair.selection", "line_number": 60, "usage_type": "call"}, {"api_name": "altair.selection", "line_number": 62, "usage_type": "call"}, {"api_name": "altair.Chart", "line_number": 72, "usage_type": "call"}, {"api_name": "altair.condition", "line_number": 81, "usage_type": "call"}, {"api_name": "altair.value", "line_number": 81, "usage_type": "call"}, {"api_name": "altair.condition", "line_number": 82, "usage_type": "call"}, {"api_name": "altair.value", "line_number": 82, "usage_type": "call"}, {"api_name": "altair.Tooltip", "line_number": 84, "usage_type": "call"}, {"api_name": "altair.Tooltip", "line_number": 85, "usage_type": "call"}, {"api_name": "altair.TitleParams", "line_number": 89, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 97, "usage_type": "call"}, {"api_name": "altair.Chart", "line_number": 101, "usage_type": "call"}, {"api_name": "altair.X", "line_number": 104, "usage_type": "call"}, {"api_name": "altair.Y", "line_number": 105, "usage_type": "call"}, {"api_name": "altair.Color", "line_number": 106, "usage_type": "call"}, {"api_name": "altair.Scale", "line_number": 107, "usage_type": "call"}, {"api_name": "altair.Chart", "line_number": 113, "usage_type": "call"}, {"api_name": "altair.X", "line_number": 116, "usage_type": "call"}, {"api_name": "altair.Y", "line_number": 117, "usage_type": "call"}, {"api_name": "altair.condition", "line_number": 121, "usage_type": "call"}, {"api_name": "altair.value", "line_number": 121, "usage_type": "call"}, {"api_name": "altair.hconcat", "line_number": 133, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 141, "usage_type": "attribute"}, {"api_name": "typing.Dict", "line_number": 144, "usage_type": "name"}, {"api_name": "clustering.PixelSegmenter.bic", "line_number": 146, "usage_type": "call"}, {"api_name": "clustering.PixelSegmenter", "line_number": 146, "usage_type": "name"}, {"api_name": "plotly.graph_objects.Figure", "line_number": 147, "usage_type": "call"}, {"api_name": "plotly.graph_objects", "line_number": 147, "usage_type": "name"}, {"api_name": "plotly.graph_objects.Scatter", "line_number": 148, "usage_type": "call"}, {"api_name": "plotly.graph_objects", "line_number": 148, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 149, "usage_type": "call"}, {"api_name": "plotly.graph_objects.Layout", "line_number": 153, "usage_type": "call"}, {"api_name": "plotly.graph_objects", "line_number": 153, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 167, "usage_type": "call"}, {"api_name": "ipywidgets.Text", "line_number": 170, "usage_type": "call"}, {"api_name": "ipywidgets.Button", "line_number": 178, "usage_type": "call"}, {"api_name": "ipywidgets.Output", "line_number": 179, "usage_type": "call"}, {"api_name": "ipywidgets.HBox", "line_number": 188, "usage_type": "call"}, {"api_name": "IPython.display.display", "line_number": 189, "usage_type": "call"}, {"api_name": "IPython.display.display", "line_number": 190, "usage_type": "call"}, {"api_name": "ipywidgets.Text", "line_number": 193, "usage_type": "call"}, {"api_name": "ipywidgets.Layout", "line_number": 199, "usage_type": "call"}, {"api_name": "ipywidgets.Text", "line_number": 201, "usage_type": "call"}, {"api_name": "ipywidgets.Layout", "line_number": 207, "usage_type": "call"}, {"api_name": "ipywidgets.BoundedIntText", "line_number": 209, "usage_type": "call"}, {"api_name": "ipywidgets.Layout", "line_number": 217, "usage_type": "call"}, {"api_name": "ipywidgets.BoundedFloatText", "line_number": 219, "usage_type": "call"}, {"api_name": "ipywidgets.Layout", "line_number": 225, "usage_type": "call"}, {"api_name": "ipywidgets.Button", "line_number": 227, "usage_type": "call"}, {"api_name": "ipywidgets.Output", "line_number": 228, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 233, "usage_type": "call"}, {"api_name": "os.path", "line_number": 233, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 234, "usage_type": "call"}, {"api_name": "matplotlib.figure", "line_number": 235, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 236, "usage_type": "call"}, {"api_name": "os.path", "line_number": 236, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 243, "usage_type": "call"}, {"api_name": "os.path", "line_number": 243, "usage_type": "attribute"}, {"api_name": "os.path.isdir", "line_number": 244, "usage_type": "call"}, {"api_name": "os.path", "line_number": 244, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 245, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 247, "usage_type": "call"}, {"api_name": "os.path", "line_number": 247, "usage_type": "attribute"}, {"api_name": "ipywidgets.HBox", "line_number": 260, "usage_type": "call"}, {"api_name": "ipywidgets.Layout", "line_number": 261, "usage_type": "call"}, {"api_name": "IPython.display.display", "line_number": 263, "usage_type": "call"}, {"api_name": "IPython.display.display", "line_number": 264, "usage_type": "call"}]} +{"seq_id": "497446383", "text": "#9-13_ordereddict_rewrite.py\n\nfrom collections import OrderedDict\n\nglossary = OrderedDict()\n\nglossary['statement'] = 'block of code'\nglossary['variable'] = 'placeholder for text and numbers'\nglossary['PEP 8'] = 'recommendations on how to write python code'\nglossary['method'] = \"function that runs on an object\"\nglossary['list comprehension'] = 'compact way to process a sequence and '\nglossary['list comprehension']+= 'return a list'\nglossary['set'] = 'similar to a list except each item must be unique'\n\n\nfor word, meaning in glossary.items():\n\tprint(word.title()+\": \" + meaning + '\\n')", "repo_name": "hellomansour/python_work", "sub_path": "Chapter 9/ordereddict_rewrite.py", "file_name": "ordereddict_rewrite.py", "file_ext": "py", "file_size_in_byte": 588, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "collections.OrderedDict", "line_number": 5, "usage_type": "call"}]} +{"seq_id": "5615561428", "text": "from django.db import models\nfrom dreamcare.apps.accounts.models import User\n\n\nclass ServiceCategory(models.Model):\n name = models.CharField(max_length=250, unique=True)\n created_at = models.DateTimeField(auto_now_add=True, blank=True)\n is_inactive = models.BooleanField(default=False, db_index=True)\n is_deleted = models.BooleanField(default=False, db_index=True)\n\n def __str__(self):\n return str(self.name)\n\n\nclass ServiceSubCategory(models.Model):\n name = models.CharField(max_length=256)\n category = models.ForeignKey(ServiceCategory, on_delete=models.CASCADE)\n created_at = models.DateTimeField(auto_now_add=True, blank=True)\n is_inactive = models.BooleanField(default=False, db_index=True)\n is_deleted = models.BooleanField(default=False, db_index=True)\n\n def __str__(self):\n return str(self.name)\n\n\nclass ProviderServices(models.Model):\n provider = models.ForeignKey(User, on_delete=models.CASCADE)\n service_category = models.ForeignKey(ServiceCategory, on_delete=models.CASCADE)\n service_subcategory = models.ForeignKey(ServiceSubCategory, on_delete=models.CASCADE)\n\n", "repo_name": "kumawat0008/dream-care", "sub_path": "dreamcare/apps/service/models.py", "file_name": "models.py", "file_ext": "py", "file_size_in_byte": 1131, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "django.db.models.Model", "line_number": 5, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 5, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 6, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 6, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 7, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 7, "usage_type": "name"}, {"api_name": "django.db.models.BooleanField", "line_number": 8, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 8, "usage_type": "name"}, {"api_name": "django.db.models.BooleanField", "line_number": 9, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 9, "usage_type": "name"}, {"api_name": "django.db.models.Model", "line_number": 15, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 15, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 16, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 16, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 17, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 17, "usage_type": "name"}, {"api_name": "django.db.models.CASCADE", "line_number": 17, "usage_type": "attribute"}, {"api_name": "django.db.models.DateTimeField", "line_number": 18, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 18, "usage_type": "name"}, {"api_name": "django.db.models.BooleanField", "line_number": 19, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 19, "usage_type": "name"}, {"api_name": "django.db.models.BooleanField", "line_number": 20, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 20, "usage_type": "name"}, {"api_name": "django.db.models.Model", "line_number": 26, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 26, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 27, "usage_type": "call"}, {"api_name": "dreamcare.apps.accounts.models.User", "line_number": 27, "usage_type": "argument"}, {"api_name": "django.db.models", "line_number": 27, "usage_type": "name"}, {"api_name": "django.db.models.CASCADE", "line_number": 27, "usage_type": "attribute"}, {"api_name": "django.db.models.ForeignKey", "line_number": 28, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 28, "usage_type": "name"}, {"api_name": "django.db.models.CASCADE", "line_number": 28, "usage_type": "attribute"}, {"api_name": "django.db.models.ForeignKey", "line_number": 29, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 29, "usage_type": "name"}, {"api_name": "django.db.models.CASCADE", "line_number": 29, "usage_type": "attribute"}]} +{"seq_id": "3559013452", "text": "\"\"\"\nDjango project structure:\n \n myproject/\n releases/\n myproject.tar\n ...\n\nServer app structure:\n \n my_app/\n staging/\n myproject\n release - Symbolic link to a concrete release\n production/\n ... - Same as above\n\"\"\"\nimport os\nfrom fabric.api import env, run, prompt, local, put, cd\nfrom fabric.utils import puts\nfrom fabric import colors\nfrom fabric.state import output\nfrom fabric.contrib.files import exists\n\nenv.name = 'MYPROJECT_ENV'\nenv.hosts = []\nenv.user = ''\n\ndependencies = []\n\nproject_folder = os.path.basename(os.path.dirname(__file__))\n\n# releases_dir is the directory where to puto all the \n# released versions\n# path is the path in the server to the app (staging or production)\n# virtualenv is the path in the server to the used virtualenv (staging or production)\nSETTINGS = {\n 'releases_dir': os.path.join(project_folder, 'releases'),\n 'staging': {\n 'path': '',\n 'virtualenv': ''\n },\n 'production': {\n 'path': '',\n 'virtualenv': ''\n }\n}\n\n\ndef staging():\n \"\"\"\n Upload the archived project to the staging servers\n \"\"\"\n project_name = _get_project_name()\n origin = '%s/%s.tar' % (SETTINGS['releases_dir'], project_name)\n destination = '%s/%s.tar' % (SETTINGS['staging']['path'], project_name)\n put(origin, destination)\n\n with cd(SETTINGS['staging']['path']):\n run('mkdir -p %(rel)s && cd %(rel)s && tar xf ../%(rel)s.tar && rm -f ../%(rel)s.tar' % {'rel': project_name})\n if exists('./release'):\n run('rm -rf release')\n # Point to the new release\n run('ln -sf %(path)s/%(rel)s %(path)s/release' % {'path': SETTINGS['staging']['path'], 'rel': project_name})\n syncdb('staging')\n\n\ndef production():\n \"\"\"\n Upload the archived project to the production servers\n \"\"\"\n pass\n\n\ndef deploy():\n \"\"\"\n Run tests\n Archive the project for uploading\n \"\"\"\n project_name = _get_project_name()\n puts(project_name)\n local('python manage.py test --noinput --failfast')\n local('git-archive-all --format=tar -o %s/%s.tar HEAD' % (SETTINGS['releases_dir'], project_name))\n puts('Project stored at %s' % colors.red('%s/%s.tar' % (SETTINGS['releases_dir'], project_name), bold=True))\n\n\ndef rollback(appenv, steps=1):\n \"\"\"\n Go backs `steps`times in the deployment history\n \"\"\"\n project_name = _get_project_name('HEAD~%s' % steps)\n with cd(SETTINGS[appenv]['path']):\n if exists(project_name):\n run('rm -rf release')\n # Point to the new release\n run('ln -sf %(path)s/%(rel)s %(path)s/release' % {'path': SETTINGS[appenv]['path'], 'rel': project_name})\n else:\n puts(colors.red('The previous version `%s`does not exist' % project_name))\n\n\ndef syncdb(appenv):\n \"\"\"\n Django syncdb command [see: http://docs.djangoproject.com/en/1.3/ref/django-admin/#syncdb]\n \"\"\"\n with cd('%s/release' % SETTINGS[appenv]['path']):\n run('export %s=%s && source %s/bin/activate && python manage.py syncdb --noinput' % (env.name, appenv, SETTINGS[appenv]['virtualenv']))\n\n\ndef _get_project_name(revision='HEAD'):\n \"\"\"\n Returns the a unique name composed in this form:\n \n `{project_name}_{git_commit_id}`\n \"\"\"\n if revision:\n commit_id = local('git rev-parse %s' % (revision,), capture=True)\n else:\n commit_id = 'norev'\n return '%s_%s' % (project_folder, commit_id)\n\n", "repo_name": "anler/Git-Django-Fabric-Deploy", "sub_path": "fabfile.py", "file_name": "fabfile.py", "file_ext": "py", "file_size_in_byte": 3539, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 6, "dataset": "github-code", "pt": "37", "api": [{"api_name": "fabric.api.env.name", "line_number": 25, "usage_type": "attribute"}, {"api_name": "fabric.api.env", "line_number": 25, "usage_type": "name"}, {"api_name": "fabric.api.env.hosts", "line_number": 26, "usage_type": "attribute"}, {"api_name": "fabric.api.env", "line_number": 26, "usage_type": "name"}, {"api_name": "fabric.api.env.user", "line_number": 27, "usage_type": "attribute"}, {"api_name": "fabric.api.env", "line_number": 27, "usage_type": "name"}, {"api_name": "os.path.basename", "line_number": 31, "usage_type": "call"}, {"api_name": "os.path", "line_number": 31, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 31, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 38, "usage_type": "call"}, {"api_name": "os.path", "line_number": 38, "usage_type": "attribute"}, {"api_name": "fabric.api.put", "line_number": 57, "usage_type": "call"}, {"api_name": "fabric.api.cd", "line_number": 59, "usage_type": "call"}, {"api_name": "fabric.api.run", "line_number": 60, "usage_type": "call"}, {"api_name": "fabric.contrib.files.exists", "line_number": 61, "usage_type": "call"}, {"api_name": "fabric.api.run", "line_number": 62, "usage_type": "call"}, {"api_name": "fabric.api.run", "line_number": 64, "usage_type": "call"}, {"api_name": "fabric.utils.puts", "line_number": 81, "usage_type": "call"}, {"api_name": "fabric.api.local", "line_number": 82, "usage_type": "call"}, {"api_name": "fabric.api.local", "line_number": 83, "usage_type": "call"}, {"api_name": "fabric.utils.puts", "line_number": 84, "usage_type": "call"}, {"api_name": "fabric.colors.red", "line_number": 84, "usage_type": "call"}, {"api_name": "fabric.colors", "line_number": 84, "usage_type": "name"}, {"api_name": "fabric.api.cd", "line_number": 92, "usage_type": "call"}, {"api_name": "fabric.contrib.files.exists", "line_number": 93, "usage_type": "call"}, {"api_name": "fabric.api.run", "line_number": 94, "usage_type": "call"}, {"api_name": "fabric.api.run", "line_number": 96, "usage_type": "call"}, {"api_name": "fabric.utils.puts", "line_number": 98, "usage_type": "call"}, {"api_name": "fabric.colors.red", "line_number": 98, "usage_type": "call"}, {"api_name": "fabric.colors", "line_number": 98, "usage_type": "name"}, {"api_name": "fabric.api.cd", "line_number": 105, "usage_type": "call"}, {"api_name": "fabric.api.run", "line_number": 106, "usage_type": "call"}, {"api_name": "fabric.api.env.name", "line_number": 106, "usage_type": "attribute"}, {"api_name": "fabric.api.env", "line_number": 106, "usage_type": "name"}, {"api_name": "fabric.api.local", "line_number": 116, "usage_type": "call"}]} +{"seq_id": "15467742918", "text": "import numpy as np\nimport pandas as pd\nfrom pandas import read_csv\nimport matplotlib.pyplot as plt\nfrom sklearn.preprocessing import MinMaxScaler\nfrom tensorflow.keras.layers import GRU, Dense\nfrom tensorflow import keras\nfrom keras.callbacks import EarlyStopping\n\n# 讀取數據\ndataItem = read_csv('./PD_adddate2.csv', usecols=[15], engine='python')\n\ndata = dataItem.values\ndata = data.astype('float32')\n# 繪製原始數據\nplt.figure(figsize=(14, 6))\nprint(data.shape)\nplt.plot(data)\nplt.show()\n\n# 數據預處理\ndef GetDataAndLabel(data, TimeStep):\n trainData, trainLabel = [], []\n for i in range(len(data)-TimeStep):\n TrainDataOne = data[i:(i+TimeStep), 0]\n trainData.append(TrainDataOne)\n trainLabel.append(data[i+TimeStep, 0])\n return np.array(trainData), np.array(trainLabel)\n\n# 歸一化\nscaler = MinMaxScaler(feature_range=(0, 1))\ndata = scaler.fit_transform(data)\n\n# 切分訓練集和測試集\nTrainDataNum = int(len(data) * 0.8)\nTestDataNum = len(data) - TrainDataNum\ntrainData = data[0:TrainDataNum, :]\ntestData = data[TrainDataNum:len(data), :]\n# 數據標籤\nTimeStep = 14\ntraindataNew, trainLabelNew = GetDataAndLabel(trainData, TimeStep)\ntestdataNew, testLabelNew = GetDataAndLabel(testData, TimeStep)\nprint(\"traindataNew.shape :\",traindataNew.shape)\nprint(\"trainLabelNew.shape :\",trainLabelNew.shape)\nprint(\"testdataNew.shape :\",testdataNew.shape)\nprint(\"testLabelNew.shape :\",testLabelNew.shape)\n\n# 改變維度\ntraindataNew = np.reshape(traindataNew, (traindataNew.shape[0], traindataNew.shape[1], 1))\ntestdataNew = np.reshape(testdataNew, (testdataNew.shape[0], testdataNew.shape[1], 1))\nprint(\"traindataNew.shape :\",traindataNew.shape)\nprint(\"testdataNew.shape :\",testdataNew.shape)\n\n# 建立模型\nmodel = keras.Sequential()\nmodel.add(GRU(256, input_shape=(TimeStep, 1), return_sequences=True))\nmodel.add(GRU(128, input_shape=(TimeStep, 1), return_sequences=True))\nmodel.add(GRU(64, input_shape=(TimeStep, 1)))\nmodel.add(Dense(1))\nprint(model.summary())\n\n# 編譯和訓練模型\noptimizer = keras.optimizers.Adam(learning_rate=0.001)\nmodel.compile(loss='mean_squared_error', optimizer=optimizer)\n# early_stopping = EarlyStopping(monitor='loss', patience=50, verbose=1)\nhist = model.fit(traindataNew, trainLabelNew, epochs=500, batch_size=32, verbose=1)\n# hist = model.fit(traindataNew, trainLabelNew, epochs=500, batch_size=32, verbose=1, callbacks=[early_stopping])\n\n# 繪製訓練損失\nloss = hist.history[\"loss\"]\nepochs = range(len(loss))\nplt.plot(epochs, loss, 'r-', label=\"Training loss\")\nplt.title('Training Loss')\nplt.xlabel(\"Epochs\")\nplt.ylabel(\"Loss\")\nplt.legend()\nplt.show()\n\n# 預測\ntrainPredict = model.predict(traindataNew)\ntestPredict = model.predict(testdataNew)\nlen(trainPredict)\n\n# 逆歸一化\ntrainRealPredict = scaler.inverse_transform(trainPredict)\ntrainY = scaler.inverse_transform([trainLabelNew])\ntestRealPredict = scaler.inverse_transform(testPredict)\ntestY = scaler.inverse_transform([testLabelNew])\n# 繪製預測結果\nPredtrainingData = np.empty_like(data)\nPredtestData = np.empty_like(data)\noriginaldata = scaler.inverse_transform(data)\nPredtrainingData[:, :] = np.nan\nPredtestData[:, :] = np.nan\n\nPredtrainingData[TimeStep: len(trainPredict) + TimeStep, :] = trainRealPredict\nPredtestData[len(trainPredict) + (TimeStep * 2) - 1: len(data) - 1, :] = testRealPredict\n\nplt.figure(figsize=(14, 6))\nplt.plot(originaldata, color='green', label=\"Original data\")\nplt.plot(PredtrainingData, color='red', label=\"Train data Predict\")\nplt.plot(PredtestData, color='blue', label=\"Test data Predict\")\nplt.legend()\nplt.show()\n\n# 預測未來3個月銷售額\nfuture_days = 90\ninput_data = data[-TimeStep:]\ninput_data = np.reshape(input_data, (1, TimeStep, 1))\n\nfuture_predictions = []\nfor i in range(future_days):\n prediction = model.predict(input_data)\n future_predictions.append(prediction[0])\n input_data = np.concatenate((input_data[:, 1:, :], prediction.reshape(1, 1, 1)), axis=1)\n\nfuture_predictions = scaler.inverse_transform(future_predictions)\n\nimport pandas as pd\nfrom datetime import datetime, timedelta\n\n# 繪製未來預測結果\nplt.figure(figsize=(14, 6))\nplt.plot(range(future_days), future_predictions, color='blue', label=\"Future predictions\")\nplt.xlabel(\"Days\")\nplt.ylabel(\"Sales\")\nplt.legend()\nplt.show()\n\n# 創建日期範圍\nstart_date = datetime.today()\ndate_range = [start_date + timedelta(days=x) for x in range(future_days)]\n\n# 創建 DataFrame 並顯示\npredictions_df = pd.DataFrame({'Date': date_range, 'Predicted Sales': np.squeeze(future_predictions)})\npd.set_option('display.max_rows', None) # 顯示所有行\nprint(predictions_df)", "repo_name": "samhuang95/robo_advisor", "sub_path": "sale_models/Forecasting future/Forecasting_future.py", "file_name": "Forecasting_future.py", "file_ext": "py", "file_size_in_byte": 4646, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "37", "api": [{"api_name": "pandas.read_csv", "line_number": 11, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 16, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 16, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 18, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 18, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 19, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 19, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 28, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.MinMaxScaler", "line_number": 31, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 49, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 50, "usage_type": "call"}, {"api_name": "tensorflow.keras.Sequential", "line_number": 55, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 55, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.GRU", "line_number": 56, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.GRU", "line_number": 57, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.GRU", "line_number": 58, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Dense", "line_number": 59, "usage_type": "call"}, {"api_name": "tensorflow.keras.optimizers.Adam", "line_number": 63, "usage_type": "call"}, {"api_name": "tensorflow.keras.optimizers", "line_number": 63, "usage_type": "attribute"}, {"api_name": "tensorflow.keras", "line_number": 63, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 72, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 72, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 73, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 73, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 74, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 74, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 75, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 75, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 76, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 76, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 77, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 77, "usage_type": "name"}, {"api_name": "numpy.empty_like", "line_number": 90, "usage_type": "call"}, {"api_name": "numpy.empty_like", "line_number": 91, "usage_type": "call"}, {"api_name": "numpy.nan", "line_number": 93, "usage_type": "attribute"}, {"api_name": "numpy.nan", "line_number": 94, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 99, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 99, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 100, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 100, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 101, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 101, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 102, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 102, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 103, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 103, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 104, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 104, "usage_type": "name"}, {"api_name": "numpy.reshape", "line_number": 109, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 115, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 123, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 123, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 124, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 124, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 125, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 125, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 126, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 126, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 127, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 127, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 128, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 128, "usage_type": "name"}, {"api_name": "datetime.datetime.today", "line_number": 131, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 131, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 132, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 135, "usage_type": "call"}, {"api_name": "numpy.squeeze", "line_number": 135, "usage_type": "call"}, {"api_name": "pandas.set_option", "line_number": 136, "usage_type": "call"}]} +{"seq_id": "71498898989", "text": "\r\nfrom kivy.app import App\r\nfrom kivy.uix.button import Button\r\nfrom kivy.uix.boxlayout import BoxLayout\r\nfrom kivy.uix.label import Label\r\nimport requests\r\nfrom bs4 import BeautifulSoup\r\n\r\nclass Yazbel(App):\r\n\r\n\r\n def build(self):\r\n\r\n \r\n self.yazi=Label(text = \"merhaba\",font_size = \"12sp\",halign = \"center\")\r\n self.govde = BoxLayout(orientation = \"vertical\")\r\n\r\n \r\n self.buton = Button(text = \"Tıkla\",size_hint_y = .3)\r\n\r\n self.buton.bind(on_press = self.press)\r\n \r\n\r\n \r\n self.govde.add_widget(self.yazi)\r\n self.govde.add_widget(self.buton)\r\n\r\n return self.govde\r\n\r\n def press(self,q):\r\n r = requests.get('https://www.gunlukburc.net/gunluk-burc-yorumlari/yay.html')\r\n source = BeautifulSoup(r.content,\"lxml\")\r\n a=source.find(\"p\").text\r\n s=a.split(\".\")\r\n for q in s :\r\n \r\n w = Label(text = q,font_size = \"14sp\",halign = \"center\")\r\n self.govde.add_widget(w)\r\n \r\n \r\n\r\n\r\nYazbel().run() \r\n", "repo_name": "dovahkinnn/python_kivy_burc", "sub_path": "burc.py", "file_name": "burc.py", "file_ext": "py", "file_size_in_byte": 1231, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "kivy.app.App", "line_number": 9, "usage_type": "name"}, {"api_name": "kivy.uix.label.Label", "line_number": 15, "usage_type": "call"}, {"api_name": "kivy.uix.boxlayout.BoxLayout", "line_number": 16, "usage_type": "call"}, {"api_name": "kivy.uix.button.Button", "line_number": 19, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 31, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 32, "usage_type": "call"}, {"api_name": "kivy.uix.label.Label", "line_number": 37, "usage_type": "call"}]} +{"seq_id": "10981734991", "text": "import urllib2\r\nimport re\r\nimport datetime\r\nfrom pymongo import MongoClient\r\nfrom beebotte import *\r\nurl = 'http://www.numeroalazar.com.ar/'\r\n\r\nrespuesta = urllib2.urlopen(url)\r\ncontenidoWeb = respuesta.read()\r\n\r\n\r\nelemento=re.findall('\\d?\\d?\\d[.]\\d\\d
    ', contenidoWeb)\r\n\r\nformato_fecha=\"%d/%m/%y\"\r\nformato_hora=\"%H:%M\"\r\nfecha=datetime.datetime.utcnow()\r\n\r\nclient = MongoClient()\r\ndb = client.test\r\nresult = db.numbers.insert_one({\"number\" : float(elemento[0].strip('
    ')),\"date\" : fecha.strftime(formato_fecha),\"hour\" : fecha.strftime(formato_hora)})\r\n\r\n\r\n_hostname = 'api.beebotte.com'\r\n_token = '1513363009433_8m2ywpK0NiNpOPJ3'\r\nbbt = BBT(token = _token, hostname = _hostname)\r\nbbt.write(\"Numbers_Database\", \"numbers\", float(elemento[0].strip('
    ')))\r\nbbt.write(\"Numbers_Database\", \"dates\", str(\"13/12/17\"))\r\nbbt.write(\"Numbers_Database\", \"hour\", str(\"18:14\"))\r\n", "repo_name": "abaldominos/random_numbers", "sub_path": "app/runtime.py", "file_name": "runtime.py", "file_ext": "py", "file_size_in_byte": 878, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "urllib2.urlopen", "line_number": 8, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 12, "usage_type": "call"}, {"api_name": "datetime.datetime.utcnow", "line_number": 16, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 16, "usage_type": "attribute"}, {"api_name": "pymongo.MongoClient", "line_number": 18, "usage_type": "call"}]} +{"seq_id": "10757496590", "text": "from typing import Tuple, Dict, Any, List\n\n\nclass Memory(object):\n def __init__(self):\n self._bytes: Dict[int, Tuple[int, bool, bool]] = {}\n\n def map_byte(self, ea: int, val: int, can_write: bool, can_exec: bool):\n self._bytes[ea] = (int(val & 0xFF), can_write, can_exec)\n\n def proto(self) -> List[Dict[str, Any]]:\n proto: List[Dict[str, Any]] = []\n if not len(self._bytes):\n return proto\n\n for ea in sorted(self._bytes.keys()):\n val, can_write, can_exec = self._bytes[ea]\n if not len(proto) or \\\n proto[-1][\"is_writeable\"] != can_write or \\\n proto[-1][\"is_executable\"] != can_exec or \\\n (proto[-1][\"address\"] + (len(proto[-1][\"data\"]) / 2)) != ea:\n proto.append({\n \"address\": ea,\n \"is_executable\": can_exec,\n \"is_writeable\": can_write,\n \"data\": \"\"\n })\n proto[-1][\"data\"] += \"{:02x}\".format(val & 0xFF)\n\n return proto\n", "repo_name": "lifting-bits/anvill", "sub_path": "python/anvill/mem.py", "file_name": "mem.py", "file_ext": "py", "file_size_in_byte": 1061, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 313, "dataset": "github-code", "pt": "37", "api": [{"api_name": "typing.Dict", "line_number": 6, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 6, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 12, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 12, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 12, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 11, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 11, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 11, "usage_type": "name"}]} +{"seq_id": "16470413588", "text": "#User function Template for python3\n\n\nfrom typing import List\nfrom collections import deque\n\nclass Solution:\n def chefAndWells(self, n : int, m : int, l : List[List[str]]) -> List[List[int]]:\n res =[]\n dx = [1,-1,0,0]\n dy = [0,0,-1,1]\n for i in range(n):\n res.append([-1]*m)\n vis =[]\n for i in range(n):\n vis.append([False]*m)\n new = deque()\n for i in range(n):\n for j in range(m):\n if l[i][j]=='W':\n new.append([i,j])\n vis[i][j] = True\n if l[i][j]=='W' or l[i][j]=='N' or l[i][j]=='.':\n res[i][j]=0\n \n dis = 2\n while len(new)>0:\n size = len(new)\n while size>0:\n size=size-1\n k =new.popleft()\n for i in range(4):\n x = k[0]+dx[i]\n y = k[1]+dy[i]\n if x>=0 and y>=0 and x List[str]:\n if path:\n with open(path, \"r\") as f:\n labels = f.read().splitlines()\n if \"O\" not in labels:\n labels = [\"O\"] + labels\n return labels\n else:\n return [\"O\", \"B-MISC\", \"I-MISC\", \"B-PER\", \"I-PER\", \"B-ORG\", \"I-ORG\", \"B-LOC\", \"I-LOC\"]\n\n\ndef example2feature(example, tokenizer, label_map, max_seq_length, weight=1):\n add_label = 'X'\n # tokenize_count = []\n tokens = ['[CLS]']\n predict_mask = [0]\n label_ids = [label_map['[CLS]']]\n features = []\n\n for i in range(len(example.features)):\n features.append([0])\n\n for i, w in enumerate(example.words):\n # use bertTokenizer to split words\n # 1996-08-22 => 1996 - 08 - 22\n # sheepmeat => sheep ##me ##at\n sub_words = tokenizer.tokenize(w)\n if not sub_words:\n sub_words = ['[UNK]']\n tokens.extend(sub_words)\n\n for j in range(len(sub_words)):\n if j == 0:\n predict_mask.append(1)\n label_ids.append(label_map[example.labels[i]])\n else:\n # '##xxx' -> 'X' (see bert paper)\n predict_mask.append(0)\n label_ids.append(label_map[add_label])\n for idx in range(len(example.features)):\n features[idx].append(example.features[idx][i])\n\n # truncate\n if len(tokens) > max_seq_length - 1:\n print('Example No.{} is too long, length is {}, truncated to {}!'.format(example.guid, len(tokens), max_seq_length))\n tokens = tokens[0:(max_seq_length - 1)]\n predict_mask = predict_mask[0:(max_seq_length - 1)]\n label_ids = label_ids[0:(max_seq_length - 1)]\n for i in range(len(features)):\n features[i] = features[i][0:(max_seq_length - 1)]\n\n tokens.append('[SEP]')\n predict_mask.append(0)\n label_ids.append(label_map['[SEP]'])\n for i in range(len(features)):\n features[i] += [0]\n\n input_ids = tokenizer.convert_tokens_to_ids(tokens)\n segment_ids = [0] * len(input_ids)\n input_mask = [1] * len(input_ids)\n\n padding_length = max_seq_length - len(input_ids)\n mask_padding_with_zero = True\n pad_token = tokenizer.pad_token_id\n pad_token_segment_id = 0\n pad_token_label_id = -100\n input_ids += [pad_token] * padding_length\n input_mask += [0 if mask_padding_with_zero else 1] * padding_length\n segment_ids += [pad_token_segment_id] * padding_length\n predict_mask += [0] * padding_length\n label_ids += [pad_token_label_id] * padding_length\n for i in range(len(features)):\n features[i] += [0] * padding_length\n assert len(input_ids) == max_seq_length\n assert len(input_mask) == max_seq_length\n assert len(segment_ids) == max_seq_length\n assert len(label_ids) == max_seq_length\n for i in range(len(features)):\n assert len(features[i]) == max_seq_length\n\n feat = InputFeatures(\n input_ids=input_ids,\n attention_mask=input_mask,\n token_type_ids=segment_ids,\n # predict_mask=predict_mask,\n label_ids=label_ids,\n features=features,\n predict_mask=predict_mask,\n weight=weight)\n\n return feat\n\n\nclass NerDataset(data.Dataset):\n def __init__(self, examples, tokenizer, label_map, max_seq_length, weights=None):\n self.examples = examples\n self.tokenizer = tokenizer\n self.label_map = label_map\n self.max_seq_length = max_seq_length\n\n if weights is None:\n self.weights = [1] * len(examples)\n else:\n self.weights = weights\n\n def __len__(self):\n return len(self.examples)\n\n def __getitem__(self, idx):\n feat = example2feature(self.examples[idx], self.tokenizer, self.label_map, self.max_seq_length, self.weights[idx])\n return feat\n\n @classmethod\n def pad(cls, batch):\n input_ids_list = torch.LongTensor([sample.input_ids for sample in batch])\n attention_mask_list = torch.LongTensor([sample.attention_mask for sample in batch])\n token_type_ids_list = torch.LongTensor([sample.token_type_ids for sample in batch])\n label_ids_list = torch.LongTensor([sample.label_ids for sample in batch])\n features_list = torch.LongTensor([sample.features for sample in batch])\n predict_mask_list = torch.LongTensor([sample.predict_mask for sample in batch])\n weights_list = torch.Tensor([sample.weight for sample in batch])\n\n return input_ids_list, attention_mask_list, token_type_ids_list, label_ids_list, features_list, predict_mask_list, weights_list\n\n @classmethod\n def dynamic_collator(cls, pad_token_id):\n # return data collator\n @dataclass\n class DyCollator:\n pad_token_id: int\n\n def __call__(self, batch: List[InputFeatures]) -> Dict[str, torch.Tensor]:\n input_ids = torch.LongTensor([sample.input_ids for sample in batch])\n attention_mask = torch.LongTensor([sample.attention_mask for sample in batch])\n token_type_ids = torch.LongTensor([sample.token_type_ids for sample in batch])\n label_ids = torch.LongTensor([sample.label_ids for sample in batch])\n features = torch.LongTensor([sample.features for sample in batch])\n predict_mask = torch.LongTensor([sample.predict_mask for sample in batch])\n weights = torch.Tensor([sample.weight for sample in batch])\n\n # exclude broken samples\n valid_samples = (label_ids >= 0).long().sum(1) > 0\n if not valid_samples.all():\n print(\"Invalid Samples Appear!!!!!\")\n\n lens = (input_ids[valid_samples] != self.pad_token_id).long().sum(-1)\n maxlen = max(1, max(lens))\n\n return {\"input_ids\": input_ids[valid_samples, :maxlen],\n \"attention_mask\": attention_mask[valid_samples, :maxlen],\n \"token_type_ids\": token_type_ids[valid_samples, :maxlen],\n \"labels\": label_ids[valid_samples, :maxlen],\n \"features\": features[valid_samples, :maxlen],\n \"predict_mask\": predict_mask[valid_samples, :maxlen],\n \"weights\": weights[valid_samples],\n }\n\n return DyCollator(pad_token_id=pad_token_id)\n", "repo_name": "amzn/amazon-weak-ner-needle", "sub_path": "bert-ner/datautils.py", "file_name": "datautils.py", "file_ext": "py", "file_size_in_byte": 7117, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 99, "dataset": "github-code", "pt": "37", "api": [{"api_name": "enum.Enum", "line_number": 23, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 29, "usage_type": "name"}, {"api_name": "torch.utils.data.Dataset", "line_number": 122, "usage_type": "attribute"}, {"api_name": "torch.utils.data", "line_number": 122, "usage_type": "name"}, {"api_name": "torch.LongTensor", "line_number": 143, "usage_type": "call"}, {"api_name": "torch.LongTensor", "line_number": 144, "usage_type": "call"}, {"api_name": "torch.LongTensor", "line_number": 145, "usage_type": "call"}, {"api_name": "torch.LongTensor", "line_number": 146, "usage_type": "call"}, {"api_name": "torch.LongTensor", "line_number": 147, "usage_type": "call"}, {"api_name": "torch.LongTensor", "line_number": 148, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 149, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 160, "usage_type": "name"}, {"api_name": "torch.LongTensor", "line_number": 161, "usage_type": "call"}, {"api_name": "torch.LongTensor", "line_number": 162, "usage_type": "call"}, {"api_name": "torch.LongTensor", "line_number": 163, "usage_type": "call"}, {"api_name": "torch.LongTensor", "line_number": 164, "usage_type": "call"}, {"api_name": "torch.LongTensor", "line_number": 165, "usage_type": "call"}, {"api_name": "torch.LongTensor", "line_number": 166, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 167, "usage_type": "call"}, {"api_name": "typing.Dict", "line_number": 160, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 160, "usage_type": "attribute"}, {"api_name": "dataclasses.dataclass", "line_number": 156, "usage_type": "name"}]} +{"seq_id": "13089435978", "text": "import datetime\nimport sys\n\nimport sickchill\nfrom sickchill.helper.common import dateTimeFormat\n\nfrom .common import Quality\n\n\nclass SearchResult(object):\n \"\"\"\n Represents a search result from an indexer.\n \"\"\"\n\n def __init__(self, episodes):\n self.provider = None\n\n # release show object\n self.show = None\n\n # URL to the NZB/torrent file\n self.url = \"\"\n\n # used by some providers to store extra info associated with the result\n self.extraInfo = []\n\n # list of TVEpisode objects that this result is associated with\n self.episodes = episodes\n\n # quality of the release\n self.quality = Quality.UNKNOWN\n\n # release name\n self.name = \"\"\n\n # size of the release (-1 = n/a)\n self.size = -1\n\n # release group\n self.release_group = \"\"\n\n # version\n self.version = -1\n\n # hash\n self.hash = None\n\n # content\n self.content = None\n\n self.resultType = \"\"\n\n self.priority = 0\n\n def from_json(self, result_dict):\n self.name = result_dict.get(\"name\")\n self.url = result_dict.get(\"url\")\n self.size = result_dict.get(\"size\")\n self.version = result_dict.get(\"version\")\n self.release_group = result_dict.get(\"release_group\")\n self.quality = int(result_dict.get(\"quality\"))\n self.provider = sickchill.oldbeard.providers.getProviderClass(result_dict.get(\"provider\"))\n\n @classmethod\n def make_result(cls, result_dict):\n show = sickchill.show.Show.Show._validate_indexer_id(result_dict.get(\"indexerid\"))\n if not show[1]:\n return show[0]\n\n show = show[1]\n episode_objects = [show.getEpisode(result_dict.get(\"season\"), ep) for ep in result_dict.get(\"episodes\").split(\"|\") if ep]\n result = cls(episode_objects)\n result.from_json(result_dict)\n result.show = show\n\n return result\n\n def __str__(self):\n if self.provider is None:\n return \"Invalid provider, unable to print self\"\n\n my_string = f\"{self.provider.name} @ {self.url}\\n\"\n my_string += \"Extra Info:\\n\"\n for extra in self.extraInfo:\n my_string += f\" {extra}\\n\"\n\n my_string += \"Episodes:\\n\"\n for ep in self.episodes:\n my_string += f\" {ep}\\n\"\n\n my_string += f\"Quality: {Quality.qualityStrings[self.quality]}\\n\"\n my_string += f\"Name: {self.name}\\n\"\n my_string += f\"Size: {self.size}\\n\"\n my_string += f\"Release Group: {self.release_group}\\n\"\n\n return my_string\n\n\nclass NZBSearchResult(SearchResult):\n \"\"\"\n Regular NZB result with an URL to the NZB\n \"\"\"\n\n def __init__(self, episodes):\n super().__init__(episodes)\n self.resultType = \"nzb\"\n\n\nclass NZBDataSearchResult(SearchResult):\n \"\"\"\n NZB result where the actual NZB XML data is stored in the extraInfo\n \"\"\"\n\n def __init__(self, episodes):\n super().__init__(episodes)\n self.resultType = \"nzbdata\"\n\n\nclass TorrentSearchResult(SearchResult):\n \"\"\"\n Torrent result with an URL to the torrent\n \"\"\"\n\n def __init__(self, episodes):\n super().__init__(episodes)\n self.resultType = \"torrent\"\n\n\nclass Proper(object):\n def __init__(self, name, url, date, show):\n self.name = name\n self.url = url\n self.date = date\n self.provider = None\n self.quality = Quality.UNKNOWN\n self.release_group = None\n self.version = -1\n\n self.show = show\n self.indexer = None\n self.indexerid = -1\n self.season = -1\n self.episode = -1\n self.scene_season = -1\n self.scene_episode = -1\n\n def __str__(self):\n return \"{date} {name} {season}x{episode} of {series_id} from {indexer}\".format(\n date=self.date, name=self.name, season=self.season, episode=self.episode, series_id=self.indexerid, indexer=sickchill.indexer.name(self.indexer)\n )\n\n\nclass ErrorViewer(object):\n \"\"\"\n Keeps a static list of UIErrors to be displayed on the UI and allows\n the list to be cleared.\n \"\"\"\n\n errors = []\n\n def __init__(self):\n ErrorViewer.errors = []\n\n @staticmethod\n def add(error):\n ErrorViewer.errors = [e for e in ErrorViewer.errors if e.message != error.message]\n ErrorViewer.errors.append(error)\n\n @staticmethod\n def clear():\n ErrorViewer.errors = []\n\n @staticmethod\n def get():\n return ErrorViewer.errors\n\n\nclass WarningViewer(object):\n \"\"\"\n Keeps a static list of (warning) UIErrors to be displayed on the UI and allows\n the list to be cleared.\n \"\"\"\n\n errors = []\n\n def __init__(self):\n WarningViewer.errors = []\n\n @staticmethod\n def add(error):\n WarningViewer.errors = [e for e in WarningViewer.errors if e.message != error.message]\n WarningViewer.errors.append(error)\n\n @staticmethod\n def clear():\n WarningViewer.errors = []\n\n @staticmethod\n def get():\n return WarningViewer.errors\n\n\nclass UIError(object):\n \"\"\"\n Represents an error to be displayed in the web UI.\n \"\"\"\n\n def __init__(self, message):\n self.title = sys.exc_info()[-2] or message\n self.message = message\n self.time = datetime.datetime.now().strftime(dateTimeFormat)\n", "repo_name": "SickChill/sickchill", "sub_path": "sickchill/oldbeard/classes.py", "file_name": "classes.py", "file_ext": "py", "file_size_in_byte": 5365, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2371, "dataset": "github-code", "pt": "37", "api": [{"api_name": "common.Quality.UNKNOWN", "line_number": 31, "usage_type": "attribute"}, {"api_name": "common.Quality", "line_number": 31, "usage_type": "name"}, {"api_name": "sickchill.oldbeard.providers.getProviderClass", "line_number": 62, "usage_type": "call"}, {"api_name": "sickchill.oldbeard", "line_number": 62, "usage_type": "attribute"}, {"api_name": "sickchill.show.Show.Show._validate_indexer_id", "line_number": 66, "usage_type": "call"}, {"api_name": "sickchill.show", "line_number": 66, "usage_type": "attribute"}, {"api_name": "common.Quality.qualityStrings", "line_number": 91, "usage_type": "attribute"}, {"api_name": "common.Quality", "line_number": 91, "usage_type": "name"}, {"api_name": "common.Quality.UNKNOWN", "line_number": 135, "usage_type": "attribute"}, {"api_name": "common.Quality", "line_number": 135, "usage_type": "name"}, {"api_name": "sickchill.indexer.name", "line_number": 149, "usage_type": "call"}, {"api_name": "sickchill.indexer", "line_number": 149, "usage_type": "attribute"}, {"api_name": "sys.exc_info", "line_number": 209, "usage_type": "call"}, {"api_name": "sickchill.helper.common.dateTimeFormat", "line_number": 211, "usage_type": "argument"}, {"api_name": "datetime.datetime.now", "line_number": 211, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 211, "usage_type": "attribute"}]} +{"seq_id": "38803320825", "text": "import requests\nimport datetime\nfrom utils import send_line\nfrom constants import *\n\ndef aachen_an(year: str, month: str):\n user_agent = \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/103.0.0.0 Safari/537.36\"\n url = f'https://qtermin.de/api/timeslots?date={year}-{month}-01&serviceid=94948&rangesearch=1&caching=false&capacity=1&duration=10&cluster=false&slottype=0&fillcalendarstrategy=0&showavcap=false&appfuture=70&appdeadline=0&appdeadlinewm=0&oneoff=null&msdcm=0&calendarid=57095,57096,57097,74724,74725,133598'\n headers = {\"User-Agent\": user_agent, \"webid\": 'bahnhofplatzkatschhof'} \n res = requests.get(url, headers=headers).json()\n\n message = ''\n for t in res: \n if t['start'][5:7] == month:\n message += '\\n'\n message += t['start'][:10]\n ft = \"%H:%M:%S%z\" \n t = datetime.datetime.now().strftime(ft)\n if message:\n send_line(NOTIFY_URL, TOKEN, message) \n print(f'{message} at {t}')\n else: \n print(f'No available appointment in month {month} at {t}')\n\ndef aachen_permit():\n user_agent = \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/106.0.0.0 Safari/537.36\" \n headers = {\"User-Agent\": user_agent}\n \n url_1 = 'https://termine.staedteregion-aachen.de/auslaenderamt/'\n url_2 = 'https://termine.staedteregion-aachen.de/auslaenderamt/select2?md=1'\n url_3 = \"https://termine.staedteregion-aachen.de/auslaenderamt/suggest?mdt=52&select_cnc=1&cnc-204=0&cnc-205=0&cnc-198=0&cnc-201=0&cnc-202=0&cnc-189=0&cnc-203=0&cnc-196=0&cnc-200=0&cnc-199=0&cnc-188=0&cnc-186=0&cnc-193=0&cnc-183=0&cnc-184=0&cnc-185=0&cnc-187=0&cnc-190=0&cnc-195=0&cnc-191=1&cnc-194=0&cnc-197=0&cnc-192=0\"\n url_4 = 'https://termine.staedteregion-aachen.de/auslaenderamt/suggest?cnc-191=1&loc=28'\n\n res_1 = requests.get(url_1, headers=headers)\n res_2 = requests.get(url_2, headers=headers,cookies=res_1.cookies)\n res_3 = requests.get(url_3, headers=headers,cookies=res_2.cookies)\n res_4 = requests.get(url_4, headers=headers,cookies=res_3.cookies)\n ft = \"%H:%M:%S%z\" \n t = datetime.datetime.now().strftime(ft)\n if \"Kein freier Termin verfügbar\" not in res_4.text:\n send_line(NOTIFY_URL, TOKEN, \"延簽有預約名額\") \n print(f'{\"延簽有預約名額\"} at {t}')\n else:\n print(f'{\"延簽沒有預約名額\"} at {t}')\n ", "repo_name": "noworneverev/aachen-termin-alert", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 2460, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "requests.get", "line_number": 10, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 18, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 18, "usage_type": "attribute"}, {"api_name": "utils.send_line", "line_number": 20, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 34, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 35, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 36, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 37, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 39, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 39, "usage_type": "attribute"}, {"api_name": "utils.send_line", "line_number": 41, "usage_type": "call"}]} +{"seq_id": "73859105706", "text": "# -*- coding: utf-8 -*-\nimport pygame\nfrom pygame.locals import *\n\nfrom core import *\nimport os\n\nclass Image(pygame.rect.Rect):\n def __init__(self, x=0 ,y=0, path=u\"\"):\n self.image = pygame.image.load(path).convert()\n self.rect = self.image.get_rect()\n super(Image, self).__init__(x,y,self.rect.w,self.rect.h)\n self.width = self.rect.width\n self.height = self.rect.height\n self.x = x\n self.y = y\n \n def render(self, x=None, y=None):\n if x:\n self.x = x\n if y:\n self.y = y\n self.rect.x = self.x\n self.rect.y = self.y\n Game.get_screen().blit(self.image, self.rect)\n \nclass Font(pygame.sprite.Sprite):\n pass", "repo_name": "giginet/Flamesc", "sub_path": "src/pywaz/graphic.py", "file_name": "graphic.py", "file_ext": "py", "file_size_in_byte": 732, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "37", "api": [{"api_name": "pygame.rect", "line_number": 8, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 10, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 10, "usage_type": "attribute"}, {"api_name": "pygame.sprite", "line_number": 27, "usage_type": "attribute"}]} +{"seq_id": "28136623473", "text": "__author__ = \"Giacomo Tanganelli\"\n__copyright__ = \"Copyright (c) 2018, University of Pisa, Italy\"\n__version__ = \"0.1.0\"\n\n# Definition of Local Control Program that is in place for monitoring and controlling CoAP Congestion Control.\n\n\ndef ecoap_local_monitoring_program_cocoa_cc(control_engine):\n # do all needed imports here!!!\n import gevent\n\n # Specific Congestion Control Functions\n import random as rnd\n import _thread\n import time\n import math\n\n rto_strong = 2000\n rto_weak = 2000\n rto_overall = 2000\n rtt_strong = None\n rtt_weak = None\n rttvar_strong = None\n rttvar_weak = None\n timestamp = 0\n agingThread_started = False\n\n def event(interface, event_name, info):\n\n #if interface is None:\n #interface = \"lowpan1\" # testbed\n\n if event_name == \"coap_rx_success\":\n tx_success(interface,info)\n if event_name == \"coap_tx_failed\":\n tx_failed(interface, info)\n\n def send_rto(interface, rto):\n\n control_engine.blocking(True).net.iface(interface).set_parameters_net({'coap_rto': rto})\n\n # Send back to the controller the rto for statistical purposes\n control_engine.send_upstream( {\"msg_type\": \"event\", \"interface\": interface, \"event_name\": 'coap_rto', \"event_value\": rto})\n\n\n pass\n\n def agingThread(interface):\n nonlocal timestamp\n nonlocal rto_overall\n\n DEFAULT = 3000\n THRESHOLD = 60000\n\n while True:\n now = int(round(time.time() * 1000))\n if rto_overall > DEFAULT and now - timestamp > 4 * rto_overall:\n rto_overall = int((2000 + rto_overall) / rto_overall * 1000)\n rto = compute_rto_init(rto_overall)\n timestamp = now\n _thread.start_new_thread(send_rto, (interface, rto,))\n elif rto_overall < 1000 and now - timestamp > 16*rto_overall:\n rto_overall = 1000\n rto = compute_rto_init(rto_overall)\n timestamp = now\n _thread.start_new_thread(send_rto, (interface, rto,))\n gevent.sleep(1)\n\n\n def tx_success(interface, info):\n\n nonlocal rto_strong\n nonlocal rto_weak\n nonlocal rto_overall\n nonlocal rtt_strong\n nonlocal rtt_weak\n nonlocal rttvar_strong\n nonlocal rttvar_weak\n nonlocal timestamp\n nonlocal agingThread_started\n\n alpha = 0.125\n beta = 0.25\n k_strong = 4\n k_weak = 1\n lambda_strong = 0.5\n lambda_weak = 0.25\n\n r = int(info[1])\n retransmissions = int(info[3]) > 0\n if rtt_strong is None:\n rtt_strong = r\n rttvar_strong = float(r / 2)\n rtt_weak = r\n rttvar_weak = float(r / 2)\n elif not retransmissions:\n rtt_strong = (1 - alpha) * rtt_strong + alpha * r\n rttvar_strong = (1 - beta) * rttvar_strong + beta * abs(rtt_strong - r)\n rto_strong = rtt_strong + k_strong * rttvar_strong\n if rto_strong > 60000:\n rto_strong = 60000\n rto_overall = int(lambda_strong * rto_strong + (1 - lambda_strong) * rto_overall)\n else:\n rtt_weak = (1 - alpha) * rtt_weak + alpha * r\n rttvar_weak = (1 - beta) * rttvar_weak + beta * abs(rtt_weak - r)\n rto_weak = rtt_weak + k_weak * rttvar_weak\n if rto_weak > 60000:\n rto_weak = 60000\n rto_overall = int(lambda_weak * rto_weak + (1 - lambda_weak) * rto_overall)\n timestamp = int(round(time.time() * 1000))\n\n rto = compute_rto_init(rto_overall)\n\n _thread.start_new_thread(send_rto, (interface, rto,))\n if not agingThread_started:\n _thread.start_new_thread(agingThread, (interface,))\n agingThread_started = True\n\n def tx_failed(interface, info):\n\n pass\n\n # end specific CC functions\n\n def compute_rto_init(rto):\n\n FACTOR = 1.5\n rto_init = rnd.randrange(rto, int(math.ceil(FACTOR * rto)))\n if int(rto) < 1000:\n vbf = 3\n elif 1000 >= int(rto) <= 3000:\n vbf = 2\n else:\n vbf = 1.3\n\n rto_lst = [rto_init]\n rto_previous = rto_init\n for i in range(1, 4):\n rto_new = int(rto_previous * vbf)\n rto_previous = rto_new\n if rto_new < 4294967295:\n rto_lst.append(rto_new)\n else:\n rto_lst.append(4294967295)\n rto_tuple = tuple(rto_lst)\n return rto_tuple\n\n @control_engine.set_default_callback()\n def default_callback(cmd, data):\n control_engine.send_upstream({\"msg_type\": \"cmd_result\", \"cmd\": cmd, \"result\": data})\n pass\n\n def event_handler(interface, event_name, event_value):\n control_engine.send_upstream({\"msg_type\": \"event\", \"interface\": interface, \"event_name\": event_name, \"event_value\": event_value})\n event(interface,event_name, event_value)\n pass\n\n def report_callback(interface, report):\n control_engine.send_upstream({\"msg_type\": \"report\", \"interface\": interface, \"report\": report})\n pass\n\n print((\"local monitor cp started - Name: {}, Id: {} - STARTED\".format(control_engine.name, control_engine.id)))\n\n # control loop\n while not control_engine.is_stopped():\n msg = control_engine.recv(block=False)\n if msg is not None and type(msg) is dict and 'command' in msg:\n if 'interface' in msg:\n ifaces = msg['interface']\n else:\n ifaces = control_engine.blocking(True).radio.iface(\"lowpan0\").get_radio_platforms()\n if msg['command'] == 'SUBSCRIBE_EVENT':\n for iface in ifaces:\n if msg['upi_type'] == 'net':\n control_engine.blocking(False).net.iface(iface).subscribe_events_net(msg['event_key_list'], event_handler, msg['event_duration'])\n elif msg['upi_type'] == 'radio':\n control_engine.blocking(False).radio.iface(iface).subscribe_events(msg['event_key_list'], event_handler, msg['event_duration'])\n else:\n print(\"async event listener unsupported upi_type {}\".format(msg['upi_type']))\n elif msg['command'] == 'GET_MEASUREMENTS_PERIODIC':\n for iface in ifaces:\n if msg['upi_type'] == 'net':\n control_engine.blocking(False).iface(iface).net.get_measurements_periodic_net(msg['measurement_key_list'], msg['collect_period'], msg['report_period'], msg['num_iterations'], report_callback)\n elif msg['upi_type'] == 'radio':\n control_engine.blocking(False).iface(iface).radio.get_measurements_periodic(msg['measurement_key_list'], msg['collect_period'], msg['report_period'], msg['num_iterations'], report_callback)\n else:\n print(\"periodic measurement collector unsupported upi_type {}\".format(msg['upi_type']))\n else:\n print(\"local monitoring unknown command {}\".format(msg['command']))\n elif type(msg) is dict:\n print(\"local monitoring unknown msg type {}\".format(msg))\n\n gevent.sleep(1)\n\n print((\"local monitor cp - Name: {}, Id: {} - STOPPED\".format(control_engine.name, control_engine.id)))\n", "repo_name": "ECOAP/examples", "sub_path": "contiki/contiki_helpers/ecoap_helpers/ecoap_local_control_cocoa_cc.py", "file_name": "ecoap_local_control_cocoa_cc.py", "file_ext": "py", "file_size_in_byte": 7411, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "github-code", "pt": "37", "api": [{"api_name": "time.time", "line_number": 56, "usage_type": "call"}, {"api_name": "_thread.start_new_thread", "line_number": 61, "usage_type": "call"}, {"api_name": "_thread.start_new_thread", "line_number": 66, "usage_type": "call"}, {"api_name": "gevent.sleep", "line_number": 67, "usage_type": "call"}, {"api_name": "time.time", "line_number": 110, "usage_type": "call"}, {"api_name": "_thread.start_new_thread", "line_number": 114, "usage_type": "call"}, {"api_name": "_thread.start_new_thread", "line_number": 116, "usage_type": "call"}, {"api_name": "random.randrange", "line_number": 128, "usage_type": "call"}, {"api_name": "math.ceil", "line_number": 128, "usage_type": "call"}, {"api_name": "gevent.sleep", "line_number": 193, "usage_type": "call"}]} +{"seq_id": "16022009511", "text": "# core lib\nimport argparse\nimport json\nimport os\nimport sys\nimport uvicorn\n\n# custom lib\nimport SetupJailEnv\nimport CmdRestart\nimport CmdBootstrap\nimport CmdTemplate\nimport CmdConsole\nimport CmdClient\nimport CmdServer\nimport CmdHandler\n\nCMD_CREATE = 'create'\nCMD_INIT = 'init'\nCMD_RESTART = 'restart'\nCMD_CONSOLE = 'console'\nCMD_CONSOLE2 = 'con'\nCMD_BOOTSTRAP = 'bootstrap'\nCMD_CMD ='cmd'\nCMD_CLONE = 'clone'\nCMD_CONVERT = 'convert'\nCMD_TEMPLATE = 'template'\nCMD_CLIENT = 'client'\nCMD_SERVER = 'server'\n\nCmdChoices = [CMD_CREATE, CMD_INIT, \n CMD_RESTART, CMD_CONSOLE, \n CMD_CONSOLE2, CMD_BOOTSTRAP,\n CMD_CMD, CMD_CLONE,\n CMD_CONVERT, \n # CMD_TEMPLATE,\n CMD_CLIENT, CMD_SERVER]\n\ndef getParsedArgs():\n parser = argparse.ArgumentParser(prog='jailmin', description=\"Jailmin command line\")\n\n subparser = parser.add_subparsers(dest = 'cmd')\n\n TemplateParser = subparser.add_parser(CMD_TEMPLATE)\n TemplateParser.add_argument('JailId', help = 'Jail Id')\n TemplateParser.add_argument('TemplatePath', help = 'Template folder')\n TemplateParser.add_argument('-v', dest='vars', nargs=1, help = 'Full path to variables file (YAML format)')\n\n ConsoleParser = subparser.add_parser(CMD_CONSOLE)\n ConsoleParser.add_argument('JailId', help = 'Jail Id')\n\n CmdHandler.registerParsers(subparser)\n\n ServerParser = subparser.add_parser(CMD_SERVER)\n\n ClientParser = subparser.add_parser(CMD_CLIENT)\n ClientParser.add_argument('artifacts', nargs='*')\n\n RestartParser = subparser.add_parser(CMD_RESTART)\n RestartParser.add_argument('JailId', help = 'Jail Id')\n\n # parser.add_argument('cmd', choices = CmdChoices)\n # parser.add_argument('CmdArgs', nargs='*')\n\n # parser.add_argument('jailname', metavar='jail', type=str, nargs='?', help='jail name')\n # parser.add_argument('-c', '--config', nargs=1)\n # parser.add_argument('-v', '--vars', nargs=1)\n\n args = parser.parse_args()\n args.func(args)\n return args\n\n if len(sys.argv) < 2:\n print (parser.print_help())\n sys.exit(0)\n else:\n return parser.parse_args()\n\n\n\ndef onTemplate(**kwargs):\n print ('hmm')\n\ndef main():\n args = getParsedArgs()\n return\n\n if args.cmd == CMD_CREATE:\n print (args)\n # elif args.cmd == CMD_LIST or args.cmd == CMD_LIST2:\n # print (Bastille.getAllJails())\n elif args.cmd == CMD_INIT:\n SetupJailEnv.do()\n elif args.cmd == CMD_RESTART:\n CmdRestart.execCmd(args)\n elif args.cmd == CMD_CONSOLE or args.cmd == CMD_CONSOLE2:\n CmdConsole.execCmd(args)\n elif args.cmd == CMD_BOOTSTRAP:\n CmdBootstrap.execCmd(args)\n elif args.cmd == CMD_TEMPLATE:\n CmdTemplate.execCmd(args)\n elif args.cmd == CMD_CLIENT:\n CmdClient.execCmd(args)\n elif args.cmd == CMD_SERVER:\n HOST = os.environ['JAILMIN_SVC_HOST'] if 'JAILMIN_SVC_HOST' in os.environ else '0.0.0.0'\n PORT = os.environ['JAILMIN_SVC_PORT'] if 'JAILMIN_SVC_PORT' in os.environ else 3003\n\n print (f'Listening on {HOST}:{PORT}')\n # IF statement below must be run in the main script file\n # print ('__name__: ' + __name__)\n if __name__ == '__main__':\n instance = uvicorn.run('server:app', \n host=HOST,\n port=PORT,\n reload=True)\n print(instance)\n else:\n CmdServer.execCmd(args)\nmain()\n\n", "repo_name": "jhfoo/jailmin", "sub_path": "src/testarg.py", "file_name": "testarg.py", "file_ext": "py", "file_size_in_byte": 3197, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 40, "usage_type": "call"}, {"api_name": "CmdHandler.registerParsers", "line_number": 52, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 73, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 75, "usage_type": "call"}, {"api_name": "SetupJailEnv.do", "line_number": 93, "usage_type": "call"}, {"api_name": "CmdRestart.execCmd", "line_number": 95, "usage_type": "call"}, {"api_name": "CmdConsole.execCmd", "line_number": 97, "usage_type": "call"}, {"api_name": "CmdBootstrap.execCmd", "line_number": 99, "usage_type": "call"}, {"api_name": "CmdTemplate.execCmd", "line_number": 101, "usage_type": "call"}, {"api_name": "CmdClient.execCmd", "line_number": 103, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 105, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 106, "usage_type": "attribute"}, {"api_name": "uvicorn.run", "line_number": 112, "usage_type": "call"}, {"api_name": "CmdServer.execCmd", "line_number": 118, "usage_type": "call"}]} +{"seq_id": "40050203013", "text": "import numpy as np\nimport torch\nimport torch.optim as optim\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom dqn_utils import ReplayBuffer, update_model_parameters\n\nDEVICE = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n\nACTOR_PREFIX = 'actor_'\nCRITIC_PREFIX = 'critic_'\n\nclass CriticNetwork(nn.Module):\n\n def __init__(self, state_size, action_size, first_layer_output,\n second_layer_output):\n super(CriticNetwork, self).__init__()\n\n first_layer_input = state_size + action_size\n\n self.first_linear_layer = nn.Linear(in_features=first_layer_input,\n out_features=first_layer_output)\n\n self.second_linear_layer = nn.Linear(in_features=first_layer_output,\n out_features=second_layer_output)\n self.third_linear_layer = nn.Linear(in_features=second_layer_output,\n out_features=1)\n\n def forward(self, state, action):\n data_in_transit = torch.cat((state, action), dim=1)\n data_in_transit = F.relu(self.first_linear_layer(data_in_transit))\n data_in_transit = F.relu(self.second_linear_layer(data_in_transit))\n\n return self.third_linear_layer(data_in_transit)\n\n\nclass ReacherAgent():\n\n def __init__(self, state_size, action_size, action_min=-1, action_max=1,\n buffer_size=int(1e5), min_learning_samples=128,\n actor_learning_rate=1e-4, actor_2nd_input=400,\n actor_2nd_output=300, critic_1st_output=400, critic_2nd_output=300,\n critic_learning_rate=1e-3, gamma=0.9, tau=1e-3, noise_stdev=0.1):\n\n self.state_size = state_size\n self.action_size = action_size\n self.action_min = action_min\n self.action_max = action_max\n self.min_learning_samples = min_learning_samples\n self.gamma = gamma\n self.tau = tau\n self.noise_stdev = noise_stdev\n\n self.actor_local_network = self.get_actor_network(second_layer_input=actor_2nd_input,\n second_layer_output=actor_2nd_output)\n self.actor_target_network = self.get_actor_network(second_layer_input=actor_2nd_input,\n second_layer_output=actor_2nd_output)\n update_model_parameters(tau=1.0, local_network=self.actor_local_network,\n target_network=self.actor_target_network)\n self.actor_optimizer = optim.Adam(self.actor_local_network.parameters(),\n lr=actor_learning_rate)\n\n self.critic_local_network = self.get_critic_network(first_layer_output=critic_1st_output,\n second_layer_output=critic_2nd_output)\n self.critic_target_network = self.get_critic_network(first_layer_output=critic_1st_output,\n second_layer_output=critic_2nd_output)\n update_model_parameters(tau=1.0, local_network=self.critic_local_network,\n target_network=self.critic_target_network)\n self.critic_optimizer = optim.Adam(self.critic_local_network.parameters(),\n lr=critic_learning_rate)\n\n self.replay_buffer = ReplayBuffer(buffer_size=buffer_size,\n action_type=np.float32,\n training_batch_size=min_learning_samples,\n device=DEVICE)\n\n def get_actor_network(self, second_layer_input, second_layer_output):\n model = nn.Sequential(\n nn.Linear(in_features=self.state_size, out_features=second_layer_input),\n nn.ReLU(),\n nn.Linear(in_features=second_layer_input, out_features=second_layer_output),\n nn.ReLU(),\n nn.Linear(in_features=second_layer_output, out_features=self.action_size),\n nn.Tanh())\n\n return model.to(DEVICE)\n\n def get_critic_network(self, first_layer_output, second_layer_output):\n model = CriticNetwork(state_size=self.state_size,\n action_size=self.action_size,\n first_layer_output=first_layer_output,\n second_layer_output=second_layer_output)\n\n return model.to(DEVICE)\n\n def act(self, state, action_parameters):\n\n state = torch.from_numpy(state).float().unsqueeze(0).to(DEVICE)\n self.actor_local_network.eval()\n with torch.no_grad():\n action = self.actor_local_network(state).numpy()\n self.actor_local_network.train()\n\n add_noise = action_parameters['add_noise']\n if add_noise:\n action += self.noise_stdev * np.random.randn(self.action_size)\n\n return np.clip(action, self.action_min, self.action_max)\n\n def step(self, state, action, reward, next_state, done):\n \"\"\"\n Receives information from the environment. Is in charge of storing\n experiences in the replay buffer and triggering agent learning.\n \"\"\"\n\n self.replay_buffer.add(state, action, reward, next_state, done)\n\n if len(self.replay_buffer) > self.min_learning_samples:\n\n learning_samples = self.replay_buffer.sample()\n self.learn(learning_samples)\n\n def learn(self, learning_samples):\n states, actions, rewards, next_states, dones = learning_samples\n\n next_actions = self.actor_target_network(next_states)\n q_values_next_state = self.critic_target_network(next_states,\n next_actions.detach())\n q_value_current_state = rewards + (self.gamma * q_values_next_state * (1 - dones))\n q_value_expected = self.critic_local_network(states, actions)\n\n critic_loss = F.mse_loss(input=q_value_expected,\n target=q_value_current_state)\n self.critic_optimizer.zero_grad()\n critic_loss.backward()\n self.critic_optimizer.step()\n\n predicted_actions = self.actor_local_network(states)\n actor_loss = -self.critic_local_network(states, predicted_actions).mean()\n self.actor_optimizer.zero_grad()\n actor_loss.backward()\n self.actor_optimizer.step()\n\n update_model_parameters(tau=self.tau,\n local_network=self.critic_local_network,\n target_network=self.critic_target_network)\n update_model_parameters(tau=self.tau,\n local_network=self.actor_local_network,\n target_network=self.actor_target_network)\n\n def save_trained_weights(self, network_file):\n actor_network_file = ACTOR_PREFIX + network_file\n torch.save(self.actor_local_network.state_dict(), actor_network_file)\n\n critic_network_file = CRITIC_PREFIX + network_file\n torch.save(self.critic_local_network.state_dict(), critic_network_file)\n\n def load_trained_weights(self, network_file):\n \"\"\"\n Takes weights from a file and assigns them to the local network.\n \"\"\"\n actor_network_file = ACTOR_PREFIX + network_file\n self.actor_local_network.load_state_dict(torch.load(actor_network_file))\n print(\"Actor Network state loaded from \", actor_network_file)\n\n critic_network_file = CRITIC_PREFIX + network_file\n self.critic_local_network.load_state_dict(torch.load(critic_network_file))\n print(\"Critic Network state loaded from \", critic_network_file)\n", "repo_name": "cptanalatriste/luke-the-reacher", "sub_path": "luke_reacher.py", "file_name": "luke_reacher.py", "file_ext": "py", "file_size_in_byte": 7721, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "37", "api": [{"api_name": "torch.device", "line_number": 9, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 9, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 9, "usage_type": "attribute"}, {"api_name": "torch.nn.Module", "line_number": 14, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 14, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 22, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 22, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 25, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 25, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 27, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 27, "usage_type": "name"}, {"api_name": "torch.cat", "line_number": 31, "usage_type": "call"}, {"api_name": "torch.nn.functional.relu", "line_number": 32, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 32, "usage_type": "name"}, {"api_name": "torch.nn.functional.relu", "line_number": 33, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 33, "usage_type": "name"}, {"api_name": "dqn_utils.update_model_parameters", "line_number": 59, "usage_type": "call"}, {"api_name": "torch.optim.Adam", "line_number": 61, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 61, "usage_type": "name"}, {"api_name": "dqn_utils.update_model_parameters", "line_number": 68, "usage_type": "call"}, {"api_name": "torch.optim.Adam", "line_number": 70, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 70, "usage_type": "name"}, {"api_name": "dqn_utils.ReplayBuffer", "line_number": 73, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 74, "usage_type": "attribute"}, {"api_name": "torch.nn.Sequential", "line_number": 79, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 79, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 80, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 80, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 81, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 81, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 82, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 82, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 83, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 83, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 84, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 84, "usage_type": "name"}, {"api_name": "torch.nn.Tanh", "line_number": 85, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 85, "usage_type": "name"}, {"api_name": "torch.from_numpy", "line_number": 99, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 101, "usage_type": "call"}, {"api_name": "numpy.random.randn", "line_number": 107, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 107, "usage_type": "attribute"}, {"api_name": "numpy.clip", "line_number": 109, "usage_type": "call"}, {"api_name": "torch.nn.functional.mse_loss", "line_number": 133, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 133, "usage_type": "name"}, {"api_name": "dqn_utils.update_model_parameters", "line_number": 145, "usage_type": "call"}, {"api_name": "dqn_utils.update_model_parameters", "line_number": 148, "usage_type": "call"}, {"api_name": "torch.save", "line_number": 154, "usage_type": "call"}, {"api_name": "torch.save", "line_number": 157, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 164, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 168, "usage_type": "call"}]} +{"seq_id": "12468731928", "text": "#@Author Raunaq Suri\n#March 5, 2014\n#Same as the others, except gets subjects from the units file\n#PRE: Requries getUnitsFromFaculty.py to already have ran\n\nimport json\nimport urllib.request\nimport urllib.parse\nfrom pprint import pprint\n\n#To prevent sql queries from messing up\ndef escapeQuotes( string ):\n\tfor char in string:\n\t\tif (char == \"'\"):\n\t\t\tstring = string.replace(char, \"''\")\n\t\telif(char == \"\\\"\"):\n\t\t\tstring = string.replace(char,\"\\\"\\\"\")\n\n\treturn string\n\nprint(\"Getting all the courses and writing the sql queries\")\n\n#gets all the subjects \nbaseUrl = \"https://api.uwaterloo.ca/v2/courses/\"\napi_key = input(\"Enter the api key here: \") #asked to enter api key because having the api key as open source in code is not a smart idea\n\n\nsubjects = open('subjects.csv','r')\ncourses = open('courses.csv','w+')\n\n#CREATE SQL QUERY FILES HERE\n#sqlInsertFile = open('insertTables.sql', 'w+')\n#sqlCreateFile = open('createTables.sql', 'w+')\n#writes the header\ncourses.write('Course ID, Course Code, Course Name, Course Description, Faculty ID\\n')\n\nfor line in subjects:\n\tsplitLine = line.split(',') #splits line into a list between the comma\n\tsubjectName = splitLine[0] #name of the subject\n\tfacultyID = splitLine[1] #faculty\n\tfacultyName = splitLine[2]\n\n\t#skips the first line in the file\n\tif(facultyID == \"FacultyID\"):\n\t\tpass\n\n\t#calls the api to get all the courses from that subject\n\turl = baseUrl + subjectName + \".json?key=\"+api_key\n\tprint(url)\n\twebsite = urllib.request.urlopen(url)\n\n\t#gets the user's data\n\tdata = json.loads(website.read().decode(\"utf-8\"))\n\n\t#now loops through the courses to get the necessary info\n\tfor items in data['data']:\n\t\t#starts getting the required stuff\n\t\t#for now I'm just writing to a file all the data we need\n\t\tif(items['academic_level'] == \"undergraduate\"):\n\t\t\tcourseID = items['course_id']\n\t\t\tcourseCode = items['subject'] + items['catalog_number']\n\t\t\tcourseName = items['title']\n\t\t\tcourseDescription = items['description']\n\n\t\t\t#writes to the file\n\t\t\tlineToWrite = courseID + ',' + courseCode +','+ courseName +','+ \"\\\"\"+courseDescription +\"\\\"\" + \",\" + facultyID +\"\\n\"\n\t\t\tcourses.write(lineToWrite)\n\t\t\nsubjects.close()\ncourses.close()\n#sqlInsertFile.close()\n#sqlCreateFile.close()\ninput(\"Enter any key to exit: \")\n", "repo_name": "Tamini/UWCourseGooseRepo", "sub_path": "data/getCourses.py", "file_name": "getCourses.py", "file_ext": "py", "file_size_in_byte": 2250, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "urllib.request.request.urlopen", "line_number": 50, "usage_type": "call"}, {"api_name": "urllib.request.request", "line_number": 50, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 50, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 53, "usage_type": "call"}]} +{"seq_id": "38877115590", "text": "from selenium import webdriver\nfrom selenium.webdriver.support import expected_conditions as EC\nfrom selenium.webdriver.support.ui import WebDriverWait\nfrom selenium.webdriver.common.by import By\nfrom selenium.common.exceptions import TimeoutException\nfrom selenium.webdriver.support.ui import Select\nfrom fixture.navigation import NavigationHelper\nfrom fixture.session import SessionHelper\nfrom fixture.project import ProjectHelper\nfrom fixture.soap import SOAPHelper\n\n\nclass Application:\n\n def __init__(self, browser, base_url, user, password):\n if browser == \"firefox\":\n self.wd = webdriver.Firefox()\n elif browser == \"chrome\":\n self.wd = webdriver.Chrome()\n elif browser == \"ie\":\n self.wd = webdriver.Ie()\n else:\n raise ValueError(\"Unrecognized browser %s\" % browser)\n self.base_url = base_url\n self.user = user\n self.password = password\n self.navigation = NavigationHelper(self)\n self.session = SessionHelper(self)\n self.project = ProjectHelper(self)\n self.soap = SOAPHelper(self)\n\n def is_valid(self):\n try:\n self.wd.current_url\n return True\n except:\n return False\n\n def ensure_confirm_page(self, text):\n try:\n WebDriverWait(self.wd, 5).until(EC.text_to_be_present_in_element((By.CSS_SELECTOR, \".success-msg\"), text))\n except TimeoutException:\n raise TimeoutException(\"\\nConfirmation page was not loaded in due time.\\n\")\n\n def update_textbox(self, field, value):\n if value is not None:\n self.wd.find_element_by_id(field).click()\n self.wd.find_element_by_id(field).clear()\n self.wd.find_element_by_id(field).send_keys(value)\n\n def update_dropdown(self, field, value):\n if value is not None:\n item = Select(self.wd.find_element_by_id(field))\n item.select_by_visible_text(value)\n\n def update_checkbox(self, field, value):\n if not value:\n self.wd.find_element_by_id(field).click()\n\n def destroy(self):\n self.wd.quit()\n", "repo_name": "spcartman/mantis_tests", "sub_path": "fixture/application.py", "file_name": "application.py", "file_ext": "py", "file_size_in_byte": 2139, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "selenium.webdriver.Firefox", "line_number": 17, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 17, "usage_type": "name"}, {"api_name": "selenium.webdriver.Chrome", "line_number": 19, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 19, "usage_type": "name"}, {"api_name": "selenium.webdriver.Ie", "line_number": 21, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 21, "usage_type": "name"}, {"api_name": "fixture.navigation.NavigationHelper", "line_number": 27, "usage_type": "call"}, {"api_name": "fixture.session.SessionHelper", "line_number": 28, "usage_type": "call"}, {"api_name": "fixture.project.ProjectHelper", "line_number": 29, "usage_type": "call"}, {"api_name": "fixture.soap.SOAPHelper", "line_number": 30, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.ui.WebDriverWait", "line_number": 41, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.expected_conditions.text_to_be_present_in_element", "line_number": 41, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.expected_conditions", "line_number": 41, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.CSS_SELECTOR", "line_number": 41, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 41, "usage_type": "name"}, {"api_name": "selenium.common.exceptions.TimeoutException", "line_number": 42, "usage_type": "name"}, {"api_name": "selenium.common.exceptions.TimeoutException", "line_number": 43, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.ui.Select", "line_number": 53, "usage_type": "call"}]} +{"seq_id": "13894743895", "text": "\"\"\"\nCS131 - Computer Vision: Foundations and Applications\nAssignment 2\nAuthor: Donsuk Lee (donlee90@stanford.edu)\nDate created: 07/2017\nLast modified: 10/18/2017\nPython Version: 3.5+\n\"\"\"\n\nimport numpy as np\n\ndef conv(image, kernel):\n \"\"\" An implementation of convolution filter.\n\n This function uses element-wise multiplication and np.sum()\n to efficiently compute weighted sum of neighborhood at each\n pixel.\n\n Args:\n image: numpy array of shape (Hi, Wi).\n kernel: numpy array of shape (Hk, Wk).\n\n Returns:\n out: numpy array of shape (Hi, Wi).\n \"\"\"\n Hi, Wi = image.shape\n Hk, Wk = kernel.shape\n out = np.zeros((Hi, Wi))\n\n # For this assignment, we will use edge values to pad the images.\n # Zero padding will make derivatives at the image boundary very big,\n # whereas we want to ignore the edges at the boundary.\n pad_width0 = Hk // 2\n pad_width1 = Wk // 2\n pad_width = ((pad_width0,pad_width0),(pad_width1,pad_width1))\n padded = np.pad(image, pad_width, mode='edge')\n\n ### YOUR CODE HERE\n kernel = np.flipud(np.fliplr(kernel))\n for i in range(Hi):\n for j in range(Wi):\n out[i, j] = np.sum(padded[i: i+Hk, j: j+Wk] * kernel)\n ### END YOUR CODE\n\n return out\n\ndef gaussian_kernel(size, sigma):\n \"\"\" Implementation of Gaussian Kernel.\n\n This function follows the gaussian kernel formula,\n and creates a kernel matrix.\n\n Hints:\n - Use np.pi and np.exp to compute pi and exp.\n\n Args:\n size: int of the size of output matrix.\n sigma: float of sigma to calculate kernel.\n\n Returns:\n kernel: numpy array of shape (size, size).\n \"\"\"\n\n kernel = np.zeros((size, size))\n\n ### YOUR CODE HERE\n for i in range(size):\n for j in range(size):\n kernel[i][j] = (1/(2*np.pi*sigma**2)) * np.exp(-((i - size//2)**2 + (j - size//2)**2) / float(2*sigma**2))\n\n ### END YOUR CODE\n\n return kernel\n\ndef partial_x(img):\n \"\"\" Computes partial x-derivative of input img.\n\n Hints:\n - You may use the conv function in defined in this file.\n\n Args:\n img: numpy array of shape (H, W).\n Returns:\n out: x-derivative image.\n \"\"\"\n\n out = None\n\n ### YOUR CODE HERE\n kernel = np.array([[0.5,0,-0.5]])\n\n out = conv(img, kernel)\n ### END YOUR CODE\n\n return out\n\ndef partial_y(img):\n \"\"\" Computes partial y-derivative of input img.\n\n Hints:\n - You may use the conv function in defined in this file.\n\n Args:\n img: numpy array of shape (H, W).\n Returns:\n out: y-derivative image.\n \"\"\"\n\n out = None\n\n ### YOUR CODE HERE\n kernel = np.array([[0.5],[0],[-0.5]])\n\n out = conv(img, kernel)\n ### END YOUR CODE\n\n return out\n\ndef gradient(img):\n \"\"\" Returns gradient magnitude and direction of input img.\n\n Args:\n img: Grayscale image. Numpy array of shape (H, W).\n\n Returns:\n G: Magnitude of gradient at each pixel in img.\n Numpy array of shape (H, W).\n theta: Direction(in degrees, 0 <= theta < 360) of gradient\n at each pixel in img. Numpy array of shape (H, W).\n\n Hints:\n - Use np.sqrt and np.arctan2 to calculate square root and arctan\n \"\"\"\n G = np.zeros(img.shape)\n theta = np.zeros(img.shape)\n\n ### YOUR CODE HERE\n gx = partial_x(img)\n gy = partial_y(img)\n G = np.sqrt(gx ** 2 + gy ** 2)\n # G = [[np.sqrt(i**2 + j**2) for i, j in zip(r1, r2)] for r1, r2 in zip(gx, gy)]\n theta = (np.rad2deg(np.arctan2(gy, gx)) + 180) % 360\n ### END YOUR CODE\n\n return G, theta\n\n\ndef non_maximum_suppression(G, theta):\n \"\"\" Performs non-maximum suppression.\n\n This function performs non-maximum suppression along the direction\n of gradient (theta) on the gradient magnitude image (G).\n\n Args:\n G: gradient magnitude image with shape of (H, W).\n theta: direction of gradients with shape of (H, W).\n\n Returns:\n out: non-maxima suppressed image.\n \"\"\"\n H, W = G.shape\n out = np.zeros((H, W))\n\n # Round the gradient direction to the nearest 45 degrees\n theta = np.floor((theta + 22.5) / 45) * 45\n\n ### YOUR CODE HERE\n for i in range(1, H-1):\n for j in range(1, W-1):\n ang = int(theta[i][j]%360)\n if (ang%180 == 0):\n l = [G[i][j-1], G[i][j+1]]\n elif (ang%180 == 45):\n l = [G[i-1][j-1], G[i+1][j+1]]\n elif (ang%180 == 90):\n l = [G[i-1][j], G[i+1][j]]\n elif (ang%180 == 135):\n l = [G[i-1][j+1], G[i+1][j-1]]\n if G[i,j] >= np.max(l):\n out[i,j] = G[i,j]\n else:\n out[i, j] = 0\n ### END YOUR CODE\n\n return out\n\ndef double_thresholding(img, high, low):\n \"\"\"\n Args:\n img: numpy array of shape (H, W) representing NMS edge response.\n high: high threshold(float) for strong edges.\n low: low threshold(float) for weak edges.\n\n Returns:\n strong_edges: Boolean array representing strong edges.\n Strong edeges are the pixels with the values greater than\n the higher threshold.\n weak_edges: Boolean array representing weak edges.\n Weak edges are the pixels with the values smaller or equal to the\n higher threshold and greater than the lower threshold.\n \"\"\"\n\n strong_edges = np.zeros(img.shape, dtype=np.bool)\n weak_edges = np.zeros(img.shape, dtype=np.bool)\n\n ### YOUR CODE HERE\n strong_edges = img > high\n weak_edges = (img < high) & (img > low)\n\n ### END YOUR CODE\n\n return strong_edges, weak_edges\n\n\ndef get_neighbors(y, x, H, W):\n \"\"\" Return indices of valid neighbors of (y, x).\n\n Return indices of all the valid neighbors of (y, x) in an array of\n shape (H, W). An index (i, j) of a valid neighbor should satisfy\n the following:\n 1. i >= 0 and i < H\n 2. j >= 0 and j < W\n 3. (i, j) != (y, x)\n\n Args:\n y, x: location of the pixel.\n H, W: size of the image.\n Returns:\n neighbors: list of indices of neighboring pixels [(i, j)].\n \"\"\"\n neighbors = []\n\n for i in (y-1, y, y+1):\n for j in (x-1, x, x+1):\n if (i >= 0 and i < H and j >= 0 and j < W):\n if (i != y or j != x):\n neighbors.append((i, j))\n\n return neighbors\n\ndef link_edges(strong_edges, weak_edges):\n \"\"\" Find weak edges connected to strong edges and link them.\n\n Iterate over each pixel in strong_edges and perform breadth first\n search across the connected pixels in weak_edges to link them.\n Here we consider a pixel (a, b) is connected to a pixel (c, d)\n if (a, b) is one of the eight neighboring pixels of (c, d).\n\n Args:\n strong_edges: binary image of shape (H, W).\n weak_edges: binary image of shape (H, W).\n \n Returns:\n edges: numpy boolean array of shape(H, W).\n \"\"\"\n\n H, W = strong_edges.shape\n indices = np.stack(np.nonzero(strong_edges)).T\n edges = np.zeros((H, W), dtype=np.bool)\n\n # Make new instances of arguments to leave the original\n # references intact\n weak_edges = np.copy(weak_edges)\n edges = np.copy(strong_edges)\n\n ### YOUR CODE HERE\n for i in range(1, H-1):\n for j in range(1, W-1):\n neighbor = get_neighbors(j, i, H, W)\n if (weak_edges[i][j] and np.any(edges[x][y] for x, y in neighbor)):\n edges[i][j] = True\n\n\n ### END YOUR CODE\n return edges\n\ndef canny(img, kernel_size=5, sigma=1.4, high=20, low=15):\n \"\"\" Implement canny edge detector by calling functions above.\n\n Args:\n img: binary image of shape (H, W).\n kernel_size: int of size for kernel matrix.\n sigma: float for calculating kernel.\n high: high threshold for strong edges.\n low: low threashold for weak edges.\n Returns:\n edge: numpy array of shape(H, W).\n \"\"\"\n ### YOUR CODE HERE\n G, theta = gradient(conv(img, gaussian_kernel(kernel_size, sigma))) \n nms = non_maximum_suppression(G, theta)\n strong_edges, weak_edges = double_thresholding(nms, high, low)\n edge = link_edges(strong_edges, weak_edges)\n ### END YOUR CODE\n return edge\n\n\ndef hough_transform(img):\n \"\"\" Transform points in the input image into Hough space.\n\n Use the parameterization:\n rho = x * cos(theta) + y * sin(theta)\n to transform a point (x,y) to a sine-like function in Hough space.\n\n Args:\n img: binary image of shape (H, W).\n \n Returns:\n accumulator: numpy array of shape (m, n).\n rhos: numpy array of shape (m, ).\n thetas: numpy array of shape (n, ).\n \"\"\"\n # Set rho and theta ranges\n W, H = img.shape\n diag_len = int(np.ceil(np.sqrt(W * W + H * H)))\n print(diag_len)\n rhos = np.linspace(-diag_len, diag_len, diag_len * 2 + 1)\n # rhos = np.linspace(-diag_len, diag_len, diag_len * 2.0 + 1)\n thetas = np.deg2rad(np.arange(-90.0, 90.0))\n\n # Cache some reusable values\n cos_t = np.cos(thetas)\n sin_t = np.sin(thetas)\n num_thetas = len(thetas)\n\n # Initialize accumulator in the Hough space\n accumulator = np.zeros((2 * diag_len + 1, num_thetas), dtype=np.uint64)\n ys, xs = np.nonzero(img)\n\n # Transform each point (x, y) in image\n # Find rho corresponding to values in thetas\n # and increment the accumulator in the corresponding coordiate.\n ### YOUR CODE HERE\n for i, j in zip(ys, xs):\n for k in range(thetas.shape[0]):\n r = j * cos_t[k] + i * sin_t[k]\n accumulator[int(r + diag_len), k] += 1\n ### END YOUR CODE\n\n return accumulator, rhos, thetas\n\n# A-1 starts\n\nfrom edge import conv, gaussian_kernel\nfrom matplotlib import pyplot as plt\nfrom skimage import io\n\n# Define 3x3 Gaussian kernel with std = 1\nkernel = gaussian_kernel(3, 1)\nkernel_test = np.array(\n [[0.05854983, 0.09653235, 0.05854983],\n [0.09653235, 0.15915494, 0.09653235],\n [0.05854983, 0.09653235, 0.05854983]]\n)\n# Test Gaussian kernel\nif not np.allclose(kernel, kernel_test):\n print('Incorrect values! Please check your implementation')\n\n# A-1 ends\n# A-2 starts\n\n# Test with different kernel_size and sigma\nkernel_size = 5\nsigma = 1.4\n\n# Load image\nimg = io.imread('iguana.png', as_gray=True)\n\n# Define 5x5 Gaussian kernel with std = sigma\nkernel = gaussian_kernel(kernel_size, sigma)\n\n# Convolve image with kernel to achieve smoothed effect\nsmoothed = conv(img, kernel)\n\nplt.subplot(1, 2, 1)\nplt.imshow(img)\nplt.title('Original image')\nplt.axis('off')\n\nplt.subplot(1, 2, 2)\nplt.imshow(smoothed)\nplt.title('Smoothed image')\nplt.axis('off')\n\nplt.show()\n\n# A-2 ends\n# B-1 starts\n\nfrom edge import partial_x, partial_y\n\n# Test input\nI = np.array(\n [[0, 0, 0],\n [0, 1, 0],\n [0, 0, 0]]\n)\n\n# Expected outputs\nI_x_test = np.array(\n [[0, 0, 0],\n [0.5, 0, -0.5],\n [0, 0, 0]]\n)\n\nI_y_test = np.array(\n [[0, 0.5, 0],\n [0, 0, 0],\n [0, -0.5, 0]]\n)\n\n# Compute partial derivatives\nI_x = partial_x(I)\nI_y = partial_y(I)\n\n# Test correctness of partial_x and partial_y\nif (not np.all(I_x == I_x_test)):\n print('partial_x incorrect')\n\nif (not np.all(I_y == I_y_test)):\n print('partial_y incorrect')\n\n# Compute parital derivatives of smoothed image\nGx = partial_x(smoothed)\nGy = partial_y(smoothed)\n\nplt.subplot(1, 2, 1)\nplt.imshow(Gx)\nplt.title('Derivative in x direction')\nplt.axis('off')\n\nplt.subplot(1, 2, 2)\nplt.imshow(Gy)\nplt.title('Derivative in y direction')\nplt.axis('off')\n\nplt.show()\n\n# B-1 ends\n# B-3 starts\n\nfrom edge import gradient\n\nG, theta = gradient(smoothed)\n\nif (not np.all(G >= 0)):\n print('Magnitude of gradients should be non-negative')\n\nif (not np.all((theta >= 0) * (theta < 360))):\n print('Direction of gradients should be in range 0 <= theta < 360')\n\nplt.imshow(G)\nplt.title('Gradient magnitude')\nplt.axis('off')\nplt.show()\n\n# B-3 ends\n# C starts\n\nfrom edge import non_maximum_suppression\n\n# Test input\ng = np.array(\n [[0.4, 0.5, 0.6],\n [0.3, 0.5, 0.7],\n [0.4, 0.5, 0.6]]\n)\n\n# Print out non-maximum suppressed output\n# Varying theta\nfor angle in range(0, 180, 45):\n print('Thetas:', angle)\n t = np.ones((3, 3)) * angle\n print(non_maximum_suppression(g, t))\n\nnms = non_maximum_suppression(G, theta)\nplt.imshow(nms)\nplt.title('Non-maximum suppressed')\nplt.axis('off')\nplt.show()\n\nplt.subplot(1, 3, 1)\nplt.imshow(nms)\nplt.axis('off')\nplt.title('Your Result')\n\nplt.subplot(1, 3, 2)\nreference = np.load('references/iguana_non_max_suppressed.npy')\nplt.imshow(reference)\nplt.axis('off')\nplt.title('Reference')\n\nplt.subplot(1, 3, 3)\nplt.imshow(nms - reference)\nplt.title('Difference')\nplt.axis('off')\nplt.show()\n\n# C ends\n# D starts\n\nfrom edge import double_thresholding\n\nlow_threshold = 0.02\nhigh_threshold = 0.03\n\nstrong_edges, weak_edges = double_thresholding(nms, high_threshold, low_threshold)\nassert(np.sum(strong_edges & weak_edges) == 0)\n\nedges = strong_edges * 1.0 + weak_edges * 0.5\n\nplt.subplot(1, 2, 1)\nplt.imshow(strong_edges)\nplt.title('Strong Edges')\nplt.axis('off')\n\nplt.subplot(1, 2, 2)\nplt.imshow(edges)\nplt.title('Strong+Weak Edges')\nplt.axis('off')\n\nplt.show()\n\n# D ends\n# E starts\n\nfrom edge import get_neighbors, link_edges\n\ntest_strong = np.array(\n [[1, 0, 0, 0],\n [0, 0, 0, 0],\n [0, 0, 0, 0],\n [0, 0, 0, 1]],\n dtype=np.bool\n)\n\ntest_weak = np.array(\n [[0, 0, 0, 1],\n [0, 1, 0, 0],\n [1, 0, 0, 0],\n [0, 0, 1, 0]],\n dtype=np.bool\n)\n\ntest_linked = link_edges(test_strong, test_weak)\n\nplt.subplot(1, 3, 1)\nplt.imshow(test_strong)\nplt.title('Strong edges')\n\nplt.subplot(1, 3, 2)\nplt.imshow(test_weak)\nplt.title('Weak edges')\n\nplt.subplot(1, 3, 3)\nplt.imshow(test_linked)\nplt.title('Linked edges')\nplt.show()\n\nedges = link_edges(strong_edges, weak_edges)\n\nplt.imshow(edges)\nplt.axis('off')\nplt.show()\n\nplt.subplot(1, 3, 1)\nplt.imshow(edges)\nplt.axis('off')\nplt.title('Your result')\n\nplt.subplot(1, 3, 2)\nreference = np.load('references/iguana_edge_tracking.npy')\nplt.imshow(reference)\nplt.axis('off')\nplt.title('Reference')\n\nplt.subplot(1, 3, 3)\nplt.imshow(edges ^ reference)\nplt.title('Difference')\nplt.axis('off')\nplt.show()\n\n# E ends\n# F starts\n\nfrom edge import canny\n\n# Load image\nimg = io.imread('iguana.png', as_gray=True)\n\n# Run Canny edge detector\nedges = canny(img, kernel_size=5, sigma=1.4, high=0.03, low=0.02)\nprint(edges.shape)\n\nplt.subplot(1, 3, 1)\nplt.imshow(edges)\nplt.axis('off')\nplt.title('Your result')\n\nplt.subplot(1, 3, 2)\nreference = np.load('references/iguana_canny.npy')\nplt.imshow(reference)\nplt.axis('off')\nplt.title('Reference')\n\nplt.subplot(1, 3, 3)\nplt.imshow(edges ^ reference)\nplt.title('Difference')\nplt.axis('off')\nplt.show()\n\n# F ends\n# 2.A starts\n\nfrom edge import canny\n\n# Load image\nimg = io.imread('road.jpg', as_gray=True)\n\n# Run canny edge detector\nedges = canny(img, kernel_size=5, sigma=1.4, high=0.03, low=0.02)\n\nplt.subplot(211)\nplt.imshow(img)\nplt.axis('off')\nplt.title('Input image')\n\nplt.subplot(212)\nplt.imshow(edges)\nplt.axis('off')\nplt.title('Edges')\nplt.show()\n\n# 2.A ends\n# 2.B starts\n\nH, W = img.shape\n\n# Generate mask for ROI(Region of Interest)\nmask = np.zeros((H, W))\nfor i in range(H):\n for j in range(W):\n if (i>(H/W) * j and i > -(H/W) * j + H):\n mask[i, j] = i\n\n# Extract edges in ROI\nroi = edges * mask\n\nplt.subplot(1, 2, 1)\nplt.imshow(mask)\nplt.title('Mask')\nplt.axis('off')\n\nplt.subplot(1, 2, 2)\nplt.imshow(roi)\nplt.title('Edges in ROI')\nplt.axis('off')\nplt.show()\n\n# 2.B ends\n# 2.C starts\n\nfrom edge import hough_transform\n\n# Perform Hough transform on the ROI\nacc, rhos, thetas = hough_transform(roi)\n\n# Coordinates for right lane\nxs_right = []\nys_right = []\n\n# Coordinates for left lane\nxs_left = []\nys_left = []\n\nfor i in range(20):\n idx = np.argmax(acc)\n r_idx = idx // acc.shape[1]\n t_idx = idx % acc.shape[1]\n acc[r_idx, t_idx] = 0 # zero out the max value in accumulator\n\n rho = rhos[r_idx]\n theta = thetas[t_idx]\n\n # Transform a point in Hough space to a line in xy-space.\n a = -(np.cos(theta) / np.sin(theta)) # slope of the line\n b = (rho/np.sin(theta)) # y-intersect of the line\n\n # Break if both right and left lanes are detected\n if (xs_right and xs_left):\n break\n\n if (a<0): # Left lane\n if (xs_left):\n continue\n xs = xs_left\n ys = ys_left\n \n else: # Right lane\n if (xs_right):\n continue\n xs = xs_right\n ys = ys_right\n \n for x in range(img.shape[1]):\n y = a * x + b\n if (y > img.shape[0] * 0.6 and y < img.shape[0]):\n xs.append(x)\n ys.append(int(round(y)))\n\nplt.imshow(img)\nplt.plot(xs_left, ys_left, linewidth=5.0)\nplt.plot(xs_right, ys_right, linewidth=5.0)\nplt.axis('off')\nplt.show()", "repo_name": "seydouxxx/cvClass", "sub_path": "21CV_hw3/edge.py", "file_name": "edge.py", "file_ext": "py", "file_size_in_byte": 16878, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "numpy.zeros", "line_number": 28, "usage_type": "call"}, {"api_name": "numpy.pad", "line_number": 36, "usage_type": "call"}, {"api_name": "numpy.flipud", "line_number": 39, "usage_type": "call"}, {"api_name": "numpy.fliplr", "line_number": 39, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 42, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 64, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 69, "usage_type": "attribute"}, {"api_name": "numpy.exp", "line_number": 69, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 90, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 112, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 134, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 135, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 140, "usage_type": "call"}, {"api_name": "numpy.rad2deg", "line_number": 142, "usage_type": "call"}, {"api_name": "numpy.arctan2", "line_number": 142, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 162, "usage_type": "call"}, {"api_name": "numpy.floor", "line_number": 165, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 179, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 203, "usage_type": "call"}, {"api_name": "numpy.bool", "line_number": 203, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 204, "usage_type": "call"}, {"api_name": "numpy.bool", "line_number": 204, "usage_type": "attribute"}, {"api_name": "numpy.stack", "line_number": 258, "usage_type": "call"}, {"api_name": "numpy.nonzero", "line_number": 258, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 259, "usage_type": "call"}, {"api_name": "numpy.bool", "line_number": 259, "usage_type": "attribute"}, {"api_name": "numpy.copy", "line_number": 263, "usage_type": "call"}, {"api_name": "numpy.copy", "line_number": 264, "usage_type": "call"}, {"api_name": "numpy.any", "line_number": 270, "usage_type": "call"}, {"api_name": "numpy.ceil", "line_number": 315, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 315, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 317, "usage_type": "call"}, {"api_name": "numpy.deg2rad", "line_number": 319, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 319, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 322, "usage_type": "call"}, {"api_name": "numpy.sin", "line_number": 323, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 327, "usage_type": "call"}, {"api_name": "numpy.uint64", "line_number": 327, "usage_type": "attribute"}, {"api_name": "numpy.nonzero", "line_number": 328, "usage_type": "call"}, {"api_name": "edge.gaussian_kernel", "line_number": 349, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 350, "usage_type": "call"}, {"api_name": "numpy.allclose", "line_number": 356, "usage_type": "call"}, {"api_name": "skimage.io.imread", "line_number": 367, "usage_type": "call"}, {"api_name": "skimage.io", "line_number": 367, "usage_type": "name"}, {"api_name": "edge.gaussian_kernel", "line_number": 370, "usage_type": "call"}, {"api_name": "edge.conv", "line_number": 373, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 375, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 375, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 376, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 376, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 377, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 377, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axis", "line_number": 378, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 378, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 380, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 380, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 381, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 381, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 382, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 382, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axis", "line_number": 383, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 383, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 385, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 385, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 393, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 400, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 406, "usage_type": "call"}, {"api_name": "edge.partial_x", "line_number": 413, "usage_type": "call"}, {"api_name": "edge.partial_y", "line_number": 414, "usage_type": "call"}, {"api_name": "numpy.all", "line_number": 417, "usage_type": "call"}, {"api_name": "numpy.all", "line_number": 420, "usage_type": "call"}, {"api_name": "edge.partial_x", "line_number": 424, "usage_type": "call"}, {"api_name": "edge.partial_y", "line_number": 425, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 427, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 427, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 428, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 428, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 429, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 429, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axis", "line_number": 430, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 430, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 432, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 432, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 433, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 433, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 434, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 434, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axis", "line_number": 435, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 435, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 437, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 437, "usage_type": "name"}, {"api_name": "edge.gradient", "line_number": 444, "usage_type": "call"}, {"api_name": "numpy.all", "line_number": 446, "usage_type": "call"}, {"api_name": "numpy.all", "line_number": 449, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 452, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 452, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 453, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 453, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axis", "line_number": 454, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 454, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 455, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 455, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 463, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 473, "usage_type": "call"}, {"api_name": "edge.non_maximum_suppression", "line_number": 474, "usage_type": "call"}, {"api_name": "edge.non_maximum_suppression", "line_number": 476, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 477, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 477, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 478, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 478, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axis", "line_number": 479, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 479, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 480, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 480, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 482, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 482, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 483, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 483, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axis", "line_number": 484, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 484, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 485, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 485, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 487, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 487, "usage_type": "name"}, {"api_name": "numpy.load", "line_number": 488, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 489, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 489, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axis", "line_number": 490, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 490, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 491, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 491, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 493, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 493, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 494, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 494, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 495, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 495, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axis", "line_number": 496, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 496, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 497, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 497, "usage_type": "name"}, {"api_name": "edge.double_thresholding", "line_number": 507, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 508, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 512, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 512, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 513, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 513, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 514, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 514, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axis", "line_number": 515, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 515, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 517, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 517, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 518, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 518, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 519, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 519, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axis", "line_number": 520, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 520, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 522, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 522, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 529, "usage_type": "call"}, {"api_name": "numpy.bool", "line_number": 534, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 537, "usage_type": "call"}, {"api_name": "numpy.bool", "line_number": 542, "usage_type": "attribute"}, {"api_name": "edge.link_edges", "line_number": 545, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 547, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 547, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 548, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 548, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 549, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 549, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 551, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 551, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 552, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 552, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 553, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 553, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 555, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 555, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 556, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 556, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 557, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 557, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 558, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 558, "usage_type": "name"}, {"api_name": "edge.link_edges", "line_number": 560, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 562, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 562, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axis", "line_number": 563, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 563, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 564, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 564, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 566, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 566, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 567, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 567, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axis", "line_number": 568, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 568, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 569, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 569, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 571, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 571, "usage_type": "name"}, {"api_name": "numpy.load", "line_number": 572, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 573, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 573, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axis", "line_number": 574, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 574, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 575, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 575, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 577, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 577, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 578, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 578, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 579, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 579, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axis", "line_number": 580, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 580, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 581, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 581, "usage_type": "name"}, {"api_name": "skimage.io.imread", "line_number": 589, "usage_type": "call"}, {"api_name": "skimage.io", "line_number": 589, "usage_type": "name"}, {"api_name": "edge.canny", "line_number": 592, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 595, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 595, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 596, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 596, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axis", "line_number": 597, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 597, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 598, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 598, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 600, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 600, "usage_type": "name"}, {"api_name": "numpy.load", "line_number": 601, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 602, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 602, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axis", "line_number": 603, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 603, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 604, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 604, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 606, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 606, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 607, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 607, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 608, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 608, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axis", "line_number": 609, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 609, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 610, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 610, "usage_type": "name"}, {"api_name": "skimage.io.imread", "line_number": 618, "usage_type": "call"}, {"api_name": "skimage.io", "line_number": 618, "usage_type": "name"}, {"api_name": "edge.canny", "line_number": 621, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 623, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 623, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 624, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 624, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axis", "line_number": 625, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 625, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 626, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 626, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 628, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 628, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 629, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 629, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axis", "line_number": 630, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 630, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 631, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 631, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 632, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 632, "usage_type": "name"}, {"api_name": "numpy.zeros", "line_number": 640, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 649, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 649, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 650, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 650, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 651, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 651, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axis", "line_number": 652, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 652, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 654, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 654, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 655, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 655, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 656, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 656, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axis", "line_number": 657, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 657, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 658, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 658, "usage_type": "name"}, {"api_name": "edge.hough_transform", "line_number": 666, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 677, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 686, "usage_type": "call"}, {"api_name": "numpy.sin", "line_number": 686, "usage_type": "call"}, {"api_name": "numpy.sin", "line_number": 687, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 711, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 711, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 712, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 712, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 713, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 713, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axis", "line_number": 714, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 714, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 715, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 715, "usage_type": "name"}]} +{"seq_id": "21354377681", "text": "\"\"\"\nHate speech classification baseline using sklearn\nDataset: https://www.kaggle.com/c/jigsaw-toxic-comment-classification-challenge/data\n\"\"\"\nfrom sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n\n__author__ = \"don.tuggener@zhaw.ch\"\n\nimport keras\nimport sys\nimport pickle\nimport utils_classifier\nimport numpy as np\n\nfrom keras.models import Sequential\nfrom keras import layers\nimport matplotlib.pyplot as plt\n\nfrom sklearn.feature_extraction.text import TfidfVectorizer\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import f1_score\n\n\ndef plot_history(history):\n acc = history.history['accuracy']\n val_acc = history.history['val_accuracy']\n loss = history.history['loss']\n val_loss = history.history['val_loss']\n x = range(1, len(acc) + 1)\n\n plt.figure(figsize=(12, 5))\n plt.subplot(1, 2, 1)\n plt.plot(x, acc, 'b', label='Training acc')\n plt.plot(x, val_acc, 'r', label='Validation acc')\n plt.title('Training and validation accuracy')\n plt.legend()\n plt.subplot(1, 2, 2)\n plt.plot(x, loss, 'b', label='Training loss')\n plt.plot(x, val_loss, 'r', label='Validation loss')\n plt.title('Training and validation loss')\n plt.legend()\n plt.savefig('learning_accuracy_loss.png')\n plt.show()\n\n\nif __name__ == '__main__':\n\n print('Loading data', file=sys.stderr)\n X, Y = utils_classifier.read_data_classifier(reprocess=True)\n\n print('Vectorizing with TFIDF', file=sys.stderr)\n tfidfizer = TfidfVectorizer(stop_words='english', max_features=1000)\n X_tfidf_matrix = tfidfizer.fit_transform(X)\n print('Data shape:', X_tfidf_matrix.shape)\n\n with open('vectorizer.pk', 'wb') as fin:\n pickle.dump(tfidfizer, fin)\n\n do_downsample = True\n if do_downsample: # Only take 20% of the data\n X_tfidf_matrix, X_, Y, Y_ = train_test_split(X_tfidf_matrix, Y, test_size=0.8, random_state=42, stratify=Y)\n print('Downsampled data shape:', X_tfidf_matrix.shape)\n\n print('Classification and evaluation', file=sys.stderr)\n\n # Randomly split data into 80% training and 20% testing, preserve class distribution with stratify\n X_train, X_test, Y_train, Y_test = train_test_split(X_tfidf_matrix, Y, test_size=0.2, random_state=42, stratify=Y)\n\n print('x_train shape:', X_train.shape)\n print(X_train.shape[0], 'train samples')\n print(X_test.shape[0], 'test samples')\n\n num_classes = 2\n batch_size = 512\n epochs = 20\n\n Y_train = keras.utils.to_categorical(Y_train, num_classes)\n Y_test = keras.utils.to_categorical(Y_test, num_classes)\n\n clf = Sequential()\n input_dim = X_train.shape[1]\n clf.add(layers.Dense(16, input_dim=input_dim, activation='relu'))\n clf.add(layers.Dense(16, input_dim=input_dim, activation='relu'))\n clf.add(layers.Dense(2, input_dim=input_dim, activation='sigmoid'))\n clf.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n clf.summary()\n\n history = clf.fit(X_train, Y_train,\n batch_size=batch_size,\n epochs=epochs,\n verbose=False,\n validation_data=(X_test, Y_test))\n\n loss, accuracy = clf.evaluate(X_train, Y_train, verbose=False)\n print(\"Training Accuracy: {:.4f}\".format(accuracy))\n print(\"Training loss: {:.4f}\".format(loss))\n\n loss, accuracy = clf.evaluate(X_test, Y_test, verbose=False)\n print(\"Testing Accuracy: {:.4f}\".format(accuracy))\n print(\"Testing loss: {:.4f}\".format(loss))\n\n # predict probabilities for test set\n yhat_probs = clf.predict(X_test, verbose=0)\n # predict crisp classes for test set\n yhat_classes = clf.predict_classes(X_test, verbose=0)\n # reduce to 1d array\n yhat_probs = yhat_probs[:, 0]\n\n w, h = 2, len(yhat_classes)\n y_test_2d = [[0 for x in range(w)] for y in range(h)]\n for i in range(len(yhat_classes)):\n if yhat_classes[i] == 1:\n y_test_2d[i] = [0, 1]\n else:\n y_test_2d[i] = [1, 0]\n\n # w, h = 6, len(yhat_classes)\n # y_test_2d = [[0 for x in range(w)] for y in range(h)]\n # for i in range(len(yhat_classes)):\n # if yhat_classes[i] == 0:\n # y_test_2d[i] = [1, 0, 0, 0, 0, 0]\n # elif yhat_classes[i] == 1:\n # y_test_2d[i] = [0, 1, 0, 0, 0, 0]\n # elif yhat_classes[i] == 2:\n # y_test_2d[i] = [0, 0, 1, 0, 0, 0]\n # elif yhat_classes[i] == 3:\n # y_test_2d[i] = [0, 0, 0, 1, 0, 0]\n # elif yhat_classes[i] == 4:\n # y_test_2d[i] = [0, 0, 0, 0, 1, 0]\n # else:\n # y_test_2d[i] = [0, 0, 0, 0, 0, 1]\n\n # # accuracy: (tp + tn) / (p + n)\n # accuracy = accuracy_score(Y_test, yhat_classes)\n # print('Accuracy: %f' % accuracy)\n # # precision tp / (tp + fp)\n # precision = precision_score(Y_test, yhat_classes)\n # print('Precision: %f' % precision)\n # # recall: tp / (tp + fn)\n # recall = recall_score(Y_test, yhat_classes)\n # print('Recall: %f' % recall)\n # f1: 2 tp / (2 tp + fp + fn)\n f1 = f1_score(Y_test, np.array(y_test_2d), average='weighted')\n print('F1 score weighted: %f' % f1)\n\n f1 = f1_score(Y_test, np.array(y_test_2d), average='micro')\n print('F1 score micro: %f' % f1)\n\n f1 = f1_score(Y_test, np.array(y_test_2d), average='macro')\n print('F1 score macro: %f' % f1)\n\n plt.style.use('ggplot')\n plot_history(history)\n\n clf.save('Sentiment.h5')\n\n\n", "repo_name": "dubelbog/Learning", "sub_path": "hate_speech_classifier/hate_speech_classification_neural_nets.py", "file_name": "hate_speech_classification_neural_nets.py", "file_ext": "py", "file_size_in_byte": 5448, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "matplotlib.pyplot.figure", "line_number": 31, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 31, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 32, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 32, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 33, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 33, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 34, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 34, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 35, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 35, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 36, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 36, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 37, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 37, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 38, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 38, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 39, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 39, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 40, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 40, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 41, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 41, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 42, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 42, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 43, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 43, "usage_type": "name"}, {"api_name": "sys.stderr", "line_number": 48, "usage_type": "attribute"}, {"api_name": "utils_classifier.read_data_classifier", "line_number": 49, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 51, "usage_type": "attribute"}, {"api_name": "sklearn.feature_extraction.text.TfidfVectorizer", "line_number": 52, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 57, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 61, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 64, "usage_type": "attribute"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 67, "usage_type": "call"}, {"api_name": "keras.utils.to_categorical", "line_number": 77, "usage_type": "call"}, {"api_name": "keras.utils", "line_number": 77, "usage_type": "attribute"}, {"api_name": "keras.utils.to_categorical", "line_number": 78, "usage_type": "call"}, {"api_name": "keras.utils", "line_number": 78, "usage_type": "attribute"}, {"api_name": "keras.models.Sequential", "line_number": 80, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 82, "usage_type": "call"}, {"api_name": "keras.layers", "line_number": 82, "usage_type": "name"}, {"api_name": "keras.layers.Dense", "line_number": 83, "usage_type": "call"}, {"api_name": "keras.layers", "line_number": 83, "usage_type": "name"}, {"api_name": "keras.layers.Dense", "line_number": 84, "usage_type": "call"}, {"api_name": "keras.layers", "line_number": 84, "usage_type": "name"}, {"api_name": "sklearn.metrics.f1_score", "line_number": 143, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 143, "usage_type": "call"}, {"api_name": "sklearn.metrics.f1_score", "line_number": 146, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 146, "usage_type": "call"}, {"api_name": "sklearn.metrics.f1_score", "line_number": 149, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 149, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.style.use", "line_number": 152, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.style", "line_number": 152, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 152, "usage_type": "name"}]} +{"seq_id": "20260642419", "text": "from keras.models import load_model\nfrom keras.preprocessing.image import img_to_array, load_img\nfrom keras.models import Sequential\nfrom keras.layers import Conv2D, MaxPooling2D\nfrom keras.layers import Activation, Dropout, Flatten, Dense\nfrom keras import backend as K\nimport numpy as np\n\nimg_width, img_height = 150, 150\n\nif K.image_data_format() == 'channels_first':\n input_shape = (3, img_width, img_height)\nelse:\n input_shape = (img_width, img_height, 3)\n\nmodel = Sequential()\nmodel.add(Conv2D(32, (3, 3), input_shape=input_shape))\nmodel.add(Activation('relu'))\nmodel.add(MaxPooling2D(pool_size=(2, 2)))\n\nmodel.add(Conv2D(32, (3, 3)))\nmodel.add(Activation('relu'))\nmodel.add(MaxPooling2D(pool_size=(2, 2)))\n\nmodel.add(Conv2D(64, (3, 3)))\nmodel.add(Activation('relu'))\nmodel.add(MaxPooling2D(pool_size=(2, 2)))\n\nmodel.add(Flatten())\nmodel.add(Dense(64))\nmodel.add(Activation('relu'))\nmodel.add(Dropout(0.5))\nmodel.add(Dense(1))\nmodel.add(Activation('sigmoid'))\n\n\nmodel.load_weights('first_try.h5')\n\n\nimg = load_img('frame_0.jpg',False,target_size=(img_width,img_height))\nx = img_to_array(img)\nx = np.expand_dims(x, axis=0)\npreds = model.predict_classes(x)\nprob = model.predict_proba(x)\nprint(preds, prob)\n", "repo_name": "cxlisolaf/asurada_robotics", "sub_path": "src/stop_sign/src/detect_stop.py", "file_name": "detect_stop.py", "file_ext": "py", "file_size_in_byte": 1216, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "keras.backend.image_data_format", "line_number": 11, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 11, "usage_type": "name"}, {"api_name": "keras.models.Sequential", "line_number": 16, "usage_type": "call"}, {"api_name": "keras.layers.Conv2D", "line_number": 17, "usage_type": "call"}, {"api_name": "keras.layers.Activation", "line_number": 18, "usage_type": "call"}, {"api_name": "keras.layers.MaxPooling2D", "line_number": 19, "usage_type": "call"}, {"api_name": "keras.layers.Conv2D", "line_number": 21, "usage_type": "call"}, {"api_name": "keras.layers.Activation", "line_number": 22, "usage_type": "call"}, {"api_name": "keras.layers.MaxPooling2D", "line_number": 23, "usage_type": "call"}, {"api_name": "keras.layers.Conv2D", "line_number": 25, "usage_type": "call"}, {"api_name": "keras.layers.Activation", "line_number": 26, "usage_type": "call"}, {"api_name": "keras.layers.MaxPooling2D", "line_number": 27, "usage_type": "call"}, {"api_name": "keras.layers.Flatten", "line_number": 29, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 30, "usage_type": "call"}, {"api_name": "keras.layers.Activation", "line_number": 31, "usage_type": "call"}, {"api_name": "keras.layers.Dropout", "line_number": 32, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 33, "usage_type": "call"}, {"api_name": "keras.layers.Activation", "line_number": 34, "usage_type": "call"}, {"api_name": "keras.preprocessing.image.load_img", "line_number": 40, "usage_type": "call"}, {"api_name": "keras.preprocessing.image.img_to_array", "line_number": 41, "usage_type": "call"}, {"api_name": "numpy.expand_dims", "line_number": 42, "usage_type": "call"}]} +{"seq_id": "16444266720", "text": "import pandas as pd\nfrom google.cloud import bigquery\nfrom google.oauth2 import service_account\nfrom reports.quarterly_etl import QuarterlyReport, extract\n\n\nclass PolkaholicExtractor:\n \"\"\"Extract data from Polkaholic, either via Big Query or directly from\n Polkaholic API.\n \"\"\"\n\n def __init__(self, from_bigquery=True, credentials=\"service-account.json\",\n route=\"\"):\n self.from_bigquery = from_bigquery\n if from_bigquery:\n credentials = service_account.Credentials.from_service_account_file(\n credentials)\n self.client = bigquery.Client(credentials=credentials)\n else:\n self.method = \"GET\"\n self.url = f\"https://api.polkaholic.io{route}\"\n\n def extract_bigquery(self, query_template,\n start=QuarterlyReport().start_time,\n end=QuarterlyReport().end_time, **kwargs):\n \"\"\"\n Keyword arguments:\n start: start point of the time range of interest\n end: end point of the time range of interest\n \"\"\"\n start, end = [t.strftime(\"%Y-%m-%d\") for t in [start, end]]\n query = query_template.substitute(start=start, end=end, **kwargs)\n data = self.client.query(query)\n\n return data\n\n def extract_api(self, params):\n data = extract(self.method, self.url, params=params)\n\n return data\n\n def extract(self, *args, **kwargs):\n if self.from_bigquery:\n data = self.extract_bigquery(*args, **kwargs)\n else:\n data = self.extract_api(*args)\n\n return data\n\n\nclass PolkaholicTransformer:\n \"\"\"Convert json-encoded content to a dataframe.\"\"\"\n\n def __init__(self, data):\n self.data = data\n\n def to_frame(self):\n if isinstance(self.data, bigquery.QueryJob):\n df = self.data.to_dataframe()\n else:\n df = pd.json_normalize(self.data, sep=\"_\")\n\n return df\n", "repo_name": "tara-nguyen/crypto-data", "sub_path": "sources/polkaholic.py", "file_name": "polkaholic.py", "file_ext": "py", "file_size_in_byte": 1971, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "google.oauth2.service_account.Credentials.from_service_account_file", "line_number": 16, "usage_type": "call"}, {"api_name": "google.oauth2.service_account.Credentials", "line_number": 16, "usage_type": "attribute"}, {"api_name": "google.oauth2.service_account", "line_number": 16, "usage_type": "name"}, {"api_name": "google.cloud.bigquery.Client", "line_number": 18, "usage_type": "call"}, {"api_name": "google.cloud.bigquery", "line_number": 18, "usage_type": "name"}, {"api_name": "reports.quarterly_etl.QuarterlyReport", "line_number": 24, "usage_type": "call"}, {"api_name": "reports.quarterly_etl.QuarterlyReport", "line_number": 25, "usage_type": "call"}, {"api_name": "reports.quarterly_etl.extract", "line_number": 38, "usage_type": "call"}, {"api_name": "google.cloud.bigquery.QueryJob", "line_number": 58, "usage_type": "attribute"}, {"api_name": "google.cloud.bigquery", "line_number": 58, "usage_type": "name"}, {"api_name": "pandas.json_normalize", "line_number": 61, "usage_type": "call"}]} +{"seq_id": "17985470006", "text": "###########################################\n# #\n# #\n# SeriSR - A CLI Project #\n# #\n# By - OpenPrattay #\n###########################################\nimport time\nimport serial\nimport sys\nimport colorama\nfrom colorama import Fore, Back, Style\n\n# Check if arguments are missing\nif len(sys.argv) != 3:\n print(Fore.RED + Style.BRIGHT + \"Usage: python script.py \" + Style.RESET_ALL)\n sys.exit(1)\n\n# Get the arguments\ncomport = sys.argv[1]\nbaudrate = sys.argv[2]\n\n# Check if any argument is empty\nif not comport or not baudrate:\n print(Fore.RED + Style.BRIGHT + \"Error: Both arguments are required.\\nUsage: python script.py \"\n + Style.RESET_ALL)\n sys.exit(1)\n\n# Use the arguments\nprint(Fore.GREEN + Style.BRIGHT + \"---- SeriSR 1.0 By OpenPrattay ----\")\nprint(Style.RESET_ALL + \"COM PORT Given: \" + comport)\nprint(Style.RESET_ALL + \"BAUD RATE Given: \" + baudrate)\nprint(Fore.GREEN + Style.BRIGHT + \"Trying To Connect To Serial Port...\")\ntry:\n ser = serial.Serial(comport, baudrate)\nexcept KeyboardInterrupt:\n sys.exit(0)\nexcept serial.SerialException:\n print(Fore.RED + Style.BRIGHT + \"Details Are Wrong, Please Check!\" + Style.RESET_ALL)\n sys.exit()\nexcept:\n print(Fore.RED + Style.BRIGHT + \"Something Has Gone Really Wrong!\\nClosing Program...\" + Style.RESET_ALL)\n sys.exit(2)\nprint(Fore.GREEN + Style.BRIGHT + \"Connected To Serial Port!\" + Style.RESET_ALL)\ndatatobesent = input(\"What Do You Want To Send?: \")\nif len(str(datatobesent)) == 0:\n print(Fore.RED + Style.BRIGHT + \"Data Can't Be Empty!\" + Style.RESET_ALL)\n exit(1)\n", "repo_name": "OrginalPrattaySarker/SeriSR", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 1770, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "sys.argv", "line_number": 15, "usage_type": "attribute"}, {"api_name": "colorama.Fore.RED", "line_number": 16, "usage_type": "attribute"}, {"api_name": "colorama.Fore", "line_number": 16, "usage_type": "name"}, {"api_name": "colorama.Style.BRIGHT", "line_number": 16, "usage_type": "attribute"}, {"api_name": "colorama.Style", "line_number": 16, "usage_type": "name"}, {"api_name": "colorama.Style.RESET_ALL", "line_number": 16, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 17, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 20, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 21, "usage_type": "attribute"}, {"api_name": "colorama.Fore.RED", "line_number": 25, "usage_type": "attribute"}, {"api_name": "colorama.Fore", "line_number": 25, "usage_type": "name"}, {"api_name": "colorama.Style.BRIGHT", "line_number": 25, "usage_type": "attribute"}, {"api_name": "colorama.Style", "line_number": 25, "usage_type": "name"}, {"api_name": "colorama.Style.RESET_ALL", "line_number": 26, "usage_type": "attribute"}, {"api_name": "colorama.Style", "line_number": 26, "usage_type": "name"}, {"api_name": "sys.exit", "line_number": 27, "usage_type": "call"}, {"api_name": "colorama.Fore.GREEN", "line_number": 30, "usage_type": "attribute"}, {"api_name": "colorama.Fore", "line_number": 30, "usage_type": "name"}, {"api_name": "colorama.Style.BRIGHT", "line_number": 30, "usage_type": "attribute"}, {"api_name": "colorama.Style", "line_number": 30, "usage_type": "name"}, {"api_name": "colorama.Style.RESET_ALL", "line_number": 31, "usage_type": "attribute"}, {"api_name": "colorama.Style", "line_number": 31, "usage_type": "name"}, {"api_name": "colorama.Style.RESET_ALL", "line_number": 32, "usage_type": "attribute"}, {"api_name": "colorama.Style", "line_number": 32, "usage_type": "name"}, {"api_name": "colorama.Fore.GREEN", "line_number": 33, "usage_type": "attribute"}, {"api_name": "colorama.Fore", "line_number": 33, "usage_type": "name"}, {"api_name": "colorama.Style.BRIGHT", "line_number": 33, "usage_type": "attribute"}, {"api_name": "colorama.Style", "line_number": 33, "usage_type": "name"}, {"api_name": "serial.Serial", "line_number": 35, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 37, "usage_type": "call"}, {"api_name": "serial.SerialException", "line_number": 38, "usage_type": "attribute"}, {"api_name": "colorama.Fore.RED", "line_number": 39, "usage_type": "attribute"}, {"api_name": "colorama.Fore", "line_number": 39, "usage_type": "name"}, {"api_name": "colorama.Style.BRIGHT", "line_number": 39, "usage_type": "attribute"}, {"api_name": "colorama.Style", "line_number": 39, "usage_type": "name"}, {"api_name": "colorama.Style.RESET_ALL", "line_number": 39, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 40, "usage_type": "call"}, {"api_name": "colorama.Fore.RED", "line_number": 42, "usage_type": "attribute"}, {"api_name": "colorama.Fore", "line_number": 42, "usage_type": "name"}, {"api_name": "colorama.Style.BRIGHT", "line_number": 42, "usage_type": "attribute"}, {"api_name": "colorama.Style", "line_number": 42, "usage_type": "name"}, {"api_name": "colorama.Style.RESET_ALL", "line_number": 42, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 43, "usage_type": "call"}, {"api_name": "colorama.Fore.GREEN", "line_number": 44, "usage_type": "attribute"}, {"api_name": "colorama.Fore", "line_number": 44, "usage_type": "name"}, {"api_name": "colorama.Style.BRIGHT", "line_number": 44, "usage_type": "attribute"}, {"api_name": "colorama.Style", "line_number": 44, "usage_type": "name"}, {"api_name": "colorama.Style.RESET_ALL", "line_number": 44, "usage_type": "attribute"}, {"api_name": "colorama.Fore.RED", "line_number": 47, "usage_type": "attribute"}, {"api_name": "colorama.Fore", "line_number": 47, "usage_type": "name"}, {"api_name": "colorama.Style.BRIGHT", "line_number": 47, "usage_type": "attribute"}, {"api_name": "colorama.Style", "line_number": 47, "usage_type": "name"}, {"api_name": "colorama.Style.RESET_ALL", "line_number": 47, "usage_type": "attribute"}]} +{"seq_id": "21202822919", "text": "# -*- coding: utf-8 -*-\n__author__ = 'SlovEnt'\n__date__ = '2019/8/28 18:37'\n\nimport time\nimport os\nfrom bs4 import BeautifulSoup\nfrom collections import OrderedDict\nfrom chs_tools.get_html_page import get_html_all_content, chrome_get_html_all_content\nfrom chs_tools.print_log import C_PrintLog\nfrom chs_tools.param_info import rtn_parainfo\nimport traceback\n\nplog = C_PrintLog()\nPARAINFO = rtn_parainfo()\nDOWN_FLODERS = PARAINFO[\"NOVEL_DOWN_FLODERS\"]\n\nROOT_URL = \"https://www.lewenxiaoshuo.com\" # 网站根目录\nGENERAL_PATH = \"books\" # 通用路径\nNOVEL_SUB_ID = \"xiaoxiaojiaofeiyangchengji\" # 目录页面ID\nENCODING = \"gbk\" # 页面文字编码\nCHAPTER_POST = 1\n\"https://www.lewenxiaoshuo.com/books/xiaoxiaojiaofeiyangchengji/\"\nif GENERAL_PATH == \"\":\n FULL_URL = \"{0}/{1}/\".format(ROOT_URL, NOVEL_SUB_ID)\nelse:\n FULL_URL = \"{0}/{1}/{2}/\".format(ROOT_URL, GENERAL_PATH, NOVEL_SUB_ID)\nplog.debug(\"小说下载首页为：{0}\".format(FULL_URL))\n\n\ndef rtn_chapter_list_info(html):\n soup = BeautifulSoup(html, 'html.parser')\n novelName = soup.find_all(name=\"div\", attrs={\"id\": \"info\"})[0].h1.text\n # novelName = novelName.split(\"《\")[1]\n # novelName = novelName.split(\"》\")[0]\n # novelName = \"妾本惊华\"\n novelName = novelName.replace(\" 最佳来源\", \"\")\n plog.debug(\"开始下载《{0}》\".format(novelName))\n\n chapterListInfoSoup = soup.find_all(name=\"dd\")\n # print(chapterListInfoSoup)\n\n chapterListInfoArr = []\n\n n = 0\n for ddItem in chapterListInfoSoup:\n # print(ddItem)\n n += 1\n\n # if n <= 12:\n # continue\n\n chapterListInfoDict = OrderedDict()\n chapterListInfoDict2 = OrderedDict()\n\n if \"href\" not in str(ddItem):\n continue\n\n if n < CHAPTER_POST:\n continue\n\n chapterListInfoDict[\"text\"] = ddItem.a.text.replace(\"WwW.lwxs520.Com\", \"\")\n chapterListInfoDict[\"href\"] = ddItem.a[\"href\"]\n chapterListInfoArr.append(chapterListInfoDict)\n\n # chapterListInfoDict2[\"text\"] = \"接\"\n # nextPageUrl = ddItem.a[\"href\"].split(\".\")\n # nextPageUrl = \"{0}_2.{1}\".format(nextPageUrl[0], nextPageUrl[1])\n # chapterListInfoDict2[\"href\"] = nextPageUrl\n # chapterListInfoArr.append(chapterListInfoDict2)\n\n plog.tmpinfo(chapterListInfoDict)\n\n return chapterListInfoArr, novelName\n\ndef rtn_chapter_txt(chapterHtml):\n\n\n # print(\"---------------chapterHtml-----------------\\n\",chapterHtml,\"\\n\\n\\n\\n\")\n chapterHtml = chapterHtml.replace(\"\", \"\")\n\n soup = BeautifulSoup(chapterHtml, 'html.parser')\n\n try:\n soupSub = soup.find_all(name=\"div\", attrs={\"id\": \"content\"})[0]\n # soupSubStr = str(soupSub)\n # print(\"---------------soupSubStr-----------------\\n\",soupSubStr,\"\\n\\n\\n\\n\")\n # soupSubStr = \"{0}{1}\".format(soupSubStr.split(\"\")\n\n # soupSub = BeautifulSoup(soupSubStr, 'html.parser')\n\n txtContent = soupSub.text\n txtContent = txtContent.replace(\" 　　\", \"\")\n txtContent = txtContent.replace(\"　　\", \"\\n\")\n # txtContent = txtContent.replace(\"\\n\\n\", \"\\n\")\n txtContent = txtContent.replace(\"\\xa0\", \"\")\n txtContent = txtContent.replace(\"\\n此段不计入字数\", \"\")\n\n txtContent = txtContent + \"\\n\"\n\n\n # txtContent = txtContent.split(\"/c/o/m\")[1] + \"\\n\"\n print(txtContent)\n return txtContent\n\n except:\n time.sleep(2)\n traceback.print_exc()\n print(\"--------------- chapterHtml error -----------------\\n\", chapterHtml)\n return False\n\ndef write_txt_content(txtFileName, chapterName, chapterTxt, encoding):\n with open(txtFileName, 'a', encoding=encoding) as f:\n chapterName = chapterName.replace(\"www.ggdown.com\", \"\")\n chapterName = chapterName.replace(\" ：\", \"\")\n if chapterName == \"接\":\n pass\n else:\n f.write(\"\\n\" + chapterName)\n # print(chapterTxt)\n f.write(chapterTxt)\n\nif __name__ == '__main__':\n\n try:\n\n headers = {'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/60.0.3112.101 Safari/537.36'}\n headers = {'Host': \"www.lewenxiaoshuo.com\"}\n headers = {'Referer': \"https://www.google.com/\"}\n headers = {'Upgrade-Insecure-Requests': '1'}\n headers = {'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,image/apng,*/*;q=0.8'}\n headers = {'Accept-Encoding': 'gzip, deflate, br'}\n\n html = get_html_all_content(FULL_URL, \"info\", ENCODING, headers)\n\n # 返回章节信息\n chapterListInfo, novelName = rtn_chapter_list_info(html)\n\n novelFilePath = r\"{0}\\{1}.txt\".format(DOWN_FLODERS, novelName)\n\n if CHAPTER_POST == 1:\n if (os.path.exists(novelFilePath)):\n os.remove(novelFilePath)\n\n n = 0\n for chapterInfo in chapterListInfo:\n\n n += 1\n\n chapterUrl = \"{0}\".format(chapterInfo[\"href\"])\n\n plog.debug(\"{3}/{4} 网址：{0}，页面章节标题：{2}，文件路径：{1} ！！！\".format(chapterUrl, novelFilePath, chapterInfo[\"text\"], n, len(chapterListInfo)))\n\n chapterHtml = get_html_all_content(chapterUrl, \"content\", ENCODING, headers)\n\n chapterTxt = rtn_chapter_txt(chapterHtml)\n # print(str(chapterHtml))\n\n if chapterTxt is not False:\n write_txt_content(novelFilePath, chapterInfo[\"text\"], chapterTxt, ENCODING)\n else:\n plog.error(\"获取失败！！！！！！\")\n\n except Exception as e:\n traceback.print_exc()\n print(e)", "repo_name": "SlovEnt/Web_Craler_Series", "sub_path": "crawler_novels/www.lewenxiaoshuo.com.py", "file_name": "www.lewenxiaoshuo.com.py", "file_ext": "py", "file_size_in_byte": 5767, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "chs_tools.print_log.C_PrintLog", "line_number": 14, "usage_type": "call"}, {"api_name": "chs_tools.param_info.rtn_parainfo", "line_number": 15, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 32, "usage_type": "call"}, {"api_name": "collections.OrderedDict", "line_number": 53, "usage_type": "call"}, {"api_name": "collections.OrderedDict", "line_number": 54, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 82, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 107, "usage_type": "call"}, {"api_name": "traceback.print_exc", "line_number": 108, "usage_type": "call"}, {"api_name": "chs_tools.get_html_page.get_html_all_content", "line_number": 134, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 142, "usage_type": "call"}, {"api_name": "os.path", "line_number": 142, "usage_type": "attribute"}, {"api_name": "os.remove", "line_number": 143, "usage_type": "call"}, {"api_name": "chs_tools.get_html_page.get_html_all_content", "line_number": 154, "usage_type": "call"}, {"api_name": "traceback.print_exc", "line_number": 165, "usage_type": "call"}]} +{"seq_id": "30384602766", "text": "import json\nimport os\n\nJSON_TYPE_TAG = '__type__'\n\n\ndef _decode_config(root_path):\n def _decode(obj):\n res = {}\n if '#include' in obj:\n includes = obj['#include']\n if not isinstance(includes, list):\n includes = [includes]\n for path in includes:\n if not os.path.isabs(path):\n path = os.path.join(os.path.dirname(root_path), path)\n\n sub_conf = Configuration.from_json(path)\n res.update(sub_conf.__dict__)\n del obj['#include']\n\n if JSON_TYPE_TAG in obj and str(Configuration) == obj[JSON_TYPE_TAG]:\n res.update(obj)\n return Configuration.from_dict(res)\n else:\n res.update(obj)\n return res\n\n return _decode\n\n\nclass Configuration(object):\n \"\"\"Configuration object which offers convenience methods for serializing\n and deserializing\n \"\"\"\n def __init__(self):\n self.seed = 0\n self.__dict__[JSON_TYPE_TAG] = str(type(self))\n super(Configuration, self).__init__()\n\n def __str__(self):\n \"\"\"Pretty stringify configuration\"\"\"\n s = 'Configuration object\\n'\n for key, value in self.__dict__.items():\n s += ' {}: {}\\n'.format(key, value)\n return s\n\n @property\n def file(self):\n return self._src_file\n\n def has_attr(self, key):\n \"\"\"Checks if configuration has an attribute\n\n Parameters\n ----------\n key : string\n Name of attribute\n \"\"\"\n return hasattr(self, key)\n\n def get_attr(self, key, default=None, alternative=None):\n \"\"\"Returns attribute of configuration or default value\n\n Parameters\n ----------\n key : string\n Name of attribute\n default : object\n Value to return if attribute does not exist\n alternative : string\n Key of alternative attribute to use if configuration does not have\n requested key. Raises an error if the alternative key does also not exist\n \"\"\"\n if hasattr(self, key):\n return getattr(self, key)\n else:\n value = default\n if alternative is not None:\n value = self.get_attr(alternative)\n if value is None:\n raise ValueError(('Configuration did not contain {} '\n ' or alternative {}').format(key, alternative))\n return value\n\n def serialize(self, dst):\n \"\"\"Serialize configuration to JSON file\n\n Parameters\n ----------\n dst : string\n Destination file path\n \"\"\"\n with open(dst, 'w') as f:\n json.dump(self.__dict__, f,\n default=lambda obj: obj.__dict__,\n indent=2)\n\n def update(self, values_by_keys):\n \"\"\"Adds values to this configuration\n\n Attempts to convert string values into python primitive types. Currently\n supported are the types bool, int, float, list.\n If no conversion succeeds, uses the value as string.\n\n Parameters\n ----------\n values_by_keys : dict of string -> string\n Dictionary which contains key: value pairs to update configuration from\n \"\"\"\n def convert(s):\n if (s.startswith('[') and s.endswith(']')) or \\\n (s.startswith('(') and s.endswith(')')):\n # This, of course, breaks badly for nested lists\n return [convert(elem.strip()) for elem in s[1:-1].split(',')]\n\n if s == 'False':\n return False\n elif s == 'True':\n return True\n\n try:\n return int(s)\n except ValueError:\n pass\n\n try:\n return float(s)\n except ValueError:\n pass\n\n return s\n\n for key, value in values_by_keys.items():\n self.__dict__[key] = convert(value)\n\n def to_param_dict(self, required_params=[], optional_params=[],\n key_renames={}):\n \"\"\"Converts configuration to a dict which can be passed to function call\n\n Parameters\n ----------\n required_params : list of string\n List of attribute names the configuration is required to have for the\n conversion to work\n optional_params : list of string or dict of string -> object\n If list, contains the keys of optional configuration attributes to be\n inserted in the result. If dict, additionally specifies default values\n to be used if the configuration does not contain an optional attribute\n key_renames : dict of string -> string\n Dictionary which maps configuration keys to keys in the output dictionary\n \"\"\"\n params = {}\n for key in required_params:\n value = self.get_attr(key)\n assert value is not None, \\\n 'Parameter {} is marked as required'.format(key)\n params[key] = value\n\n if isinstance(optional_params, dict):\n for key, default_value in optional_params.items():\n value = self.get_attr(key, default=default_value)\n params[key] = value\n else:\n for key in optional_params:\n value = self.get_attr(key)\n if value is not None:\n params[key] = value\n\n return {key_renames.get(key, key): value for key, value in params.items()}\n\n @staticmethod\n def from_dict(dictionary):\n \"\"\"Construct configuration from dictionary\n\n Parameters\n ----------\n dictionary : dict\n Dictionary to convert\n\n Returns : Configuration\n -------\n Converted configuration object\n \"\"\"\n if isinstance(dictionary, Configuration):\n return dictionary\n conf = Configuration()\n conf.__dict__.update(dictionary)\n return conf\n\n @staticmethod\n def from_json(src):\n \"\"\"Deserialize configuration from JSON file\n\n Parameters\n ----------\n src : string\n Source file path\n\n Returns : Configuration\n -------\n Deserialized configuration object\n \"\"\"\n with open(src, 'r') as f:\n conf = json.load(f, object_hook=_decode_config(src))\n\n if isinstance(conf, dict):\n conf = Configuration.from_dict(conf)\n\n conf._src_file = src\n\n if hasattr(conf, 'include'):\n for key, path in conf.include.items():\n if not os.path.isabs(path):\n path = os.path.join(os.path.dirname(src), path)\n\n sub_conf = Configuration.from_json(path)\n\n if key == '':\n conf.__dict__ = dict(**sub_conf.__dict__, **conf.__dict__)\n else:\n saved_value = conf.get_attr(key, default=None)\n conf.__dict__[key] = sub_conf.__dict__\n if (isinstance(conf.__dict__[key], dict) and\n isinstance(saved_value, dict)):\n conf.__dict__[key].update(saved_value)\n del conf.__dict__['include']\n\n return conf\n", "repo_name": "mseitzer/srgan", "sub_path": "utils/config.py", "file_name": "config.py", "file_ext": "py", "file_size_in_byte": 6384, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 41, "dataset": "github-code", "pt": "37", "api": [{"api_name": "os.path.isabs", "line_number": 15, "usage_type": "call"}, {"api_name": "os.path", "line_number": 15, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 16, "usage_type": "call"}, {"api_name": "os.path", "line_number": 16, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 16, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 95, "usage_type": "call"}, {"api_name": "json.load", "line_number": 205, "usage_type": "call"}, {"api_name": "os.path.isabs", "line_number": 214, "usage_type": "call"}, {"api_name": "os.path", "line_number": 214, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 215, "usage_type": "call"}, {"api_name": "os.path", "line_number": 215, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 215, "usage_type": "call"}]} +{"seq_id": "16535837076", "text": "from bs4 import BeautifulSoup\nimport requests\nfrom urllib.parse import parse_qs, quote, urlparse\n\n# Function to scrape data from a given URL using css selectors(reviews)\ndef scrape_data_from_url(url, target_class):\n try:\n response = requests.get(url)\n response.raise_for_status() # Raise an exception for 4xx or 5xx status codes\n soup = BeautifulSoup(response.content, 'html.parser')\n elements = soup.find_all(class_=target_class)\n scraped_data = [element.get_text(strip=True) for element in elements]\n return scraped_data\n except requests.exceptions.RequestException as e:\n print(f\"Error occurred while fetching data from {url}: {e}\")\n return None\n \ndef extract_website_from_url(url):\n print(\"Extracting website from URL\")\n parsed_url = urlparse(url)\n query_params = parse_qs(parsed_url.query)\n\n # Check if the URL contains the 'redirect_url' parameter\n if 'redirect_url' in query_params:\n redirect_url = query_params['redirect_url'][0]\n parsed_redirect_url = urlparse(redirect_url)\n\n # Get the 'url' parameter from the redirected URL\n if 'url' in parse_qs(parsed_redirect_url.query):\n website_link = parse_qs(parsed_redirect_url.query)['url'][0]\n return website_link\n\n return None\n\n# transform the city name to the format that Yelp uses\ndef encode_city_name(city_name):\n return quote(city_name)", "repo_name": "alexbud1/yelp-crawler", "sub_path": "utils.py", "file_name": "utils.py", "file_ext": "py", "file_size_in_byte": 1433, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "37", "api": [{"api_name": "requests.get", "line_number": 8, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 10, "usage_type": "call"}, {"api_name": "requests.exceptions", "line_number": 14, "usage_type": "attribute"}, {"api_name": "urllib.parse.urlparse", "line_number": 20, "usage_type": "call"}, {"api_name": "urllib.parse.parse_qs", "line_number": 21, "usage_type": "call"}, {"api_name": "urllib.parse.urlparse", "line_number": 26, "usage_type": "call"}, {"api_name": "urllib.parse.parse_qs", "line_number": 29, "usage_type": "call"}, {"api_name": "urllib.parse.parse_qs", "line_number": 30, "usage_type": "call"}, {"api_name": "urllib.parse.quote", "line_number": 37, "usage_type": "call"}]} +{"seq_id": "71988728106", "text": "import matplotlib.pyplot as plt\nimport matplotlib.image as mping\nimport numpy as np\nimport cv2\nimport glob\nimport pickle\n\n\ndef color_gradient_threshold(image_undistorted):\n ksize = 15\n hsv= cv2.cvtColor(image_undistorted,cv2.COLOR_RGB2HSV)\n s_channel = hsv[:,:,1]\n# 原图进行梯度（边缘）检测\n gradx=abs_sobel_thresh(image_undistorted,orient='x',sobel_kernel=ksize,thresh=(50,90))\n grady=abs_sobel_thresh(image_undistorted,orient='y',sobel_kernel=ksize,thresh=(30,90))\n# 原图进行颜色阈检测\n c_binary=color_thresh(image_undistorted,s_thresh=(70,100),l_thresh=(60,255),b_thresh=(50,255),v_thresh=(150,255))\n rgb_binary=rgb_select(image_undistorted,r_thresh=(225,255),g_thresh=(225,255),b_thresh=(0,255))\n combined_binary = np.zeros_like(s_channel)\n# 将梯度检测结果和颜色阈检测结果进行组合叠加\n combined_binary[((gradx == 1) & (grady == 1) | (c_binary == 1) | (rgb_binary==1))] = 255\n# 输出处理后的图片\n color_binary = combined_binary\n return color_binary, combined_binary\n\ndef abs_sobel_thresh(image, orient='x', sobel_kernel=3, thresh=(0, 255)):\n# 计算X或Y方向的方向梯度\n # 转化成灰度图像\n gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)\n # 求X或Y方向的方向梯度\n if orient == 'x':\n abs_sobel = np.absolute(cv2.Sobel(gray, cv2.CV_64F, 1, 0, ksize=sobel_kernel))\n if orient == 'y':\n abs_sobel = np.absolute(cv2.Sobel(gray, cv2.CV_64F, 0, 1, ksize=sobel_kernel))\n # 数据重新缩放\n scaled_sobel = np.uint8(255*abs_sobel/np.max(abs_sobel))\n # 创建一个空矩阵，黑图片\n grad_binary = np.zeros_like(scaled_sobel)\n # 梯度在阈值范围内的，图片点亮\n grad_binary[(scaled_sobel >= thresh[0]) & (scaled_sobel <= thresh[1])] = 1\n return grad_binary\n\n# 使用hsv中的s通道，lab中的b通道，luv中的l通道，hsv中的v通道\ndef color_thresh(image, s_thresh, l_thresh, b_thresh, v_thresh):\n # 颜色阈变化，分别将RGB图像转化成LUV，HLS，HSV，lab图像（分别用LUV，HLS，HSV，LAB\n # 方式来表示同一张RGB图像）\n luv= cv2.cvtColor(image, cv2.COLOR_RGB2LUV)\n hls = cv2.cvtColor(image, cv2.COLOR_RGB2HLS)\n hsv = cv2.cvtColor(image,cv2.COLOR_RGB2HSV)\n lab=cv2.cvtColor(image, cv2.COLOR_RGB2LAB)\n # 提取hsv中的s通道，lab中的b通道，luv中的l通道，hsv中的v通道\n s_channel = hsv[:,:,1]\n b_channel=lab[:,:,2]\n l_channel = luv[:,:,0]\n v_channel= hsv[:,:,2]\n # 提取S通道中符合阈值的像素点\n s_binary = np.zeros_like(s_channel)\n s_binary[(s_channel > s_thresh[0]) & (s_channel <= s_thresh[1])] = 1\n # 提取b通道中符合阈值的像素点\n b_binary = np.zeros_like(b_channel)\n b_binary[(b_channel > b_thresh[0]) & (b_channel <= b_thresh[1])] = 1\n # 提取l通道中符合阈值的像素点\n l_binary = np.zeros_like(l_channel)\n l_binary[(l_channel > l_thresh[0]) & (l_channel <= l_thresh[1])] = 1\n # 提取v通道中符合阈值的像素点\n v_binary = np.zeros_like(v_channel)\n v_binary[(v_channel > v_thresh[0]) & (v_channel <= v_thresh[1])] = 1\n # 提取同时满足以上4个通道阈值的像素点\n combined = np.zeros_like(s_channel)\n combined[((s_binary == 1) & (b_binary == 1) & (l_binary == 1) & (v_binary == 1))] = 1\n \n return combined\n\ndef rgb_select(img, r_thresh, g_thresh, b_thresh):\n r_channel = img[:,:,0]\n g_channel=img[:,:,1]\n b_channel = img[:,:,2]\n r_binary = np.zeros_like(r_channel)\n r_binary[(r_channel > r_thresh[0]) & (r_channel <= r_thresh[1])] = 1\n \n g_binary = np.zeros_like(g_channel)\n g_binary[(g_channel > g_thresh[0]) & (g_channel <= g_thresh[1])] = 1\n \n b_binary = np.zeros_like(b_channel)\n b_binary[(b_channel > b_thresh[0]) & (b_channel <= b_thresh[1])] = 1\n \n combined = np.zeros_like(r_channel)\n combined[((r_binary == 1) & (g_binary == 1) & (b_binary == 1))] = 1\n return combined\n\ndef region_of_interest(img, vertices):\n mask = np.zeros_like(img) \n if len(img.shape) > 2:\n channel_count = img.shape[2] # i.e. 3 or 4 depending on your image\n ignore_mask_color = (255,) * channel_count\n else:\n ignore_mask_color = 255\n cv2.fillPoly(mask, vertices, ignore_mask_color)\n masked_image = cv2.bitwise_and(img, mask)\n return masked_image\n\ndef apply_region_of_interest_mask(image):\n x_factor = 40\n y_factor = 60\n vertices = np.array([[\n (0,image.shape[0]),\n (((image.shape[1]/2)- x_factor), (image.shape[0]/2)+ y_factor), \n (((image.shape[1]/2) + x_factor), (image.shape[0]/2)+ y_factor), \n (image.shape[1],image.shape[0])]], dtype=np.int32)\n return region_of_interest(image, vertices)\n\n# 透视变换，输入为原图和进行阈值检测后的图\ndef perspective_transform(image_undistorted, combined_binary):\n# 定义原图中待映射的4个点坐标\n top_left = [560, 470]\n top_right = [730, 470]\n bottom_right = [1080, 720]\n bottom_left = [200, 720]\n# 定义映射后的4个点的坐标\n top_left_dst = [200,0]\n top_right_dst = [1100,0]\n bottom_right_dst = [1100,720]\n bottom_left_dst = [200,720]\n img_size = (image_undistorted.shape[1], image_undistorted.shape[0])\n# 数据格式整理\n src = np.float32([top_left,top_right, bottom_right, bottom_left] )\n dst = np.float32([top_left_dst, top_right_dst, bottom_right_dst, bottom_left_dst])\n# 求映射的关系矩阵\n M = cv2.getPerspectiveTransform(src, dst)\n Minv = cv2.getPerspectiveTransform(dst, src)\n# 输出透视变换后的图片\n warped = cv2.warpPerspective(combined_binary, M, img_size)\n return warped, Minv\n\ndef finding_line(warped):\n # 将warped中从360行开始加到720行；\n histogram2 = np.sum(warped[warped.shape[0]//2:,:], axis=0)\n out_img = np.dstack((warped, warped, warped))*255\n midpoint = np.int(histogram2.shape[0]/2)\n\n leftx_base = np.argmax(histogram2[:midpoint])\n rightx_base = np.argmax(histogram2[midpoint:])+midpoint\n nwindows = 5\n window_height = np.int(warped.shape[0]/nwindows)\n nonzero = warped.nonzero()\n \n nonzeroy = np.array(nonzero[0])\n nonzerox = np.array(nonzero[1])\n leftx_current = leftx_base\n rightx_current = rightx_base\n margin = 100\n minpix = 50\n left_lane_inds = []\n right_lane_inds = []\n \n for window in range(nwindows):\n win_y_low = warped.shape[0]-(window+1)*window_height\n win_y_high = warped.shape[0]-window*window_height\n win_xleft_low = leftx_current-margin\n win_xleft_high = leftx_current+margin\n win_xright_low = rightx_current - margin\n win_xright_high = rightx_current + margin\n cv2.rectangle(out_img,(win_xleft_low,win_y_low),(win_xleft_high,win_y_high),\n (0,255,0), 2) \n cv2.rectangle(out_img,(win_xright_low,win_y_low),(win_xright_high,win_y_high),\n (0,255,0), 2) \n good_left_inds = ((nonzeroy >= win_y_low) & (nonzeroy < win_y_high) & \n (nonzerox >= win_xleft_low) & (nonzerox < win_xleft_high)).nonzero()[0]\n good_right_inds = ((nonzeroy >= win_y_low) & (nonzeroy < win_y_high) & \n (nonzerox >= win_xright_low) & (nonzerox < win_xright_high)).nonzero()[0]\n left_lane_inds.append(good_left_inds)\n right_lane_inds.append(good_right_inds)\n \n \n if len(good_left_inds) > minpix:\n leftx_current = np.int(np.mean(nonzerox[good_left_inds]))\n if len(good_right_inds) > minpix: \n rightx_current = np.int(np.mean(nonzerox[good_right_inds]))\n \n left_lane_inds = np.concatenate(left_lane_inds)\n right_lane_inds = np.concatenate(right_lane_inds)\n \n leftx = nonzerox[left_lane_inds]\n lefty = nonzeroy[left_lane_inds] \n rightx = nonzerox[right_lane_inds]\n righty = nonzeroy[right_lane_inds] \n left_fit = np.polyfit(lefty, leftx, 2)\n right_fit = np.polyfit(righty, rightx, 2)\n ploty = np.linspace(0, warped.shape[0]-1, warped.shape[0] )\n left_fitx = left_fit[0]*ploty**2 + left_fit[1]*ploty + left_fit[2]\n right_fitx = right_fit[0]*ploty**2 + right_fit[1]*ploty + right_fit[2]\n# out_img[nonzeroy[left_lane_inds], nonzerox[left_lane_inds]] = [255, 0, 0]\n out_img[nonzeroy[right_lane_inds], nonzerox[right_lane_inds]] = [0, 0, 255]\n \n \n # 找出左车道线附近的像素点序号；\n left_lane_inds = ((nonzerox > (left_fit[0]*(nonzeroy**2) + left_fit[1]*nonzeroy + \n left_fit[2] - margin)) & (nonzerox < (left_fit[0]*(nonzeroy**2) + \n left_fit[1]*nonzeroy + left_fit[2] + margin))) \n # 找出右车道线附近的像素点序号；\n right_lane_inds = ((nonzerox > (right_fit[0]*(nonzeroy**2) + right_fit[1]*nonzeroy + \n right_fit[2] - margin)) & (nonzerox < (right_fit[0]*(nonzeroy**2) + \n right_fit[1]*nonzeroy + right_fit[2] + margin))) \n print(left_lane_inds)\n print(right_lane_inds)\n\n return left_fitx, right_fitx,out_img, left_fit, right_fit,left_lane_inds,right_lane_inds\n\ndef CalculateCurvature(binary_image, left_fit, right_fit, l_lane_inds, r_lane_inds):\n\n img_size = (binary_image.shape[1], binary_image.shape[0])\n ploty = np.linspace(0, img_size[1]-1, img_size[1])\n y_eval = np.max(ploty)\n ym_per_pix = 30/720 # y方向720个像素，对应30米\n xm_per_pix = 3.7/960 # x方向960个像素，对应3.7米 \n # 找到图像中不为零的所有像素点的像素坐标\n nonzero = binary_image.nonzero()\n nonzeroy = np.array(nonzero[0])\n nonzerox = np.array(nonzero[1])\n # 将这些不为零的像素点坐标分成x，y车道线中\n leftx = nonzerox[l_lane_inds]\n lefty = nonzeroy[l_lane_inds] \n rightx = nonzerox[r_lane_inds]\n righty = nonzeroy[r_lane_inds]\n # 将这些像素点对应到世界坐标系中，然后拟合成二次曲线\n left_fit_cr = np.polyfit(lefty*ym_per_pix, leftx*xm_per_pix, 2)\n right_fit_cr = np.polyfit(righty*ym_per_pix, rightx*xm_per_pix, 2) \n # 计算曲线的曲率\n left_curverad = ((1 + (2*left_fit_cr[0]*y_eval*ym_per_pix + left_fit_cr[1])**2)**1.5) / np.absolute(2*left_fit_cr[0])\n right_curverad = ((1 + (2*right_fit_cr[0]*y_eval*ym_per_pix + right_fit_cr[1])**2)**1.5) / np.absolute(2*right_fit_cr[0])\n # 左右车道线曲率平均\n avg_curverad = (left_curverad + right_curverad) / 2 \n## 以下计算本车在车道线中心的位置\n dist_from_center = 0.0\n if right_fit is not None:\n if left_fit is not None:\n # 摄像头位于图像中间，也是本车的中心\n camera_pos = img_size[0] / 2\n # 左右车道线最底端x坐标\n left_lane_pix = np.polyval(left_fit, binary_image.shape[0])\n right_lane_pix = np.polyval(right_fit, binary_image.shape[0])\n # 左右车道线中点x坐标\n center_of_lane_pix = (left_lane_pix + right_lane_pix) / 2\n # 摄像头（本车中心）与车道线中心的距离\n dist_from_center = (camera_pos - center_of_lane_pix) * 3.7/960\n return avg_curverad, dist_from_center\n\ndef overlay_text_on_image (image, avg_curverad, dist_from_center):\n \n new_img = np.copy(image)\n # 字体和字体颜色 \n font = cv2.FONT_HERSHEY_SIMPLEX\n font_color = (255,255,255)\n \n num_format = '{:04.2f}'\n text = 'Radius of Curvature: ' + num_format.format(avg_curverad) + 'm'\n cv2.putText(new_img, text, (40,70), font, 1.5, font_color, 2, cv2.LINE_AA)\n \n direction = 'left'\n if dist_from_center > 0:\n direction = 'right'\n abs_dist = abs(dist_from_center)\n text = 'Vehicle is ' + num_format.format(abs_dist) + ' m ' + direction + ' of center'\n cv2.putText(new_img, text, (40,120), font, 1.5, font_color, 2, cv2.LINE_AA) \n return new_img\n\ndef main_pipline(image_0):\n \n #1 畸变矫正\n #image_0 = cv2.undistort(image_0, mtx, dist, None, mtx)\n\n #2 颜色与梯度阈\n color_binary, combined_binary = color_gradient_threshold(image_0)\n\n #3 感兴趣区域\n masked = apply_region_of_interest_mask(combined_binary)\n\n #4 透视变换\n warped_0, Minv = perspective_transform(masked, combined_binary)\n \n #5 滑动窗车道线提取\n left_fitx, right_fitx, out_img,left_fit, right_fit,left_lane_inds,right_lane_inds = finding_line(warped_0)\n \n #6 计算车道线的曲率\n avg_curverad, dist_from_center = CalculateCurvature(warped_0,left_fit, right_fit, left_lane_inds, right_lane_inds)\n \n #7 在图像上画车道线\n \n warp_zero = np.zeros_like(warped_0).astype(np.uint8)\n color_warp = np.dstack((warp_zero, warp_zero, warp_zero))\n pts_left = np.array([np.transpose(np.vstack([left_fitx, ploty]))])\n pts_right = np.array([np.flipud(np.transpose(np.vstack([right_fitx, ploty])))])\n pts = np.hstack((pts_left, pts_right))\n cv2.fillPoly(color_warp, np.int_([pts]), (0,255, 0))\n newwarp = cv2.warpPerspective(color_warp, Minv, (image_0.shape[1], image_0.shape[0])) \n result = cv2.addWeighted(image_0, 1, newwarp, 0.3, 0)\n\n #8 图像上显示文字\n result = overlay_text_on_image (result, avg_curverad, dist_from_center)\n return result\n\n\n\n\n", "repo_name": "HumanAutomationInteractionLab-HAIL/DrivingSimulatorDetection", "sub_path": "LaneDetection/method.py", "file_name": "method.py", "file_ext": "py", "file_size_in_byte": 13154, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "37", "api": [{"api_name": "cv2.cvtColor", "line_number": 11, "usage_type": "call"}, {"api_name": "cv2.COLOR_RGB2HSV", "line_number": 11, "usage_type": "attribute"}, {"api_name": "numpy.zeros_like", "line_number": 19, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 29, "usage_type": "call"}, {"api_name": "cv2.COLOR_RGB2GRAY", "line_number": 29, "usage_type": "attribute"}, {"api_name": "numpy.absolute", "line_number": 32, "usage_type": "call"}, {"api_name": "cv2.Sobel", "line_number": 32, "usage_type": "call"}, {"api_name": "cv2.CV_64F", "line_number": 32, "usage_type": "attribute"}, {"api_name": "numpy.absolute", "line_number": 34, "usage_type": "call"}, {"api_name": "cv2.Sobel", "line_number": 34, "usage_type": "call"}, {"api_name": "cv2.CV_64F", "line_number": 34, "usage_type": "attribute"}, {"api_name": "numpy.uint8", "line_number": 36, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 36, "usage_type": "call"}, {"api_name": "numpy.zeros_like", "line_number": 38, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 47, "usage_type": "call"}, {"api_name": "cv2.COLOR_RGB2LUV", "line_number": 47, "usage_type": "attribute"}, {"api_name": "cv2.cvtColor", "line_number": 48, "usage_type": "call"}, {"api_name": "cv2.COLOR_RGB2HLS", "line_number": 48, "usage_type": "attribute"}, {"api_name": "cv2.cvtColor", "line_number": 49, "usage_type": "call"}, {"api_name": "cv2.COLOR_RGB2HSV", "line_number": 49, "usage_type": "attribute"}, {"api_name": "cv2.cvtColor", "line_number": 50, "usage_type": "call"}, {"api_name": "cv2.COLOR_RGB2LAB", "line_number": 50, "usage_type": "attribute"}, {"api_name": "numpy.zeros_like", "line_number": 57, "usage_type": "call"}, {"api_name": "numpy.zeros_like", "line_number": 60, "usage_type": "call"}, {"api_name": "numpy.zeros_like", "line_number": 63, "usage_type": "call"}, {"api_name": "numpy.zeros_like", "line_number": 66, "usage_type": "call"}, {"api_name": "numpy.zeros_like", "line_number": 69, "usage_type": "call"}, {"api_name": "numpy.zeros_like", "line_number": 78, "usage_type": "call"}, {"api_name": "numpy.zeros_like", "line_number": 81, "usage_type": "call"}, {"api_name": "numpy.zeros_like", "line_number": 84, "usage_type": "call"}, {"api_name": "numpy.zeros_like", "line_number": 87, "usage_type": "call"}, {"api_name": "numpy.zeros_like", "line_number": 92, "usage_type": "call"}, {"api_name": "cv2.fillPoly", "line_number": 98, "usage_type": "call"}, {"api_name": "cv2.bitwise_and", "line_number": 99, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 105, "usage_type": "call"}, {"api_name": "numpy.int32", "line_number": 109, "usage_type": "attribute"}, {"api_name": "numpy.float32", "line_number": 126, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 127, "usage_type": "call"}, {"api_name": "cv2.getPerspectiveTransform", "line_number": 129, "usage_type": "call"}, {"api_name": "cv2.getPerspectiveTransform", "line_number": 130, "usage_type": "call"}, {"api_name": "cv2.warpPerspective", "line_number": 132, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 137, "usage_type": "call"}, {"api_name": "numpy.dstack", "line_number": 138, "usage_type": "call"}, {"api_name": "numpy.int", "line_number": 139, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 141, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 142, "usage_type": "call"}, {"api_name": "numpy.int", "line_number": 144, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 147, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 148, "usage_type": "call"}, {"api_name": "cv2.rectangle", "line_number": 163, "usage_type": "call"}, {"api_name": "cv2.rectangle", "line_number": 165, "usage_type": "call"}, {"api_name": "numpy.int", "line_number": 176, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 176, "usage_type": "call"}, {"api_name": "numpy.int", "line_number": 178, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 178, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 180, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 181, "usage_type": "call"}, {"api_name": "numpy.polyfit", "line_number": 187, "usage_type": "call"}, {"api_name": "numpy.polyfit", "line_number": 188, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 189, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 212, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 213, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 218, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 219, "usage_type": "call"}, {"api_name": "numpy.polyfit", "line_number": 226, "usage_type": "call"}, {"api_name": "numpy.polyfit", "line_number": 227, "usage_type": "call"}, {"api_name": "numpy.absolute", "line_number": 229, "usage_type": "call"}, {"api_name": "numpy.absolute", "line_number": 230, "usage_type": "call"}, {"api_name": "numpy.polyval", "line_number": 240, "usage_type": "call"}, {"api_name": "numpy.polyval", "line_number": 241, "usage_type": "call"}, {"api_name": "numpy.copy", "line_number": 250, "usage_type": "call"}, {"api_name": "cv2.FONT_HERSHEY_SIMPLEX", "line_number": 252, "usage_type": "attribute"}, {"api_name": "cv2.putText", "line_number": 257, "usage_type": "call"}, {"api_name": "cv2.LINE_AA", "line_number": 257, "usage_type": "attribute"}, {"api_name": "cv2.putText", "line_number": 264, "usage_type": "call"}, {"api_name": "cv2.LINE_AA", "line_number": 264, "usage_type": "attribute"}, {"api_name": "numpy.zeros_like", "line_number": 289, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 289, "usage_type": "attribute"}, {"api_name": "numpy.dstack", "line_number": 290, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 291, "usage_type": "call"}, {"api_name": "numpy.transpose", "line_number": 291, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 291, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 292, "usage_type": "call"}, {"api_name": "numpy.flipud", "line_number": 292, "usage_type": "call"}, {"api_name": "numpy.transpose", "line_number": 292, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 292, "usage_type": "call"}, {"api_name": "numpy.hstack", "line_number": 293, "usage_type": "call"}, {"api_name": "cv2.fillPoly", "line_number": 294, "usage_type": "call"}, {"api_name": "numpy.int_", "line_number": 294, "usage_type": "call"}, {"api_name": "cv2.warpPerspective", "line_number": 295, "usage_type": "call"}, {"api_name": "cv2.addWeighted", "line_number": 296, "usage_type": "call"}]} +{"seq_id": "1315171477", "text": "import pygame\r\nfrom math import sqrt, sin, pi, atan, cos\r\nfrom random import randrange, randint, random\r\nfrom time import sleep\r\n\r\n\r\nspeed = 20\r\nmirror = 6\r\npygame.init()\r\nscreen = pygame.display.set_mode((700, 700))\r\nw, h = screen.get_width(), screen.get_height()\r\nangle = 2 * pi / mirror # nel mio caso pi / 3\r\nmin_range = -4\r\nmax_range = (-min_range) + 1\r\nstart_y = 0 #sin(angle / 4) * w / 4\r\nstart_x = w / 2 - 10\r\nradius = (2, 4)\r\ncompleted = False\r\n\r\n\r\ndef constrain(x, maxim, minim):\r\n return max(min(x, maxim), minim)\r\n\r\n\r\ndef distance(x, y, x1, y1):\r\n return sqrt((x - x1) ** 2 + (y - y1) ** 2)\r\n\r\n\r\nclass snowflake:\r\n def __init__(self, x=start_x, y=start_y, stuck=False):\r\n self.x = x\r\n self.y = y\r\n self.stuck = stuck\r\n self.vx = -0.5\r\n self.radius = randint(*radius)\r\n r = randint(0, 200)\r\n if random() > 0: # qui\r\n if random() > 0: # qui\r\n self.color = (255 - r, 255 - r, 255)\r\n else:\r\n self.color = (55 + r, 100, 50)\r\n else:\r\n self.color = (255 - r, 255 - r, 255)\r\n\r\n def move(self): # per qualche miracolo divino funziona, forse #\r\n if not self.stuck:\r\n self.x += self.vx\r\n self.y += randrange(min_range, max_range)\r\n self.y = constrain(self.y, self.x * sin(angle / 2), 0)\r\n # pygame.draw.line(screen, (255, 255, 255), (w / 2, h / 2), (w, h / 2 + self.x * sin(angle / 2)), 3)\r\n # pygame.draw.line(screen, (255, 255, 255), (w / 2, h / 2), (w, h / 2 - self.x * sin(angle / 2)), 3)\r\n\r\n def collision(self, sn): # anche questo #\r\n if not self.stuck:\r\n for z in sn:\r\n if z is not self and z.stuck:\r\n if distance(self.x, self.y, z.x, z.y) <= self.radius + z.radius:\r\n self.stuck = True\r\n return special_add(self.x, self.y, self.radius, self.color)\r\n\r\n\r\ndef special_add(x, y, r, c):\r\n angle1 = atan(y / x)\r\n radius1 = distance(0, 0, x, y)\r\n\r\n for i in range(mirror):\r\n x1 = radius1 * cos(angle * i + angle1 + angle / 2) + w / 2\r\n x2 = radius1 * cos(angle * i - angle1 + angle / 2) + w / 2\r\n y1 = radius1 * sin(angle * i + angle1 + angle / 2) + h / 2\r\n y2 = radius1 * sin(angle * i - angle1 + angle / 2) + h / 2\r\n\r\n pygame.draw.circle(screen, c, (x1, y1), r)\r\n pygame.draw.circle(screen, c, (x2, y2), r)\r\n\r\n\r\nstart = snowflake(0, 0, True)\r\ncrystal = [start]\r\npygame.draw.circle(screen, start.color, (start.x + w / 2, start.y + h / 2), start.radius)\r\ncounter = 0\r\nnumber = 0\r\n\r\nwhile True:\r\n for event in pygame.event.get():\r\n if event.type == pygame.QUIT:\r\n pygame.quit()\r\n break\r\n\r\n for s in crystal:\r\n s.collision(crystal)\r\n s.move()\r\n if s.stuck and s.x == start_x and s.y == start_y:\r\n completed = True\r\n\r\n counter += 1\r\n if number == 20:\r\n pygame.quit()\r\n break\r\n\r\n pygame.display.update()\r\n if counter % speed == 0 and not completed:\r\n crystal.append(snowflake())\r\n elif completed:\r\n pygame.image.save(screen, f\"{number}.jpg\")\r\n number += 1\r\n print(number)\r\n sleep(2)\r\n screen.fill((0, 0, 0))\r\n start = snowflake(0, 0, True)\r\n crystal = [start]\r\n pygame.draw.circle(screen, start.color, (start.x + w / 2, start.y + h / 2), start.radius)\r\n counter = 0\r\n completed = False\r\n", "repo_name": "Sierpinski22/Python-Projects", "sub_path": "Simulazioni/Brownian_snowflake/Snowflake2.py", "file_name": "Snowflake2.py", "file_ext": "py", "file_size_in_byte": 3506, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "pygame.init", "line_number": 9, "usage_type": "call"}, {"api_name": "pygame.display.set_mode", "line_number": 10, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 10, "usage_type": "attribute"}, {"api_name": "math.pi", "line_number": 12, "usage_type": "name"}, {"api_name": "math.sqrt", "line_number": 26, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 35, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 36, "usage_type": "call"}, {"api_name": "random.random", "line_number": 37, "usage_type": "call"}, {"api_name": "random.random", "line_number": 38, "usage_type": "call"}, {"api_name": "random.randrange", "line_number": 48, "usage_type": "call"}, {"api_name": "math.sin", "line_number": 49, "usage_type": "call"}, {"api_name": "math.atan", "line_number": 63, "usage_type": "call"}, {"api_name": "math.cos", "line_number": 67, "usage_type": "call"}, {"api_name": "math.cos", "line_number": 68, "usage_type": "call"}, {"api_name": "math.sin", "line_number": 69, "usage_type": "call"}, {"api_name": "math.sin", "line_number": 70, "usage_type": "call"}, {"api_name": "pygame.draw.circle", "line_number": 72, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 72, "usage_type": "attribute"}, {"api_name": "pygame.draw.circle", "line_number": 73, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 73, "usage_type": "attribute"}, {"api_name": "pygame.draw.circle", "line_number": 78, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 78, "usage_type": "attribute"}, {"api_name": "pygame.event.get", "line_number": 83, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 83, "usage_type": "attribute"}, {"api_name": "pygame.QUIT", "line_number": 84, "usage_type": "attribute"}, {"api_name": "pygame.quit", "line_number": 85, "usage_type": "call"}, {"api_name": "pygame.quit", "line_number": 96, "usage_type": "call"}, {"api_name": "pygame.display.update", "line_number": 99, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 99, "usage_type": "attribute"}, {"api_name": "pygame.image.save", "line_number": 103, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 103, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 106, "usage_type": "call"}, {"api_name": "pygame.draw.circle", "line_number": 110, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 110, "usage_type": "attribute"}]} +{"seq_id": "74055013867", "text": "from fastapi import APIRouter, Request\nfrom fastapi.responses import JSONResponse\nfrom fastapi import HTTPException\n\nrouter = APIRouter()\n\nfrom services.ethereum.embedding import train as embedding_train\n\n# --------------- GET ----------------- # \n\n@router.get(\n \"/api/v1/train\", \n response_description=\"List news related to certain coin\",\n responses={404: {\"description\": \"Not found\"}}\n)\nasync def train(code: int):\n try:\n if code != 111111:\n raise ValueError(\"Invalid code\")\n\n news = embedding_train()\n return \"Train successfully\"\n\n except ValueError as e:\n raise HTTPException(status_code=400, detail=str(e))\n\n", "repo_name": "shiyivei/chatdata-insight", "sub_path": "backend/api/v1/endpoints/train.py", "file_name": "train.py", "file_ext": "py", "file_size_in_byte": 679, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "fastapi.APIRouter", "line_number": 5, "usage_type": "call"}, {"api_name": "services.ethereum.embedding.train", "line_number": 21, "usage_type": "call"}, {"api_name": "fastapi.HTTPException", "line_number": 25, "usage_type": "call"}]} +{"seq_id": "2296531991", "text": "import urllib.request\nimport urllib.parse\nimport json\nimport subprocess\nimport os.path\nfrom multiprocessing import Pool\n\nout_path=\"out\"\n\ndef fetch_schema_list():\n with urllib.request.urlopen(\"https://www.schemastore.org/api/json/catalog.json\") as response:\n return json.load(response)\n\ndef fetch_schema(schema_descr):\n with urllib.request.urlopen(schema_descr[\"url\"]) as response:\n return response.read()\n\ndef parse_attr_name(raw_url):\n url = urllib.parse.urlparse(raw_url)\n original_filename = os.path.basename(url.path)\n return os.path.splitext(original_filename)[0]\n\ndef convert_to_nickel(raw_schema, dest_file_name):\n os.makedirs(os.path.dirname(dest_file_name), exist_ok = True)\n with open(dest_file_name, \"w+\") as out:\n js2n = subprocess.run([\"json-schema-to-nickel\"],\n input = raw_schema,\n stdout = out,\n check = True,\n )\n\ndef process_one_schema(schema_descr):\n name = schema_descr[\"name\"]\n attr_name = schema_descr[\"name\"]\n dest_file_name = os.path.join(out_path, attr_name + \".ncl\")\n try:\n raw_schema = fetch_schema(schema_descr)\n convert_to_nickel(raw_schema, dest_file_name)\n print(f\"“{name}”: OK\")\n except Exception as e:\n with open(dest_file_name, \"w+\") as dest:\n print(\"null\", file=dest)\n print(f\"“{name}”: Failure\")\n print(e)\n return f'\"{attr_name}\" = import \"{dest_file_name}\",'\n\ndef main():\n schemas = fetch_schema_list()[\"schemas\"]\n\n with Pool(16) as p:\n record_fields = p.map(process_one_schema, schemas)\n\n with open(\"main.ncl\", \"w+\") as entrypoint:\n print(\"{\", file=entrypoint)\n for line in record_fields:\n print(line, file=entrypoint)\n print(\"}\", file=entrypoint)\n\nmain()\n", "repo_name": "thufschmitt/nickel-schemastore", "sub_path": "extract-schemas.py", "file_name": "extract-schemas.py", "file_ext": "py", "file_size_in_byte": 1804, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "37", "api": [{"api_name": "urllib.request.request.urlopen", "line_number": 11, "usage_type": "call"}, {"api_name": "urllib.request.request", "line_number": 11, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 11, "usage_type": "name"}, {"api_name": "json.load", "line_number": 12, "usage_type": "call"}, {"api_name": "urllib.request.request.urlopen", "line_number": 15, "usage_type": "call"}, {"api_name": "urllib.request.request", "line_number": 15, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 15, "usage_type": "name"}, {"api_name": "urllib.request.parse.urlparse", "line_number": 19, "usage_type": "call"}, {"api_name": "urllib.request.parse", "line_number": 19, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 19, "usage_type": "name"}, {"api_name": "os.path.path.basename", "line_number": 20, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 20, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 20, "usage_type": "name"}, {"api_name": "os.path.path.splitext", "line_number": 21, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 21, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 21, "usage_type": "name"}, {"api_name": "os.path.makedirs", "line_number": 24, "usage_type": "call"}, {"api_name": "os.path", "line_number": 24, "usage_type": "name"}, {"api_name": "os.path.path.dirname", "line_number": 24, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 24, "usage_type": "attribute"}, {"api_name": "subprocess.run", "line_number": 26, "usage_type": "call"}, {"api_name": "os.path.path.join", "line_number": 35, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 35, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 35, "usage_type": "name"}, {"api_name": "multiprocessing.Pool", "line_number": 50, "usage_type": "call"}]} +{"seq_id": "38601716179", "text": "#!/usr/bin/python\nimport errno\nimport re\nimport os\nimport subprocess\nfrom ansible.module_utils.basic import AnsibleModule\nfrom ansible.module_utils.six import PY2\n\n\nANSIBLE_METADATA = {\n 'metadata_version': '1.0',\n 'status': ['preview'],\n 'supported_by': 'community'\n}\n\n\nDOCUMENTATION = '''\n---\nmodule: krb_principal\n\nshort_description: Create Kerberos principals and export keytabs\ndescription:\n - This module uses /usr/sbin/kadmin.local to create users and export\n keytab files.\n\noptions:\n name:\n description:\n - The name of the kerberos principal, for example \"kdreyer@EXAMPLE.COM\"\n required: true\n state:\n description:\n - The only allowed value is \"present\".\n required: false\n choices: [present]\n keytab:\n description:\n - If specified, Ansible will extract a .keytab file to this path.\n - If any file already exists at this path, then Ansible will delete the\n file and create a new keytab if Ansible also created a new principal.\n If Ansible did not create a new principal, then it will not delete or\n edit this file if it exists.\n - Ansible does not validate that the pre-existing file is a keytab.\n required: false\nrequirements:\n - \"python >= 2.7\"\n'''\n\nEXAMPLES = '''\n- name: Create a user and a keytab.\n hosts: localhost\n tasks:\n - name: Create a kdreyer keytab\n krb_principal:\n name: kdreyer@EXAMPLE.COM\n keytab: /var/local/kdreyer.keytab\n'''\n\n\ndef kadmin(query):\n \"\"\" Call \"kadmin.local -q\" with this query. \"\"\"\n cmd = ('/usr/sbin/kadmin.local', '-q', query)\n output = subprocess.check_output(cmd, stderr=subprocess.STDOUT)\n if PY2:\n return output\n return output.decode('utf-8')\n\n\ndef get_principal(name):\n \"\"\" Return the full principal name, or None if it does not exist. \"\"\"\n output = kadmin('getprinc %s' % name)\n for line in output.splitlines():\n if line.startswith('Principal:'):\n _, principal = line.split(':')\n return principal\n\n\ndef create_principal(name):\n \"\"\" Create an account, and return the full principal name. \"\"\"\n output = kadmin('addprinc -randkey %s' % name)\n for line in output.splitlines():\n m = re.match('Principal \"[^\"]+\" created.', line)\n if m:\n return m.group(0)\n raise RuntimeError('could not scrape addprinc output: %s' % output)\n\n\ndef extract_keytab(principal, keytab):\n \"\"\" Extract a keytab file for a principal. \"\"\"\n kadmin('ktadd -k %s -norandkey %s' % (keytab, principal))\n\n\ndef run_module():\n module_args = dict(\n name=dict(required=True),\n keytab=dict(type='path'),\n state=dict(choices=['present'], default='present'),\n )\n module = AnsibleModule(\n argument_spec=module_args,\n supports_check_mode=True\n )\n\n check_mode = module.check_mode\n params = module.params\n name = params['name']\n keytab = params['keytab']\n\n changes = []\n principal = get_principal(name)\n if not principal:\n changes.append('create principal %s' % name)\n if not check_mode:\n principal = create_principal(name)\n\n if keytab:\n if changes and not check_mode:\n # Delete the keytab\n try:\n os.remove(keytab)\n except OSError as e:\n if e.errno != errno.ENOENT:\n raise\n if not os.path.exists(keytab):\n changes.append('extract keytab %s' % keytab)\n if not check_mode:\n extract_keytab(principal, keytab)\n\n if not changes:\n module.exit_json(changed=False)\n\n module.exit_json(changed=True, stdout_lines=changes)\n\n\ndef main():\n run_module()\n\n\nif __name__ == '__main__':\n main()\n", "repo_name": "ktdreyer/koji-playbooks", "sub_path": "roles/kdc/library/krb_principal.py", "file_name": "krb_principal.py", "file_ext": "py", "file_size_in_byte": 3754, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 14, "dataset": "github-code", "pt": "37", "api": [{"api_name": "subprocess.check_output", "line_number": 63, "usage_type": "call"}, {"api_name": "subprocess.STDOUT", "line_number": 63, "usage_type": "attribute"}, {"api_name": "ansible.module_utils.six.PY2", "line_number": 64, "usage_type": "name"}, {"api_name": "re.match", "line_number": 82, "usage_type": "call"}, {"api_name": "ansible.module_utils.basic.AnsibleModule", "line_number": 99, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 120, "usage_type": "call"}, {"api_name": "errno.ENOENT", "line_number": 122, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 124, "usage_type": "call"}, {"api_name": "os.path", "line_number": 124, "usage_type": "attribute"}]} +{"seq_id": "34710249103", "text": "from django.contrib import admin\nfrom django.urls import path, include\nfrom django.conf import settings\nfrom django.conf.urls.static import static\n\nfrom tracker.views import *\nfrom tracker.utils import *\n\n\nurlpatterns = [\n path('', DashboardView.as_view(), name='dashboard'),\n\n path('employees/', ListEmployeesView.as_view(), name='list_employees'),\n path('employee/add/', CreateEmployeeView.as_view(), name='add_employee'),\n path('employee/upload/', UploadEmployeeDocView.as_view(), name='upload_emp_doc'),\n path('employee/getdocs/', GetEmployeeDocuments.as_view(), name='get_emp_docs'),\n path('employee/delete_doc//', DeleteEmployeeDocument.as_view(), name='delete_emp_doc'),\n path('employee//edit/', UpdateEmployeeView.as_view(), name='update_employee'),\n path('employee//delete/', DeleteEmployeeView.as_view(), name='delete_employee'),\n path('employees/download/', export_emps_xls, name='download_emps'),\n\n path('employee//expenses/', ListEmpExpensesView.as_view(), name='list_emp_expenses'),\n path('employee//expense/add/', CreateEmpExpenseView.as_view(), name='add_emp_expense'),\n path('employee//expense//edit/', UpdateEmpExpenseView.as_view(), name='update_emp_expense'),\n path('employee//expense//delete/', DeleteEmpExpenseView.as_view(), name='delete_emp_expense'),\n\n path('clients/', ListClientsView.as_view(), name='list_clients'),\n path('client/add/', CreateClientView.as_view(), name='add_client'),\n path('client//edit/', UpdateClientView.as_view(), name='update_client'),\n path('client//delete/', DeleteClientView.as_view(), name='delete_client'),\n\n path('users/', ListUsersView.as_view(), name='list_users'),\n path('user/add/', CreateUserView.as_view(), name='add_user'),\n path('user//edit/', UpdateUserView.as_view(), name='update_user'),\n path('user//delete/', DeleteUserView.as_view(), name='delete_user'),\n\n path('projects/', ListProjectsView.as_view(), name='list_projects'),\n path('project/add/', CreateProjectView.as_view(), name='add_project'),\n path('project/upload/', UploadProjectDocView.as_view(), name='upload_project_doc'),\n path('project/getdocs/', GetProjectDocuments.as_view(), name='get_project_docs'),\n path('project/delete_doc//', DeleteProjectDocument.as_view(), name='delete_project_doc'),\n path('project//edit/', UpdateProjectView.as_view(), name='update_project'),\n path('project//delete/', DeleteProjectView.as_view(), name='delete_project'),\n\n path('project//expenses/', ListProjectExpensesView.as_view(), name='list_project_expenses'),\n path('project//expense/add/', CreateProjectExpenseView.as_view(), name='add_project_expense'),\n path('project//expense//edit/', UpdateProjectExpenseView.as_view(),\n name='update_project_expense'),\n path('project//expense//delete/', DeleteProjectExpenseView.as_view(),\n name='delete_project_expense'),\n\n path('getcontracts/', GetContracts.as_view(), name='get_contracts'),\n path('contracts/', ListContractsView.as_view(), name='list_contracts'),\n path('contract/add/', CreateContractView.as_view(), name='add_contract'),\n path('contract//edit/', UpdateContractView.as_view(), name='update_contract'),\n path('contract//delete/', DeleteContractView.as_view(), name='delete_contract'),\n\n path('timesheets/', ListTimesheetsView.as_view(), name='list_timesheets'),\n path('timesheet/add/', CreateTimesheetView.as_view(), name='add_timesheet'),\n path('timesheet//edit/', UpdateTimesheetView.as_view(), name='update_timesheet'),\n path('timesheet//delete/', DeleteTimesheetView.as_view(), name='delete_timesheet'),\n\n path('generictimesheet/add/', GenericTimesheetView.as_view(), name='add_generic_timesheet'),\n\n path('assignments/', ListAssignmentsView.as_view(), name='list_assignments'),\n path('assignment/add/', CreateAssignmentView.as_view(), name='add_assignment'),\n path('assignment//edit/', UpdateAssignmentView.as_view(), name='update_assignment'),\n path('assignment//delete/', DeleteAssignmentView.as_view(), name='delete_assignment'),\n\n path('vendors/', ListVendorsView.as_view(), name='list_vendors'),\n path('vendor/add/', CreateVendorView.as_view(), name='add_vendor'),\n path('vendor/upload/', UploadVendorDocView.as_view(), name='upload_vendor_doc'),\n path('vendor/getdocs/', GetVendorDocuments.as_view(), name='get_vendor_docs'),\n path('vendor/delete_doc//', DeleteVendorDocument.as_view(), name='delete_vendor_doc'),\n path('vendor//edit/', UpdateVendorView.as_view(), name='update_vendor'),\n path('vendor//delete/', DeleteVendorView.as_view(), name='delete_vendor'),\n\n path('vendor//expenses/', ListVendorExpensesView.as_view(), name='list_vendor_expenses'),\n path('vendor//expense/add/', CreateVendorExpenseView.as_view(), name='add_vendor_expense'),\n path('vendor//expense//edit/', UpdateVendorExpenseView.as_view(),\n name='update_vendor_expense'),\n path('vendor//expense//delete/', DeleteVendorExpenseView.as_view(),\n name='delete_vendor_expense'),\n\n path('referrals/', ListReferralsView.as_view(), name='list_referrals'),\n path('referral/add/', CreateReferralView.as_view(), name='add_referral'),\n path('referral//edit/', UpdateReferralView.as_view(), name='update_referral'),\n path('referral//delete/', DeleteReferralView.as_view(), name='delete_referral'),\n\n path('search/', ReportView.as_view(), name='report'),\n path('search_expense/', ExpenseReportView.as_view(), name='expense_report'),\n path('search/defaulters/', SearchDefaulterView.as_view(), name='get_defaulters'),\n\n path('gettenants/', GetTenants.as_view(), name='get_tenants'),\n\n path('messages/', InboxMessagesView.as_view(), name='inbox_messages'),\n path('message/add/', CreateMessageView.as_view(), name='create_message'),\n path('message//edit/', UpdateMessageView.as_view(), name='update_message'),\n path('message//delete/', DeleteMessageView.as_view(), name='delete_message'),\n\n path('tasks/', ListTasksView.as_view(), name='list_tasks'),\n path('task/add/', CreateTaskView.as_view(), name='add_task'),\n path('task//edit/', UpdateTaskView.as_view(), name='update_task'),\n path('task//delete/', DeleteTaskView.as_view(), name='delete_task'),\n\n path('invoices/', ListInvoicesView.as_view(), name='list_invoices'),\n path('invoice/add/', CreateInvoiceView.as_view(), name='add_invoice'),\n path('invoice//edit/', UpdateInvoiceView.as_view(), name='update_invoice'),\n path('invoice//delete/', DeleteInvoiceView.as_view(), name='delete_invoice'),\n path('invoice//send/', SendInvoiceView.as_view(), name='send_invoice'),\n]+ static(settings.MEDIA_URL, document_root=settings.MEDIA_ROOT)", "repo_name": "FirojNeosoft/TimesheetManagementSystem", "sub_path": "tracker/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 7189, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "django.urls.path", "line_number": 11, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 13, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 14, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 15, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 16, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 17, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 18, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 19, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 20, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 22, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 23, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 24, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 25, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 27, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 28, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 29, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 30, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 32, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 33, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 34, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 35, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 37, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 38, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 39, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 40, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 41, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 42, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 43, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 45, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 46, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 47, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 49, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 52, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 53, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 54, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 55, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 56, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 58, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 59, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 60, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 61, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 63, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 65, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 66, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 67, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 68, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 70, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 71, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 72, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 73, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 74, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 75, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 76, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 78, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 79, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 80, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 82, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 85, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 86, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 87, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 88, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 90, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 91, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 92, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 94, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 96, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 97, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 98, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 99, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 101, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 102, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 103, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 104, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 106, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 107, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 108, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 109, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 110, "usage_type": "call"}, {"api_name": "django.conf.urls.static.static", "line_number": 111, "usage_type": "call"}, {"api_name": "django.conf.settings.MEDIA_URL", "line_number": 111, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 111, "usage_type": "name"}, {"api_name": "django.conf.settings.MEDIA_ROOT", "line_number": 111, "usage_type": "attribute"}]} +{"seq_id": "42868150173", "text": "from collections import defaultdict\n\ngrafo = {\n 'RS': ['SC'],\n 'SC': ['PR'],\n 'PR': ['SP', 'MS'],\n 'SP': ['MS', 'MG', 'RJ'],\n 'MS': ['MT', 'GO', 'MG'],\n 'RJ': ['ES', 'MG'],\n 'MG': ['GO', 'ES', 'DF'],\n 'GO': ['BA'],\n 'MT': ['RO', 'PA', 'TO'],\n 'ES': ['BA'],\n 'DF': ['GO'],\n 'BA': ['PE', 'AL', 'PI', 'TO', 'MG'],\n 'SE': ['BA', 'AL'],\n 'AL': ['PE'],\n 'PE': ['CE', 'PI', 'PB'],\n 'PI': ['CE', 'TO', 'MA'],\n 'TO': ['PA', 'MA'],\n 'PA': ['AM', 'RR'],\n 'RO': ['AC'],\n 'AC': ['AM'],\n 'AM': ['RO'],\n 'RR': ['AM'],\n 'AP': ['PA'],\n 'MA': ['PA'],\n 'CE': ['PB'],\n 'PB': ['RN'],\n 'RN': ['CE']\n }\n\n\ni = 0\nvizinhos = defaultdict(list)\nfor v in grafo:\n for u in grafo[v]:\n vizinhos[v].append(u)\n vizinhos[u].append(v)\n \ng = 0\nfrequencia = dict()\nfor v in vizinhos:\n for u in vizinhos[v]:\n g += 1\n frequencia[v] = [g]\n g = 0 \n \n \n\nprint(\"\\n===============================================================\")\n\nmaior = 0\nfor v in frequencia:\n for u in frequencia[v]:\n if u > maior:\n maior = u\n vertMaior = v \n\nmenor = maior\nfor f in frequencia:\n for g in frequencia[f]:\n if g < menor:\n menor = g\n vertMenor = f \n\n\nprint(\"\\nEstado com o maior numero de vizinhos: \", vertMaior, \"=>\", maior, \"vizinhos\")\n\nprint(\"\\n===============================================================\")\n\nprint(\"\\nEstado com o menor numero de vizinhos: \", vertMenor, \"=>\", menor, \"vizinho\")\n\nprint(\"\\n===============================================================\")\n\nprint(\"\\nVertices e seus respectivos vizinhos:\\n \")\nfor v in vizinhos:\n print(v, \"=> \", vizinhos[v])\n \nprint(\"\\n===============================================================\")\n\nprint(\"\\nFrequencia do grau de cada vertice: \")\nprint()\nfor v in frequencia:\n print(v, \"=> \", frequencia[v], \"arestas\")\n\nprint(\"\\n===============================================================\")\n\nvertices = 0\narestas = 0\nfor v in grafo:\n vertices += 1\n for u in grafo[v]:\n arestas += 1\n\nprint('\\nDensidade do grafo:', arestas/vertices)\n\nprint(\"\\n===============================================================\")\n\n\n", "repo_name": "pedrxlz/grafos", "sub_path": "Trabalho1.py", "file_name": "Trabalho1.py", "file_ext": "py", "file_size_in_byte": 2375, "program_lang": "python", "lang": "pt", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "collections.defaultdict", "line_number": 35, "usage_type": "call"}]} +{"seq_id": "32273703593", "text": "import torch\r\nfrom torch import nn\r\nfrom typing import Optional, Union, List\r\n\r\nfrom segmentation_models_pytorch.encoders import get_encoder\r\nfrom segmentation_models_pytorch.base import (\r\n SegmentationModel,\r\n SegmentationHead,\r\n ClassificationHead,\r\n)\r\nfrom segmentation_models_pytorch.decoders.unet.decoder import UnetDecoder\r\n\r\n\r\n\r\nclass SSLModel(torch.nn.Module):\r\n def initialize(self):\r\n # init.initialize_decoder(self.decoder)\r\n # init.initialize_head(self.segmentation_head)\r\n # if self.classification_head is not None:\r\n # init.initialize_head(self.classification_head)\r\n pass\r\n\r\n def check_input_shape(self, x):\r\n\r\n h, w = x.shape[-2:]\r\n output_stride = self.encoder.output_stride\r\n if h % output_stride != 0 or w % output_stride != 0:\r\n new_h = (h // output_stride + 1) * output_stride if h % output_stride != 0 else h\r\n new_w = (w // output_stride + 1) * output_stride if w % output_stride != 0 else w\r\n raise RuntimeError(\r\n f\"Wrong input shape height={h}, width={w}. Expected image height and width \"\r\n f\"divisible by {output_stride}. Consider pad your images to shape ({new_h}, {new_w}).\"\r\n )\r\n\r\n def forward(self, x):\r\n \"\"\"Sequentially pass `x` trough model`s encoder, decoder and heads\"\"\"\r\n\r\n self.check_input_shape(x)\r\n features = self.encoder(x)\r\n for afeatures in features:\r\n print(\"Encoder: \", afeatures.shape)\r\n decoder_output = self.decoder.forward_list(*features)\r\n for fea in decoder_output:\r\n print(\"Decoder: \", fea.shape)\r\n return decoder_output\r\n # masks = self.segmentation_head(decoder_output)\r\n # if self.classification_head is not None:\r\n # labels = self.classification_head(features[-1])\r\n # return masks, labels\r\n # return masks\r\n\r\n @torch.no_grad()\r\n def predict(self, x):\r\n if self.training:\r\n self.eval()\r\n\r\n x = self.forward(x)\r\n return x\r\n\r\n\r\nclass Unet(SSLModel):\r\n def __init__(\r\n self,\r\n encoder_name: str = \"resnet34\",\r\n encoder_depth: int = 5,\r\n encoder_weights: Optional[str] = \"imagenet\",\r\n decoder_use_batchnorm: bool = True,\r\n decoder_channels: List[int] = (256, 128, 64, 32, 32),\r\n decoder_attention_type: Optional[str] = None,\r\n in_channels: int = 1,\r\n classes: int = 1,\r\n activation: Optional[Union[str, callable]] = None,\r\n aux_params: Optional[dict] = None,\r\n ):\r\n super().__init__()\r\n\r\n self.encoder = get_encoder(\r\n encoder_name,\r\n in_channels=in_channels,\r\n depth=encoder_depth,\r\n weights=encoder_weights,\r\n )\r\n\r\n self.decoder = UnetDecoder_extend(\r\n encoder_channels=self.encoder.out_channels,\r\n decoder_channels=decoder_channels,\r\n n_blocks=encoder_depth,\r\n use_batchnorm=decoder_use_batchnorm,\r\n center=True if encoder_name.startswith(\"vgg\") else False,\r\n attention_type=decoder_attention_type,\r\n )\r\n\r\n self.segmentation_head = SegmentationHead(\r\n in_channels=decoder_channels[-1],\r\n out_channels=classes,\r\n activation=activation,\r\n kernel_size=3,\r\n )\r\n length_embedding = 16\r\n self.trans_5 = nn.Conv2d(512, length_embedding, kernel_size=1, padding=0)\r\n self.trans_4 = nn.Conv2d(256, length_embedding, kernel_size=1, padding=0)\r\n self.trans_3 = nn.Conv2d(128, length_embedding, kernel_size=1, padding=0)\r\n self.trans_2 = nn.Conv2d(64, length_embedding, kernel_size=1, padding=0)\r\n self.trans_1 = nn.Conv2d(32, length_embedding, kernel_size=1, padding=0)\r\n # self.trans_0 = nn.Conv2d(32, length_embedding, kernel_size=1, padding=0)\r\n\r\n if aux_params is not None:\r\n self.classification_head = ClassificationHead(in_channels=self.encoder.out_channels[-1], **aux_params)\r\n else:\r\n self.classification_head = None\r\n\r\n self.name = \"u-{}\".format(encoder_name)\r\n self.initialize()\r\n\r\n def forward(self, x):\r\n \"\"\"Sequentially pass `x` trough model`s encoder, decoder and heads\"\"\"\r\n b, c, h, w = x.shape\r\n if c > 1:\r\n x = x[:, 0, :, :].unsqueeze(1)\r\n\r\n self.check_input_shape(x)\r\n features = self.encoder(x)\r\n out = self.decoder.forward_list(*features)\r\n fea0, fea1, fea2, fea3, fea4 = out[0], out[1], out[2], out[3], out[4]\r\n fea0 = self.trans_5(fea0)\r\n fea1 = self.trans_4(fea1)\r\n fea2 = self.trans_3(fea2)\r\n fea3 = self.trans_2(fea3)\r\n fea4 = self.trans_1(fea4)\r\n return [fea0, fea1, fea2, fea3, fea4]\r\n\r\n\r\nclass UnetDecoder_extend(UnetDecoder):\r\n def __init__(self, *args, **kwargs):\r\n super(UnetDecoder_extend, self).__init__(*args, **kwargs)\r\n\r\n def forward_list(self, *features):\r\n features = features[1:] # remove first skip with same spatial resolution\r\n features = features[::-1] # reverse channels to start from head of encoder\r\n head = features[0]\r\n skips = features[1:]\r\n\r\n x = self.center(head)\r\n output_list = []\r\n for i, decoder_block in enumerate(self.blocks):\r\n output_list.append(x)\r\n skip = skips[i] if i < len(skips) else None\r\n x = decoder_block(x, skip)\r\n return output_list\r\n\r\n\r\ndef test_model():\r\n model_cfg = {\r\n 'ENCODER_NAME': 'resnet34',\r\n 'ENCODER_WEIGHTS': 'imagenet',\r\n 'DECODER_CHANNELS': [256,128,64,32,32],\r\n 'IN_CHANNELS': 1,\r\n }\r\n\r\n model = Unet()\r\n testdata = torch.ones((4,1,384,384))\r\n output = model(testdata)\r\n for fea in output:\r\n print(\"test model: \", fea.shape)\r\n import ipdb; ipdb.set_trace()\r\n\r\n\r\nif __name__ == '__main__':\r\n test_model()", "repo_name": "transcendentsky/pixel-aug", "sub_path": "sc/ssl2/ssl_smp_model.py", "file_name": "ssl_smp_model.py", "file_ext": "py", "file_size_in_byte": 5993, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "37", "api": [{"api_name": "torch.nn", "line_number": 15, "usage_type": "attribute"}, {"api_name": "torch.no_grad", "line_number": 52, "usage_type": "call"}, {"api_name": "typing.Optional", "line_number": 66, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 68, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 69, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 72, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 72, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 73, "usage_type": "name"}, {"api_name": "segmentation_models_pytorch.encoders.get_encoder", "line_number": 77, "usage_type": "call"}, {"api_name": "segmentation_models_pytorch.base.SegmentationHead", "line_number": 93, "usage_type": "call"}, {"api_name": "torch.nn.Conv2d", "line_number": 100, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 100, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 101, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 101, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 102, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 102, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 103, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 103, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 104, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 104, "usage_type": "name"}, {"api_name": "segmentation_models_pytorch.base.ClassificationHead", "line_number": 108, "usage_type": "call"}, {"api_name": "segmentation_models_pytorch.decoders.unet.decoder.UnetDecoder", "line_number": 133, "usage_type": "name"}, {"api_name": "torch.ones", "line_number": 161, "usage_type": "call"}, {"api_name": "ipdb.set_trace", "line_number": 165, "usage_type": "call"}]} +{"seq_id": "22063396961", "text": "import typer\nimport json\nfrom operator import itemgetter\n\napp = typer.Typer()\n\n\ndef parse_opponent(opponent):\n if opponent is None:\n return None\n else:\n id, name, image_url = itemgetter(\"id\", \"name\", \"image_url\")(opponent)\n return {\"id\": id, \"name\": name, \"image_url\": image_url}\n\n\n@app.command()\ndef parse_json(input_file: str, output_file: str):\n \"\"\"\n Parse a pandascore api response and write it to a json file.\n \"\"\"\n\n with open(input_file, 'r') as f:\n data = json.load(f)\n\n parsed_data = []\n for match in data:\n id, name, status, begin_at, end_at, results = itemgetter(\n \"id\", \"name\", \"status\", \"begin_at\", \"end_at\", \"results\")(match)\n match_dict = {\n \"id\": id,\n \"name\": name,\n \"status\": status,\n \"begin_at\": begin_at,\n \"end_at\": end_at,\n \"results\": results\n }\n match_dict[\"winner\"] = parse_opponent(match[\"winner\"])\n match_dict[\"teams\"] = [parse_opponent(\n opp[\"opponent\"]) for opp in match[\"opponents\"]]\n\n if match_dict[\"winner\"] is not None:\n del match_dict[\"winner\"][\"image_url\"]\n parsed_data.append(match_dict)\n\n with open(output_file, 'w') as f:\n json.dump(parsed_data, f)\n\n\nif __name__ == \"__main__\":\n app()\n", "repo_name": "ekojsalim/roundrobin-tiebreaker", "sub_path": "service/parse-pandascore.py", "file_name": "parse-pandascore.py", "file_ext": "py", "file_size_in_byte": 1332, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "37", "api": [{"api_name": "typer.Typer", "line_number": 5, "usage_type": "call"}, {"api_name": "operator.itemgetter", "line_number": 12, "usage_type": "call"}, {"api_name": "json.load", "line_number": 23, "usage_type": "call"}, {"api_name": "operator.itemgetter", "line_number": 27, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 46, "usage_type": "call"}]} +{"seq_id": "14018594257", "text": "# -*- coding: utf-8 -*-\n\nimport logging\nimport urllib\nfrom telezombie import types\nfrom tornado import gen, httpclient\nimport ujson\nfrom . import text_plugin_entry_point, TextPluginMixin\n\nDUCKDUCKGO_SEARCH_API_ENDPOINT = 'http://api.duckduckgo.com/'\n\nlogger = logging.getLogger('plugins')\n\n\n# @text_plugin_entry_point(u'(?:/г|г|/g|g)\\s+?(.*)')\n@text_plugin_entry_point(u'/d')\nclass SequencedDuckduckgoTextSearch(TextPluginMixin):\n __help_string__ = u'/d - search in DuckDuckGo'\n\n @staticmethod\n @gen.coroutine\n def first_entry_point(bot, message, match, *_):\n try:\n res = yield bot.send_message(\n message.chat.id_,\n \"duck wat?\",\n reply_to_message_id=message.message_id,\n reply_markup=types.ForceReply(True)\n )\n raise gen.Return(res)\n except httpclient.HTTPError as e:\n logger.debug(str(e))\n\n @staticmethod\n @gen.coroutine\n def subsequent_entry_point(bot, message, *_):\n # google_res = google.search(message.text, 1, lang='ru')\n # if len(google_res) > 0 and google_res[0] is not None and google_res[0].link:\n # link = google_res[0].link\n # else:\n # link = \"Don't know\"\n\n query = message.text.strip(' ').encode('utf-8')\n params_dict = {\n 'format': 'json',\n 'q': query\n }\n params = urllib.urlencode(params_dict)\n url = DUCKDUCKGO_SEARCH_API_ENDPOINT + '?' + params\n\n httpc = httpclient.AsyncHTTPClient()\n try:\n resp = yield httpc.fetch(url)\n link = ujson.loads(resp.body)['AbstractURL']\n if link == u'':\n link = 'No results'\n except httpclient.HTTPError as e:\n logger.debug('DuckDuckGo plugin search error {} with query {}'.format(str(e), query))\n link = 'DuckDuckGo error'\n\n try:\n yield bot.send_message(\n message.chat.id_,\n link,\n reply_to_message_id=message.message_id\n )\n except httpclient.HTTPError as e:\n logger.debug(str(e))\n\n httpc.close()\n return\n\n\n@text_plugin_entry_point(u'/d\\s+?(.+)')\nclass DuckduckgoTextSearch(TextPluginMixin):\n __help_string__ = u'/d query - search in DuckDuckGo immediately'\n\n @staticmethod\n @gen.coroutine\n def first_entry_point(bot, message, match, *_):\n query = match.group(1).encode('utf-8')\n params_dict = {\n 'format': 'json',\n 'q': query\n }\n params = urllib.urlencode(params_dict)\n url = DUCKDUCKGO_SEARCH_API_ENDPOINT + '?' + params\n\n httpc = httpclient.AsyncHTTPClient()\n try:\n resp = yield httpc.fetch(url)\n link = ujson.loads(resp.body)['AbstractURL']\n if link == u'':\n link = 'No results'\n except httpclient.HTTPError as e:\n logger.debug('DuckDuckGo plugin search error {} with query {}'.format(str(e), query))\n link = 'DuckDuckGo error'\n\n try:\n yield bot.send_message(\n message.chat.id_,\n link,\n reply_to_message_id=message.message_id\n )\n except httpclient.HTTPError as e:\n logger.debug(str(e))\n\n httpc.close()\n return\n\n @staticmethod\n @gen.coroutine\n def subsequent_entry_point(bot, *_):\n pass\n", "repo_name": "olegvg/telebot", "sub_path": "telebot/plugins/duckduckgo_search.py", "file_name": "duckduckgo_search.py", "file_ext": "py", "file_size_in_byte": 3458, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "logging.getLogger", "line_number": 12, "usage_type": "call"}, {"api_name": "telezombie.types.ForceReply", "line_number": 28, "usage_type": "call"}, {"api_name": "telezombie.types", "line_number": 28, "usage_type": "name"}, {"api_name": "tornado.gen.Return", "line_number": 30, "usage_type": "call"}, {"api_name": "tornado.gen", "line_number": 30, "usage_type": "name"}, {"api_name": "tornado.httpclient.HTTPError", "line_number": 31, "usage_type": "attribute"}, {"api_name": "tornado.httpclient", "line_number": 31, "usage_type": "name"}, {"api_name": "tornado.gen.coroutine", "line_number": 21, "usage_type": "attribute"}, {"api_name": "tornado.gen", "line_number": 21, "usage_type": "name"}, {"api_name": "urllib.urlencode", "line_number": 48, "usage_type": "call"}, {"api_name": "tornado.httpclient.AsyncHTTPClient", "line_number": 51, "usage_type": "call"}, {"api_name": "tornado.httpclient", "line_number": 51, "usage_type": "name"}, {"api_name": "ujson.loads", "line_number": 54, "usage_type": "call"}, {"api_name": "tornado.httpclient.HTTPError", "line_number": 57, "usage_type": "attribute"}, {"api_name": "tornado.httpclient", "line_number": 57, "usage_type": "name"}, {"api_name": "tornado.httpclient.HTTPError", "line_number": 67, "usage_type": "attribute"}, {"api_name": "tornado.httpclient", "line_number": 67, "usage_type": "name"}, {"api_name": "tornado.gen.coroutine", "line_number": 35, "usage_type": "attribute"}, {"api_name": "tornado.gen", "line_number": 35, "usage_type": "name"}, {"api_name": "urllib.urlencode", "line_number": 86, "usage_type": "call"}, {"api_name": "tornado.httpclient.AsyncHTTPClient", "line_number": 89, "usage_type": "call"}, {"api_name": "tornado.httpclient", "line_number": 89, "usage_type": "name"}, {"api_name": "ujson.loads", "line_number": 92, "usage_type": "call"}, {"api_name": "tornado.httpclient.HTTPError", "line_number": 95, "usage_type": "attribute"}, {"api_name": "tornado.httpclient", "line_number": 95, "usage_type": "name"}, {"api_name": "tornado.httpclient.HTTPError", "line_number": 105, "usage_type": "attribute"}, {"api_name": "tornado.httpclient", "line_number": 105, "usage_type": "name"}, {"api_name": "tornado.gen.coroutine", "line_number": 79, "usage_type": "attribute"}, {"api_name": "tornado.gen", "line_number": 79, "usage_type": "name"}, {"api_name": "tornado.gen.coroutine", "line_number": 112, "usage_type": "attribute"}, {"api_name": "tornado.gen", "line_number": 112, "usage_type": "name"}]} +{"seq_id": "26879941554", "text": "from typing import List\n\n\nclass Solution:\n\n def subsets(self, nums: List[int]) -> List[List[int]]:\n return self.subsets_backtracking(nums)\n\n def subsets_backtracking(self, nums: List[int]) -> List[List[int]]:\n\n self.power_list = []\n\n def _recurse(max_len, cur_len, index, path):\n if cur_len == max_len:\n self.power_list.append(path)\n return\n\n for i, n in enumerate(nums[index:], start=index):\n _recurse(max_len, cur_len + 1, i + 1, path + [n])\n\n for length in range(len(nums) + 1):\n _recurse(length, 0, 0, [])\n\n return self.power_list\n\n def subsets_dfs(self, nums: List[int]) -> List[List[int]]:\n temp_arr = [\"\"] * len(nums)\n subsets = []\n\n def recurse(subsets, nums, temp_arr, index):\n if index == (len(nums)):\n subsets.append([x for x in temp_arr if x != \"\"])\n return\n\n temp_arr[index] = \"\"\n recurse(subsets, nums, [x for x in temp_arr], index + 1)\n temp_arr[index] = nums[index]\n recurse(subsets, nums, [x for x in temp_arr], index + 1)\n\n recurse(subsets, nums, temp_arr, 0)\n return subsets\n\n\n\"\"\"\nRuntime: O(N 2^N)\n\nRuntime: 36 ms, faster than 29.84% of Python3 online submissions for Subsets.\nMemory Usage: 13 MB, less than 100.00% of Python3 online submissions for Subsets.\n\"\"\"\n", "repo_name": "SamSamhuns/wallbreakers_projekts", "sub_path": "Leetcode/week_4/p0078_subsets.py", "file_name": "p0078_subsets.py", "file_ext": "py", "file_size_in_byte": 1425, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "37", "api": [{"api_name": "typing.List", "line_number": 6, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 9, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 26, "usage_type": "name"}]} +{"seq_id": "3176519760", "text": "import asyncio\nimport json\nimport time\nimport socks\nfrom telethon.errors.rpcerrorlist import AuthKeyDuplicatedError\nfrom telethon.sync import TelegramClient\nimport os\n\nclass Login:\n\n def __init__(self,ip:str,port:int,authorized,username:str,password:str,proxy_type:str):\n\n if proxy_type == 'SOCKS5':\n self.proxy = (socks.SOCKS5, ip, port, authorized, username, password)\n\n\n async def get_custom_TelegramClient(self,client_settings):\n\n client_settings['proxy'] = self.proxy\n client = TelegramClient(session=client_settings['session'],\n api_id=client_settings['api_id'],\n api_hash=client_settings['api_hash']\n )\n if TelegramClient.is_user_authorized(client):\n await client.start()\n print(await client.get_me())\n print(\"Chats: \" + str(len(await client.get_dialogs())))\n\n return client\n else:\n return None\n\n\nasync def check(acc_list,proxy):\n accounts = []\n login = Login(**proxy)\n\n for el in acc_list:\n client = await login.get_custom_TelegramClient(el)\n if client != None:\n accounts.append(el)\n\n with open('valid_account.json','w+') as f:\n json.dump(accounts,f)\n\nif __name__ == '__main__':\n ## Замінити на Базу\n with open('proxy.json', 'r') as f:\n proxy = json.loads(f.read())\n\n with open('account.json', 'r') as f:\n acc_list = json.loads(f.read())\n\n\n asyncio.set_event_loop(asyncio.SelectorEventLoop())\n loop = asyncio.get_event_loop()\n loop.run_until_complete(check(acc_list,proxy))\n", "repo_name": "bohdan-fedoryshyn/TelegramSoft", "sub_path": "Spam/Login.py", "file_name": "Login.py", "file_ext": "py", "file_size_in_byte": 1672, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "socks.SOCKS5", "line_number": 14, "usage_type": "attribute"}, {"api_name": "telethon.sync.TelegramClient", "line_number": 20, "usage_type": "call"}, {"api_name": "telethon.sync.TelegramClient.is_user_authorized", "line_number": 24, "usage_type": "call"}, {"api_name": "telethon.sync.TelegramClient", "line_number": 24, "usage_type": "name"}, {"api_name": "json.dump", "line_number": 44, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 49, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 52, "usage_type": "call"}, {"api_name": "asyncio.set_event_loop", "line_number": 55, "usage_type": "call"}, {"api_name": "asyncio.SelectorEventLoop", "line_number": 55, "usage_type": "call"}, {"api_name": "asyncio.get_event_loop", "line_number": 56, "usage_type": "call"}]} +{"seq_id": "39214468589", "text": "from datetime import timedelta\nfrom typing import Optional\n\nfrom fastapi import FastAPI, Request, File, Form\nfrom fastapi.responses import HTMLResponse, StreamingResponse\nfrom fastapi.staticfiles import StaticFiles\nfrom fastapi.templating import Jinja2Templates\nimport ffmpeg\nimport numpy as np\nimport srt as srt\nimport stable_whisper\n\n\ndef get_audio_buffer(filename: str, start: int, length: int):\n \"\"\"\n input: filename of the audio file, start time in seconds, length of the audio in seconds\n output: np array of the audio data which the model's transcribe function can take as input\n \"\"\"\n out, _ = (\n ffmpeg.input(filename, threads=0)\n .output(\"-\", format=\"s16le\", acodec=\"pcm_s16le\", ac=1, ar=16000, ss=start, t=length)\n .run(cmd=[\"ffmpeg\", \"-nostdin\"], capture_stdout=True, capture_stderr=True)\n )\n\n return np.frombuffer(out, np.int16).flatten().astype(np.float32) / 32768.0\n\n\ndef transcribe_time_stamps(segments: list):\n \"\"\"\n input: a list of segments from the model's transcribe function\n output: a string of the timestamps and the text of each segment\n \"\"\"\n string = \"\"\n for seg in segments:\n string += \" \".join([str(seg.start), \"->\", str(seg.end), \": \", seg.text.strip(), \"\\n\"])\n return string\n\n\ndef make_srt_subtitles(segments: list):\n subtitles = []\n for i, seg in enumerate(segments, start=1):\n start_time = seg.start\n end_time = seg.end\n text = seg.text.strip()\n\n subtitle = srt.Subtitle(\n index=i,\n start=timedelta(seconds=start_time),\n end=timedelta(seconds=end_time),\n content=text\n )\n subtitles.append(subtitle)\n\n return srt.compose(subtitles)\n\n\napp = FastAPI()\n\napp.mount('/static', StaticFiles(directory='static'), name='static')\ntemplate = Jinja2Templates(directory='templates')\n\n\n@app.get('/', response_class=HTMLResponse)\ndef index(request: Request):\n return template.TemplateResponse('index.html', {\"request\": request, \"text\": None})\n\n\n@app.post('/download/')\nasync def download_subtitle(\n request: Request,\n file: bytes = File(),\n model_type: str = \"tiny\",\n timestamps: Optional[str] = Form(\"False\"),\n filename: str = \"subtitles\",\n file_type: str = \"srt\"\n):\n # Save the uploaded file\n with open('audio.mp3', 'wb') as f:\n f.write(file)\n\n # Load the model and transcribe the audio\n model = stable_whisper.load_model(model_type)\n result = model.transcribe(\"audio.mp3\", regroup=False)\n\n subtitle_file = \"subtitle.srt\"\n # Create the subtitle file\n if file_type == \"srt\":\n subtitle_file = f\"{filename}.srt\"\n with open(subtitle_file, \"w\") as f:\n if timestamps:\n f.write(make_srt_subtitles(result.segments))\n else:\n f.write(result.text)\n elif file_type == \"vtt\":\n subtitle_file = f\"{filename}.vtt\"\n with open(subtitle_file, \"w\") as f:\n if timestamps:\n f.write(result.to_vtt())\n else:\n f.write(result.text)\n\n # Create a streaming response with the file\n media_type = \"application/octet-stream\"\n response = StreamingResponse(\n open(subtitle_file, 'rb'),\n media_type=media_type,\n headers={'Content-Disposition': f'attachment;filename={subtitle_file}'}\n )\n\n return response", "repo_name": "Kabanosk/whisper-website", "sub_path": "src/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 3397, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 197, "dataset": "github-code", "pt": "37", "api": [{"api_name": "ffmpeg.input", "line_number": 20, "usage_type": "call"}, {"api_name": "numpy.frombuffer", "line_number": 25, "usage_type": "call"}, {"api_name": "numpy.int16", "line_number": 25, "usage_type": "attribute"}, {"api_name": "numpy.float32", "line_number": 25, "usage_type": "attribute"}, {"api_name": "srt.Subtitle", "line_number": 46, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 48, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 49, "usage_type": "call"}, {"api_name": "srt.compose", "line_number": 54, "usage_type": "call"}, {"api_name": "fastapi.FastAPI", "line_number": 57, "usage_type": "call"}, {"api_name": "fastapi.staticfiles.StaticFiles", "line_number": 59, "usage_type": "call"}, {"api_name": "fastapi.templating.Jinja2Templates", "line_number": 60, "usage_type": "call"}, {"api_name": "fastapi.Request", "line_number": 64, "usage_type": "name"}, {"api_name": "fastapi.responses.HTMLResponse", "line_number": 63, "usage_type": "name"}, {"api_name": "fastapi.Request", "line_number": 70, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 73, "usage_type": "name"}, {"api_name": "fastapi.File", "line_number": 71, "usage_type": "call"}, {"api_name": "fastapi.Form", "line_number": 73, "usage_type": "call"}, {"api_name": "stable_whisper.load_model", "line_number": 82, "usage_type": "call"}, {"api_name": "fastapi.responses.StreamingResponse", "line_number": 104, "usage_type": "call"}]} +{"seq_id": "4184061946", "text": "import logging\nimport os\nimport os.path\nimport time\nimport re\nimport requests as rq\nimport json\n\nimport bs4\nimport pandas as pd\n\nimport config\n\nMARKET_REGION = '10000030'\n\nlogger = logging.getLogger(__name__)\n\ndef get_history(type_id, region_id=MARKET_REGION):\n \"\"\"Get the market history for item with given type_id\"\"\"\n\n crest_history_url = ('https://crest-tq.eveonline.com/market/{region_id}/history/?type='\n 'https://public-crest.eveonline.com/inventory/types/{type_id}/')\n\n try:\n req = rq.get(crest_history_url.format(region_id=MARKET_REGION, type_id=type_id))\n\n except Exception as e:\n logger.error('Failed to get info for typeID - {} - with error {}'.format(type_id, e))\n return None\n\n return json.dumps(req.json())\n\n\ndef get_orders(type_id, region_id=MARKET_REGION):\n \"\"\"Get the current market orders for an item in a region.\n API has 6 min cache time.\"\"\"\n\n crest_order_url = ('https://crest-tq.eveonline.com/market/{region_id}/orders/{order_type}/?type='\n 'https://public-crest.eveonline.com/inventory/types/{type_id}/')\n\n # api only returns buys or sells, so need two calls to get both\n responses = []\n for order_type in ['buy', 'sell']:\n resp = rq.get(crest_order_url.format(region_id=region_id, \n order_type=order_type, \n type_id=type_id))\n resp_json = resp.json()\n responses.extend(resp_json['items'])\n\n return json.dumps(responses)\n\n\ndef get_transactions(key_id, access_code):\n \"\"\"Get the transaction history for the character with the given API key\"\"\"\n\n xml_url = 'https://api.eveonline.com/char/WalletTransactions.xml.aspx?keyID={}&vCode={}&rowCount={}'\n\n key_id = '442571'\n access_code = '7qdTnpfrBfL3Gw2elwKaT9SsGkn6O5gwV3QUM77S3pHPanRBzzDyql5pCUU7V0bS'\n\n # query the API and parse the returned xml with beautifulsoup\n response = rq.get(xml_url.format(key_id, access_code, 2560))\n soup = bs4.BeautifulSoup(response.text, 'lxml')\n\n # put the transactions in a dataframe\n rows = soup.findAll('row')\n row_dicts = [row.attrs for row in soup.findAll('row')]\n df = pd.DataFrame(row_dicts)\n\n # convert columns to the appropriate datatypes\n numeric_cols = ['clientid', 'clienttypeid', 'journaltransactionid',\n 'price', 'quantity', 'stationid', 'transactionid','typeid']\n\n datetime_cols = ['transactiondatetime']\n\n df[numeric_cols] = df[numeric_cols].apply(pd.to_numeric)\n df[datetime_cols] = df[datetime_cols].apply(pd.to_datetime)\n\n return df.to_csv(index=False)\n\n\ndef process_orders_json(filename):\n \"\"\"Read a scraped market order json file and return a Dataframe\n with the raw market data\"\"\"\n\n df = pd.read_json(os.path.join(config.orders_dir, filename))\n \n try:\n # pull the type_id and name out of the 'type' dictionary\n df['type_id'] = df.type.apply(lambda row: int(row['id']))\n df['type_name'] = df.type.apply(lambda row: row['name'])\n \n # pull the location_id and name out of the 'location' dictionary\n df['location_id'] = df.location.apply(lambda row: int(row['id']))\n df['location_name'] = df.location.apply(lambda row: row['name'])\n \n # add information about when the file was created\n modified = time.gmtime(os.path.getmtime(os.path.join(config.orders_dir, filename)))\n df['recorded'] = pd.to_datetime(time.strftime('%Y-%m-%d %H:%M', modified))\n\n # convert the 'issued' column to a datetime\n df['issued'] = pd.to_datetime(df['issued'])\n\n # drop unneccesary columns\n str_cols = [col for col in df.columns if '_str' in col]\n df = df.drop(str_cols, axis=1)\n df = df.drop(['href', 'location', 'type'], axis=1)\n \n return df\n\n except:\n\n return pd.DataFrame()\n \n\ndef process_history_json(filename):\n \"\"\"Read a scraped market history json file and return a Dataframe\n with the raw market data\"\"\"\n\n # pull the type_id out of the filename since it's not saved in the json\n type_id = re.match(\"history_([0-9]*)\\.json\", filename).group(1)\n\n with open(os.path.join(config.history_dir, filename)) as data_file:\n data = json.load(data_file)\n\n raw_df = pd.DataFrame.from_dict(data['items'], orient='columns')\n\n # convert date column to datetime objects\n raw_df['date'] = pd.to_datetime(raw_df['date'])\n\n # add a type_id column\n raw_df['type_id'] = int(type_id)\n\n # add information about when the file was created\n modified = time.gmtime(os.path.getmtime(os.path.join(config.history_dir, filename)))\n raw_df['recorded'] = pd.to_datetime(time.strftime('%Y-%m-%d %H:%M', modified))\n\n # add a column with the volume in ISK\n raw_df['volume_isk'] = raw_df.avgPrice * raw_df.volume\n\n # drop some columns\n del raw_df['orderCount_str']\n del raw_df['volume_str']\n\n return raw_df\n", "repo_name": "hinnefe2/evetrader", "sub_path": "api.py", "file_name": "api.py", "file_ext": "py", "file_size_in_byte": 4963, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "37", "api": [{"api_name": "logging.getLogger", "line_number": 16, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 25, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 31, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 44, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 50, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 62, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 63, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 68, "usage_type": "call"}, {"api_name": "pandas.to_numeric", "line_number": 76, "usage_type": "attribute"}, {"api_name": "pandas.to_datetime", "line_number": 77, "usage_type": "attribute"}, {"api_name": "pandas.read_json", "line_number": 86, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 86, "usage_type": "call"}, {"api_name": "os.path", "line_number": 86, "usage_type": "attribute"}, {"api_name": "config.orders_dir", "line_number": 86, "usage_type": "attribute"}, {"api_name": "time.gmtime", "line_number": 98, "usage_type": "call"}, {"api_name": "os.path.getmtime", "line_number": 98, "usage_type": "call"}, {"api_name": "os.path", "line_number": 98, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 98, "usage_type": "call"}, {"api_name": "config.orders_dir", "line_number": 98, "usage_type": "attribute"}, {"api_name": "pandas.to_datetime", "line_number": 99, "usage_type": "call"}, {"api_name": "time.strftime", "line_number": 99, "usage_type": "call"}, {"api_name": "pandas.to_datetime", "line_number": 102, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 113, "usage_type": "call"}, {"api_name": "re.match", "line_number": 121, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 123, "usage_type": "call"}, {"api_name": "os.path", "line_number": 123, "usage_type": "attribute"}, {"api_name": "config.history_dir", "line_number": 123, "usage_type": "attribute"}, {"api_name": "json.load", "line_number": 124, "usage_type": "call"}, {"api_name": "pandas.DataFrame.from_dict", "line_number": 126, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 126, "usage_type": "attribute"}, {"api_name": "pandas.to_datetime", "line_number": 129, "usage_type": "call"}, {"api_name": "time.gmtime", "line_number": 135, "usage_type": "call"}, {"api_name": "os.path.getmtime", "line_number": 135, "usage_type": "call"}, {"api_name": "os.path", "line_number": 135, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 135, "usage_type": "call"}, {"api_name": "config.history_dir", "line_number": 135, "usage_type": "attribute"}, {"api_name": "pandas.to_datetime", "line_number": 136, "usage_type": "call"}, {"api_name": "time.strftime", "line_number": 136, "usage_type": "call"}]} +{"seq_id": "25661672764", "text": "import numpy as np\nimport matplotlib.pyplot as plt\nfrom Electric_pot import Electric_pot\nfrom gradient import Grad\nplt.rcParams[\"font.family\"] = \"helvetica\"\nplt.style.use('seaborn-white')\n\n\n# point charge 1 info\ndist_1 = 0.5\n\n# point charge 2 info\n\ndist_2 = -0.5\n\n# Creating points in a square grid\n\nx = np.linspace(-1.0, 1.0, 201)\ny = np.linspace(-1.0, 1.0, 201)\nX, Y = np.meshgrid(x, y)\n\n# These points are used to just show the region \nx_s = np.linspace(-1.0, 1.0, 11)\ny_s = np.linspace(-1.0, 1.0, 11)\nX_s, Y_s = np.meshgrid(x_s, y_s)\n\n# Calculating electric potential at all points\n\npotential = Electric_pot(-0.52, 0, X, Y, 1) + Electric_pot(.52, 0, X, Y, 1)\n\n# Calculating the E field at those points & and the speed for vector field\n\nfield = Grad(potential, 0.1)\n\n# Create figure\n\nfig, axes = plt.subplots(nrows=2, ncols=2, figsize=(11, 11), gridspec_kw={\n 'width_ratios': [5, 5],\n 'height_ratios': [5, 5]})\n\naxes[1, 1].axis(\"off\")\n\n# Plot the points made(gray) and particle (red and blue)\n\n\n# axes[0, 0].scatter(X, Y, color=\"grey\") # True points measured\naxes[0, 0].scatter(X_s, Y_s, color=\"grey\") # Points for a visual understanding\naxes[0, 0].grid()\naxes[0, 0].scatter(dist_1, 0, color=\"r\")\naxes[0, 0].scatter(dist_2, 0, color=\"b\")\naxes[0, 0].title.set_text(\"Region to Find Potentials(Charges are the red and blue points)\")\naxes[0, 0].set_xlabel(\"X(m)\")\naxes[0, 0].set_ylabel(\"Y(m)\")\n\n# Plots image of potential\n\ncmap = axes[0, 1].imshow(potential / 1e10, vmin=0, vmax=5, extent=[\n 0, 1, 0, 1], origin=\"lower\", cmap=\"copper_r\")\naxes[0, 1].title.set_text(\"Map of Potentials (Volts * 10e10)\")\naxes[0, 1].set_xlabel(\"X(m)\")\naxes[0, 1].set_ylabel(\"Y(m)\")\n\n# Plots E-field\n\naxes[1, 0].streamplot(X, Y, field[0] * -1, field[1] * -1, color=\"k\", density=[\n 0.5, 1])\naxes[1, 0].scatter(dist_1, 0, color=\"r\")\naxes[1, 0].scatter(dist_2, 0, color=\"b\")\naxes[1, 0].title.set_text(\"Electric Field (Volts / m)\")\naxes[1, 0].set_xlabel(\"X(m)\")\naxes[1, 0].set_ylabel(\"Y(m)\")\n\nplt.colorbar(cmap, ax=axes[0, 1], fraction=0.046, pad=0.04)\nfig.suptitle(\"Homework #3\")\nplt.show()\n", "repo_name": "AstroG12/CompMethods_ImaniD", "sub_path": "Hw_3.py", "file_name": "Hw_3.py", "file_ext": "py", "file_size_in_byte": 2079, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "matplotlib.pyplot.rcParams", "line_number": 5, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 5, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.style.use", "line_number": 6, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.style", "line_number": 6, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 6, "usage_type": "name"}, {"api_name": "numpy.linspace", "line_number": 18, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 19, "usage_type": "call"}, {"api_name": "numpy.meshgrid", "line_number": 20, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 23, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 24, "usage_type": "call"}, {"api_name": "numpy.meshgrid", "line_number": 25, "usage_type": "call"}, {"api_name": "Electric_pot.Electric_pot", "line_number": 29, "usage_type": "call"}, {"api_name": "gradient.Grad", "line_number": 33, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 37, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 37, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.colorbar", "line_number": 73, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 73, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 75, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 75, "usage_type": "name"}]} +{"seq_id": "22611707887", "text": "\"\"\"Sensor script for Raspberry Pi. Sends to Mongo DB. \nCMP2804M Team Software Engineering - Assignment 3.\n\"\"\"\n\nimport sys # Used for command line arguments.\nimport getopt # Used to parse command line arguments.\nimport pymongo # Used for HTML POST request to backend.\nfrom os import path # Used for file logging.\nfrom os import getenv # Used for hiding API key.\nfrom datetime import datetime # Used for log files.\nfrom time import sleep # Used for timing sending data.\nfrom threading import Thread # Used for multi-threading (for timing).\nimport RPi.GPIO as GPIO # Used for sensors.\nfrom dotenv import load_dotenv # Hide API key.\n\n\ndef parse_command_line(argv: list) -> tuple:\n \"\"\"Get the command line arguments, if any.\n Returns tuple of sensor_type, pin, count.\n \"\"\"\n # Defaults - can set at command line.\n polling_interval = 1 # If motion, save every 1 minute.\n pin = 17 \n sensor_type = \"motion\"\n \n if len(argv) == 1: return sensor_type, pin, polling_interval\n\n # Command line arguments and parsing.\n help_statement = str(argv[0]) + \" -s -p -i \"\n\n try:\n opts, args = getopt.getopt(argv[1:],\"hs:p:i:\",[\"sensor=\",\"pin=\",\"interval=\"])\n\n except getopt.GetoptError:\n print(help_statement)\n sys.exit(2)\n\n for opt, arg in opts:\n if opt == '-h':\n print(help_statement)\n sys.exit()\n\n elif opt in (\"-s\", \"--sensor\"):\n sensor_type = arg\n if sensor_type == \"motion\": continue\n elif sensor_type == \"ir\": continue\n else:\n print(\"Invalid sensor. \" + help_statement)\n sys.exit()\n\n elif opt in (\"-p\", \"--pin\"):\n pin = arg\n try:\n pin = int(pin)\n except:\n print(\"Pin must be a whole number.\")\n sys.exit()\n if pin < 0:\n print(\"Pin must be a positive number.\")\n sys.exit()\n\n elif opt in (\"-i\", \"--interval\"):\n polling_interval = arg\n try:\n polling_interval = int(polling_interval)\n except:\n print(\"Polling Interval must be a whole number.\")\n sys.exit()\n if polling_interval < 0:\n print(\"Polling Interval of \" + str(polling_interval) + \"is too short.\")\n sys.exit()\n \n \ndef start_motion_sensor(data: dict, pin: int, count: int) -> tuple:\n \"\"\"Attempts to start the motion sensor.\n Initialises count to 0.\n Returns sensor and data dictionary.\n \"\"\"\n try:\n GPIO.setmode(GPIO.BCM)\n GPIO.setup(pin, GPIO.IN)\n count = 0 # Shows motion sensor is ready.\n\n msg = \"Initialising motion sensor on pin \" + str(pin) + \".\"\n log_entry(msg)\n print(msg)\n\n sleep(10) # Give sensor time to start.\n\n msg = \"Initialised motion sensor on pin \" + str(pin) + \".\"\n log_entry(msg)\n print(msg)\n\n except:\n msg = \"Error initialising motion sensor on pin \" + str(pin) + \".\"\n print(msg)\n log_entry(msg)\n sys.exit(2)\n\n return data, count\n\n\ndef start_ir_sensor(data: dict, pin: int, count: int) -> tuple:\n \"\"\"Attempts to start the IR sensor.\n Initialises count to 0.\n Returns sensor and data dictionary.\n \"\"\"\n try:\n GPIO.setmode(GPIO.BCM)\n GPIO.setup(pin, GPIO.IN) \n count = 0 # Shows IR sensor is ready. \n\n msg = \"Initialising IR sensor on pin \" + str(pin) + \".\"\n log_entry(msg)\n print(msg) \n\n sleep(10) # Give sensor time to start.\n\n msg = \"Initialised IR sensor on pin \" + str(pin) + \".\"\n log_entry(msg)\n print(msg)\n\n except:\n error_msg = \"Error initialising IR sensor on pin \" + str(pin) + \".\"\n log_entry(error_msg)\n print(error_msg)\n sys.exit(2)\n\n return data, count\n\n\ndef save_data(data: dict, count: int) -> tuple:\n \"\"\"Save the time and count to the dictionary.\"\"\"\n now = datetime.now().strftime(\"%H:%M\")\n msg = \"Saving count of \" + str(count) + \".\"\n log_entry(msg)\n print(msg)\n data[\"timeRecieved\"][now] = count\n count = 0\n return data, count\n\n\ndef send_data(data: dict, col: pymongo.MongoClient) -> dict:\n \"\"\"Send sensor data to backend. Returns data (reset if successful).\"\"\"\n empty_dict = {\n \"timeSent\" : \"\",\n \"timeRecieved\" : {}\n }\n if data == empty_dict: return\n\n # Append the current time to the data dictionary before sending.\n now = datetime.now().strftime(\"%d/%m/%Y-%H:%M\")\n data[\"timeSent\"] = now\n\n # Attempt to send the data to the backend.\n try:\n col.insert_one(data)\n # Response from the backend.\n msg = \"Saved to the database.\"\n log_entry(msg)\n print(msg)\n return empty_dict\n except:\n msg = \"Failed logging to the database. Will try again in 10 minutes.\"\n log_entry(msg)\n print(msg)\n return data\n\n\ndef log_entry(message: str) -> None:\n \"\"\"Append a timestamp + message to the dated log file. No return\"\"\"\n # Guard clauses.\n if message == None: return\n if message == \"\": return\n\n now = datetime.now()\n now_date = now.strftime(\"%d_%m_%Y\")\n now_time = now.strftime(\"%H:%M:%S\")\n\n filename = now_date + \" Sensor Log\"\n directory = path.dirname(path.realpath(__file__))\n\n with open(directory + '/' +filename, 'a') as log:\n log.write(now_time + \" \" + message + \"\\n\")\n\n return\n\n\ndef main(argv: list) -> None:\n load_dotenv()\n API_KEY = getenv('API_KEY')\n # Mongo setup.\n client = pymongo.MongoClient(API_KEY)\n db = client.LoneWorking\n col = db[\"SensorData\"]\n\n # Other.\n sensor_type, pin, polling_interval = parse_command_line(argv)\n data = {\n \"timeSent\" : \"\",\n \"timeRecieved\" : {}\n }\n count = -1\n\n # Main loop. Count each signal from the motion sensor. \n while (True):\n # Check sensors are started, extra guarding.\n if count == -1:\n if sensor_type == \"motion\":\n data, count = start_motion_sensor(data, pin, count)\n elif sensor_type == \"ir\":\n data, count = start_ir_sensor(data, pin, count) \n sleep(1)\n continue\n\n # Save data every minute.\n now_seconds = datetime.now().strftime(\"%S\")\n if now_seconds == \"00\":\n data, count = save_data(data, count)\n\n # Send data every ten minutes.\n now_minute_seconds = datetime.now().strftime(\"%M:%S\")\n if now_minute_seconds[1:] == \"0:00\":\n data = send_data(data, col)\n\n if sensor_type == \"motion\":\n if GPIO.input(pin): \n count += 1\n print(\"count=\" + str(count))\n sleep(polling_interval)\n\n elif sensor_type == \"ir\":\n if GPIO.input(pin):\n count += 1\n print(\"count=\" + str(count))\n sleep(polling_interval)\n\n\nif __name__ == '__main__':\n main(sys.argv)\n\n\n", "repo_name": "benjaminpnicholls/pi-sensor", "sub_path": "sensor.py", "file_name": "sensor.py", "file_ext": "py", "file_size_in_byte": 7270, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "getopt.getopt", "line_number": 32, "usage_type": "call"}, {"api_name": "getopt.GetoptError", "line_number": 34, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 36, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 41, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 49, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 57, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 60, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 68, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 71, "usage_type": "call"}, {"api_name": "RPi.GPIO.setmode", "line_number": 80, "usage_type": "call"}, {"api_name": "RPi.GPIO", "line_number": 80, "usage_type": "name"}, {"api_name": "RPi.GPIO.BCM", "line_number": 80, "usage_type": "attribute"}, {"api_name": "RPi.GPIO.setup", "line_number": 81, "usage_type": "call"}, {"api_name": "RPi.GPIO", "line_number": 81, "usage_type": "name"}, {"api_name": "RPi.GPIO.IN", "line_number": 81, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 88, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 98, "usage_type": "call"}, {"api_name": "RPi.GPIO.setmode", "line_number": 109, "usage_type": "call"}, {"api_name": "RPi.GPIO", "line_number": 109, "usage_type": "name"}, {"api_name": "RPi.GPIO.BCM", "line_number": 109, "usage_type": "attribute"}, {"api_name": "RPi.GPIO.setup", "line_number": 110, "usage_type": "call"}, {"api_name": "RPi.GPIO", "line_number": 110, "usage_type": "name"}, {"api_name": "RPi.GPIO.IN", "line_number": 110, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 117, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 127, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 134, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 134, "usage_type": "name"}, {"api_name": "pymongo.MongoClient", "line_number": 143, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 152, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 152, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 176, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 176, "usage_type": "name"}, {"api_name": "os.path.dirname", "line_number": 181, "usage_type": "call"}, {"api_name": "os.path", "line_number": 181, "usage_type": "name"}, {"api_name": "os.path.realpath", "line_number": 181, "usage_type": "call"}, {"api_name": "dotenv.load_dotenv", "line_number": 190, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 191, "usage_type": "call"}, {"api_name": "pymongo.MongoClient", "line_number": 193, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 213, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 217, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 217, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 222, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 222, "usage_type": "name"}, {"api_name": "RPi.GPIO.input", "line_number": 227, "usage_type": "call"}, {"api_name": "RPi.GPIO", "line_number": 227, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 230, "usage_type": "call"}, {"api_name": "RPi.GPIO.input", "line_number": 233, "usage_type": "call"}, {"api_name": "RPi.GPIO", "line_number": 233, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 236, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 240, "usage_type": "attribute"}]} +{"seq_id": "34510640276", "text": "import serial\nimport csv\nimport time\n\n# Configuration\n# Change this to your Arduino's port (COM# for Windows, /dev/tty.* for macOS/Linux)\nSERIAL_PORT = '/dev/cu.usbmodem11401'\nBAUD_RATE = 115200\nCSV_FILENAME = 'imu_data.csv'\nCOLLECTION_DURATION = 60 # Collect data for 1 minute\n\n# Set up the serial connection\nser = serial.Serial(SERIAL_PORT, BAUD_RATE)\ntime.sleep(2) # Allow connection to establish\n\n# Create/open the CSV file\nwith open(CSV_FILENAME, 'w', newline='') as csvfile:\n csv_writer = csv.writer(csvfile)\n\n # Write the header\n csv_writer.writerow([\"ax\", \"ay\", \"az\", \"gx\", \"gy\", \"gz\", \"timestamp\"])\n\n # Start data collection\n start_time = time.time()\n while time.time() - start_time < COLLECTION_DURATION:\n # Read the line from serial\n line = ser.readline().decode('utf-8').strip()\n\n # Split and parse the values\n ax, ay, az, gx, gy, gz = map(float, line.split('\\t'))\n current_time = time.time()\n\n # Write to CSV\n csv_writer.writerow([ax, ay, az, gx, gy, gz, current_time])\n\nser.close()\nprint(\"Data collection complete. Saved to\", CSV_FILENAME)\n", "repo_name": "iamharkirat/BMI598", "sub_path": "data_collector.py", "file_name": "data_collector.py", "file_ext": "py", "file_size_in_byte": 1123, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "serial.Serial", "line_number": 13, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 14, "usage_type": "call"}, {"api_name": "csv.writer", "line_number": 18, "usage_type": "call"}, {"api_name": "time.time", "line_number": 24, "usage_type": "call"}, {"api_name": "time.time", "line_number": 25, "usage_type": "call"}, {"api_name": "time.time", "line_number": 31, "usage_type": "call"}]} +{"seq_id": "21311418281", "text": "import pdfplumber\nfrom pdfplumber.page import Page\nfrom pdfplumber.table import Table\nfrom typing import List, Dict\nfrom tools.headers import Headers\n# 一些全局参数\nSIT_DEVATION = 10e-6 # 坐标中存在大量的浮点数，当两个坐标的差的绝对值小于10e-6时，认为两个坐标是相同的。\nHEADER_MAX_ROW = 10 # 在确定页眉内容时最多只检查HEADER_MAX_ROW个字块\n'''\n关于坐标位置的说明\nbbox记录四个位置信息(x0, top, x1, bottom)用来标记一个矩形区域,\n下面是项目地址(https://github.com/jsvine/pdfplumber)中的部分文档说明\nx0\tDistance of left-side extremity from left side of page.\nx1\tDistance of right-side extremity from left side of page.\ny0\tDistance of bottom extremity from bottom of page.\ny1\tDistance of top extremity bottom of page.\ntop\tDistance of top of line from top of page.\nbottom\tDistance of bottom of the line from top of page.\ndoctop\tDistance of top of line from top of document.\n'''\n\n\ndef show_page(page: Page, bbox_or_obj=None):\n im = page.to_image()\n if bbox_or_obj is not None:\n try:\n im.draw_rect(bbox_or_obj)\n except:\n print('from show_page:图形绘制失败')\n im.show()\n\n\nclass Parser:\n def __init__(self, path: str = None) -> None:\n self.header_is_find = False\n self.pdf: pdfplumber.pdf.PDF = None\n self.pdf_file = None\n self.pdf_num = 0\n self.all_page_words: List[List[Dict[str, str]]] = []\n self.all_page_tables: List[List[Table]] = []\n self.open(path=path)\n self.read_pdf()\n\n self.headers = Headers(pages_num=self.pages_num)\n self.find_header()\n\n self.cropped_pages: List[Page] = []\n self.crop_pages() # 剪切掉所有页面的页眉页脚\n self.new_page_words: List[List[Dict[str, str]]] = []\n self.load_new_pages()\n\n self.possible_tocs: List[Dict[str, str]] = []\n self.find_toc()\n\n def open(self, path: str):\n self.pdf_file = open(path, 'rb')\n\n def read_pdf(self):\n self.pdf = pdfplumber.open(path_or_fp=self.pdf_file)\n self.pages_num = len(self.pdf.pages)\n for page in self.pdf.pages:\n self.all_page_words.append(page.extract_words())\n self.all_page_tables.append(page.find_tables())\n def load_new_pages(self):\n for page in self.cropped_pages:\n self.new_page_words.append(page.extract_words())\n # 确定页眉的内容\n def _find_header(self) -> bool:\n if self.header_is_find:\n return True\n word_num = 0\n word = ''\n while True:\n for pagination in range(len(self.all_page_words)):\n if not pagination+1 < len(self.all_page_words):\n break\n # 拿到当前页的所有字块\n now_page_words = self.all_page_words[pagination]\n # 拿到下一页的所有字块\n next_page_words = self.all_page_words[pagination+1]\n # 判断当前字块序号是否超出索引范围\n if not word_num < len(now_page_words) or not word_num < len(next_page_words):\n continue\n # 判断前后两页相同序号的字块是否相同，如果相同则有可能是页眉\n if now_page_words[word_num]['text'] == next_page_words[word_num]['text']:\n word = now_page_words[word_num]\n if pagination == 0:\n self.headers.append(\n word=word, pagination=pagination)\n self.headers.append(\n word=word, pagination=pagination+1)\n if self.headers.header_is_find(word_num=word_num, word_text=word['text']):\n return True\n word_num += 1\n if word_num > HEADER_MAX_ROW:\n return False\n\n def find_header(self): # 深嵌套多耦合，完全没有可读性,如无必要，不要尝试读懂它\n if self._find_header():\n msg = ' '\n msg_list = self.headers.get_header_text_list()\n print(f'Msg from find_header: 页眉为\\'{msg.join(msg_list)}\\'')\n else:\n print('Error from find_header: 无法确认页眉')\n\n def crop_pages(self):\n header_num = self.headers.total_header\n header_text_list = self.headers.get_header_text_list()\n header_exist = True\n for page_index, page in enumerate(self.pdf.pages):\n words = page.extract_words()\n bottom: int = words[-3]['top']-1\n for i in range(header_num):\n if self.all_page_words[page_index][i]['text'] != header_text_list[i]:\n header_exist = False\n break\n if header_exist:\n top: int = self.all_page_words[page_index][header_num-1]['bottom']\n top += 10 # 冗余\n self.cropped_pages.append(\n page.crop(bbox=[0, top, page.width, bottom]))\n header_exist = True\n\n # own_header 表示该页是否有页眉\n def consolidate_tables(self, pagination: int, table_num: int, own_header: bool = True) -> dict[str, list[int]]:\n # 此函数用来判断下一页的第一张表格是否与上一页最后一张表格是同一张表格\n def table_is_consecutive(header_msg: list[Dict[str, str]], page_words: list[Dict[str, str]], table_top: str):\n try:\n for word_num, header_word in enumerate(page_words):\n if word_num < len(header_msg):\n if not header_msg[word_num]['text'] == header_word['text']:\n return False\n else:\n if table_top <= header_word['top']:\n return True\n else:\n return False\n\n except Exception as e:\n print(e)\n return False\n\n self.table_statue: Dict[str, list] = {\n 'pagination': [pagination],\n 'list': [table_num]\n }\n header_msg = self.headers.get_header()\n while True:\n if not pagination+1 < self.pages_num:\n break\n page = self.pdf.pages[pagination+1]\n tables = page.find_tables()\n table_top = tables[0].bbox[1]\n page_words = page.extract_words()\n if table_is_consecutive(header_msg=header_msg, page_words=page_words, table_top=table_top):\n self.table_statue['pagination'].append(pagination+1)\n self.table_statue['list'].append(1)\n if not len(tables) == 1:\n break\n pagination += 1\n return self.table_statue\n\n def find_toc(self):\n page_numbers: List[int] = []\n for page in self.pdf.pages:\n if len(page.annots):\n print(f'Msg from find_toc: 在第{page.page_number}页找到目录')\n page_numbers.append(page.page_number)\n toc_crop_list: List[Page] = []\n for page_number in page_numbers:\n page = self.pdf.pages[page_number-1]\n for annot in page.annots:\n bbox = [annot['x0'], annot['top'],\n annot['x1'], annot['bottom']]\n toc_crop_list.append(page.crop(bbox=bbox))\n for toc in toc_crop_list:\n words = toc.extract_words()\n tmp_dict = {}\n tmp_dict['section_num'] = words[0]['text']\n tmp_dict['section_name'] = words[1]['text']\n tmp_dict['section_page'] = words[3]['text']\n self.possible_tocs.append(tmp_dict)\n\n def rerange_words(self,words: List[Dict[str, str]], position: str = 'top'):#根据坐标信息重新排列字词块\n index_dict:Dict[float,int]={}\n rerange_words_list: List[List[Dict[str, str]]] = []\n if position not in {'x0', 'x1', 'top', 'doctop', 'bottom'} or len(words) == 0:\n return rerange_words_list,index_dict\n pre_word_position = None\n index=0\n for i in range(1, len(words)):\n word = words[i]\n if word[position] != pre_word_position:\n index_dict[word[position]]=index\n index+=1\n rerange_words_list.append([word])\n pre_word_position = word[position]\n else:\n rerange_words_list[-1].append(word)\n return rerange_words_list,index_dict\n\n def find_words_in_pdf(self,target_str_or_list=None):#words_str_or_list=\"√不适用,√适用\"\n def match_words(match_words,target_words):\n tmp_page_words = []\n for match in match_words:\n for word in target_words:\n if word == match['text']:\n tmp_page_words.append(match)\n return tmp_page_words\n words_msg: List[List[Dict[str]]] = [] # 列表中的每一个元素存储一个页的目标字词块信息\n target_words = target_str_or_list\n if isinstance(target_str_or_list, str):\n target_words = target_str_or_list.split(',')\n for matchs in self.all_page_words:\n tmp_page_words=match_words(match_words=matchs,target_words=target_words)\n words_msg.append(tmp_page_words)\n return words_msg\n\n\n def get_toc(self):\n pass\n\n def close(self):\n self.pdf_file.close()\n self.pdf.close()\n\n def __enter__(self):\n return self\n\n def __exit__(self, exc_type, exc_val, exc_tb):\n self.close()\n return False\n\n\nif __name__ == '__main__':\n with Parser('test.PDF') as parser:\n print('Debug')\n", "repo_name": "Lingwuxin/PDFParser", "sub_path": "pdfparser.py", "file_name": "pdfparser.py", "file_ext": "py", "file_size_in_byte": 9736, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "pdfplumber.page.Page", "line_number": 23, "usage_type": "name"}, {"api_name": "pdfplumber.pdf", "line_number": 36, "usage_type": "attribute"}, {"api_name": "typing.List", "line_number": 39, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 39, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 40, "usage_type": "name"}, {"api_name": "pdfplumber.table.Table", "line_number": 40, "usage_type": "name"}, {"api_name": "tools.headers.Headers", "line_number": 44, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 47, "usage_type": "name"}, {"api_name": "pdfplumber.page.Page", "line_number": 47, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 49, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 49, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 52, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 52, "usage_type": "name"}, {"api_name": "pdfplumber.open", "line_number": 59, "usage_type": "call"}, {"api_name": "typing.Dict", "line_number": 127, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 143, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 164, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 169, "usage_type": "name"}, {"api_name": "pdfplumber.page.Page", "line_number": 169, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 184, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 184, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 185, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 186, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 186, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 210, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 210, "usage_type": "name"}]} +{"seq_id": "7334778154", "text": "import logging\nfrom vc_exporters.vc_exporter import VCExporter\nfrom prometheus_client import Gauge\nfrom pyVmomi import vim\n\n\nclass VcDatastoreMetrics(VCExporter):\n\n def __init__(self, exporterType, exporterConfig):\n super().__init__(exporterType, exporterConfig)\n\n self.gauge = {}\n\n self.gauge['vcenter_datastore_accessible'] = Gauge('vcenter_datastore_accessible',\n 'vcenter_datastore_accessible',\n ['region', 'datacenter', 'name', 'type'])\n\n self.gauge['vcenter_datastore_maintenance'] = Gauge('vcenter_datastore_maintenance',\n 'vcenter_datastore_maintenance',\n ['region', 'datacenter', 'name', 'type'])\n\n self.gauge['vcenter_datastore_capacity_bytes'] = Gauge('vcenter_datastore_capacity_bytes',\n 'vcenter_datastore_capacity_bytes',\n ['region', 'datacenter', 'name', 'type'])\n\n self.gauge['vcenter_datastore_free_space_bytes'] = Gauge('vcenter_datastore_free_space_bytes',\n 'vcenter_datastore_free_space_bytes',\n ['region', 'datacenter', 'name', 'type'])\n\n self.gauge['vcenter_datastore_accessible_from_host'] = Gauge('vcenter_datastore_accessible_from_host',\n 'vcenter_datastore_accessible_from_host',\n ['region', 'datacenter', 'name', 'type', 'host'])\n\n\n self.gauge['vcenter_datastore_vm_stored'] = Gauge('vcenter_datastore_vm_stored',\n 'vcenter_datastore_vm_stored',\n ['region', 'datacenter', 'name', 'type'])\n\n self.datastore_properties =[\n \"summary.name\",\n \"summary.type\",\n \"summary.url\",\n \"summary.accessible\",\n \"summary.maintenanceMode\",\n \"summary.capacity\",\n \"summary.freeSpace\",\n \"host\",\n \"vm\"\n ]\n\n self.content = self.si.RetrieveContent()\n\n self.datastores = self.si.content.viewManager.CreateContainerView(\n container=self.content.rootFolder,\n type=[vim.Datastore],\n recursive=True\n )\n\n def collect(self):\n\n self.data, self.mors = self.collect_properties(self.si, self.datastores, vim.Datastore, self.datastore_properties, True)\n\n def export(self):\n\n self.metric_count = 0\n region = self.vcenterInfo['hostname'].split('.')[2]\n datacenter = region + (self.vcenterInfo['hostname'].split('.')[0]).split('-')[1]\n\n\n for datastore in self.data:\n # discard management datastores\n\n if 'Management' in datastore['summary.name']:\n continue\n\n logging.debug('datastore: %s %s, maintenanceMode: %s, accessible: %s' %(datastore['summary.type'],\n datastore['summary.name'],\n datastore['summary.maintenanceMode'],\n datastore['summary.accessible']))\n\n self.metric_count += 1\n\n try:\n\n # accessible state\n if datastore['summary.accessible']:\n self.gauge['vcenter_datastore_accessible'].labels(region, datacenter, datastore['summary.name'],\n datastore['summary.type']).set(1)\n else:\n self.gauge['vcenter_datastore_accessible'].labels(region, datacenter, datastore['summary.name'],\n datastore['summary.type']).set(0)\n\n # maintenance mode\n if datastore['summary.maintenanceMode'] == \"normal\":\n self.gauge['vcenter_datastore_maintenance'].labels(region, datacenter, datastore['summary.name'],\n datastore['summary.type']).set(1)\n else:\n self.gauge['vcenter_datastore_maintenance'].labels(region, datacenter, datastore['summary.name'],\n datastore['summary.type']).set(0)\n\n # capacity\n self.gauge['vcenter_datastore_capacity_bytes'].labels(region, datacenter, datastore['summary.name'],\n datastore['summary.type']).set(str(datastore['summary.capacity']))\n\n # free space\n self.gauge['vcenter_datastore_free_space_bytes'].labels(region, datacenter, datastore['summary.name'],\n datastore['summary.type']).set(str(datastore['summary.freeSpace']))\n\n\n # number of virtual machines associated to the host\n self.gauge['vcenter_datastore_vm_stored'].labels(region, datacenter, datastore['summary.name'],\n datastore['summary.type']).set(len(datastore['vm']))\n\n # access from mounted host\n for host in datastore['host']:\n logging.debug(\"Mounted host: %s, inMaintenance: %s, can access ds: %s\" %(host.key.name,\n host.key.runtime.inMaintenanceMode,\n host.mountInfo.accessible))\n\n if not host.key.runtime.inMaintenanceMode: # only hosts which are not in maintenance\n if host.mountInfo.accessible:\n self.gauge['vcenter_datastore_accessible_from_host'].labels(region, datacenter,\n datastore['summary.name'],\n datastore['summary.type'],\n host.key.name).set(1)\n else:\n self.gauge['vcenter_datastore_accessible_from_host'].labels(region, datacenter,\n datastore['summary.name'],\n datastore['summary.type'],\n host.key.name).set(0)\n\n except Exception as e:\n logging.debug('Could not get data for datastore %s' % str(e))", "repo_name": "sapcc/infrastructure-exporters", "sub_path": "vc_exporters/vc_exporter_types/vcdatastoremetrics.py", "file_name": "vcdatastoremetrics.py", "file_ext": "py", "file_size_in_byte": 7307, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "37", "api": [{"api_name": "vc_exporters.vc_exporter.VCExporter", "line_number": 7, "usage_type": "name"}, {"api_name": "prometheus_client.Gauge", "line_number": 14, "usage_type": "call"}, {"api_name": "prometheus_client.Gauge", "line_number": 18, "usage_type": "call"}, {"api_name": "prometheus_client.Gauge", "line_number": 22, "usage_type": "call"}, {"api_name": "prometheus_client.Gauge", "line_number": 26, "usage_type": "call"}, {"api_name": "prometheus_client.Gauge", "line_number": 30, "usage_type": "call"}, {"api_name": "prometheus_client.Gauge", "line_number": 35, "usage_type": "call"}, {"api_name": "pyVmomi.vim.Datastore", "line_number": 55, "usage_type": "attribute"}, {"api_name": "pyVmomi.vim", "line_number": 55, "usage_type": "name"}, {"api_name": "pyVmomi.vim.Datastore", "line_number": 61, "usage_type": "attribute"}, {"api_name": "pyVmomi.vim", "line_number": 61, "usage_type": "name"}, {"api_name": "logging.debug", "line_number": 76, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 116, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 133, "usage_type": "call"}]} +{"seq_id": "18102436677", "text": "import numpy as np\nimport pandas as pd\nimport pytest\n\nfrom raiutils.exceptions import UserConfigValidationException\nfrom responsibleai._data_validations import \\\n _validate_unique_operation_on_categorical_columns\n\nTARGET = 'target'\n\n\nclass TestDataValidations:\n def test_dirty_train_test_data(self):\n X_train = pd.DataFrame(data=[['1', np.nan], ['2', '3']],\n columns=['c1', 'c2'])\n y_train = np.array([1, 0])\n X_test = pd.DataFrame(data=[['1', '2'], ['2', '3']],\n columns=['c1', 'c2'])\n y_test = np.array([1, 0])\n\n X_train[TARGET] = y_train\n X_test[TARGET] = y_test\n\n with pytest.raises(UserConfigValidationException) as ucve:\n _validate_unique_operation_on_categorical_columns(\n train_data=X_train,\n test_data=X_test,\n categorical_features=['c2'])\n\n assert 'Error finding unique values in column c2. ' + \\\n 'Please check your train data.' in str(ucve.value)\n\n with pytest.raises(UserConfigValidationException) as ucve:\n _validate_unique_operation_on_categorical_columns(\n train_data=X_test,\n test_data=X_train,\n categorical_features=['c2'])\n\n assert 'Error finding unique values in column c2. ' + \\\n 'Please check your test data.' in str(ucve.value)\n", "repo_name": "microsoft/responsible-ai-toolbox", "sub_path": "responsibleai/tests/test_data_validations.py", "file_name": "test_data_validations.py", "file_ext": "py", "file_size_in_byte": 1420, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1031, "dataset": "github-code", "pt": "37", "api": [{"api_name": "pandas.DataFrame", "line_number": 14, "usage_type": "call"}, {"api_name": "numpy.nan", "line_number": 14, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 16, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 17, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 19, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 24, "usage_type": "call"}, {"api_name": "raiutils.exceptions.UserConfigValidationException", "line_number": 24, "usage_type": "argument"}, {"api_name": "responsibleai._data_validations._validate_unique_operation_on_categorical_columns", "line_number": 25, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 33, "usage_type": "call"}, {"api_name": "raiutils.exceptions.UserConfigValidationException", "line_number": 33, "usage_type": "argument"}, {"api_name": "responsibleai._data_validations._validate_unique_operation_on_categorical_columns", "line_number": 34, "usage_type": "call"}]} +{"seq_id": "26710969209", "text": "# the solver of the 4 = 10 problem\n\"\"\"four single dgits and available operators are given,\n creating a formula that results 10 is required\"\"\"\n \n# MAINTENANCE IS NOT CONSIDERED\n\nfrom operator import truediv\nfrom itertools import permutations, product\n\nbrackPos = [(0, 4), (0, 6), (2, 6), (2, 8), (4, 8)]\n\ndef Solver(nums, opers, bracket=True) :\n numList = list(set(permutations(nums, 4)))\n operitem = [opers, opers, opers]\n operList = list(product(*operitem))\n \n for i in numList :\n for j in operList :\n form = str(i[0]) + j[0] + str(i[1]) + j[1] + str(i[2]) + j[2] + str(i[3])\n \n try :\n if eval(form) == 10 : print(form)\n except ZeroDivisionError : continue \n\n if bracket :\n for k in brackPos :\n tmpform = form[0:k[0]] + \"(\" + form[k[0]:k[1]-1] + \")\" + form[k[1]-1:9] \n try :\n if eval(tmpform) == 10 : print(tmpform)\n except ZeroDivisionError : continue\n \n\n\n############################################\n############# MODIFY HERE ##################\n############################################\n \nnumbers = [1, 2, 3, 4] \noperators = ['+', '-', '*', '/'] \nbracket = False \n \n############################################\n############################################\n############################################\n\nSolver(numbers, operators, bracket)\n", "repo_name": "Jenix8/4equal10-Solver", "sub_path": "4=10.py", "file_name": "4=10.py", "file_ext": "py", "file_size_in_byte": 1594, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "itertools.permutations", "line_number": 13, "usage_type": "call"}, {"api_name": "itertools.product", "line_number": 15, "usage_type": "call"}]} +{"seq_id": "42075764940", "text": "# mypy: ignore-errors\nimport argparse\nimport matplotlib\nimport matplotlib.pyplot as plt\nimport matplotlib.animation as animation\nimport numpy as np\nfrom rlo import sparser\n\n# Collection is used to indicate something which can be iterated through repeatedly, but where the order is not significant;\n# and Sequence for where the order/position is significant (even if we may not index into it).\nfrom typing import Callable, Collection, Sequence, Tuple, Optional\n\nmatplotlib.use(\"Agg\")\n\nfrom rlo import experiment_result\nfrom rlo.summations import SummationsExpert\nfrom rlo import plotting\nfrom rlo import utils\n\nDEFAULT_FPS = 2\n\n\ndef flatten(seqs):\n for seq in seqs:\n yield from seq\n\n\ndef plot_hist(ax, tgt_then_fitted: Sequence[Sequence[Tuple[float, float]]], tgt_type):\n fitted_for_tgt = {k: [] for k in range(3)}\n for tgt, fitted in flatten(tgt_then_fitted):\n # We use NaN to exclude particular time-lefts from the expert plots.\n if not np.isnan(tgt):\n fitted_for_tgt[tgt].append(fitted)\n for tgt, fitteds in fitted_for_tgt.items():\n ax.hist(fitteds, histtype=\"step\", label=f\"{tgt_type} value {tgt}\")\n ax.set_xlabel(\"Fitted value\")\n ax.set_xlim(-0.3, 2.3)\n ax.set_ylabel(\"#points\")\n ax.set_yscale(\"log\")\n ax.set_ylim(1, 1e5)\n ax.legend(loc=\"upper center\")\n\n\ndef group_by_target_then_tl(\n tgt_and_fitted_per_expr: Collection[Sequence[Tuple[float, float]]]\n) -> Sequence[Sequence[Sequence[float]]]:\n \"\"\" Input: a collection (index not significant) of\n sequences (indexed by time-left) of\n pairs of (target value, fitted value) - NaN targets to be skipped\n Output - a sequence (where index == target value i.e. 0,1,2) of\n sequences (indexed by time-left) of\n collections (index not significant) of\n fitted values\n \"\"\"\n max_len = max(len(seq) for seq in tgt_and_fitted_per_expr)\n\n tgt_and_fitted_by_tl = [[] for tl in range(max_len)]\n for seq in tgt_and_fitted_per_expr:\n for tl, val_at_tl in enumerate(seq):\n tgt_and_fitted_by_tl[tl].append(val_at_tl)\n return [\n [\n [fitted for tgt, fitted in tgt_and_fitteds if tgt == tgt_val]\n for tgt_and_fitteds in tgt_and_fitted_by_tl\n ]\n for tgt_val in range(3)\n ]\n\n\ndef plot_violins(ax, fitted_by_tgt_then_tl: Sequence[Sequence[Collection[float]]]):\n for tgt_val, fitted_by_tl in enumerate(fitted_by_tgt_then_tl):\n vals_with_tl = [(tl, vs) for tl, vs in enumerate(fitted_by_tl) if len(vs) > 0]\n if len(vals_with_tl) > 0:\n xs_with_offset, vals = zip(\n *[(tl + (tgt_val / 3), vs) for tl, vs in vals_with_tl]\n )\n ax.violinplot(\n vals,\n xs_with_offset,\n points=100,\n widths=0.4,\n showmeans=False,\n showmedians=False,\n showextrema=False,\n )\n ax.set_ylabel(\"Fitted value\")\n ax.set_ylim(-0.3, 2.3)\n ax.set_xlabel(\"Time_left\")\n\n\ndef plot_counts(count_ax, fitted_by_tgt_then_tl: Sequence[Sequence[Collection[float]]]):\n counts_by_tgt_then_tl = np.array(\n [\n [len(pts_for_tl) for pts_for_tl in fitted_by_tl]\n for fitted_by_tl in fitted_by_tgt_then_tl\n ]\n )\n counts_by_tl = counts_by_tgt_then_tl.sum(axis=0) # Sum all target values\n xs = range(1, len(counts_by_tl) + 1)\n for tgt_val, counts_for_tgt in enumerate(counts_by_tgt_then_tl):\n count_ax.plot(xs, counts_for_tgt, label=f\"#points for {tgt_val}\")\n count_ax.plot(xs, counts_by_tl, color=\"black\", label=\"total #points\")\n\n\ndef plot_row(\n row,\n tgt_and_fitted_per_expr: Collection[Sequence[Tuple[float, float]]],\n tgt_type: str,\n):\n ax1, ax2, count_ax = row\n plot_hist(ax1, tgt_and_fitted_per_expr, tgt_type)\n fitted_by_tgt_then_tl = group_by_target_then_tl(tgt_and_fitted_per_expr)\n plot_violins(ax2, fitted_by_tgt_then_tl)\n plot_counts(count_ax, fitted_by_tgt_then_tl)\n\n\ndef plot_summations_fit_animated(\n outfile,\n events,\n expert_fn: Callable[[int], Optional[SummationsExpert]] = lambda _: None,\n repetition=0,\n suffix=\"\",\n fps=DEFAULT_FPS,\n):\n \"\"\" expert_fn: function from max_depth to optional expert. \"\"\"\n fit_events = [\n e\n for e in events\n if e[\"event\"] == \"distill_fit\" and e[\"repetition\"] == repetition\n ]\n target_and_fitteds_by_gen = {\n gen: utils.single_elem(evt)[\"target_and_fitted\"]\n for gen, evt in utils.group_by(fit_events, lambda e: e[\"generation\"]).items()\n }\n max_num_exprs = max(len(vs) for vs in target_and_fitteds_by_gen.values())\n\n expert = expert_fn(\n max(len(seq) for seq in flatten(target_and_fitteds_by_gen.values()))\n )\n rows = 1 if expert is None else 4\n fig, axss = plt.subplots(rows, 2, figsize=(16, 8 * rows), squeeze=False)\n rows = [list(axs) + [axs[1].twinx()] for axs in axss]\n\n def plot_gen(gen_num):\n for ax in flatten(rows):\n ax.clear()\n for _1, _2, count_ax in rows:\n count_ax.set_ylim(1, max_num_exprs)\n if gen_num not in target_and_fitteds_by_gen.keys():\n print(\"Skipping \", gen_num)\n return\n print(\"Plotting \", gen_num)\n target_and_fitted_by_expr = target_and_fitteds_by_gen[gen_num]\n\n # Top plots: fitted vs empirical (target value on which model just trained)\n plot_row(rows[0], target_and_fitted_by_expr.values(), \"Empirical\")\n\n if len(rows) > 1:\n # Bottom plots use expert values\n target_expert_fittedss = [\n [\n (target, expert, fitted)\n for expert, (target, fitted) in zip(\n utils.single_elem(\n expert.evaluate_all_time_left([sparser.parse_expr(expr)])\n ),\n target_and_fitteds,\n )\n ]\n for expr, target_and_fitteds in target_and_fitted_by_expr.items()\n ]\n\n def get_seqs(target_fn):\n return [\n [\n (target_fn(target, expert), fitted)\n for target, expert, fitted in seq\n ]\n for seq in target_expert_fittedss\n ]\n\n # fitted values for target values less than the expert\n plot_row(\n rows[1],\n get_seqs(\n lambda target, expert: target if target < expert else float(\"nan\")\n ),\n \"Empirical (< expert)\",\n )\n # fitted values for target values equal to the expert\n plot_row(\n rows[2],\n get_seqs(\n lambda target, expert: target if target == expert else float(\"nan\")\n ),\n \"Empirical ()== expert)\",\n )\n # fitted values against expert values\n plot_row(\n rows[3], get_seqs(lambda _target, expert: expert), \"Expert\",\n )\n\n plt.suptitle(f\"Generation {gen_num} summations fit {suffix}\")\n\n ani = animation.FuncAnimation(\n fig,\n plot_gen,\n init_func=lambda: plot_gen(0),\n frames=range(\n min(target_and_fitteds_by_gen.keys()), max(target_and_fitteds_by_gen.keys())\n ),\n )\n ani.save(outfile, fps=fps, dpi=200, writer=\"ffmpeg\")\n plt.close()\n\n\ndef plot_summations_expert_fit_from_config(config, events, **_kwargs):\n \"\"\"This is a verbose plot. Provide with verbose_events\"\"\"\n\n plot_summations_fit_animated(\n plotting.format_figure_filename(config, \"summations_expert_fit.mp4\"),\n events,\n suffix=plotting.config_suffix(config),\n expert_fn=SummationsExpert,\n )\n\n\ndef plot_summations_fit_animated_from_config(config, events, **_kwargs):\n \"\"\"This is a verbose plot. Provide with verbose_events\"\"\"\n\n plot_summations_fit_animated(\n plotting.format_figure_filename(config, \"summations_fit.mp4\"),\n events,\n suffix=plotting.config_suffix(config),\n )\n\n\ndef main():\n parser = argparse.ArgumentParser()\n parser.add_argument(\n \"run_id\",\n type=str,\n help=\"a run ID (e.g., 2019_01_06_13_15_48_13172) or path to a config.json file\",\n )\n parser.add_argument(\n \"--fps\",\n type=float,\n default=DEFAULT_FPS,\n help=\"speed of the animation - frames per second\",\n )\n parser.add_argument(\n \"--repetition\", type=int, default=0, help=\"which repetition to plot\"\n )\n parser.add_argument(\n \"--expert\",\n action=\"store_true\",\n help=\"include plots of datapoints grouped by expert value (slow!)\",\n )\n args = parser.parse_args()\n\n config, events = experiment_result.load_config_events(args.run_id, verbosity=1)\n outfile = plotting.format_figure_filename(\n config, \"summations_expert_fit.mp4\" if args.expert else \"summations_fit.mp4\"\n )\n\n plot_summations_fit_animated(\n outfile,\n events,\n repetition=args.repetition,\n suffix=plotting.config_suffix(config),\n expert_fn=SummationsExpert if args.expert else lambda _: None,\n fps=args.fps,\n )\n\n\nif __name__ == \"__main__\":\n main()\n", "repo_name": "microsoft/knossos-ksc", "sub_path": "rlo/src/rlo/plot_summations_fit_animated.py", "file_name": "plot_summations_fit_animated.py", "file_ext": "py", "file_size_in_byte": 9396, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 45, "dataset": "github-code", "pt": "37", "api": [{"api_name": "matplotlib.use", "line_number": 13, "usage_type": "call"}, {"api_name": "typing.Sequence", "line_number": 28, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 28, "usage_type": "name"}, {"api_name": "numpy.isnan", "line_number": 32, "usage_type": "call"}, {"api_name": "typing.Collection", "line_number": 45, "usage_type": "name"}, {"api_name": "typing.Sequence", "line_number": 45, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 45, "usage_type": "name"}, {"api_name": "typing.Sequence", "line_number": 46, "usage_type": "name"}, {"api_name": "typing.Sequence", "line_number": 70, "usage_type": "name"}, {"api_name": "typing.Collection", "line_number": 70, "usage_type": "name"}, {"api_name": "typing.Sequence", "line_number": 91, "usage_type": "name"}, {"api_name": "typing.Collection", "line_number": 91, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 92, "usage_type": "call"}, {"api_name": "typing.Collection", "line_number": 107, "usage_type": "name"}, {"api_name": "typing.Sequence", "line_number": 107, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 107, "usage_type": "name"}, {"api_name": "typing.Callable", "line_number": 120, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 120, "usage_type": "name"}, {"api_name": "rlo.summations.SummationsExpert", "line_number": 120, "usage_type": "name"}, {"api_name": "rlo.utils.single_elem", "line_number": 132, "usage_type": "call"}, {"api_name": "rlo.utils", "line_number": 132, "usage_type": "name"}, {"api_name": "rlo.utils.group_by", "line_number": 133, "usage_type": "call"}, {"api_name": "rlo.utils", "line_number": 133, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 141, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 141, "usage_type": "name"}, {"api_name": "rlo.utils.single_elem", "line_number": 164, "usage_type": "call"}, {"api_name": "rlo.utils", "line_number": 164, "usage_type": "name"}, {"api_name": "rlo.sparser.parse_expr", "line_number": 165, "usage_type": "call"}, {"api_name": "rlo.sparser", "line_number": 165, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.suptitle", "line_number": 203, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 203, "usage_type": "name"}, {"api_name": "matplotlib.animation.FuncAnimation", "line_number": 205, "usage_type": "call"}, {"api_name": "matplotlib.animation", "line_number": 205, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.close", "line_number": 214, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 214, "usage_type": "name"}, {"api_name": "rlo.plotting.format_figure_filename", "line_number": 221, "usage_type": "call"}, {"api_name": "rlo.plotting", "line_number": 221, "usage_type": "name"}, {"api_name": "rlo.plotting.config_suffix", "line_number": 223, "usage_type": "call"}, {"api_name": "rlo.plotting", "line_number": 223, "usage_type": "name"}, {"api_name": "rlo.summations.SummationsExpert", "line_number": 224, "usage_type": "name"}, {"api_name": "rlo.plotting.format_figure_filename", "line_number": 232, "usage_type": "call"}, {"api_name": "rlo.plotting", "line_number": 232, "usage_type": "name"}, {"api_name": "rlo.plotting.config_suffix", "line_number": 234, "usage_type": "call"}, {"api_name": "rlo.plotting", "line_number": 234, "usage_type": "name"}, {"api_name": "argparse.ArgumentParser", "line_number": 239, "usage_type": "call"}, {"api_name": "rlo.experiment_result.load_config_events", "line_number": 261, "usage_type": "call"}, {"api_name": "rlo.experiment_result", "line_number": 261, "usage_type": "name"}, {"api_name": "rlo.plotting.format_figure_filename", "line_number": 262, "usage_type": "call"}, {"api_name": "rlo.plotting", "line_number": 262, "usage_type": "name"}, {"api_name": "rlo.plotting.config_suffix", "line_number": 270, "usage_type": "call"}, {"api_name": "rlo.plotting", "line_number": 270, "usage_type": "name"}, {"api_name": "rlo.summations.SummationsExpert", "line_number": 271, "usage_type": "name"}]} +{"seq_id": "32000628334", "text": "from __future__ import print_function\n\nfrom types import MethodType\n\n\n__all__ = [\n 'Connection',\n 'ExitStack',\n 'PY2',\n 'PY3',\n 'StringIO',\n 'gevent',\n 'gyield',\n 'items',\n 'keys',\n 'range',\n 'reduce',\n 'zip',\n]\n\n\ntry:\n reduce = reduce\n PY2 = True\nexcept NameError:\n from functools import reduce\n PY2 = False\n\nPY3 = not PY2\n\ntry:\n import gevent\n\n def gyield():\n gevent.sleep(0.01)\nexcept ImportError:\n gevent = None\n\n def gyield():\n pass\n\nif PY2:\n try:\n from cStringIO import StringIO\n except ImportError:\n from StringIO import StringIO\n\n from contextlib2 import ExitStack\n import itertools\n\n boundmethod = MethodType\n filter = itertools.ifilter\n input = raw_input # NOQA\n items = dict.iteritems\n keys = dict.iterkeys\n map = itertools.imap\n range = xrange # NOQA\n zip = itertools.izip\n\nelse:\n from contextlib import ExitStack\n from io import StringIO\n\n def boundmethod(f, instance, owner):\n return MethodType(f, instance)\n\n filter = filter\n input = input # NOQA\n items = dict.items\n keys = dict.keys\n map = map\n range = range\n zip = zip\n\nprint_ = print\n\n\nclass Connection(object):\n \"\"\"\n A wrapper for a multiprocessing connection to emulate gipc pipes.\n \"\"\"\n def __init__(self, conn):\n self.__conn = conn\n\n def put(self, *args, **kwargs):\n return self.__conn.send(*args, **kwargs)\n\n def get(self, *args, **kwargs):\n return self.__conn.recv(*args, **kwargs)\n\n def __getattr__(self, name):\n return getattr(self.__conn, name)\n\n\ndef with_metaclass(metaclass, *bases):\n \"\"\"\n Adds a new base in the mro for python 2 and 3 compatible\n metaclass syntax.\n \"\"\"\n return metaclass('SurrogateBase', bases, {})\n", "repo_name": "adamtheturtle/qdb", "sub_path": "qdb/compat.py", "file_name": "compat.py", "file_ext": "py", "file_size_in_byte": 1825, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "github-code", "pt": "37", "api": [{"api_name": "gevent.sleep", "line_number": 35, "usage_type": "call"}, {"api_name": "types.MethodType", "line_number": 51, "usage_type": "name"}, {"api_name": "itertools.ifilter", "line_number": 52, "usage_type": "attribute"}, {"api_name": "itertools.imap", "line_number": 56, "usage_type": "attribute"}, {"api_name": "itertools.izip", "line_number": 58, "usage_type": "attribute"}, {"api_name": "types.MethodType", "line_number": 65, "usage_type": "call"}]} +{"seq_id": "74745411306", "text": "## A. Wright, E.-P. Damskägg, and V. Välimäki, ‘Real-time black-box modelling with recurrent neural networks’, in 22nd international conference on digital audio effects (DAFx-19), 2019, pp. 1–8.\n\nimport torch \nimport json\nfrom json import JSONEncoder\n\nimport torch.nn.functional as F\n\nclass SimpleLSTM(torch.nn.Module):\n \"\"\"\n LSTM Model after\n A. Wright, E.-P. Damskägg, and V. Välimäki, ‘Real-time black-box modelling with recurrent neural networks’, in 22nd international conference on digital audio effects (DAFx-19), 2019, pp. 1–8.\n uses 32 hidden by default.\n Wright et al. showed decent performance for 32, but \n even better going up to 96\n \"\"\"\n\n model_type = \"LSTM\"\n input_dim = 1\n\n def __init__(self, hidden_size=32, num_layers=1, dropout=0, param=False): \n super().__init__()\n # Batch first means input data is [batch,sequence,feature]\n\n if param:\n self.input_dim = 2\n\n self.lstm = torch.nn.LSTM(input_size=self.input_dim, hidden_size=hidden_size, batch_first=True,\n num_layers=num_layers, dropout=dropout)\n self.dense = torch.nn.Linear(hidden_size, 1)# from 8 hidden back to 1 output\n self.drop_hidden = True\n\n\n def zero_on_next_forward(self):\n \"\"\"\n next time forward is called, the network will\n run it with zeroed hidden+cell values\n \"\"\"\n self.drop_hidden = True \n \n def forward(self, torch_in):\n if self.drop_hidden:\n batch_size = torch_in.shape[0]\n # h_size = (num_layers,batch_size,hidden_count)\n h_shape = [self.lstm.num_layers, batch_size, self.lstm.hidden_size]\n #print(\"dropping hidden, shape probably \", h_shape)\n hidden = torch.zeros(h_shape).to(torch_in.device)\n cell = torch.zeros(h_shape).to(torch_in.device)\n x, _ = self.lstm(torch_in, (hidden, cell))\n self.drop_hidden = False\n else:\n x, _ = self.lstm(torch_in)\n\n return self.dense(x)\n \n def save_for_rtneural(self, outfile):\n ## used for saving \n class EncodeTensor(JSONEncoder):\n def default(self, obj):\n if isinstance(obj, torch.Tensor):\n return obj.cpu().detach().numpy().tolist()\n return super(json.NpEncoder, self).default(obj)\n \n with open(outfile, 'w') as json_file:\n json.dump(self.state_dict(), json_file,cls=EncodeTensor)\n\n# -------------------------------------------------------------------------------------------------------- #\n\nclass SimpleConv1D(torch.nn.Module):\n\n model_type = \"Conv1D\"\n input_dim = 1\n\n def __init__(self, param=False):\n super().__init__()\n\n if param:\n self.input_dim = 2\n\n self.conv1 = torch.nn.Conv1d(self.input_dim, 16, kernel_size=5, stride=1, padding='same')\n self.act1 = torch.nn.PReLU()\n\n self.conv2 = torch.nn.Conv1d(16, 32, kernel_size=5, stride=1, padding='same')\n self.act2 = torch.nn.PReLU()\n\n # commented for dist\n\n self.conv3 = torch.nn.Conv1d(32, 64, kernel_size=5, stride=1, padding='same')\n self.act3 = torch.nn.PReLU()\n\n self.conv4 = torch.nn.Conv1d(64, 32, kernel_size=5, stride=1, padding='same')\n self.act4 = torch.nn.PReLU()\n\n # till here\n\n self.conv5 = torch.nn.Conv1d(32, 16, kernel_size=5, stride=1, padding='same')\n self.act5 = torch.nn.PReLU()\n\n self.conv6 = torch.nn.Conv1d(16, 1, kernel_size=5, stride=1, padding='same')\n self.act6 = torch.nn.PReLU()\n \n def forward(self, x):\n #print(\"in forward\\ninput shape: \" + str(x.size()))\n # x, phase = self.batch_stft(x)\n #print(\"input shape post stft: \" + str(x.size()))\n \n x = x.transpose(-2, -1)\n #print(f\"shape after transp: {x.size()}\")\n\n #print(\"input shape after transpose: \" + str(x.size()))\n\n # input 1xFxT, output 16xFxT\n x = self.act1(self.conv1(x))\n # input 16 channels, output 32 channels\n x = self.act2(self.conv2(x))\n\n # input 32 channels, output 64 channels\n x = self.act3(self.conv3(x))\n\n # input 64 channels, output 32 channels\n x = self.act4(self.conv4(x))\n\n # input 32 channels, output 16 channels\n x = self.act5(self.conv5(x))\n\n # input 16 channels, output 1 channel\n x = self.act6(self.conv6(x))\n\n\n x = x.transpose(-2, -1)\n\n #print(\"output shape: \" + str(x.size()))\n\n return x\n\n def save_for_rtneural(self, outfile):\n ## used for saving \n class EncodeTensor(JSONEncoder):\n def default(self, obj):\n if isinstance(obj, torch.Tensor):\n return obj.cpu().detach().numpy().tolist()\n return super(json.NpEncoder, self).default(obj)\n \n with open(outfile, 'w') as json_file:\n json.dump(self.state_dict(), json_file,cls=EncodeTensor)\n\n# -------------------------------------------------------------------------------------------------------- #\n\nclass SimpleConv2D(torch.nn.Module):\n\n model_type = \"Conv2D\"\n input_dim = 1\n\n def __init__(self, param=False):\n super().__init__()\n\n if param:\n self.input_dim = 2\n\n self.conv1 = torch.nn.Conv2d(self.input_dim, 16, kernel_size=(3,3), stride=1, padding='same')\n self.act1 = torch.nn.ReLU()\n\n self.conv2 = torch.nn.Conv2d(16, 32, kernel_size=(3,3), stride=1, padding='same')\n self.act2 = torch.nn.ReLU()\n\n self.conv3 = torch.nn.Conv2d(32, 64, kernel_size=(3,3), stride=1, padding='same')\n self.act3 = torch.nn.ReLU()\n\n self.conv4 = torch.nn.Conv2d(64, 32, kernel_size=(3,3), stride=1, padding='same')\n self.act4 = torch.nn.ReLU()\n\n self.conv5 = torch.nn.Conv2d(32, 16, kernel_size=(3,3), stride=1, padding='same')\n self.act5 = torch.nn.ReLU()\n\n self.conv6 = torch.nn.Conv2d(16, 1, kernel_size=(3,3), stride=1, padding='same')\n self.act6 = torch.nn.ReLU()\n \n def forward(self, x):\n \n # print(\"input shape: \" + str(x.size()))\n x, phase = self.batch_stft(x)\n #print(\"input shape post stft: \" + str(x.size()))\n\n x = x.transpose(0, 1)\n\n #print(\"input shape after transpose: \" + str(x.size()))\n\n # input 1xFxT, output 16xFxT\n x = self.act1(self.conv1(x))\n # input 16 channels, output 32 channels\n x = self.act2(self.conv2(x))\n\n # input 32 channels, output 64 channels\n x = self.act3(self.conv3(x))\n\n # input 64 channels, output 32 channels\n x = self.act4(self.conv4(x))\n\n # input 32 channels, output 16 channels\n x = self.act5(self.conv5(x))\n\n # input 16 channels, output 1 channel\n x = self.act6(self.conv6(x))\n\n x = x.transpose(0, 1)\n\n\n #print(\"output shape pre istft: \" + str(x.size()))\n x = self.batch_istft(x, phase, trim_length=None)\n\n x = torch.unsqueeze(x, 2)\n # print(\"output shape post istft: \" + str(x.size()))\n\n return x\n \n def batch_stft(self, x, pad: bool = True, return_complex: bool = False):\n\n x = torch.squeeze(x)\n #print(\"x shape after squeeze: \" + str(x.size()))\n\n\n x = x.reshape(-1, x.size()[-1])\n\n #print(\"x shape after reshaping: \" + str(x.size()))\n \n\n #if pad:\n #x = self.pad_stft_input(x)\n\n #print(\"x sizes after padding: \" + str(x.size()))\n \n\n S = self._stft(x)\n\n #print(\"S shape: \" + str(S.shape[:]))\n\n #print(\"x_shape[:-1]: \" + str(x_shape[:-1]))\n\n #print(\"S.shape[-2:]: \" + str(S.shape[-2:]))\n\n #S = S.reshape(x_shape[:-1] + S.shape[-2:]) # aggiunge una dimensione alla S: da [2, 2049, 1347] -> [1, 2, 2049, 1347]\n\n S = torch.unsqueeze(S, 0)\n\n #print(\"S shape(), post unsqueeze: \" + str(S.shape[:]))\n\n if return_complex:\n return S\n return S.abs(), S.angle()\n\n def batch_istft(self, magnitude, phase, trim_length=None):\n S = torch.polar(magnitude, phase)\n #S = S.reshape(-1, S.size()[-2], S.size()[-1])\n S = torch.squeeze(S, 0)\n #print(\"Nell' istft S shape: \" + str(S.size()))\n y = self._istft(S, trim_length)\n #x = x.reshape(S_shape[:-2] + x.shape[-1:])\n y_shape = y.size()\n #print(\"Nell'istft y shape: \" + str(y_shape))\n return y\n\n def _stft(self, x):\n return torch.stft(input=x,\n n_fft=4096,\n window=torch.hann_window(4096, periodic=True),#.to(device),\n win_length=4096,\n hop_length=4096//8,\n center=True,\n return_complex=True\n )\n\n def _istft(self, x, trim_length=None):\n return torch.istft(input=x,\n n_fft=4096,\n window=torch.hann_window(4096, periodic=True),#to.(device)\n win_length=4096,\n hop_length=4096//8,\n center=True,\n length=trim_length\n )\n \n def pad_stft_input(self, x):\n pad_len = (-(x.size(-1) - 4096) % (4096//4)) % 4096\n return F.pad(x, (0, pad_len*4))\n\n def save_for_rtneural(self, outfile):\n ## used for saving \n class EncodeTensor(JSONEncoder):\n def default(self, obj):\n if isinstance(obj, torch.Tensor):\n return obj.cpu().detach().numpy().tolist()\n return super(json.NpEncoder, self).default(obj)\n \n with open(outfile, 'w') as json_file:\n json.dump(self.state_dict(), json_file,cls=EncodeTensor)", "repo_name": "EdoardoMor/MagicKnobRep", "sub_path": "MagicKnob/python/myk_models.py", "file_name": "myk_models.py", "file_ext": "py", "file_size_in_byte": 9885, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "torch.nn", "line_number": 9, "usage_type": "attribute"}, {"api_name": "torch.nn.LSTM", "line_number": 28, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 28, "usage_type": "attribute"}, {"api_name": "torch.nn.Linear", "line_number": 30, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 30, "usage_type": "attribute"}, {"api_name": "torch.zeros", "line_number": 47, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 48, "usage_type": "call"}, {"api_name": "json.JSONEncoder", "line_number": 58, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 60, "usage_type": "attribute"}, {"api_name": "json.NpEncoder", "line_number": 62, "usage_type": "attribute"}, {"api_name": "json.dump", "line_number": 65, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 69, "usage_type": "attribute"}, {"api_name": "torch.nn.Conv1d", "line_number": 80, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 80, "usage_type": "attribute"}, {"api_name": "torch.nn.PReLU", "line_number": 81, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 81, "usage_type": "attribute"}, {"api_name": "torch.nn.Conv1d", "line_number": 83, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 83, "usage_type": "attribute"}, {"api_name": "torch.nn.PReLU", "line_number": 84, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 84, "usage_type": "attribute"}, {"api_name": "torch.nn.Conv1d", "line_number": 88, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 88, "usage_type": "attribute"}, {"api_name": "torch.nn.PReLU", "line_number": 89, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 89, "usage_type": "attribute"}, {"api_name": "torch.nn.Conv1d", "line_number": 91, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 91, "usage_type": "attribute"}, {"api_name": "torch.nn.PReLU", "line_number": 92, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 92, "usage_type": "attribute"}, {"api_name": "torch.nn.Conv1d", "line_number": 96, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 96, "usage_type": "attribute"}, {"api_name": "torch.nn.PReLU", "line_number": 97, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 97, "usage_type": "attribute"}, {"api_name": "torch.nn.Conv1d", "line_number": 99, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 99, "usage_type": "attribute"}, {"api_name": "torch.nn.PReLU", "line_number": 100, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 100, "usage_type": "attribute"}, {"api_name": "json.JSONEncoder", "line_number": 138, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 140, "usage_type": "attribute"}, {"api_name": "json.NpEncoder", "line_number": 142, "usage_type": "attribute"}, {"api_name": "json.dump", "line_number": 145, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 149, "usage_type": "attribute"}, {"api_name": "torch.nn.Conv2d", "line_number": 160, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 160, "usage_type": "attribute"}, {"api_name": "torch.nn.ReLU", "line_number": 161, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 161, "usage_type": "attribute"}, {"api_name": "torch.nn.Conv2d", "line_number": 163, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 163, "usage_type": "attribute"}, {"api_name": "torch.nn.ReLU", "line_number": 164, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 164, "usage_type": "attribute"}, {"api_name": "torch.nn.Conv2d", "line_number": 166, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 166, "usage_type": "attribute"}, {"api_name": "torch.nn.ReLU", "line_number": 167, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 167, "usage_type": "attribute"}, {"api_name": "torch.nn.Conv2d", "line_number": 169, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 169, "usage_type": "attribute"}, {"api_name": "torch.nn.ReLU", "line_number": 170, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 170, "usage_type": "attribute"}, {"api_name": "torch.nn.Conv2d", "line_number": 172, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 172, "usage_type": "attribute"}, {"api_name": "torch.nn.ReLU", "line_number": 173, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 173, "usage_type": "attribute"}, {"api_name": "torch.nn.Conv2d", "line_number": 175, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 175, "usage_type": "attribute"}, {"api_name": "torch.nn.ReLU", "line_number": 176, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 176, "usage_type": "attribute"}, {"api_name": "torch.unsqueeze", "line_number": 211, "usage_type": "call"}, {"api_name": "torch.squeeze", "line_number": 218, "usage_type": "call"}, {"api_name": "torch.unsqueeze", "line_number": 243, "usage_type": "call"}, {"api_name": "torch.polar", "line_number": 252, "usage_type": "call"}, {"api_name": "torch.squeeze", "line_number": 254, "usage_type": "call"}, {"api_name": "torch.stft", "line_number": 263, "usage_type": "call"}, {"api_name": "torch.hann_window", "line_number": 265, "usage_type": "call"}, {"api_name": "torch.istft", "line_number": 273, "usage_type": "call"}, {"api_name": "torch.hann_window", "line_number": 275, "usage_type": "call"}, {"api_name": "torch.nn.functional.pad", "line_number": 284, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 284, "usage_type": "name"}, {"api_name": "json.JSONEncoder", "line_number": 288, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 290, "usage_type": "attribute"}, {"api_name": "json.NpEncoder", "line_number": 292, "usage_type": "attribute"}, {"api_name": "json.dump", "line_number": 295, "usage_type": "call"}]} +{"seq_id": "41586327510", "text": "import multiprocessing\nimport sys\nimport time\nfrom functools import partial\n\nimport pandas as pd\nimport torch\n\nfrom .utils.log import logger\nfrom .utils.util import map_column_name_to_dimension_space\n\nDIMENSIONS_WHEN_COMPUTING_DEVICE_TO_DEVICE_DISTANCES = 2\n\n\ndef device_to_device_cdist_iterable(idx, df_devices_a, df_devices_b, lsuffix, rsuffix, v_count, v_start, lock, size):\n \"\"\"\n Background runnable function to compute distances between devices.\n\n :param idx: Dataframe lookup index (time based)\n :param df_devices_a: Devices dataframe A\n :param df_devices_b: Devices dataframe B\n :param v_count: Iteration count\n :param v_start: Timer for logging progress\n :param lock: Multiprocessing lock for variable manipulation\n :param size: Total number of records for processing\n :return: Dataframe of joined devices with computed distance\n \"\"\"\n if lsuffix is None:\n lsuffix = \"_a\"\n\n if rsuffix is None:\n rsuffix = \"_b\"\n\n with lock:\n if v_count.value % 1000 == 0:\n logger.info(\n \"Computing device <-> device distances: Processed {}/{} - Time {}s\".format(\n v_count.value, size, time.time() - v_start.value\n )\n )\n sys.stdout.flush()\n v_start.value = time.time()\n\n v_count.value += 1\n\n if idx not in df_devices_a.index:\n return None\n df_device_a_by_idx = df_devices_a.loc[[idx]]\n\n if idx not in df_devices_b.index:\n return None\n df_device_b_by_idx = df_devices_b.loc[[idx]]\n\n position_cols = map_column_name_to_dimension_space(\"position\", DIMENSIONS_WHEN_COMPUTING_DEVICE_TO_DEVICE_DISTANCES)\n\n df_device_to_device_join = df_device_a_by_idx.join(\n df_device_b_by_idx, how=\"inner\", lsuffix=lsuffix, rsuffix=rsuffix\n )\n distances = torch.cdist(\n torch.tensor(df_device_a_by_idx[position_cols].to_numpy()),\n torch.tensor(df_device_b_by_idx[position_cols].to_numpy()),\n )\n\n return df_device_to_device_join.assign(device_to_device_distance=distances.flatten())\n\n\ndef generate_device_to_device_distances(df_device_features_a, df_device_features_b, lsuffix=\"_a\", rsuffix=\"_b\"):\n \"\"\"\n Use multi-processing to generate distances between candidate devices\n across all recorded features (computationally heavy because distance is generated\n for every timestamp between every pair)\n\n :param df_device_features_a:\n :param df_device_features_b:\n :return:\n \"\"\"\n p = multiprocessing.Pool()\n m = multiprocessing.Manager()\n\n lock = m.Lock()\n start = time.time()\n v_count = m.Value(\"i\", 0) # Keep track of iterations\n v_start = m.Value(\"f\", start) # Share timer object\n time_indexes = df_device_features_a.index.unique(level=0)\n\n # This computes distances between all devices\n df_device_to_device_distances = pd.concat(\n p.map(\n partial(\n device_to_device_cdist_iterable,\n df_devices_a=df_device_features_a,\n df_devices_b=df_device_features_b,\n lsuffix=lsuffix,\n rsuffix=rsuffix,\n v_count=v_count,\n v_start=v_start,\n lock=lock,\n size=len(time_indexes),\n ),\n time_indexes,\n )\n )\n logger.info(\n \"Finished computing device <-> device distances: {}/{} - Total time: {}s\".format(\n len(time_indexes), len(time_indexes), time.time() - start\n )\n )\n\n p.close()\n p.join()\n\n return df_device_to_device_distances\n", "repo_name": "WildflowerSchools/wf-process-cuwb-data", "sub_path": "process_cuwb_data/uwb_motion_distance.py", "file_name": "uwb_motion_distance.py", "file_ext": "py", "file_size_in_byte": 3582, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "utils.log.logger.info", "line_number": 36, "usage_type": "call"}, {"api_name": "utils.log.logger", "line_number": 36, "usage_type": "name"}, {"api_name": "time.time", "line_number": 38, "usage_type": "call"}, {"api_name": "sys.stdout.flush", "line_number": 41, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 41, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 42, "usage_type": "call"}, {"api_name": "utils.util.map_column_name_to_dimension_space", "line_number": 54, "usage_type": "call"}, {"api_name": "torch.cdist", "line_number": 59, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 60, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 61, "usage_type": "call"}, {"api_name": "multiprocessing.Pool", "line_number": 77, "usage_type": "call"}, {"api_name": "multiprocessing.Manager", "line_number": 78, "usage_type": "call"}, {"api_name": "time.time", "line_number": 81, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 87, "usage_type": "call"}, {"api_name": "functools.partial", "line_number": 89, "usage_type": "call"}, {"api_name": "utils.log.logger.info", "line_number": 103, "usage_type": "call"}, {"api_name": "utils.log.logger", "line_number": 103, "usage_type": "name"}, {"api_name": "time.time", "line_number": 105, "usage_type": "call"}]} +{"seq_id": "43100052383", "text": "import os\nfrom abc import ABC, abstractmethod\nfrom typing import List, Tuple\nfrom termcolor import colored\nfrom schema.board import Board\n# from ..schema.board import Board\n# from schema.board import Board\n\n\nclass SolverStrategy(ABC):\n @abstractmethod\n def play(\n self, board: Board, states_graph: List, full_pipes: int, empty_pipes: int\n ) -> Tuple[List[Board], int]:\n pass\n\n\nclass UserSolverStrategy(SolverStrategy):\n def play(\n self, board: Board, states_graph: List, full_pipes: int, empty_pipes: int\n ) -> Tuple[List[Board], int] :\n print(board)\n cost = 0\n while not board.is_finished():\n\n states_graph.append(board.copy_current_state())\n try:\n actions = board.get_possible_actions()\n if len(actions) == 0:\n print(\"Game over\")\n return states_graph\n print(f\"possible actions (S,D):{actions}\")\n source = int(input(\"Move a ball from: \"))\n dest = int(input(\"To: \"))\n if (\n dest > full_pipes + empty_pipes\n or source > full_pipes + empty_pipes\n or source == dest\n ):\n raise ValueError()\n except ValueError:\n print(colored(\"Bad input\", \"red\"))\n continue\n if board.move(source, dest):\n cost += 1\n os.system(\"clear\")\n print(board)\n else:\n print(colored(\"Incorrect movement\", \"red\"))\n print(colored(\"You are Brilliant *_*\", \"green\"))\n return states_graph, cost\n\n\nclass BFSSolverStrategy(SolverStrategy):\n def play(\n self, board: Board, states_graph: List, full_pipes: int, empty_pipes: int\n ) -> Tuple[List[Board], int]:\n bfs_queue = []\n bfs_queue.append(board.copy_current_state())\n is_finished = False\n cost = 0\n while not is_finished and len(bfs_queue) > 0:\n current_state = bfs_queue.pop(0)\n states_graph.append(current_state)\n actions = current_state.get_possible_actions()\n if len(actions) == 0:\n continue\n for source, dest in actions:\n temp = current_state.copy_current_state()\n if temp.move(source, dest):\n cost += 1\n if temp.is_finished():\n states_graph.append(temp)\n is_finished = True\n break\n if not temp in bfs_queue:\n bfs_queue.append(temp.copy_current_state())\n return states_graph, cost\n\n\nclass DFSSolverStrategy(SolverStrategy):\n def play(\n self, board: Board, states_graph: List, full_pipes: int, empty_pipes: int\n ) -> Tuple[List[Board], int]:\n dfs_stack = []\n dfs_stack.append(board.copy_current_state())\n is_finished = False\n cost = 0\n while not is_finished and len(dfs_stack) > 0:\n current_state = dfs_stack.pop()\n states_graph.append(current_state)\n actions = current_state.get_possible_actions()\n if current_state.is_finished():\n return states_graph\n if len(actions) == 0:\n continue\n i = 1\n while i <= len(actions):\n temp = current_state.copy_current_state()\n if temp.move(actions[-i][0], actions[-i][1]):\n if not temp in states_graph:\n dfs_stack.append(temp.copy_current_state())\n cost += 1\n i += 1\n return states_graph, cost\n", "repo_name": "Hussain-Khallouf/Bobble-Ball-Sort-Game", "sub_path": "game/strategies/solver_strategy.py", "file_name": "solver_strategy.py", "file_ext": "py", "file_size_in_byte": 3751, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "abc.ABC", "line_number": 10, "usage_type": "name"}, {"api_name": "schema.board.Board", "line_number": 13, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 13, "usage_type": "name"}, {"api_name": "abc.abstractmethod", "line_number": 11, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 14, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 14, "usage_type": "name"}, {"api_name": "schema.board.Board", "line_number": 14, "usage_type": "name"}, {"api_name": "schema.board.Board", "line_number": 20, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 20, "usage_type": "name"}, {"api_name": "termcolor.colored", "line_number": 42, "usage_type": "call"}, {"api_name": "os.system", "line_number": 46, "usage_type": "call"}, {"api_name": "termcolor.colored", "line_number": 49, "usage_type": "call"}, {"api_name": "termcolor.colored", "line_number": 50, "usage_type": "call"}, {"api_name": "typing.Tuple", "line_number": 21, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 21, "usage_type": "name"}, {"api_name": "schema.board.Board", "line_number": 21, "usage_type": "name"}, {"api_name": "schema.board.Board", "line_number": 56, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 56, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 57, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 57, "usage_type": "name"}, {"api_name": "schema.board.Board", "line_number": 57, "usage_type": "name"}, {"api_name": "schema.board.Board", "line_number": 83, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 83, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 84, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 84, "usage_type": "name"}, {"api_name": "schema.board.Board", "line_number": 84, "usage_type": "name"}]} +{"seq_id": "9964210209", "text": "from openerp.osv import fields,osv\nimport datetime\n\nclass wizard_purchase_receipt_details(osv.osv_memory):\n\t_name=\"wizard.purchase.receipt.details\"\n\t_columns={\n\t\t\"date_start\" : fields.date(\"Date From\", required=False),\n\t\t\"date_stop\"\t: fields.date(\"Date To\", required=False),\n\t\t\"purchase_type\" : fields.selection([('import','Import'),('local','Local'),('all','All')],\"Purchase Type\",required=True),\n\t\t# 'output_type':fields.selection([('xls','Excel'),('pdf','PDF')],'Output Type',required=True),\n\t\t\"goods_type\" : fields.selection([('packing','Packing'),('stores','Stores'),('raw','Raw Material')], \"Goods Type\"),\n\t}\n\t_defaults={\n\t\t\"date_start\" :lambda self,cr,uid,context:datetime.date.today().strftime(\"2016-01-01\"),\n\t\t\"date_stop\" :lambda self,cr,uid,context:datetime.date.today().strftime(\"2016-01-30\"),\n\t\t# \"output_type\" :'pdf',\n\t\t\"purchase_type\" :lambda *p:'local',\n\t\t# \"goods_type\":lambda *p:'packing',\n\t}\n\n\tdef print_report(self,cr,uid,ids,context=None):\n\t\tif not context:\n\t\t\tcontext={}\n\t\tform_data=self.read(cr,uid,ids)[0]\n\t\tdatas={\n\t\t\t'ids': context.get('active_ids',[]),\n\t\t\t'model' :'wizard.purchase.receipt.details',\n\t\t\t'form' :form_data,\n\t\t}\n\n\t\t# if form_data['output_type']=='pdf':\n\t\t# \treturn{\n\t\t# \t'type' :'ir.actions.report.xml',\n\t\t# \t'report_name' :'pending.purchase.order.report',\n\t\t# \t'report_type' :'webkit',\n\t\t# \t'datas' :datas,\n\t\t# \t}\n\t\t# else:\n\t\treturn{\n\t\t\t'type' :'ir.actions.report.xml',\n\t\t\t'report_name' :'purchase.receipt_details',\n\t\t\t'report_type' :'xls',\n\t\t\t'datas' :datas,\n\t\t\t}\n", "repo_name": "hendrasaputra0501/btxjalan", "sub_path": "ad_purchases_report/wizard_purchase_receipt_details.py", "file_name": "wizard_purchase_receipt_details.py", "file_ext": "py", "file_size_in_byte": 1507, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "openerp.osv.osv.osv_memory", "line_number": 4, "usage_type": "attribute"}, {"api_name": "openerp.osv.osv", "line_number": 4, "usage_type": "name"}, {"api_name": "openerp.osv.fields.date", "line_number": 7, "usage_type": "call"}, {"api_name": "openerp.osv.fields", "line_number": 7, "usage_type": "name"}, {"api_name": "openerp.osv.fields.date", "line_number": 8, "usage_type": "call"}, {"api_name": "openerp.osv.fields", "line_number": 8, "usage_type": "name"}, {"api_name": "openerp.osv.fields.selection", "line_number": 9, "usage_type": "call"}, {"api_name": "openerp.osv.fields", "line_number": 9, "usage_type": "name"}, {"api_name": "openerp.osv.fields.selection", "line_number": 11, "usage_type": "call"}, {"api_name": "openerp.osv.fields", "line_number": 11, "usage_type": "name"}, {"api_name": "datetime.date.today", "line_number": 14, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 14, "usage_type": "attribute"}, {"api_name": "datetime.date.today", "line_number": 15, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 15, "usage_type": "attribute"}]} +{"seq_id": "38432940037", "text": "from django.conf.urls import patterns, include, url\n\nfrom django.contrib import admin\nfrom litsey_exam import settings\nfrom django.contrib.staticfiles.urls import staticfiles_urlpatterns\nadmin.autodiscover()\n\nurlpatterns = patterns('',\n # Examples:\n # url(r'^$', 'litsey_exam.views.home', name='home'),\n # url(r'^blog/', include('blog.urls')),\n url(r'^$', 'litsey_exam.views.MainRedirect', name='home'),\n url(r'^admin/', include(admin.site.urls)),\n url(r'^journal/', include('journal.urls', namespace='journal')),\n url(r'^media/(?P.*)$','django.views.static.serve',\n \t\t{'document_root' : settings.MEDIA_ROOT}\n \t\t),\n)\n\nurlpatterns += staticfiles_urlpatterns()\n", "repo_name": "mezun7/litsey-exam", "sub_path": "litsey_exam/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 688, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "37", "api": [{"api_name": "django.contrib.admin.autodiscover", "line_number": 6, "usage_type": "call"}, {"api_name": "django.contrib.admin", "line_number": 6, "usage_type": "name"}, {"api_name": "django.conf.urls.patterns", "line_number": 8, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 12, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 13, "usage_type": "call"}, {"api_name": "django.conf.urls.include", "line_number": 13, "usage_type": "call"}, {"api_name": "django.contrib.admin.site", "line_number": 13, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 13, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 14, "usage_type": "call"}, {"api_name": "django.conf.urls.include", "line_number": 14, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 15, "usage_type": "call"}, {"api_name": "litsey_exam.settings.MEDIA_ROOT", "line_number": 16, "usage_type": "attribute"}, {"api_name": "litsey_exam.settings", "line_number": 16, "usage_type": "name"}, {"api_name": "django.contrib.staticfiles.urls.staticfiles_urlpatterns", "line_number": 20, "usage_type": "call"}]} +{"seq_id": "70387318507", "text": "import argparse\nimport torch\nfrom transformers import BlenderbotTokenizer, BlenderbotForConditionalGeneration\nfrom transformers import BlenderbotTokenizer, BlenderbotForConditionalGeneration\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser()\n parser.add_argument('--message', type=str, default='')\n args = parser.parse_args()\n device = \"cuda:0\" if torch.cuda.is_available() else \"cpu\"\n\n #download and setup the model and tokenizer\n model_name = 'facebook/blenderbot-400M-distill'\n tokenizer = BlenderbotTokenizer.from_pretrained(model_name)\n model = BlenderbotForConditionalGeneration.from_pretrained(model_name).to(device)\n\n inputs = tokenizer(args.message, return_tensors=\"pt\").to(device)\n result = model.generate(**inputs)\n print(tokenizer.decode(result[0]).replace('','').replace('','').strip())", "repo_name": "ma7dev/OnlySudo", "sub_path": "src/ai/chat/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 852, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 14, "dataset": "github-code", "pt": "37", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 7, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 10, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 10, "usage_type": "attribute"}, {"api_name": "transformers.BlenderbotTokenizer.from_pretrained", "line_number": 14, "usage_type": "call"}, {"api_name": "transformers.BlenderbotTokenizer", "line_number": 14, "usage_type": "name"}, {"api_name": "transformers.BlenderbotForConditionalGeneration.from_pretrained", "line_number": 15, "usage_type": "call"}, {"api_name": "transformers.BlenderbotForConditionalGeneration", "line_number": 15, "usage_type": "name"}]} +{"seq_id": "1548633982", "text": "import re\nimport requests\nimport os\n\ndomain_dict = {\n \"github.com\": \"https://github.com.ipaddress.com/\",\n \"assets-cdn.github.com\": \"https://github.com.ipaddress.com/assets-cdn.github.com\",\n \"github.global.ssl.fastly.net\": \"https://fastly.net.ipaddress.com/github.global.ssl.fastly.net\"\n}\nhosts_dict = {}\n\nfor domain, url in domain_dict.items():\n method = \"GET\"\n req = requests.request(method=method, url=url)\n pattern = r'IPv4 Addresses